fumiyau/python_comments_free_500000 · Datasets at Hugging Face

f731817c0c67a924ce2ccfc634deaf68c68b5c57

54,791

py

Python

numpy/polynomial/hermite.py

ivanov/numpy

6d2665626e40f346bb5af8d780579f5a429ff9ba

[ "BSD-3-Clause" ]

null

numpy/polynomial/hermite.py

ivanov/numpy

6d2665626e40f346bb5af8d780579f5a429ff9ba

[ "BSD-3-Clause" ]

null

numpy/polynomial/hermite.py

ivanov/numpy

6d2665626e40f346bb5af8d780579f5a429ff9ba

[ "BSD-3-Clause" ]

null

""" Objects for dealing with Hermite series. This module provides a number of objects (mostly functions) useful for dealing with Hermite series, including a `Hermite` class that encapsulates the usual arithmetic operations. (General information on how this module represents and works with such polynomials is in the docstring for its "parent" sub-package, `numpy.polynomial`). Constants --------- - `hermdomain` -- Hermite series default domain, [-1,1]. - `hermzero` -- Hermite series that evaluates identically to 0. - `hermone` -- Hermite series that evaluates identically to 1. - `hermx` -- Hermite series for the identity map, ``f(x) = x``. Arithmetic ---------- - `hermmulx` -- multiply a Hermite series in ``P_i(x)`` by ``x``. - `hermadd` -- add two Hermite series. - `hermsub` -- subtract one Hermite series from another. - `hermmul` -- multiply two Hermite series. - `hermdiv` -- divide one Hermite series by another. - `hermval` -- evaluate a Hermite series at given points. - `hermval2d` -- evaluate a 2D Hermite series at given points. - `hermval3d` -- evaluate a 3D Hermite series at given points. - `hermgrid2d` -- evaluate a 2D Hermite series on a Cartesian product. - `hermgrid3d` -- evaluate a 3D Hermite series on a Cartesian product. Calculus -------- - `hermder` -- differentiate a Hermite series. - `hermint` -- integrate a Hermite series. Misc Functions -------------- - `hermfromroots` -- create a Hermite series with specified roots. - `hermroots` -- find the roots of a Hermite series. - `hermvander` -- Vandermonde-like matrix for Hermite polynomials. - `hermvander2d` -- Vandermonde-like matrix for 2D power series. - `hermvander3d` -- Vandermonde-like matrix for 3D power series. - `hermgauss` -- Gauss-Hermite quadrature, points and weights. - `hermweight` -- Hermite weight function. - `hermcompanion` -- symmetrized companion matrix in Hermite form. - `hermfit` -- least-squares fit returning a Hermite series. - `hermtrim` -- trim leading coefficients from a Hermite series. - `hermline` -- Hermite series of given straight line. - `herm2poly` -- convert a Hermite series to a polynomial. - `poly2herm` -- convert a polynomial to a Hermite series. Classes ------- - `Hermite` -- A Hermite series class. See also -------- `numpy.polynomial` """ from __future__ import division, absolute_import import numpy as np import numpy.linalg as la from . import polyutils as pu import warnings from .polytemplate import polytemplate __all__ = ['hermzero', 'hermone', 'hermx', 'hermdomain', 'hermline', 'hermadd', 'hermsub', 'hermmulx', 'hermmul', 'hermdiv', 'hermpow', 'hermval', 'hermder', 'hermint', 'herm2poly', 'poly2herm', 'hermfromroots', 'hermvander', 'hermfit', 'hermtrim', 'hermroots', 'Hermite', 'hermval2d', 'hermval3d', 'hermgrid2d', 'hermgrid3d', 'hermvander2d', 'hermvander3d', 'hermcompanion', 'hermgauss', 'hermweight'] hermtrim = pu.trimcoef def poly2herm(pol) : """ poly2herm(pol) Convert a polynomial to a Hermite series. Convert an array representing the coefficients of a polynomial (relative to the "standard" basis) ordered from lowest degree to highest, to an array of the coefficients of the equivalent Hermite series, ordered from lowest to highest degree. Parameters ---------- pol : array_like 1-D array containing the polynomial coefficients Returns ------- c : ndarray 1-D array containing the coefficients of the equivalent Hermite series. See Also -------- herm2poly Notes ----- The easy way to do conversions between polynomial basis sets is to use the convert method of a class instance. Examples -------- >>> from numpy.polynomial.hermite_e import poly2herme >>> poly2herm(np.arange(4)) array([ 1. , 2.75 , 0.5 , 0.375]) """ [pol] = pu.as_series([pol]) deg = len(pol) - 1 res = 0 for i in range(deg, -1, -1) : res = hermadd(hermmulx(res), pol[i]) return res def herm2poly(c) : """ Convert a Hermite series to a polynomial. Convert an array representing the coefficients of a Hermite series, ordered from lowest degree to highest, to an array of the coefficients of the equivalent polynomial (relative to the "standard" basis) ordered from lowest to highest degree. Parameters ---------- c : array_like 1-D array containing the Hermite series coefficients, ordered from lowest order term to highest. Returns ------- pol : ndarray 1-D array containing the coefficients of the equivalent polynomial (relative to the "standard" basis) ordered from lowest order term to highest. See Also -------- poly2herm Notes ----- The easy way to do conversions between polynomial basis sets is to use the convert method of a class instance. Examples -------- >>> from numpy.polynomial.hermite import herm2poly >>> herm2poly([ 1. , 2.75 , 0.5 , 0.375]) array([ 0., 1., 2., 3.]) """ from .polynomial import polyadd, polysub, polymulx [c] = pu.as_series([c]) n = len(c) if n == 1: return c if n == 2: c[1] *= 2 return c else: c0 = c[-2] c1 = c[-1] # i is the current degree of c1 for i in range(n - 1, 1, -1) : tmp = c0 c0 = polysub(c[i - 2], c1*(2*(i - 1))) c1 = polyadd(tmp, polymulx(c1)*2) return polyadd(c0, polymulx(c1)*2) # # These are constant arrays are of integer type so as to be compatible # with the widest range of other types, such as Decimal. # # Hermite hermdomain = np.array([-1,1]) # Hermite coefficients representing zero. hermzero = np.array([0]) # Hermite coefficients representing one. hermone = np.array([1]) # Hermite coefficients representing the identity x. hermx = np.array([0, 1/2]) def hermline(off, scl) : """ Hermite series whose graph is a straight line. Parameters ---------- off, scl : scalars The specified line is given by ``off + scl*x``. Returns ------- y : ndarray This module's representation of the Hermite series for ``off + scl*x``. See Also -------- polyline, chebline Examples -------- >>> from numpy.polynomial.hermite import hermline, hermval >>> hermval(0,hermline(3, 2)) 3.0 >>> hermval(1,hermline(3, 2)) 5.0 """ if scl != 0 : return np.array([off,scl/2]) else : return np.array([off]) def hermfromroots(roots) : """ Generate a Hermite series with given roots. The function returns the coefficients of the polynomial .. math:: p(x) = (x - r_0) * (x - r_1) * ... * (x - r_n), in Hermite form, where the `r_n` are the roots specified in `roots`. If a zero has multiplicity n, then it must appear in `roots` n times. For instance, if 2 is a root of multiplicity three and 3 is a root of multiplicity 2, then `roots` looks something like [2, 2, 2, 3, 3]. The roots can appear in any order. If the returned coefficients are `c`, then .. math:: p(x) = c_0 + c_1 * H_1(x) + ... + c_n * H_n(x) The coefficient of the last term is not generally 1 for monic polynomials in Hermite form. Parameters ---------- roots : array_like Sequence containing the roots. Returns ------- out : ndarray 1-D array of coefficients. If all roots are real then `out` is a real array, if some of the roots are complex, then `out` is complex even if all the coefficients in the result are real (see Examples below). See Also -------- polyfromroots, legfromroots, lagfromroots, chebfromroots, hermefromroots. Examples -------- >>> from numpy.polynomial.hermite import hermfromroots, hermval >>> coef = hermfromroots((-1, 0, 1)) >>> hermval((-1, 0, 1), coef) array([ 0., 0., 0.]) >>> coef = hermfromroots((-1j, 1j)) >>> hermval((-1j, 1j), coef) array([ 0.+0.j, 0.+0.j]) """ if len(roots) == 0 : return np.ones(1) else : [roots] = pu.as_series([roots], trim=False) roots.sort() p = [hermline(-r, 1) for r in roots] n = len(p) while n > 1: m, r = divmod(n, 2) tmp = [hermmul(p[i], p[i+m]) for i in range(m)] if r: tmp[0] = hermmul(tmp[0], p[-1]) p = tmp n = m return p[0] def hermadd(c1, c2): """ Add one Hermite series to another. Returns the sum of two Hermite series `c1` + `c2`. The arguments are sequences of coefficients ordered from lowest order term to highest, i.e., [1,2,3] represents the series ``P_0 + 2*P_1 + 3*P_2``. Parameters ---------- c1, c2 : array_like 1-D arrays of Hermite series coefficients ordered from low to high. Returns ------- out : ndarray Array representing the Hermite series of their sum. See Also -------- hermsub, hermmul, hermdiv, hermpow Notes ----- Unlike multiplication, division, etc., the sum of two Hermite series is a Hermite series (without having to "reproject" the result onto the basis set) so addition, just like that of "standard" polynomials, is simply "component-wise." Examples -------- >>> from numpy.polynomial.hermite import hermadd >>> hermadd([1, 2, 3], [1, 2, 3, 4]) array([ 2., 4., 6., 4.]) """ # c1, c2 are trimmed copies [c1, c2] = pu.as_series([c1, c2]) if len(c1) > len(c2) : c1[:c2.size] += c2 ret = c1 else : c2[:c1.size] += c1 ret = c2 return pu.trimseq(ret) def hermsub(c1, c2): """ Subtract one Hermite series from another. Returns the difference of two Hermite series `c1` - `c2`. The sequences of coefficients are from lowest order term to highest, i.e., [1,2,3] represents the series ``P_0 + 2*P_1 + 3*P_2``. Parameters ---------- c1, c2 : array_like 1-D arrays of Hermite series coefficients ordered from low to high. Returns ------- out : ndarray Of Hermite series coefficients representing their difference. See Also -------- hermadd, hermmul, hermdiv, hermpow Notes ----- Unlike multiplication, division, etc., the difference of two Hermite series is a Hermite series (without having to "reproject" the result onto the basis set) so subtraction, just like that of "standard" polynomials, is simply "component-wise." Examples -------- >>> from numpy.polynomial.hermite import hermsub >>> hermsub([1, 2, 3, 4], [1, 2, 3]) array([ 0., 0., 0., 4.]) """ # c1, c2 are trimmed copies [c1, c2] = pu.as_series([c1, c2]) if len(c1) > len(c2) : c1[:c2.size] -= c2 ret = c1 else : c2 = -c2 c2[:c1.size] += c1 ret = c2 return pu.trimseq(ret) def hermmulx(c): """Multiply a Hermite series by x. Multiply the Hermite series `c` by x, where x is the independent variable. Parameters ---------- c : array_like 1-D array of Hermite series coefficients ordered from low to high. Returns ------- out : ndarray Array representing the result of the multiplication. Notes ----- The multiplication uses the recursion relationship for Hermite polynomials in the form .. math:: xP_i(x) = (P_{i + 1}(x)/2 + i*P_{i - 1}(x)) Examples -------- >>> from numpy.polynomial.hermite import hermmulx >>> hermmulx([1, 2, 3]) array([ 2. , 6.5, 1. , 1.5]) """ # c is a trimmed copy [c] = pu.as_series([c]) # The zero series needs special treatment if len(c) == 1 and c[0] == 0: return c prd = np.empty(len(c) + 1, dtype=c.dtype) prd[0] = c[0]*0 prd[1] = c[0]/2 for i in range(1, len(c)): prd[i + 1] = c[i]/2 prd[i - 1] += c[i]*i return prd def hermmul(c1, c2): """ Multiply one Hermite series by another. Returns the product of two Hermite series `c1` * `c2`. The arguments are sequences of coefficients, from lowest order "term" to highest, e.g., [1,2,3] represents the series ``P_0 + 2*P_1 + 3*P_2``. Parameters ---------- c1, c2 : array_like 1-D arrays of Hermite series coefficients ordered from low to high. Returns ------- out : ndarray Of Hermite series coefficients representing their product. See Also -------- hermadd, hermsub, hermdiv, hermpow Notes ----- In general, the (polynomial) product of two C-series results in terms that are not in the Hermite polynomial basis set. Thus, to express the product as a Hermite series, it is necessary to "reproject" the product onto said basis set, which may produce "unintuitive" (but correct) results; see Examples section below. Examples -------- >>> from numpy.polynomial.hermite import hermmul >>> hermmul([1, 2, 3], [0, 1, 2]) array([ 52., 29., 52., 7., 6.]) """ # s1, s2 are trimmed copies [c1, c2] = pu.as_series([c1, c2]) if len(c1) > len(c2): c = c2 xs = c1 else: c = c1 xs = c2 if len(c) == 1: c0 = c[0]*xs c1 = 0 elif len(c) == 2: c0 = c[0]*xs c1 = c[1]*xs else : nd = len(c) c0 = c[-2]*xs c1 = c[-1]*xs for i in range(3, len(c) + 1) : tmp = c0 nd = nd - 1 c0 = hermsub(c[-i]*xs, c1*(2*(nd - 1))) c1 = hermadd(tmp, hermmulx(c1)*2) return hermadd(c0, hermmulx(c1)*2) def hermdiv(c1, c2): """ Divide one Hermite series by another. Returns the quotient-with-remainder of two Hermite series `c1` / `c2`. The arguments are sequences of coefficients from lowest order "term" to highest, e.g., [1,2,3] represents the series ``P_0 + 2*P_1 + 3*P_2``. Parameters ---------- c1, c2 : array_like 1-D arrays of Hermite series coefficients ordered from low to high. Returns ------- [quo, rem] : ndarrays Of Hermite series coefficients representing the quotient and remainder. See Also -------- hermadd, hermsub, hermmul, hermpow Notes ----- In general, the (polynomial) division of one Hermite series by another results in quotient and remainder terms that are not in the Hermite polynomial basis set. Thus, to express these results as a Hermite series, it is necessary to "reproject" the results onto the Hermite basis set, which may produce "unintuitive" (but correct) results; see Examples section below. Examples -------- >>> from numpy.polynomial.hermite import hermdiv >>> hermdiv([ 52., 29., 52., 7., 6.], [0, 1, 2]) (array([ 1., 2., 3.]), array([ 0.])) >>> hermdiv([ 54., 31., 52., 7., 6.], [0, 1, 2]) (array([ 1., 2., 3.]), array([ 2., 2.])) >>> hermdiv([ 53., 30., 52., 7., 6.], [0, 1, 2]) (array([ 1., 2., 3.]), array([ 1., 1.])) """ # c1, c2 are trimmed copies [c1, c2] = pu.as_series([c1, c2]) if c2[-1] == 0 : raise ZeroDivisionError() lc1 = len(c1) lc2 = len(c2) if lc1 < lc2 : return c1[:1]*0, c1 elif lc2 == 1 : return c1/c2[-1], c1[:1]*0 else : quo = np.empty(lc1 - lc2 + 1, dtype=c1.dtype) rem = c1 for i in range(lc1 - lc2, - 1, -1): p = hermmul([0]*i + [1], c2) q = rem[-1]/p[-1] rem = rem[:-1] - q*p[:-1] quo[i] = q return quo, pu.trimseq(rem) def hermpow(c, pow, maxpower=16) : """Raise a Hermite series to a power. Returns the Hermite series `c` raised to the power `pow`. The argument `c` is a sequence of coefficients ordered from low to high. i.e., [1,2,3] is the series ``P_0 + 2*P_1 + 3*P_2.`` Parameters ---------- c : array_like 1-D array of Hermite series coefficients ordered from low to high. pow : integer Power to which the series will be raised maxpower : integer, optional Maximum power allowed. This is mainly to limit growth of the series to unmanageable size. Default is 16 Returns ------- coef : ndarray Hermite series of power. See Also -------- hermadd, hermsub, hermmul, hermdiv Examples -------- >>> from numpy.polynomial.hermite import hermpow >>> hermpow([1, 2, 3], 2) array([ 81., 52., 82., 12., 9.]) """ # c is a trimmed copy [c] = pu.as_series([c]) power = int(pow) if power != pow or power < 0 : raise ValueError("Power must be a non-negative integer.") elif maxpower is not None and power > maxpower : raise ValueError("Power is too large") elif power == 0 : return np.array([1], dtype=c.dtype) elif power == 1 : return c else : # This can be made more efficient by using powers of two # in the usual way. prd = c for i in range(2, power + 1) : prd = hermmul(prd, c) return prd def hermder(c, m=1, scl=1, axis=0) : """ Differentiate a Hermite series. Returns the Hermite series coefficients `c` differentiated `m` times along `axis`. At each iteration the result is multiplied by `scl` (the scaling factor is for use in a linear change of variable). The argument `c` is an array of coefficients from low to high degree along each axis, e.g., [1,2,3] represents the series ``1*H_0 + 2*H_1 + 3*H_2`` while [[1,2],[1,2]] represents ``1*H_0(x)*H_0(y) + 1*H_1(x)*H_0(y) + 2*H_0(x)*H_1(y) + 2*H_1(x)*H_1(y)`` if axis=0 is ``x`` and axis=1 is ``y``. Parameters ---------- c : array_like Array of Hermite series coefficients. If `c` is multidimensional the different axis correspond to different variables with the degree in each axis given by the corresponding index. m : int, optional Number of derivatives taken, must be non-negative. (Default: 1) scl : scalar, optional Each differentiation is multiplied by `scl`. The end result is multiplication by ``scl**m``. This is for use in a linear change of variable. (Default: 1) axis : int, optional Axis over which the derivative is taken. (Default: 0). .. versionadded:: 1.7.0 Returns ------- der : ndarray Hermite series of the derivative. See Also -------- hermint Notes ----- In general, the result of differentiating a Hermite series does not resemble the same operation on a power series. Thus the result of this function may be "unintuitive," albeit correct; see Examples section below. Examples -------- >>> from numpy.polynomial.hermite import hermder >>> hermder([ 1. , 0.5, 0.5, 0.5]) array([ 1., 2., 3.]) >>> hermder([-0.5, 1./2., 1./8., 1./12., 1./16.], m=2) array([ 1., 2., 3.]) """ c = np.array(c, ndmin=1, copy=1) if c.dtype.char in '?bBhHiIlLqQpP': c = c.astype(np.double) cnt, iaxis = [int(t) for t in [m, axis]] if cnt != m: raise ValueError("The order of derivation must be integer") if cnt < 0: raise ValueError("The order of derivation must be non-negative") if iaxis != axis: raise ValueError("The axis must be integer") if not -c.ndim <= iaxis < c.ndim: raise ValueError("The axis is out of range") if iaxis < 0: iaxis += c.ndim if cnt == 0: return c c = np.rollaxis(c, iaxis) n = len(c) if cnt >= n: c = c[:1]*0 else : for i in range(cnt): n = n - 1 c *= scl der = np.empty((n,) + c.shape[1:], dtype=c.dtype) for j in range(n, 0, -1): der[j - 1] = (2*j)*c[j] c = der c = np.rollaxis(c, 0, iaxis + 1) return c def hermint(c, m=1, k=[], lbnd=0, scl=1, axis=0): """ Integrate a Hermite series. Returns the Hermite series coefficients `c` integrated `m` times from `lbnd` along `axis`. At each iteration the resulting series is **multiplied** by `scl` and an integration constant, `k`, is added. The scaling factor is for use in a linear change of variable. ("Buyer beware": note that, depending on what one is doing, one may want `scl` to be the reciprocal of what one might expect; for more information, see the Notes section below.) The argument `c` is an array of coefficients from low to high degree along each axis, e.g., [1,2,3] represents the series ``H_0 + 2*H_1 + 3*H_2`` while [[1,2],[1,2]] represents ``1*H_0(x)*H_0(y) + 1*H_1(x)*H_0(y) + 2*H_0(x)*H_1(y) + 2*H_1(x)*H_1(y)`` if axis=0 is ``x`` and axis=1 is ``y``. Parameters ---------- c : array_like Array of Hermite series coefficients. If c is multidimensional the different axis correspond to different variables with the degree in each axis given by the corresponding index. m : int, optional Order of integration, must be positive. (Default: 1) k : {[], list, scalar}, optional Integration constant(s). The value of the first integral at ``lbnd`` is the first value in the list, the value of the second integral at ``lbnd`` is the second value, etc. If ``k == []`` (the default), all constants are set to zero. If ``m == 1``, a single scalar can be given instead of a list. lbnd : scalar, optional The lower bound of the integral. (Default: 0) scl : scalar, optional Following each integration the result is *multiplied* by `scl` before the integration constant is added. (Default: 1) axis : int, optional Axis over which the integral is taken. (Default: 0). .. versionadded:: 1.7.0 Returns ------- S : ndarray Hermite series coefficients of the integral. Raises ------ ValueError If ``m < 0``, ``len(k) > m``, ``np.isscalar(lbnd) == False``, or ``np.isscalar(scl) == False``. See Also -------- hermder Notes ----- Note that the result of each integration is *multiplied* by `scl`. Why is this important to note? Say one is making a linear change of variable :math:`u = ax + b` in an integral relative to `x`. Then .. math::`dx = du/a`, so one will need to set `scl` equal to :math:`1/a` - perhaps not what one would have first thought. Also note that, in general, the result of integrating a C-series needs to be "reprojected" onto the C-series basis set. Thus, typically, the result of this function is "unintuitive," albeit correct; see Examples section below. Examples -------- >>> from numpy.polynomial.hermite import hermint >>> hermint([1,2,3]) # integrate once, value 0 at 0. array([ 1. , 0.5, 0.5, 0.5]) >>> hermint([1,2,3], m=2) # integrate twice, value & deriv 0 at 0 array([-0.5 , 0.5 , 0.125 , 0.08333333, 0.0625 ]) >>> hermint([1,2,3], k=1) # integrate once, value 1 at 0. array([ 2. , 0.5, 0.5, 0.5]) >>> hermint([1,2,3], lbnd=-1) # integrate once, value 0 at -1 array([-2. , 0.5, 0.5, 0.5]) >>> hermint([1,2,3], m=2, k=[1,2], lbnd=-1) array([ 1.66666667, -0.5 , 0.125 , 0.08333333, 0.0625 ]) """ c = np.array(c, ndmin=1, copy=1) if c.dtype.char in '?bBhHiIlLqQpP': c = c.astype(np.double) if not np.iterable(k): k = [k] cnt, iaxis = [int(t) for t in [m, axis]] if cnt != m: raise ValueError("The order of integration must be integer") if cnt < 0 : raise ValueError("The order of integration must be non-negative") if len(k) > cnt : raise ValueError("Too many integration constants") if iaxis != axis: raise ValueError("The axis must be integer") if not -c.ndim <= iaxis < c.ndim: raise ValueError("The axis is out of range") if iaxis < 0: iaxis += c.ndim if cnt == 0: return c c = np.rollaxis(c, iaxis) k = list(k) + [0]*(cnt - len(k)) for i in range(cnt) : n = len(c) c *= scl if n == 1 and np.all(c[0] == 0): c[0] += k[i] else: tmp = np.empty((n + 1,) + c.shape[1:], dtype=c.dtype) tmp[0] = c[0]*0 tmp[1] = c[0]/2 for j in range(1, n): tmp[j + 1] = c[j]/(2*(j + 1)) tmp[0] += k[i] - hermval(lbnd, tmp) c = tmp c = np.rollaxis(c, 0, iaxis + 1) return c def hermval(x, c, tensor=True): """ Evaluate an Hermite series at points x. If `c` is of length `n + 1`, this function returns the value: .. math:: p(x) = c_0 * H_0(x) + c_1 * H_1(x) + ... + c_n * H_n(x) The parameter `x` is converted to an array only if it is a tuple or a list, otherwise it is treated as a scalar. In either case, either `x` or its elements must support multiplication and addition both with themselves and with the elements of `c`. If `c` is a 1-D array, then `p(x)` will have the same shape as `x`. If `c` is multidimensional, then the shape of the result depends on the value of `tensor`. If `tensor` is true the shape will be c.shape[1:] + x.shape. If `tensor` is false the shape will be c.shape[1:]. Note that scalars have shape (,). Trailing zeros in the coefficients will be used in the evaluation, so they should be avoided if efficiency is a concern. Parameters ---------- x : array_like, compatible object If `x` is a list or tuple, it is converted to an ndarray, otherwise it is left unchanged and treated as a scalar. In either case, `x` or its elements must support addition and multiplication with with themselves and with the elements of `c`. c : array_like Array of coefficients ordered so that the coefficients for terms of degree n are contained in c[n]. If `c` is multidimensional the remaining indices enumerate multiple polynomials. In the two dimensional case the coefficients may be thought of as stored in the columns of `c`. tensor : boolean, optional If True, the shape of the coefficient array is extended with ones on the right, one for each dimension of `x`. Scalars have dimension 0 for this action. The result is that every column of coefficients in `c` is evaluated for every element of `x`. If False, `x` is broadcast over the columns of `c` for the evaluation. This keyword is useful when `c` is multidimensional. The default value is True. .. versionadded:: 1.7.0 Returns ------- values : ndarray, algebra_like The shape of the return value is described above. See Also -------- hermval2d, hermgrid2d, hermval3d, hermgrid3d Notes ----- The evaluation uses Clenshaw recursion, aka synthetic division. Examples -------- >>> from numpy.polynomial.hermite import hermval >>> coef = [1,2,3] >>> hermval(1, coef) 11.0 >>> hermval([[1,2],[3,4]], coef) array([[ 11., 51.], [ 115., 203.]]) """ c = np.array(c, ndmin=1, copy=0) if c.dtype.char in '?bBhHiIlLqQpP': c = c.astype(np.double) if isinstance(x, (tuple, list)): x = np.asarray(x) if isinstance(x, np.ndarray) and tensor: c = c.reshape(c.shape + (1,)*x.ndim) x2 = x*2 if len(c) == 1 : c0 = c[0] c1 = 0 elif len(c) == 2 : c0 = c[0] c1 = c[1] else : nd = len(c) c0 = c[-2] c1 = c[-1] for i in range(3, len(c) + 1) : tmp = c0 nd = nd - 1 c0 = c[-i] - c1*(2*(nd - 1)) c1 = tmp + c1*x2 return c0 + c1*x2 def hermval2d(x, y, c): """ Evaluate a 2-D Hermite series at points (x, y). This function returns the values: .. math:: p(x,y) = \\sum_{i,j} c_{i,j} * H_i(x) * H_j(y) The parameters `x` and `y` are converted to arrays only if they are tuples or a lists, otherwise they are treated as a scalars and they must have the same shape after conversion. In either case, either `x` and `y` or their elements must support multiplication and addition both with themselves and with the elements of `c`. If `c` is a 1-D array a one is implicitly appended to its shape to make it 2-D. The shape of the result will be c.shape[2:] + x.shape. Parameters ---------- x, y : array_like, compatible objects The two dimensional series is evaluated at the points `(x, y)`, where `x` and `y` must have the same shape. If `x` or `y` is a list or tuple, it is first converted to an ndarray, otherwise it is left unchanged and if it isn't an ndarray it is treated as a scalar. c : array_like Array of coefficients ordered so that the coefficient of the term of multi-degree i,j is contained in ``c[i,j]``. If `c` has dimension greater than two the remaining indices enumerate multiple sets of coefficients. Returns ------- values : ndarray, compatible object The values of the two dimensional polynomial at points formed with pairs of corresponding values from `x` and `y`. See Also -------- hermval, hermgrid2d, hermval3d, hermgrid3d Notes ----- .. versionadded::1.7.0 """ try: x, y = np.array((x, y), copy=0) except: raise ValueError('x, y are incompatible') c = hermval(x, c) c = hermval(y, c, tensor=False) return c def hermgrid2d(x, y, c): """ Evaluate a 2-D Hermite series on the Cartesian product of x and y. This function returns the values: .. math:: p(a,b) = \sum_{i,j} c_{i,j} * H_i(a) * H_j(b) where the points `(a, b)` consist of all pairs formed by taking `a` from `x` and `b` from `y`. The resulting points form a grid with `x` in the first dimension and `y` in the second. The parameters `x` and `y` are converted to arrays only if they are tuples or a lists, otherwise they are treated as a scalars. In either case, either `x` and `y` or their elements must support multiplication and addition both with themselves and with the elements of `c`. If `c` has fewer than two dimensions, ones are implicitly appended to its shape to make it 2-D. The shape of the result will be c.shape[2:] + x.shape. Parameters ---------- x, y : array_like, compatible objects The two dimensional series is evaluated at the points in the Cartesian product of `x` and `y`. If `x` or `y` is a list or tuple, it is first converted to an ndarray, otherwise it is left unchanged and, if it isn't an ndarray, it is treated as a scalar. c : array_like Array of coefficients ordered so that the coefficients for terms of degree i,j are contained in ``c[i,j]``. If `c` has dimension greater than two the remaining indices enumerate multiple sets of coefficients. Returns ------- values : ndarray, compatible object The values of the two dimensional polynomial at points in the Cartesian product of `x` and `y`. See Also -------- hermval, hermval2d, hermval3d, hermgrid3d Notes ----- .. versionadded::1.7.0 """ c = hermval(x, c) c = hermval(y, c) return c def hermval3d(x, y, z, c): """ Evaluate a 3-D Hermite series at points (x, y, z). This function returns the values: .. math:: p(x,y,z) = \\sum_{i,j,k} c_{i,j,k} * H_i(x) * H_j(y) * H_k(z) The parameters `x`, `y`, and `z` are converted to arrays only if they are tuples or a lists, otherwise they are treated as a scalars and they must have the same shape after conversion. In either case, either `x`, `y`, and `z` or their elements must support multiplication and addition both with themselves and with the elements of `c`. If `c` has fewer than 3 dimensions, ones are implicitly appended to its shape to make it 3-D. The shape of the result will be c.shape[3:] + x.shape. Parameters ---------- x, y, z : array_like, compatible object The three dimensional series is evaluated at the points `(x, y, z)`, where `x`, `y`, and `z` must have the same shape. If any of `x`, `y`, or `z` is a list or tuple, it is first converted to an ndarray, otherwise it is left unchanged and if it isn't an ndarray it is treated as a scalar. c : array_like Array of coefficients ordered so that the coefficient of the term of multi-degree i,j,k is contained in ``c[i,j,k]``. If `c` has dimension greater than 3 the remaining indices enumerate multiple sets of coefficients. Returns ------- values : ndarray, compatible object The values of the multidimensional polynomial on points formed with triples of corresponding values from `x`, `y`, and `z`. See Also -------- hermval, hermval2d, hermgrid2d, hermgrid3d Notes ----- .. versionadded::1.7.0 """ try: x, y, z = np.array((x, y, z), copy=0) except: raise ValueError('x, y, z are incompatible') c = hermval(x, c) c = hermval(y, c, tensor=False) c = hermval(z, c, tensor=False) return c def hermgrid3d(x, y, z, c): """ Evaluate a 3-D Hermite series on the Cartesian product of x, y, and z. This function returns the values: .. math:: p(a,b,c) = \\sum_{i,j,k} c_{i,j,k} * H_i(a) * H_j(b) * H_k(c) where the points `(a, b, c)` consist of all triples formed by taking `a` from `x`, `b` from `y`, and `c` from `z`. The resulting points form a grid with `x` in the first dimension, `y` in the second, and `z` in the third. The parameters `x`, `y`, and `z` are converted to arrays only if they are tuples or a lists, otherwise they are treated as a scalars. In either case, either `x`, `y`, and `z` or their elements must support multiplication and addition both with themselves and with the elements of `c`. If `c` has fewer than three dimensions, ones are implicitly appended to its shape to make it 3-D. The shape of the result will be c.shape[3:] + x.shape + y.shape + z.shape. Parameters ---------- x, y, z : array_like, compatible objects The three dimensional series is evaluated at the points in the Cartesian product of `x`, `y`, and `z`. If `x`,`y`, or `z` is a list or tuple, it is first converted to an ndarray, otherwise it is left unchanged and, if it isn't an ndarray, it is treated as a scalar. c : array_like Array of coefficients ordered so that the coefficients for terms of degree i,j are contained in ``c[i,j]``. If `c` has dimension greater than two the remaining indices enumerate multiple sets of coefficients. Returns ------- values : ndarray, compatible object The values of the two dimensional polynomial at points in the Cartesian product of `x` and `y`. See Also -------- hermval, hermval2d, hermgrid2d, hermval3d Notes ----- .. versionadded::1.7.0 """ c = hermval(x, c) c = hermval(y, c) c = hermval(z, c) return c def hermvander(x, deg) : """Pseudo-Vandermonde matrix of given degree. Returns the pseudo-Vandermonde matrix of degree `deg` and sample points `x`. The pseudo-Vandermonde matrix is defined by .. math:: V[..., i] = H_i(x), where `0 <= i <= deg`. The leading indices of `V` index the elements of `x` and the last index is the degree of the Hermite polynomial. If `c` is a 1-D array of coefficients of length `n + 1` and `V` is the array ``V = hermvander(x, n)``, then ``np.dot(V, c)`` and ``hermval(x, c)`` are the same up to roundoff. This equivalence is useful both for least squares fitting and for the evaluation of a large number of Hermite series of the same degree and sample points. Parameters ---------- x : array_like Array of points. The dtype is converted to float64 or complex128 depending on whether any of the elements are complex. If `x` is scalar it is converted to a 1-D array. deg : int Degree of the resulting matrix. Returns ------- vander : ndarray The pseudo-Vandermonde matrix. The shape of the returned matrix is ``x.shape + (deg + 1,)``, where The last index is the degree of the corresponding Hermite polynomial. The dtype will be the same as the converted `x`. Examples -------- >>> from numpy.polynomial.hermite import hermvander >>> x = np.array([-1, 0, 1]) >>> hermvander(x, 3) array([[ 1., -2., 2., 4.], [ 1., 0., -2., -0.], [ 1., 2., 2., -4.]]) """ ideg = int(deg) if ideg != deg: raise ValueError("deg must be integer") if ideg < 0: raise ValueError("deg must be non-negative") x = np.array(x, copy=0, ndmin=1) + 0.0 dims = (ideg + 1,) + x.shape dtyp = x.dtype v = np.empty(dims, dtype=dtyp) v[0] = x*0 + 1 if ideg > 0 : x2 = x*2 v[1] = x2 for i in range(2, ideg + 1) : v[i] = (v[i-1]*x2 - v[i-2]*(2*(i - 1))) return np.rollaxis(v, 0, v.ndim) def hermvander2d(x, y, deg) : """Pseudo-Vandermonde matrix of given degrees. Returns the pseudo-Vandermonde matrix of degrees `deg` and sample points `(x, y)`. The pseudo-Vandermonde matrix is defined by .. math:: V[..., deg[1]*i + j] = H_i(x) * H_j(y), where `0 <= i <= deg[0]` and `0 <= j <= deg[1]`. The leading indices of `V` index the points `(x, y)` and the last index encodes the degrees of the Hermite polynomials. If ``V = hermvander2d(x, y, [xdeg, ydeg])``, then the columns of `V` correspond to the elements of a 2-D coefficient array `c` of shape (xdeg + 1, ydeg + 1) in the order .. math:: c_{00}, c_{01}, c_{02} ... , c_{10}, c_{11}, c_{12} ... and ``np.dot(V, c.flat)`` and ``hermval2d(x, y, c)`` will be the same up to roundoff. This equivalence is useful both for least squares fitting and for the evaluation of a large number of 2-D Hermite series of the same degrees and sample points. Parameters ---------- x, y : array_like Arrays of point coordinates, all of the same shape. The dtypes will be converted to either float64 or complex128 depending on whether any of the elements are complex. Scalars are converted to 1-D arrays. deg : list of ints List of maximum degrees of the form [x_deg, y_deg]. Returns ------- vander2d : ndarray The shape of the returned matrix is ``x.shape + (order,)``, where :math:`order = (deg[0]+1)*(deg([1]+1)`. The dtype will be the same as the converted `x` and `y`. See Also -------- hermvander, hermvander3d. hermval2d, hermval3d Notes ----- .. versionadded::1.7.0 """ ideg = [int(d) for d in deg] is_valid = [id == d and id >= 0 for id, d in zip(ideg, deg)] if is_valid != [1, 1]: raise ValueError("degrees must be non-negative integers") degx, degy = ideg x, y = np.array((x, y), copy=0) + 0.0 vx = hermvander(x, degx) vy = hermvander(y, degy) v = vx[..., None]*vy[..., None, :] return v.reshape(v.shape[:-2] + (-1,)) def hermvander3d(x, y, z, deg) : """Pseudo-Vandermonde matrix of given degrees. Returns the pseudo-Vandermonde matrix of degrees `deg` and sample points `(x, y, z)`. If `l, m, n` are the given degrees in `x, y, z`, then The pseudo-Vandermonde matrix is defined by .. math:: V[..., (m+1)(n+1)i + (n+1)j + k] = H_i(x)*H_j(y)*H_k(z), where `0 <= i <= l`, `0 <= j <= m`, and `0 <= j <= n`. The leading indices of `V` index the points `(x, y, z)` and the last index encodes the degrees of the Hermite polynomials. If ``V = hermvander3d(x, y, z, [xdeg, ydeg, zdeg])``, then the columns of `V` correspond to the elements of a 3-D coefficient array `c` of shape (xdeg + 1, ydeg + 1, zdeg + 1) in the order .. math:: c_{000}, c_{001}, c_{002},... , c_{010}, c_{011}, c_{012},... and ``np.dot(V, c.flat)`` and ``hermval3d(x, y, z, c)`` will be the same up to roundoff. This equivalence is useful both for least squares fitting and for the evaluation of a large number of 3-D Hermite series of the same degrees and sample points. Parameters ---------- x, y, z : array_like Arrays of point coordinates, all of the same shape. The dtypes will be converted to either float64 or complex128 depending on whether any of the elements are complex. Scalars are converted to 1-D arrays. deg : list of ints List of maximum degrees of the form [x_deg, y_deg, z_deg]. Returns ------- vander3d : ndarray The shape of the returned matrix is ``x.shape + (order,)``, where :math:`order = (deg[0]+1)*(deg([1]+1)*(deg[2]+1)`. The dtype will be the same as the converted `x`, `y`, and `z`. See Also -------- hermvander, hermvander3d. hermval2d, hermval3d Notes ----- .. versionadded::1.7.0 """ ideg = [int(d) for d in deg] is_valid = [id == d and id >= 0 for id, d in zip(ideg, deg)] if is_valid != [1, 1, 1]: raise ValueError("degrees must be non-negative integers") degx, degy, degz = ideg x, y, z = np.array((x, y, z), copy=0) + 0.0 vx = hermvander(x, degx) vy = hermvander(y, degy) vz = hermvander(z, degz) v = vx[..., None, None]*vy[..., None, :, None]*vz[..., None, None, :] return v.reshape(v.shape[:-3] + (-1,)) def hermfit(x, y, deg, rcond=None, full=False, w=None): """ Least squares fit of Hermite series to data. Return the coefficients of a Hermite series of degree `deg` that is the least squares fit to the data values `y` given at points `x`. If `y` is 1-D the returned coefficients will also be 1-D. If `y` is 2-D multiple fits are done, one for each column of `y`, and the resulting coefficients are stored in the corresponding columns of a 2-D return. The fitted polynomial(s) are in the form .. math:: p(x) = c_0 + c_1 * H_1(x) + ... + c_n * H_n(x), where `n` is `deg`. Since numpy version 1.7.0, hermfit also supports NA. If any of the elements of `x`, `y`, or `w` are NA, then the corresponding rows of the linear least squares problem (see Notes) are set to 0. If `y` is 2-D, then an NA in any row of `y` invalidates that whole row. Parameters ---------- x : array_like, shape (M,) x-coordinates of the M sample points ``(x[i], y[i])``. y : array_like, shape (M,) or (M, K) y-coordinates of the sample points. Several data sets of sample points sharing the same x-coordinates can be fitted at once by passing in a 2D-array that contains one dataset per column. deg : int Degree of the fitting polynomial rcond : float, optional Relative condition number of the fit. Singular values smaller than this relative to the largest singular value will be ignored. The default value is len(x)*eps, where eps is the relative precision of the float type, about 2e-16 in most cases. full : bool, optional Switch determining nature of return value. When it is False (the default) just the coefficients are returned, when True diagnostic information from the singular value decomposition is also returned. w : array_like, shape (`M`,), optional Weights. If not None, the contribution of each point ``(x[i],y[i])`` to the fit is weighted by `w[i]`. Ideally the weights are chosen so that the errors of the products ``w[i]*y[i]`` all have the same variance. The default value is None. Returns ------- coef : ndarray, shape (M,) or (M, K) Hermite coefficients ordered from low to high. If `y` was 2-D, the coefficients for the data in column k of `y` are in column `k`. [residuals, rank, singular_values, rcond] : present when `full` = True Residuals of the least-squares fit, the effective rank of the scaled Vandermonde matrix and its singular values, and the specified value of `rcond`. For more details, see `linalg.lstsq`. Warns ----- RankWarning The rank of the coefficient matrix in the least-squares fit is deficient. The warning is only raised if `full` = False. The warnings can be turned off by >>> import warnings >>> warnings.simplefilter('ignore', RankWarning) See Also -------- chebfit, legfit, lagfit, polyfit, hermefit hermval : Evaluates a Hermite series. hermvander : Vandermonde matrix of Hermite series. hermweight : Hermite weight function linalg.lstsq : Computes a least-squares fit from the matrix. scipy.interpolate.UnivariateSpline : Computes spline fits. Notes ----- The solution is the coefficients of the Hermite series `p` that minimizes the sum of the weighted squared errors .. math:: E = \\sum_j w_j^2 * |y_j - p(x_j)|^2, where the :math:`w_j` are the weights. This problem is solved by setting up the (typically) overdetermined matrix equation .. math:: V(x) * c = w * y, where `V` is the weighted pseudo Vandermonde matrix of `x`, `c` are the coefficients to be solved for, `w` are the weights, `y` are the observed values. This equation is then solved using the singular value decomposition of `V`. If some of the singular values of `V` are so small that they are neglected, then a `RankWarning` will be issued. This means that the coefficient values may be poorly determined. Using a lower order fit will usually get rid of the warning. The `rcond` parameter can also be set to a value smaller than its default, but the resulting fit may be spurious and have large contributions from roundoff error. Fits using Hermite series are probably most useful when the data can be approximated by ``sqrt(w(x)) * p(x)``, where `w(x)` is the Hermite weight. In that case the weight ``sqrt(w(x[i])`` should be used together with data values ``y[i]/sqrt(w(x[i])``. The weight function is available as `hermweight`. References ---------- .. [1] Wikipedia, "Curve fitting", http://en.wikipedia.org/wiki/Curve_fitting Examples -------- >>> from numpy.polynomial.hermite import hermfit, hermval >>> x = np.linspace(-10, 10) >>> err = np.random.randn(len(x))/10 >>> y = hermval(x, [1, 2, 3]) + err >>> hermfit(x, y, 2) array([ 0.97902637, 1.99849131, 3.00006 ]) """ order = int(deg) + 1 x = np.asarray(x) + 0.0 y = np.asarray(y) + 0.0 # check arguments. if deg < 0 : raise ValueError("expected deg >= 0") if x.ndim != 1: raise TypeError("expected 1D vector for x") if x.size == 0: raise TypeError("expected non-empty vector for x") if y.ndim < 1 or y.ndim > 2 : raise TypeError("expected 1D or 2D array for y") if len(x) != len(y): raise TypeError("expected x and y to have same length") # set up the least squares matrices in transposed form lhs = hermvander(x, deg).T rhs = y.T if w is not None: w = np.asarray(w) + 0.0 if w.ndim != 1: raise TypeError("expected 1D vector for w") if len(x) != len(w): raise TypeError("expected x and w to have same length") # apply weights. Don't use inplace operations as they # can cause problems with NA. lhs = lhs * w rhs = rhs * w # set rcond if rcond is None : rcond = len(x)*np.finfo(x.dtype).eps # Determine the norms of the design matrix columns. if issubclass(lhs.dtype.type, np.complexfloating): scl = np.sqrt((np.square(lhs.real) + np.square(lhs.imag)).sum(1)) else: scl = np.sqrt(np.square(lhs).sum(1)) scl[scl == 0] = 1 # Solve the least squares problem. c, resids, rank, s = la.lstsq(lhs.T/scl, rhs.T, rcond) c = (c.T/scl).T # warn on rank reduction if rank != order and not full: msg = "The fit may be poorly conditioned" warnings.warn(msg, pu.RankWarning) if full : return c, [resids, rank, s, rcond] else : return c def hermcompanion(c): """Return the scaled companion matrix of c. The basis polynomials are scaled so that the companion matrix is symmetric when `c` is an Hermite basis polynomial. This provides better eigenvalue estimates than the unscaled case and for basis polynomials the eigenvalues are guaranteed to be real if `numpy.linalg.eigvalsh` is used to obtain them. Parameters ---------- c : array_like 1-D array of Hermite series coefficients ordered from low to high degree. Returns ------- mat : ndarray Scaled companion matrix of dimensions (deg, deg). Notes ----- .. versionadded::1.7.0 """ accprod = np.multiply.accumulate # c is a trimmed copy [c] = pu.as_series([c]) if len(c) < 2: raise ValueError('Series must have maximum degree of at least 1.') if len(c) == 2: return np.array(-.5*c[0]/c[1]) n = len(c) - 1 mat = np.zeros((n, n), dtype=c.dtype) scl = np.hstack((1., np.sqrt(2.*np.arange(1,n)))) scl = np.multiply.accumulate(scl) top = mat.reshape(-1)[1::n+1] bot = mat.reshape(-1)[n::n+1] top[...] = np.sqrt(.5*np.arange(1,n)) bot[...] = top mat[:,-1] -= (c[:-1]/c[-1])*(scl/scl[-1])*.5 return mat def hermroots(c): """ Compute the roots of a Hermite series. Return the roots (a.k.a. "zeros") of the polynomial .. math:: p(x) = \\sum_i c[i] * H_i(x). Parameters ---------- c : 1-D array_like 1-D array of coefficients. Returns ------- out : ndarray Array of the roots of the series. If all the roots are real, then `out` is also real, otherwise it is complex. See Also -------- polyroots, legroots, lagroots, chebroots, hermeroots Notes ----- The root estimates are obtained as the eigenvalues of the companion matrix, Roots far from the origin of the complex plane may have large errors due to the numerical instability of the series for such values. Roots with multiplicity greater than 1 will also show larger errors as the value of the series near such points is relatively insensitive to errors in the roots. Isolated roots near the origin can be improved by a few iterations of Newton's method. The Hermite series basis polynomials aren't powers of `x` so the results of this function may seem unintuitive. Examples -------- >>> from numpy.polynomial.hermite import hermroots, hermfromroots >>> coef = hermfromroots([-1, 0, 1]) >>> coef array([ 0. , 0.25 , 0. , 0.125]) >>> hermroots(coef) array([ -1.00000000e+00, -1.38777878e-17, 1.00000000e+00]) """ # c is a trimmed copy [c] = pu.as_series([c]) if len(c) <= 1 : return np.array([], dtype=c.dtype) if len(c) == 2 : return np.array([-.5*c[0]/c[1]]) m = hermcompanion(c) r = la.eigvals(m) r.sort() return r def hermgauss(deg): """ Gauss-Hermite quadrature. Computes the sample points and weights for Gauss-Hermite quadrature. These sample points and weights will correctly integrate polynomials of degree :math:`2*deg - 1` or less over the interval :math:`[-\inf, \inf]` with the weight function :math:`f(x) = \exp(-x^2)`. Parameters ---------- deg : int Number of sample points and weights. It must be >= 1. Returns ------- x : ndarray 1-D ndarray containing the sample points. y : ndarray 1-D ndarray containing the weights. Notes ----- .. versionadded::1.7.0 The results have only been tested up to degree 100, higher degrees may be problematic. The weights are determined by using the fact that .. math:: w_k = c / (H'_n(x_k) * H_{n-1}(x_k)) where :math:`c` is a constant independent of :math:`k` and :math:`x_k` is the k'th root of :math:`H_n`, and then scaling the results to get the right value when integrating 1. """ ideg = int(deg) if ideg != deg or ideg < 1: raise ValueError("deg must be a non-negative integer") # first approximation of roots. We use the fact that the companion # matrix is symmetric in this case in order to obtain better zeros. c = np.array([0]*deg + [1]) m = hermcompanion(c) x = la.eigvals(m) x.sort() # improve roots by one application of Newton dy = hermval(x, c) df = hermval(x, hermder(c)) x -= dy/df # compute the weights. We scale the factor to avoid possible numerical # overflow. fm = hermval(x, c[1:]) fm /= np.abs(fm).max() df /= np.abs(df).max() w = 1/(fm * df) # for Hermite we can also symmetrize w = (w + w[::-1])/2 x = (x - x[::-1])/2 # scale w to get the right value w *= np.sqrt(np.pi) / w.sum() return x, w def hermweight(x): """ Weight function of the Hermite polynomials. The weight function is :math:`\exp(-x^2)` and the interval of integration is :math:`[-\inf, \inf]`. the Hermite polynomials are orthogonal, but not normalized, with respect to this weight function. Parameters ---------- x : array_like Values at which the weight function will be computed. Returns ------- w : ndarray The weight function at `x`. Notes ----- .. versionadded::1.7.0 """ w = np.exp(-x**2) return w # # Hermite series class # exec(polytemplate.substitute(name='Hermite', nick='herm', domain='[-1,1]'))

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0.597416

from __future__ import division, absolute_import import numpy as np import numpy.linalg as la from . import polyutils as pu import warnings from .polytemplate import polytemplate __all__ = ['hermzero', 'hermone', 'hermx', 'hermdomain', 'hermline', 'hermadd', 'hermsub', 'hermmulx', 'hermmul', 'hermdiv', 'hermpow', 'hermval', 'hermder', 'hermint', 'herm2poly', 'poly2herm', 'hermfromroots', 'hermvander', 'hermfit', 'hermtrim', 'hermroots', 'Hermite', 'hermval2d', 'hermval3d', 'hermgrid2d', 'hermgrid3d', 'hermvander2d', 'hermvander3d', 'hermcompanion', 'hermgauss', 'hermweight'] hermtrim = pu.trimcoef def poly2herm(pol) : [pol] = pu.as_series([pol]) deg = len(pol) - 1 res = 0 for i in range(deg, -1, -1) : res = hermadd(hermmulx(res), pol[i]) return res def herm2poly(c) : from .polynomial import polyadd, polysub, polymulx [c] = pu.as_series([c]) n = len(c) if n == 1: return c if n == 2: c[1] *= 2 return c else: c0 = c[-2] c1 = c[-1] for i in range(n - 1, 1, -1) : tmp = c0 c0 = polysub(c[i - 2], c1*(2*(i - 1))) c1 = polyadd(tmp, polymulx(c1)*2) return polyadd(c0, polymulx(c1)*2) hermdomain = np.array([-1,1]) hermzero = np.array([0]) hermone = np.array([1]) hermx = np.array([0, 1/2]) def hermline(off, scl) : if scl != 0 : return np.array([off,scl/2]) else : return np.array([off]) def hermfromroots(roots) : if len(roots) == 0 : return np.ones(1) else : [roots] = pu.as_series([roots], trim=False) roots.sort() p = [hermline(-r, 1) for r in roots] n = len(p) while n > 1: m, r = divmod(n, 2) tmp = [hermmul(p[i], p[i+m]) for i in range(m)] if r: tmp[0] = hermmul(tmp[0], p[-1]) p = tmp n = m return p[0] def hermadd(c1, c2): [c1, c2] = pu.as_series([c1, c2]) if len(c1) > len(c2) : c1[:c2.size] += c2 ret = c1 else : c2[:c1.size] += c1 ret = c2 return pu.trimseq(ret) def hermsub(c1, c2): [c1, c2] = pu.as_series([c1, c2]) if len(c1) > len(c2) : c1[:c2.size] -= c2 ret = c1 else : c2 = -c2 c2[:c1.size] += c1 ret = c2 return pu.trimseq(ret) def hermmulx(c): [c] = pu.as_series([c]) if len(c) == 1 and c[0] == 0: return c prd = np.empty(len(c) + 1, dtype=c.dtype) prd[0] = c[0]*0 prd[1] = c[0]/2 for i in range(1, len(c)): prd[i + 1] = c[i]/2 prd[i - 1] += c[i]*i return prd def hermmul(c1, c2): [c1, c2] = pu.as_series([c1, c2]) if len(c1) > len(c2): c = c2 xs = c1 else: c = c1 xs = c2 if len(c) == 1: c0 = c[0]*xs c1 = 0 elif len(c) == 2: c0 = c[0]*xs c1 = c[1]*xs else : nd = len(c) c0 = c[-2]*xs c1 = c[-1]*xs for i in range(3, len(c) + 1) : tmp = c0 nd = nd - 1 c0 = hermsub(c[-i]*xs, c1*(2*(nd - 1))) c1 = hermadd(tmp, hermmulx(c1)*2) return hermadd(c0, hermmulx(c1)*2) def hermdiv(c1, c2): [c1, c2] = pu.as_series([c1, c2]) if c2[-1] == 0 : raise ZeroDivisionError() lc1 = len(c1) lc2 = len(c2) if lc1 < lc2 : return c1[:1]*0, c1 elif lc2 == 1 : return c1/c2[-1], c1[:1]*0 else : quo = np.empty(lc1 - lc2 + 1, dtype=c1.dtype) rem = c1 for i in range(lc1 - lc2, - 1, -1): p = hermmul([0]*i + [1], c2) q = rem[-1]/p[-1] rem = rem[:-1] - q*p[:-1] quo[i] = q return quo, pu.trimseq(rem) def hermpow(c, pow, maxpower=16) : [c] = pu.as_series([c]) power = int(pow) if power != pow or power < 0 : raise ValueError("Power must be a non-negative integer.") elif maxpower is not None and power > maxpower : raise ValueError("Power is too large") elif power == 0 : return np.array([1], dtype=c.dtype) elif power == 1 : return c else : prd = c for i in range(2, power + 1) : prd = hermmul(prd, c) return prd def hermder(c, m=1, scl=1, axis=0) : c = np.array(c, ndmin=1, copy=1) if c.dtype.char in '?bBhHiIlLqQpP': c = c.astype(np.double) cnt, iaxis = [int(t) for t in [m, axis]] if cnt != m: raise ValueError("The order of derivation must be integer") if cnt < 0: raise ValueError("The order of derivation must be non-negative") if iaxis != axis: raise ValueError("The axis must be integer") if not -c.ndim <= iaxis < c.ndim: raise ValueError("The axis is out of range") if iaxis < 0: iaxis += c.ndim if cnt == 0: return c c = np.rollaxis(c, iaxis) n = len(c) if cnt >= n: c = c[:1]*0 else : for i in range(cnt): n = n - 1 c *= scl der = np.empty((n,) + c.shape[1:], dtype=c.dtype) for j in range(n, 0, -1): der[j - 1] = (2*j)*c[j] c = der c = np.rollaxis(c, 0, iaxis + 1) return c def hermint(c, m=1, k=[], lbnd=0, scl=1, axis=0): c = np.array(c, ndmin=1, copy=1) if c.dtype.char in '?bBhHiIlLqQpP': c = c.astype(np.double) if not np.iterable(k): k = [k] cnt, iaxis = [int(t) for t in [m, axis]] if cnt != m: raise ValueError("The order of integration must be integer") if cnt < 0 : raise ValueError("The order of integration must be non-negative") if len(k) > cnt : raise ValueError("Too many integration constants") if iaxis != axis: raise ValueError("The axis must be integer") if not -c.ndim <= iaxis < c.ndim: raise ValueError("The axis is out of range") if iaxis < 0: iaxis += c.ndim if cnt == 0: return c c = np.rollaxis(c, iaxis) k = list(k) + [0]*(cnt - len(k)) for i in range(cnt) : n = len(c) c *= scl if n == 1 and np.all(c[0] == 0): c[0] += k[i] else: tmp = np.empty((n + 1,) + c.shape[1:], dtype=c.dtype) tmp[0] = c[0]*0 tmp[1] = c[0]/2 for j in range(1, n): tmp[j + 1] = c[j]/(2*(j + 1)) tmp[0] += k[i] - hermval(lbnd, tmp) c = tmp c = np.rollaxis(c, 0, iaxis + 1) return c def hermval(x, c, tensor=True): c = np.array(c, ndmin=1, copy=0) if c.dtype.char in '?bBhHiIlLqQpP': c = c.astype(np.double) if isinstance(x, (tuple, list)): x = np.asarray(x) if isinstance(x, np.ndarray) and tensor: c = c.reshape(c.shape + (1,)*x.ndim) x2 = x*2 if len(c) == 1 : c0 = c[0] c1 = 0 elif len(c) == 2 : c0 = c[0] c1 = c[1] else : nd = len(c) c0 = c[-2] c1 = c[-1] for i in range(3, len(c) + 1) : tmp = c0 nd = nd - 1 c0 = c[-i] - c1*(2*(nd - 1)) c1 = tmp + c1*x2 return c0 + c1*x2 def hermval2d(x, y, c): try: x, y = np.array((x, y), copy=0) except: raise ValueError('x, y are incompatible') c = hermval(x, c) c = hermval(y, c, tensor=False) return c def hermgrid2d(x, y, c): c = hermval(x, c) c = hermval(y, c) return c def hermval3d(x, y, z, c): try: x, y, z = np.array((x, y, z), copy=0) except: raise ValueError('x, y, z are incompatible') c = hermval(x, c) c = hermval(y, c, tensor=False) c = hermval(z, c, tensor=False) return c def hermgrid3d(x, y, z, c): c = hermval(x, c) c = hermval(y, c) c = hermval(z, c) return c def hermvander(x, deg) : ideg = int(deg) if ideg != deg: raise ValueError("deg must be integer") if ideg < 0: raise ValueError("deg must be non-negative") x = np.array(x, copy=0, ndmin=1) + 0.0 dims = (ideg + 1,) + x.shape dtyp = x.dtype v = np.empty(dims, dtype=dtyp) v[0] = x*0 + 1 if ideg > 0 : x2 = x*2 v[1] = x2 for i in range(2, ideg + 1) : v[i] = (v[i-1]*x2 - v[i-2]*(2*(i - 1))) return np.rollaxis(v, 0, v.ndim) def hermvander2d(x, y, deg) : ideg = [int(d) for d in deg] is_valid = [id == d and id >= 0 for id, d in zip(ideg, deg)] if is_valid != [1, 1]: raise ValueError("degrees must be non-negative integers") degx, degy = ideg x, y = np.array((x, y), copy=0) + 0.0 vx = hermvander(x, degx) vy = hermvander(y, degy) v = vx[..., None]*vy[..., None, :] return v.reshape(v.shape[:-2] + (-1,)) def hermvander3d(x, y, z, deg) : ideg = [int(d) for d in deg] is_valid = [id == d and id >= 0 for id, d in zip(ideg, deg)] if is_valid != [1, 1, 1]: raise ValueError("degrees must be non-negative integers") degx, degy, degz = ideg x, y, z = np.array((x, y, z), copy=0) + 0.0 vx = hermvander(x, degx) vy = hermvander(y, degy) vz = hermvander(z, degz) v = vx[..., None, None]*vy[..., None, :, None]*vz[..., None, None, :] return v.reshape(v.shape[:-3] + (-1,)) def hermfit(x, y, deg, rcond=None, full=False, w=None): order = int(deg) + 1 x = np.asarray(x) + 0.0 y = np.asarray(y) + 0.0 if deg < 0 : raise ValueError("expected deg >= 0") if x.ndim != 1: raise TypeError("expected 1D vector for x") if x.size == 0: raise TypeError("expected non-empty vector for x") if y.ndim < 1 or y.ndim > 2 : raise TypeError("expected 1D or 2D array for y") if len(x) != len(y): raise TypeError("expected x and y to have same length") lhs = hermvander(x, deg).T rhs = y.T if w is not None: w = np.asarray(w) + 0.0 if w.ndim != 1: raise TypeError("expected 1D vector for w") if len(x) != len(w): raise TypeError("expected x and w to have same length") # can cause problems with NA. lhs = lhs * w rhs = rhs * w # set rcond if rcond is None : rcond = len(x)*np.finfo(x.dtype).eps # Determine the norms of the design matrix columns. if issubclass(lhs.dtype.type, np.complexfloating): scl = np.sqrt((np.square(lhs.real) + np.square(lhs.imag)).sum(1)) else: scl = np.sqrt(np.square(lhs).sum(1)) scl[scl == 0] = 1 # Solve the least squares problem. c, resids, rank, s = la.lstsq(lhs.T/scl, rhs.T, rcond) c = (c.T/scl).T # warn on rank reduction if rank != order and not full: msg = "The fit may be poorly conditioned" warnings.warn(msg, pu.RankWarning) if full : return c, [resids, rank, s, rcond] else : return c def hermcompanion(c): accprod = np.multiply.accumulate # c is a trimmed copy [c] = pu.as_series([c]) if len(c) < 2: raise ValueError('Series must have maximum degree of at least 1.') if len(c) == 2: return np.array(-.5*c[0]/c[1]) n = len(c) - 1 mat = np.zeros((n, n), dtype=c.dtype) scl = np.hstack((1., np.sqrt(2.*np.arange(1,n)))) scl = np.multiply.accumulate(scl) top = mat.reshape(-1)[1::n+1] bot = mat.reshape(-1)[n::n+1] top[...] = np.sqrt(.5*np.arange(1,n)) bot[...] = top mat[:,-1] -= (c[:-1]/c[-1])*(scl/scl[-1])*.5 return mat def hermroots(c): # c is a trimmed copy [c] = pu.as_series([c]) if len(c) <= 1 : return np.array([], dtype=c.dtype) if len(c) == 2 : return np.array([-.5*c[0]/c[1]]) m = hermcompanion(c) r = la.eigvals(m) r.sort() return r def hermgauss(deg): ideg = int(deg) if ideg != deg or ideg < 1: raise ValueError("deg must be a non-negative integer") # first approximation of roots. We use the fact that the companion # matrix is symmetric in this case in order to obtain better zeros. c = np.array([0]*deg + [1]) m = hermcompanion(c) x = la.eigvals(m) x.sort() # improve roots by one application of Newton dy = hermval(x, c) df = hermval(x, hermder(c)) x -= dy/df # compute the weights. We scale the factor to avoid possible numerical # overflow. fm = hermval(x, c[1:]) fm /= np.abs(fm).max() df /= np.abs(df).max() w = 1/(fm * df) # for Hermite we can also symmetrize w = (w + w[::-1])/2 x = (x - x[::-1])/2 # scale w to get the right value w *= np.sqrt(np.pi) / w.sum() return x, w def hermweight(x): w = np.exp(-x**2) return w # # Hermite series class # exec(polytemplate.substitute(name='Hermite', nick='herm', domain='[-1,1]'))

true

f73182b901161d28b67b66626d7189fdce69c7df

368

py

Python

utilities/models.py

codes-dev/qubwebs-blog

0ba9372b2ec83ad181dbd31eb009a17a4a7acaf0

[ "MIT" ]

null

utilities/models.py

codes-dev/qubwebs-blog

0ba9372b2ec83ad181dbd31eb009a17a4a7acaf0

[ "MIT" ]

null

utilities/models.py

codes-dev/qubwebs-blog

0ba9372b2ec83ad181dbd31eb009a17a4a7acaf0

[ "MIT" ]

null

from django.db import models # Create your models here. class TimeStampMixin(models.Model): """ An abstract base class model that provides self-updating ``created_at`` and ``updated_at`` fields. """ created_at = models.DateTimeField(auto_now_add=True) updated_at = models.DateTimeField(auto_now=True) class Meta: abstract = True

24.533333

60

0.701087

from django.db import models class TimeStampMixin(models.Model): created_at = models.DateTimeField(auto_now_add=True) updated_at = models.DateTimeField(auto_now=True) class Meta: abstract = True

true

f73182c1280def3ee7b87cc1f8310cdf39c87b12

16,051

py

Python

fairseq/fairseq/data/indexed_dataset.py

oguzdemirbasci/DynamicVocabAbstractiveSummariser

2e8ba9efd6eddd7d1870d540638f05c80bfe9894

[ "MIT" ]

null

fairseq/fairseq/data/indexed_dataset.py

oguzdemirbasci/DynamicVocabAbstractiveSummariser

2e8ba9efd6eddd7d1870d540638f05c80bfe9894

[ "MIT" ]

null

fairseq/fairseq/data/indexed_dataset.py

oguzdemirbasci/DynamicVocabAbstractiveSummariser

2e8ba9efd6eddd7d1870d540638f05c80bfe9894

[ "MIT" ]

null

# Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. from functools import lru_cache import os import shutil import struct import numpy as np import torch from . import FairseqDataset def __best_fitting_dtype(vocab_size=None): if vocab_size is not None and vocab_size < 65500: return np.uint16 else: return np.int32 def get_available_dataset_impl(): return ['raw', 'lazy', 'cached', 'mmap'] def infer_dataset_impl(path): if IndexedRawTextDataset.exists(path): return 'raw' elif IndexedDataset.exists(path): with open(index_file_path(path), 'rb') as f: magic = f.read(8) if magic == IndexedDataset._HDR_MAGIC: return 'cached' elif magic == MMapIndexedDataset.Index._HDR_MAGIC[:8]: return 'mmap' else: return None else: return None def make_builder(out_file, impl, vocab_size=None): if impl == 'mmap': return MMapIndexedDatasetBuilder(out_file, dtype=__best_fitting_dtype(vocab_size)) else: return IndexedDatasetBuilder(out_file) def make_dataset(path, impl, fix_lua_indexing=False, dictionary=None): if impl == 'raw' and IndexedRawTextDataset.exists(path): assert dictionary is not None return IndexedRawTextDataset(path, dictionary) elif impl == 'lazy' and IndexedDataset.exists(path): return IndexedDataset(path, fix_lua_indexing=fix_lua_indexing) elif impl == 'cached' and IndexedDataset.exists(path): return IndexedCachedDataset(path, fix_lua_indexing=fix_lua_indexing) elif impl == 'mmap' and MMapIndexedDataset.exists(path): return MMapIndexedDataset(path) return None def dataset_exists(path, impl): if impl == 'raw': return IndexedRawTextDataset.exists(path) elif impl == 'mmap': return MMapIndexedDataset.exists(path) else: return IndexedDataset.exists(path) def read_longs(f, n): a = np.empty(n, dtype=np.int64) f.readinto(a) return a def write_longs(f, a): f.write(np.array(a, dtype=np.int64)) dtypes = { 1: np.uint8, 2: np.int8, 3: np.int16, 4: np.int32, 5: np.int64, 6: np.float, 7: np.double, 8: np.uint16 } def code(dtype): for k in dtypes.keys(): if dtypes[k] == dtype: return k raise ValueError(dtype) def index_file_path(prefix_path): return prefix_path + '.idx' def data_file_path(prefix_path): return prefix_path + '.bin' class IndexedDataset(FairseqDataset): """Loader for TorchNet IndexedDataset""" _HDR_MAGIC = b'TNTIDX\x00\x00' def __init__(self, path, fix_lua_indexing=False): super().__init__() self.path = path self.fix_lua_indexing = fix_lua_indexing self.data_file = None self.read_index(path) def read_index(self, path): with open(index_file_path(path), 'rb') as f: magic = f.read(8) assert magic == self._HDR_MAGIC, ( 'Index file doesn\'t match expected format. ' 'Make sure that --dataset-impl is configured properly.' ) version = f.read(8) assert struct.unpack('<Q', version) == (1,) code, self.element_size = struct.unpack('<QQ', f.read(16)) self.dtype = dtypes[code] self._len, self.s = struct.unpack('<QQ', f.read(16)) self.dim_offsets = read_longs(f, self._len + 1) self.data_offsets = read_longs(f, self._len + 1) self.sizes = read_longs(f, self.s) def read_data(self, path): self.data_file = open(data_file_path(path), 'rb', buffering=0) def check_index(self, i): if i < 0 or i >= self._len: raise IndexError('index out of range') def __del__(self): if self.data_file: self.data_file.close() @lru_cache(maxsize=8) def __getitem__(self, i): if not self.data_file: self.read_data(self.path) self.check_index(i) tensor_size = self.sizes[self.dim_offsets[i]:self.dim_offsets[i + 1]] a = np.empty(tensor_size, dtype=self.dtype) self.data_file.seek(self.data_offsets[i] * self.element_size) self.data_file.readinto(a) item = torch.from_numpy(a).long() if self.fix_lua_indexing: item -= 1 # subtract 1 for 0-based indexing return item def __len__(self): return self._len def num_tokens(self, index): return self.sizes[index] def size(self, index): return self.sizes[index] @staticmethod def exists(path): return ( os.path.exists(index_file_path(path)) and os.path.exists(data_file_path(path)) ) @property def supports_prefetch(self): return False # avoid prefetching to save memory class IndexedCachedDataset(IndexedDataset): def __init__(self, path, fix_lua_indexing=False): super().__init__(path, fix_lua_indexing=fix_lua_indexing) self.cache = None self.cache_index = {} @property def supports_prefetch(self): return True def prefetch(self, indices): if all(i in self.cache_index for i in indices): return if not self.data_file: self.read_data(self.path) indices = sorted(set(indices)) total_size = 0 for i in indices: total_size += self.data_offsets[i + 1] - self.data_offsets[i] self.cache = np.empty(total_size, dtype=self.dtype) ptx = 0 self.cache_index.clear() for i in indices: self.cache_index[i] = ptx size = self.data_offsets[i + 1] - self.data_offsets[i] a = self.cache[ptx: ptx + size] self.data_file.seek(self.data_offsets[i] * self.element_size) self.data_file.readinto(a) ptx += size if self.data_file: # close and delete data file after prefetch so we can pickle self.data_file.close() self.data_file = None @lru_cache(maxsize=8) def __getitem__(self, i): self.check_index(i) tensor_size = self.sizes[self.dim_offsets[i]:self.dim_offsets[i + 1]] a = np.empty(tensor_size, dtype=self.dtype) ptx = self.cache_index[i] np.copyto(a, np.reshape(self.cache[ptx: ptx + a.size], tensor_size)) item = torch.from_numpy(a).long() if self.fix_lua_indexing: item -= 1 # subtract 1 for 0-based indexing return item class IndexedRawTextDataset(FairseqDataset): """Takes a text file as input and binarizes it in memory at instantiation. Original lines are also kept in memory""" def __init__(self, path, dictionary, append_eos=True, reverse_order=False): self.tokens_list = [] self.lines = [] self.sizes = [] self.append_eos = append_eos self.reverse_order = reverse_order self.read_data(path, dictionary) self.size = len(self.tokens_list) def read_data(self, path, dictionary): with open(path, 'r', encoding='utf-8') as f: for line in f: self.lines.append(line.strip('\n')) tokens = dictionary.encode_line( line, add_if_not_exist=False, append_eos=self.append_eos, reverse_order=self.reverse_order, ).long() self.tokens_list.append(tokens) self.sizes.append(len(tokens)) self.sizes = np.array(self.sizes) def check_index(self, i): if i < 0 or i >= self.size: raise IndexError('index out of range') @lru_cache(maxsize=8) def __getitem__(self, i): self.check_index(i) return self.tokens_list[i] def get_original_text(self, i): self.check_index(i) return self.lines[i] def __del__(self): pass def __len__(self): return self.size def num_tokens(self, index): return self.sizes[index] def size(self, index): return self.sizes[index] @staticmethod def exists(path): return os.path.exists(path) class IndexedDatasetBuilder(object): element_sizes = { np.uint8: 1, np.int8: 1, np.int16: 2, np.int32: 4, np.int64: 8, np.float: 4, np.double: 8 } def __init__(self, out_file, dtype=np.int32): self.out_file = open(out_file, 'wb') self.dtype = dtype self.data_offsets = [0] self.dim_offsets = [0] self.sizes = [] self.element_size = self.element_sizes[self.dtype] def add_item(self, tensor): # +1 for Lua compatibility bytes = self.out_file.write(np.array(tensor.numpy() + 1, dtype=self.dtype)) self.data_offsets.append(self.data_offsets[-1] + bytes / self.element_size) for s in tensor.size(): self.sizes.append(s) self.dim_offsets.append(self.dim_offsets[-1] + len(tensor.size())) def merge_file_(self, another_file): index = IndexedDataset(another_file) assert index.dtype == self.dtype begin = self.data_offsets[-1] for offset in index.data_offsets[1:]: self.data_offsets.append(begin + offset) self.sizes.extend(index.sizes) begin = self.dim_offsets[-1] for dim_offset in index.dim_offsets[1:]: self.dim_offsets.append(begin + dim_offset) with open(data_file_path(another_file), 'rb') as f: while True: data = f.read(1024) if data: self.out_file.write(data) else: break def finalize(self, index_file): self.out_file.close() index = open(index_file, 'wb') index.write(b'TNTIDX\x00\x00') index.write(struct.pack('<Q', 1)) index.write(struct.pack('<QQ', code(self.dtype), self.element_size)) index.write(struct.pack('<QQ', len(self.data_offsets) - 1, len(self.sizes))) write_longs(index, self.dim_offsets) write_longs(index, self.data_offsets) write_longs(index, self.sizes) index.close() def _warmup_mmap_file(path): with open(path, 'rb') as stream: while stream.read(100 * 1024 * 1024): pass class MMapIndexedDataset(torch.utils.data.Dataset): class Index(object): _HDR_MAGIC = b'MMIDIDX\x00\x00' @classmethod def writer(cls, path, dtype): class _Writer(object): def __enter__(self): self._file = open(path, 'wb') self._file.write(cls._HDR_MAGIC) self._file.write(struct.pack('<Q', 1)) self._file.write(struct.pack('<B', code(dtype))) return self @staticmethod def _get_pointers(sizes): dtype_size = dtype().itemsize address = 0 pointers = [] for size in sizes: pointers.append(address) address += size * dtype_size return pointers def write(self, sizes): pointers = self._get_pointers(sizes) self._file.write(struct.pack('<Q', len(sizes))) sizes = np.array(sizes, dtype=np.int32) self._file.write(sizes.tobytes(order='C')) del sizes pointers = np.array(pointers, dtype=np.int64) self._file.write(pointers.tobytes(order='C')) del pointers def __exit__(self, exc_type, exc_val, exc_tb): self._file.close() return _Writer() def __init__(self, path): with open(path, 'rb') as stream: magic_test = stream.read(9) assert self._HDR_MAGIC == magic_test, ( 'Index file doesn\'t match expected format. ' 'Make sure that --dataset-impl is configured properly.' ) version = struct.unpack('<Q', stream.read(8)) assert (1,) == version dtype_code, = struct.unpack('<B', stream.read(1)) self._dtype = dtypes[dtype_code] self._dtype_size = self._dtype().itemsize self._len = struct.unpack('<Q', stream.read(8))[0] offset = stream.tell() _warmup_mmap_file(path) self._bin_buffer_mmap = np.memmap(path, mode='r', order='C') self._bin_buffer = memoryview(self._bin_buffer_mmap) self._sizes = np.frombuffer(self._bin_buffer, dtype=np.int32, count=self._len, offset=offset) self._pointers = np.frombuffer(self._bin_buffer, dtype=np.int64, count=self._len, offset=offset + self._sizes.nbytes) def __del__(self): self._bin_buffer_mmap._mmap.close() del self._bin_buffer_mmap @property def dtype(self): return self._dtype @property def sizes(self): return self._sizes @lru_cache(maxsize=8) def __getitem__(self, i): return self._pointers[i], self._sizes[i] def __len__(self): return self._len def __init__(self, path): super().__init__() self._path = None self._index = None self._bin_buffer = None self._do_init(path) def __getstate__(self): return self._path def __setstate__(self, state): self._do_init(state) def _do_init(self, path): self._path = path self._index = self.Index(index_file_path(self._path)) _warmup_mmap_file(data_file_path(self._path)) self._bin_buffer_mmap = np.memmap(data_file_path(self._path), mode='r', order='C') self._bin_buffer = memoryview(self._bin_buffer_mmap) def __del__(self): self._bin_buffer_mmap._mmap.close() del self._bin_buffer_mmap del self._index def __len__(self): return len(self._index) @lru_cache(maxsize=8) def __getitem__(self, i): ptr, size = self._index[i] np_array = np.frombuffer(self._bin_buffer, dtype=self._index.dtype, count=size, offset=ptr) if self._index.dtype != np.int64: np_array = np_array.astype(np.int64) return torch.from_numpy(np_array) @property def sizes(self): return self._index.sizes @property def supports_prefetch(self): return False @staticmethod def exists(path): return ( os.path.exists(index_file_path(path)) and os.path.exists(data_file_path(path)) ) class MMapIndexedDatasetBuilder(object): def __init__(self, out_file, dtype=np.int64): self._data_file = open(out_file, 'wb') self._dtype = dtype self._sizes = [] def add_item(self, tensor): np_array = np.array(tensor.numpy(), dtype=self._dtype) self._data_file.write(np_array.tobytes(order='C')) self._sizes.append(np_array.size) def merge_file_(self, another_file): # Concatenate index index = MMapIndexedDataset.Index(index_file_path(another_file)) assert index.dtype == self._dtype for size in index.sizes: self._sizes.append(size) # Concatenate data with open(data_file_path(another_file), 'rb') as f: shutil.copyfileobj(f, self._data_file) def finalize(self, index_file): self._data_file.close() with MMapIndexedDataset.Index.writer(index_file, self._dtype) as index: index.write(self._sizes)

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from functools import lru_cache import os import shutil import struct import numpy as np import torch from . import FairseqDataset def __best_fitting_dtype(vocab_size=None): if vocab_size is not None and vocab_size < 65500: return np.uint16 else: return np.int32 def get_available_dataset_impl(): return ['raw', 'lazy', 'cached', 'mmap'] def infer_dataset_impl(path): if IndexedRawTextDataset.exists(path): return 'raw' elif IndexedDataset.exists(path): with open(index_file_path(path), 'rb') as f: magic = f.read(8) if magic == IndexedDataset._HDR_MAGIC: return 'cached' elif magic == MMapIndexedDataset.Index._HDR_MAGIC[:8]: return 'mmap' else: return None else: return None def make_builder(out_file, impl, vocab_size=None): if impl == 'mmap': return MMapIndexedDatasetBuilder(out_file, dtype=__best_fitting_dtype(vocab_size)) else: return IndexedDatasetBuilder(out_file) def make_dataset(path, impl, fix_lua_indexing=False, dictionary=None): if impl == 'raw' and IndexedRawTextDataset.exists(path): assert dictionary is not None return IndexedRawTextDataset(path, dictionary) elif impl == 'lazy' and IndexedDataset.exists(path): return IndexedDataset(path, fix_lua_indexing=fix_lua_indexing) elif impl == 'cached' and IndexedDataset.exists(path): return IndexedCachedDataset(path, fix_lua_indexing=fix_lua_indexing) elif impl == 'mmap' and MMapIndexedDataset.exists(path): return MMapIndexedDataset(path) return None def dataset_exists(path, impl): if impl == 'raw': return IndexedRawTextDataset.exists(path) elif impl == 'mmap': return MMapIndexedDataset.exists(path) else: return IndexedDataset.exists(path) def read_longs(f, n): a = np.empty(n, dtype=np.int64) f.readinto(a) return a def write_longs(f, a): f.write(np.array(a, dtype=np.int64)) dtypes = { 1: np.uint8, 2: np.int8, 3: np.int16, 4: np.int32, 5: np.int64, 6: np.float, 7: np.double, 8: np.uint16 } def code(dtype): for k in dtypes.keys(): if dtypes[k] == dtype: return k raise ValueError(dtype) def index_file_path(prefix_path): return prefix_path + '.idx' def data_file_path(prefix_path): return prefix_path + '.bin' class IndexedDataset(FairseqDataset): _HDR_MAGIC = b'TNTIDX\x00\x00' def __init__(self, path, fix_lua_indexing=False): super().__init__() self.path = path self.fix_lua_indexing = fix_lua_indexing self.data_file = None self.read_index(path) def read_index(self, path): with open(index_file_path(path), 'rb') as f: magic = f.read(8) assert magic == self._HDR_MAGIC, ( 'Index file doesn\'t match expected format. ' 'Make sure that --dataset-impl is configured properly.' ) version = f.read(8) assert struct.unpack('<Q', version) == (1,) code, self.element_size = struct.unpack('<QQ', f.read(16)) self.dtype = dtypes[code] self._len, self.s = struct.unpack('<QQ', f.read(16)) self.dim_offsets = read_longs(f, self._len + 1) self.data_offsets = read_longs(f, self._len + 1) self.sizes = read_longs(f, self.s) def read_data(self, path): self.data_file = open(data_file_path(path), 'rb', buffering=0) def check_index(self, i): if i < 0 or i >= self._len: raise IndexError('index out of range') def __del__(self): if self.data_file: self.data_file.close() @lru_cache(maxsize=8) def __getitem__(self, i): if not self.data_file: self.read_data(self.path) self.check_index(i) tensor_size = self.sizes[self.dim_offsets[i]:self.dim_offsets[i + 1]] a = np.empty(tensor_size, dtype=self.dtype) self.data_file.seek(self.data_offsets[i] * self.element_size) self.data_file.readinto(a) item = torch.from_numpy(a).long() if self.fix_lua_indexing: item -= 1 # subtract 1 for 0-based indexing return item def __len__(self): return self._len def num_tokens(self, index): return self.sizes[index] def size(self, index): return self.sizes[index] @staticmethod def exists(path): return ( os.path.exists(index_file_path(path)) and os.path.exists(data_file_path(path)) ) @property def supports_prefetch(self): return False # avoid prefetching to save memory class IndexedCachedDataset(IndexedDataset): def __init__(self, path, fix_lua_indexing=False): super().__init__(path, fix_lua_indexing=fix_lua_indexing) self.cache = None self.cache_index = {} @property def supports_prefetch(self): return True def prefetch(self, indices): if all(i in self.cache_index for i in indices): return if not self.data_file: self.read_data(self.path) indices = sorted(set(indices)) total_size = 0 for i in indices: total_size += self.data_offsets[i + 1] - self.data_offsets[i] self.cache = np.empty(total_size, dtype=self.dtype) ptx = 0 self.cache_index.clear() for i in indices: self.cache_index[i] = ptx size = self.data_offsets[i + 1] - self.data_offsets[i] a = self.cache[ptx: ptx + size] self.data_file.seek(self.data_offsets[i] * self.element_size) self.data_file.readinto(a) ptx += size if self.data_file: # close and delete data file after prefetch so we can pickle self.data_file.close() self.data_file = None @lru_cache(maxsize=8) def __getitem__(self, i): self.check_index(i) tensor_size = self.sizes[self.dim_offsets[i]:self.dim_offsets[i + 1]] a = np.empty(tensor_size, dtype=self.dtype) ptx = self.cache_index[i] np.copyto(a, np.reshape(self.cache[ptx: ptx + a.size], tensor_size)) item = torch.from_numpy(a).long() if self.fix_lua_indexing: item -= 1 # subtract 1 for 0-based indexing return item class IndexedRawTextDataset(FairseqDataset): def __init__(self, path, dictionary, append_eos=True, reverse_order=False): self.tokens_list = [] self.lines = [] self.sizes = [] self.append_eos = append_eos self.reverse_order = reverse_order self.read_data(path, dictionary) self.size = len(self.tokens_list) def read_data(self, path, dictionary): with open(path, 'r', encoding='utf-8') as f: for line in f: self.lines.append(line.strip('\n')) tokens = dictionary.encode_line( line, add_if_not_exist=False, append_eos=self.append_eos, reverse_order=self.reverse_order, ).long() self.tokens_list.append(tokens) self.sizes.append(len(tokens)) self.sizes = np.array(self.sizes) def check_index(self, i): if i < 0 or i >= self.size: raise IndexError('index out of range') @lru_cache(maxsize=8) def __getitem__(self, i): self.check_index(i) return self.tokens_list[i] def get_original_text(self, i): self.check_index(i) return self.lines[i] def __del__(self): pass def __len__(self): return self.size def num_tokens(self, index): return self.sizes[index] def size(self, index): return self.sizes[index] @staticmethod def exists(path): return os.path.exists(path) class IndexedDatasetBuilder(object): element_sizes = { np.uint8: 1, np.int8: 1, np.int16: 2, np.int32: 4, np.int64: 8, np.float: 4, np.double: 8 } def __init__(self, out_file, dtype=np.int32): self.out_file = open(out_file, 'wb') self.dtype = dtype self.data_offsets = [0] self.dim_offsets = [0] self.sizes = [] self.element_size = self.element_sizes[self.dtype] def add_item(self, tensor): # +1 for Lua compatibility bytes = self.out_file.write(np.array(tensor.numpy() + 1, dtype=self.dtype)) self.data_offsets.append(self.data_offsets[-1] + bytes / self.element_size) for s in tensor.size(): self.sizes.append(s) self.dim_offsets.append(self.dim_offsets[-1] + len(tensor.size())) def merge_file_(self, another_file): index = IndexedDataset(another_file) assert index.dtype == self.dtype begin = self.data_offsets[-1] for offset in index.data_offsets[1:]: self.data_offsets.append(begin + offset) self.sizes.extend(index.sizes) begin = self.dim_offsets[-1] for dim_offset in index.dim_offsets[1:]: self.dim_offsets.append(begin + dim_offset) with open(data_file_path(another_file), 'rb') as f: while True: data = f.read(1024) if data: self.out_file.write(data) else: break def finalize(self, index_file): self.out_file.close() index = open(index_file, 'wb') index.write(b'TNTIDX\x00\x00') index.write(struct.pack('<Q', 1)) index.write(struct.pack('<QQ', code(self.dtype), self.element_size)) index.write(struct.pack('<QQ', len(self.data_offsets) - 1, len(self.sizes))) write_longs(index, self.dim_offsets) write_longs(index, self.data_offsets) write_longs(index, self.sizes) index.close() def _warmup_mmap_file(path): with open(path, 'rb') as stream: while stream.read(100 * 1024 * 1024): pass class MMapIndexedDataset(torch.utils.data.Dataset): class Index(object): _HDR_MAGIC = b'MMIDIDX\x00\x00' @classmethod def writer(cls, path, dtype): class _Writer(object): def __enter__(self): self._file = open(path, 'wb') self._file.write(cls._HDR_MAGIC) self._file.write(struct.pack('<Q', 1)) self._file.write(struct.pack('<B', code(dtype))) return self @staticmethod def _get_pointers(sizes): dtype_size = dtype().itemsize address = 0 pointers = [] for size in sizes: pointers.append(address) address += size * dtype_size return pointers def write(self, sizes): pointers = self._get_pointers(sizes) self._file.write(struct.pack('<Q', len(sizes))) sizes = np.array(sizes, dtype=np.int32) self._file.write(sizes.tobytes(order='C')) del sizes pointers = np.array(pointers, dtype=np.int64) self._file.write(pointers.tobytes(order='C')) del pointers def __exit__(self, exc_type, exc_val, exc_tb): self._file.close() return _Writer() def __init__(self, path): with open(path, 'rb') as stream: magic_test = stream.read(9) assert self._HDR_MAGIC == magic_test, ( 'Index file doesn\'t match expected format. ' 'Make sure that --dataset-impl is configured properly.' ) version = struct.unpack('<Q', stream.read(8)) assert (1,) == version dtype_code, = struct.unpack('<B', stream.read(1)) self._dtype = dtypes[dtype_code] self._dtype_size = self._dtype().itemsize self._len = struct.unpack('<Q', stream.read(8))[0] offset = stream.tell() _warmup_mmap_file(path) self._bin_buffer_mmap = np.memmap(path, mode='r', order='C') self._bin_buffer = memoryview(self._bin_buffer_mmap) self._sizes = np.frombuffer(self._bin_buffer, dtype=np.int32, count=self._len, offset=offset) self._pointers = np.frombuffer(self._bin_buffer, dtype=np.int64, count=self._len, offset=offset + self._sizes.nbytes) def __del__(self): self._bin_buffer_mmap._mmap.close() del self._bin_buffer_mmap @property def dtype(self): return self._dtype @property def sizes(self): return self._sizes @lru_cache(maxsize=8) def __getitem__(self, i): return self._pointers[i], self._sizes[i] def __len__(self): return self._len def __init__(self, path): super().__init__() self._path = None self._index = None self._bin_buffer = None self._do_init(path) def __getstate__(self): return self._path def __setstate__(self, state): self._do_init(state) def _do_init(self, path): self._path = path self._index = self.Index(index_file_path(self._path)) _warmup_mmap_file(data_file_path(self._path)) self._bin_buffer_mmap = np.memmap(data_file_path(self._path), mode='r', order='C') self._bin_buffer = memoryview(self._bin_buffer_mmap) def __del__(self): self._bin_buffer_mmap._mmap.close() del self._bin_buffer_mmap del self._index def __len__(self): return len(self._index) @lru_cache(maxsize=8) def __getitem__(self, i): ptr, size = self._index[i] np_array = np.frombuffer(self._bin_buffer, dtype=self._index.dtype, count=size, offset=ptr) if self._index.dtype != np.int64: np_array = np_array.astype(np.int64) return torch.from_numpy(np_array) @property def sizes(self): return self._index.sizes @property def supports_prefetch(self): return False @staticmethod def exists(path): return ( os.path.exists(index_file_path(path)) and os.path.exists(data_file_path(path)) ) class MMapIndexedDatasetBuilder(object): def __init__(self, out_file, dtype=np.int64): self._data_file = open(out_file, 'wb') self._dtype = dtype self._sizes = [] def add_item(self, tensor): np_array = np.array(tensor.numpy(), dtype=self._dtype) self._data_file.write(np_array.tobytes(order='C')) self._sizes.append(np_array.size) def merge_file_(self, another_file): index = MMapIndexedDataset.Index(index_file_path(another_file)) assert index.dtype == self._dtype for size in index.sizes: self._sizes.append(size) with open(data_file_path(another_file), 'rb') as f: shutil.copyfileobj(f, self._data_file) def finalize(self, index_file): self._data_file.close() with MMapIndexedDataset.Index.writer(index_file, self._dtype) as index: index.write(self._sizes)

true

f731833e514a80da7c83975fe621231e7f8586e1

994

py

Python

assets/code/practice-example-four.py

david-story/david-story.github.io

4a02854207b86946befd933d6f23773f6239d7da

[ "MIT" ]

null

assets/code/practice-example-four.py

david-story/david-story.github.io

4a02854207b86946befd933d6f23773f6239d7da

[ "MIT" ]

null

assets/code/practice-example-four.py

david-story/david-story.github.io

4a02854207b86946befd933d6f23773f6239d7da

[ "MIT" ]

null

""" Practice File 4 Created by David Story Description: Some nice examples of things you can do with functions from the following libraries: - sys - os - time - datetime """ import sys import os import time import datetime # get the time print(time.time()) # get the current date print(datetime.date.today()) # prints the current working directory print(os.getcwd()) # saves your current working directory your_working_directory = os.getcwd() # changes directory to the C:\\ drive os.chdir("C:\\") # prints the current working directory print(os.getcwd()) # change to the original current working directory os.chdir(your_working_directory) # prints the directory print(os.getcwd()) # gets number of cores on your cpu computer_cpu_count = os.cpu_count() print("This computer has this many CPU cores:", computer_cpu_count) seconds = 2 time_1 = time.time() time.sleep(seconds) time_2 = time.time() print("The sleep operation took this many seconds:", (time_2-time_1))

19.490196

97

0.740443

import sys import os import time import datetime print(time.time()) print(datetime.date.today()) print(os.getcwd()) your_working_directory = os.getcwd() os.chdir("C:\\") print(os.getcwd()) os.chdir(your_working_directory) print(os.getcwd()) computer_cpu_count = os.cpu_count() print("This computer has this many CPU cores:", computer_cpu_count) seconds = 2 time_1 = time.time() time.sleep(seconds) time_2 = time.time() print("The sleep operation took this many seconds:", (time_2-time_1))

true

f731835a4a4d1abb694ef9dec2e9777f59218f60

390

py

Python

NewRelicApiParser/REST/AlertsViolations/__init__.py

Bharat23/newrelic-api-parser

c55d508387fde33af9bdc93f16aae3cb2a2e5f13

[ "MIT" ]

null

NewRelicApiParser/REST/AlertsViolations/__init__.py

Bharat23/newrelic-api-parser

c55d508387fde33af9bdc93f16aae3cb2a2e5f13

[ "MIT" ]

1

2021-07-30T17:32:37.000Z

NewRelicApiParser/REST/AlertsViolations/__init__.py

Bharat23/newrelic-api-parser

c55d508387fde33af9bdc93f16aae3cb2a2e5f13

[ "MIT" ]

null

from NewRelicApiParser.Base import BaseNewRelic class AlertsViolations(BaseNewRelic): def __init__(self, API_KEY): super().__init__(API_KEY) def get_list(self, options: dict = {}) -> dict: """ fetch the alert violations for new relic """ url = self.BASE_URI + '/alerts_violations.json' return super().get_data(url, options=options)

30

55

0.651282

from NewRelicApiParser.Base import BaseNewRelic class AlertsViolations(BaseNewRelic): def __init__(self, API_KEY): super().__init__(API_KEY) def get_list(self, options: dict = {}) -> dict: url = self.BASE_URI + '/alerts_violations.json' return super().get_data(url, options=options)

true

f731839f44074592ff4c8fc120911ce2b706f06b

2,652

py

Python

utils/audio.py

deciding/Voicefilter

dda34da9d1cfca48102b2d1b4274bfd76e5a2e1c

[ "Apache-2.0", "MIT" ]

3

2020-03-25T06:27:20.000Z

2021-02-28T12:59:14.000Z

utils/audio.py

deciding/Voicefilter

dda34da9d1cfca48102b2d1b4274bfd76e5a2e1c

[ "Apache-2.0", "MIT" ]

null

utils/audio.py

deciding/Voicefilter

dda34da9d1cfca48102b2d1b4274bfd76e5a2e1c

[ "Apache-2.0", "MIT" ]

null

# adapted from Keith Ito's tacotron implementation # https://github.com/keithito/tacotron/blob/master/util/audio.py import librosa import numpy as np class Audio(): def __init__(self, hp): self.hp = hp self.mel_basis = librosa.filters.mel(sr=hp.audio.sample_rate, n_fft=hp.embedder.n_fft, n_mels=hp.embedder.num_mels) self.audio_mel_basis = librosa.filters.mel(sr=hp.audio.sample_rate, n_fft=hp.audio.n_fft, n_mels=80) def get_mel(self, y): y = librosa.core.stft(y=y, n_fft=self.hp.embedder.n_fft, hop_length=self.hp.audio.hop_length, win_length=self.hp.audio.win_length, window='hann') magnitudes = np.abs(y) ** 2 mel = np.log10(np.dot(self.mel_basis, magnitudes) + 1e-6) return mel def wav2spec(self, y): D = self.stft(y) S = self.amp_to_db(np.abs(D)) - self.hp.audio.ref_level_db S, D = self.normalize(S), np.angle(D) S, D = S.T, D.T # to make [time, freq] return S, D def wav2mel(self, y): D = self.stft(y) S = np.dot(self.audio_mel_basis, np.abs(D)) S = self.amp_to_db(S) - self.hp.audio.ref_level_db S = self.normalize(S) return S.T def spec2wav(self, spectrogram, phase): spectrogram, phase = spectrogram.T, phase.T # used during inference only # spectrogram: enhanced output # phase: use noisy input's phase, so no GLA is required S = self.db_to_amp(self.denormalize(spectrogram) + self.hp.audio.ref_level_db) return self.istft(S, phase) def stft(self, y): return librosa.stft(y=y, n_fft=self.hp.audio.n_fft, hop_length=self.hp.audio.hop_length, win_length=self.hp.audio.win_length) def istft(self, mag, phase): stft_matrix = mag * np.exp(1j*phase) return librosa.istft(stft_matrix, hop_length=self.hp.audio.hop_length, win_length=self.hp.audio.win_length) def amp_to_db(self, x): return 20.0 * np.log10(np.maximum(1e-5, x)) def db_to_amp(self, x): return np.power(10.0, x * 0.05) def normalize(self, S): return np.clip(S / -self.hp.audio.min_level_db, -1.0, 0.0) + 1.0 def denormalize(self, S): return (np.clip(S, 0.0, 1.0) - 1.0) * -self.hp.audio.min_level_db

36.833333

86

0.549397

# https://github.com/keithito/tacotron/blob/master/util/audio.py import librosa import numpy as np class Audio(): def __init__(self, hp): self.hp = hp self.mel_basis = librosa.filters.mel(sr=hp.audio.sample_rate, n_fft=hp.embedder.n_fft, n_mels=hp.embedder.num_mels) self.audio_mel_basis = librosa.filters.mel(sr=hp.audio.sample_rate, n_fft=hp.audio.n_fft, n_mels=80) def get_mel(self, y): y = librosa.core.stft(y=y, n_fft=self.hp.embedder.n_fft, hop_length=self.hp.audio.hop_length, win_length=self.hp.audio.win_length, window='hann') magnitudes = np.abs(y) ** 2 mel = np.log10(np.dot(self.mel_basis, magnitudes) + 1e-6) return mel def wav2spec(self, y): D = self.stft(y) S = self.amp_to_db(np.abs(D)) - self.hp.audio.ref_level_db S, D = self.normalize(S), np.angle(D) S, D = S.T, D.T # to make [time, freq] return S, D def wav2mel(self, y): D = self.stft(y) S = np.dot(self.audio_mel_basis, np.abs(D)) S = self.amp_to_db(S) - self.hp.audio.ref_level_db S = self.normalize(S) return S.T def spec2wav(self, spectrogram, phase): spectrogram, phase = spectrogram.T, phase.T # used during inference only # spectrogram: enhanced output # phase: use noisy input's phase, so no GLA is required S = self.db_to_amp(self.denormalize(spectrogram) + self.hp.audio.ref_level_db) return self.istft(S, phase) def stft(self, y): return librosa.stft(y=y, n_fft=self.hp.audio.n_fft, hop_length=self.hp.audio.hop_length, win_length=self.hp.audio.win_length) def istft(self, mag, phase): stft_matrix = mag * np.exp(1j*phase) return librosa.istft(stft_matrix, hop_length=self.hp.audio.hop_length, win_length=self.hp.audio.win_length) def amp_to_db(self, x): return 20.0 * np.log10(np.maximum(1e-5, x)) def db_to_amp(self, x): return np.power(10.0, x * 0.05) def normalize(self, S): return np.clip(S / -self.hp.audio.min_level_db, -1.0, 0.0) + 1.0 def denormalize(self, S): return (np.clip(S, 0.0, 1.0) - 1.0) * -self.hp.audio.min_level_db

true

f73183abb453edfa3f42627d23629fd71241bbd0

1,790

py

Python

keymint_cli/verb/__init__.py

keymint/keymint_cli

977995ade32cf2b3a6394bda1c05f80a9fcc3369

[ "Apache-2.0" ]

null

keymint_cli/verb/__init__.py

keymint/keymint_cli

977995ade32cf2b3a6394bda1c05f80a9fcc3369

[ "Apache-2.0" ]

null

keymint_cli/verb/__init__.py

keymint/keymint_cli

977995ade32cf2b3a6394bda1c05f80a9fcc3369

[ "Apache-2.0" ]

null

# Copyright 2016-2017 Dirk Thomas # Copyright 2017 Open Source Robotics Foundation, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from keymint_cli.plugin_system import instantiate_extensions from keymint_cli.plugin_system import PLUGIN_SYSTEM_VERSION from keymint_cli.plugin_system import satisfies_version class VerbExtension: """ The interface for verb extensions. The following properties must be defined: * `NAME` (will be set to the entry point name) The following methods must be defined: * `main` """ NAME = None EXTENSION_POINT_VERSION = '0.1' def __init__(self): super(VerbExtension, self).__init__() satisfies_version(PLUGIN_SYSTEM_VERSION, '^0.1') def get_verb_extensions(name): extensions = instantiate_extensions(name) for name, extension in extensions.items(): extension.NAME = name return extensions def add_task_arguments(parser, task_name): plugins = get_verb_extensions(task_name) for plugin_name, plugin in plugins.items(): group = parser.add_argument_group( title="Arguments for '{plugin_name}' packages" .format_map(locals())) func = getattr(plugin, 'add_%s_arguments' % task_name, None) if func: func(group)

31.964286

74

0.721229

from keymint_cli.plugin_system import instantiate_extensions from keymint_cli.plugin_system import PLUGIN_SYSTEM_VERSION from keymint_cli.plugin_system import satisfies_version class VerbExtension: NAME = None EXTENSION_POINT_VERSION = '0.1' def __init__(self): super(VerbExtension, self).__init__() satisfies_version(PLUGIN_SYSTEM_VERSION, '^0.1') def get_verb_extensions(name): extensions = instantiate_extensions(name) for name, extension in extensions.items(): extension.NAME = name return extensions def add_task_arguments(parser, task_name): plugins = get_verb_extensions(task_name) for plugin_name, plugin in plugins.items(): group = parser.add_argument_group( title="Arguments for '{plugin_name}' packages" .format_map(locals())) func = getattr(plugin, 'add_%s_arguments' % task_name, None) if func: func(group)

true

f7318473c64262e9daec3211788af8404125cccc

722

py

Python

webview/monitor/migrations/0002_auto_20191022_1952.py

dw0rdptr/2019_IoT_GoToDouble

7f8a3005710b57199e918be1a0d8b9047918bce2

[ "MIT" ]

4

2019-10-23T04:43:09.000Z

2019-10-24T11:25:37.000Z

webview/monitor/migrations/0002_auto_20191022_1952.py

dw0rdptr/2019_IoT_GoToDouble

7f8a3005710b57199e918be1a0d8b9047918bce2

[ "MIT" ]

10

2019-10-22T12:06:42.000Z

2019-10-24T17:39:31.000Z

webview/monitor/migrations/0002_auto_20191022_1952.py

epikjjh/2019_IoT_GoToDouble

1d80eb87b7d59da90a7d0a9225209bfb4704a045

[ "MIT" ]

5

2019-10-22T08:04:26.000Z

2019-10-24T17:31:40.000Z

# Generated by Django 2.2.6 on 2019-10-22 10:52 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('monitor', '0001_initial'), ] operations = [ migrations.CreateModel( name='Sposition', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('angle', models.FloatField()), ('distance', models.FloatField()), ('time', models.DateTimeField(auto_now_add=True)), ], ), migrations.RenameModel( old_name='Position', new_name='Fposition', ), ]

26.740741

114

0.548476

from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('monitor', '0001_initial'), ] operations = [ migrations.CreateModel( name='Sposition', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('angle', models.FloatField()), ('distance', models.FloatField()), ('time', models.DateTimeField(auto_now_add=True)), ], ), migrations.RenameModel( old_name='Position', new_name='Fposition', ), ]

true

f73184b05282cc90c8544a41fce552460381a5d0

17,311

py

Python

src/k2hash/tests/test_k2hash.py

ggtakec/k2hash_python

9d20d36aaf28d0dd2497d39b43286d50e7200fcc

[ "MIT" ]

1

2022-03-02T10:27:43.000Z

src/k2hash/tests/test_k2hash.py

ggtakec/k2hash_python

9d20d36aaf28d0dd2497d39b43286d50e7200fcc

[ "MIT" ]

null

src/k2hash/tests/test_k2hash.py

ggtakec/k2hash_python

9d20d36aaf28d0dd2497d39b43286d50e7200fcc

[ "MIT" ]

null

# -*- coding: utf-8 -*- # # K2hash Python Driver # # Copyright (c) 2022 Yahoo Japan Corporation # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. # # AUTHOR: Hirotaka Wakabayashi # CREATE: Tue Feb 08 2022 # REVISION: # import unittest import k2hash import logging import ctypes import time class TestK2hashIterator(unittest.TestCase): def test_K2hashIterator_construct(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) ki = k2hash.K2hashIterator(db) self.assertTrue(isinstance(ki, k2hash.K2hashIterator)) self.assertTrue(key == next(ki)) db.close() def test_K2hashIterator_construct_key(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) subkey = "subkey1" subval = "subval1" self.assertTrue(db.add_subkey(key, subkey, subval), True) ki = k2hash.K2hashIterator(db, key) self.assertTrue(isinstance(ki, k2hash.K2hashIterator)) self.assertTrue(subkey == next(ki)) db.close() class TestK2hash(unittest.TestCase): def test_K2hash_construct(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) db.close() def test_K2hash_get_iterator(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) ki = db.get_iterator() # Note: handle should be undefined before setting no keys. self.assertTrue(isinstance(ki, k2hash.K2hashIterator)) db.close() def test_K2hash_set(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) db.close() def test_K2hash_get(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) db.close() @unittest.skip("skipping because no plugin lib prepared") def test_K2hash_add_attribute_plugin_lib(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) db.close() def test_K2hash_add_decryption_password(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) password = "secretstring" self.assertTrue(db.add_decryption_password(password), True) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key, password), val) self.assertTrue(db.get(key), "") db.close() def test_K2hash_add_subkey(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) subkey = "subkey" subval = "subval" self.assertTrue(db.set(subkey, subval), True) self.assertTrue(db.get(subkey), subval) self.assertTrue(db.add_subkey(key, subkey, subval), True) self.assertTrue(db.get_subkeys(key) == [subkey]) db.close() def test_K2hash_begin_tx(self): db = k2hash.K2hash() # for debugging, uncomment the following line. # db.set_log_level(k2hash.LogLevel.ERROR) self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) k2h_tx_log = "test.log" self.assertTrue(db.begin_tx(k2h_tx_log), True) # TODO how to check whether transaction is enabled. db.close() def test_K2hash_close(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) self.assertTrue(db.close(), True) def test_K2hash_create(self): k2h_file = "test.k2h" self.assertTrue(k2hash.K2hash.create(k2h_file), True) def test_K2hash_dump_to_file(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) k2h_file = "test.k2h" self.assertTrue(db.dump_to_file(k2h_file), val) db.close() def test_K2hash_enable_encryption(self): db = k2hash.K2hash() # db.set_log_level(k2hash.LogLevel.ERROR) self.assertTrue(isinstance(db, k2hash.K2hash)) password = "secretstring" # Calls set_default_encryption_password before calling enable_encryption self.assertTrue(db.set_default_encryption_password(password), True) self.assertTrue(db.enable_encryption(), True) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key, password), val) # for debugging, uncomment the following line. db.close() def test_K2hash_enable_history(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) self.assertTrue(db.enable_history(), True) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) db.close() def test_K2hash_enable_mtime(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) self.assertTrue(db.enable_mtime(), True) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) db.close() def test_K2hash_get_attributes(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) attr_key = "attrkey1" attr_val = "attrval1" attrs = {attr_key: attr_val} self.assertTrue(db.set_attribute(key, attr_key, attr_val), True) self.assertTrue(db.get_attributes(key), attrs) db.close() def test_K2hash_handle(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) # TODO insufficient check self.assertTrue(db.handle != k2hash.K2hash.K2H_INVALID_HANDLE) db.close() def test_K2hash_get_subkeys(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) subkey = "subkey" subval = "subval" self.assertTrue(db.set(subkey, subval), True) self.assertTrue(db.get(subkey), subval) self.assertTrue(db.add_subkey(key, subkey, subval), True) self.assertTrue(db.get_subkeys(key) == [subkey]) db.close() def test_K2hash_get_tx_file_fd(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) k2h_tx_log = "test.log" self.assertTrue(db.begin_tx(k2h_tx_log), True) # TODO how to check whether transaction is enabled. self.assertTrue(db.get_tx_file_fd() != k2hash.K2hash.K2H_INVALID_HANDLE) db.close() def test_K2hash_get_tx_pool_size(self): self.assertTrue(k2hash.K2hash.get_tx_pool_size() == 0) def test_K2hash_libk2hash(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) self.assertTrue(isinstance(db.libk2hash, ctypes.CDLL)) db.close() def test_K2hash_libc(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) self.assertTrue(isinstance(db.libc, ctypes.CDLL)) db.close() def test_K2hash_load_from_file(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) k2h_file = "test.k2h" self.assertTrue(db.dump_to_file(k2h_file), val) db.close() db = None db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) self.assertTrue(db.load_from_file(k2h_file), val) self.assertTrue(db.get(key), val) db.close() def test_K2hash_print_attribute_plugins(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) self.assertTrue(db.print_attribute_plugins(), True) db.close() def test_K2hash_print_attributes(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) self.assertTrue(db.print_attributes(), True) db.close() def test_K2hash_print_data_stats(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) self.assertTrue(db.print_data_stats(), True) db.close() def test_K2hash_print_table_stats(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) self.assertTrue(db.print_table_stats(), True) db.close() def test_K2hash_remove(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) self.assertTrue(db.remove(key), True) self.assertTrue(db.get(key) == "") db.close() def test_K2hash_remove_subkeys(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) subkey = "subkey" subval = "subval" self.assertTrue(db.set(subkey, subval), True) self.assertTrue(db.get(subkey), subval) self.assertTrue(db.add_subkey(key, subkey, subval), True) self.assertTrue(db.get_subkeys(key) == [subkey]) self.assertTrue(db.remove_subkeys(key, [subkey]), True) self.assertTrue(db.get_subkeys(key) == []) self.assertTrue(db.get(subkey) == "") db.close() def test_K2hash_rename(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" newkey = key[::-1] self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) self.assertTrue(db.rename(key, newkey), val) self.assertTrue(db.get(newkey), val) db.close() def test_K2hash_set_attribute(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) attr_key = "attrkey1" attr_val = "attrval1" attrs = {attr_key: attr_val} self.assertTrue(db.set_attribute(key, attr_key, attr_val), True) self.assertTrue(db.get_attributes(key), attrs) db.close() def test_K2hash_set_encryption_password_file(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) # 1. make the password file for test password_file="password.txt" password = "secretstring" import os with open(password_file, 'w') as f: print("{}".format(password), file=f) # 2. call the api self.assertTrue(db.set_encryption_password_file(password_file), True) db.close() def test_K2hash_set_expiration_duration(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) duration = 3 self.assertTrue(db.set_expiration_duration(duration), True) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) time.sleep(duration + 1) self.assertTrue(db.get(key) == "") db.close() def test_K2hash_set_log_level(self): db = k2hash.K2hash() db.set_log_level(k2hash.LogLevel.ERROR) self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) db.set_log_level(k2hash.LogLevel.WARNING) db.close() def test_K2hash_set_subkeys(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) subkey = "subkey" subval = "subval" self.assertTrue(db.set_subkeys(key, {subkey: subval}), True) self.assertTrue(db.get_subkeys(key) == [subkey]) db.close() def test_K2hash_set_tx_pool_size(self): self.assertTrue(k2hash.K2hash.set_tx_pool_size(1), True) self.assertTrue(k2hash.K2hash.get_tx_pool_size() == 1) def test_K2hash_stop_tx(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) k2h_tx_log = "test.log" self.assertTrue(db.begin_tx(k2h_tx_log), True) # TODO how to check whether transaction is enabled. self.assertTrue(db.get_tx_file_fd() != k2hash.K2hash.K2H_INVALID_HANDLE) self.assertTrue(db.stop_tx(), True) db.close() def test_K2hash_repr(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) self.assertRegex(repr(db), "<_K2hash _k2hfile=.*") db.close() def test_K2hash_version(self): self.assertTrue(k2hash.K2hash.version() == None) if __name__ == '__main__': unittest.main() # # Local variables: # tab-width: 4 # c-basic-offset: 4 # End: # vim600: expandtab sw=4 ts=4 fdm=marker # vim<600: expandtab sw=4 ts=4 #

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import unittest import k2hash import logging import ctypes import time class TestK2hashIterator(unittest.TestCase): def test_K2hashIterator_construct(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) ki = k2hash.K2hashIterator(db) self.assertTrue(isinstance(ki, k2hash.K2hashIterator)) self.assertTrue(key == next(ki)) db.close() def test_K2hashIterator_construct_key(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) subkey = "subkey1" subval = "subval1" self.assertTrue(db.add_subkey(key, subkey, subval), True) ki = k2hash.K2hashIterator(db, key) self.assertTrue(isinstance(ki, k2hash.K2hashIterator)) self.assertTrue(subkey == next(ki)) db.close() class TestK2hash(unittest.TestCase): def test_K2hash_construct(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) db.close() def test_K2hash_get_iterator(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) ki = db.get_iterator() self.assertTrue(isinstance(ki, k2hash.K2hashIterator)) db.close() def test_K2hash_set(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) db.close() def test_K2hash_get(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) db.close() @unittest.skip("skipping because no plugin lib prepared") def test_K2hash_add_attribute_plugin_lib(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) db.close() def test_K2hash_add_decryption_password(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) password = "secretstring" self.assertTrue(db.add_decryption_password(password), True) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key, password), val) self.assertTrue(db.get(key), "") db.close() def test_K2hash_add_subkey(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) subkey = "subkey" subval = "subval" self.assertTrue(db.set(subkey, subval), True) self.assertTrue(db.get(subkey), subval) self.assertTrue(db.add_subkey(key, subkey, subval), True) self.assertTrue(db.get_subkeys(key) == [subkey]) db.close() def test_K2hash_begin_tx(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) k2h_tx_log = "test.log" self.assertTrue(db.begin_tx(k2h_tx_log), True) db.close() def test_K2hash_close(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) self.assertTrue(db.close(), True) def test_K2hash_create(self): k2h_file = "test.k2h" self.assertTrue(k2hash.K2hash.create(k2h_file), True) def test_K2hash_dump_to_file(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) k2h_file = "test.k2h" self.assertTrue(db.dump_to_file(k2h_file), val) db.close() def test_K2hash_enable_encryption(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) password = "secretstring" self.assertTrue(db.set_default_encryption_password(password), True) self.assertTrue(db.enable_encryption(), True) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key, password), val) db.close() def test_K2hash_enable_history(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) self.assertTrue(db.enable_history(), True) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) db.close() def test_K2hash_enable_mtime(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) self.assertTrue(db.enable_mtime(), True) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) db.close() def test_K2hash_get_attributes(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) attr_key = "attrkey1" attr_val = "attrval1" attrs = {attr_key: attr_val} self.assertTrue(db.set_attribute(key, attr_key, attr_val), True) self.assertTrue(db.get_attributes(key), attrs) db.close() def test_K2hash_handle(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) self.assertTrue(db.handle != k2hash.K2hash.K2H_INVALID_HANDLE) db.close() def test_K2hash_get_subkeys(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) subkey = "subkey" subval = "subval" self.assertTrue(db.set(subkey, subval), True) self.assertTrue(db.get(subkey), subval) self.assertTrue(db.add_subkey(key, subkey, subval), True) self.assertTrue(db.get_subkeys(key) == [subkey]) db.close() def test_K2hash_get_tx_file_fd(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) k2h_tx_log = "test.log" self.assertTrue(db.begin_tx(k2h_tx_log), True) self.assertTrue(db.get_tx_file_fd() != k2hash.K2hash.K2H_INVALID_HANDLE) db.close() def test_K2hash_get_tx_pool_size(self): self.assertTrue(k2hash.K2hash.get_tx_pool_size() == 0) def test_K2hash_libk2hash(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) self.assertTrue(isinstance(db.libk2hash, ctypes.CDLL)) db.close() def test_K2hash_libc(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) self.assertTrue(isinstance(db.libc, ctypes.CDLL)) db.close() def test_K2hash_load_from_file(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) k2h_file = "test.k2h" self.assertTrue(db.dump_to_file(k2h_file), val) db.close() db = None db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) self.assertTrue(db.load_from_file(k2h_file), val) self.assertTrue(db.get(key), val) db.close() def test_K2hash_print_attribute_plugins(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) self.assertTrue(db.print_attribute_plugins(), True) db.close() def test_K2hash_print_attributes(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) self.assertTrue(db.print_attributes(), True) db.close() def test_K2hash_print_data_stats(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) self.assertTrue(db.print_data_stats(), True) db.close() def test_K2hash_print_table_stats(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) self.assertTrue(db.print_table_stats(), True) db.close() def test_K2hash_remove(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) self.assertTrue(db.remove(key), True) self.assertTrue(db.get(key) == "") db.close() def test_K2hash_remove_subkeys(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) subkey = "subkey" subval = "subval" self.assertTrue(db.set(subkey, subval), True) self.assertTrue(db.get(subkey), subval) self.assertTrue(db.add_subkey(key, subkey, subval), True) self.assertTrue(db.get_subkeys(key) == [subkey]) self.assertTrue(db.remove_subkeys(key, [subkey]), True) self.assertTrue(db.get_subkeys(key) == []) self.assertTrue(db.get(subkey) == "") db.close() def test_K2hash_rename(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" newkey = key[::-1] self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) self.assertTrue(db.rename(key, newkey), val) self.assertTrue(db.get(newkey), val) db.close() def test_K2hash_set_attribute(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) attr_key = "attrkey1" attr_val = "attrval1" attrs = {attr_key: attr_val} self.assertTrue(db.set_attribute(key, attr_key, attr_val), True) self.assertTrue(db.get_attributes(key), attrs) db.close() def test_K2hash_set_encryption_password_file(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) password_file="password.txt" password = "secretstring" import os with open(password_file, 'w') as f: print("{}".format(password), file=f) self.assertTrue(db.set_encryption_password_file(password_file), True) db.close() def test_K2hash_set_expiration_duration(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) duration = 3 self.assertTrue(db.set_expiration_duration(duration), True) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) time.sleep(duration + 1) self.assertTrue(db.get(key) == "") db.close() def test_K2hash_set_log_level(self): db = k2hash.K2hash() db.set_log_level(k2hash.LogLevel.ERROR) self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) db.set_log_level(k2hash.LogLevel.WARNING) db.close() def test_K2hash_set_subkeys(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) subkey = "subkey" subval = "subval" self.assertTrue(db.set_subkeys(key, {subkey: subval}), True) self.assertTrue(db.get_subkeys(key) == [subkey]) db.close() def test_K2hash_set_tx_pool_size(self): self.assertTrue(k2hash.K2hash.set_tx_pool_size(1), True) self.assertTrue(k2hash.K2hash.get_tx_pool_size() == 1) def test_K2hash_stop_tx(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) k2h_tx_log = "test.log" self.assertTrue(db.begin_tx(k2h_tx_log), True) self.assertTrue(db.get_tx_file_fd() != k2hash.K2hash.K2H_INVALID_HANDLE) self.assertTrue(db.stop_tx(), True) db.close() def test_K2hash_repr(self): db = k2hash.K2hash() self.assertTrue(isinstance(db, k2hash.K2hash)) key = "hello" val = "world" self.assertTrue(db.set(key, val), True) self.assertTrue(db.get(key), val) self.assertRegex(repr(db), "<_K2hash _k2hfile=.*") db.close() def test_K2hash_version(self): self.assertTrue(k2hash.K2hash.version() == None) if __name__ == '__main__': unittest.main()

true

f73184b337ca7e743751238f2a113e71bb8c75e0

7,364

py

Python

tests/rundb/test_sqldb.py

EdmondIguazio/mlrun

e63b34a610788ebe522ce7a46642e26927e39882

[ "Apache-2.0" ]

null

tests/rundb/test_sqldb.py

EdmondIguazio/mlrun

e63b34a610788ebe522ce7a46642e26927e39882

[ "Apache-2.0" ]

null

tests/rundb/test_sqldb.py

EdmondIguazio/mlrun

e63b34a610788ebe522ce7a46642e26927e39882

[ "Apache-2.0" ]

1

2021-05-05T14:19:46.000Z

# Copyright 2019 Iguazio # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """SQLDB specific tests, common tests should be in test_dbs.py""" from collections import defaultdict from contextlib import contextmanager from datetime import datetime, timedelta from unittest.mock import Mock import pytest from sqlalchemy.orm import Session from mlrun.api.db.sqldb.db import SQLDB from mlrun.api.db.sqldb.models import _tagged from tests.conftest import new_run @contextmanager def patch(obj, **kw): old = {} for k, v in kw.items(): old[k] = getattr(obj, k) setattr(obj, k, v) try: yield obj finally: for k, v in old.items(): setattr(obj, k, v) def test_list_artifact_tags(db: SQLDB, db_session: Session): db.store_artifact(db_session, "k1", {}, "1", tag="t1", project="p1") db.store_artifact(db_session, "k1", {}, "2", tag="t2", project="p1") db.store_artifact(db_session, "k1", {}, "2", tag="t2", project="p2") tags = db.list_artifact_tags(db_session, "p1") assert {"t1", "t2"} == set(tags), "bad tags" def test_list_artifact_date(db: SQLDB, db_session: Session): t1 = datetime(2020, 2, 16) t2 = t1 - timedelta(days=7) t3 = t2 - timedelta(days=7) prj = "p7" db.store_artifact(db_session, "k1", {"updated": t1}, "u1", project=prj) db.store_artifact(db_session, "k2", {"updated": t2}, "u2", project=prj) db.store_artifact(db_session, "k3", {"updated": t3}, "u3", project=prj) arts = db.list_artifacts(db_session, project=prj, since=t3, tag="*") assert 3 == len(arts), "since t3" arts = db.list_artifacts(db_session, project=prj, since=t2, tag="*") assert 2 == len(arts), "since t2" arts = db.list_artifacts( db_session, project=prj, since=t1 + timedelta(days=1), tag="*" ) assert not arts, "since t1+" arts = db.list_artifacts(db_session, project=prj, until=t2, tag="*") assert 2 == len(arts), "until t2" arts = db.list_artifacts(db_session, project=prj, since=t2, until=t2, tag="*") assert 1 == len(arts), "since/until t2" def test_list_projects(db: SQLDB, db_session: Session): for i in range(10): run = new_run("s1", {"l1": "v1", "l2": "v2"}, x=1) db.store_run(db_session, run, "u7", project=f"prj{i % 3}", iter=i) assert {"prj0", "prj1", "prj2"} == {p.name for p in db.list_projects(db_session)} def test_run_iter0(db: SQLDB, db_session: Session): uid, prj = "uid39", "lemon" run = new_run("s1", {"l1": "v1", "l2": "v2"}, x=1) for i in range(7): db.store_run(db_session, run, uid, prj, i) db._get_run(db_session, uid, prj, 0) # See issue 140 def test_artifacts_latest(db: SQLDB, db_session: Session): k1, u1, art1 = "k1", "u1", {"a": 1} prj = "p38" db.store_artifact(db_session, k1, art1, u1, project=prj) arts = db.list_artifacts(db_session, project=prj, tag="latest") assert art1["a"] == arts[0]["a"], "bad artifact" u2, art2 = "u2", {"a": 17} db.store_artifact(db_session, k1, art2, u2, project=prj) arts = db.list_artifacts(db_session, project=prj, tag="latest") assert 2 == len(arts), "count" assert art2["a"] == arts[1]["a"], "bad artifact" k2, u3, art3 = "k2", "u3", {"a": 99} db.store_artifact(db_session, k2, art3, u3, project=prj) arts = db.list_artifacts(db_session, project=prj, tag="latest") assert 3 == len(arts), "number" assert {1, 17, 99} == set(art["a"] for art in arts), "latest" @pytest.mark.parametrize("cls", _tagged) def test_tags(db: SQLDB, db_session: Session, cls): p1, n1 = "prj1", "name1" obj1, obj2, obj3 = cls(), cls(), cls() db_session.add(obj1) db_session.add(obj2) db_session.add(obj3) db_session.commit() db.tag_objects(db_session, [obj1, obj2], p1, n1) objs = db.find_tagged(db_session, p1, n1) assert {obj1, obj2} == set(objs), "find tags" db.del_tag(db_session, p1, n1) objs = db.find_tagged(db_session, p1, n1) assert [] == objs, "find tags after del" def _tag_objs(db: SQLDB, db_session: Session, count, project, tags): by_tag = defaultdict(list) for i in range(count): cls = _tagged[i % len(_tagged)] obj = cls() by_tag[tags[i % len(tags)]].append(obj) db_session.add(obj) db_session.commit() for tag, objs in by_tag.items(): db.tag_objects(db_session, objs, project, tag) def test_list_tags(db: SQLDB, db_session: Session): p1, tags1 = "prj1", ["a", "b", "c"] _tag_objs(db, db_session, 17, p1, tags1) p2, tags2 = "prj2", ["b", "c", "d", "e"] _tag_objs(db, db_session, 11, p2, tags2) tags = db.list_tags(db_session, p1) assert set(tags) == set(tags1), "tags" def test_projects(db: SQLDB, db_session: Session): prj1 = { "name": "p1", "description": "banana", # 'users': ['u1', 'u2'], "spec": {"company": "ACME"}, "state": "active", "created": datetime.now(), } pid1 = db.add_project(db_session, prj1) p1 = db.get_project(db_session, project_id=pid1) assert p1, f"project {pid1} not found" out = { "name": p1.name, "description": p1.description, # 'users': sorted(u.name for u in p1.users), "spec": p1.spec, "state": p1.state, "created": p1.created, } assert prj1 == out, "bad project" data = {"description": "lemon"} db.update_project(db_session, p1.name, data) p1 = db.get_project(db_session, project_id=pid1) assert data["description"] == p1.description, "bad update" prj2 = {"name": "p2"} db.add_project(db_session, prj2) prjs = {p.name for p in db.list_projects(db_session)} assert {prj1["name"], prj2["name"]} == prjs, "list" def test_cache_projects(db: SQLDB, db_session: Session): assert 0 == len(db._projects), "empty cache" name = "prj348" db.add_project(db_session, {"name": name}) assert {name} == db._projects, "project" mock = Mock() with patch(db, add_project=mock): db._ensure_project(db_session, name) mock.assert_not_called() mock = Mock() with patch(db, add_project=mock): db._ensure_project(db_session, name + "-new") mock.assert_called_once() project_2_name = "project-2" db.add_project(db_session, {"name": project_2_name}) db._projects = set() mock = Mock() with patch(db, add_project=mock): db._ensure_project(db_session, name) mock.assert_not_called() # def test_function_latest(db: SQLDB, db_session: Session): # fn1, t1 = {'x': 1}, 'u83' # fn2, t2 = {'x': 2}, 'u23' # prj, name = 'p388', 'n3023' # db.store_function(db_session, fn1, name, prj, t1) # db.store_function(db_session, fn2, name, prj, t2) # # fn = db.get_function(db_session, name, prj, 'latest') # assert fn2 == fn, 'latest'

33.022422

85

0.629957

from collections import defaultdict from contextlib import contextmanager from datetime import datetime, timedelta from unittest.mock import Mock import pytest from sqlalchemy.orm import Session from mlrun.api.db.sqldb.db import SQLDB from mlrun.api.db.sqldb.models import _tagged from tests.conftest import new_run @contextmanager def patch(obj, **kw): old = {} for k, v in kw.items(): old[k] = getattr(obj, k) setattr(obj, k, v) try: yield obj finally: for k, v in old.items(): setattr(obj, k, v) def test_list_artifact_tags(db: SQLDB, db_session: Session): db.store_artifact(db_session, "k1", {}, "1", tag="t1", project="p1") db.store_artifact(db_session, "k1", {}, "2", tag="t2", project="p1") db.store_artifact(db_session, "k1", {}, "2", tag="t2", project="p2") tags = db.list_artifact_tags(db_session, "p1") assert {"t1", "t2"} == set(tags), "bad tags" def test_list_artifact_date(db: SQLDB, db_session: Session): t1 = datetime(2020, 2, 16) t2 = t1 - timedelta(days=7) t3 = t2 - timedelta(days=7) prj = "p7" db.store_artifact(db_session, "k1", {"updated": t1}, "u1", project=prj) db.store_artifact(db_session, "k2", {"updated": t2}, "u2", project=prj) db.store_artifact(db_session, "k3", {"updated": t3}, "u3", project=prj) arts = db.list_artifacts(db_session, project=prj, since=t3, tag="*") assert 3 == len(arts), "since t3" arts = db.list_artifacts(db_session, project=prj, since=t2, tag="*") assert 2 == len(arts), "since t2" arts = db.list_artifacts( db_session, project=prj, since=t1 + timedelta(days=1), tag="*" ) assert not arts, "since t1+" arts = db.list_artifacts(db_session, project=prj, until=t2, tag="*") assert 2 == len(arts), "until t2" arts = db.list_artifacts(db_session, project=prj, since=t2, until=t2, tag="*") assert 1 == len(arts), "since/until t2" def test_list_projects(db: SQLDB, db_session: Session): for i in range(10): run = new_run("s1", {"l1": "v1", "l2": "v2"}, x=1) db.store_run(db_session, run, "u7", project=f"prj{i % 3}", iter=i) assert {"prj0", "prj1", "prj2"} == {p.name for p in db.list_projects(db_session)} def test_run_iter0(db: SQLDB, db_session: Session): uid, prj = "uid39", "lemon" run = new_run("s1", {"l1": "v1", "l2": "v2"}, x=1) for i in range(7): db.store_run(db_session, run, uid, prj, i) db._get_run(db_session, uid, prj, 0) def test_artifacts_latest(db: SQLDB, db_session: Session): k1, u1, art1 = "k1", "u1", {"a": 1} prj = "p38" db.store_artifact(db_session, k1, art1, u1, project=prj) arts = db.list_artifacts(db_session, project=prj, tag="latest") assert art1["a"] == arts[0]["a"], "bad artifact" u2, art2 = "u2", {"a": 17} db.store_artifact(db_session, k1, art2, u2, project=prj) arts = db.list_artifacts(db_session, project=prj, tag="latest") assert 2 == len(arts), "count" assert art2["a"] == arts[1]["a"], "bad artifact" k2, u3, art3 = "k2", "u3", {"a": 99} db.store_artifact(db_session, k2, art3, u3, project=prj) arts = db.list_artifacts(db_session, project=prj, tag="latest") assert 3 == len(arts), "number" assert {1, 17, 99} == set(art["a"] for art in arts), "latest" @pytest.mark.parametrize("cls", _tagged) def test_tags(db: SQLDB, db_session: Session, cls): p1, n1 = "prj1", "name1" obj1, obj2, obj3 = cls(), cls(), cls() db_session.add(obj1) db_session.add(obj2) db_session.add(obj3) db_session.commit() db.tag_objects(db_session, [obj1, obj2], p1, n1) objs = db.find_tagged(db_session, p1, n1) assert {obj1, obj2} == set(objs), "find tags" db.del_tag(db_session, p1, n1) objs = db.find_tagged(db_session, p1, n1) assert [] == objs, "find tags after del" def _tag_objs(db: SQLDB, db_session: Session, count, project, tags): by_tag = defaultdict(list) for i in range(count): cls = _tagged[i % len(_tagged)] obj = cls() by_tag[tags[i % len(tags)]].append(obj) db_session.add(obj) db_session.commit() for tag, objs in by_tag.items(): db.tag_objects(db_session, objs, project, tag) def test_list_tags(db: SQLDB, db_session: Session): p1, tags1 = "prj1", ["a", "b", "c"] _tag_objs(db, db_session, 17, p1, tags1) p2, tags2 = "prj2", ["b", "c", "d", "e"] _tag_objs(db, db_session, 11, p2, tags2) tags = db.list_tags(db_session, p1) assert set(tags) == set(tags1), "tags" def test_projects(db: SQLDB, db_session: Session): prj1 = { "name": "p1", "description": "banana", "spec": {"company": "ACME"}, "state": "active", "created": datetime.now(), } pid1 = db.add_project(db_session, prj1) p1 = db.get_project(db_session, project_id=pid1) assert p1, f"project {pid1} not found" out = { "name": p1.name, "description": p1.description, "spec": p1.spec, "state": p1.state, "created": p1.created, } assert prj1 == out, "bad project" data = {"description": "lemon"} db.update_project(db_session, p1.name, data) p1 = db.get_project(db_session, project_id=pid1) assert data["description"] == p1.description, "bad update" prj2 = {"name": "p2"} db.add_project(db_session, prj2) prjs = {p.name for p in db.list_projects(db_session)} assert {prj1["name"], prj2["name"]} == prjs, "list" def test_cache_projects(db: SQLDB, db_session: Session): assert 0 == len(db._projects), "empty cache" name = "prj348" db.add_project(db_session, {"name": name}) assert {name} == db._projects, "project" mock = Mock() with patch(db, add_project=mock): db._ensure_project(db_session, name) mock.assert_not_called() mock = Mock() with patch(db, add_project=mock): db._ensure_project(db_session, name + "-new") mock.assert_called_once() project_2_name = "project-2" db.add_project(db_session, {"name": project_2_name}) db._projects = set() mock = Mock() with patch(db, add_project=mock): db._ensure_project(db_session, name) mock.assert_not_called()

true

f731851b0bbc67a1b6af6e831923eba84d6c28aa

10,964

py

Python

rustplus/api/rust_api.py

olijeffers0n/rustplus.py

0ecaaecc345848b47abe27bc1fd8b7ef7aebfd23

[ "MIT" ]

25

2021-05-03T11:08:55.000Z

2022-03-14T00:56:50.000Z

rustplus/api/rust_api.py

olijeffers0n/rustplus.py

0ecaaecc345848b47abe27bc1fd8b7ef7aebfd23

[ "MIT" ]

9

2021-06-15T10:38:42.000Z

2022-03-26T11:45:03.000Z

rustplus/api/rust_api.py

olijeffers0n/rustplus.py

0ecaaecc345848b47abe27bc1fd8b7ef7aebfd23

[ "MIT" ]

13

2021-06-08T18:35:17.000Z

2022-03-26T00:44:08.000Z

import asyncio from asyncio.futures import Future from typing import List from PIL import Image from io import BytesIO from importlib import resources from datetime import datetime from collections import defaultdict from .base_rust_api import BaseRustSocket from .structures import RustTime, RustInfo, RustMap, RustMarker, RustChatMessage, RustTeamInfo, RustTeamMember, RustTeamNote, RustEntityInfo, RustContents, RustItem from .remote.rustplus_pb2 import * from .remote import HeartBeat from ..commands import CommandOptions from ..exceptions import * from ..utils import * class RustSocket(BaseRustSocket): def __init__(self, ip: str = None, port: str = None, steamid: int = None, playertoken: int = None, command_options : CommandOptions = None, raise_ratelimit_exception : bool = True, ratelimit_limit : int = 25, ratelimit_refill : int = 3) -> None: super().__init__(ip=ip, port=port, steamid=steamid, playertoken=playertoken, command_options=command_options, raise_ratelimit_exception=raise_ratelimit_exception, ratelimit_limit=ratelimit_limit, ratelimit_refill=ratelimit_refill, heartbeat=HeartBeat(self)) def entity_event(self, eid): """ Decorator to register a smart device listener """ def wrap_func(coro): def entity_event_callback(future : Future): try: entity_info : RustEntityInfo = future.result() self.remote.event_handler.register_event(eid, (coro, loop, entity_info.type)) except: raise SmartDeviceRegistrationError("Not Found") loop = asyncio.get_event_loop() future = asyncio.run_coroutine_threadsafe(self.get_entity_info(eid), loop) future.add_done_callback(entity_event_callback) return wrap_func async def get_time(self) -> RustTime: await self._handle_ratelimit() app_request = self._generate_protobuf() app_request.getTime.CopyFrom(AppEmpty()) await self.remote.send_message(app_request) response = await self.remote.get_response(app_request.seq, app_request) return format_time(response) async def send_team_message(self, message: str) -> None: await self._handle_ratelimit(2) app_send_message = AppSendMessage() app_send_message.message = message app_request = self._generate_protobuf() app_request.sendTeamMessage.CopyFrom(app_send_message) self.remote.ignored_responses.append(app_request.seq) await self.remote.send_message(app_request) async def get_info(self) -> RustInfo: await self._handle_ratelimit() app_request = self._generate_protobuf() app_request.getInfo.CopyFrom(AppEmpty()) await self.remote.send_message(app_request) response = await self.remote.get_response(app_request.seq, app_request) return RustInfo(response.response.info) async def get_team_chat(self) -> List[RustChatMessage]: await self._handle_ratelimit() app_request = self._generate_protobuf() app_request.getTeamChat.CopyFrom(AppEmpty()) await self.remote.send_message(app_request) messages = (await self.remote.get_response(app_request.seq, app_request)).response.teamChat.messages return [RustChatMessage(message) for message in messages] async def get_team_info(self): await self._handle_ratelimit() app_request = self._generate_protobuf() app_request.getTeamInfo.CopyFrom(AppEmpty()) await self.remote.send_message(app_request) app_message = await self.remote.get_response(app_request.seq, app_request) return RustTeamInfo(app_message.response.teamInfo) async def get_markers(self) -> List[RustMarker]: await self._handle_ratelimit() app_request = self._generate_protobuf() app_request.getMapMarkers.CopyFrom(AppEmpty()) await self.remote.send_message(app_request) app_message = await self.remote.get_response(app_request.seq, app_request) return [RustMarker(marker) for marker in app_message.response.mapMarkers.markers] async def get_raw_map_data(self) -> RustMap: await self._handle_ratelimit(5) app_request = self._generate_protobuf() app_request.getMap.CopyFrom(AppEmpty()) await self.remote.send_message(app_request) app_message = await self.remote.get_response(app_request.seq, app_request) return RustMap(app_message.response.map) async def get_map(self, add_icons: bool = False, add_events: bool = False, add_vending_machines: bool = False, override_images: dict = {}) -> Image: MAPSIZE = int((await self.get_info()).size) await self._handle_ratelimit(5 + 1 if [add_icons, add_events, add_vending_machines].count(True) >= 1 else 0) app_request = self._generate_protobuf() app_request.getMap.CopyFrom(AppEmpty()) await self.remote.send_message(app_request) app_message = await self.remote.get_response(app_request.seq, app_request) map = app_message.response.map monuments = list(map.monuments) try: image = Image.open(BytesIO(map.jpgImage)) except: raise ImageError("Invalid bytes for the image") image = image.crop((500,500,map.height-500,map.width-500)) map = image.resize((MAPSIZE,MAPSIZE), Image.ANTIALIAS) if add_icons or add_events or add_vending_machines: mapMarkers = await self.get_markers() if add_icons: for monument in monuments: if str(monument.token) == "DungeonBase": continue icon = convert_monument(monument.token, override_images) if monument.token in override_images: icon = icon.resize((150, 150)) if str(monument.token) == "train_tunnel_display_name": icon = icon.resize((100, 125)) map.paste(icon, (format_cood(int(monument.x), int(monument.y), MAPSIZE)), icon) if add_vending_machines: with resources.path("rustplus.api.icons", "vending_machine.png") as path: vendingMachine = Image.open(path).convert("RGBA") vendingMachine = vendingMachine.resize((100, 100)) for marker in mapMarkers: if add_events: if marker.type == 2 or marker.type == 4 or marker.type == 5 or marker.type == 6: icon = convert_marker(str(marker.type), marker.rotation) if marker.type == 6: x = marker.x y = marker.y if y > MAPSIZE: y = MAPSIZE if y < 0: y = 100 if x > MAPSIZE: x = MAPSIZE - 75 if x < 0: x = 50 map.paste(icon, (int(x), MAPSIZE - int(y)), icon) else: map.paste(icon, (format_cood(int(marker.x), int(marker.y), MAPSIZE)), icon) if add_vending_machines and marker.type == 3: map.paste(vendingMachine, (int(marker.x) - 50, MAPSIZE - int(marker.y) - 50), vendingMachine) return map.resize((2000, 2000), Image.ANTIALIAS) async def get_entity_info(self, eid: int = None) -> RustEntityInfo: await self._handle_ratelimit() if eid is None: raise ValueError("EID cannot be None") app_request = self._generate_protobuf() app_request.entityId = eid app_request.getEntityInfo.CopyFrom(AppEmpty()) await self.remote.send_message(app_request) app_message = await self.remote.get_response(app_request.seq, app_request) return RustEntityInfo(app_message.response.entityInfo) async def _update_smart_device(self, eid : int, value : bool) -> None: await self._handle_ratelimit() entityValue = AppSetEntityValue() entityValue.value = value app_request = self._generate_protobuf() app_request.entityId = eid app_request.setEntityValue.CopyFrom(entityValue) self.remote.ignored_responses.append(app_request.seq) await self.remote.send_message(app_request) async def turn_on_smart_switch(self, eid: int = None) -> None: if eid is None: raise ValueError("EID cannot be None") await self._update_smart_device(eid, True) async def turn_off_smart_switch(self, eid: int = None) -> None: if eid is None: raise ValueError("EID cannot be None") await self._update_smart_device(eid, False) async def promote_to_team_leader(self, steamid: int = None) -> None: if steamid is None: raise ValueError("SteamID cannot be None") await self._handle_ratelimit() leaderPacket = AppPromoteToLeader() leaderPacket.steamId = steamid app_request = self._generate_protobuf() app_request.promoteToLeader.CopyFrom(leaderPacket) self.remote.ignored_responses.append(app_request.seq) await self.remote.send_message(app_request) async def get_current_events(self) -> List[RustMarker]: return [marker for marker in (await self.get_markers()) if marker.type == 2 or marker.type == 4 or marker.type == 5 or marker.type == 6] async def get_tc_storage_contents(self, eid: int = None, combine_stacks: bool = False) -> RustContents: if eid is None: raise ValueError("EID cannot be None") returnedData = await self.get_entity_info(eid) targetTime = datetime.utcfromtimestamp(int(returnedData.protectionExpiry)) difference = targetTime - datetime.utcnow() items = [] for item in returnedData.items: items.append(RustItem(translate_id_to_stack(item.itemId), item.itemId, item.quantity, item.itemIsBlueprint)) if combine_stacks: mergedMap = defaultdict(tuple) for item in items: data = mergedMap[str(item.itemId)] if data: count = int(data[0]) + int(item.quantity) mergedMap[str(item.itemId)] = (count, bool(item.isBlueprint)) else: mergedMap[str(item.itemId)] = (int(item.quantity), bool(item.isBlueprint)) items = [] for key in mergedMap.keys(): items.append(RustItem(translate_id_to_stack(key), key, int(mergedMap[key][0]), bool(mergedMap[key][1]))) return RustContents(difference, bool(returnedData.hasProtection), items)

36.915825

265

0.639639

import asyncio from asyncio.futures import Future from typing import List from PIL import Image from io import BytesIO from importlib import resources from datetime import datetime from collections import defaultdict from .base_rust_api import BaseRustSocket from .structures import RustTime, RustInfo, RustMap, RustMarker, RustChatMessage, RustTeamInfo, RustTeamMember, RustTeamNote, RustEntityInfo, RustContents, RustItem from .remote.rustplus_pb2 import * from .remote import HeartBeat from ..commands import CommandOptions from ..exceptions import * from ..utils import * class RustSocket(BaseRustSocket): def __init__(self, ip: str = None, port: str = None, steamid: int = None, playertoken: int = None, command_options : CommandOptions = None, raise_ratelimit_exception : bool = True, ratelimit_limit : int = 25, ratelimit_refill : int = 3) -> None: super().__init__(ip=ip, port=port, steamid=steamid, playertoken=playertoken, command_options=command_options, raise_ratelimit_exception=raise_ratelimit_exception, ratelimit_limit=ratelimit_limit, ratelimit_refill=ratelimit_refill, heartbeat=HeartBeat(self)) def entity_event(self, eid): def wrap_func(coro): def entity_event_callback(future : Future): try: entity_info : RustEntityInfo = future.result() self.remote.event_handler.register_event(eid, (coro, loop, entity_info.type)) except: raise SmartDeviceRegistrationError("Not Found") loop = asyncio.get_event_loop() future = asyncio.run_coroutine_threadsafe(self.get_entity_info(eid), loop) future.add_done_callback(entity_event_callback) return wrap_func async def get_time(self) -> RustTime: await self._handle_ratelimit() app_request = self._generate_protobuf() app_request.getTime.CopyFrom(AppEmpty()) await self.remote.send_message(app_request) response = await self.remote.get_response(app_request.seq, app_request) return format_time(response) async def send_team_message(self, message: str) -> None: await self._handle_ratelimit(2) app_send_message = AppSendMessage() app_send_message.message = message app_request = self._generate_protobuf() app_request.sendTeamMessage.CopyFrom(app_send_message) self.remote.ignored_responses.append(app_request.seq) await self.remote.send_message(app_request) async def get_info(self) -> RustInfo: await self._handle_ratelimit() app_request = self._generate_protobuf() app_request.getInfo.CopyFrom(AppEmpty()) await self.remote.send_message(app_request) response = await self.remote.get_response(app_request.seq, app_request) return RustInfo(response.response.info) async def get_team_chat(self) -> List[RustChatMessage]: await self._handle_ratelimit() app_request = self._generate_protobuf() app_request.getTeamChat.CopyFrom(AppEmpty()) await self.remote.send_message(app_request) messages = (await self.remote.get_response(app_request.seq, app_request)).response.teamChat.messages return [RustChatMessage(message) for message in messages] async def get_team_info(self): await self._handle_ratelimit() app_request = self._generate_protobuf() app_request.getTeamInfo.CopyFrom(AppEmpty()) await self.remote.send_message(app_request) app_message = await self.remote.get_response(app_request.seq, app_request) return RustTeamInfo(app_message.response.teamInfo) async def get_markers(self) -> List[RustMarker]: await self._handle_ratelimit() app_request = self._generate_protobuf() app_request.getMapMarkers.CopyFrom(AppEmpty()) await self.remote.send_message(app_request) app_message = await self.remote.get_response(app_request.seq, app_request) return [RustMarker(marker) for marker in app_message.response.mapMarkers.markers] async def get_raw_map_data(self) -> RustMap: await self._handle_ratelimit(5) app_request = self._generate_protobuf() app_request.getMap.CopyFrom(AppEmpty()) await self.remote.send_message(app_request) app_message = await self.remote.get_response(app_request.seq, app_request) return RustMap(app_message.response.map) async def get_map(self, add_icons: bool = False, add_events: bool = False, add_vending_machines: bool = False, override_images: dict = {}) -> Image: MAPSIZE = int((await self.get_info()).size) await self._handle_ratelimit(5 + 1 if [add_icons, add_events, add_vending_machines].count(True) >= 1 else 0) app_request = self._generate_protobuf() app_request.getMap.CopyFrom(AppEmpty()) await self.remote.send_message(app_request) app_message = await self.remote.get_response(app_request.seq, app_request) map = app_message.response.map monuments = list(map.monuments) try: image = Image.open(BytesIO(map.jpgImage)) except: raise ImageError("Invalid bytes for the image") image = image.crop((500,500,map.height-500,map.width-500)) map = image.resize((MAPSIZE,MAPSIZE), Image.ANTIALIAS) if add_icons or add_events or add_vending_machines: mapMarkers = await self.get_markers() if add_icons: for monument in monuments: if str(monument.token) == "DungeonBase": continue icon = convert_monument(monument.token, override_images) if monument.token in override_images: icon = icon.resize((150, 150)) if str(monument.token) == "train_tunnel_display_name": icon = icon.resize((100, 125)) map.paste(icon, (format_cood(int(monument.x), int(monument.y), MAPSIZE)), icon) if add_vending_machines: with resources.path("rustplus.api.icons", "vending_machine.png") as path: vendingMachine = Image.open(path).convert("RGBA") vendingMachine = vendingMachine.resize((100, 100)) for marker in mapMarkers: if add_events: if marker.type == 2 or marker.type == 4 or marker.type == 5 or marker.type == 6: icon = convert_marker(str(marker.type), marker.rotation) if marker.type == 6: x = marker.x y = marker.y if y > MAPSIZE: y = MAPSIZE if y < 0: y = 100 if x > MAPSIZE: x = MAPSIZE - 75 if x < 0: x = 50 map.paste(icon, (int(x), MAPSIZE - int(y)), icon) else: map.paste(icon, (format_cood(int(marker.x), int(marker.y), MAPSIZE)), icon) if add_vending_machines and marker.type == 3: map.paste(vendingMachine, (int(marker.x) - 50, MAPSIZE - int(marker.y) - 50), vendingMachine) return map.resize((2000, 2000), Image.ANTIALIAS) async def get_entity_info(self, eid: int = None) -> RustEntityInfo: await self._handle_ratelimit() if eid is None: raise ValueError("EID cannot be None") app_request = self._generate_protobuf() app_request.entityId = eid app_request.getEntityInfo.CopyFrom(AppEmpty()) await self.remote.send_message(app_request) app_message = await self.remote.get_response(app_request.seq, app_request) return RustEntityInfo(app_message.response.entityInfo) async def _update_smart_device(self, eid : int, value : bool) -> None: await self._handle_ratelimit() entityValue = AppSetEntityValue() entityValue.value = value app_request = self._generate_protobuf() app_request.entityId = eid app_request.setEntityValue.CopyFrom(entityValue) self.remote.ignored_responses.append(app_request.seq) await self.remote.send_message(app_request) async def turn_on_smart_switch(self, eid: int = None) -> None: if eid is None: raise ValueError("EID cannot be None") await self._update_smart_device(eid, True) async def turn_off_smart_switch(self, eid: int = None) -> None: if eid is None: raise ValueError("EID cannot be None") await self._update_smart_device(eid, False) async def promote_to_team_leader(self, steamid: int = None) -> None: if steamid is None: raise ValueError("SteamID cannot be None") await self._handle_ratelimit() leaderPacket = AppPromoteToLeader() leaderPacket.steamId = steamid app_request = self._generate_protobuf() app_request.promoteToLeader.CopyFrom(leaderPacket) self.remote.ignored_responses.append(app_request.seq) await self.remote.send_message(app_request) async def get_current_events(self) -> List[RustMarker]: return [marker for marker in (await self.get_markers()) if marker.type == 2 or marker.type == 4 or marker.type == 5 or marker.type == 6] async def get_tc_storage_contents(self, eid: int = None, combine_stacks: bool = False) -> RustContents: if eid is None: raise ValueError("EID cannot be None") returnedData = await self.get_entity_info(eid) targetTime = datetime.utcfromtimestamp(int(returnedData.protectionExpiry)) difference = targetTime - datetime.utcnow() items = [] for item in returnedData.items: items.append(RustItem(translate_id_to_stack(item.itemId), item.itemId, item.quantity, item.itemIsBlueprint)) if combine_stacks: mergedMap = defaultdict(tuple) for item in items: data = mergedMap[str(item.itemId)] if data: count = int(data[0]) + int(item.quantity) mergedMap[str(item.itemId)] = (count, bool(item.isBlueprint)) else: mergedMap[str(item.itemId)] = (int(item.quantity), bool(item.isBlueprint)) items = [] for key in mergedMap.keys(): items.append(RustItem(translate_id_to_stack(key), key, int(mergedMap[key][0]), bool(mergedMap[key][1]))) return RustContents(difference, bool(returnedData.hasProtection), items)

true

f73185ddbd238f0f843033c23cdf25d8077121c5

989

py

Python

tests/sparseml/onnx/test_imports.py

clementpoiret/sparseml

8442a6ef8ba11fb02f5e51472dd68b72438539b9

[ "Apache-2.0" ]

922

2021-02-04T17:51:54.000Z

2022-03-31T20:49:26.000Z

tests/sparseml/onnx/test_imports.py

clementpoiret/sparseml

8442a6ef8ba11fb02f5e51472dd68b72438539b9

[ "Apache-2.0" ]

197

2021-02-04T22:17:21.000Z

2022-03-31T13:58:55.000Z

tests/sparseml/onnx/test_imports.py

clementpoiret/sparseml

8442a6ef8ba11fb02f5e51472dd68b72438539b9

[ "Apache-2.0" ]

80

2021-02-04T22:20:14.000Z

2022-03-30T19:36:15.000Z

# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. def test_imports(): # flake8: noqa from sparseml.onnx import ( check_onnx_install, check_onnxruntime_install, detect_framework, framework_info, is_supported, onnx, onnx_err, onnxruntime, onnxruntime_err, require_onnx, require_onnxruntime, sparsification_info, )

30.90625

76

0.695652

def test_imports(): from sparseml.onnx import ( check_onnx_install, check_onnxruntime_install, detect_framework, framework_info, is_supported, onnx, onnx_err, onnxruntime, onnxruntime_err, require_onnx, require_onnxruntime, sparsification_info, )

true

f73186d8775e20db4e181ff54bb6713045cc02fc

7,745

py

Python

demo/demo.py

hummat/detectron2

ef2f4df474b4a07049cada4793392e8e36c3e746

[ "Apache-2.0" ]

null

demo/demo.py

hummat/detectron2

ef2f4df474b4a07049cada4793392e8e36c3e746

[ "Apache-2.0" ]

null

demo/demo.py

hummat/detectron2

ef2f4df474b4a07049cada4793392e8e36c3e746

[ "Apache-2.0" ]

null

# Copyright (c) Facebook, Inc. and its affiliates. import argparse import glob import multiprocessing as mp import numpy as np import os import tempfile import time import warnings import cv2 import tqdm from detectron2.config import get_cfg from detectron2.data.detection_utils import read_image from detectron2.utils.logger import setup_logger from predictor import VisualizationDemo from detectron2.data import DatasetCatalog, MetadataCatalog from detectron2.data.datasets import load_coco_json path_to_coco_json = "/home/matthias/Data/Ubuntu/data/datasets/porta_filter/front3d/coco_data/coco_annotations.json" path_to_images = "/home/matthias/Data/Ubuntu/data/datasets/porta_filter/front3d" # path_to_config_yaml = "/home/matthias/Data/Ubuntu/git/detectron2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml" DatasetCatalog.register("porta_filter", lambda: load_coco_json(path_to_coco_json, path_to_images)) MetadataCatalog.get("porta_filter").set(thing_classes=["porta filter"], json_file=path_to_coco_json, image_root=path_to_images) # constants WINDOW_NAME = "COCO detections" def setup_cfg(args): # load config from file and command-line arguments cfg = get_cfg() # To use demo for Panoptic-DeepLab, please uncomment the following two lines. # from detectron2.projects.panoptic_deeplab import add_panoptic_deeplab_config # noqa # add_panoptic_deeplab_config(cfg) cfg.merge_from_file(args.config_file) cfg.merge_from_list(args.opts) # Set score_threshold for builtin models cfg.MODEL.RETINANET.SCORE_THRESH_TEST = args.confidence_threshold cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = args.confidence_threshold cfg.MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH = args.confidence_threshold cfg.freeze() return cfg def get_parser(): parser = argparse.ArgumentParser(description="Detectron2 demo for builtin configs") parser.add_argument( "--config-file", default="configs/quick_schedules/mask_rcnn_R_50_FPN_inference_acc_test.yaml", metavar="FILE", help="path to config file", ) parser.add_argument("--webcam", action="store_true", help="Take inputs from webcam.") parser.add_argument("--video-input", help="Path to video file.") parser.add_argument( "--input", nargs="+", help="A list of space separated input images; " "or a single glob pattern such as 'directory/*.jpg'", ) parser.add_argument( "--output", help="A file or directory to save output visualizations. " "If not given, will show output in an OpenCV window.", ) parser.add_argument( "--confidence-threshold", type=float, default=0.5, help="Minimum score for instance predictions to be shown", ) parser.add_argument( "--opts", help="Modify config options using the command-line 'KEY VALUE' pairs", default=[], nargs=argparse.REMAINDER, ) return parser def test_opencv_video_format(codec, file_ext): with tempfile.TemporaryDirectory(prefix="video_format_test") as dir: filename = os.path.join(dir, "test_file" + file_ext) writer = cv2.VideoWriter( filename=filename, fourcc=cv2.VideoWriter_fourcc(*codec), fps=float(30), frameSize=(10, 10), isColor=True, ) [writer.write(np.zeros((10, 10, 3), np.uint8)) for _ in range(30)] writer.release() if os.path.isfile(filename): return True return False if __name__ == "__main__": mp.set_start_method("spawn", force=True) args = get_parser().parse_args() setup_logger(name="fvcore") logger = setup_logger() logger.info("Arguments: " + str(args)) cfg = setup_cfg(args) demo = VisualizationDemo(cfg) if args.input: if len(args.input) == 1: args.input = glob.glob(os.path.expanduser(args.input[0])) assert args.input, "The input path(s) was not found" for path in tqdm.tqdm(args.input, disable=not args.output): # use PIL, to be consistent with evaluation img = read_image(path, format="BGR") start_time = time.time() predictions, visualized_output = demo.run_on_image(img) logger.info( "{}: {} in {:.2f}s".format( path, "detected {} instances".format(len(predictions["instances"])) if "instances" in predictions else "finished", time.time() - start_time, ) ) if args.output: if os.path.isdir(args.output): assert os.path.isdir(args.output), args.output out_filename = os.path.join(args.output, os.path.basename(path)) else: assert len(args.input) == 1, "Please specify a directory with args.output" out_filename = args.output visualized_output.save(out_filename) else: cv2.namedWindow(WINDOW_NAME, cv2.WINDOW_NORMAL) cv2.imshow(WINDOW_NAME, visualized_output.get_image()[:, :, ::-1]) if cv2.waitKey(0) == 27: break # esc to quit elif args.webcam: assert args.input is None, "Cannot have both --input and --webcam!" assert args.output is None, "output not yet supported with --webcam!" cam = cv2.VideoCapture(0) for vis in tqdm.tqdm(demo.run_on_video(cam)): cv2.namedWindow(WINDOW_NAME, cv2.WINDOW_NORMAL) cv2.imshow(WINDOW_NAME, vis) if cv2.waitKey(1) == 27: break # esc to quit cam.release() cv2.destroyAllWindows() elif args.video_input: video = cv2.VideoCapture(args.video_input) width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH)) height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT)) frames_per_second = video.get(cv2.CAP_PROP_FPS) num_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT)) basename = os.path.basename(args.video_input) codec, file_ext = ( ("x264", ".mkv") if test_opencv_video_format("x264", ".mkv") else ("mp4v", ".mp4") ) if codec == ".mp4v": warnings.warn("x264 codec not available, switching to mp4v") if args.output: if os.path.isdir(args.output): output_fname = os.path.join(args.output, basename) output_fname = os.path.splitext(output_fname)[0] + file_ext else: output_fname = args.output assert not os.path.isfile(output_fname), output_fname output_file = cv2.VideoWriter( filename=output_fname, # some installation of opencv may not support x264 (due to its license), # you can try other format (e.g. MPEG) fourcc=cv2.VideoWriter_fourcc(*codec), fps=float(frames_per_second), frameSize=(width, height), isColor=True, ) assert os.path.isfile(args.video_input) for vis_frame in tqdm.tqdm(demo.run_on_video(video), total=num_frames): if args.output: output_file.write(vis_frame) else: cv2.namedWindow(basename, cv2.WINDOW_NORMAL) cv2.imshow(basename, vis_frame) if cv2.waitKey(1) == 27: break # esc to quit video.release() if args.output: output_file.release() else: cv2.destroyAllWindows()

39.116162

128

0.628922

import argparse import glob import multiprocessing as mp import numpy as np import os import tempfile import time import warnings import cv2 import tqdm from detectron2.config import get_cfg from detectron2.data.detection_utils import read_image from detectron2.utils.logger import setup_logger from predictor import VisualizationDemo from detectron2.data import DatasetCatalog, MetadataCatalog from detectron2.data.datasets import load_coco_json path_to_coco_json = "/home/matthias/Data/Ubuntu/data/datasets/porta_filter/front3d/coco_data/coco_annotations.json" path_to_images = "/home/matthias/Data/Ubuntu/data/datasets/porta_filter/front3d" DatasetCatalog.register("porta_filter", lambda: load_coco_json(path_to_coco_json, path_to_images)) MetadataCatalog.get("porta_filter").set(thing_classes=["porta filter"], json_file=path_to_coco_json, image_root=path_to_images) WINDOW_NAME = "COCO detections" def setup_cfg(args): cfg = get_cfg() cfg.merge_from_file(args.config_file) cfg.merge_from_list(args.opts) cfg.MODEL.RETINANET.SCORE_THRESH_TEST = args.confidence_threshold cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = args.confidence_threshold cfg.MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH = args.confidence_threshold cfg.freeze() return cfg def get_parser(): parser = argparse.ArgumentParser(description="Detectron2 demo for builtin configs") parser.add_argument( "--config-file", default="configs/quick_schedules/mask_rcnn_R_50_FPN_inference_acc_test.yaml", metavar="FILE", help="path to config file", ) parser.add_argument("--webcam", action="store_true", help="Take inputs from webcam.") parser.add_argument("--video-input", help="Path to video file.") parser.add_argument( "--input", nargs="+", help="A list of space separated input images; " "or a single glob pattern such as 'directory/*.jpg'", ) parser.add_argument( "--output", help="A file or directory to save output visualizations. " "If not given, will show output in an OpenCV window.", ) parser.add_argument( "--confidence-threshold", type=float, default=0.5, help="Minimum score for instance predictions to be shown", ) parser.add_argument( "--opts", help="Modify config options using the command-line 'KEY VALUE' pairs", default=[], nargs=argparse.REMAINDER, ) return parser def test_opencv_video_format(codec, file_ext): with tempfile.TemporaryDirectory(prefix="video_format_test") as dir: filename = os.path.join(dir, "test_file" + file_ext) writer = cv2.VideoWriter( filename=filename, fourcc=cv2.VideoWriter_fourcc(*codec), fps=float(30), frameSize=(10, 10), isColor=True, ) [writer.write(np.zeros((10, 10, 3), np.uint8)) for _ in range(30)] writer.release() if os.path.isfile(filename): return True return False if __name__ == "__main__": mp.set_start_method("spawn", force=True) args = get_parser().parse_args() setup_logger(name="fvcore") logger = setup_logger() logger.info("Arguments: " + str(args)) cfg = setup_cfg(args) demo = VisualizationDemo(cfg) if args.input: if len(args.input) == 1: args.input = glob.glob(os.path.expanduser(args.input[0])) assert args.input, "The input path(s) was not found" for path in tqdm.tqdm(args.input, disable=not args.output): img = read_image(path, format="BGR") start_time = time.time() predictions, visualized_output = demo.run_on_image(img) logger.info( "{}: {} in {:.2f}s".format( path, "detected {} instances".format(len(predictions["instances"])) if "instances" in predictions else "finished", time.time() - start_time, ) ) if args.output: if os.path.isdir(args.output): assert os.path.isdir(args.output), args.output out_filename = os.path.join(args.output, os.path.basename(path)) else: assert len(args.input) == 1, "Please specify a directory with args.output" out_filename = args.output visualized_output.save(out_filename) else: cv2.namedWindow(WINDOW_NAME, cv2.WINDOW_NORMAL) cv2.imshow(WINDOW_NAME, visualized_output.get_image()[:, :, ::-1]) if cv2.waitKey(0) == 27: break elif args.webcam: assert args.input is None, "Cannot have both --input and --webcam!" assert args.output is None, "output not yet supported with --webcam!" cam = cv2.VideoCapture(0) for vis in tqdm.tqdm(demo.run_on_video(cam)): cv2.namedWindow(WINDOW_NAME, cv2.WINDOW_NORMAL) cv2.imshow(WINDOW_NAME, vis) if cv2.waitKey(1) == 27: break cam.release() cv2.destroyAllWindows() elif args.video_input: video = cv2.VideoCapture(args.video_input) width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH)) height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT)) frames_per_second = video.get(cv2.CAP_PROP_FPS) num_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT)) basename = os.path.basename(args.video_input) codec, file_ext = ( ("x264", ".mkv") if test_opencv_video_format("x264", ".mkv") else ("mp4v", ".mp4") ) if codec == ".mp4v": warnings.warn("x264 codec not available, switching to mp4v") if args.output: if os.path.isdir(args.output): output_fname = os.path.join(args.output, basename) output_fname = os.path.splitext(output_fname)[0] + file_ext else: output_fname = args.output assert not os.path.isfile(output_fname), output_fname output_file = cv2.VideoWriter( filename=output_fname, fourcc=cv2.VideoWriter_fourcc(*codec), fps=float(frames_per_second), frameSize=(width, height), isColor=True, ) assert os.path.isfile(args.video_input) for vis_frame in tqdm.tqdm(demo.run_on_video(video), total=num_frames): if args.output: output_file.write(vis_frame) else: cv2.namedWindow(basename, cv2.WINDOW_NORMAL) cv2.imshow(basename, vis_frame) if cv2.waitKey(1) == 27: break video.release() if args.output: output_file.release() else: cv2.destroyAllWindows()

true

f731877388c6e4f2d7121cb542ca1fe6ff75cc62

1,466

py

Python

xcc/util.py

BastianZim/xanadu-cloud-client

3a2d9d5373d90339a1047ee939bacef1bd4019ed

[ "Apache-2.0" ]

3

2021-11-12T21:44:35.000Z

2022-02-10T15:07:20.000Z

xcc/util.py

BastianZim/xanadu-cloud-client

3a2d9d5373d90339a1047ee939bacef1bd4019ed

[ "Apache-2.0" ]

2

2021-11-08T16:39:56.000Z

2022-01-20T14:47:29.000Z

xcc/util.py

BastianZim/xanadu-cloud-client

3a2d9d5373d90339a1047ee939bacef1bd4019ed

[ "Apache-2.0" ]

2

2021-12-01T19:07:05.000Z

2022-01-20T14:26:47.000Z

""" This module contains utilities which are shared between other modules. """ from typing import Any, Callable class cached_property: # pylint: disable=invalid-name """Descriptor that transforms a class method into a property whose value is computed once and then cached for subsequent accesses. Args: func (Callable[[Any], Any]): class method whose value should be cached .. note:: Each class instance is associated with an independent cache. .. warning:: Unlike ``functools.cached_property``, this descriptor is *not* safe for concurrent use. """ def __init__(self, func: Callable[[Any], Any]) -> None: self.func = func self.caches = {} self.__doc__ = func.__doc__ def __get__(self, instance: Any, _) -> Any: """Returns the (cached) value associated with the given instance.""" # Edge case to support getattr() and generate Sphinx documentation. if instance is None: return self if instance not in self.caches: self.caches[instance] = self.func(instance) return self.caches[instance] def __set__(self, instance: Any, value: Any) -> None: """Sets the cache of the given instance to the provided value.""" self.caches[instance] = value def __delete__(self, instance: Any) -> None: """Clears the cache of the given instance.""" self.caches.pop(instance, None)

31.191489

79

0.645975

from typing import Any, Callable class cached_property: def __init__(self, func: Callable[[Any], Any]) -> None: self.func = func self.caches = {} self.__doc__ = func.__doc__ def __get__(self, instance: Any, _) -> Any: if instance is None: return self if instance not in self.caches: self.caches[instance] = self.func(instance) return self.caches[instance] def __set__(self, instance: Any, value: Any) -> None: self.caches[instance] = value def __delete__(self, instance: Any) -> None: self.caches.pop(instance, None)

true

f73187898e8bb312c2b27b9f5d0d2fa9a2a19d87

79,267

py

Python

flaml/model.py

rserran/FLAML

7d6822aa40883550e72c4ee24adb765c6e937ce7

[ "MIT" ]

null

flaml/model.py

rserran/FLAML

7d6822aa40883550e72c4ee24adb765c6e937ce7

[ "MIT" ]

null

flaml/model.py

rserran/FLAML

7d6822aa40883550e72c4ee24adb765c6e937ce7

[ "MIT" ]

null

# ! # * Copyright (c) FLAML authors. All rights reserved. # * Licensed under the MIT License. See LICENSE file in the # * project root for license information. from contextlib import contextmanager from functools import partial import signal import os from typing import Callable, List import numpy as np import time from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier from sklearn.ensemble import ExtraTreesRegressor, ExtraTreesClassifier from sklearn.linear_model import LogisticRegression from sklearn.dummy import DummyClassifier, DummyRegressor from scipy.sparse import issparse import logging import shutil from pandas import DataFrame, Series, to_datetime import sys import math from . import tune from .data import ( group_counts, CLASSIFICATION, TS_FORECASTREGRESSION, TS_TIMESTAMP_COL, TS_VALUE_COL, SEQCLASSIFICATION, SEQREGRESSION, TOKENCLASSIFICATION, SUMMARIZATION, NLG_TASKS, MULTICHOICECLASSIFICATION, ) try: import psutil except ImportError: psutil = None try: import resource except ImportError: resource = None logger = logging.getLogger("flaml.automl") FREE_MEM_RATIO = 0.2 def TimeoutHandler(sig, frame): raise TimeoutError(sig, frame) @contextmanager def limit_resource(memory_limit, time_limit): if memory_limit > 0: soft, hard = resource.getrlimit(resource.RLIMIT_AS) if soft < 0 and (hard < 0 or memory_limit <= hard) or memory_limit < soft: try: resource.setrlimit(resource.RLIMIT_AS, (int(memory_limit), hard)) except ValueError: # According to https://bugs.python.org/issue40518, it's a mac-specific error. pass main_thread = False if time_limit is not None: try: signal.signal(signal.SIGALRM, TimeoutHandler) signal.alarm(int(time_limit) or 1) main_thread = True except ValueError: pass try: yield finally: if main_thread: signal.alarm(0) if memory_limit > 0: resource.setrlimit(resource.RLIMIT_AS, (soft, hard)) class BaseEstimator: """The abstract class for all learners. Typical examples: * XGBoostEstimator: for regression. * XGBoostSklearnEstimator: for classification. * LGBMEstimator, RandomForestEstimator, LRL1Classifier, LRL2Classifier: for both regression and classification. """ def __init__(self, task="binary", **config): """Constructor. Args: task: A string of the task type, one of 'binary', 'multiclass', 'regression', 'rank', 'seq-classification', 'seq-regression', 'token-classification', 'multichoice-classification', 'summarization', 'ts_forecast', 'ts_forecast_classification'. config: A dictionary containing the hyperparameter names, 'n_jobs' as keys. n_jobs is the number of parallel threads. """ self._task = task self.params = self.config2params(config) self.estimator_class = self._model = None if "_estimator_type" in config: self._estimator_type = self.params.pop("_estimator_type") else: self._estimator_type = ( "classifier" if task in CLASSIFICATION else "regressor" ) def get_params(self, deep=False): params = self.params.copy() params["task"] = self._task if hasattr(self, "_estimator_type"): params["_estimator_type"] = self._estimator_type return params @property def classes_(self): return self._model.classes_ @property def n_features_in_(self): return self._model.n_features_in_ @property def model(self): """Trained model after fit() is called, or None before fit() is called.""" return self._model @property def estimator(self): """Trained model after fit() is called, or None before fit() is called.""" return self._model def _preprocess(self, X): return X def _fit(self, X_train, y_train, **kwargs): current_time = time.time() if "groups" in kwargs: kwargs = kwargs.copy() groups = kwargs.pop("groups") if self._task == "rank": kwargs["group"] = group_counts(groups) # groups_val = kwargs.get('groups_val') # if groups_val is not None: # kwargs['eval_group'] = [group_counts(groups_val)] # kwargs['eval_set'] = [ # (kwargs['X_val'], kwargs['y_val'])] # kwargs['verbose'] = False # del kwargs['groups_val'], kwargs['X_val'], kwargs['y_val'] X_train = self._preprocess(X_train) model = self.estimator_class(**self.params) if logger.level == logging.DEBUG: # xgboost 1.6 doesn't display all the params in the model str logger.debug(f"flaml.model - {model} fit started with params {self.params}") model.fit(X_train, y_train, **kwargs) if logger.level == logging.DEBUG: logger.debug(f"flaml.model - {model} fit finished") train_time = time.time() - current_time self._model = model return train_time def fit(self, X_train, y_train, budget=None, **kwargs): """Train the model from given training data. Args: X_train: A numpy array or a dataframe of training data in shape n*m. y_train: A numpy array or a series of labels in shape n*1. budget: A float of the time budget in seconds. Returns: train_time: A float of the training time in seconds. """ if ( getattr(self, "limit_resource", None) and resource is not None and (budget is not None or psutil is not None) ): start_time = time.time() mem = psutil.virtual_memory() if psutil is not None else None try: with limit_resource( mem.available * (1 - FREE_MEM_RATIO) + psutil.Process(os.getpid()).memory_info().rss if mem is not None else -1, budget, ): train_time = self._fit(X_train, y_train, **kwargs) except (MemoryError, TimeoutError) as e: logger.warning(f"{e.__class__} {e}") if self._task in CLASSIFICATION: model = DummyClassifier() else: model = DummyRegressor() X_train = self._preprocess(X_train) model.fit(X_train, y_train) self._model = model train_time = time.time() - start_time else: train_time = self._fit(X_train, y_train, **kwargs) return train_time def predict(self, X, **kwargs): """Predict label from features. Args: X: A numpy array or a dataframe of featurized instances, shape n*m. Returns: A numpy array of shape n*1. Each element is the label for a instance. """ if self._model is not None: X = self._preprocess(X) return self._model.predict(X) else: logger.warning( "Estimator is not fit yet. Please run fit() before predict()." ) return np.ones(X.shape[0]) def predict_proba(self, X, **kwargs): """Predict the probability of each class from features. Only works for classification problems Args: X: A numpy array of featurized instances, shape n*m. Returns: A numpy array of shape n*c. c is the # classes. Each element at (i,j) is the probability for instance i to be in class j. """ assert self._task in CLASSIFICATION, "predict_proba() only for classification." X = self._preprocess(X) return self._model.predict_proba(X) def score(self, X_val: DataFrame, y_val: Series, **kwargs): """Report the evaluation score of a trained estimator. Args: X_val: A pandas dataframe of the validation input data. y_val: A pandas series of the validation label. kwargs: keyword argument of the evaluation function, for example: - metric: A string of the metric name or a function e.g., 'accuracy', 'roc_auc', 'roc_auc_ovr', 'roc_auc_ovo', 'f1', 'micro_f1', 'macro_f1', 'log_loss', 'mae', 'mse', 'r2', 'mape'. Default is 'auto'. If metric is given, the score will report the user specified metric. If metric is not given, the metric is set to accuracy for classification and r2 for regression. You can also pass a customized metric function, for examples on how to pass a customized metric function, please check [test/nlp/test_autohf_custom_metric.py](https://github.com/microsoft/FLAML/blob/main/test/nlp/test_autohf_custom_metric.py) and [test/automl/test_multiclass.py](https://github.com/microsoft/FLAML/blob/main/test/automl/test_multiclass.py). Returns: The evaluation score on the validation dataset. """ from .ml import metric_loss_score from .ml import is_min_metric if self._model is not None: if self._task == "rank": raise NotImplementedError( "AutoML.score() is not implemented for ranking" ) else: X_val = self._preprocess(X_val) metric = kwargs.get("metric", None) if metric: y_pred = self.predict(X_val, **kwargs) if is_min_metric(metric): return metric_loss_score(metric, y_pred, y_val) else: return 1.0 - metric_loss_score(metric, y_pred, y_val) else: return self._model.score(X_val, y_val, **kwargs) else: logger.warning( "Estimator is not fit yet. Please run fit() before predict()." ) return 0.0 def cleanup(self): del self._model self._model = None @classmethod def search_space(cls, data_size, task, **params): """[required method] search space. Args: data_size: A tuple of two integers, number of rows and columns. task: A str of the task type, e.g., "binary", "multiclass", "regression". Returns: A dictionary of the search space. Each key is the name of a hyperparameter, and value is a dict with its domain (required) and low_cost_init_value, init_value, cat_hp_cost (if applicable). e.g., ```{'domain': tune.randint(lower=1, upper=10), 'init_value': 1}```. """ return {} @classmethod def size(cls, config: dict) -> float: """[optional method] memory size of the estimator in bytes. Args: config: A dict of the hyperparameter config. Returns: A float of the memory size required by the estimator to train the given config. """ return 1.0 @classmethod def cost_relative2lgbm(cls) -> float: """[optional method] relative cost compared to lightgbm.""" return 1.0 @classmethod def init(cls): """[optional method] initialize the class.""" pass def config2params(self, config: dict) -> dict: """[optional method] config dict to params dict Args: config: A dict of the hyperparameter config. Returns: A dict that will be passed to self.estimator_class's constructor. """ params = config.copy() if "FLAML_sample_size" in params: params.pop("FLAML_sample_size") return params class TransformersEstimator(BaseEstimator): """The class for fine-tuning language models, using huggingface transformers API.""" ITER_HP = "global_max_steps" def __init__(self, task="seq-classification", **config): super().__init__(task, **config) import uuid self.trial_id = str(uuid.uuid1().hex)[:8] if task not in NLG_TASKS: # TODO: not in NLG_TASKS from .nlp.huggingface.training_args import ( TrainingArgumentsForAuto as TrainingArguments, ) else: from .nlp.huggingface.training_args import ( Seq2SeqTrainingArgumentsForAuto as TrainingArguments, ) self._TrainingArguments = TrainingArguments @staticmethod def _join(X_train, y_train, task): y_train = DataFrame(y_train, index=X_train.index) y_train.columns = ["label"] if task != TOKENCLASSIFICATION else ["labels"] train_df = X_train.join(y_train) return train_df @classmethod def search_space(cls, data_size, task, **params): search_space_dict = { "learning_rate": { "domain": tune.loguniform(lower=1e-6, upper=1e-3), "init_value": 1e-5, }, "num_train_epochs": { "domain": tune.loguniform(lower=0.1, upper=10.0), "init_value": 3.0, # to be consistent with roberta }, "per_device_train_batch_size": { "domain": tune.choice([4, 8, 16, 32]), "init_value": 32, }, "warmup_ratio": { "domain": tune.uniform(lower=0.0, upper=0.3), "init_value": 0.0, }, "weight_decay": { "domain": tune.uniform(lower=0.0, upper=0.3), "init_value": 0.0, }, "adam_epsilon": { "domain": tune.loguniform(lower=1e-8, upper=1e-6), "init_value": 1e-6, }, "seed": {"domain": tune.choice(list(range(40, 45))), "init_value": 42}, "global_max_steps": { "domain": sys.maxsize, "init_value": sys.maxsize, }, } return search_space_dict @property def checkpoint_freq(self): return ( int( min(self._training_args.num_train_epochs, 1) * len(self._X_train) / self._training_args.per_device_train_batch_size / self._training_args.ckpt_per_epoch ) + 1 ) @property def fp16(self): return self._kwargs.get("gpu_per_trial") and self._training_args.fp16 @property def no_cuda(self): return not self._kwargs.get("gpu_per_trial") def _set_training_args(self, **kwargs): from .nlp.utils import date_str, Counter for (key, val) in kwargs.items(): assert key not in self.params, ( "Since {} is in the search space, it cannot exist in 'custom_fit_kwargs' at the same time." "If you need to fix the value of {} to {}, the only way is to add a single-value domain in the search " "space by adding:\n '{}': {{ 'domain': {} }} to 'custom_hp'. For example:" 'automl_settings["custom_hp"] = {{ "transformer": {{ "model_path": {{ "domain" : ' '"google/electra-small-discriminator" }} }} }}'.format( key, key, val, key, val ) ) """ If use has specified any custom args for TrainingArguments, update these arguments """ self._training_args = self._TrainingArguments(**kwargs) """ Update the attributes in TrainingArguments with self.params values """ for key, val in self.params.items(): if hasattr(self._training_args, key): setattr(self._training_args, key, val) """ Update the attributes in TrainingArguments that depends on the values of self.params """ local_dir = os.path.join( self._training_args.output_dir, "train_{}".format(date_str()) ) if self._use_ray is True: import ray self._training_args.output_dir = ray.tune.get_trial_dir() else: self._training_args.output_dir = Counter.get_trial_fold_name( local_dir, self.params, self.trial_id ) self._training_args.eval_steps = ( self._training_args.logging_steps ) = self._training_args.saving_steps = self.checkpoint_freq self._training_args.fp16 = self.fp16 self._training_args.no_cuda = self.no_cuda def _preprocess(self, X, y=None, **kwargs): from .nlp.utils import tokenize_text, is_a_list_of_str is_str = str(X.dtypes[0]) in ("string", "str") is_list_of_str = is_a_list_of_str(X[list(X.keys())[0]].to_list()[0]) if is_str or is_list_of_str: return tokenize_text( X=X, Y=y, task=self._task, hf_args=self._training_args, tokenizer=self.tokenizer, ) else: return X, None def _model_init(self): from .nlp.utils import load_model this_model = load_model( checkpoint_path=self._training_args.model_path, task=self._task, num_labels=self.num_labels, ) return this_model def preprocess_data(self, X, y): from datasets import Dataset if (self._task not in NLG_TASKS) and (self._task != TOKENCLASSIFICATION): processed_X, _ = self._preprocess(X=X, **self._kwargs) processed_y = y else: processed_X, processed_y = self._preprocess(X=X, y=y, **self._kwargs) processed_dataset = Dataset.from_pandas( TransformersEstimator._join(processed_X, processed_y, self._task) ) return processed_dataset, processed_X, processed_y @property def num_labels(self): from .data import SEQCLASSIFICATION, SEQREGRESSION, TOKENCLASSIFICATION if self._task == SEQREGRESSION: return 1 elif self._task == SEQCLASSIFICATION: return len(set(self._y_train)) elif self._task == TOKENCLASSIFICATION: return len(set([a for b in self._y_train.tolist() for a in b])) else: return None @property def tokenizer(self): from transformers import AutoTokenizer if self._task == SUMMARIZATION: return AutoTokenizer.from_pretrained( pretrained_model_name_or_path=self._training_args.model_path, cache_dir=None, use_fast=True, revision="main", use_auth_token=None, ) else: return AutoTokenizer.from_pretrained( self._training_args.model_path, use_fast=True, add_prefix_space=True if "roberta" in self._training_args.model_path else False, # If roberta model, must set add_prefix_space to True to avoid the assertion error at # https://github.com/huggingface/transformers/blob/main/src/transformers/models/roberta/tokenization_roberta_fast.py#L249 ) @property def data_collator(self): from .nlp.huggingface.data_collator import task_to_datacollator_class return ( task_to_datacollator_class[self._task]( tokenizer=self.tokenizer, pad_to_multiple_of=8, # if self._training_args.fp16 else None, ) if self._task in (MULTICHOICECLASSIFICATION, TOKENCLASSIFICATION) else None ) def fit( self, X_train: DataFrame, y_train: Series, budget=None, X_val=None, y_val=None, gpu_per_trial=None, metric=None, **kwargs, ): import transformers transformers.logging.set_verbosity_error() from transformers import TrainerCallback from transformers.trainer_utils import set_seed from .nlp.huggingface.trainer import TrainerForAuto try: from ray.tune import is_session_enabled self._use_ray = is_session_enabled() except ImportError: self._use_ray = False this_params = self.params self._kwargs = kwargs self._X_train, self._y_train = X_train, y_train self._set_training_args(**kwargs) train_dataset, self._X_train, self._y_train = self.preprocess_data( X_train, y_train ) if X_val is not None: eval_dataset, self._X_val, self._y_val = self.preprocess_data(X_val, y_val) else: eval_dataset, self._X_val, self._y_val = None, None, None set_seed(self.params.get("seed", self._training_args.seed)) self._metric = metric class EarlyStoppingCallbackForAuto(TrainerCallback): def on_train_begin(self, args, state, control, **callback_kwargs): self.train_begin_time = time.time() def on_step_begin(self, args, state, control, **callback_kwargs): self.step_begin_time = time.time() def on_step_end(self, args, state, control, **callback_kwargs): if state.global_step == 1: self.time_per_iter = time.time() - self.step_begin_time if ( budget and ( time.time() + self.time_per_iter > self.train_begin_time + budget ) or state.global_step >= this_params[TransformersEstimator.ITER_HP] ): control.should_training_stop = True control.should_save = True control.should_evaluate = True return control def on_epoch_end(self, args, state, control, **callback_kwargs): if ( control.should_training_stop or state.epoch + 1 >= args.num_train_epochs ): control.should_save = True control.should_evaluate = True self._trainer = TrainerForAuto( args=self._training_args, model_init=self._model_init, train_dataset=train_dataset, eval_dataset=eval_dataset, tokenizer=self.tokenizer, data_collator=self.data_collator, compute_metrics=self._compute_metrics_by_dataset_name, callbacks=[EarlyStoppingCallbackForAuto], ) if self._task in NLG_TASKS: setattr(self._trainer, "_is_seq2seq", True) """ When not using ray for tuning, set the limit of CUDA_VISIBLE_DEVICES to math.ceil(gpu_per_trial), so each estimator does not see all the GPUs """ if gpu_per_trial is not None: tmp_cuda_visible_devices = os.environ.get("CUDA_VISIBLE_DEVICES", "") self._trainer.args._n_gpu = gpu_per_trial # if gpu_per_trial == 0: # os.environ["CUDA_VISIBLE_DEVICES"] = "" if tmp_cuda_visible_devices.count(",") != math.ceil(gpu_per_trial) - 1: os.environ["CUDA_VISIBLE_DEVICES"] = ",".join( [str(x) for x in range(math.ceil(gpu_per_trial))] ) import time start_time = time.time() self._trainer.train() if gpu_per_trial is not None: os.environ["CUDA_VISIBLE_DEVICES"] = tmp_cuda_visible_devices self.params[self.ITER_HP] = self._trainer.state.global_step self._checkpoint_path = self._select_checkpoint(self._trainer) self._ckpt_remains = list(self._trainer.ckpt_to_metric.keys()) if hasattr(self._trainer, "intermediate_results"): self.intermediate_results = [ x[1] for x in sorted( self._trainer.intermediate_results.items(), key=lambda x: x[0] ) ] self._trainer = None return time.time() - start_time def _delete_one_ckpt(self, ckpt_location): if self._use_ray is False: try: shutil.rmtree(ckpt_location) except FileNotFoundError: logger.warning("checkpoint {} not found".format(ckpt_location)) def cleanup(self): super().cleanup() if hasattr(self, "_ckpt_remains"): for each_ckpt in self._ckpt_remains: self._delete_one_ckpt(each_ckpt) def _select_checkpoint(self, trainer): from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR if trainer.ckpt_to_metric: best_ckpt, _ = min( trainer.ckpt_to_metric.items(), key=lambda x: x[1]["eval_loss"] ) best_ckpt_global_step = trainer.ckpt_to_global_step[best_ckpt] for each_ckpt in list(trainer.ckpt_to_metric): if each_ckpt != best_ckpt: del trainer.ckpt_to_metric[each_ckpt] del trainer.ckpt_to_global_step[each_ckpt] self._delete_one_ckpt(each_ckpt) else: best_ckpt_global_step = trainer.state.global_step best_ckpt = os.path.join( trainer.args.output_dir, f"{PREFIX_CHECKPOINT_DIR}-{best_ckpt_global_step}", ) self.params[self.ITER_HP] = best_ckpt_global_step logger.debug(trainer.state.global_step) logger.debug(trainer.ckpt_to_global_step) return best_ckpt def _compute_metrics_by_dataset_name(self, eval_pred): if isinstance(self._metric, str): from .ml import metric_loss_score from .nlp.utils import postprocess_text predictions, labels = eval_pred if self._task in NLG_TASKS: if isinstance(predictions, tuple): predictions = np.argmax(predictions[0], axis=2) decoded_preds = self.tokenizer.batch_decode( predictions, skip_special_tokens=True ) labels = np.where(labels != -100, labels, self.tokenizer.pad_token_id) decoded_labels = self.tokenizer.batch_decode( labels, skip_special_tokens=True ) predictions, labels = postprocess_text(decoded_preds, decoded_labels) else: predictions = ( np.squeeze(predictions) if self._task == SEQREGRESSION else np.argmax(predictions, axis=2) if self._task == TOKENCLASSIFICATION else np.argmax(predictions, axis=1) ) metric_dict = { "automl_metric": metric_loss_score( metric_name=self._metric, y_predict=predictions, y_true=labels, labels=self._training_args.label_list, ) } else: loss, metric_dict = self._metric( X_test=self._X_val, y_test=self._y_val, estimator=self, labels=None, X_train=self._X_train, y_train=self._y_train, ) metric_dict["automl_metric"] = loss return metric_dict def _init_model_for_predict(self): from .nlp.huggingface.trainer import TrainerForAuto """ Need to reinit training_args because of a bug in deepspeed: if not reinit, the deepspeed config will be inconsistent with HF config https://github.com/huggingface/transformers/blob/main/src/transformers/training_args.py#L947 """ training_args = self._TrainingArguments( local_rank=-1, model_path=self._checkpoint_path, fp16=self.fp16 ) for key, val in self._training_args.__dict__.items(): if key not in ("local_rank", "model_path", "fp16"): setattr(training_args, key, val) self._training_args = training_args new_trainer = TrainerForAuto( model=self._model_init(), args=self._training_args, data_collator=self.data_collator, compute_metrics=self._compute_metrics_by_dataset_name, ) if self._task in NLG_TASKS: setattr(new_trainer, "_is_seq2seq", True) return new_trainer def predict_proba(self, X, **pred_kwargs): from datasets import Dataset if pred_kwargs: for key, val in pred_kwargs.items(): setattr(self._training_args, key, val) assert ( self._task in CLASSIFICATION ), "predict_proba() only for classification tasks." X_test, _ = self._preprocess(X, **self._kwargs) test_dataset = Dataset.from_pandas(X_test) new_trainer = self._init_model_for_predict() predictions = new_trainer.predict(test_dataset) return predictions.predictions def score(self, X_val: DataFrame, y_val: Series, **kwargs): import transformers transformers.logging.set_verbosity_error() self._metric = kwargs["metric"] eval_dataset, X_val, y_val = self.preprocess_data(X_val, y_val) new_trainer = self._init_model_for_predict() return new_trainer.evaluate(eval_dataset) def predict(self, X, **pred_kwargs): import transformers from datasets import Dataset transformers.logging.set_verbosity_error() if pred_kwargs: for key, val in pred_kwargs.items(): setattr(self._training_args, key, val) X_test, _ = self._preprocess(X, **self._kwargs) test_dataset = Dataset.from_pandas(X_test) new_trainer = self._init_model_for_predict() if self._task not in NLG_TASKS: predictions = new_trainer.predict(test_dataset) else: predictions = new_trainer.predict( test_dataset, metric_key_prefix="predict", ) if self._task == SEQCLASSIFICATION: return np.argmax(predictions.predictions, axis=1) elif self._task == SEQREGRESSION: return predictions.predictions.reshape((len(predictions.predictions),)) elif self._task == TOKENCLASSIFICATION: return np.argmax(predictions.predictions, axis=2) elif self._task == SUMMARIZATION: decoded_preds = self.tokenizer.batch_decode( predictions.predictions, skip_special_tokens=True ) return decoded_preds elif self._task == MULTICHOICECLASSIFICATION: return np.argmax(predictions.predictions, axis=1) def config2params(self, config: dict) -> dict: params = super().config2params(config) params[TransformersEstimator.ITER_HP] = params.get( TransformersEstimator.ITER_HP, sys.maxsize ) return params class TransformersEstimatorModelSelection(TransformersEstimator): def __init__(self, task="seq-classification", **config): super().__init__(task, **config) @classmethod def search_space(cls, data_size, task, **params): search_space_dict = TransformersEstimator.search_space( data_size, task, **params ) """ For model selection, use the same search space regardless of memory constraint If OOM, user should change the search space themselves """ search_space_dict["model_path"] = { "domain": tune.choice( [ "google/electra-base-discriminator", "bert-base-uncased", "roberta-base", "facebook/muppet-roberta-base", "google/electra-small-discriminator", ] ), "init_value": "facebook/muppet-roberta-base", } return search_space_dict class SKLearnEstimator(BaseEstimator): """The base class for tuning scikit-learn estimators.""" def __init__(self, task="binary", **config): super().__init__(task, **config) def _preprocess(self, X): if isinstance(X, DataFrame): cat_columns = X.select_dtypes(include=["category"]).columns if not cat_columns.empty: X = X.copy() X[cat_columns] = X[cat_columns].apply(lambda x: x.cat.codes) elif isinstance(X, np.ndarray) and X.dtype.kind not in "buif": # numpy array is not of numeric dtype X = DataFrame(X) for col in X.columns: if isinstance(X[col][0], str): X[col] = X[col].astype("category").cat.codes X = X.to_numpy() return X class LGBMEstimator(BaseEstimator): """The class for tuning LGBM, using sklearn API.""" ITER_HP = "n_estimators" HAS_CALLBACK = True DEFAULT_ITER = 100 @classmethod def search_space(cls, data_size, **params): upper = max(5, min(32768, int(data_size[0]))) # upper must be larger than lower return { "n_estimators": { "domain": tune.lograndint(lower=4, upper=upper), "init_value": 4, "low_cost_init_value": 4, }, "num_leaves": { "domain": tune.lograndint(lower=4, upper=upper), "init_value": 4, "low_cost_init_value": 4, }, "min_child_samples": { "domain": tune.lograndint(lower=2, upper=2**7 + 1), "init_value": 20, }, "learning_rate": { "domain": tune.loguniform(lower=1 / 1024, upper=1.0), "init_value": 0.1, }, "log_max_bin": { # log transformed with base 2 "domain": tune.lograndint(lower=3, upper=11), "init_value": 8, }, "colsample_bytree": { "domain": tune.uniform(lower=0.01, upper=1.0), "init_value": 1.0, }, "reg_alpha": { "domain": tune.loguniform(lower=1 / 1024, upper=1024), "init_value": 1 / 1024, }, "reg_lambda": { "domain": tune.loguniform(lower=1 / 1024, upper=1024), "init_value": 1.0, }, } def config2params(self, config: dict) -> dict: params = super().config2params(config) if "log_max_bin" in params: params["max_bin"] = (1 << params.pop("log_max_bin")) - 1 return params @classmethod def size(cls, config): num_leaves = int( round( config.get("num_leaves") or config.get("max_leaves") or 1 << config.get("max_depth", 16) ) ) n_estimators = int(round(config["n_estimators"])) return (num_leaves * 3 + (num_leaves - 1) * 4 + 1.0) * n_estimators * 8 def __init__(self, task="binary", **config): super().__init__(task, **config) if "verbose" not in self.params: self.params["verbose"] = -1 if "regression" == task: from lightgbm import LGBMRegressor self.estimator_class = LGBMRegressor elif "rank" == task: from lightgbm import LGBMRanker self.estimator_class = LGBMRanker else: from lightgbm import LGBMClassifier self.estimator_class = LGBMClassifier self._time_per_iter = None self._train_size = 0 self._mem_per_iter = -1 self.HAS_CALLBACK = self.HAS_CALLBACK and self._callbacks(0, 0) is not None def _preprocess(self, X): if ( not isinstance(X, DataFrame) and issparse(X) and np.issubdtype(X.dtype, np.integer) ): X = X.astype(float) elif isinstance(X, np.ndarray) and X.dtype.kind not in "buif": # numpy array is not of numeric dtype X = DataFrame(X) for col in X.columns: if isinstance(X[col][0], str): X[col] = X[col].astype("category").cat.codes X = X.to_numpy() return X def fit(self, X_train, y_train, budget=None, **kwargs): start_time = time.time() deadline = start_time + budget if budget else np.inf n_iter = self.params.get(self.ITER_HP, self.DEFAULT_ITER) trained = False if not self.HAS_CALLBACK: mem0 = psutil.virtual_memory().available if psutil is not None else 1 if ( ( not self._time_per_iter or abs(self._train_size - X_train.shape[0]) > 4 ) and budget is not None or self._mem_per_iter < 0 and psutil is not None ) and n_iter > 1: self.params[self.ITER_HP] = 1 self._t1 = self._fit(X_train, y_train, **kwargs) if budget is not None and self._t1 >= budget or n_iter == 1: return self._t1 mem1 = psutil.virtual_memory().available if psutil is not None else 1 self._mem1 = mem0 - mem1 self.params[self.ITER_HP] = min(n_iter, 4) self._t2 = self._fit(X_train, y_train, **kwargs) mem2 = psutil.virtual_memory().available if psutil is not None else 1 self._mem2 = max(mem0 - mem2, self._mem1) # if self._mem1 <= 0: # self._mem_per_iter = self._mem2 / (self.params[self.ITER_HP] + 1) # elif self._mem2 <= 0: # self._mem_per_iter = self._mem1 # else: self._mem_per_iter = min( self._mem1, self._mem2 / self.params[self.ITER_HP] ) # if self._mem_per_iter <= 1 and psutil is not None: # n_iter = self.params[self.ITER_HP] self._time_per_iter = ( (self._t2 - self._t1) / (self.params[self.ITER_HP] - 1) if self._t2 > self._t1 else self._t1 if self._t1 else 0.001 ) self._train_size = X_train.shape[0] if ( budget is not None and self._t1 + self._t2 >= budget or n_iter == self.params[self.ITER_HP] ): # self.params[self.ITER_HP] = n_iter return time.time() - start_time trained = True # logger.debug(mem0) # logger.debug(self._mem_per_iter) if n_iter > 1: max_iter = min( n_iter, int( (budget - time.time() + start_time - self._t1) / self._time_per_iter + 1 ) if budget is not None else n_iter, int((1 - FREE_MEM_RATIO) * mem0 / self._mem_per_iter) if psutil is not None and self._mem_per_iter > 0 else n_iter, ) if trained and max_iter <= self.params[self.ITER_HP]: return time.time() - start_time # when not trained, train at least one iter self.params[self.ITER_HP] = max(max_iter, 1) if self.HAS_CALLBACK: kwargs_callbacks = kwargs.get("callbacks") if kwargs_callbacks: callbacks = kwargs_callbacks + self._callbacks(start_time, deadline) kwargs.pop("callbacks") else: callbacks = self._callbacks(start_time, deadline) if isinstance(self, XGBoostSklearnEstimator): from xgboost import __version__ if __version__ >= "1.6.0": # since xgboost>=1.6.0, callbacks can't be passed in fit() self.params["callbacks"] = callbacks callbacks = None self._fit( X_train, y_train, callbacks=callbacks, **kwargs, ) if callbacks is None: # for xgboost>=1.6.0, pop callbacks to enable pickle callbacks = self.params.pop("callbacks") self._model.set_params(callbacks=callbacks[:-1]) best_iteration = ( self._model.get_booster().best_iteration if isinstance(self, XGBoostSklearnEstimator) else self._model.best_iteration_ ) if best_iteration is not None: self._model.set_params(n_estimators=best_iteration + 1) else: self._fit(X_train, y_train, **kwargs) train_time = time.time() - start_time return train_time def _callbacks(self, start_time, deadline) -> List[Callable]: return [partial(self._callback, start_time, deadline)] def _callback(self, start_time, deadline, env) -> None: from lightgbm.callback import EarlyStopException now = time.time() if env.iteration == 0: self._time_per_iter = now - start_time if now + self._time_per_iter > deadline: raise EarlyStopException(env.iteration, env.evaluation_result_list) if psutil is not None: mem = psutil.virtual_memory() if mem.available / mem.total < FREE_MEM_RATIO: raise EarlyStopException(env.iteration, env.evaluation_result_list) class XGBoostEstimator(SKLearnEstimator): """The class for tuning XGBoost regressor, not using sklearn API.""" DEFAULT_ITER = 10 @classmethod def search_space(cls, data_size, **params): upper = max(5, min(32768, int(data_size[0]))) # upper must be larger than lower return { "n_estimators": { "domain": tune.lograndint(lower=4, upper=upper), "init_value": 4, "low_cost_init_value": 4, }, "max_leaves": { "domain": tune.lograndint(lower=4, upper=upper), "init_value": 4, "low_cost_init_value": 4, }, "max_depth": { "domain": tune.choice([0, 6, 12]), "init_value": 0, }, "min_child_weight": { "domain": tune.loguniform(lower=0.001, upper=128), "init_value": 1.0, }, "learning_rate": { "domain": tune.loguniform(lower=1 / 1024, upper=1.0), "init_value": 0.1, }, "subsample": { "domain": tune.uniform(lower=0.1, upper=1.0), "init_value": 1.0, }, "colsample_bylevel": { "domain": tune.uniform(lower=0.01, upper=1.0), "init_value": 1.0, }, "colsample_bytree": { "domain": tune.uniform(lower=0.01, upper=1.0), "init_value": 1.0, }, "reg_alpha": { "domain": tune.loguniform(lower=1 / 1024, upper=1024), "init_value": 1 / 1024, }, "reg_lambda": { "domain": tune.loguniform(lower=1 / 1024, upper=1024), "init_value": 1.0, }, } @classmethod def size(cls, config): return LGBMEstimator.size(config) @classmethod def cost_relative2lgbm(cls): return 1.6 def config2params(self, config: dict) -> dict: params = super().config2params(config) max_depth = params["max_depth"] = params.get("max_depth", 0) if max_depth == 0: params["grow_policy"] = params.get("grow_policy", "lossguide") params["tree_method"] = params.get("tree_method", "hist") # params["booster"] = params.get("booster", "gbtree") params["use_label_encoder"] = params.get("use_label_encoder", False) if "n_jobs" in config: params["nthread"] = params.pop("n_jobs") return params def __init__( self, task="regression", **config, ): super().__init__(task, **config) self.params["verbosity"] = 0 def fit(self, X_train, y_train, budget=None, **kwargs): import xgboost as xgb start_time = time.time() deadline = start_time + budget if budget else np.inf if issparse(X_train): if xgb.__version__ < "1.6.0": # "auto" fails for sparse input since xgboost 1.6.0 self.params["tree_method"] = "auto" else: X_train = self._preprocess(X_train) if "sample_weight" in kwargs: dtrain = xgb.DMatrix(X_train, label=y_train, weight=kwargs["sample_weight"]) else: dtrain = xgb.DMatrix(X_train, label=y_train) objective = self.params.get("objective") if isinstance(objective, str): obj = None else: obj = objective if "objective" in self.params: del self.params["objective"] _n_estimators = self.params.pop("n_estimators") callbacks = XGBoostEstimator._callbacks(start_time, deadline) if callbacks: self._model = xgb.train( self.params, dtrain, _n_estimators, obj=obj, callbacks=callbacks, ) self.params["n_estimators"] = self._model.best_iteration + 1 else: self._model = xgb.train(self.params, dtrain, _n_estimators, obj=obj) self.params["n_estimators"] = _n_estimators self.params["objective"] = objective del dtrain train_time = time.time() - start_time return train_time def predict(self, X, **kwargs): import xgboost as xgb if not issparse(X): X = self._preprocess(X) dtest = xgb.DMatrix(X) return super().predict(dtest) @classmethod def _callbacks(cls, start_time, deadline): try: from xgboost.callback import TrainingCallback except ImportError: # for xgboost<1.3 return None class ResourceLimit(TrainingCallback): def after_iteration(self, model, epoch, evals_log) -> bool: now = time.time() if epoch == 0: self._time_per_iter = now - start_time if now + self._time_per_iter > deadline: return True if psutil is not None: mem = psutil.virtual_memory() if mem.available / mem.total < FREE_MEM_RATIO: return True return False return [ResourceLimit()] class XGBoostSklearnEstimator(SKLearnEstimator, LGBMEstimator): """The class for tuning XGBoost with unlimited depth, using sklearn API.""" DEFAULT_ITER = 10 @classmethod def search_space(cls, data_size, **params): space = XGBoostEstimator.search_space(data_size) space.pop("max_depth") return space @classmethod def cost_relative2lgbm(cls): return XGBoostEstimator.cost_relative2lgbm() def config2params(self, config: dict) -> dict: params = super().config2params(config) max_depth = params["max_depth"] = params.get("max_depth", 0) if max_depth == 0: params["grow_policy"] = params.get("grow_policy", "lossguide") params["tree_method"] = params.get("tree_method", "hist") params["use_label_encoder"] = params.get("use_label_encoder", False) return params def __init__( self, task="binary", **config, ): super().__init__(task, **config) del self.params["verbose"] self.params["verbosity"] = 0 import xgboost as xgb self.estimator_class = xgb.XGBRegressor if "rank" == task: self.estimator_class = xgb.XGBRanker elif task in CLASSIFICATION: self.estimator_class = xgb.XGBClassifier self._xgb_version = xgb.__version__ def fit(self, X_train, y_train, budget=None, **kwargs): if issparse(X_train) and self._xgb_version < "1.6.0": # "auto" fails for sparse input since xgboost 1.6.0 self.params["tree_method"] = "auto" if kwargs.get("gpu_per_trial"): self.params["tree_method"] = "gpu_hist" kwargs.pop("gpu_per_trial") return super().fit(X_train, y_train, budget, **kwargs) def _callbacks(self, start_time, deadline) -> List[Callable]: return XGBoostEstimator._callbacks(start_time, deadline) class XGBoostLimitDepthEstimator(XGBoostSklearnEstimator): """The class for tuning XGBoost with limited depth, using sklearn API.""" @classmethod def search_space(cls, data_size, **params): space = XGBoostEstimator.search_space(data_size) space.pop("max_leaves") upper = max(6, int(np.log2(data_size[0]))) space["max_depth"] = { "domain": tune.randint(lower=1, upper=min(upper, 16)), "init_value": 6, "low_cost_init_value": 1, } space["learning_rate"]["init_value"] = 0.3 space["n_estimators"]["init_value"] = 10 return space @classmethod def cost_relative2lgbm(cls): return 64 class RandomForestEstimator(SKLearnEstimator, LGBMEstimator): """The class for tuning Random Forest.""" HAS_CALLBACK = False nrows = 101 @classmethod def search_space(cls, data_size, task, **params): RandomForestEstimator.nrows = int(data_size[0]) upper = min(2048, RandomForestEstimator.nrows) init = 1 / np.sqrt(data_size[1]) if task in CLASSIFICATION else 1 lower = min(0.1, init) space = { "n_estimators": { "domain": tune.lograndint(lower=4, upper=max(5, upper)), "init_value": 4, "low_cost_init_value": 4, }, "max_features": { "domain": tune.loguniform(lower=lower, upper=1.0), "init_value": init, }, "max_leaves": { "domain": tune.lograndint( lower=4, upper=max(5, min(32768, RandomForestEstimator.nrows >> 1)), # ), "init_value": 4, "low_cost_init_value": 4, }, } if task in CLASSIFICATION: space["criterion"] = { "domain": tune.choice(["gini", "entropy"]), # "init_value": "gini", } return space @classmethod def cost_relative2lgbm(cls): return 2 def config2params(self, config: dict) -> dict: params = super().config2params(config) if "max_leaves" in params: params["max_leaf_nodes"] = params.get( "max_leaf_nodes", params.pop("max_leaves") ) if self._task not in CLASSIFICATION and "criterion" in config: params.pop("criterion") return params def __init__( self, task="binary", **params, ): super().__init__(task, **params) self.params["verbose"] = 0 self.estimator_class = RandomForestRegressor if task in CLASSIFICATION: self.estimator_class = RandomForestClassifier class ExtraTreesEstimator(RandomForestEstimator): """The class for tuning Extra Trees.""" @classmethod def cost_relative2lgbm(cls): return 1.9 def __init__(self, task="binary", **params): super().__init__(task, **params) if "regression" in task: self.estimator_class = ExtraTreesRegressor else: self.estimator_class = ExtraTreesClassifier class LRL1Classifier(SKLearnEstimator): """The class for tuning Logistic Regression with L1 regularization.""" @classmethod def search_space(cls, **params): return { "C": { "domain": tune.loguniform(lower=0.03125, upper=32768.0), "init_value": 1.0, }, } @classmethod def cost_relative2lgbm(cls): return 160 def config2params(self, config: dict) -> dict: params = super().config2params(config) params["tol"] = params.get("tol", 0.0001) params["solver"] = params.get("solver", "saga") params["penalty"] = params.get("penalty", "l1") return params def __init__(self, task="binary", **config): super().__init__(task, **config) assert task in CLASSIFICATION, "LogisticRegression for classification task only" self.estimator_class = LogisticRegression class LRL2Classifier(SKLearnEstimator): """The class for tuning Logistic Regression with L2 regularization.""" limit_resource = True @classmethod def search_space(cls, **params): return LRL1Classifier.search_space(**params) @classmethod def cost_relative2lgbm(cls): return 25 def config2params(self, config: dict) -> dict: params = super().config2params(config) params["tol"] = params.get("tol", 0.0001) params["solver"] = params.get("solver", "lbfgs") params["penalty"] = params.get("penalty", "l2") return params def __init__(self, task="binary", **config): super().__init__(task, **config) assert task in CLASSIFICATION, "LogisticRegression for classification task only" self.estimator_class = LogisticRegression class CatBoostEstimator(BaseEstimator): """The class for tuning CatBoost.""" ITER_HP = "n_estimators" DEFAULT_ITER = 1000 @classmethod def search_space(cls, data_size, **params): upper = max(min(round(1500000 / data_size[0]), 150), 12) return { "early_stopping_rounds": { "domain": tune.lograndint(lower=10, upper=upper), "init_value": 10, "low_cost_init_value": 10, }, "learning_rate": { "domain": tune.loguniform(lower=0.005, upper=0.2), "init_value": 0.1, }, "n_estimators": { "domain": 8192, "init_value": 8192, }, } @classmethod def size(cls, config): n_estimators = config.get("n_estimators", 8192) max_leaves = 64 return (max_leaves * 3 + (max_leaves - 1) * 4 + 1.0) * n_estimators * 8 @classmethod def cost_relative2lgbm(cls): return 15 def _preprocess(self, X): if isinstance(X, DataFrame): cat_columns = X.select_dtypes(include=["category"]).columns if not cat_columns.empty: X = X.copy() X[cat_columns] = X[cat_columns].apply( lambda x: x.cat.rename_categories( [ str(c) if isinstance(c, float) else c for c in x.cat.categories ] ) ) elif isinstance(X, np.ndarray) and X.dtype.kind not in "buif": # numpy array is not of numeric dtype X = DataFrame(X) for col in X.columns: if isinstance(X[col][0], str): X[col] = X[col].astype("category").cat.codes X = X.to_numpy() return X def config2params(self, config: dict) -> dict: params = super().config2params(config) params["n_estimators"] = params.get("n_estimators", 8192) if "n_jobs" in params: params["thread_count"] = params.pop("n_jobs") return params def __init__( self, task="binary", **config, ): super().__init__(task, **config) self.params.update( { "verbose": config.get("verbose", False), "random_seed": config.get("random_seed", 10242048), } ) from catboost import CatBoostRegressor self.estimator_class = CatBoostRegressor if task in CLASSIFICATION: from catboost import CatBoostClassifier self.estimator_class = CatBoostClassifier def fit(self, X_train, y_train, budget=None, **kwargs): start_time = time.time() deadline = start_time + budget if budget else np.inf train_dir = f"catboost_{str(start_time)}" X_train = self._preprocess(X_train) if isinstance(X_train, DataFrame): cat_features = list(X_train.select_dtypes(include="category").columns) else: cat_features = [] n = max(int(len(y_train) * 0.9), len(y_train) - 1000) X_tr, y_tr = X_train[:n], y_train[:n] if "sample_weight" in kwargs: weight = kwargs["sample_weight"] if weight is not None: kwargs["sample_weight"] = weight[:n] else: weight = None from catboost import Pool, __version__ model = self.estimator_class(train_dir=train_dir, **self.params) if __version__ >= "0.26": model.fit( X_tr, y_tr, cat_features=cat_features, eval_set=Pool( data=X_train[n:], label=y_train[n:], cat_features=cat_features ), callbacks=CatBoostEstimator._callbacks(start_time, deadline), **kwargs, ) else: model.fit( X_tr, y_tr, cat_features=cat_features, eval_set=Pool( data=X_train[n:], label=y_train[n:], cat_features=cat_features ), **kwargs, ) shutil.rmtree(train_dir, ignore_errors=True) if weight is not None: kwargs["sample_weight"] = weight self._model = model self.params[self.ITER_HP] = self._model.tree_count_ train_time = time.time() - start_time return train_time @classmethod def _callbacks(cls, start_time, deadline): class ResourceLimit: def after_iteration(self, info) -> bool: now = time.time() if info.iteration == 1: self._time_per_iter = now - start_time if now + self._time_per_iter > deadline: return False if psutil is not None: mem = psutil.virtual_memory() if mem.available / mem.total < FREE_MEM_RATIO: return False return True # can continue return [ResourceLimit()] class KNeighborsEstimator(BaseEstimator): @classmethod def search_space(cls, data_size, **params): upper = min(512, int(data_size[0] / 2)) return { "n_neighbors": { "domain": tune.lograndint(lower=1, upper=max(2, upper)), "init_value": 5, "low_cost_init_value": 1, }, } @classmethod def cost_relative2lgbm(cls): return 30 def config2params(self, config: dict) -> dict: params = super().config2params(config) params["weights"] = params.get("weights", "distance") return params def __init__(self, task="binary", **config): super().__init__(task, **config) if task in CLASSIFICATION: from sklearn.neighbors import KNeighborsClassifier self.estimator_class = KNeighborsClassifier else: from sklearn.neighbors import KNeighborsRegressor self.estimator_class = KNeighborsRegressor def _preprocess(self, X): if isinstance(X, DataFrame): cat_columns = X.select_dtypes(["category"]).columns if X.shape[1] == len(cat_columns): raise ValueError("kneighbor requires at least one numeric feature") X = X.drop(cat_columns, axis=1) elif isinstance(X, np.ndarray) and X.dtype.kind not in "buif": # drop categocial columns if any X = DataFrame(X) cat_columns = [] for col in X.columns: if isinstance(X[col][0], str): cat_columns.append(col) X = X.drop(cat_columns, axis=1) X = X.to_numpy() return X class Prophet(SKLearnEstimator): """The class for tuning Prophet.""" @classmethod def search_space(cls, **params): space = { "changepoint_prior_scale": { "domain": tune.loguniform(lower=0.001, upper=0.05), "init_value": 0.05, "low_cost_init_value": 0.001, }, "seasonality_prior_scale": { "domain": tune.loguniform(lower=0.01, upper=10), "init_value": 10, }, "holidays_prior_scale": { "domain": tune.loguniform(lower=0.01, upper=10), "init_value": 10, }, "seasonality_mode": { "domain": tune.choice(["additive", "multiplicative"]), "init_value": "multiplicative", }, } return space def __init__(self, task="ts_forecast", n_jobs=1, **params): super().__init__(task, **params) def _join(self, X_train, y_train): assert TS_TIMESTAMP_COL in X_train, ( "Dataframe for training ts_forecast model must have column" f' "{TS_TIMESTAMP_COL}" with the dates in X_train.' ) y_train = DataFrame(y_train, columns=[TS_VALUE_COL]) train_df = X_train.join(y_train) return train_df def fit(self, X_train, y_train, budget=None, **kwargs): from prophet import Prophet current_time = time.time() train_df = self._join(X_train, y_train) train_df = self._preprocess(train_df) cols = list(train_df) cols.remove(TS_TIMESTAMP_COL) cols.remove(TS_VALUE_COL) logging.getLogger("prophet").setLevel(logging.WARNING) model = Prophet(**self.params) for regressor in cols: model.add_regressor(regressor) with suppress_stdout_stderr(): model.fit(train_df) train_time = time.time() - current_time self._model = model return train_time def predict(self, X, **kwargs): if isinstance(X, int): raise ValueError( "predict() with steps is only supported for arima/sarimax." " For Prophet, pass a dataframe with the first column containing" " the timestamp values." ) if self._model is not None: X = self._preprocess(X) forecast = self._model.predict(X) return forecast["yhat"] else: logger.warning( "Estimator is not fit yet. Please run fit() before predict()." ) return np.ones(X.shape[0]) def score(self, X_val: DataFrame, y_val: Series, **kwargs): from sklearn.metrics import r2_score from .ml import metric_loss_score y_pred = self.predict(X_val) self._metric = kwargs.get("metric", None) if self._metric: return metric_loss_score(self._metric, y_pred, y_val) else: return r2_score(y_pred, y_val) class ARIMA(Prophet): """The class for tuning ARIMA.""" @classmethod def search_space(cls, **params): space = { "p": { "domain": tune.qrandint(lower=0, upper=10, q=1), "init_value": 2, "low_cost_init_value": 0, }, "d": { "domain": tune.qrandint(lower=0, upper=10, q=1), "init_value": 2, "low_cost_init_value": 0, }, "q": { "domain": tune.qrandint(lower=0, upper=10, q=1), "init_value": 1, "low_cost_init_value": 0, }, } return space def _join(self, X_train, y_train): train_df = super()._join(X_train, y_train) train_df.index = to_datetime(train_df[TS_TIMESTAMP_COL]) train_df = train_df.drop(TS_TIMESTAMP_COL, axis=1) return train_df def fit(self, X_train, y_train, budget=None, **kwargs): import warnings warnings.filterwarnings("ignore") from statsmodels.tsa.arima.model import ARIMA as ARIMA_estimator current_time = time.time() train_df = self._join(X_train, y_train) train_df = self._preprocess(train_df) regressors = list(train_df) regressors.remove(TS_VALUE_COL) if regressors: model = ARIMA_estimator( train_df[[TS_VALUE_COL]], exog=train_df[regressors], order=(self.params["p"], self.params["d"], self.params["q"]), enforce_stationarity=False, enforce_invertibility=False, ) else: model = ARIMA_estimator( train_df, order=(self.params["p"], self.params["d"], self.params["q"]), enforce_stationarity=False, enforce_invertibility=False, ) with suppress_stdout_stderr(): model = model.fit() train_time = time.time() - current_time self._model = model return train_time def predict(self, X, **kwargs): if self._model is not None: if isinstance(X, int): forecast = self._model.forecast(steps=X) elif isinstance(X, DataFrame): start = X[TS_TIMESTAMP_COL].iloc[0] end = X[TS_TIMESTAMP_COL].iloc[-1] if len(X.columns) > 1: X = self._preprocess(X.drop(columns=TS_TIMESTAMP_COL)) regressors = list(X) forecast = self._model.predict( start=start, end=end, exog=X[regressors] ) else: forecast = self._model.predict(start=start, end=end) else: raise ValueError( "X needs to be either a pandas Dataframe with dates as the first column" " or an int number of periods for predict()." ) return forecast else: return np.ones(X if isinstance(X, int) else X.shape[0]) class SARIMAX(ARIMA): """The class for tuning SARIMA.""" @classmethod def search_space(cls, **params): space = { "p": { "domain": tune.qrandint(lower=0, upper=10, q=1), "init_value": 2, "low_cost_init_value": 0, }, "d": { "domain": tune.qrandint(lower=0, upper=10, q=1), "init_value": 2, "low_cost_init_value": 0, }, "q": { "domain": tune.qrandint(lower=0, upper=10, q=1), "init_value": 1, "low_cost_init_value": 0, }, "P": { "domain": tune.qrandint(lower=0, upper=10, q=1), "init_value": 1, "low_cost_init_value": 0, }, "D": { "domain": tune.qrandint(lower=0, upper=10, q=1), "init_value": 1, "low_cost_init_value": 0, }, "Q": { "domain": tune.qrandint(lower=0, upper=10, q=1), "init_value": 1, "low_cost_init_value": 0, }, "s": { "domain": tune.choice([1, 4, 6, 12]), "init_value": 12, }, } return space def fit(self, X_train, y_train, budget=None, **kwargs): import warnings warnings.filterwarnings("ignore") from statsmodels.tsa.statespace.sarimax import SARIMAX as SARIMAX_estimator current_time = time.time() train_df = self._join(X_train, y_train) train_df = self._preprocess(train_df) regressors = list(train_df) regressors.remove(TS_VALUE_COL) if regressors: model = SARIMAX_estimator( train_df[[TS_VALUE_COL]], exog=train_df[regressors], order=(self.params["p"], self.params["d"], self.params["q"]), seasonality_order=( self.params["P"], self.params["D"], self.params["Q"], self.params["s"], ), enforce_stationarity=False, enforce_invertibility=False, ) else: model = SARIMAX_estimator( train_df, order=(self.params["p"], self.params["d"], self.params["q"]), seasonality_order=( self.params["P"], self.params["D"], self.params["Q"], self.params["s"], ), enforce_stationarity=False, enforce_invertibility=False, ) with suppress_stdout_stderr(): model = model.fit() train_time = time.time() - current_time self._model = model return train_time class TS_SKLearn(SKLearnEstimator): """The class for tuning SKLearn Regressors for time-series forecasting, using hcrystalball""" base_class = SKLearnEstimator @classmethod def search_space(cls, data_size, pred_horizon, **params): space = cls.base_class.search_space(data_size, **params) space.update( { "optimize_for_horizon": { "domain": tune.choice([True, False]), "init_value": False, "low_cost_init_value": False, }, "lags": { "domain": tune.randint( lower=1, upper=max(2, int(np.sqrt(data_size[0]))) ), "init_value": 3, }, } ) return space def __init__(self, task="ts_forecast", **params): super().__init__(task, **params) self.hcrystaball_model = None self.ts_task = ( "regression" if task in TS_FORECASTREGRESSION else "classification" ) def transform_X(self, X): cols = list(X) if len(cols) == 1: ds_col = cols[0] X = DataFrame(index=X[ds_col]) elif len(cols) > 1: ds_col = cols[0] exog_cols = cols[1:] X = X[exog_cols].set_index(X[ds_col]) return X def _fit(self, X_train, y_train, budget=None, **kwargs): from hcrystalball.wrappers import get_sklearn_wrapper X_train = self.transform_X(X_train) X_train = self._preprocess(X_train) params = self.params.copy() lags = params.pop("lags") optimize_for_horizon = params.pop("optimize_for_horizon") estimator = self.base_class(task=self.ts_task, **params) self.hcrystaball_model = get_sklearn_wrapper(estimator.estimator_class) self.hcrystaball_model.lags = int(lags) self.hcrystaball_model.fit(X_train, y_train) if optimize_for_horizon: # Direct Multi-step Forecast Strategy - fit a seperate model for each horizon model_list = [] for i in range(1, kwargs["period"] + 1): ( X_fit, y_fit, ) = self.hcrystaball_model._transform_data_to_tsmodel_input_format( X_train, y_train, i ) self.hcrystaball_model.model.set_params(**estimator.params) model = self.hcrystaball_model.model.fit(X_fit, y_fit) model_list.append(model) self._model = model_list else: ( X_fit, y_fit, ) = self.hcrystaball_model._transform_data_to_tsmodel_input_format( X_train, y_train, kwargs["period"] ) self.hcrystaball_model.model.set_params(**estimator.params) model = self.hcrystaball_model.model.fit(X_fit, y_fit) self._model = model def fit(self, X_train, y_train, budget=None, **kwargs): current_time = time.time() self._fit(X_train, y_train, budget=budget, **kwargs) train_time = time.time() - current_time return train_time def predict(self, X, **kwargs): if self._model is not None: X = self.transform_X(X) X = self._preprocess(X) if isinstance(self._model, list): assert len(self._model) == len( X ), "Model is optimized for horizon, length of X must be equal to `period`." preds = [] for i in range(1, len(self._model) + 1): ( X_pred, _, ) = self.hcrystaball_model._transform_data_to_tsmodel_input_format( X.iloc[:i, :] ) preds.append(self._model[i - 1].predict(X_pred)[-1]) forecast = DataFrame( data=np.asarray(preds).reshape(-1, 1), columns=[self.hcrystaball_model.name], index=X.index, ) else: ( X_pred, _, ) = self.hcrystaball_model._transform_data_to_tsmodel_input_format(X) forecast = self._model.predict(X_pred) return forecast else: logger.warning( "Estimator is not fit yet. Please run fit() before predict()." ) return np.ones(X.shape[0]) class LGBM_TS(TS_SKLearn): """The class for tuning LGBM Regressor for time-series forecasting""" base_class = LGBMEstimator class XGBoost_TS(TS_SKLearn): """The class for tuning XGBoost Regressor for time-series forecasting""" base_class = XGBoostSklearnEstimator # catboost regressor is invalid because it has a `name` parameter, making it incompatible with hcrystalball # class CatBoost_TS_Regressor(TS_Regressor): # base_class = CatBoostEstimator class RF_TS(TS_SKLearn): """The class for tuning Random Forest Regressor for time-series forecasting""" base_class = RandomForestEstimator class ExtraTrees_TS(TS_SKLearn): """The class for tuning Extra Trees Regressor for time-series forecasting""" base_class = ExtraTreesEstimator class XGBoostLimitDepth_TS(TS_SKLearn): """The class for tuning XGBoost Regressor with unlimited depth for time-series forecasting""" base_class = XGBoostLimitDepthEstimator class suppress_stdout_stderr(object): def __init__(self): # Open a pair of null files self.null_fds = [os.open(os.devnull, os.O_RDWR) for x in range(2)] # Save the actual stdout (1) and stderr (2) file descriptors. self.save_fds = (os.dup(1), os.dup(2)) def __enter__(self): # Assign the null pointers to stdout and stderr. os.dup2(self.null_fds[0], 1) os.dup2(self.null_fds[1], 2) def __exit__(self, *_): # Re-assign the real stdout/stderr back to (1) and (2) os.dup2(self.save_fds[0], 1) os.dup2(self.save_fds[1], 2) # Close the null files os.close(self.null_fds[0]) os.close(self.null_fds[1])

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from contextlib import contextmanager from functools import partial import signal import os from typing import Callable, List import numpy as np import time from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier from sklearn.ensemble import ExtraTreesRegressor, ExtraTreesClassifier from sklearn.linear_model import LogisticRegression from sklearn.dummy import DummyClassifier, DummyRegressor from scipy.sparse import issparse import logging import shutil from pandas import DataFrame, Series, to_datetime import sys import math from . import tune from .data import ( group_counts, CLASSIFICATION, TS_FORECASTREGRESSION, TS_TIMESTAMP_COL, TS_VALUE_COL, SEQCLASSIFICATION, SEQREGRESSION, TOKENCLASSIFICATION, SUMMARIZATION, NLG_TASKS, MULTICHOICECLASSIFICATION, ) try: import psutil except ImportError: psutil = None try: import resource except ImportError: resource = None logger = logging.getLogger("flaml.automl") FREE_MEM_RATIO = 0.2 def TimeoutHandler(sig, frame): raise TimeoutError(sig, frame) @contextmanager def limit_resource(memory_limit, time_limit): if memory_limit > 0: soft, hard = resource.getrlimit(resource.RLIMIT_AS) if soft < 0 and (hard < 0 or memory_limit <= hard) or memory_limit < soft: try: resource.setrlimit(resource.RLIMIT_AS, (int(memory_limit), hard)) except ValueError: pass main_thread = False if time_limit is not None: try: signal.signal(signal.SIGALRM, TimeoutHandler) signal.alarm(int(time_limit) or 1) main_thread = True except ValueError: pass try: yield finally: if main_thread: signal.alarm(0) if memory_limit > 0: resource.setrlimit(resource.RLIMIT_AS, (soft, hard)) class BaseEstimator: def __init__(self, task="binary", **config): self._task = task self.params = self.config2params(config) self.estimator_class = self._model = None if "_estimator_type" in config: self._estimator_type = self.params.pop("_estimator_type") else: self._estimator_type = ( "classifier" if task in CLASSIFICATION else "regressor" ) def get_params(self, deep=False): params = self.params.copy() params["task"] = self._task if hasattr(self, "_estimator_type"): params["_estimator_type"] = self._estimator_type return params @property def classes_(self): return self._model.classes_ @property def n_features_in_(self): return self._model.n_features_in_ @property def model(self): return self._model @property def estimator(self): return self._model def _preprocess(self, X): return X def _fit(self, X_train, y_train, **kwargs): current_time = time.time() if "groups" in kwargs: kwargs = kwargs.copy() groups = kwargs.pop("groups") if self._task == "rank": kwargs["group"] = group_counts(groups) # groups_val = kwargs.get('groups_val') # if groups_val is not None: # kwargs['eval_group'] = [group_counts(groups_val)] # kwargs['eval_set'] = [ # (kwargs['X_val'], kwargs['y_val'])] # kwargs['verbose'] = False # del kwargs['groups_val'], kwargs['X_val'], kwargs['y_val'] X_train = self._preprocess(X_train) model = self.estimator_class(**self.params) if logger.level == logging.DEBUG: # xgboost 1.6 doesn't display all the params in the model str logger.debug(f"flaml.model - {model} fit started with params {self.params}") model.fit(X_train, y_train, **kwargs) if logger.level == logging.DEBUG: logger.debug(f"flaml.model - {model} fit finished") train_time = time.time() - current_time self._model = model return train_time def fit(self, X_train, y_train, budget=None, **kwargs): if ( getattr(self, "limit_resource", None) and resource is not None and (budget is not None or psutil is not None) ): start_time = time.time() mem = psutil.virtual_memory() if psutil is not None else None try: with limit_resource( mem.available * (1 - FREE_MEM_RATIO) + psutil.Process(os.getpid()).memory_info().rss if mem is not None else -1, budget, ): train_time = self._fit(X_train, y_train, **kwargs) except (MemoryError, TimeoutError) as e: logger.warning(f"{e.__class__} {e}") if self._task in CLASSIFICATION: model = DummyClassifier() else: model = DummyRegressor() X_train = self._preprocess(X_train) model.fit(X_train, y_train) self._model = model train_time = time.time() - start_time else: train_time = self._fit(X_train, y_train, **kwargs) return train_time def predict(self, X, **kwargs): if self._model is not None: X = self._preprocess(X) return self._model.predict(X) else: logger.warning( "Estimator is not fit yet. Please run fit() before predict()." ) return np.ones(X.shape[0]) def predict_proba(self, X, **kwargs): assert self._task in CLASSIFICATION, "predict_proba() only for classification." X = self._preprocess(X) return self._model.predict_proba(X) def score(self, X_val: DataFrame, y_val: Series, **kwargs): from .ml import metric_loss_score from .ml import is_min_metric if self._model is not None: if self._task == "rank": raise NotImplementedError( "AutoML.score() is not implemented for ranking" ) else: X_val = self._preprocess(X_val) metric = kwargs.get("metric", None) if metric: y_pred = self.predict(X_val, **kwargs) if is_min_metric(metric): return metric_loss_score(metric, y_pred, y_val) else: return 1.0 - metric_loss_score(metric, y_pred, y_val) else: return self._model.score(X_val, y_val, **kwargs) else: logger.warning( "Estimator is not fit yet. Please run fit() before predict()." ) return 0.0 def cleanup(self): del self._model self._model = None @classmethod def search_space(cls, data_size, task, **params): return {} @classmethod def size(cls, config: dict) -> float: return 1.0 @classmethod def cost_relative2lgbm(cls) -> float: return 1.0 @classmethod def init(cls): pass def config2params(self, config: dict) -> dict: params = config.copy() if "FLAML_sample_size" in params: params.pop("FLAML_sample_size") return params class TransformersEstimator(BaseEstimator): ITER_HP = "global_max_steps" def __init__(self, task="seq-classification", **config): super().__init__(task, **config) import uuid self.trial_id = str(uuid.uuid1().hex)[:8] if task not in NLG_TASKS: from .nlp.huggingface.training_args import ( TrainingArgumentsForAuto as TrainingArguments, ) else: from .nlp.huggingface.training_args import ( Seq2SeqTrainingArgumentsForAuto as TrainingArguments, ) self._TrainingArguments = TrainingArguments @staticmethod def _join(X_train, y_train, task): y_train = DataFrame(y_train, index=X_train.index) y_train.columns = ["label"] if task != TOKENCLASSIFICATION else ["labels"] train_df = X_train.join(y_train) return train_df @classmethod def search_space(cls, data_size, task, **params): search_space_dict = { "learning_rate": { "domain": tune.loguniform(lower=1e-6, upper=1e-3), "init_value": 1e-5, }, "num_train_epochs": { "domain": tune.loguniform(lower=0.1, upper=10.0), "init_value": 3.0, }, "per_device_train_batch_size": { "domain": tune.choice([4, 8, 16, 32]), "init_value": 32, }, "warmup_ratio": { "domain": tune.uniform(lower=0.0, upper=0.3), "init_value": 0.0, }, "weight_decay": { "domain": tune.uniform(lower=0.0, upper=0.3), "init_value": 0.0, }, "adam_epsilon": { "domain": tune.loguniform(lower=1e-8, upper=1e-6), "init_value": 1e-6, }, "seed": {"domain": tune.choice(list(range(40, 45))), "init_value": 42}, "global_max_steps": { "domain": sys.maxsize, "init_value": sys.maxsize, }, } return search_space_dict @property def checkpoint_freq(self): return ( int( min(self._training_args.num_train_epochs, 1) * len(self._X_train) / self._training_args.per_device_train_batch_size / self._training_args.ckpt_per_epoch ) + 1 ) @property def fp16(self): return self._kwargs.get("gpu_per_trial") and self._training_args.fp16 @property def no_cuda(self): return not self._kwargs.get("gpu_per_trial") def _set_training_args(self, **kwargs): from .nlp.utils import date_str, Counter for (key, val) in kwargs.items(): assert key not in self.params, ( "Since {} is in the search space, it cannot exist in 'custom_fit_kwargs' at the same time." "If you need to fix the value of {} to {}, the only way is to add a single-value domain in the search " "space by adding:\n '{}': {{ 'domain': {} }} to 'custom_hp'. For example:" 'automl_settings["custom_hp"] = {{ "transformer": {{ "model_path": {{ "domain" : ' '"google/electra-small-discriminator" }} }} }}'.format( key, key, val, key, val ) ) self._training_args = self._TrainingArguments(**kwargs) for key, val in self.params.items(): if hasattr(self._training_args, key): setattr(self._training_args, key, val) local_dir = os.path.join( self._training_args.output_dir, "train_{}".format(date_str()) ) if self._use_ray is True: import ray self._training_args.output_dir = ray.tune.get_trial_dir() else: self._training_args.output_dir = Counter.get_trial_fold_name( local_dir, self.params, self.trial_id ) self._training_args.eval_steps = ( self._training_args.logging_steps ) = self._training_args.saving_steps = self.checkpoint_freq self._training_args.fp16 = self.fp16 self._training_args.no_cuda = self.no_cuda def _preprocess(self, X, y=None, **kwargs): from .nlp.utils import tokenize_text, is_a_list_of_str is_str = str(X.dtypes[0]) in ("string", "str") is_list_of_str = is_a_list_of_str(X[list(X.keys())[0]].to_list()[0]) if is_str or is_list_of_str: return tokenize_text( X=X, Y=y, task=self._task, hf_args=self._training_args, tokenizer=self.tokenizer, ) else: return X, None def _model_init(self): from .nlp.utils import load_model this_model = load_model( checkpoint_path=self._training_args.model_path, task=self._task, num_labels=self.num_labels, ) return this_model def preprocess_data(self, X, y): from datasets import Dataset if (self._task not in NLG_TASKS) and (self._task != TOKENCLASSIFICATION): processed_X, _ = self._preprocess(X=X, **self._kwargs) processed_y = y else: processed_X, processed_y = self._preprocess(X=X, y=y, **self._kwargs) processed_dataset = Dataset.from_pandas( TransformersEstimator._join(processed_X, processed_y, self._task) ) return processed_dataset, processed_X, processed_y @property def num_labels(self): from .data import SEQCLASSIFICATION, SEQREGRESSION, TOKENCLASSIFICATION if self._task == SEQREGRESSION: return 1 elif self._task == SEQCLASSIFICATION: return len(set(self._y_train)) elif self._task == TOKENCLASSIFICATION: return len(set([a for b in self._y_train.tolist() for a in b])) else: return None @property def tokenizer(self): from transformers import AutoTokenizer if self._task == SUMMARIZATION: return AutoTokenizer.from_pretrained( pretrained_model_name_or_path=self._training_args.model_path, cache_dir=None, use_fast=True, revision="main", use_auth_token=None, ) else: return AutoTokenizer.from_pretrained( self._training_args.model_path, use_fast=True, add_prefix_space=True if "roberta" in self._training_args.model_path else False, ) @property def data_collator(self): from .nlp.huggingface.data_collator import task_to_datacollator_class return ( task_to_datacollator_class[self._task]( tokenizer=self.tokenizer, pad_to_multiple_of=8, ) if self._task in (MULTICHOICECLASSIFICATION, TOKENCLASSIFICATION) else None ) def fit( self, X_train: DataFrame, y_train: Series, budget=None, X_val=None, y_val=None, gpu_per_trial=None, metric=None, **kwargs, ): import transformers transformers.logging.set_verbosity_error() from transformers import TrainerCallback from transformers.trainer_utils import set_seed from .nlp.huggingface.trainer import TrainerForAuto try: from ray.tune import is_session_enabled self._use_ray = is_session_enabled() except ImportError: self._use_ray = False this_params = self.params self._kwargs = kwargs self._X_train, self._y_train = X_train, y_train self._set_training_args(**kwargs) train_dataset, self._X_train, self._y_train = self.preprocess_data( X_train, y_train ) if X_val is not None: eval_dataset, self._X_val, self._y_val = self.preprocess_data(X_val, y_val) else: eval_dataset, self._X_val, self._y_val = None, None, None set_seed(self.params.get("seed", self._training_args.seed)) self._metric = metric class EarlyStoppingCallbackForAuto(TrainerCallback): def on_train_begin(self, args, state, control, **callback_kwargs): self.train_begin_time = time.time() def on_step_begin(self, args, state, control, **callback_kwargs): self.step_begin_time = time.time() def on_step_end(self, args, state, control, **callback_kwargs): if state.global_step == 1: self.time_per_iter = time.time() - self.step_begin_time if ( budget and ( time.time() + self.time_per_iter > self.train_begin_time + budget ) or state.global_step >= this_params[TransformersEstimator.ITER_HP] ): control.should_training_stop = True control.should_save = True control.should_evaluate = True return control def on_epoch_end(self, args, state, control, **callback_kwargs): if ( control.should_training_stop or state.epoch + 1 >= args.num_train_epochs ): control.should_save = True control.should_evaluate = True self._trainer = TrainerForAuto( args=self._training_args, model_init=self._model_init, train_dataset=train_dataset, eval_dataset=eval_dataset, tokenizer=self.tokenizer, data_collator=self.data_collator, compute_metrics=self._compute_metrics_by_dataset_name, callbacks=[EarlyStoppingCallbackForAuto], ) if self._task in NLG_TASKS: setattr(self._trainer, "_is_seq2seq", True) if gpu_per_trial is not None: tmp_cuda_visible_devices = os.environ.get("CUDA_VISIBLE_DEVICES", "") self._trainer.args._n_gpu = gpu_per_trial if tmp_cuda_visible_devices.count(",") != math.ceil(gpu_per_trial) - 1: os.environ["CUDA_VISIBLE_DEVICES"] = ",".join( [str(x) for x in range(math.ceil(gpu_per_trial))] ) import time start_time = time.time() self._trainer.train() if gpu_per_trial is not None: os.environ["CUDA_VISIBLE_DEVICES"] = tmp_cuda_visible_devices self.params[self.ITER_HP] = self._trainer.state.global_step self._checkpoint_path = self._select_checkpoint(self._trainer) self._ckpt_remains = list(self._trainer.ckpt_to_metric.keys()) if hasattr(self._trainer, "intermediate_results"): self.intermediate_results = [ x[1] for x in sorted( self._trainer.intermediate_results.items(), key=lambda x: x[0] ) ] self._trainer = None return time.time() - start_time def _delete_one_ckpt(self, ckpt_location): if self._use_ray is False: try: shutil.rmtree(ckpt_location) except FileNotFoundError: logger.warning("checkpoint {} not found".format(ckpt_location)) def cleanup(self): super().cleanup() if hasattr(self, "_ckpt_remains"): for each_ckpt in self._ckpt_remains: self._delete_one_ckpt(each_ckpt) def _select_checkpoint(self, trainer): from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR if trainer.ckpt_to_metric: best_ckpt, _ = min( trainer.ckpt_to_metric.items(), key=lambda x: x[1]["eval_loss"] ) best_ckpt_global_step = trainer.ckpt_to_global_step[best_ckpt] for each_ckpt in list(trainer.ckpt_to_metric): if each_ckpt != best_ckpt: del trainer.ckpt_to_metric[each_ckpt] del trainer.ckpt_to_global_step[each_ckpt] self._delete_one_ckpt(each_ckpt) else: best_ckpt_global_step = trainer.state.global_step best_ckpt = os.path.join( trainer.args.output_dir, f"{PREFIX_CHECKPOINT_DIR}-{best_ckpt_global_step}", ) self.params[self.ITER_HP] = best_ckpt_global_step logger.debug(trainer.state.global_step) logger.debug(trainer.ckpt_to_global_step) return best_ckpt def _compute_metrics_by_dataset_name(self, eval_pred): if isinstance(self._metric, str): from .ml import metric_loss_score from .nlp.utils import postprocess_text predictions, labels = eval_pred if self._task in NLG_TASKS: if isinstance(predictions, tuple): predictions = np.argmax(predictions[0], axis=2) decoded_preds = self.tokenizer.batch_decode( predictions, skip_special_tokens=True ) labels = np.where(labels != -100, labels, self.tokenizer.pad_token_id) decoded_labels = self.tokenizer.batch_decode( labels, skip_special_tokens=True ) predictions, labels = postprocess_text(decoded_preds, decoded_labels) else: predictions = ( np.squeeze(predictions) if self._task == SEQREGRESSION else np.argmax(predictions, axis=2) if self._task == TOKENCLASSIFICATION else np.argmax(predictions, axis=1) ) metric_dict = { "automl_metric": metric_loss_score( metric_name=self._metric, y_predict=predictions, y_true=labels, labels=self._training_args.label_list, ) } else: loss, metric_dict = self._metric( X_test=self._X_val, y_test=self._y_val, estimator=self, labels=None, X_train=self._X_train, y_train=self._y_train, ) metric_dict["automl_metric"] = loss return metric_dict def _init_model_for_predict(self): from .nlp.huggingface.trainer import TrainerForAuto training_args = self._TrainingArguments( local_rank=-1, model_path=self._checkpoint_path, fp16=self.fp16 ) for key, val in self._training_args.__dict__.items(): if key not in ("local_rank", "model_path", "fp16"): setattr(training_args, key, val) self._training_args = training_args new_trainer = TrainerForAuto( model=self._model_init(), args=self._training_args, data_collator=self.data_collator, compute_metrics=self._compute_metrics_by_dataset_name, ) if self._task in NLG_TASKS: setattr(new_trainer, "_is_seq2seq", True) return new_trainer def predict_proba(self, X, **pred_kwargs): from datasets import Dataset if pred_kwargs: for key, val in pred_kwargs.items(): setattr(self._training_args, key, val) assert ( self._task in CLASSIFICATION ), "predict_proba() only for classification tasks." X_test, _ = self._preprocess(X, **self._kwargs) test_dataset = Dataset.from_pandas(X_test) new_trainer = self._init_model_for_predict() predictions = new_trainer.predict(test_dataset) return predictions.predictions def score(self, X_val: DataFrame, y_val: Series, **kwargs): import transformers transformers.logging.set_verbosity_error() self._metric = kwargs["metric"] eval_dataset, X_val, y_val = self.preprocess_data(X_val, y_val) new_trainer = self._init_model_for_predict() return new_trainer.evaluate(eval_dataset) def predict(self, X, **pred_kwargs): import transformers from datasets import Dataset transformers.logging.set_verbosity_error() if pred_kwargs: for key, val in pred_kwargs.items(): setattr(self._training_args, key, val) X_test, _ = self._preprocess(X, **self._kwargs) test_dataset = Dataset.from_pandas(X_test) new_trainer = self._init_model_for_predict() if self._task not in NLG_TASKS: predictions = new_trainer.predict(test_dataset) else: predictions = new_trainer.predict( test_dataset, metric_key_prefix="predict", ) if self._task == SEQCLASSIFICATION: return np.argmax(predictions.predictions, axis=1) elif self._task == SEQREGRESSION: return predictions.predictions.reshape((len(predictions.predictions),)) elif self._task == TOKENCLASSIFICATION: return np.argmax(predictions.predictions, axis=2) elif self._task == SUMMARIZATION: decoded_preds = self.tokenizer.batch_decode( predictions.predictions, skip_special_tokens=True ) return decoded_preds elif self._task == MULTICHOICECLASSIFICATION: return np.argmax(predictions.predictions, axis=1) def config2params(self, config: dict) -> dict: params = super().config2params(config) params[TransformersEstimator.ITER_HP] = params.get( TransformersEstimator.ITER_HP, sys.maxsize ) return params class TransformersEstimatorModelSelection(TransformersEstimator): def __init__(self, task="seq-classification", **config): super().__init__(task, **config) @classmethod def search_space(cls, data_size, task, **params): search_space_dict = TransformersEstimator.search_space( data_size, task, **params ) search_space_dict["model_path"] = { "domain": tune.choice( [ "google/electra-base-discriminator", "bert-base-uncased", "roberta-base", "facebook/muppet-roberta-base", "google/electra-small-discriminator", ] ), "init_value": "facebook/muppet-roberta-base", } return search_space_dict class SKLearnEstimator(BaseEstimator): def __init__(self, task="binary", **config): super().__init__(task, **config) def _preprocess(self, X): if isinstance(X, DataFrame): cat_columns = X.select_dtypes(include=["category"]).columns if not cat_columns.empty: X = X.copy() X[cat_columns] = X[cat_columns].apply(lambda x: x.cat.codes) elif isinstance(X, np.ndarray) and X.dtype.kind not in "buif": X = DataFrame(X) for col in X.columns: if isinstance(X[col][0], str): X[col] = X[col].astype("category").cat.codes X = X.to_numpy() return X class LGBMEstimator(BaseEstimator): ITER_HP = "n_estimators" HAS_CALLBACK = True DEFAULT_ITER = 100 @classmethod def search_space(cls, data_size, **params): upper = max(5, min(32768, int(data_size[0]))) return { "n_estimators": { "domain": tune.lograndint(lower=4, upper=upper), "init_value": 4, "low_cost_init_value": 4, }, "num_leaves": { "domain": tune.lograndint(lower=4, upper=upper), "init_value": 4, "low_cost_init_value": 4, }, "min_child_samples": { "domain": tune.lograndint(lower=2, upper=2**7 + 1), "init_value": 20, }, "learning_rate": { "domain": tune.loguniform(lower=1 / 1024, upper=1.0), "init_value": 0.1, }, "log_max_bin": { "domain": tune.lograndint(lower=3, upper=11), "init_value": 8, }, "colsample_bytree": { "domain": tune.uniform(lower=0.01, upper=1.0), "init_value": 1.0, }, "reg_alpha": { "domain": tune.loguniform(lower=1 / 1024, upper=1024), "init_value": 1 / 1024, }, "reg_lambda": { "domain": tune.loguniform(lower=1 / 1024, upper=1024), "init_value": 1.0, }, } def config2params(self, config: dict) -> dict: params = super().config2params(config) if "log_max_bin" in params: params["max_bin"] = (1 << params.pop("log_max_bin")) - 1 return params @classmethod def size(cls, config): num_leaves = int( round( config.get("num_leaves") or config.get("max_leaves") or 1 << config.get("max_depth", 16) ) ) n_estimators = int(round(config["n_estimators"])) return (num_leaves * 3 + (num_leaves - 1) * 4 + 1.0) * n_estimators * 8 def __init__(self, task="binary", **config): super().__init__(task, **config) if "verbose" not in self.params: self.params["verbose"] = -1 if "regression" == task: from lightgbm import LGBMRegressor self.estimator_class = LGBMRegressor elif "rank" == task: from lightgbm import LGBMRanker self.estimator_class = LGBMRanker else: from lightgbm import LGBMClassifier self.estimator_class = LGBMClassifier self._time_per_iter = None self._train_size = 0 self._mem_per_iter = -1 self.HAS_CALLBACK = self.HAS_CALLBACK and self._callbacks(0, 0) is not None def _preprocess(self, X): if ( not isinstance(X, DataFrame) and issparse(X) and np.issubdtype(X.dtype, np.integer) ): X = X.astype(float) elif isinstance(X, np.ndarray) and X.dtype.kind not in "buif": X = DataFrame(X) for col in X.columns: if isinstance(X[col][0], str): X[col] = X[col].astype("category").cat.codes X = X.to_numpy() return X def fit(self, X_train, y_train, budget=None, **kwargs): start_time = time.time() deadline = start_time + budget if budget else np.inf n_iter = self.params.get(self.ITER_HP, self.DEFAULT_ITER) trained = False if not self.HAS_CALLBACK: mem0 = psutil.virtual_memory().available if psutil is not None else 1 if ( ( not self._time_per_iter or abs(self._train_size - X_train.shape[0]) > 4 ) and budget is not None or self._mem_per_iter < 0 and psutil is not None ) and n_iter > 1: self.params[self.ITER_HP] = 1 self._t1 = self._fit(X_train, y_train, **kwargs) if budget is not None and self._t1 >= budget or n_iter == 1: return self._t1 mem1 = psutil.virtual_memory().available if psutil is not None else 1 self._mem1 = mem0 - mem1 self.params[self.ITER_HP] = min(n_iter, 4) self._t2 = self._fit(X_train, y_train, **kwargs) mem2 = psutil.virtual_memory().available if psutil is not None else 1 self._mem2 = max(mem0 - mem2, self._mem1) self._mem_per_iter = min( self._mem1, self._mem2 / self.params[self.ITER_HP] ) self._time_per_iter = ( (self._t2 - self._t1) / (self.params[self.ITER_HP] - 1) if self._t2 > self._t1 else self._t1 if self._t1 else 0.001 ) self._train_size = X_train.shape[0] if ( budget is not None and self._t1 + self._t2 >= budget or n_iter == self.params[self.ITER_HP] ): return time.time() - start_time trained = True if n_iter > 1: max_iter = min( n_iter, int( (budget - time.time() + start_time - self._t1) / self._time_per_iter + 1 ) if budget is not None else n_iter, int((1 - FREE_MEM_RATIO) * mem0 / self._mem_per_iter) if psutil is not None and self._mem_per_iter > 0 else n_iter, ) if trained and max_iter <= self.params[self.ITER_HP]: return time.time() - start_time self.params[self.ITER_HP] = max(max_iter, 1) if self.HAS_CALLBACK: kwargs_callbacks = kwargs.get("callbacks") if kwargs_callbacks: callbacks = kwargs_callbacks + self._callbacks(start_time, deadline) kwargs.pop("callbacks") else: callbacks = self._callbacks(start_time, deadline) if isinstance(self, XGBoostSklearnEstimator): from xgboost import __version__ if __version__ >= "1.6.0": self.params["callbacks"] = callbacks callbacks = None self._fit( X_train, y_train, callbacks=callbacks, **kwargs, ) if callbacks is None: # for xgboost>=1.6.0, pop callbacks to enable pickle callbacks = self.params.pop("callbacks") self._model.set_params(callbacks=callbacks[:-1]) best_iteration = ( self._model.get_booster().best_iteration if isinstance(self, XGBoostSklearnEstimator) else self._model.best_iteration_ ) if best_iteration is not None: self._model.set_params(n_estimators=best_iteration + 1) else: self._fit(X_train, y_train, **kwargs) train_time = time.time() - start_time return train_time def _callbacks(self, start_time, deadline) -> List[Callable]: return [partial(self._callback, start_time, deadline)] def _callback(self, start_time, deadline, env) -> None: from lightgbm.callback import EarlyStopException now = time.time() if env.iteration == 0: self._time_per_iter = now - start_time if now + self._time_per_iter > deadline: raise EarlyStopException(env.iteration, env.evaluation_result_list) if psutil is not None: mem = psutil.virtual_memory() if mem.available / mem.total < FREE_MEM_RATIO: raise EarlyStopException(env.iteration, env.evaluation_result_list) class XGBoostEstimator(SKLearnEstimator): DEFAULT_ITER = 10 @classmethod def search_space(cls, data_size, **params): upper = max(5, min(32768, int(data_size[0]))) # upper must be larger than lower return { "n_estimators": { "domain": tune.lograndint(lower=4, upper=upper), "init_value": 4, "low_cost_init_value": 4, }, "max_leaves": { "domain": tune.lograndint(lower=4, upper=upper), "init_value": 4, "low_cost_init_value": 4, }, "max_depth": { "domain": tune.choice([0, 6, 12]), "init_value": 0, }, "min_child_weight": { "domain": tune.loguniform(lower=0.001, upper=128), "init_value": 1.0, }, "learning_rate": { "domain": tune.loguniform(lower=1 / 1024, upper=1.0), "init_value": 0.1, }, "subsample": { "domain": tune.uniform(lower=0.1, upper=1.0), "init_value": 1.0, }, "colsample_bylevel": { "domain": tune.uniform(lower=0.01, upper=1.0), "init_value": 1.0, }, "colsample_bytree": { "domain": tune.uniform(lower=0.01, upper=1.0), "init_value": 1.0, }, "reg_alpha": { "domain": tune.loguniform(lower=1 / 1024, upper=1024), "init_value": 1 / 1024, }, "reg_lambda": { "domain": tune.loguniform(lower=1 / 1024, upper=1024), "init_value": 1.0, }, } @classmethod def size(cls, config): return LGBMEstimator.size(config) @classmethod def cost_relative2lgbm(cls): return 1.6 def config2params(self, config: dict) -> dict: params = super().config2params(config) max_depth = params["max_depth"] = params.get("max_depth", 0) if max_depth == 0: params["grow_policy"] = params.get("grow_policy", "lossguide") params["tree_method"] = params.get("tree_method", "hist") # params["booster"] = params.get("booster", "gbtree") params["use_label_encoder"] = params.get("use_label_encoder", False) if "n_jobs" in config: params["nthread"] = params.pop("n_jobs") return params def __init__( self, task="regression", **config, ): super().__init__(task, **config) self.params["verbosity"] = 0 def fit(self, X_train, y_train, budget=None, **kwargs): import xgboost as xgb start_time = time.time() deadline = start_time + budget if budget else np.inf if issparse(X_train): if xgb.__version__ < "1.6.0": # "auto" fails for sparse input since xgboost 1.6.0 self.params["tree_method"] = "auto" else: X_train = self._preprocess(X_train) if "sample_weight" in kwargs: dtrain = xgb.DMatrix(X_train, label=y_train, weight=kwargs["sample_weight"]) else: dtrain = xgb.DMatrix(X_train, label=y_train) objective = self.params.get("objective") if isinstance(objective, str): obj = None else: obj = objective if "objective" in self.params: del self.params["objective"] _n_estimators = self.params.pop("n_estimators") callbacks = XGBoostEstimator._callbacks(start_time, deadline) if callbacks: self._model = xgb.train( self.params, dtrain, _n_estimators, obj=obj, callbacks=callbacks, ) self.params["n_estimators"] = self._model.best_iteration + 1 else: self._model = xgb.train(self.params, dtrain, _n_estimators, obj=obj) self.params["n_estimators"] = _n_estimators self.params["objective"] = objective del dtrain train_time = time.time() - start_time return train_time def predict(self, X, **kwargs): import xgboost as xgb if not issparse(X): X = self._preprocess(X) dtest = xgb.DMatrix(X) return super().predict(dtest) @classmethod def _callbacks(cls, start_time, deadline): try: from xgboost.callback import TrainingCallback except ImportError: # for xgboost<1.3 return None class ResourceLimit(TrainingCallback): def after_iteration(self, model, epoch, evals_log) -> bool: now = time.time() if epoch == 0: self._time_per_iter = now - start_time if now + self._time_per_iter > deadline: return True if psutil is not None: mem = psutil.virtual_memory() if mem.available / mem.total < FREE_MEM_RATIO: return True return False return [ResourceLimit()] class XGBoostSklearnEstimator(SKLearnEstimator, LGBMEstimator): DEFAULT_ITER = 10 @classmethod def search_space(cls, data_size, **params): space = XGBoostEstimator.search_space(data_size) space.pop("max_depth") return space @classmethod def cost_relative2lgbm(cls): return XGBoostEstimator.cost_relative2lgbm() def config2params(self, config: dict) -> dict: params = super().config2params(config) max_depth = params["max_depth"] = params.get("max_depth", 0) if max_depth == 0: params["grow_policy"] = params.get("grow_policy", "lossguide") params["tree_method"] = params.get("tree_method", "hist") params["use_label_encoder"] = params.get("use_label_encoder", False) return params def __init__( self, task="binary", **config, ): super().__init__(task, **config) del self.params["verbose"] self.params["verbosity"] = 0 import xgboost as xgb self.estimator_class = xgb.XGBRegressor if "rank" == task: self.estimator_class = xgb.XGBRanker elif task in CLASSIFICATION: self.estimator_class = xgb.XGBClassifier self._xgb_version = xgb.__version__ def fit(self, X_train, y_train, budget=None, **kwargs): if issparse(X_train) and self._xgb_version < "1.6.0": # "auto" fails for sparse input since xgboost 1.6.0 self.params["tree_method"] = "auto" if kwargs.get("gpu_per_trial"): self.params["tree_method"] = "gpu_hist" kwargs.pop("gpu_per_trial") return super().fit(X_train, y_train, budget, **kwargs) def _callbacks(self, start_time, deadline) -> List[Callable]: return XGBoostEstimator._callbacks(start_time, deadline) class XGBoostLimitDepthEstimator(XGBoostSklearnEstimator): @classmethod def search_space(cls, data_size, **params): space = XGBoostEstimator.search_space(data_size) space.pop("max_leaves") upper = max(6, int(np.log2(data_size[0]))) space["max_depth"] = { "domain": tune.randint(lower=1, upper=min(upper, 16)), "init_value": 6, "low_cost_init_value": 1, } space["learning_rate"]["init_value"] = 0.3 space["n_estimators"]["init_value"] = 10 return space @classmethod def cost_relative2lgbm(cls): return 64 class RandomForestEstimator(SKLearnEstimator, LGBMEstimator): HAS_CALLBACK = False nrows = 101 @classmethod def search_space(cls, data_size, task, **params): RandomForestEstimator.nrows = int(data_size[0]) upper = min(2048, RandomForestEstimator.nrows) init = 1 / np.sqrt(data_size[1]) if task in CLASSIFICATION else 1 lower = min(0.1, init) space = { "n_estimators": { "domain": tune.lograndint(lower=4, upper=max(5, upper)), "init_value": 4, "low_cost_init_value": 4, }, "max_features": { "domain": tune.loguniform(lower=lower, upper=1.0), "init_value": init, }, "max_leaves": { "domain": tune.lograndint( lower=4, upper=max(5, min(32768, RandomForestEstimator.nrows >> 1)), # ), "init_value": 4, "low_cost_init_value": 4, }, } if task in CLASSIFICATION: space["criterion"] = { "domain": tune.choice(["gini", "entropy"]), # "init_value": "gini", } return space @classmethod def cost_relative2lgbm(cls): return 2 def config2params(self, config: dict) -> dict: params = super().config2params(config) if "max_leaves" in params: params["max_leaf_nodes"] = params.get( "max_leaf_nodes", params.pop("max_leaves") ) if self._task not in CLASSIFICATION and "criterion" in config: params.pop("criterion") return params def __init__( self, task="binary", **params, ): super().__init__(task, **params) self.params["verbose"] = 0 self.estimator_class = RandomForestRegressor if task in CLASSIFICATION: self.estimator_class = RandomForestClassifier class ExtraTreesEstimator(RandomForestEstimator): @classmethod def cost_relative2lgbm(cls): return 1.9 def __init__(self, task="binary", **params): super().__init__(task, **params) if "regression" in task: self.estimator_class = ExtraTreesRegressor else: self.estimator_class = ExtraTreesClassifier class LRL1Classifier(SKLearnEstimator): @classmethod def search_space(cls, **params): return { "C": { "domain": tune.loguniform(lower=0.03125, upper=32768.0), "init_value": 1.0, }, } @classmethod def cost_relative2lgbm(cls): return 160 def config2params(self, config: dict) -> dict: params = super().config2params(config) params["tol"] = params.get("tol", 0.0001) params["solver"] = params.get("solver", "saga") params["penalty"] = params.get("penalty", "l1") return params def __init__(self, task="binary", **config): super().__init__(task, **config) assert task in CLASSIFICATION, "LogisticRegression for classification task only" self.estimator_class = LogisticRegression class LRL2Classifier(SKLearnEstimator): limit_resource = True @classmethod def search_space(cls, **params): return LRL1Classifier.search_space(**params) @classmethod def cost_relative2lgbm(cls): return 25 def config2params(self, config: dict) -> dict: params = super().config2params(config) params["tol"] = params.get("tol", 0.0001) params["solver"] = params.get("solver", "lbfgs") params["penalty"] = params.get("penalty", "l2") return params def __init__(self, task="binary", **config): super().__init__(task, **config) assert task in CLASSIFICATION, "LogisticRegression for classification task only" self.estimator_class = LogisticRegression class CatBoostEstimator(BaseEstimator): ITER_HP = "n_estimators" DEFAULT_ITER = 1000 @classmethod def search_space(cls, data_size, **params): upper = max(min(round(1500000 / data_size[0]), 150), 12) return { "early_stopping_rounds": { "domain": tune.lograndint(lower=10, upper=upper), "init_value": 10, "low_cost_init_value": 10, }, "learning_rate": { "domain": tune.loguniform(lower=0.005, upper=0.2), "init_value": 0.1, }, "n_estimators": { "domain": 8192, "init_value": 8192, }, } @classmethod def size(cls, config): n_estimators = config.get("n_estimators", 8192) max_leaves = 64 return (max_leaves * 3 + (max_leaves - 1) * 4 + 1.0) * n_estimators * 8 @classmethod def cost_relative2lgbm(cls): return 15 def _preprocess(self, X): if isinstance(X, DataFrame): cat_columns = X.select_dtypes(include=["category"]).columns if not cat_columns.empty: X = X.copy() X[cat_columns] = X[cat_columns].apply( lambda x: x.cat.rename_categories( [ str(c) if isinstance(c, float) else c for c in x.cat.categories ] ) ) elif isinstance(X, np.ndarray) and X.dtype.kind not in "buif": # numpy array is not of numeric dtype X = DataFrame(X) for col in X.columns: if isinstance(X[col][0], str): X[col] = X[col].astype("category").cat.codes X = X.to_numpy() return X def config2params(self, config: dict) -> dict: params = super().config2params(config) params["n_estimators"] = params.get("n_estimators", 8192) if "n_jobs" in params: params["thread_count"] = params.pop("n_jobs") return params def __init__( self, task="binary", **config, ): super().__init__(task, **config) self.params.update( { "verbose": config.get("verbose", False), "random_seed": config.get("random_seed", 10242048), } ) from catboost import CatBoostRegressor self.estimator_class = CatBoostRegressor if task in CLASSIFICATION: from catboost import CatBoostClassifier self.estimator_class = CatBoostClassifier def fit(self, X_train, y_train, budget=None, **kwargs): start_time = time.time() deadline = start_time + budget if budget else np.inf train_dir = f"catboost_{str(start_time)}" X_train = self._preprocess(X_train) if isinstance(X_train, DataFrame): cat_features = list(X_train.select_dtypes(include="category").columns) else: cat_features = [] n = max(int(len(y_train) * 0.9), len(y_train) - 1000) X_tr, y_tr = X_train[:n], y_train[:n] if "sample_weight" in kwargs: weight = kwargs["sample_weight"] if weight is not None: kwargs["sample_weight"] = weight[:n] else: weight = None from catboost import Pool, __version__ model = self.estimator_class(train_dir=train_dir, **self.params) if __version__ >= "0.26": model.fit( X_tr, y_tr, cat_features=cat_features, eval_set=Pool( data=X_train[n:], label=y_train[n:], cat_features=cat_features ), callbacks=CatBoostEstimator._callbacks(start_time, deadline), **kwargs, ) else: model.fit( X_tr, y_tr, cat_features=cat_features, eval_set=Pool( data=X_train[n:], label=y_train[n:], cat_features=cat_features ), **kwargs, ) shutil.rmtree(train_dir, ignore_errors=True) if weight is not None: kwargs["sample_weight"] = weight self._model = model self.params[self.ITER_HP] = self._model.tree_count_ train_time = time.time() - start_time return train_time @classmethod def _callbacks(cls, start_time, deadline): class ResourceLimit: def after_iteration(self, info) -> bool: now = time.time() if info.iteration == 1: self._time_per_iter = now - start_time if now + self._time_per_iter > deadline: return False if psutil is not None: mem = psutil.virtual_memory() if mem.available / mem.total < FREE_MEM_RATIO: return False return True # can continue return [ResourceLimit()] class KNeighborsEstimator(BaseEstimator): @classmethod def search_space(cls, data_size, **params): upper = min(512, int(data_size[0] / 2)) return { "n_neighbors": { "domain": tune.lograndint(lower=1, upper=max(2, upper)), "init_value": 5, "low_cost_init_value": 1, }, } @classmethod def cost_relative2lgbm(cls): return 30 def config2params(self, config: dict) -> dict: params = super().config2params(config) params["weights"] = params.get("weights", "distance") return params def __init__(self, task="binary", **config): super().__init__(task, **config) if task in CLASSIFICATION: from sklearn.neighbors import KNeighborsClassifier self.estimator_class = KNeighborsClassifier else: from sklearn.neighbors import KNeighborsRegressor self.estimator_class = KNeighborsRegressor def _preprocess(self, X): if isinstance(X, DataFrame): cat_columns = X.select_dtypes(["category"]).columns if X.shape[1] == len(cat_columns): raise ValueError("kneighbor requires at least one numeric feature") X = X.drop(cat_columns, axis=1) elif isinstance(X, np.ndarray) and X.dtype.kind not in "buif": # drop categocial columns if any X = DataFrame(X) cat_columns = [] for col in X.columns: if isinstance(X[col][0], str): cat_columns.append(col) X = X.drop(cat_columns, axis=1) X = X.to_numpy() return X class Prophet(SKLearnEstimator): @classmethod def search_space(cls, **params): space = { "changepoint_prior_scale": { "domain": tune.loguniform(lower=0.001, upper=0.05), "init_value": 0.05, "low_cost_init_value": 0.001, }, "seasonality_prior_scale": { "domain": tune.loguniform(lower=0.01, upper=10), "init_value": 10, }, "holidays_prior_scale": { "domain": tune.loguniform(lower=0.01, upper=10), "init_value": 10, }, "seasonality_mode": { "domain": tune.choice(["additive", "multiplicative"]), "init_value": "multiplicative", }, } return space def __init__(self, task="ts_forecast", n_jobs=1, **params): super().__init__(task, **params) def _join(self, X_train, y_train): assert TS_TIMESTAMP_COL in X_train, ( "Dataframe for training ts_forecast model must have column" f' "{TS_TIMESTAMP_COL}" with the dates in X_train.' ) y_train = DataFrame(y_train, columns=[TS_VALUE_COL]) train_df = X_train.join(y_train) return train_df def fit(self, X_train, y_train, budget=None, **kwargs): from prophet import Prophet current_time = time.time() train_df = self._join(X_train, y_train) train_df = self._preprocess(train_df) cols = list(train_df) cols.remove(TS_TIMESTAMP_COL) cols.remove(TS_VALUE_COL) logging.getLogger("prophet").setLevel(logging.WARNING) model = Prophet(**self.params) for regressor in cols: model.add_regressor(regressor) with suppress_stdout_stderr(): model.fit(train_df) train_time = time.time() - current_time self._model = model return train_time def predict(self, X, **kwargs): if isinstance(X, int): raise ValueError( "predict() with steps is only supported for arima/sarimax." " For Prophet, pass a dataframe with the first column containing" " the timestamp values." ) if self._model is not None: X = self._preprocess(X) forecast = self._model.predict(X) return forecast["yhat"] else: logger.warning( "Estimator is not fit yet. Please run fit() before predict()." ) return np.ones(X.shape[0]) def score(self, X_val: DataFrame, y_val: Series, **kwargs): from sklearn.metrics import r2_score from .ml import metric_loss_score y_pred = self.predict(X_val) self._metric = kwargs.get("metric", None) if self._metric: return metric_loss_score(self._metric, y_pred, y_val) else: return r2_score(y_pred, y_val) class ARIMA(Prophet): @classmethod def search_space(cls, **params): space = { "p": { "domain": tune.qrandint(lower=0, upper=10, q=1), "init_value": 2, "low_cost_init_value": 0, }, "d": { "domain": tune.qrandint(lower=0, upper=10, q=1), "init_value": 2, "low_cost_init_value": 0, }, "q": { "domain": tune.qrandint(lower=0, upper=10, q=1), "init_value": 1, "low_cost_init_value": 0, }, } return space def _join(self, X_train, y_train): train_df = super()._join(X_train, y_train) train_df.index = to_datetime(train_df[TS_TIMESTAMP_COL]) train_df = train_df.drop(TS_TIMESTAMP_COL, axis=1) return train_df def fit(self, X_train, y_train, budget=None, **kwargs): import warnings warnings.filterwarnings("ignore") from statsmodels.tsa.arima.model import ARIMA as ARIMA_estimator current_time = time.time() train_df = self._join(X_train, y_train) train_df = self._preprocess(train_df) regressors = list(train_df) regressors.remove(TS_VALUE_COL) if regressors: model = ARIMA_estimator( train_df[[TS_VALUE_COL]], exog=train_df[regressors], order=(self.params["p"], self.params["d"], self.params["q"]), enforce_stationarity=False, enforce_invertibility=False, ) else: model = ARIMA_estimator( train_df, order=(self.params["p"], self.params["d"], self.params["q"]), enforce_stationarity=False, enforce_invertibility=False, ) with suppress_stdout_stderr(): model = model.fit() train_time = time.time() - current_time self._model = model return train_time def predict(self, X, **kwargs): if self._model is not None: if isinstance(X, int): forecast = self._model.forecast(steps=X) elif isinstance(X, DataFrame): start = X[TS_TIMESTAMP_COL].iloc[0] end = X[TS_TIMESTAMP_COL].iloc[-1] if len(X.columns) > 1: X = self._preprocess(X.drop(columns=TS_TIMESTAMP_COL)) regressors = list(X) forecast = self._model.predict( start=start, end=end, exog=X[regressors] ) else: forecast = self._model.predict(start=start, end=end) else: raise ValueError( "X needs to be either a pandas Dataframe with dates as the first column" " or an int number of periods for predict()." ) return forecast else: return np.ones(X if isinstance(X, int) else X.shape[0]) class SARIMAX(ARIMA): @classmethod def search_space(cls, **params): space = { "p": { "domain": tune.qrandint(lower=0, upper=10, q=1), "init_value": 2, "low_cost_init_value": 0, }, "d": { "domain": tune.qrandint(lower=0, upper=10, q=1), "init_value": 2, "low_cost_init_value": 0, }, "q": { "domain": tune.qrandint(lower=0, upper=10, q=1), "init_value": 1, "low_cost_init_value": 0, }, "P": { "domain": tune.qrandint(lower=0, upper=10, q=1), "init_value": 1, "low_cost_init_value": 0, }, "D": { "domain": tune.qrandint(lower=0, upper=10, q=1), "init_value": 1, "low_cost_init_value": 0, }, "Q": { "domain": tune.qrandint(lower=0, upper=10, q=1), "init_value": 1, "low_cost_init_value": 0, }, "s": { "domain": tune.choice([1, 4, 6, 12]), "init_value": 12, }, } return space def fit(self, X_train, y_train, budget=None, **kwargs): import warnings warnings.filterwarnings("ignore") from statsmodels.tsa.statespace.sarimax import SARIMAX as SARIMAX_estimator current_time = time.time() train_df = self._join(X_train, y_train) train_df = self._preprocess(train_df) regressors = list(train_df) regressors.remove(TS_VALUE_COL) if regressors: model = SARIMAX_estimator( train_df[[TS_VALUE_COL]], exog=train_df[regressors], order=(self.params["p"], self.params["d"], self.params["q"]), seasonality_order=( self.params["P"], self.params["D"], self.params["Q"], self.params["s"], ), enforce_stationarity=False, enforce_invertibility=False, ) else: model = SARIMAX_estimator( train_df, order=(self.params["p"], self.params["d"], self.params["q"]), seasonality_order=( self.params["P"], self.params["D"], self.params["Q"], self.params["s"], ), enforce_stationarity=False, enforce_invertibility=False, ) with suppress_stdout_stderr(): model = model.fit() train_time = time.time() - current_time self._model = model return train_time class TS_SKLearn(SKLearnEstimator): base_class = SKLearnEstimator @classmethod def search_space(cls, data_size, pred_horizon, **params): space = cls.base_class.search_space(data_size, **params) space.update( { "optimize_for_horizon": { "domain": tune.choice([True, False]), "init_value": False, "low_cost_init_value": False, }, "lags": { "domain": tune.randint( lower=1, upper=max(2, int(np.sqrt(data_size[0]))) ), "init_value": 3, }, } ) return space def __init__(self, task="ts_forecast", **params): super().__init__(task, **params) self.hcrystaball_model = None self.ts_task = ( "regression" if task in TS_FORECASTREGRESSION else "classification" ) def transform_X(self, X): cols = list(X) if len(cols) == 1: ds_col = cols[0] X = DataFrame(index=X[ds_col]) elif len(cols) > 1: ds_col = cols[0] exog_cols = cols[1:] X = X[exog_cols].set_index(X[ds_col]) return X def _fit(self, X_train, y_train, budget=None, **kwargs): from hcrystalball.wrappers import get_sklearn_wrapper X_train = self.transform_X(X_train) X_train = self._preprocess(X_train) params = self.params.copy() lags = params.pop("lags") optimize_for_horizon = params.pop("optimize_for_horizon") estimator = self.base_class(task=self.ts_task, **params) self.hcrystaball_model = get_sklearn_wrapper(estimator.estimator_class) self.hcrystaball_model.lags = int(lags) self.hcrystaball_model.fit(X_train, y_train) if optimize_for_horizon: # Direct Multi-step Forecast Strategy - fit a seperate model for each horizon model_list = [] for i in range(1, kwargs["period"] + 1): ( X_fit, y_fit, ) = self.hcrystaball_model._transform_data_to_tsmodel_input_format( X_train, y_train, i ) self.hcrystaball_model.model.set_params(**estimator.params) model = self.hcrystaball_model.model.fit(X_fit, y_fit) model_list.append(model) self._model = model_list else: ( X_fit, y_fit, ) = self.hcrystaball_model._transform_data_to_tsmodel_input_format( X_train, y_train, kwargs["period"] ) self.hcrystaball_model.model.set_params(**estimator.params) model = self.hcrystaball_model.model.fit(X_fit, y_fit) self._model = model def fit(self, X_train, y_train, budget=None, **kwargs): current_time = time.time() self._fit(X_train, y_train, budget=budget, **kwargs) train_time = time.time() - current_time return train_time def predict(self, X, **kwargs): if self._model is not None: X = self.transform_X(X) X = self._preprocess(X) if isinstance(self._model, list): assert len(self._model) == len( X ), "Model is optimized for horizon, length of X must be equal to `period`." preds = [] for i in range(1, len(self._model) + 1): ( X_pred, _, ) = self.hcrystaball_model._transform_data_to_tsmodel_input_format( X.iloc[:i, :] ) preds.append(self._model[i - 1].predict(X_pred)[-1]) forecast = DataFrame( data=np.asarray(preds).reshape(-1, 1), columns=[self.hcrystaball_model.name], index=X.index, ) else: ( X_pred, _, ) = self.hcrystaball_model._transform_data_to_tsmodel_input_format(X) forecast = self._model.predict(X_pred) return forecast else: logger.warning( "Estimator is not fit yet. Please run fit() before predict()." ) return np.ones(X.shape[0]) class LGBM_TS(TS_SKLearn): base_class = LGBMEstimator class XGBoost_TS(TS_SKLearn): base_class = XGBoostSklearnEstimator # catboost regressor is invalid because it has a `name` parameter, making it incompatible with hcrystalball # class CatBoost_TS_Regressor(TS_Regressor): # base_class = CatBoostEstimator class RF_TS(TS_SKLearn): base_class = RandomForestEstimator class ExtraTrees_TS(TS_SKLearn): base_class = ExtraTreesEstimator class XGBoostLimitDepth_TS(TS_SKLearn): base_class = XGBoostLimitDepthEstimator class suppress_stdout_stderr(object): def __init__(self): # Open a pair of null files self.null_fds = [os.open(os.devnull, os.O_RDWR) for x in range(2)] # Save the actual stdout (1) and stderr (2) file descriptors. self.save_fds = (os.dup(1), os.dup(2)) def __enter__(self): # Assign the null pointers to stdout and stderr. os.dup2(self.null_fds[0], 1) os.dup2(self.null_fds[1], 2) def __exit__(self, *_): # Re-assign the real stdout/stderr back to (1) and (2) os.dup2(self.save_fds[0], 1) os.dup2(self.save_fds[1], 2) # Close the null files os.close(self.null_fds[0]) os.close(self.null_fds[1])

true

f7318945e70a68298c48631812fa81322c407bc9

12,916

py

Python

env/lib/python3.6/site-packages/nibabel/tests/test_image_load_save.py

Raniac/NEURO-LEARN

3c3acc55de8ba741e673063378e6cbaf10b64c7a

[ "Apache-2.0" ]

8

2019-05-29T09:38:30.000Z

2021-01-20T03:36:59.000Z

env/lib/python3.6/site-packages/nibabel/tests/test_image_load_save.py

Raniac/neurolearn_dev

3c3acc55de8ba741e673063378e6cbaf10b64c7a

[ "Apache-2.0" ]

12

2021-03-09T03:01:16.000Z

2022-03-11T23:59:36.000Z

env/lib/python3.6/site-packages/nibabel/tests/test_image_load_save.py

Raniac/NEURO-LEARN

3c3acc55de8ba741e673063378e6cbaf10b64c7a

[ "Apache-2.0" ]

1

2020-07-17T12:49:49.000Z

# emacs: -*- mode: python-mode; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the NiBabel package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## ''' Tests for loader function ''' from __future__ import division, print_function, absolute_import from io import BytesIO import shutil from os.path import dirname, join as pjoin from tempfile import mkdtemp import numpy as np from .. import analyze as ana from .. import spm99analyze as spm99 from .. import spm2analyze as spm2 from .. import nifti1 as ni1 from .. import loadsave as nils from .. import (Nifti1Image, Nifti1Header, Nifti1Pair, Nifti2Image, Nifti2Pair, Minc1Image, Minc2Image, Spm2AnalyzeImage, Spm99AnalyzeImage, AnalyzeImage, MGHImage, all_image_classes) from ..tmpdirs import InTemporaryDirectory from ..volumeutils import native_code, swapped_code from ..optpkg import optional_package from ..spatialimages import SpatialImage from numpy.testing import assert_array_equal, assert_array_almost_equal from nose.tools import assert_true, assert_equal, assert_raises _, have_scipy, _ = optional_package('scipy') # No scipy=>no SPM-format writing DATA_PATH = pjoin(dirname(__file__), 'data') MGH_DATA_PATH = pjoin(dirname(__file__), '..', 'freesurfer', 'tests', 'data') def round_trip(img): # round trip a nifti single sio = BytesIO() img.file_map['image'].fileobj = sio img.to_file_map() img2 = Nifti1Image.from_file_map(img.file_map) return img2 def test_conversion_spatialimages(): shape = (2, 4, 6) affine = np.diag([1, 2, 3, 1]) klasses = [klass for klass in all_image_classes if klass.rw and issubclass(klass, SpatialImage)] for npt in np.float32, np.int16: data = np.arange(np.prod(shape), dtype=npt).reshape(shape) for r_class in klasses: if not r_class.makeable: continue img = r_class(data, affine) img.set_data_dtype(npt) for w_class in klasses: if not w_class.makeable: continue img2 = w_class.from_image(img) assert_array_equal(img2.get_data(), data) assert_array_equal(img2.affine, affine) def test_save_load_endian(): shape = (2, 4, 6) affine = np.diag([1, 2, 3, 1]) data = np.arange(np.prod(shape), dtype='f4').reshape(shape) # Native endian image img = Nifti1Image(data, affine) assert_equal(img.header.endianness, native_code) img2 = round_trip(img) assert_equal(img2.header.endianness, native_code) assert_array_equal(img2.get_data(), data) # byte swapped endian image bs_hdr = img.header.as_byteswapped() bs_img = Nifti1Image(data, affine, bs_hdr) assert_equal(bs_img.header.endianness, swapped_code) # of course the data is the same because it's not written to disk assert_array_equal(bs_img.get_data(), data) # Check converting to another image cbs_img = AnalyzeImage.from_image(bs_img) # this will make the header native by doing the header conversion cbs_hdr = cbs_img.header assert_equal(cbs_hdr.endianness, native_code) # and the byte order follows it back into another image cbs_img2 = Nifti1Image.from_image(cbs_img) cbs_hdr2 = cbs_img2.header assert_equal(cbs_hdr2.endianness, native_code) # Try byteswapped round trip bs_img2 = round_trip(bs_img) bs_data2 = bs_img2.get_data() # now the data dtype was swapped endian, so the read data is too assert_equal(bs_data2.dtype.byteorder, swapped_code) assert_equal(bs_img2.header.endianness, swapped_code) assert_array_equal(bs_data2, data) # Now mix up byteswapped data and non-byteswapped header mixed_img = Nifti1Image(bs_data2, affine) assert_equal(mixed_img.header.endianness, native_code) m_img2 = round_trip(mixed_img) assert_equal(m_img2.header.endianness, native_code) assert_array_equal(m_img2.get_data(), data) def test_save_load(): shape = (2, 4, 6) npt = np.float32 data = np.arange(np.prod(shape), dtype=npt).reshape(shape) affine = np.diag([1, 2, 3, 1]) affine[:3, 3] = [3, 2, 1] img = ni1.Nifti1Image(data, affine) img.set_data_dtype(npt) with InTemporaryDirectory() as pth: nifn = 'an_image.nii' sifn = 'another_image.img' ni1.save(img, nifn) re_img = nils.load(nifn) assert_true(isinstance(re_img, ni1.Nifti1Image)) assert_array_equal(re_img.get_data(), data) assert_array_equal(re_img.affine, affine) # These and subsequent del statements are to prevent confusing # windows errors when trying to open files or delete the # temporary directory. del re_img if have_scipy: # skip we we cannot read .mat files spm2.save(img, sifn) re_img2 = nils.load(sifn) assert_true(isinstance(re_img2, spm2.Spm2AnalyzeImage)) assert_array_equal(re_img2.get_data(), data) assert_array_equal(re_img2.affine, affine) del re_img2 spm99.save(img, sifn) re_img3 = nils.load(sifn) assert_true(isinstance(re_img3, spm99.Spm99AnalyzeImage)) assert_array_equal(re_img3.get_data(), data) assert_array_equal(re_img3.affine, affine) ni1.save(re_img3, nifn) del re_img3 re_img = nils.load(nifn) assert_true(isinstance(re_img, ni1.Nifti1Image)) assert_array_equal(re_img.get_data(), data) assert_array_equal(re_img.affine, affine) del re_img def test_two_to_one(): # test going from two to one file in save shape = (2, 4, 6) npt = np.float32 data = np.arange(np.prod(shape), dtype=npt).reshape(shape) affine = np.diag([1, 2, 3, 1]) affine[:3, 3] = [3, 2, 1] # single file format img = ni1.Nifti1Image(data, affine) assert_equal(img.header['magic'], b'n+1') str_io = BytesIO() img.file_map['image'].fileobj = str_io # check that the single format vox offset stays at zero img.to_file_map() assert_equal(img.header['magic'], b'n+1') assert_equal(img.header['vox_offset'], 0) # make a new pair image, with the single image header pimg = ni1.Nifti1Pair(data, affine, img.header) isio = BytesIO() hsio = BytesIO() pimg.file_map['image'].fileobj = isio pimg.file_map['header'].fileobj = hsio pimg.to_file_map() # the offset stays at zero (but is 352 on disk) assert_equal(pimg.header['magic'], b'ni1') assert_equal(pimg.header['vox_offset'], 0) assert_array_equal(pimg.get_data(), data) # same for from_image, going from single image to pair format ana_img = ana.AnalyzeImage.from_image(img) assert_equal(ana_img.header['vox_offset'], 0) # back to the single image, save it again to a stringio str_io = BytesIO() img.file_map['image'].fileobj = str_io img.to_file_map() assert_equal(img.header['vox_offset'], 0) aimg = ana.AnalyzeImage.from_image(img) assert_equal(aimg.header['vox_offset'], 0) aimg = spm99.Spm99AnalyzeImage.from_image(img) assert_equal(aimg.header['vox_offset'], 0) aimg = spm2.Spm2AnalyzeImage.from_image(img) assert_equal(aimg.header['vox_offset'], 0) nfimg = ni1.Nifti1Pair.from_image(img) assert_equal(nfimg.header['vox_offset'], 0) # now set the vox offset directly hdr = nfimg.header hdr['vox_offset'] = 16 assert_equal(nfimg.header['vox_offset'], 16) # check it gets properly set by the nifti single image nfimg = ni1.Nifti1Image.from_image(img) assert_equal(nfimg.header['vox_offset'], 0) def test_negative_load_save(): shape = (1, 2, 5) data = np.arange(10).reshape(shape) - 10.0 affine = np.eye(4) hdr = ni1.Nifti1Header() hdr.set_data_dtype(np.int16) img = Nifti1Image(data, affine, hdr) str_io = BytesIO() img.file_map['image'].fileobj = str_io img.to_file_map() str_io.seek(0) re_img = Nifti1Image.from_file_map(img.file_map) assert_array_almost_equal(re_img.get_data(), data, 4) def test_filename_save(): # This is to test the logic in the load and save routines, relating # extensions to filetypes # Tuples of class, ext, loadedclass inklass_ext_loadklasses = ( (Nifti1Image, '.nii', Nifti1Image), (Nifti2Image, '.nii', Nifti2Image), (Nifti1Pair, '.nii', Nifti1Image), (Nifti2Pair, '.nii', Nifti2Image), (Nifti1Image, '.img', Nifti1Pair), (Nifti2Image, '.img', Nifti2Pair), (Nifti1Pair, '.img', Nifti1Pair), (Nifti2Pair, '.img', Nifti2Pair), (Nifti1Image, '.hdr', Nifti1Pair), (Nifti2Image, '.hdr', Nifti2Pair), (Nifti1Pair, '.hdr', Nifti1Pair), (Nifti2Pair, '.hdr', Nifti2Pair), (Minc1Image, '.nii', Nifti1Image), (Minc1Image, '.img', Nifti1Pair), (Spm2AnalyzeImage, '.nii', Nifti1Image), (Spm2AnalyzeImage, '.img', Spm2AnalyzeImage), (Spm99AnalyzeImage, '.nii', Nifti1Image), (Spm99AnalyzeImage, '.img', Spm2AnalyzeImage), (AnalyzeImage, '.nii', Nifti1Image), (AnalyzeImage, '.img', Spm2AnalyzeImage), ) shape = (2, 4, 6) affine = np.diag([1, 2, 3, 1]) data = np.arange(np.prod(shape), dtype='f4').reshape(shape) for inklass, out_ext, loadklass in inklass_ext_loadklasses: if not have_scipy: # We can't load a SPM analyze type without scipy. These types have # a 'mat' file (the type we can't load) if ('mat', '.mat') in loadklass.files_types: continue img = inklass(data, affine) try: pth = mkdtemp() fname = pjoin(pth, 'image' + out_ext) nils.save(img, fname) rt_img = nils.load(fname) assert_array_almost_equal(rt_img.get_data(), data) assert_true(type(rt_img) is loadklass) # delete image to allow file close. Otherwise windows # raises an error when trying to delete the directory del rt_img finally: shutil.rmtree(pth) def test_analyze_detection(): # Test detection of Analyze, Nifti1 and Nifti2 # Algorithm is as described in loadsave:which_analyze_type def wat(hdr): return nils.which_analyze_type(hdr.binaryblock) n1_hdr = Nifti1Header(b'\0' * 348, check=False) assert_equal(wat(n1_hdr), None) n1_hdr['sizeof_hdr'] = 540 assert_equal(wat(n1_hdr), 'nifti2') assert_equal(wat(n1_hdr.as_byteswapped()), 'nifti2') n1_hdr['sizeof_hdr'] = 348 assert_equal(wat(n1_hdr), 'analyze') assert_equal(wat(n1_hdr.as_byteswapped()), 'analyze') n1_hdr['magic'] = b'n+1' assert_equal(wat(n1_hdr), 'nifti1') assert_equal(wat(n1_hdr.as_byteswapped()), 'nifti1') n1_hdr['magic'] = b'ni1' assert_equal(wat(n1_hdr), 'nifti1') assert_equal(wat(n1_hdr.as_byteswapped()), 'nifti1') # Doesn't matter what magic is if it's not a nifti1 magic n1_hdr['magic'] = b'ni2' assert_equal(wat(n1_hdr), 'analyze') n1_hdr['sizeof_hdr'] = 0 n1_hdr['magic'] = b'' assert_equal(wat(n1_hdr), None) n1_hdr['magic'] = 'n+1' assert_equal(wat(n1_hdr), 'nifti1') n1_hdr['magic'] = 'ni1' assert_equal(wat(n1_hdr), 'nifti1') def test_guessed_image_type(): # Test whether we can guess the image type from example files assert_equal(nils.guessed_image_type( pjoin(DATA_PATH, 'example4d.nii.gz')), Nifti1Image) assert_equal(nils.guessed_image_type( pjoin(DATA_PATH, 'nifti1.hdr')), Nifti1Pair) assert_equal(nils.guessed_image_type( pjoin(DATA_PATH, 'example_nifti2.nii.gz')), Nifti2Image) assert_equal(nils.guessed_image_type( pjoin(DATA_PATH, 'nifti2.hdr')), Nifti2Pair) assert_equal(nils.guessed_image_type( pjoin(DATA_PATH, 'tiny.mnc')), Minc1Image) assert_equal(nils.guessed_image_type( pjoin(DATA_PATH, 'small.mnc')), Minc2Image) assert_equal(nils.guessed_image_type( pjoin(DATA_PATH, 'test.mgz')), MGHImage) assert_equal(nils.guessed_image_type( pjoin(DATA_PATH, 'analyze.hdr')), Spm2AnalyzeImage) def test_fail_save(): with InTemporaryDirectory(): dataobj = np.ones((10, 10, 10), dtype=np.float16) affine = np.eye(4, dtype=np.float32) img = SpatialImage(dataobj, affine) # Fails because float16 is not supported. with assert_raises(AttributeError): nils.save(img, 'foo.nii.gz') del img

38.440476

79

0.650743

) assert_true(isinstance(re_img2, spm2.Spm2AnalyzeImage)) assert_array_equal(re_img2.get_data(), data) assert_array_equal(re_img2.affine, affine) del re_img2 spm99.save(img, sifn) re_img3 = nils.load(sifn) assert_true(isinstance(re_img3, spm99.Spm99AnalyzeImage)) assert_array_equal(re_img3.get_data(), data) assert_array_equal(re_img3.affine, affine) ni1.save(re_img3, nifn) del re_img3 re_img = nils.load(nifn) assert_true(isinstance(re_img, ni1.Nifti1Image)) assert_array_equal(re_img.get_data(), data) assert_array_equal(re_img.affine, affine) del re_img def test_two_to_one(): # test going from two to one file in save shape = (2, 4, 6) npt = np.float32 data = np.arange(np.prod(shape), dtype=npt).reshape(shape) affine = np.diag([1, 2, 3, 1]) affine[:3, 3] = [3, 2, 1] # single file format img = ni1.Nifti1Image(data, affine) assert_equal(img.header['magic'], b'n+1') str_io = BytesIO() img.file_map['image'].fileobj = str_io # check that the single format vox offset stays at zero img.to_file_map() assert_equal(img.header['magic'], b'n+1') assert_equal(img.header['vox_offset'], 0) # make a new pair image, with the single image header pimg = ni1.Nifti1Pair(data, affine, img.header) isio = BytesIO() hsio = BytesIO() pimg.file_map['image'].fileobj = isio pimg.file_map['header'].fileobj = hsio pimg.to_file_map() # the offset stays at zero (but is 352 on disk) assert_equal(pimg.header['magic'], b'ni1') assert_equal(pimg.header['vox_offset'], 0) assert_array_equal(pimg.get_data(), data) # same for from_image, going from single image to pair format ana_img = ana.AnalyzeImage.from_image(img) assert_equal(ana_img.header['vox_offset'], 0) # back to the single image, save it again to a stringio str_io = BytesIO() img.file_map['image'].fileobj = str_io img.to_file_map() assert_equal(img.header['vox_offset'], 0) aimg = ana.AnalyzeImage.from_image(img) assert_equal(aimg.header['vox_offset'], 0) aimg = spm99.Spm99AnalyzeImage.from_image(img) assert_equal(aimg.header['vox_offset'], 0) aimg = spm2.Spm2AnalyzeImage.from_image(img) assert_equal(aimg.header['vox_offset'], 0) nfimg = ni1.Nifti1Pair.from_image(img) assert_equal(nfimg.header['vox_offset'], 0) # now set the vox offset directly hdr = nfimg.header hdr['vox_offset'] = 16 assert_equal(nfimg.header['vox_offset'], 16) # check it gets properly set by the nifti single image nfimg = ni1.Nifti1Image.from_image(img) assert_equal(nfimg.header['vox_offset'], 0) def test_negative_load_save(): shape = (1, 2, 5) data = np.arange(10).reshape(shape) - 10.0 affine = np.eye(4) hdr = ni1.Nifti1Header() hdr.set_data_dtype(np.int16) img = Nifti1Image(data, affine, hdr) str_io = BytesIO() img.file_map['image'].fileobj = str_io img.to_file_map() str_io.seek(0) re_img = Nifti1Image.from_file_map(img.file_map) assert_array_almost_equal(re_img.get_data(), data, 4) def test_filename_save(): # This is to test the logic in the load and save routines, relating # extensions to filetypes # Tuples of class, ext, loadedclass inklass_ext_loadklasses = ( (Nifti1Image, '.nii', Nifti1Image), (Nifti2Image, '.nii', Nifti2Image), (Nifti1Pair, '.nii', Nifti1Image), (Nifti2Pair, '.nii', Nifti2Image), (Nifti1Image, '.img', Nifti1Pair), (Nifti2Image, '.img', Nifti2Pair), (Nifti1Pair, '.img', Nifti1Pair), (Nifti2Pair, '.img', Nifti2Pair), (Nifti1Image, '.hdr', Nifti1Pair), (Nifti2Image, '.hdr', Nifti2Pair), (Nifti1Pair, '.hdr', Nifti1Pair), (Nifti2Pair, '.hdr', Nifti2Pair), (Minc1Image, '.nii', Nifti1Image), (Minc1Image, '.img', Nifti1Pair), (Spm2AnalyzeImage, '.nii', Nifti1Image), (Spm2AnalyzeImage, '.img', Spm2AnalyzeImage), (Spm99AnalyzeImage, '.nii', Nifti1Image), (Spm99AnalyzeImage, '.img', Spm2AnalyzeImage), (AnalyzeImage, '.nii', Nifti1Image), (AnalyzeImage, '.img', Spm2AnalyzeImage), ) shape = (2, 4, 6) affine = np.diag([1, 2, 3, 1]) data = np.arange(np.prod(shape), dtype='f4').reshape(shape) for inklass, out_ext, loadklass in inklass_ext_loadklasses: if not have_scipy: # We can't load a SPM analyze type without scipy. These types have if ('mat', '.mat') in loadklass.files_types: continue img = inklass(data, affine) try: pth = mkdtemp() fname = pjoin(pth, 'image' + out_ext) nils.save(img, fname) rt_img = nils.load(fname) assert_array_almost_equal(rt_img.get_data(), data) assert_true(type(rt_img) is loadklass) # delete image to allow file close. Otherwise windows # raises an error when trying to delete the directory del rt_img finally: shutil.rmtree(pth) def test_analyze_detection(): # Test detection of Analyze, Nifti1 and Nifti2 # Algorithm is as described in loadsave:which_analyze_type def wat(hdr): return nils.which_analyze_type(hdr.binaryblock) n1_hdr = Nifti1Header(b'\0' * 348, check=False) assert_equal(wat(n1_hdr), None) n1_hdr['sizeof_hdr'] = 540 assert_equal(wat(n1_hdr), 'nifti2') assert_equal(wat(n1_hdr.as_byteswapped()), 'nifti2') n1_hdr['sizeof_hdr'] = 348 assert_equal(wat(n1_hdr), 'analyze') assert_equal(wat(n1_hdr.as_byteswapped()), 'analyze') n1_hdr['magic'] = b'n+1' assert_equal(wat(n1_hdr), 'nifti1') assert_equal(wat(n1_hdr.as_byteswapped()), 'nifti1') n1_hdr['magic'] = b'ni1' assert_equal(wat(n1_hdr), 'nifti1') assert_equal(wat(n1_hdr.as_byteswapped()), 'nifti1') # Doesn't matter what magic is if it's not a nifti1 magic n1_hdr['magic'] = b'ni2' assert_equal(wat(n1_hdr), 'analyze') n1_hdr['sizeof_hdr'] = 0 n1_hdr['magic'] = b'' assert_equal(wat(n1_hdr), None) n1_hdr['magic'] = 'n+1' assert_equal(wat(n1_hdr), 'nifti1') n1_hdr['magic'] = 'ni1' assert_equal(wat(n1_hdr), 'nifti1') def test_guessed_image_type(): # Test whether we can guess the image type from example files assert_equal(nils.guessed_image_type( pjoin(DATA_PATH, 'example4d.nii.gz')), Nifti1Image) assert_equal(nils.guessed_image_type( pjoin(DATA_PATH, 'nifti1.hdr')), Nifti1Pair) assert_equal(nils.guessed_image_type( pjoin(DATA_PATH, 'example_nifti2.nii.gz')), Nifti2Image) assert_equal(nils.guessed_image_type( pjoin(DATA_PATH, 'nifti2.hdr')), Nifti2Pair) assert_equal(nils.guessed_image_type( pjoin(DATA_PATH, 'tiny.mnc')), Minc1Image) assert_equal(nils.guessed_image_type( pjoin(DATA_PATH, 'small.mnc')), Minc2Image) assert_equal(nils.guessed_image_type( pjoin(DATA_PATH, 'test.mgz')), MGHImage) assert_equal(nils.guessed_image_type( pjoin(DATA_PATH, 'analyze.hdr')), Spm2AnalyzeImage) def test_fail_save(): with InTemporaryDirectory(): dataobj = np.ones((10, 10, 10), dtype=np.float16) affine = np.eye(4, dtype=np.float32) img = SpatialImage(dataobj, affine) # Fails because float16 is not supported. with assert_raises(AttributeError): nils.save(img, 'foo.nii.gz') del img

true

f7318a7c9b9961d14143aebdcada5b709c5812c1

2,945

py

Python

tests/integration/qbs/test_qbs_submission_data.py

nealedj/eq-survey-runner

b8e6cddae6068f6c8fd60e21d31d58aaa79bbb34

[ "MIT" ]

null

tests/integration/qbs/test_qbs_submission_data.py

nealedj/eq-survey-runner

b8e6cddae6068f6c8fd60e21d31d58aaa79bbb34

[ "MIT" ]

1

2018-11-05T12:00:51.000Z

tests/integration/qbs/test_qbs_submission_data.py

nealedj/eq-survey-runner

b8e6cddae6068f6c8fd60e21d31d58aaa79bbb34

[ "MIT" ]

null

from tests.integration.integration_test_case import IntegrationTestCase class TestQbsSubmissionData(IntegrationTestCase): def test_submission_data_2_0001(self): self.submission_data('2', '0001') def submission_data(self, eq_id, form_type_id): self.launchSurvey(eq_id, form_type_id, roles=['dumper']) # We are on the introduction page self.assertInPage('>Start survey<') self.assertInPage('Quarterly Business Survey') # We proceed to the questionnaire self.post(action='start_questionnaire') # We are in the Questionnaire self.assertInPage('>Quarterly Business Survey</') self.assertInPage('what was the number of employees for Integration Tests?') self.assertInPage('>Save and continue<') # When I submit answers self.post(post_data={'number-of-employees-total': '10'}) self.post(post_data={'number-of-employees-male-more-30-hours': '1', 'number-of-employees-male-less-30-hours': '2', 'number-of-employees-female-more-30-hours': '3', 'number-of-employees-female-less-30-hours': '4'}) # There are no validation errors (we're on the summary screen) self.assertInUrl('summary') self.assertInPage('>Quarterly Business Survey</') self.assertInPage('>Check your answers and submit<') self.assertInPage('You can check your answers below') self.assertInPage('>Submit answers<') # And the JSON response contains the data I submitted actual = self.dumpSubmission() expected = { 'submission': { 'origin': 'uk.gov.ons.edc.eq', 'started_at': actual['submission']['started_at'], 'submitted_at': actual['submission']['submitted_at'], 'case_id': actual['submission']['case_id'], 'collection': { 'exercise_sid': '789', 'period': '201604', 'instrument_id': '0001' }, 'survey_id': '139', 'flushed': False, 'tx_id': actual['submission']['tx_id'], 'data': { '50': '10', '51': '1', '52': '2', '53': '3', '54': '4' }, 'type': 'uk.gov.ons.edc.eq:surveyresponse', 'version': '0.0.1', 'metadata': { 'ref_period_end_date': '2016-04-30', 'ref_period_start_date': '2016-04-01', 'ru_ref': '123456789012A', 'user_id': 'integration-test' } } } # Enable full dictionary diffs on test failure self.maxDiff = None self.assertDictEqual(actual, expected)

38.75

84

0.529372

from tests.integration.integration_test_case import IntegrationTestCase class TestQbsSubmissionData(IntegrationTestCase): def test_submission_data_2_0001(self): self.submission_data('2', '0001') def submission_data(self, eq_id, form_type_id): self.launchSurvey(eq_id, form_type_id, roles=['dumper']) self.assertInPage('>Start survey<') self.assertInPage('Quarterly Business Survey') self.post(action='start_questionnaire') self.assertInPage('>Quarterly Business Survey</') self.assertInPage('what was the number of employees for Integration Tests?') self.assertInPage('>Save and continue<') self.post(post_data={'number-of-employees-total': '10'}) self.post(post_data={'number-of-employees-male-more-30-hours': '1', 'number-of-employees-male-less-30-hours': '2', 'number-of-employees-female-more-30-hours': '3', 'number-of-employees-female-less-30-hours': '4'}) self.assertInUrl('summary') self.assertInPage('>Quarterly Business Survey</') self.assertInPage('>Check your answers and submit<') self.assertInPage('You can check your answers below') self.assertInPage('>Submit answers<') # And the JSON response contains the data I submitted actual = self.dumpSubmission() expected = { 'submission': { 'origin': 'uk.gov.ons.edc.eq', 'started_at': actual['submission']['started_at'], 'submitted_at': actual['submission']['submitted_at'], 'case_id': actual['submission']['case_id'], 'collection': { 'exercise_sid': '789', 'period': '201604', 'instrument_id': '0001' }, 'survey_id': '139', 'flushed': False, 'tx_id': actual['submission']['tx_id'], 'data': { '50': '10', '51': '1', '52': '2', '53': '3', '54': '4' }, 'type': 'uk.gov.ons.edc.eq:surveyresponse', 'version': '0.0.1', 'metadata': { 'ref_period_end_date': '2016-04-30', 'ref_period_start_date': '2016-04-01', 'ru_ref': '123456789012A', 'user_id': 'integration-test' } } } # Enable full dictionary diffs on test failure self.maxDiff = None self.assertDictEqual(actual, expected)

true

f7318bf8ab84bb950ae4d28f761bd4399b07f385

46,765

py

Python

src/transformers/configuration_utils.py

elusenji/transformers

af14c61973effd8b8077ac61b3f24bdd4a632f25

[ "Apache-2.0" ]

3

2022-01-15T08:06:07.000Z

2022-03-10T07:13:18.000Z

src/transformers/configuration_utils.py

arron1227/transformers

b18dfd95e1f60ae65a959a7b255fc06522170d1b

[ "Apache-2.0" ]

null

src/transformers/configuration_utils.py

arron1227/transformers

b18dfd95e1f60ae65a959a7b255fc06522170d1b

[ "Apache-2.0" ]

null

# coding=utf-8 # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Configuration base class and utilities.""" import copy import json import os import re import warnings from typing import Any, Dict, List, Optional, Tuple, Union from packaging import version from requests import HTTPError from . import __version__ from .dynamic_module_utils import custom_object_save from .utils import ( CONFIG_NAME, HUGGINGFACE_CO_RESOLVE_ENDPOINT, EntryNotFoundError, PushToHubMixin, RepositoryNotFoundError, RevisionNotFoundError, cached_path, copy_func, hf_bucket_url, is_offline_mode, is_remote_url, is_torch_available, logging, ) logger = logging.get_logger(__name__) _re_configuration_file = re.compile(r"config\.(.*)\.json") class PretrainedConfig(PushToHubMixin): r""" Base class for all configuration classes. Handles a few parameters common to all models' configurations as well as methods for loading/downloading/saving configurations. <Tip> A configuration file can be loaded and saved to disk. Loading the configuration file and using this file to initialize a model does **not** load the model weights. It only affects the model's configuration. </Tip> Class attributes (overridden by derived classes): - **model_type** (`str`) -- An identifier for the model type, serialized into the JSON file, and used to recreate the correct object in [`~transformers.AutoConfig`]. - **is_composition** (`bool`) -- Whether the config class is composed of multiple sub-configs. In this case the config has to be initialized from two or more configs of type [`~transformers.PretrainedConfig`] like: [`~transformers.EncoderDecoderConfig`] or [`~RagConfig`]. - **keys_to_ignore_at_inference** (`List[str]`) -- A list of keys to ignore by default when looking at dictionary outputs of the model during inference. - **attribute_map** (`Dict[str, str]`) -- A dict that maps model specific attribute names to the standardized naming of attributes. Common attributes (present in all subclasses): - **vocab_size** (`int`) -- The number of tokens in the vocabulary, which is also the first dimension of the embeddings matrix (this attribute may be missing for models that don't have a text modality like ViT). - **hidden_size** (`int`) -- The hidden size of the model. - **num_attention_heads** (`int`) -- The number of attention heads used in the multi-head attention layers of the model. - **num_hidden_layers** (`int`) -- The number of blocks in the model. Arg: name_or_path (`str`, *optional*, defaults to `""`): Store the string that was passed to [`PreTrainedModel.from_pretrained`] or [`TFPreTrainedModel.from_pretrained`] as `pretrained_model_name_or_path` if the configuration was created with such a method. output_hidden_states (`bool`, *optional*, defaults to `False`): Whether or not the model should return all hidden-states. output_attentions (`bool`, *optional*, defaults to `False`): Whether or not the model should returns all attentions. return_dict (`bool`, *optional*, defaults to `True`): Whether or not the model should return a [`~transformers.utils.ModelOutput`] instead of a plain tuple. is_encoder_decoder (`bool`, *optional*, defaults to `False`): Whether the model is used as an encoder/decoder or not. is_decoder (`bool`, *optional*, defaults to `False`): Whether the model is used as decoder or not (in which case it's used as an encoder). cross_attention_hidden_size** (`bool`, *optional*): The hidden size of the cross-attention layer in case the model is used as a decoder in an encoder-decoder setting and the cross-attention hidden dimension differs from `self.config.hidden_size`. add_cross_attention (`bool`, *optional*, defaults to `False`): Whether cross-attention layers should be added to the model. Note, this option is only relevant for models that can be used as decoder models within the [`EncoderDecoderModel`] class, which consists of all models in `AUTO_MODELS_FOR_CAUSAL_LM`. tie_encoder_decoder (`bool`, *optional*, defaults to `False`): Whether all encoder weights should be tied to their equivalent decoder weights. This requires the encoder and decoder model to have the exact same parameter names. prune_heads (`Dict[int, List[int]]`, *optional*, defaults to `{}`): Pruned heads of the model. The keys are the selected layer indices and the associated values, the list of heads to prune in said layer. For instance `{1: [0, 2], 2: [2, 3]}` will prune heads 0 and 2 on layer 1 and heads 2 and 3 on layer 2. chunk_size_feed_forward (`int`, *optional*, defaults to `0`): The chunk size of all feed forward layers in the residual attention blocks. A chunk size of `0` means that the feed forward layer is not chunked. A chunk size of n means that the feed forward layer processes `n` < sequence_length embeddings at a time. For more information on feed forward chunking, see [How does Feed Forward Chunking work?](../glossary.html#feed-forward-chunking). > Parameters for sequence generation max_length (`int`, *optional*, defaults to 20): Maximum length that will be used by default in the `generate` method of the model. min_length (`int`, *optional*, defaults to 10): Minimum length that will be used by default in the `generate` method of the model. do_sample (`bool`, *optional*, defaults to `False`): Flag that will be used by default in the `generate` method of the model. Whether or not to use sampling ; use greedy decoding otherwise. early_stopping (`bool`, *optional*, defaults to `False`): Flag that will be used by default in the `generate` method of the model. Whether to stop the beam search when at least `num_beams` sentences are finished per batch or not. num_beams (`int`, *optional*, defaults to 1): Number of beams for beam search that will be used by default in the `generate` method of the model. 1 means no beam search. num_beam_groups (`int`, *optional*, defaults to 1): Number of groups to divide `num_beams` into in order to ensure diversity among different groups of beams that will be used by default in the `generate` method of the model. 1 means no group beam search. diversity_penalty (`float`, *optional*, defaults to 0.0): Value to control diversity for group beam search. that will be used by default in the `generate` method of the model. 0 means no diversity penalty. The higher the penalty, the more diverse are the outputs. temperature (`float`, *optional*, defaults to 1): The value used to module the next token probabilities that will be used by default in the `generate` method of the model. Must be strictly positive. top_k (`int`, *optional*, defaults to 50): Number of highest probability vocabulary tokens to keep for top-k-filtering that will be used by default in the `generate` method of the model. top_p (`float`, *optional*, defaults to 1): Value that will be used by default in the `generate` method of the model for `top_p`. If set to float < 1, only the most probable tokens with probabilities that add up to `top_p` or higher are kept for generation. repetition_penalty (`float`, *optional*, defaults to 1): Parameter for repetition penalty that will be used by default in the `generate` method of the model. 1.0 means no penalty. length_penalty (`float`, *optional*, defaults to 1): Exponential penalty to the length that will be used by default in the `generate` method of the model. no_repeat_ngram_size (`int`, *optional*, defaults to 0) -- Value that will be used by default in the `generate` method of the model for `no_repeat_ngram_size`. If set to int > 0, all ngrams of that size can only occur once. encoder_no_repeat_ngram_size (`int`, *optional*, defaults to 0) -- Value that will be used by default in the `generate` method of the model for `encoder_no_repeat_ngram_size`. If set to int > 0, all ngrams of that size that occur in the `encoder_input_ids` cannot occur in the `decoder_input_ids`. bad_words_ids (`List[int]`, *optional*): List of token ids that are not allowed to be generated that will be used by default in the `generate` method of the model. In order to get the tokens of the words that should not appear in the generated text, use `tokenizer.encode(bad_word, add_prefix_space=True)`. num_return_sequences (`int`, *optional*, defaults to 1): Number of independently computed returned sequences for each element in the batch that will be used by default in the `generate` method of the model. output_scores (`bool`, *optional*, defaults to `False`): Whether the model should return the logits when used for generation. return_dict_in_generate (`bool`, *optional*, defaults to `False`): Whether the model should return a [`~transformers.utils.ModelOutput`] instead of a `torch.LongTensor`. forced_bos_token_id (`int`, *optional*): The id of the token to force as the first generated token after the `decoder_start_token_id`. Useful for multilingual models like [mBART](../model_doc/mbart) where the first generated token needs to be the target language token. forced_eos_token_id (`int`, *optional*): The id of the token to force as the last generated token when `max_length` is reached. remove_invalid_values (`bool`, *optional*): Whether to remove possible _nan_ and _inf_ outputs of the model to prevent the generation method to crash. Note that using `remove_invalid_values` can slow down generation. > Parameters for fine-tuning tasks architectures (`List[str]`, *optional*): Model architectures that can be used with the model pretrained weights. finetuning_task (`str`, *optional*): Name of the task used to fine-tune the model. This can be used when converting from an original (TensorFlow or PyTorch) checkpoint. id2label (`Dict[int, str]`, *optional*): A map from index (for instance prediction index, or target index) to label. label2id (`Dict[str, int]`, *optional*): A map from label to index for the model. num_labels (`int`, *optional*): Number of labels to use in the last layer added to the model, typically for a classification task. task_specific_params (`Dict[str, Any]`, *optional*): Additional keyword arguments to store for the current task. problem_type (`str`, *optional*): Problem type for `XxxForSequenceClassification` models. Can be one of `"regression"`, `"single_label_classification"` or `"multi_label_classification"`. > Parameters linked to the tokenizer tokenizer_class (`str`, *optional*): The name of the associated tokenizer class to use (if none is set, will use the tokenizer associated to the model by default). prefix (`str`, *optional*): A specific prompt that should be added at the beginning of each text before calling the model. bos_token_id (`int`, *optional*): The id of the _beginning-of-stream_ token. pad_token_id (`int`, *optional*): The id of the _padding_ token. eos_token_id (`int`, *optional*): The id of the _end-of-stream_ token. decoder_start_token_id (`int`, *optional*): If an encoder-decoder model starts decoding with a different token than _bos_, the id of that token. sep_token_id (`int`, *optional*): The id of the _separation_ token. > PyTorch specific parameters torchscript (`bool`, *optional*, defaults to `False`): Whether or not the model should be used with Torchscript. tie_word_embeddings (`bool`, *optional*, defaults to `True`): Whether the model's input and output word embeddings should be tied. Note that this is only relevant if the model has a output word embedding layer. torch_dtype (`str`, *optional*): The `dtype` of the weights. This attribute can be used to initialize the model to a non-default `dtype` (which is normally `float32`) and thus allow for optimal storage allocation. For example, if the saved model is `float16`, ideally we want to load it back using the minimal amount of memory needed to load `float16` weights. Since the config object is stored in plain text, this attribute contains just the floating type string without the `torch.` prefix. For example, for `torch.float16` ``torch_dtype` is the `"float16"` string. This attribute is currently not being used during model loading time, but this may change in the future versions. But we can already start preparing for the future by saving the dtype with save_pretrained. > TensorFlow specific parameters use_bfloat16 (`bool`, *optional*, defaults to `False`): Whether or not the model should use BFloat16 scalars (only used by some TensorFlow models). """ model_type: str = "" is_composition: bool = False attribute_map: Dict[str, str] = {} _auto_class: Optional[str] = None def __setattr__(self, key, value): if key in super().__getattribute__("attribute_map"): key = super().__getattribute__("attribute_map")[key] super().__setattr__(key, value) def __getattribute__(self, key): if key != "attribute_map" and key in super().__getattribute__("attribute_map"): key = super().__getattribute__("attribute_map")[key] return super().__getattribute__(key) def __init__(self, **kwargs): # Attributes with defaults self.return_dict = kwargs.pop("return_dict", True) self.output_hidden_states = kwargs.pop("output_hidden_states", False) self.output_attentions = kwargs.pop("output_attentions", False) self.torchscript = kwargs.pop("torchscript", False) # Only used by PyTorch models self.torch_dtype = kwargs.pop("torch_dtype", None) # Only used by PyTorch models self.use_bfloat16 = kwargs.pop("use_bfloat16", False) self.pruned_heads = kwargs.pop("pruned_heads", {}) self.tie_word_embeddings = kwargs.pop( "tie_word_embeddings", True ) # Whether input and output word embeddings should be tied for all MLM, LM and Seq2Seq models. # Is decoder is used in encoder-decoder models to differentiate encoder from decoder self.is_encoder_decoder = kwargs.pop("is_encoder_decoder", False) self.is_decoder = kwargs.pop("is_decoder", False) self.cross_attention_hidden_size = kwargs.pop("cross_attention_hidden_size", None) self.add_cross_attention = kwargs.pop("add_cross_attention", False) self.tie_encoder_decoder = kwargs.pop("tie_encoder_decoder", False) # Parameters for sequence generation self.max_length = kwargs.pop("max_length", 20) self.min_length = kwargs.pop("min_length", 0) self.do_sample = kwargs.pop("do_sample", False) self.early_stopping = kwargs.pop("early_stopping", False) self.num_beams = kwargs.pop("num_beams", 1) self.num_beam_groups = kwargs.pop("num_beam_groups", 1) self.diversity_penalty = kwargs.pop("diversity_penalty", 0.0) self.temperature = kwargs.pop("temperature", 1.0) self.top_k = kwargs.pop("top_k", 50) self.top_p = kwargs.pop("top_p", 1.0) self.typical_p = kwargs.pop("typical_p", 1.0) self.repetition_penalty = kwargs.pop("repetition_penalty", 1.0) self.length_penalty = kwargs.pop("length_penalty", 1.0) self.no_repeat_ngram_size = kwargs.pop("no_repeat_ngram_size", 0) self.encoder_no_repeat_ngram_size = kwargs.pop("encoder_no_repeat_ngram_size", 0) self.bad_words_ids = kwargs.pop("bad_words_ids", None) self.num_return_sequences = kwargs.pop("num_return_sequences", 1) self.chunk_size_feed_forward = kwargs.pop("chunk_size_feed_forward", 0) self.output_scores = kwargs.pop("output_scores", False) self.return_dict_in_generate = kwargs.pop("return_dict_in_generate", False) self.forced_bos_token_id = kwargs.pop("forced_bos_token_id", None) self.forced_eos_token_id = kwargs.pop("forced_eos_token_id", None) self.remove_invalid_values = kwargs.pop("remove_invalid_values", False) self.exponential_decay_length_penalty = kwargs.pop("exponential_decay_length_penalty", None) # Fine-tuning task arguments self.architectures = kwargs.pop("architectures", None) self.finetuning_task = kwargs.pop("finetuning_task", None) self.id2label = kwargs.pop("id2label", None) self.label2id = kwargs.pop("label2id", None) if self.id2label is not None: kwargs.pop("num_labels", None) self.id2label = dict((int(key), value) for key, value in self.id2label.items()) # Keys are always strings in JSON so convert ids to int here. else: self.num_labels = kwargs.pop("num_labels", 2) if self.torch_dtype is not None and isinstance(self.torch_dtype, str): # we will start using self.torch_dtype in v5, but to be consistent with # from_pretrained's torch_dtype arg convert it to an actual torch.dtype object if is_torch_available(): import torch self.torch_dtype = getattr(torch, self.torch_dtype) # Tokenizer arguments TODO: eventually tokenizer and models should share the same config self.tokenizer_class = kwargs.pop("tokenizer_class", None) self.prefix = kwargs.pop("prefix", None) self.bos_token_id = kwargs.pop("bos_token_id", None) self.pad_token_id = kwargs.pop("pad_token_id", None) self.eos_token_id = kwargs.pop("eos_token_id", None) self.sep_token_id = kwargs.pop("sep_token_id", None) self.decoder_start_token_id = kwargs.pop("decoder_start_token_id", None) # task specific arguments self.task_specific_params = kwargs.pop("task_specific_params", None) # regression / multi-label classification self.problem_type = kwargs.pop("problem_type", None) allowed_problem_types = ("regression", "single_label_classification", "multi_label_classification") if self.problem_type is not None and self.problem_type not in allowed_problem_types: raise ValueError( f"The config parameter `problem_type` was not understood: received {self.problem_type} " "but only 'regression', 'single_label_classification' and 'multi_label_classification' are valid." ) # TPU arguments if kwargs.pop("xla_device", None) is not None: logger.warning( "The `xla_device` argument has been deprecated in v4.4.0 of Transformers. It is ignored and you can " "safely remove it from your `config.json` file." ) # Name or path to the pretrained checkpoint self._name_or_path = str(kwargs.pop("name_or_path", "")) # Drop the transformers version info self.transformers_version = kwargs.pop("transformers_version", None) # Deal with gradient checkpointing if kwargs.get("gradient_checkpointing", False): warnings.warn( "Passing `gradient_checkpointing` to a config initialization is deprecated and will be removed in v5 " "Transformers. Using `model.gradient_checkpointing_enable()` instead, or if you are using the " "`Trainer` API, pass `gradient_checkpointing=True` in your `TrainingArguments`." ) # Additional attributes without default values for key, value in kwargs.items(): try: setattr(self, key, value) except AttributeError as err: logger.error(f"Can't set {key} with value {value} for {self}") raise err @property def name_or_path(self) -> str: return getattr(self, "_name_or_path", None) @name_or_path.setter def name_or_path(self, value): self._name_or_path = str(value) # Make sure that name_or_path is a string (for JSON encoding) @property def use_return_dict(self) -> bool: """ `bool`: Whether or not return [`~utils.ModelOutput`] instead of tuples. """ # If torchscript is set, force `return_dict=False` to avoid jit errors return self.return_dict and not self.torchscript @property def num_labels(self) -> int: """ `int`: The number of labels for classification models. """ return len(self.id2label) @num_labels.setter def num_labels(self, num_labels: int): if not hasattr(self, "id2label") or self.id2label is None or len(self.id2label) != num_labels: self.id2label = {i: f"LABEL_{i}" for i in range(num_labels)} self.label2id = dict(zip(self.id2label.values(), self.id2label.keys())) def save_pretrained(self, save_directory: Union[str, os.PathLike], push_to_hub: bool = False, **kwargs): """ Save a configuration object to the directory `save_directory`, so that it can be re-loaded using the [`~PretrainedConfig.from_pretrained`] class method. Args: save_directory (`str` or `os.PathLike`): Directory where the configuration JSON file will be saved (will be created if it does not exist). push_to_hub (`bool`, *optional*, defaults to `False`): Whether or not to push your model to the Hugging Face model hub after saving it. <Tip warning={true}> Using `push_to_hub=True` will synchronize the repository you are pushing to with `save_directory`, which requires `save_directory` to be a local clone of the repo you are pushing to if it's an existing folder. Pass along `temp_dir=True` to use a temporary directory instead. </Tip> kwargs: Additional key word arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method. """ if os.path.isfile(save_directory): raise AssertionError(f"Provided path ({save_directory}) should be a directory, not a file") if push_to_hub: commit_message = kwargs.pop("commit_message", None) repo = self._create_or_get_repo(save_directory, **kwargs) os.makedirs(save_directory, exist_ok=True) # If we have a custom config, we copy the file defining it in the folder and set the attributes so it can be # loaded from the Hub. if self._auto_class is not None: custom_object_save(self, save_directory, config=self) # If we save using the predefined names, we can load using `from_pretrained` output_config_file = os.path.join(save_directory, CONFIG_NAME) self.to_json_file(output_config_file, use_diff=True) logger.info(f"Configuration saved in {output_config_file}") if push_to_hub: url = self._push_to_hub(repo, commit_message=commit_message) logger.info(f"Configuration pushed to the hub in this commit: {url}") @classmethod def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig": r""" Instantiate a [`PretrainedConfig`] (or a derived class) from a pretrained model configuration. Args: pretrained_model_name_or_path (`str` or `os.PathLike`): This can be either: - a string, the *model id* of a pretrained model configuration hosted inside a model repo on huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a user or organization name, like `dbmdz/bert-base-german-cased`. - a path to a *directory* containing a configuration file saved using the [`~PretrainedConfig.save_pretrained`] method, e.g., `./my_model_directory/`. - a path or url to a saved configuration JSON *file*, e.g., `./my_model_directory/configuration.json`. cache_dir (`str` or `os.PathLike`, *optional*): Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. force_download (`bool`, *optional*, defaults to `False`): Whether or not to force to (re-)download the configuration files and override the cached versions if they exist. resume_download (`bool`, *optional*, defaults to `False`): Whether or not to delete incompletely received file. Attempts to resume the download if such a file exists. proxies (`Dict[str, str]`, *optional*): A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request. use_auth_token (`str` or *bool*, *optional*): The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated when running `transformers-cli login` (stored in `~/.huggingface`). revision (`str`, *optional*, defaults to `"main"`): The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git. return_unused_kwargs (`bool`, *optional*, defaults to `False`): If `False`, then this function returns just the final configuration object. If `True`, then this functions returns a `Tuple(config, unused_kwargs)` where *unused_kwargs* is a dictionary consisting of the key/value pairs whose keys are not configuration attributes: i.e., the part of `kwargs` which has not been used to update `config` and is otherwise ignored. kwargs (`Dict[str, Any]`, *optional*): The values in kwargs of any keys which are configuration attributes will be used to override the loaded values. Behavior concerning key/value pairs whose keys are *not* configuration attributes is controlled by the `return_unused_kwargs` keyword parameter. <Tip> Passing `use_auth_token=True` is required when you want to use a private model. </Tip> Returns: [`PretrainedConfig`]: The configuration object instantiated from this pretrained model. Examples: ```python # We can't instantiate directly the base class *PretrainedConfig* so let's show the examples on a # derived class: BertConfig config = BertConfig.from_pretrained( "bert-base-uncased" ) # Download configuration from huggingface.co and cache. config = BertConfig.from_pretrained( "./test/saved_model/" ) # E.g. config (or model) was saved using *save_pretrained('./test/saved_model/')* config = BertConfig.from_pretrained("./test/saved_model/my_configuration.json") config = BertConfig.from_pretrained("bert-base-uncased", output_attentions=True, foo=False) assert config.output_attentions == True config, unused_kwargs = BertConfig.from_pretrained( "bert-base-uncased", output_attentions=True, foo=False, return_unused_kwargs=True ) assert config.output_attentions == True assert unused_kwargs == {"foo": False} ```""" config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs) if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type: logger.warning( f"You are using a model of type {config_dict['model_type']} to instantiate a model of type " f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." ) return cls.from_dict(config_dict, **kwargs) @classmethod def get_config_dict( cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs ) -> Tuple[Dict[str, Any], Dict[str, Any]]: """ From a `pretrained_model_name_or_path`, resolve to a dictionary of parameters, to be used for instantiating a [`PretrainedConfig`] using `from_dict`. Parameters: pretrained_model_name_or_path (`str` or `os.PathLike`): The identifier of the pre-trained checkpoint from which we want the dictionary of parameters. Returns: `Tuple[Dict, Dict]`: The dictionary(ies) that will be used to instantiate the configuration object. """ original_kwargs = copy.deepcopy(kwargs) # Get config dict associated with the base config file config_dict, kwargs = cls._get_config_dict(pretrained_model_name_or_path, **kwargs) # That config file may point us toward another config file to use. if "configuration_files" in config_dict: configuration_file = get_configuration_file(config_dict["configuration_files"]) config_dict, kwargs = cls._get_config_dict( pretrained_model_name_or_path, _configuration_file=configuration_file, **original_kwargs ) return config_dict, kwargs @classmethod def _get_config_dict( cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs ) -> Tuple[Dict[str, Any], Dict[str, Any]]: cache_dir = kwargs.pop("cache_dir", None) force_download = kwargs.pop("force_download", False) resume_download = kwargs.pop("resume_download", False) proxies = kwargs.pop("proxies", None) use_auth_token = kwargs.pop("use_auth_token", None) local_files_only = kwargs.pop("local_files_only", False) revision = kwargs.pop("revision", None) from_pipeline = kwargs.pop("_from_pipeline", None) from_auto_class = kwargs.pop("_from_auto", False) user_agent = {"file_type": "config", "from_auto_class": from_auto_class} if from_pipeline is not None: user_agent["using_pipeline"] = from_pipeline if is_offline_mode() and not local_files_only: logger.info("Offline mode: forcing local_files_only=True") local_files_only = True pretrained_model_name_or_path = str(pretrained_model_name_or_path) if os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path): config_file = pretrained_model_name_or_path else: configuration_file = kwargs.pop("_configuration_file", CONFIG_NAME) if os.path.isdir(pretrained_model_name_or_path): config_file = os.path.join(pretrained_model_name_or_path, configuration_file) else: config_file = hf_bucket_url( pretrained_model_name_or_path, filename=configuration_file, revision=revision, mirror=None ) try: # Load from URL or cache if already cached resolved_config_file = cached_path( config_file, cache_dir=cache_dir, force_download=force_download, proxies=proxies, resume_download=resume_download, local_files_only=local_files_only, use_auth_token=use_auth_token, user_agent=user_agent, ) except RepositoryNotFoundError: raise EnvironmentError( f"{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier listed on " "'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a token having " "permission to this repo with `use_auth_token` or log in with `huggingface-cli login` and pass " "`use_auth_token=True`." ) except RevisionNotFoundError: raise EnvironmentError( f"{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for this " f"model name. Check the model page at 'https://huggingface.co/{pretrained_model_name_or_path}' for " "available revisions." ) except EntryNotFoundError: raise EnvironmentError( f"{pretrained_model_name_or_path} does not appear to have a file named {configuration_file}." ) except HTTPError as err: raise EnvironmentError( f"There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}" ) except ValueError: raise EnvironmentError( f"We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load this model, couldn't find it in the cached " f"files and it looks like {pretrained_model_name_or_path} is not the path to a directory containing a " "{configuration_file} file.\nCheckout your internet connection or see how to run the library in " "offline mode at 'https://huggingface.co/docs/transformers/installation#offline-mode'." ) except EnvironmentError: raise EnvironmentError( f"Can't load config for '{pretrained_model_name_or_path}'. If you were trying to load it from " "'https://huggingface.co/models', make sure you don't have a local directory with the same name. " f"Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory " f"containing a {configuration_file} file" ) try: # Load config dict config_dict = cls._dict_from_json_file(resolved_config_file) except (json.JSONDecodeError, UnicodeDecodeError): raise EnvironmentError( f"It looks like the config file at '{resolved_config_file}' is not a valid JSON file." ) if resolved_config_file == config_file: logger.info(f"loading configuration file {config_file}") else: logger.info(f"loading configuration file {config_file} from cache at {resolved_config_file}") return config_dict, kwargs @classmethod def from_dict(cls, config_dict: Dict[str, Any], **kwargs) -> "PretrainedConfig": """ Instantiates a [`PretrainedConfig`] from a Python dictionary of parameters. Args: config_dict (`Dict[str, Any]`): Dictionary that will be used to instantiate the configuration object. Such a dictionary can be retrieved from a pretrained checkpoint by leveraging the [`~PretrainedConfig.get_config_dict`] method. kwargs (`Dict[str, Any]`): Additional parameters from which to initialize the configuration object. Returns: [`PretrainedConfig`]: The configuration object instantiated from those parameters. """ return_unused_kwargs = kwargs.pop("return_unused_kwargs", False) config = cls(**config_dict) if hasattr(config, "pruned_heads"): config.pruned_heads = dict((int(key), value) for key, value in config.pruned_heads.items()) # Update config with kwargs if needed to_remove = [] for key, value in kwargs.items(): if hasattr(config, key): setattr(config, key, value) if key != "torch_dtype": to_remove.append(key) for key in to_remove: kwargs.pop(key, None) logger.info(f"Model config {config}") if return_unused_kwargs: return config, kwargs else: return config @classmethod def from_json_file(cls, json_file: Union[str, os.PathLike]) -> "PretrainedConfig": """ Instantiates a [`PretrainedConfig`] from the path to a JSON file of parameters. Args: json_file (`str` or `os.PathLike`): Path to the JSON file containing the parameters. Returns: [`PretrainedConfig`]: The configuration object instantiated from that JSON file. """ config_dict = cls._dict_from_json_file(json_file) return cls(**config_dict) @classmethod def _dict_from_json_file(cls, json_file: Union[str, os.PathLike]): with open(json_file, "r", encoding="utf-8") as reader: text = reader.read() return json.loads(text) def __eq__(self, other): return self.__dict__ == other.__dict__ def __repr__(self): return f"{self.__class__.__name__} {self.to_json_string()}" def to_diff_dict(self) -> Dict[str, Any]: """ Removes all attributes from config which correspond to the default config attributes for better readability and serializes to a Python dictionary. Returns: `Dict[str, Any]`: Dictionary of all the attributes that make up this configuration instance, """ config_dict = self.to_dict() # get the default config dict default_config_dict = PretrainedConfig().to_dict() # get class specific config dict class_config_dict = self.__class__().to_dict() if not self.is_composition else {} serializable_config_dict = {} # only serialize values that differ from the default config for key, value in config_dict.items(): if ( key not in default_config_dict or key == "transformers_version" or value != default_config_dict[key] or (key in class_config_dict and value != class_config_dict[key]) ): serializable_config_dict[key] = value self.dict_torch_dtype_to_str(serializable_config_dict) return serializable_config_dict def to_dict(self) -> Dict[str, Any]: """ Serializes this instance to a Python dictionary. Returns: `Dict[str, Any]`: Dictionary of all the attributes that make up this configuration instance. """ output = copy.deepcopy(self.__dict__) if hasattr(self.__class__, "model_type"): output["model_type"] = self.__class__.model_type if "_auto_class" in output: del output["_auto_class"] # Transformers version when serializing the model output["transformers_version"] = __version__ self.dict_torch_dtype_to_str(output) return output def to_json_string(self, use_diff: bool = True) -> str: """ Serializes this instance to a JSON string. Args: use_diff (`bool`, *optional*, defaults to `True`): If set to `True`, only the difference between the config instance and the default `PretrainedConfig()` is serialized to JSON string. Returns: `str`: String containing all the attributes that make up this configuration instance in JSON format. """ if use_diff is True: config_dict = self.to_diff_dict() else: config_dict = self.to_dict() return json.dumps(config_dict, indent=2, sort_keys=True) + "\n" def to_json_file(self, json_file_path: Union[str, os.PathLike], use_diff: bool = True): """ Save this instance to a JSON file. Args: json_file_path (`str` or `os.PathLike`): Path to the JSON file in which this configuration instance's parameters will be saved. use_diff (`bool`, *optional*, defaults to `True`): If set to `True`, only the difference between the config instance and the default `PretrainedConfig()` is serialized to JSON file. """ with open(json_file_path, "w", encoding="utf-8") as writer: writer.write(self.to_json_string(use_diff=use_diff)) def update(self, config_dict: Dict[str, Any]): """ Updates attributes of this class with attributes from `config_dict`. Args: config_dict (`Dict[str, Any]`): Dictionary of attributes that should be updated for this class. """ for key, value in config_dict.items(): setattr(self, key, value) def update_from_string(self, update_str: str): """ Updates attributes of this class with attributes from `update_str`. The expected format is ints, floats and strings as is, and for booleans use `true` or `false`. For example: "n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index" The keys to change have to already exist in the config object. Args: update_str (`str`): String with attributes that should be updated for this class. """ d = dict(x.split("=") for x in update_str.split(",")) for k, v in d.items(): if not hasattr(self, k): raise ValueError(f"key {k} isn't in the original config dict") old_v = getattr(self, k) if isinstance(old_v, bool): if v.lower() in ["true", "1", "y", "yes"]: v = True elif v.lower() in ["false", "0", "n", "no"]: v = False else: raise ValueError(f"can't derive true or false from {v} (key {k})") elif isinstance(old_v, int): v = int(v) elif isinstance(old_v, float): v = float(v) elif not isinstance(old_v, str): raise ValueError( f"You can only update int, float, bool or string values in the config, got {v} for key {k}" ) setattr(self, k, v) def dict_torch_dtype_to_str(self, d: Dict[str, Any]) -> None: """ Checks whether the passed dictionary and its nested dicts have a *torch_dtype* key and if it's not None, converts torch.dtype to a string of just the type. For example, `torch.float32` get converted into *"float32"* string, which can then be stored in the json format. """ if d.get("torch_dtype", None) is not None and not isinstance(d["torch_dtype"], str): d["torch_dtype"] = str(d["torch_dtype"]).split(".")[1] for value in d.values(): if isinstance(value, dict): self.dict_torch_dtype_to_str(value) @classmethod def register_for_auto_class(cls, auto_class="AutoConfig"): """ Register this class with a given auto class. This should only be used for custom configurations as the ones in the library are already mapped with `AutoConfig`. <Tip warning={true}> This API is experimental and may have some slight breaking changes in the next releases. </Tip> Args: auto_class (`str` or `type`, *optional*, defaults to `"AutoConfig"`): The auto class to register this new configuration with. """ if not isinstance(auto_class, str): auto_class = auto_class.__name__ import transformers.models.auto as auto_module if not hasattr(auto_module, auto_class): raise ValueError(f"{auto_class} is not a valid auto class.") cls._auto_class = auto_class def get_configuration_file(configuration_files: List[str]) -> str: """ Get the configuration file to use for this version of transformers. Args: configuration_files (`List[str]`): The list of available configuration files. Returns: `str`: The configuration file to use. """ configuration_files_map = {} for file_name in configuration_files: search = _re_configuration_file.search(file_name) if search is not None: v = search.groups()[0] configuration_files_map[v] = file_name available_versions = sorted(configuration_files_map.keys()) # Defaults to FULL_CONFIGURATION_FILE and then try to look at some newer versions. configuration_file = CONFIG_NAME transformers_version = version.parse(__version__) for v in available_versions: if version.parse(v) <= transformers_version: configuration_file = configuration_files_map[v] else: # No point going further since the versions are sorted. break return configuration_file PretrainedConfig.push_to_hub = copy_func(PretrainedConfig.push_to_hub) PretrainedConfig.push_to_hub.__doc__ = PretrainedConfig.push_to_hub.__doc__.format( object="config", object_class="AutoConfig", object_files="configuration file" )

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import copy import json import os import re import warnings from typing import Any, Dict, List, Optional, Tuple, Union from packaging import version from requests import HTTPError from . import __version__ from .dynamic_module_utils import custom_object_save from .utils import ( CONFIG_NAME, HUGGINGFACE_CO_RESOLVE_ENDPOINT, EntryNotFoundError, PushToHubMixin, RepositoryNotFoundError, RevisionNotFoundError, cached_path, copy_func, hf_bucket_url, is_offline_mode, is_remote_url, is_torch_available, logging, ) logger = logging.get_logger(__name__) _re_configuration_file = re.compile(r"config\.(.*)\.json") class PretrainedConfig(PushToHubMixin): model_type: str = "" is_composition: bool = False attribute_map: Dict[str, str] = {} _auto_class: Optional[str] = None def __setattr__(self, key, value): if key in super().__getattribute__("attribute_map"): key = super().__getattribute__("attribute_map")[key] super().__setattr__(key, value) def __getattribute__(self, key): if key != "attribute_map" and key in super().__getattribute__("attribute_map"): key = super().__getattribute__("attribute_map")[key] return super().__getattribute__(key) def __init__(self, **kwargs): self.return_dict = kwargs.pop("return_dict", True) self.output_hidden_states = kwargs.pop("output_hidden_states", False) self.output_attentions = kwargs.pop("output_attentions", False) self.torchscript = kwargs.pop("torchscript", False) self.torch_dtype = kwargs.pop("torch_dtype", None) self.use_bfloat16 = kwargs.pop("use_bfloat16", False) self.pruned_heads = kwargs.pop("pruned_heads", {}) self.tie_word_embeddings = kwargs.pop( "tie_word_embeddings", True ) self.is_encoder_decoder = kwargs.pop("is_encoder_decoder", False) self.is_decoder = kwargs.pop("is_decoder", False) self.cross_attention_hidden_size = kwargs.pop("cross_attention_hidden_size", None) self.add_cross_attention = kwargs.pop("add_cross_attention", False) self.tie_encoder_decoder = kwargs.pop("tie_encoder_decoder", False) self.max_length = kwargs.pop("max_length", 20) self.min_length = kwargs.pop("min_length", 0) self.do_sample = kwargs.pop("do_sample", False) self.early_stopping = kwargs.pop("early_stopping", False) self.num_beams = kwargs.pop("num_beams", 1) self.num_beam_groups = kwargs.pop("num_beam_groups", 1) self.diversity_penalty = kwargs.pop("diversity_penalty", 0.0) self.temperature = kwargs.pop("temperature", 1.0) self.top_k = kwargs.pop("top_k", 50) self.top_p = kwargs.pop("top_p", 1.0) self.typical_p = kwargs.pop("typical_p", 1.0) self.repetition_penalty = kwargs.pop("repetition_penalty", 1.0) self.length_penalty = kwargs.pop("length_penalty", 1.0) self.no_repeat_ngram_size = kwargs.pop("no_repeat_ngram_size", 0) self.encoder_no_repeat_ngram_size = kwargs.pop("encoder_no_repeat_ngram_size", 0) self.bad_words_ids = kwargs.pop("bad_words_ids", None) self.num_return_sequences = kwargs.pop("num_return_sequences", 1) self.chunk_size_feed_forward = kwargs.pop("chunk_size_feed_forward", 0) self.output_scores = kwargs.pop("output_scores", False) self.return_dict_in_generate = kwargs.pop("return_dict_in_generate", False) self.forced_bos_token_id = kwargs.pop("forced_bos_token_id", None) self.forced_eos_token_id = kwargs.pop("forced_eos_token_id", None) self.remove_invalid_values = kwargs.pop("remove_invalid_values", False) self.exponential_decay_length_penalty = kwargs.pop("exponential_decay_length_penalty", None) self.architectures = kwargs.pop("architectures", None) self.finetuning_task = kwargs.pop("finetuning_task", None) self.id2label = kwargs.pop("id2label", None) self.label2id = kwargs.pop("label2id", None) if self.id2label is not None: kwargs.pop("num_labels", None) self.id2label = dict((int(key), value) for key, value in self.id2label.items()) else: self.num_labels = kwargs.pop("num_labels", 2) if self.torch_dtype is not None and isinstance(self.torch_dtype, str): if is_torch_available(): import torch self.torch_dtype = getattr(torch, self.torch_dtype) # Tokenizer arguments TODO: eventually tokenizer and models should share the same config self.tokenizer_class = kwargs.pop("tokenizer_class", None) self.prefix = kwargs.pop("prefix", None) self.bos_token_id = kwargs.pop("bos_token_id", None) self.pad_token_id = kwargs.pop("pad_token_id", None) self.eos_token_id = kwargs.pop("eos_token_id", None) self.sep_token_id = kwargs.pop("sep_token_id", None) self.decoder_start_token_id = kwargs.pop("decoder_start_token_id", None) # task specific arguments self.task_specific_params = kwargs.pop("task_specific_params", None) # regression / multi-label classification self.problem_type = kwargs.pop("problem_type", None) allowed_problem_types = ("regression", "single_label_classification", "multi_label_classification") if self.problem_type is not None and self.problem_type not in allowed_problem_types: raise ValueError( f"The config parameter `problem_type` was not understood: received {self.problem_type} " "but only 'regression', 'single_label_classification' and 'multi_label_classification' are valid." ) # TPU arguments if kwargs.pop("xla_device", None) is not None: logger.warning( "The `xla_device` argument has been deprecated in v4.4.0 of Transformers. It is ignored and you can " "safely remove it from your `config.json` file." ) # Name or path to the pretrained checkpoint self._name_or_path = str(kwargs.pop("name_or_path", "")) # Drop the transformers version info self.transformers_version = kwargs.pop("transformers_version", None) # Deal with gradient checkpointing if kwargs.get("gradient_checkpointing", False): warnings.warn( "Passing `gradient_checkpointing` to a config initialization is deprecated and will be removed in v5 " "Transformers. Using `model.gradient_checkpointing_enable()` instead, or if you are using the " "`Trainer` API, pass `gradient_checkpointing=True` in your `TrainingArguments`." ) # Additional attributes without default values for key, value in kwargs.items(): try: setattr(self, key, value) except AttributeError as err: logger.error(f"Can't set {key} with value {value} for {self}") raise err @property def name_or_path(self) -> str: return getattr(self, "_name_or_path", None) @name_or_path.setter def name_or_path(self, value): self._name_or_path = str(value) @property def use_return_dict(self) -> bool: return self.return_dict and not self.torchscript @property def num_labels(self) -> int: return len(self.id2label) @num_labels.setter def num_labels(self, num_labels: int): if not hasattr(self, "id2label") or self.id2label is None or len(self.id2label) != num_labels: self.id2label = {i: f"LABEL_{i}" for i in range(num_labels)} self.label2id = dict(zip(self.id2label.values(), self.id2label.keys())) def save_pretrained(self, save_directory: Union[str, os.PathLike], push_to_hub: bool = False, **kwargs): if os.path.isfile(save_directory): raise AssertionError(f"Provided path ({save_directory}) should be a directory, not a file") if push_to_hub: commit_message = kwargs.pop("commit_message", None) repo = self._create_or_get_repo(save_directory, **kwargs) os.makedirs(save_directory, exist_ok=True) if self._auto_class is not None: custom_object_save(self, save_directory, config=self) output_config_file = os.path.join(save_directory, CONFIG_NAME) self.to_json_file(output_config_file, use_diff=True) logger.info(f"Configuration saved in {output_config_file}") if push_to_hub: url = self._push_to_hub(repo, commit_message=commit_message) logger.info(f"Configuration pushed to the hub in this commit: {url}") @classmethod def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig": config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs) if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type: logger.warning( f"You are using a model of type {config_dict['model_type']} to instantiate a model of type " f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." ) return cls.from_dict(config_dict, **kwargs) @classmethod def get_config_dict( cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs ) -> Tuple[Dict[str, Any], Dict[str, Any]]: original_kwargs = copy.deepcopy(kwargs) config_dict, kwargs = cls._get_config_dict(pretrained_model_name_or_path, **kwargs) if "configuration_files" in config_dict: configuration_file = get_configuration_file(config_dict["configuration_files"]) config_dict, kwargs = cls._get_config_dict( pretrained_model_name_or_path, _configuration_file=configuration_file, **original_kwargs ) return config_dict, kwargs @classmethod def _get_config_dict( cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs ) -> Tuple[Dict[str, Any], Dict[str, Any]]: cache_dir = kwargs.pop("cache_dir", None) force_download = kwargs.pop("force_download", False) resume_download = kwargs.pop("resume_download", False) proxies = kwargs.pop("proxies", None) use_auth_token = kwargs.pop("use_auth_token", None) local_files_only = kwargs.pop("local_files_only", False) revision = kwargs.pop("revision", None) from_pipeline = kwargs.pop("_from_pipeline", None) from_auto_class = kwargs.pop("_from_auto", False) user_agent = {"file_type": "config", "from_auto_class": from_auto_class} if from_pipeline is not None: user_agent["using_pipeline"] = from_pipeline if is_offline_mode() and not local_files_only: logger.info("Offline mode: forcing local_files_only=True") local_files_only = True pretrained_model_name_or_path = str(pretrained_model_name_or_path) if os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path): config_file = pretrained_model_name_or_path else: configuration_file = kwargs.pop("_configuration_file", CONFIG_NAME) if os.path.isdir(pretrained_model_name_or_path): config_file = os.path.join(pretrained_model_name_or_path, configuration_file) else: config_file = hf_bucket_url( pretrained_model_name_or_path, filename=configuration_file, revision=revision, mirror=None ) try: resolved_config_file = cached_path( config_file, cache_dir=cache_dir, force_download=force_download, proxies=proxies, resume_download=resume_download, local_files_only=local_files_only, use_auth_token=use_auth_token, user_agent=user_agent, ) except RepositoryNotFoundError: raise EnvironmentError( f"{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier listed on " "'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a token having " "permission to this repo with `use_auth_token` or log in with `huggingface-cli login` and pass " "`use_auth_token=True`." ) except RevisionNotFoundError: raise EnvironmentError( f"{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for this " f"model name. Check the model page at 'https://huggingface.co/{pretrained_model_name_or_path}' for " "available revisions." ) except EntryNotFoundError: raise EnvironmentError( f"{pretrained_model_name_or_path} does not appear to have a file named {configuration_file}." ) except HTTPError as err: raise EnvironmentError( f"There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}" ) except ValueError: raise EnvironmentError( f"We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load this model, couldn't find it in the cached " f"files and it looks like {pretrained_model_name_or_path} is not the path to a directory containing a " "{configuration_file} file.\nCheckout your internet connection or see how to run the library in " "offline mode at 'https://huggingface.co/docs/transformers/installation#offline-mode'." ) except EnvironmentError: raise EnvironmentError( f"Can't load config for '{pretrained_model_name_or_path}'. If you were trying to load it from " "'https://huggingface.co/models', make sure you don't have a local directory with the same name. " f"Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory " f"containing a {configuration_file} file" ) try: config_dict = cls._dict_from_json_file(resolved_config_file) except (json.JSONDecodeError, UnicodeDecodeError): raise EnvironmentError( f"It looks like the config file at '{resolved_config_file}' is not a valid JSON file." ) if resolved_config_file == config_file: logger.info(f"loading configuration file {config_file}") else: logger.info(f"loading configuration file {config_file} from cache at {resolved_config_file}") return config_dict, kwargs @classmethod def from_dict(cls, config_dict: Dict[str, Any], **kwargs) -> "PretrainedConfig": return_unused_kwargs = kwargs.pop("return_unused_kwargs", False) config = cls(**config_dict) if hasattr(config, "pruned_heads"): config.pruned_heads = dict((int(key), value) for key, value in config.pruned_heads.items()) to_remove = [] for key, value in kwargs.items(): if hasattr(config, key): setattr(config, key, value) if key != "torch_dtype": to_remove.append(key) for key in to_remove: kwargs.pop(key, None) logger.info(f"Model config {config}") if return_unused_kwargs: return config, kwargs else: return config @classmethod def from_json_file(cls, json_file: Union[str, os.PathLike]) -> "PretrainedConfig": config_dict = cls._dict_from_json_file(json_file) return cls(**config_dict) @classmethod def _dict_from_json_file(cls, json_file: Union[str, os.PathLike]): with open(json_file, "r", encoding="utf-8") as reader: text = reader.read() return json.loads(text) def __eq__(self, other): return self.__dict__ == other.__dict__ def __repr__(self): return f"{self.__class__.__name__} {self.to_json_string()}" def to_diff_dict(self) -> Dict[str, Any]: config_dict = self.to_dict() default_config_dict = PretrainedConfig().to_dict() class_config_dict = self.__class__().to_dict() if not self.is_composition else {} serializable_config_dict = {} for key, value in config_dict.items(): if ( key not in default_config_dict or key == "transformers_version" or value != default_config_dict[key] or (key in class_config_dict and value != class_config_dict[key]) ): serializable_config_dict[key] = value self.dict_torch_dtype_to_str(serializable_config_dict) return serializable_config_dict def to_dict(self) -> Dict[str, Any]: output = copy.deepcopy(self.__dict__) if hasattr(self.__class__, "model_type"): output["model_type"] = self.__class__.model_type if "_auto_class" in output: del output["_auto_class"] output["transformers_version"] = __version__ self.dict_torch_dtype_to_str(output) return output def to_json_string(self, use_diff: bool = True) -> str: if use_diff is True: config_dict = self.to_diff_dict() else: config_dict = self.to_dict() return json.dumps(config_dict, indent=2, sort_keys=True) + "\n" def to_json_file(self, json_file_path: Union[str, os.PathLike], use_diff: bool = True): with open(json_file_path, "w", encoding="utf-8") as writer: writer.write(self.to_json_string(use_diff=use_diff)) def update(self, config_dict: Dict[str, Any]): for key, value in config_dict.items(): setattr(self, key, value) def update_from_string(self, update_str: str): d = dict(x.split("=") for x in update_str.split(",")) for k, v in d.items(): if not hasattr(self, k): raise ValueError(f"key {k} isn't in the original config dict") old_v = getattr(self, k) if isinstance(old_v, bool): if v.lower() in ["true", "1", "y", "yes"]: v = True elif v.lower() in ["false", "0", "n", "no"]: v = False else: raise ValueError(f"can't derive true or false from {v} (key {k})") elif isinstance(old_v, int): v = int(v) elif isinstance(old_v, float): v = float(v) elif not isinstance(old_v, str): raise ValueError( f"You can only update int, float, bool or string values in the config, got {v} for key {k}" ) setattr(self, k, v) def dict_torch_dtype_to_str(self, d: Dict[str, Any]) -> None: if d.get("torch_dtype", None) is not None and not isinstance(d["torch_dtype"], str): d["torch_dtype"] = str(d["torch_dtype"]).split(".")[1] for value in d.values(): if isinstance(value, dict): self.dict_torch_dtype_to_str(value) @classmethod def register_for_auto_class(cls, auto_class="AutoConfig"): if not isinstance(auto_class, str): auto_class = auto_class.__name__ import transformers.models.auto as auto_module if not hasattr(auto_module, auto_class): raise ValueError(f"{auto_class} is not a valid auto class.") cls._auto_class = auto_class def get_configuration_file(configuration_files: List[str]) -> str: configuration_files_map = {} for file_name in configuration_files: search = _re_configuration_file.search(file_name) if search is not None: v = search.groups()[0] configuration_files_map[v] = file_name available_versions = sorted(configuration_files_map.keys()) configuration_file = CONFIG_NAME transformers_version = version.parse(__version__) for v in available_versions: if version.parse(v) <= transformers_version: configuration_file = configuration_files_map[v] else: break return configuration_file PretrainedConfig.push_to_hub = copy_func(PretrainedConfig.push_to_hub) PretrainedConfig.push_to_hub.__doc__ = PretrainedConfig.push_to_hub.__doc__.format( object="config", object_class="AutoConfig", object_files="configuration file" )

true

f7318c2b9fdaa9f536243ae29bdfe008ac0eb2a0

22,340

py

Python

addons/io_scene_gltf2/blender/exp/gltf2_blender_extract.py

inaber0420/glTF-Modo-IO

57f99aee4e9b6177d25b465b87d731b54a625532

[ "Apache-2.0" ]

1,084

2018-07-14T07:09:50.000Z

2022-03-30T16:34:05.000Z

addons/io_scene_gltf2/blender/exp/gltf2_blender_extract.py

inaber0420/glTF-Modo-IO

57f99aee4e9b6177d25b465b87d731b54a625532

[ "Apache-2.0" ]

1,375

2018-07-13T22:09:24.000Z

2022-03-31T00:36:36.000Z

addons/io_scene_gltf2/blender/exp/gltf2_blender_extract.py

inaber0420/glTF-Modo-IO

57f99aee4e9b6177d25b465b87d731b54a625532

[ "Apache-2.0" ]

235

2018-07-13T22:04:28.000Z

2022-03-30T09:15:53.000Z

# Copyright 2018-2021 The glTF-Blender-IO authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import numpy as np from mathutils import Vector from . import gltf2_blender_export_keys from ...io.com.gltf2_io_debug import print_console from io_scene_gltf2.blender.exp import gltf2_blender_gather_skins def extract_primitives(glTF, blender_mesh, library, blender_object, blender_vertex_groups, modifiers, export_settings): """Extract primitives from a mesh.""" print_console('INFO', 'Extracting primitive: ' + blender_mesh.name) use_normals = export_settings[gltf2_blender_export_keys.NORMALS] if use_normals: blender_mesh.calc_normals_split() use_tangents = False if use_normals and export_settings[gltf2_blender_export_keys.TANGENTS]: if blender_mesh.uv_layers.active and len(blender_mesh.uv_layers) > 0: try: blender_mesh.calc_tangents() use_tangents = True except Exception: print_console('WARNING', 'Could not calculate tangents. Please try to triangulate the mesh first.') tex_coord_max = 0 if export_settings[gltf2_blender_export_keys.TEX_COORDS]: if blender_mesh.uv_layers.active: tex_coord_max = len(blender_mesh.uv_layers) color_max = 0 if export_settings[gltf2_blender_export_keys.COLORS]: color_max = len(blender_mesh.vertex_colors) armature = None skin = None if blender_vertex_groups and export_settings[gltf2_blender_export_keys.SKINS]: if modifiers is not None: modifiers_dict = {m.type: m for m in modifiers} if "ARMATURE" in modifiers_dict: modifier = modifiers_dict["ARMATURE"] armature = modifier.object # Skin must be ignored if the object is parented to a bone of the armature # (This creates an infinite recursive error) # So ignoring skin in that case is_child_of_arma = ( armature and blender_object and blender_object.parent_type == "BONE" and blender_object.parent.name == armature.name ) if is_child_of_arma: armature = None if armature: skin = gltf2_blender_gather_skins.gather_skin(armature, export_settings) if not skin: armature = None use_morph_normals = use_normals and export_settings[gltf2_blender_export_keys.MORPH_NORMAL] use_morph_tangents = use_morph_normals and use_tangents and export_settings[gltf2_blender_export_keys.MORPH_TANGENT] key_blocks = [] if blender_mesh.shape_keys and export_settings[gltf2_blender_export_keys.MORPH]: key_blocks = [ key_block for key_block in blender_mesh.shape_keys.key_blocks if not (key_block == key_block.relative_key or key_block.mute) ] use_materials = export_settings[gltf2_blender_export_keys.MATERIALS] # Fetch vert positions and bone data (joint,weights) locs, morph_locs = __get_positions(blender_mesh, key_blocks, armature, blender_object, export_settings) if skin: vert_bones, num_joint_sets = __get_bone_data(blender_mesh, skin, blender_vertex_groups) # In Blender there is both per-vert data, like position, and also per-loop # (loop=corner-of-poly) data, like normals or UVs. glTF only has per-vert # data, so we need to split Blender verts up into potentially-multiple glTF # verts. # # First, we'll collect a "dot" for every loop: a struct that stores all the # attributes at that loop, namely the vertex index (which determines all # per-vert data), and all the per-loop data like UVs, etc. # # Each unique dot will become one unique glTF vert. # List all fields the dot struct needs. dot_fields = [('vertex_index', np.uint32)] if use_normals: dot_fields += [('nx', np.float32), ('ny', np.float32), ('nz', np.float32)] if use_tangents: dot_fields += [('tx', np.float32), ('ty', np.float32), ('tz', np.float32), ('tw', np.float32)] for uv_i in range(tex_coord_max): dot_fields += [('uv%dx' % uv_i, np.float32), ('uv%dy' % uv_i, np.float32)] for col_i in range(color_max): dot_fields += [ ('color%dr' % col_i, np.float32), ('color%dg' % col_i, np.float32), ('color%db' % col_i, np.float32), ('color%da' % col_i, np.float32), ] if use_morph_normals: for morph_i, _ in enumerate(key_blocks): dot_fields += [ ('morph%dnx' % morph_i, np.float32), ('morph%dny' % morph_i, np.float32), ('morph%dnz' % morph_i, np.float32), ] dots = np.empty(len(blender_mesh.loops), dtype=np.dtype(dot_fields)) vidxs = np.empty(len(blender_mesh.loops)) blender_mesh.loops.foreach_get('vertex_index', vidxs) dots['vertex_index'] = vidxs del vidxs if use_normals: kbs = key_blocks if use_morph_normals else [] normals, morph_normals = __get_normals( blender_mesh, kbs, armature, blender_object, export_settings ) dots['nx'] = normals[:, 0] dots['ny'] = normals[:, 1] dots['nz'] = normals[:, 2] del normals for morph_i, ns in enumerate(morph_normals): dots['morph%dnx' % morph_i] = ns[:, 0] dots['morph%dny' % morph_i] = ns[:, 1] dots['morph%dnz' % morph_i] = ns[:, 2] del morph_normals if use_tangents: tangents = __get_tangents(blender_mesh, armature, blender_object, export_settings) dots['tx'] = tangents[:, 0] dots['ty'] = tangents[:, 1] dots['tz'] = tangents[:, 2] del tangents signs = __get_bitangent_signs(blender_mesh, armature, blender_object, export_settings) dots['tw'] = signs del signs for uv_i in range(tex_coord_max): uvs = __get_uvs(blender_mesh, uv_i) dots['uv%dx' % uv_i] = uvs[:, 0] dots['uv%dy' % uv_i] = uvs[:, 1] del uvs for col_i in range(color_max): colors = __get_colors(blender_mesh, col_i) dots['color%dr' % col_i] = colors[:, 0] dots['color%dg' % col_i] = colors[:, 1] dots['color%db' % col_i] = colors[:, 2] dots['color%da' % col_i] = colors[:, 3] del colors # Calculate triangles and sort them into primitives. blender_mesh.calc_loop_triangles() loop_indices = np.empty(len(blender_mesh.loop_triangles) * 3, dtype=np.uint32) blender_mesh.loop_triangles.foreach_get('loops', loop_indices) prim_indices = {} # maps material index to TRIANGLES-style indices into dots if use_materials == "NONE": # Only for None. For placeholder and export, keep primitives # Put all vertices into one primitive prim_indices[-1] = loop_indices else: # Bucket by material index. tri_material_idxs = np.empty(len(blender_mesh.loop_triangles), dtype=np.uint32) blender_mesh.loop_triangles.foreach_get('material_index', tri_material_idxs) loop_material_idxs = np.repeat(tri_material_idxs, 3) # material index for every loop unique_material_idxs = np.unique(tri_material_idxs) del tri_material_idxs for material_idx in unique_material_idxs: prim_indices[material_idx] = loop_indices[loop_material_idxs == material_idx] # Create all the primitives. primitives = [] for material_idx, dot_indices in prim_indices.items(): # Extract just dots used by this primitive, deduplicate them, and # calculate indices into this deduplicated list. prim_dots = dots[dot_indices] prim_dots, indices = np.unique(prim_dots, return_inverse=True) if len(prim_dots) == 0: continue # Now just move all the data for prim_dots into attribute arrays attributes = {} blender_idxs = prim_dots['vertex_index'] attributes['POSITION'] = locs[blender_idxs] for morph_i, vs in enumerate(morph_locs): attributes['MORPH_POSITION_%d' % morph_i] = vs[blender_idxs] if use_normals: normals = np.empty((len(prim_dots), 3), dtype=np.float32) normals[:, 0] = prim_dots['nx'] normals[:, 1] = prim_dots['ny'] normals[:, 2] = prim_dots['nz'] attributes['NORMAL'] = normals if use_tangents: tangents = np.empty((len(prim_dots), 4), dtype=np.float32) tangents[:, 0] = prim_dots['tx'] tangents[:, 1] = prim_dots['ty'] tangents[:, 2] = prim_dots['tz'] tangents[:, 3] = prim_dots['tw'] attributes['TANGENT'] = tangents if use_morph_normals: for morph_i, _ in enumerate(key_blocks): ns = np.empty((len(prim_dots), 3), dtype=np.float32) ns[:, 0] = prim_dots['morph%dnx' % morph_i] ns[:, 1] = prim_dots['morph%dny' % morph_i] ns[:, 2] = prim_dots['morph%dnz' % morph_i] attributes['MORPH_NORMAL_%d' % morph_i] = ns if use_morph_tangents: attributes['MORPH_TANGENT_%d' % morph_i] = __calc_morph_tangents(normals, ns, tangents) for tex_coord_i in range(tex_coord_max): uvs = np.empty((len(prim_dots), 2), dtype=np.float32) uvs[:, 0] = prim_dots['uv%dx' % tex_coord_i] uvs[:, 1] = prim_dots['uv%dy' % tex_coord_i] attributes['TEXCOORD_%d' % tex_coord_i] = uvs for color_i in range(color_max): colors = np.empty((len(prim_dots), 4), dtype=np.float32) colors[:, 0] = prim_dots['color%dr' % color_i] colors[:, 1] = prim_dots['color%dg' % color_i] colors[:, 2] = prim_dots['color%db' % color_i] colors[:, 3] = prim_dots['color%da' % color_i] attributes['COLOR_%d' % color_i] = colors if skin: joints = [[] for _ in range(num_joint_sets)] weights = [[] for _ in range(num_joint_sets)] for vi in blender_idxs: bones = vert_bones[vi] for j in range(0, 4 * num_joint_sets): if j < len(bones): joint, weight = bones[j] else: joint, weight = 0, 0.0 joints[j//4].append(joint) weights[j//4].append(weight) for i, (js, ws) in enumerate(zip(joints, weights)): attributes['JOINTS_%d' % i] = js attributes['WEIGHTS_%d' % i] = ws primitives.append({ 'attributes': attributes, 'indices': indices, 'material': material_idx, }) if export_settings['gltf_loose_edges']: # Find loose edges loose_edges = [e for e in blender_mesh.edges if e.is_loose] blender_idxs = [vi for e in loose_edges for vi in e.vertices] if blender_idxs: # Export one glTF vert per unique Blender vert in a loose edge blender_idxs = np.array(blender_idxs, dtype=np.uint32) blender_idxs, indices = np.unique(blender_idxs, return_inverse=True) attributes = {} attributes['POSITION'] = locs[blender_idxs] for morph_i, vs in enumerate(morph_locs): attributes['MORPH_POSITION_%d' % morph_i] = vs[blender_idxs] if skin: joints = [[] for _ in range(num_joint_sets)] weights = [[] for _ in range(num_joint_sets)] for vi in blender_idxs: bones = vert_bones[vi] for j in range(0, 4 * num_joint_sets): if j < len(bones): joint, weight = bones[j] else: joint, weight = 0, 0.0 joints[j//4].append(joint) weights[j//4].append(weight) for i, (js, ws) in enumerate(zip(joints, weights)): attributes['JOINTS_%d' % i] = js attributes['WEIGHTS_%d' % i] = ws primitives.append({ 'attributes': attributes, 'indices': indices, 'mode': 1, # LINES 'material': 0, }) if export_settings['gltf_loose_points']: # Find loose points verts_in_edge = set(vi for e in blender_mesh.edges for vi in e.vertices) blender_idxs = [ vi for vi, _ in enumerate(blender_mesh.vertices) if vi not in verts_in_edge ] if blender_idxs: blender_idxs = np.array(blender_idxs, dtype=np.uint32) attributes = {} attributes['POSITION'] = locs[blender_idxs] for morph_i, vs in enumerate(morph_locs): attributes['MORPH_POSITION_%d' % morph_i] = vs[blender_idxs] if skin: joints = [[] for _ in range(num_joint_sets)] weights = [[] for _ in range(num_joint_sets)] for vi in blender_idxs: bones = vert_bones[vi] for j in range(0, 4 * num_joint_sets): if j < len(bones): joint, weight = bones[j] else: joint, weight = 0, 0.0 joints[j//4].append(joint) weights[j//4].append(weight) for i, (js, ws) in enumerate(zip(joints, weights)): attributes['JOINTS_%d' % i] = js attributes['WEIGHTS_%d' % i] = ws primitives.append({ 'attributes': attributes, 'mode': 0, # POINTS 'material': 0, }) print_console('INFO', 'Primitives created: %d' % len(primitives)) return primitives def __get_positions(blender_mesh, key_blocks, armature, blender_object, export_settings): locs = np.empty(len(blender_mesh.vertices) * 3, dtype=np.float32) source = key_blocks[0].relative_key.data if key_blocks else blender_mesh.vertices source.foreach_get('co', locs) locs = locs.reshape(len(blender_mesh.vertices), 3) morph_locs = [] for key_block in key_blocks: vs = np.empty(len(blender_mesh.vertices) * 3, dtype=np.float32) key_block.data.foreach_get('co', vs) vs = vs.reshape(len(blender_mesh.vertices), 3) morph_locs.append(vs) # Transform for skinning if armature and blender_object: apply_matrix = armature.matrix_world.inverted_safe() @ blender_object.matrix_world loc_transform = armature.matrix_world @ apply_matrix loc_transform = blender_object.matrix_world locs[:] = __apply_mat_to_all(loc_transform, locs) for vs in morph_locs: vs[:] = __apply_mat_to_all(loc_transform, vs) # glTF stores deltas in morph targets for vs in morph_locs: vs -= locs if export_settings[gltf2_blender_export_keys.YUP]: __zup2yup(locs) for vs in morph_locs: __zup2yup(vs) return locs, morph_locs def __get_normals(blender_mesh, key_blocks, armature, blender_object, export_settings): """Get normal for each loop.""" if key_blocks: normals = key_blocks[0].relative_key.normals_split_get() normals = np.array(normals, dtype=np.float32) else: normals = np.empty(len(blender_mesh.loops) * 3, dtype=np.float32) blender_mesh.calc_normals_split() blender_mesh.loops.foreach_get('normal', normals) normals = normals.reshape(len(blender_mesh.loops), 3) morph_normals = [] for key_block in key_blocks: ns = np.array(key_block.normals_split_get(), dtype=np.float32) ns = ns.reshape(len(blender_mesh.loops), 3) morph_normals.append(ns) # Transform for skinning if armature and blender_object: apply_matrix = (armature.matrix_world.inverted_safe() @ blender_object.matrix_world) apply_matrix = apply_matrix.to_3x3().inverted_safe().transposed() normal_transform = armature.matrix_world.to_3x3() @ apply_matrix normals[:] = __apply_mat_to_all(normal_transform, normals) __normalize_vecs(normals) for ns in morph_normals: ns[:] = __apply_mat_to_all(normal_transform, ns) __normalize_vecs(ns) for ns in [normals, *morph_normals]: # Replace zero normals with the unit UP vector. # Seems to happen sometimes with degenerate tris? is_zero = ~ns.any(axis=1) ns[is_zero, 2] = 1 # glTF stores deltas in morph targets for ns in morph_normals: ns -= normals if export_settings[gltf2_blender_export_keys.YUP]: __zup2yup(normals) for ns in morph_normals: __zup2yup(ns) return normals, morph_normals def __get_tangents(blender_mesh, armature, blender_object, export_settings): """Get an array of the tangent for each loop.""" tangents = np.empty(len(blender_mesh.loops) * 3, dtype=np.float32) blender_mesh.loops.foreach_get('tangent', tangents) tangents = tangents.reshape(len(blender_mesh.loops), 3) # Transform for skinning if armature and blender_object: apply_matrix = armature.matrix_world.inverted_safe() @ blender_object.matrix_world tangent_transform = apply_matrix.to_quaternion().to_matrix() tangents = __apply_mat_to_all(tangent_transform, tangents) __normalize_vecs(tangents) if export_settings[gltf2_blender_export_keys.YUP]: __zup2yup(tangents) return tangents def __get_bitangent_signs(blender_mesh, armature, blender_object, export_settings): signs = np.empty(len(blender_mesh.loops), dtype=np.float32) blender_mesh.loops.foreach_get('bitangent_sign', signs) # Transform for skinning if armature and blender_object: # Bitangent signs should flip when handedness changes # TODO: confirm apply_matrix = armature.matrix_world.inverted_safe() @ blender_object.matrix_world tangent_transform = apply_matrix.to_quaternion().to_matrix() flipped = tangent_transform.determinant() < 0 if flipped: signs *= -1 # No change for Zup -> Yup return signs def __calc_morph_tangents(normals, morph_normal_deltas, tangents): # TODO: check if this works morph_tangent_deltas = np.empty((len(normals), 3), dtype=np.float32) for i in range(len(normals)): n = Vector(normals[i]) morph_n = n + Vector(morph_normal_deltas[i]) # convert back to non-delta t = Vector(tangents[i, :3]) rotation = morph_n.rotation_difference(n) t_morph = Vector(t) t_morph.rotate(rotation) morph_tangent_deltas[i] = t_morph - t # back to delta return morph_tangent_deltas def __get_uvs(blender_mesh, uv_i): layer = blender_mesh.uv_layers[uv_i] uvs = np.empty(len(blender_mesh.loops) * 2, dtype=np.float32) layer.data.foreach_get('uv', uvs) uvs = uvs.reshape(len(blender_mesh.loops), 2) # Blender UV space -> glTF UV space # u,v -> u,1-v uvs[:, 1] *= -1 uvs[:, 1] += 1 return uvs def __get_colors(blender_mesh, color_i): layer = blender_mesh.vertex_colors[color_i] colors = np.empty(len(blender_mesh.loops) * 4, dtype=np.float32) layer.data.foreach_get('color', colors) colors = colors.reshape(len(blender_mesh.loops), 4) # sRGB -> Linear rgb = colors[:, :-1] not_small = rgb >= 0.04045 small_result = np.where(rgb < 0.0, 0.0, rgb * (1.0 / 12.92)) large_result = np.power((rgb + 0.055) * (1.0 / 1.055), 2.4, where=not_small) rgb[:] = np.where(not_small, large_result, small_result) return colors def __get_bone_data(blender_mesh, skin, blender_vertex_groups): joint_name_to_index = {joint.name: index for index, joint in enumerate(skin.joints)} group_to_joint = [joint_name_to_index.get(g.name) for g in blender_vertex_groups] # List of (joint, weight) pairs for each vert vert_bones = [] max_num_influences = 0 for vertex in blender_mesh.vertices: bones = [] if vertex.groups: for group_element in vertex.groups: weight = group_element.weight if weight <= 0.0: continue try: joint = group_to_joint[group_element.group] except Exception: continue if joint is None: continue bones.append((joint, weight)) bones.sort(key=lambda x: x[1], reverse=True) if not bones: bones = ((0, 1.0),) # HACK for verts with zero weight (#308) vert_bones.append(bones) if len(bones) > max_num_influences: max_num_influences = len(bones) # How many joint sets do we need? 1 set = 4 influences num_joint_sets = (max_num_influences + 3) // 4 return vert_bones, num_joint_sets def __zup2yup(array): # x,y,z -> x,z,-y array[:, [1,2]] = array[:, [2,1]] # x,z,y array[:, 2] *= -1 # x,z,-y def __apply_mat_to_all(matrix, vectors): """Given matrix m and vectors [v1,v2,...], computes [m@v1,m@v2,...]""" # Linear part m = matrix.to_3x3() if len(matrix) == 4 else matrix res = np.matmul(vectors, np.array(m.transposed())) # Translation part if len(matrix) == 4: res += np.array(matrix.translation) return res def __normalize_vecs(vectors): norms = np.linalg.norm(vectors, axis=1, keepdims=True) np.divide(vectors, norms, out=vectors, where=norms != 0)

37.35786

120

0.616025

import numpy as np from mathutils import Vector from . import gltf2_blender_export_keys from ...io.com.gltf2_io_debug import print_console from io_scene_gltf2.blender.exp import gltf2_blender_gather_skins def extract_primitives(glTF, blender_mesh, library, blender_object, blender_vertex_groups, modifiers, export_settings): print_console('INFO', 'Extracting primitive: ' + blender_mesh.name) use_normals = export_settings[gltf2_blender_export_keys.NORMALS] if use_normals: blender_mesh.calc_normals_split() use_tangents = False if use_normals and export_settings[gltf2_blender_export_keys.TANGENTS]: if blender_mesh.uv_layers.active and len(blender_mesh.uv_layers) > 0: try: blender_mesh.calc_tangents() use_tangents = True except Exception: print_console('WARNING', 'Could not calculate tangents. Please try to triangulate the mesh first.') tex_coord_max = 0 if export_settings[gltf2_blender_export_keys.TEX_COORDS]: if blender_mesh.uv_layers.active: tex_coord_max = len(blender_mesh.uv_layers) color_max = 0 if export_settings[gltf2_blender_export_keys.COLORS]: color_max = len(blender_mesh.vertex_colors) armature = None skin = None if blender_vertex_groups and export_settings[gltf2_blender_export_keys.SKINS]: if modifiers is not None: modifiers_dict = {m.type: m for m in modifiers} if "ARMATURE" in modifiers_dict: modifier = modifiers_dict["ARMATURE"] armature = modifier.object is_child_of_arma = ( armature and blender_object and blender_object.parent_type == "BONE" and blender_object.parent.name == armature.name ) if is_child_of_arma: armature = None if armature: skin = gltf2_blender_gather_skins.gather_skin(armature, export_settings) if not skin: armature = None use_morph_normals = use_normals and export_settings[gltf2_blender_export_keys.MORPH_NORMAL] use_morph_tangents = use_morph_normals and use_tangents and export_settings[gltf2_blender_export_keys.MORPH_TANGENT] key_blocks = [] if blender_mesh.shape_keys and export_settings[gltf2_blender_export_keys.MORPH]: key_blocks = [ key_block for key_block in blender_mesh.shape_keys.key_blocks if not (key_block == key_block.relative_key or key_block.mute) ] use_materials = export_settings[gltf2_blender_export_keys.MATERIALS] locs, morph_locs = __get_positions(blender_mesh, key_blocks, armature, blender_object, export_settings) if skin: vert_bones, num_joint_sets = __get_bone_data(blender_mesh, skin, blender_vertex_groups) # attributes at that loop, namely the vertex index (which determines all # per-vert data), and all the per-loop data like UVs, etc. # # Each unique dot will become one unique glTF vert. # List all fields the dot struct needs. dot_fields = [('vertex_index', np.uint32)] if use_normals: dot_fields += [('nx', np.float32), ('ny', np.float32), ('nz', np.float32)] if use_tangents: dot_fields += [('tx', np.float32), ('ty', np.float32), ('tz', np.float32), ('tw', np.float32)] for uv_i in range(tex_coord_max): dot_fields += [('uv%dx' % uv_i, np.float32), ('uv%dy' % uv_i, np.float32)] for col_i in range(color_max): dot_fields += [ ('color%dr' % col_i, np.float32), ('color%dg' % col_i, np.float32), ('color%db' % col_i, np.float32), ('color%da' % col_i, np.float32), ] if use_morph_normals: for morph_i, _ in enumerate(key_blocks): dot_fields += [ ('morph%dnx' % morph_i, np.float32), ('morph%dny' % morph_i, np.float32), ('morph%dnz' % morph_i, np.float32), ] dots = np.empty(len(blender_mesh.loops), dtype=np.dtype(dot_fields)) vidxs = np.empty(len(blender_mesh.loops)) blender_mesh.loops.foreach_get('vertex_index', vidxs) dots['vertex_index'] = vidxs del vidxs if use_normals: kbs = key_blocks if use_morph_normals else [] normals, morph_normals = __get_normals( blender_mesh, kbs, armature, blender_object, export_settings ) dots['nx'] = normals[:, 0] dots['ny'] = normals[:, 1] dots['nz'] = normals[:, 2] del normals for morph_i, ns in enumerate(morph_normals): dots['morph%dnx' % morph_i] = ns[:, 0] dots['morph%dny' % morph_i] = ns[:, 1] dots['morph%dnz' % morph_i] = ns[:, 2] del morph_normals if use_tangents: tangents = __get_tangents(blender_mesh, armature, blender_object, export_settings) dots['tx'] = tangents[:, 0] dots['ty'] = tangents[:, 1] dots['tz'] = tangents[:, 2] del tangents signs = __get_bitangent_signs(blender_mesh, armature, blender_object, export_settings) dots['tw'] = signs del signs for uv_i in range(tex_coord_max): uvs = __get_uvs(blender_mesh, uv_i) dots['uv%dx' % uv_i] = uvs[:, 0] dots['uv%dy' % uv_i] = uvs[:, 1] del uvs for col_i in range(color_max): colors = __get_colors(blender_mesh, col_i) dots['color%dr' % col_i] = colors[:, 0] dots['color%dg' % col_i] = colors[:, 1] dots['color%db' % col_i] = colors[:, 2] dots['color%da' % col_i] = colors[:, 3] del colors # Calculate triangles and sort them into primitives. blender_mesh.calc_loop_triangles() loop_indices = np.empty(len(blender_mesh.loop_triangles) * 3, dtype=np.uint32) blender_mesh.loop_triangles.foreach_get('loops', loop_indices) prim_indices = {} # maps material index to TRIANGLES-style indices into dots if use_materials == "NONE": # Only for None. For placeholder and export, keep primitives # Put all vertices into one primitive prim_indices[-1] = loop_indices else: # Bucket by material index. tri_material_idxs = np.empty(len(blender_mesh.loop_triangles), dtype=np.uint32) blender_mesh.loop_triangles.foreach_get('material_index', tri_material_idxs) loop_material_idxs = np.repeat(tri_material_idxs, 3) # material index for every loop unique_material_idxs = np.unique(tri_material_idxs) del tri_material_idxs for material_idx in unique_material_idxs: prim_indices[material_idx] = loop_indices[loop_material_idxs == material_idx] # Create all the primitives. primitives = [] for material_idx, dot_indices in prim_indices.items(): # Extract just dots used by this primitive, deduplicate them, and # calculate indices into this deduplicated list. prim_dots = dots[dot_indices] prim_dots, indices = np.unique(prim_dots, return_inverse=True) if len(prim_dots) == 0: continue # Now just move all the data for prim_dots into attribute arrays attributes = {} blender_idxs = prim_dots['vertex_index'] attributes['POSITION'] = locs[blender_idxs] for morph_i, vs in enumerate(morph_locs): attributes['MORPH_POSITION_%d' % morph_i] = vs[blender_idxs] if use_normals: normals = np.empty((len(prim_dots), 3), dtype=np.float32) normals[:, 0] = prim_dots['nx'] normals[:, 1] = prim_dots['ny'] normals[:, 2] = prim_dots['nz'] attributes['NORMAL'] = normals if use_tangents: tangents = np.empty((len(prim_dots), 4), dtype=np.float32) tangents[:, 0] = prim_dots['tx'] tangents[:, 1] = prim_dots['ty'] tangents[:, 2] = prim_dots['tz'] tangents[:, 3] = prim_dots['tw'] attributes['TANGENT'] = tangents if use_morph_normals: for morph_i, _ in enumerate(key_blocks): ns = np.empty((len(prim_dots), 3), dtype=np.float32) ns[:, 0] = prim_dots['morph%dnx' % morph_i] ns[:, 1] = prim_dots['morph%dny' % morph_i] ns[:, 2] = prim_dots['morph%dnz' % morph_i] attributes['MORPH_NORMAL_%d' % morph_i] = ns if use_morph_tangents: attributes['MORPH_TANGENT_%d' % morph_i] = __calc_morph_tangents(normals, ns, tangents) for tex_coord_i in range(tex_coord_max): uvs = np.empty((len(prim_dots), 2), dtype=np.float32) uvs[:, 0] = prim_dots['uv%dx' % tex_coord_i] uvs[:, 1] = prim_dots['uv%dy' % tex_coord_i] attributes['TEXCOORD_%d' % tex_coord_i] = uvs for color_i in range(color_max): colors = np.empty((len(prim_dots), 4), dtype=np.float32) colors[:, 0] = prim_dots['color%dr' % color_i] colors[:, 1] = prim_dots['color%dg' % color_i] colors[:, 2] = prim_dots['color%db' % color_i] colors[:, 3] = prim_dots['color%da' % color_i] attributes['COLOR_%d' % color_i] = colors if skin: joints = [[] for _ in range(num_joint_sets)] weights = [[] for _ in range(num_joint_sets)] for vi in blender_idxs: bones = vert_bones[vi] for j in range(0, 4 * num_joint_sets): if j < len(bones): joint, weight = bones[j] else: joint, weight = 0, 0.0 joints[j//4].append(joint) weights[j//4].append(weight) for i, (js, ws) in enumerate(zip(joints, weights)): attributes['JOINTS_%d' % i] = js attributes['WEIGHTS_%d' % i] = ws primitives.append({ 'attributes': attributes, 'indices': indices, 'material': material_idx, }) if export_settings['gltf_loose_edges']: # Find loose edges loose_edges = [e for e in blender_mesh.edges if e.is_loose] blender_idxs = [vi for e in loose_edges for vi in e.vertices] if blender_idxs: # Export one glTF vert per unique Blender vert in a loose edge blender_idxs = np.array(blender_idxs, dtype=np.uint32) blender_idxs, indices = np.unique(blender_idxs, return_inverse=True) attributes = {} attributes['POSITION'] = locs[blender_idxs] for morph_i, vs in enumerate(morph_locs): attributes['MORPH_POSITION_%d' % morph_i] = vs[blender_idxs] if skin: joints = [[] for _ in range(num_joint_sets)] weights = [[] for _ in range(num_joint_sets)] for vi in blender_idxs: bones = vert_bones[vi] for j in range(0, 4 * num_joint_sets): if j < len(bones): joint, weight = bones[j] else: joint, weight = 0, 0.0 joints[j//4].append(joint) weights[j//4].append(weight) for i, (js, ws) in enumerate(zip(joints, weights)): attributes['JOINTS_%d' % i] = js attributes['WEIGHTS_%d' % i] = ws primitives.append({ 'attributes': attributes, 'indices': indices, 'mode': 1, # LINES 'material': 0, }) if export_settings['gltf_loose_points']: # Find loose points verts_in_edge = set(vi for e in blender_mesh.edges for vi in e.vertices) blender_idxs = [ vi for vi, _ in enumerate(blender_mesh.vertices) if vi not in verts_in_edge ] if blender_idxs: blender_idxs = np.array(blender_idxs, dtype=np.uint32) attributes = {} attributes['POSITION'] = locs[blender_idxs] for morph_i, vs in enumerate(morph_locs): attributes['MORPH_POSITION_%d' % morph_i] = vs[blender_idxs] if skin: joints = [[] for _ in range(num_joint_sets)] weights = [[] for _ in range(num_joint_sets)] for vi in blender_idxs: bones = vert_bones[vi] for j in range(0, 4 * num_joint_sets): if j < len(bones): joint, weight = bones[j] else: joint, weight = 0, 0.0 joints[j//4].append(joint) weights[j//4].append(weight) for i, (js, ws) in enumerate(zip(joints, weights)): attributes['JOINTS_%d' % i] = js attributes['WEIGHTS_%d' % i] = ws primitives.append({ 'attributes': attributes, 'mode': 0, # POINTS 'material': 0, }) print_console('INFO', 'Primitives created: %d' % len(primitives)) return primitives def __get_positions(blender_mesh, key_blocks, armature, blender_object, export_settings): locs = np.empty(len(blender_mesh.vertices) * 3, dtype=np.float32) source = key_blocks[0].relative_key.data if key_blocks else blender_mesh.vertices source.foreach_get('co', locs) locs = locs.reshape(len(blender_mesh.vertices), 3) morph_locs = [] for key_block in key_blocks: vs = np.empty(len(blender_mesh.vertices) * 3, dtype=np.float32) key_block.data.foreach_get('co', vs) vs = vs.reshape(len(blender_mesh.vertices), 3) morph_locs.append(vs) # Transform for skinning if armature and blender_object: apply_matrix = armature.matrix_world.inverted_safe() @ blender_object.matrix_world loc_transform = armature.matrix_world @ apply_matrix loc_transform = blender_object.matrix_world locs[:] = __apply_mat_to_all(loc_transform, locs) for vs in morph_locs: vs[:] = __apply_mat_to_all(loc_transform, vs) # glTF stores deltas in morph targets for vs in morph_locs: vs -= locs if export_settings[gltf2_blender_export_keys.YUP]: __zup2yup(locs) for vs in morph_locs: __zup2yup(vs) return locs, morph_locs def __get_normals(blender_mesh, key_blocks, armature, blender_object, export_settings): if key_blocks: normals = key_blocks[0].relative_key.normals_split_get() normals = np.array(normals, dtype=np.float32) else: normals = np.empty(len(blender_mesh.loops) * 3, dtype=np.float32) blender_mesh.calc_normals_split() blender_mesh.loops.foreach_get('normal', normals) normals = normals.reshape(len(blender_mesh.loops), 3) morph_normals = [] for key_block in key_blocks: ns = np.array(key_block.normals_split_get(), dtype=np.float32) ns = ns.reshape(len(blender_mesh.loops), 3) morph_normals.append(ns) # Transform for skinning if armature and blender_object: apply_matrix = (armature.matrix_world.inverted_safe() @ blender_object.matrix_world) apply_matrix = apply_matrix.to_3x3().inverted_safe().transposed() normal_transform = armature.matrix_world.to_3x3() @ apply_matrix normals[:] = __apply_mat_to_all(normal_transform, normals) __normalize_vecs(normals) for ns in morph_normals: ns[:] = __apply_mat_to_all(normal_transform, ns) __normalize_vecs(ns) for ns in [normals, *morph_normals]: # Replace zero normals with the unit UP vector. # Seems to happen sometimes with degenerate tris? is_zero = ~ns.any(axis=1) ns[is_zero, 2] = 1 # glTF stores deltas in morph targets for ns in morph_normals: ns -= normals if export_settings[gltf2_blender_export_keys.YUP]: __zup2yup(normals) for ns in morph_normals: __zup2yup(ns) return normals, morph_normals def __get_tangents(blender_mesh, armature, blender_object, export_settings): tangents = np.empty(len(blender_mesh.loops) * 3, dtype=np.float32) blender_mesh.loops.foreach_get('tangent', tangents) tangents = tangents.reshape(len(blender_mesh.loops), 3) # Transform for skinning if armature and blender_object: apply_matrix = armature.matrix_world.inverted_safe() @ blender_object.matrix_world tangent_transform = apply_matrix.to_quaternion().to_matrix() tangents = __apply_mat_to_all(tangent_transform, tangents) __normalize_vecs(tangents) if export_settings[gltf2_blender_export_keys.YUP]: __zup2yup(tangents) return tangents def __get_bitangent_signs(blender_mesh, armature, blender_object, export_settings): signs = np.empty(len(blender_mesh.loops), dtype=np.float32) blender_mesh.loops.foreach_get('bitangent_sign', signs) # Transform for skinning if armature and blender_object: # Bitangent signs should flip when handedness changes # TODO: confirm apply_matrix = armature.matrix_world.inverted_safe() @ blender_object.matrix_world tangent_transform = apply_matrix.to_quaternion().to_matrix() flipped = tangent_transform.determinant() < 0 if flipped: signs *= -1 # No change for Zup -> Yup return signs def __calc_morph_tangents(normals, morph_normal_deltas, tangents): # TODO: check if this works morph_tangent_deltas = np.empty((len(normals), 3), dtype=np.float32) for i in range(len(normals)): n = Vector(normals[i]) morph_n = n + Vector(morph_normal_deltas[i]) # convert back to non-delta t = Vector(tangents[i, :3]) rotation = morph_n.rotation_difference(n) t_morph = Vector(t) t_morph.rotate(rotation) morph_tangent_deltas[i] = t_morph - t # back to delta return morph_tangent_deltas def __get_uvs(blender_mesh, uv_i): layer = blender_mesh.uv_layers[uv_i] uvs = np.empty(len(blender_mesh.loops) * 2, dtype=np.float32) layer.data.foreach_get('uv', uvs) uvs = uvs.reshape(len(blender_mesh.loops), 2) # Blender UV space -> glTF UV space # u,v -> u,1-v uvs[:, 1] *= -1 uvs[:, 1] += 1 return uvs def __get_colors(blender_mesh, color_i): layer = blender_mesh.vertex_colors[color_i] colors = np.empty(len(blender_mesh.loops) * 4, dtype=np.float32) layer.data.foreach_get('color', colors) colors = colors.reshape(len(blender_mesh.loops), 4) # sRGB -> Linear rgb = colors[:, :-1] not_small = rgb >= 0.04045 small_result = np.where(rgb < 0.0, 0.0, rgb * (1.0 / 12.92)) large_result = np.power((rgb + 0.055) * (1.0 / 1.055), 2.4, where=not_small) rgb[:] = np.where(not_small, large_result, small_result) return colors def __get_bone_data(blender_mesh, skin, blender_vertex_groups): joint_name_to_index = {joint.name: index for index, joint in enumerate(skin.joints)} group_to_joint = [joint_name_to_index.get(g.name) for g in blender_vertex_groups] # List of (joint, weight) pairs for each vert vert_bones = [] max_num_influences = 0 for vertex in blender_mesh.vertices: bones = [] if vertex.groups: for group_element in vertex.groups: weight = group_element.weight if weight <= 0.0: continue try: joint = group_to_joint[group_element.group] except Exception: continue if joint is None: continue bones.append((joint, weight)) bones.sort(key=lambda x: x[1], reverse=True) if not bones: bones = ((0, 1.0),) # HACK for verts with zero weight (#308) vert_bones.append(bones) if len(bones) > max_num_influences: max_num_influences = len(bones) # How many joint sets do we need? 1 set = 4 influences num_joint_sets = (max_num_influences + 3) // 4 return vert_bones, num_joint_sets def __zup2yup(array): # x,y,z -> x,z,-y array[:, [1,2]] = array[:, [2,1]] # x,z,y array[:, 2] *= -1 # x,z,-y def __apply_mat_to_all(matrix, vectors): # Linear part m = matrix.to_3x3() if len(matrix) == 4 else matrix res = np.matmul(vectors, np.array(m.transposed())) # Translation part if len(matrix) == 4: res += np.array(matrix.translation) return res def __normalize_vecs(vectors): norms = np.linalg.norm(vectors, axis=1, keepdims=True) np.divide(vectors, norms, out=vectors, where=norms != 0)

true

f7318c381216a7a0a5e30f0ca35e0f3c326d7ead

184

py

Python

beecrowd exercises/beecrowd-1145.py

pachecosamuel/Python-Exercises

de542536dd1a2bc0ad27e81824713cda8ad34054

[ "MIT" ]

null

beecrowd exercises/beecrowd-1145.py

pachecosamuel/Python-Exercises

de542536dd1a2bc0ad27e81824713cda8ad34054

[ "MIT" ]

null

beecrowd exercises/beecrowd-1145.py

pachecosamuel/Python-Exercises

de542536dd1a2bc0ad27e81824713cda8ad34054

[ "MIT" ]

null

n1,n2 = list(map(int,input().split())) cont = 1 for i in range(1,(int((n2/n1))+1)): r = "" for y in range(n1): r += str(cont) + " " cont += 1 print(r[:-1])

20.444444

38

0.451087

n1,n2 = list(map(int,input().split())) cont = 1 for i in range(1,(int((n2/n1))+1)): r = "" for y in range(n1): r += str(cont) + " " cont += 1 print(r[:-1])

true

f7318c8d58133c98fa8052706364e5c9c4e4f7bb

116

py

Python

ComStream/tf_idf/__init__.py

alimpfard/ComStream

1bfde3a01ba0b996b4c41e0e9112089618789469

[ "MIT" ]

null

ComStream/tf_idf/__init__.py

alimpfard/ComStream

1bfde3a01ba0b996b4c41e0e9112089618789469

[ "MIT" ]

null

ComStream/tf_idf/__init__.py

alimpfard/ComStream

1bfde3a01ba0b996b4c41e0e9112089618789469

[ "MIT" ]

null

from . import Agent from . import Coordinator from . import DataManager from . import DataPoint from . import Utils

19.333333

25

0.784483

from . import Agent from . import Coordinator from . import DataManager from . import DataPoint from . import Utils

true

f7318c9eae78d9ecd4097a5f77c09245d4f5b3b0

14,635

py

Python

appmetrics/statistics.py

avalente/appmetrics

366fc7e1ca897e49a2227cbfa43bfa02a47f1acc

[ "Apache-2.0" ]

60

2015-01-19T05:32:32.000Z

2021-06-08T07:35:02.000Z

appmetrics/statistics.py

alexKeleon/appmetrics

366fc7e1ca897e49a2227cbfa43bfa02a47f1acc

[ "Apache-2.0" ]

6

2015-03-02T19:25:02.000Z

2021-03-27T17:26:07.000Z

appmetrics/statistics.py

alexKeleon/appmetrics

366fc7e1ca897e49a2227cbfa43bfa02a47f1acc

[ "Apache-2.0" ]

12

2015-01-19T05:04:14.000Z

2020-09-08T07:49:54.000Z

## Module statistics.py ## ## Copyright (c) 2014 Antonio Valente <y3sman@gmail.com> ## ## Licensed under the Apache License, Version 2.0 (the "License"); ## you may not use this file except in compliance with the License. ## You may obtain a copy of the License at ## ## http://www.apache.org/licenses/LICENSE-2.0 ## ## Unless required by applicable law or agreed to in writing, software ## distributed under the License is distributed on an "AS IS" BASIS, ## WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. ## See the License for the specific language governing permissions and ## limitations under the License. """ Statistics module. The basic functions are stolen from python 3.4 stdlib """ from __future__ import division import collections import math import operator import functools from fractions import Fraction from decimal import Decimal from .exceptions import StatisticsError from .py3comp import xrange, iteritems def isfinite(n): """Return True if x is neither an infinity nor a NaN, and False otherwise. (Note that 0.0 is considered finite.) Backported from python 3 """ return not (math.isinf(n) or math.isnan(n)) def sum(data, start=0): """sum(data [, start]) -> value Return a high-precision sum of the given numeric data. If optional argument ``start`` is given, it is added to the total. If ``data`` is empty, ``start`` (defaulting to 0) is returned. """ n, d = exact_ratio(start) T = type(start) partials = {d: n} # map {denominator: sum of numerators} # Micro-optimizations. coerce_types_ = coerce_types exact_ratio_ = exact_ratio partials_get = partials.get # Add numerators for each denominator, and track the "current" type. for x in data: T = coerce_types_(T, type(x)) n, d = exact_ratio_(x) partials[d] = partials_get(d, 0) + n if None in partials: assert issubclass(T, (float, Decimal)) assert not isfinite(partials[None]) return T(partials[None]) total = Fraction() for d, n in sorted(partials.items()): total += Fraction(n, d) if issubclass(T, int): assert total.denominator == 1 return T(total.numerator) if issubclass(T, Decimal): return T(total.numerator) / total.denominator return T(total) def exact_ratio(x): """Convert Real number x exactly to (numerator, denominator) pair. x is expected to be an int, Fraction, Decimal or float. """ try: try: # int, Fraction return x.numerator, x.denominator except AttributeError: # float try: return x.as_integer_ratio() except AttributeError: # Decimal try: return decimal_to_ratio(x) except AttributeError: msg = "can't convert type '{}' to numerator/denominator" raise TypeError(msg.format(type(x).__name__)) except (OverflowError, ValueError): # INF or NAN return (x, None) # FIXME This is faster than Fraction.from_decimal, but still too slow. def decimal_to_ratio(d): """Convert Decimal d to exact integer ratio (numerator, denominator). """ sign, digits, exp = d.as_tuple() if exp in ('F', 'n', 'N'): # INF, NAN, sNAN assert not d.is_finite() raise ValueError num = 0 for digit in digits: num = num * 10 + digit if sign: num = -num den = 10 ** -exp return (num, den) def coerce_types(T1, T2): """Coerce types T1 and T2 to a common type. Coercion is performed according to this table, where "N/A" means that a TypeError exception is raised. +----------+-----------+-----------+-----------+----------+ | | int | Fraction | Decimal | float | +----------+-----------+-----------+-----------+----------+ | int | int | Fraction | Decimal | float | | Fraction | Fraction | Fraction | N/A | float | | Decimal | Decimal | N/A | Decimal | float | | float | float | float | float | float | +----------+-----------+-----------+-----------+----------+ Subclasses trump their parent class; two subclasses of the same base class will be coerced to the second of the two. """ # Get the common/fast cases out of the way first. if T1 is T2: return T1 if T1 is int: return T2 if T2 is int: return T1 # Subclasses trump their parent class. if issubclass(T2, T1): return T2 if issubclass(T1, T2): return T1 # Floats trump everything else. if issubclass(T2, float): return T2 if issubclass(T1, float): return T1 # Subclasses of the same base class give priority to the second. if T1.__base__ is T2.__base__: return T2 # Otherwise, just give up. raise TypeError('cannot coerce types %r and %r' % (T1, T2)) def counts(data): """ Generate a table of sorted (value, frequency) pairs. """ if data is None: raise TypeError('None is not iterable') table = collections.Counter(data).most_common() if not table: return table # Extract the values with the highest frequency. maxfreq = table[0][1] for i in range(1, len(table)): if table[i][1] != maxfreq: table = table[:i] break return table # === Measures of central tendency (averages) === def mean(data): """Return the sample arithmetic mean of data. If ``data`` is empty, StatisticsError will be raised. """ if iter(data) is data: data = list(data) n = len(data) if n < 1: raise StatisticsError('mean requires at least one data point') return sum(data) / n # FIXME: investigate ways to calculate medians without sorting? Quickselect? def median(data): """Return the median (middle value) of numeric data. When the number of data points is odd, return the middle data point. When the number of data points is even, the median is interpolated by taking the average of the two middle values: """ data = sorted(data) n = len(data) if n == 0: raise StatisticsError("no median for empty data") if n % 2 == 1: return data[n // 2] else: i = n // 2 return (data[i - 1] + data[i]) / 2 def median_low(data): """Return the low median of numeric data. When the number of data points is odd, the middle value is returned. When it is even, the smaller of the two middle values is returned. """ data = sorted(data) n = len(data) if n == 0: raise StatisticsError("no median for empty data") if n % 2 == 1: return data[n // 2] else: return data[n // 2 - 1] def median_high(data): """Return the high median of data. When the number of data points is odd, the middle value is returned. When it is even, the larger of the two middle values is returned. """ data = sorted(data) n = len(data) if n == 0: raise StatisticsError("no median for empty data") return data[n // 2] def mode(data): """Return the most common data point from discrete or nominal data. ``mode`` assumes discrete data, and returns a single value. This is the standard treatment of the mode as commonly taught in schools: If there is not exactly one most common value, ``mode`` will raise StatisticsError. """ # Generate a table of sorted (value, frequency) pairs. table = counts(data) if len(table) == 1: return table[0][0] elif table: raise StatisticsError( 'no unique mode; found %d equally common values' % len(table) ) else: raise StatisticsError('no mode for empty data') # === Measures of spread === # See http://mathworld.wolfram.com/Variance.html # http://mathworld.wolfram.com/SampleVariance.html # http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance # # Under no circumstances use the so-called "computational formula for # variance", as that is only suitable for hand calculations with a small # amount of low-precision data. It has terrible numeric properties. # # See a comparison of three computational methods here: # http://www.johndcook.com/blog/2008/09/26/comparing-three-methods-of-computing-standard-deviation/ def _ss(data, c=None): """Return sum of square deviations of sequence data. If ``c`` is None, the mean is calculated in one pass, and the deviations from the mean are calculated in a second pass. Otherwise, deviations are calculated from ``c`` as given. Use the second case with care, as it can lead to garbage results. """ if c is None: c = mean(data) ss = sum((x - c) ** 2 for x in data) # The following sum should mathematically equal zero, but due to rounding # error may not. ss -= sum((x - c) for x in data) ** 2 / len(data) assert not ss < 0, 'negative sum of square deviations: %f' % ss return ss def variance(data, xbar=None): """Return the sample variance of data. data should be an iterable of Real-valued numbers, with at least two values. The optional argument xbar, if given, should be the mean of the data. If it is missing or None, the mean is automatically calculated. Use this function when your data is a sample from a population. To calculate the variance from the entire population, see ``pvariance``. If you have already calculated the mean of your data, you can pass it as the optional second argument ``xbar`` to avoid recalculating it: This function does not check that ``xbar`` is actually the mean of ``data``. Giving arbitrary values for ``xbar`` may lead to invalid or impossible results. Decimals and Fractions are supported """ if iter(data) is data: data = list(data) n = len(data) if n < 2: raise StatisticsError('variance requires at least two data points') ss = _ss(data, xbar) return ss / (n - 1) def pvariance(data, mu=None): """Return the population variance of ``data``. data should be an iterable of Real-valued numbers, with at least one value. The optional argument mu, if given, should be the mean of the data. If it is missing or None, the mean is automatically calculated. Use this function to calculate the variance from the entire population. To estimate the variance from a sample, the ``variance`` function is usually a better choice. If you have already calculated the mean of the data, you can pass it as the optional second argument to avoid recalculating it: This function does not check that ``mu`` is actually the mean of ``data``. Giving arbitrary values for ``mu`` may lead to invalid or impossible results. Decimals and Fractions are supported: """ if iter(data) is data: data = list(data) n = len(data) if n < 1: raise StatisticsError('pvariance requires at least one data point') ss = _ss(data, mu) return ss / n def stdev(data, xbar=None): """Return the square root of the sample variance. See ``variance`` for arguments and other details. """ var = variance(data, xbar) try: return var.sqrt() except AttributeError: return math.sqrt(var) def pstdev(data, mu=None): """Return the square root of the population variance. See ``pvariance`` for arguments and other details. """ var = pvariance(data, mu) try: return var.sqrt() except AttributeError: return math.sqrt(var) def geometric_mean(data): """Return the geometric mean of data """ if not data: raise StatisticsError('geometric_mean requires at least one data point') # in order to support negative or null values data = [x if x > 0 else math.e if x == 0 else 1.0 for x in data] return math.pow(math.fabs(functools.reduce(operator.mul, data)), 1.0 / len(data)) def harmonic_mean(data): """Return the harmonic mean of data """ if not data: raise StatisticsError('harmonic_mean requires at least one data point') divisor = sum(map(lambda x: 1.0 / x if x else 0.0, data)) return len(data) / divisor if divisor else 0.0 def skewness(data): """Return the skewness of the data's distribution """ if not data: raise StatisticsError('skewness requires at least one data point') size = len(data) sd = stdev(data) ** 3 if not sd: return 0.0 mn = mean(data) return sum(map(lambda x: ((x - mn) ** 3 / sd), data)) / size def kurtosis(data): """Return the kurtosis of the data's distribution """ if not data: raise StatisticsError('kurtosis requires at least one data point') size = len(data) sd = stdev(data) ** 4 if not sd: return 0.0 mn = mean(data) return sum(map(lambda x: ((x - mn) ** 4 / sd), data)) / size - 3 def percentile(data, n): """Return the n-th percentile of the given data Assume that the data are already sorted """ size = len(data) idx = (n / 100.0) * size - 0.5 if idx < 0 or idx > size: raise StatisticsError("Too few data points ({}) for {}th percentile".format(size, n)) return data[int(idx)] def get_histogram(data): """Return the histogram relative to the given data Assume that the data are already sorted """ count = len(data) if count < 2: raise StatisticsError('Too few data points ({}) for get_histogram'.format(count)) min_ = data[0] max_ = data[-1] std = stdev(data) bins = get_histogram_bins(min_, max_, std, count) res = {x: 0 for x in bins} for value in data: for bin_ in bins: if value <= bin_: res[bin_] += 1 break return sorted(iteritems(res)) def get_histogram_bins(min_, max_, std, count): """ Return optimal bins given the input parameters """ width = _get_bin_width(std, count) count = int(round((max_ - min_) / width) + 1) if count: bins = [i * width + min_ for i in xrange(1, count + 1)] else: bins = [min_] return bins def _get_bin_width(stdev, count): """Return the histogram's optimal bin width based on Sturges http://www.jstor.org/pss/2965501 """ w = int(round((3.5 * stdev) / (count ** (1.0 / 3)))) if w: return w else: return 1

28.362403

99

0.624804

partials[d] = partials_get(d, 0) + n if None in partials: assert issubclass(T, (float, Decimal)) assert not isfinite(partials[None]) return T(partials[None]) total = Fraction() for d, n in sorted(partials.items()): total += Fraction(n, d) if issubclass(T, int): assert total.denominator == 1 return T(total.numerator) if issubclass(T, Decimal): return T(total.numerator) / total.denominator return T(total) def exact_ratio(x): try: try: return x.numerator, x.denominator except AttributeError: try: return x.as_integer_ratio() except AttributeError: try: return decimal_to_ratio(x) except AttributeError: msg = "can't convert type '{}' to numerator/denominator" raise TypeError(msg.format(type(x).__name__)) except (OverflowError, ValueError): # INF or NAN return (x, None) # FIXME This is faster than Fraction.from_decimal, but still too slow. def decimal_to_ratio(d): sign, digits, exp = d.as_tuple() if exp in ('F', 'n', 'N'): # INF, NAN, sNAN assert not d.is_finite() raise ValueError num = 0 for digit in digits: num = num * 10 + digit if sign: num = -num den = 10 ** -exp return (num, den) def coerce_types(T1, T2): # Get the common/fast cases out of the way first. if T1 is T2: return T1 if T1 is int: return T2 if T2 is int: return T1 # Subclasses trump their parent class. if issubclass(T2, T1): return T2 if issubclass(T1, T2): return T1 # Floats trump everything else. if issubclass(T2, float): return T2 if issubclass(T1, float): return T1 # Subclasses of the same base class give priority to the second. if T1.__base__ is T2.__base__: return T2 # Otherwise, just give up. raise TypeError('cannot coerce types %r and %r' % (T1, T2)) def counts(data): if data is None: raise TypeError('None is not iterable') table = collections.Counter(data).most_common() if not table: return table # Extract the values with the highest frequency. maxfreq = table[0][1] for i in range(1, len(table)): if table[i][1] != maxfreq: table = table[:i] break return table # === Measures of central tendency (averages) === def mean(data): if iter(data) is data: data = list(data) n = len(data) if n < 1: raise StatisticsError('mean requires at least one data point') return sum(data) / n # FIXME: investigate ways to calculate medians without sorting? Quickselect? def median(data): data = sorted(data) n = len(data) if n == 0: raise StatisticsError("no median for empty data") if n % 2 == 1: return data[n // 2] else: i = n // 2 return (data[i - 1] + data[i]) / 2 def median_low(data): data = sorted(data) n = len(data) if n == 0: raise StatisticsError("no median for empty data") if n % 2 == 1: return data[n // 2] else: return data[n // 2 - 1] def median_high(data): data = sorted(data) n = len(data) if n == 0: raise StatisticsError("no median for empty data") return data[n // 2] def mode(data): # Generate a table of sorted (value, frequency) pairs. table = counts(data) if len(table) == 1: return table[0][0] elif table: raise StatisticsError( 'no unique mode; found %d equally common values' % len(table) ) else: raise StatisticsError('no mode for empty data') # === Measures of spread === # See http://mathworld.wolfram.com/Variance.html # http://mathworld.wolfram.com/SampleVariance.html # http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance # # Under no circumstances use the so-called "computational formula for # variance", as that is only suitable for hand calculations with a small # amount of low-precision data. It has terrible numeric properties. # # See a comparison of three computational methods here: # http://www.johndcook.com/blog/2008/09/26/comparing-three-methods-of-computing-standard-deviation/ def _ss(data, c=None): if c is None: c = mean(data) ss = sum((x - c) ** 2 for x in data) # The following sum should mathematically equal zero, but due to rounding # error may not. ss -= sum((x - c) for x in data) ** 2 / len(data) assert not ss < 0, 'negative sum of square deviations: %f' % ss return ss def variance(data, xbar=None): if iter(data) is data: data = list(data) n = len(data) if n < 2: raise StatisticsError('variance requires at least two data points') ss = _ss(data, xbar) return ss / (n - 1) def pvariance(data, mu=None): if iter(data) is data: data = list(data) n = len(data) if n < 1: raise StatisticsError('pvariance requires at least one data point') ss = _ss(data, mu) return ss / n def stdev(data, xbar=None): var = variance(data, xbar) try: return var.sqrt() except AttributeError: return math.sqrt(var) def pstdev(data, mu=None): var = pvariance(data, mu) try: return var.sqrt() except AttributeError: return math.sqrt(var) def geometric_mean(data): if not data: raise StatisticsError('geometric_mean requires at least one data point') # in order to support negative or null values data = [x if x > 0 else math.e if x == 0 else 1.0 for x in data] return math.pow(math.fabs(functools.reduce(operator.mul, data)), 1.0 / len(data)) def harmonic_mean(data): if not data: raise StatisticsError('harmonic_mean requires at least one data point') divisor = sum(map(lambda x: 1.0 / x if x else 0.0, data)) return len(data) / divisor if divisor else 0.0 def skewness(data): if not data: raise StatisticsError('skewness requires at least one data point') size = len(data) sd = stdev(data) ** 3 if not sd: return 0.0 mn = mean(data) return sum(map(lambda x: ((x - mn) ** 3 / sd), data)) / size def kurtosis(data): if not data: raise StatisticsError('kurtosis requires at least one data point') size = len(data) sd = stdev(data) ** 4 if not sd: return 0.0 mn = mean(data) return sum(map(lambda x: ((x - mn) ** 4 / sd), data)) / size - 3 def percentile(data, n): size = len(data) idx = (n / 100.0) * size - 0.5 if idx < 0 or idx > size: raise StatisticsError("Too few data points ({}) for {}th percentile".format(size, n)) return data[int(idx)] def get_histogram(data): count = len(data) if count < 2: raise StatisticsError('Too few data points ({}) for get_histogram'.format(count)) min_ = data[0] max_ = data[-1] std = stdev(data) bins = get_histogram_bins(min_, max_, std, count) res = {x: 0 for x in bins} for value in data: for bin_ in bins: if value <= bin_: res[bin_] += 1 break return sorted(iteritems(res)) def get_histogram_bins(min_, max_, std, count): width = _get_bin_width(std, count) count = int(round((max_ - min_) / width) + 1) if count: bins = [i * width + min_ for i in xrange(1, count + 1)] else: bins = [min_] return bins def _get_bin_width(stdev, count): w = int(round((3.5 * stdev) / (count ** (1.0 / 3)))) if w: return w else: return 1

true

f7318cbf69b4247d05d82297d5c1cf2c37526fa4

6,137

py

Python

lavaplayer/websocket.py

xArty4/lavaplayer

1fae7c74e5e3c1eaf45b06fc05fc5fb16a2e4d3c

[ "MIT" ]

null

lavaplayer/websocket.py

xArty4/lavaplayer

1fae7c74e5e3c1eaf45b06fc05fc5fb16a2e4d3c

[ "MIT" ]

null

lavaplayer/websocket.py

xArty4/lavaplayer

1fae7c74e5e3c1eaf45b06fc05fc5fb16a2e4d3c

[ "MIT" ]

null

import asyncio import aiohttp import logging from lavaplayer.exceptions import NodeError from .objects import ( Info, PlayerUpdateEvent, TrackStartEvent, TrackEndEvent, TrackExceptionEvent, TrackStuckEvent, WebSocketClosedEvent, ) from .emitter import Emitter import typing as t if t.TYPE_CHECKING: from .client import LavalinkClient _LOGGER = logging.getLogger("lavaplayer.ws") class WS: def __init__( self, client: "LavalinkClient", host: str, port: int, is_ssl: bool = False, ) -> None: self.ws = None self.ws_url = f"{'wss' if is_ssl else 'ws'}://{host}:{port}" self.client = client self._headers = client._headers self._loop = client._loop self.emitter: Emitter = client.event_manager self.is_connect: bool = False async def _connect(self): async with aiohttp.ClientSession(headers=self._headers, loop=self._loop) as session: self.session = session try: self.ws = await self.session.ws_connect(self.ws_url) if session is None: await self.check_connection() except (aiohttp.ClientConnectorError, aiohttp.WSServerHandshakeError, aiohttp.ServerDisconnectedError) as error: if isinstance(error, aiohttp.ClientConnectorError): _LOGGER.error(f"Could not connect to websocket: {error}") _LOGGER.warning("Reconnecting to websocket after 10 seconds") await asyncio.sleep(10) await self._connect() return elif isinstance(error, aiohttp.WSServerHandshakeError): if error.status in (403, 401): # Unauthorized or Forbidden _LOGGER.warning("Password authentication failed - closing websocket") return _LOGGER.warning("Please check your websocket port - closing websocket") elif isinstance(error, aiohttp.ServerDisconnectedError): _LOGGER.error(f"Could not connect to websocket: {error}") _LOGGER.warning("Reconnecting to websocket after 10 seconds") await asyncio.sleep(10) await self._connect() return _LOGGER.info("Connected to websocket") self.is_connect = True async for msg in self.ws: if msg.type == aiohttp.WSMsgType.TEXT: await self.callback(msg.json()) elif msg.type == aiohttp.WSMsgType.CLOSED: _LOGGER.error("Websocket closed") break elif msg.type == aiohttp.WSMsgType.ERROR: _LOGGER.error(msg.data) break async def check_connection(self): while self.ws.closed is None or not self.ws.closed or not self.is_connected: _LOGGER.warning("Websocket closed unexpectedly - reconnecting in 10 seconds") if self.client.nodes: self.client.nodes.clear() await asyncio.sleep(10) await self._connect() async def callback(self, payload: dict): if payload["op"] == "stats": self.client.info = Info( playing_players=payload["playingPlayers"], memory_used=payload["memory"]["used"], memory_free=payload["memory"]["free"], players=payload["players"], uptime=payload["uptime"] ) elif payload["op"] == "playerUpdate": data = PlayerUpdateEvent( guild_id=payload["guildId"], time=payload["state"]["time"], position=payload["state"].get("position"), connected=payload["state"]["connected"], ) self.emitter.emit("playerUpdate", data) elif payload["op"] == "event": if not payload.get("track"): return track = await self.client._decodetrack(payload["track"]) guild_id = int(payload["guildId"]) try: node = await self.client.get_guild_node(guild_id) except NodeError: node = None if payload["type"] == "TrackStartEvent": self.emitter.emit("TrackStartEvent", TrackStartEvent(track, guild_id)) elif payload["type"] == "TrackEndEvent": self.emitter.emit("TrackEndEvent", TrackEndEvent(track, guild_id, payload["reason"])) if not node: return if not node.queue: return if node.repeat: await self.client.play(guild_id, track, node.queue[0].requester, True) return del node.queue[0] await self.client.set_guild_node(guild_id, node) if len(node.queue) != 0: await self.client.play(guild_id, node.queue[0], node.queue[0].requester, True) elif payload["type"] == "TrackExceptionEvent": self.emitter.emit("TrackExceptionEvent", TrackExceptionEvent(track, guild_id, payload["exception"], payload["message"], payload["severity"], payload["cause"])) elif payload["type"] == "TrackStuckEvent": self.emitter.emit("TrackStuckEvent", TrackStuckEvent(track, guild_id, payload["thresholdMs"])) elif payload["type"] == "WebSocketClosedEvent": self.emitter.emit("WebSocketClosedEvent", WebSocketClosedEvent(track, guild_id, payload["code"], payload["reason"], payload["byRemote"])) @property def is_connected(self) -> bool: return self.is_connect and self.ws.closed is False async def send(self, payload): # only dict if not self.is_connected: _LOGGER.error("Not connected to websocket") await self.check_connection() return await self.ws.send_json(payload)

40.111111

175

0.570311

import asyncio import aiohttp import logging from lavaplayer.exceptions import NodeError from .objects import ( Info, PlayerUpdateEvent, TrackStartEvent, TrackEndEvent, TrackExceptionEvent, TrackStuckEvent, WebSocketClosedEvent, ) from .emitter import Emitter import typing as t if t.TYPE_CHECKING: from .client import LavalinkClient _LOGGER = logging.getLogger("lavaplayer.ws") class WS: def __init__( self, client: "LavalinkClient", host: str, port: int, is_ssl: bool = False, ) -> None: self.ws = None self.ws_url = f"{'wss' if is_ssl else 'ws'}://{host}:{port}" self.client = client self._headers = client._headers self._loop = client._loop self.emitter: Emitter = client.event_manager self.is_connect: bool = False async def _connect(self): async with aiohttp.ClientSession(headers=self._headers, loop=self._loop) as session: self.session = session try: self.ws = await self.session.ws_connect(self.ws_url) if session is None: await self.check_connection() except (aiohttp.ClientConnectorError, aiohttp.WSServerHandshakeError, aiohttp.ServerDisconnectedError) as error: if isinstance(error, aiohttp.ClientConnectorError): _LOGGER.error(f"Could not connect to websocket: {error}") _LOGGER.warning("Reconnecting to websocket after 10 seconds") await asyncio.sleep(10) await self._connect() return elif isinstance(error, aiohttp.WSServerHandshakeError): if error.status in (403, 401): _LOGGER.warning("Password authentication failed - closing websocket") return _LOGGER.warning("Please check your websocket port - closing websocket") elif isinstance(error, aiohttp.ServerDisconnectedError): _LOGGER.error(f"Could not connect to websocket: {error}") _LOGGER.warning("Reconnecting to websocket after 10 seconds") await asyncio.sleep(10) await self._connect() return _LOGGER.info("Connected to websocket") self.is_connect = True async for msg in self.ws: if msg.type == aiohttp.WSMsgType.TEXT: await self.callback(msg.json()) elif msg.type == aiohttp.WSMsgType.CLOSED: _LOGGER.error("Websocket closed") break elif msg.type == aiohttp.WSMsgType.ERROR: _LOGGER.error(msg.data) break async def check_connection(self): while self.ws.closed is None or not self.ws.closed or not self.is_connected: _LOGGER.warning("Websocket closed unexpectedly - reconnecting in 10 seconds") if self.client.nodes: self.client.nodes.clear() await asyncio.sleep(10) await self._connect() async def callback(self, payload: dict): if payload["op"] == "stats": self.client.info = Info( playing_players=payload["playingPlayers"], memory_used=payload["memory"]["used"], memory_free=payload["memory"]["free"], players=payload["players"], uptime=payload["uptime"] ) elif payload["op"] == "playerUpdate": data = PlayerUpdateEvent( guild_id=payload["guildId"], time=payload["state"]["time"], position=payload["state"].get("position"), connected=payload["state"]["connected"], ) self.emitter.emit("playerUpdate", data) elif payload["op"] == "event": if not payload.get("track"): return track = await self.client._decodetrack(payload["track"]) guild_id = int(payload["guildId"]) try: node = await self.client.get_guild_node(guild_id) except NodeError: node = None if payload["type"] == "TrackStartEvent": self.emitter.emit("TrackStartEvent", TrackStartEvent(track, guild_id)) elif payload["type"] == "TrackEndEvent": self.emitter.emit("TrackEndEvent", TrackEndEvent(track, guild_id, payload["reason"])) if not node: return if not node.queue: return if node.repeat: await self.client.play(guild_id, track, node.queue[0].requester, True) return del node.queue[0] await self.client.set_guild_node(guild_id, node) if len(node.queue) != 0: await self.client.play(guild_id, node.queue[0], node.queue[0].requester, True) elif payload["type"] == "TrackExceptionEvent": self.emitter.emit("TrackExceptionEvent", TrackExceptionEvent(track, guild_id, payload["exception"], payload["message"], payload["severity"], payload["cause"])) elif payload["type"] == "TrackStuckEvent": self.emitter.emit("TrackStuckEvent", TrackStuckEvent(track, guild_id, payload["thresholdMs"])) elif payload["type"] == "WebSocketClosedEvent": self.emitter.emit("WebSocketClosedEvent", WebSocketClosedEvent(track, guild_id, payload["code"], payload["reason"], payload["byRemote"])) @property def is_connected(self) -> bool: return self.is_connect and self.ws.closed is False async def send(self, payload): if not self.is_connected: _LOGGER.error("Not connected to websocket") await self.check_connection() return await self.ws.send_json(payload)

true

f7318d4e874e77b247071f0e8f618b2b791be9d7

629

py

Python

skl2onnx/operator_converters/id_op.py

xiaowuhu/sklearn-onnx

e85674a67a0a043e19c2ffe181e5d31eca8ce40b

[ "Apache-2.0" ]

323

2018-12-18T20:23:19.000Z

2022-03-25T09:47:31.000Z

skl2onnx/operator_converters/id_op.py

xiaowuhu/sklearn-onnx

e85674a67a0a043e19c2ffe181e5d31eca8ce40b

[ "Apache-2.0" ]

408

2019-01-02T12:16:10.000Z

2022-03-21T14:01:28.000Z

skl2onnx/operator_converters/id_op.py

xiaowuhu/sklearn-onnx

e85674a67a0a043e19c2ffe181e5d31eca8ce40b

[ "Apache-2.0" ]

70

2018-12-20T19:36:07.000Z

2022-03-14T06:41:36.000Z

# SPDX-License-Identifier: Apache-2.0 from ..common._apply_operation import apply_identity from ..common._registration import register_converter from ..common._topology import Scope, Operator from ..common._container import ModelComponentContainer def convert_sklearn_identity(scope: Scope, operator: Operator, container: ModelComponentContainer): apply_identity( scope, operator.inputs[0].full_name, operator.outputs[0].full_name, container, operator_name=scope.get_unique_operator_name('CIdentity')) register_converter('SklearnIdentity', convert_sklearn_identity)

33.105263

66

0.761526

from ..common._apply_operation import apply_identity from ..common._registration import register_converter from ..common._topology import Scope, Operator from ..common._container import ModelComponentContainer def convert_sklearn_identity(scope: Scope, operator: Operator, container: ModelComponentContainer): apply_identity( scope, operator.inputs[0].full_name, operator.outputs[0].full_name, container, operator_name=scope.get_unique_operator_name('CIdentity')) register_converter('SklearnIdentity', convert_sklearn_identity)

true

f7318d62eadf54fe70b5a57ab76350ff07135c26

177

py

Python

tests/cases/core/layer3_pubsub/rpc/timeout/bad_call.py

AaronJGaut/pyspoke

37bbe8b42c5abe129a3736b255c8a2ee17a4fb59

[ "MIT" ]

null

tests/cases/core/layer3_pubsub/rpc/timeout/bad_call.py

AaronJGaut/pyspoke

37bbe8b42c5abe129a3736b255c8a2ee17a4fb59

[ "MIT" ]

null

tests/cases/core/layer3_pubsub/rpc/timeout/bad_call.py

AaronJGaut/pyspoke

37bbe8b42c5abe129a3736b255c8a2ee17a4fb59

[ "MIT" ]

null

import spoke try: spoke.call("junk", None, timeout=2) except TimeoutError: print("Got expected TimeoutError") else: raise TestFailure("Didn't get a TimeoutError")

17.7

50

0.711864

import spoke try: spoke.call("junk", None, timeout=2) except TimeoutError: print("Got expected TimeoutError") else: raise TestFailure("Didn't get a TimeoutError")

true

f7318e0ce7bdc535165b2bde29c259ad0a74e64c

9,150

py

Python

tests/providers/test_internet.py

shirakia/faker

4eb4ef24f5edbcbadd38f941025b671f5b6ebe60

[ "MIT" ]

1

2019-01-16T14:02:54.000Z

tests/providers/test_internet.py

shirakia/faker

4eb4ef24f5edbcbadd38f941025b671f5b6ebe60

[ "MIT" ]

null

tests/providers/test_internet.py

shirakia/faker

4eb4ef24f5edbcbadd38f941025b671f5b6ebe60

[ "MIT" ]

1

2019-11-07T03:33:43.000Z

# coding=utf-8 from __future__ import unicode_literals from itertools import cycle import unittest import mock import pytest import six from email_validator import validate_email from faker import Faker from faker.providers.person.ja_JP import Provider as JaProvider from faker.utils import text class TestInternetProvider(unittest.TestCase): """ Tests internet """ def setUp(self): self.factory = Faker() def test_email(self): email = self.factory.email(domain='example.com') assert email.split('@')[1] == 'example.com' @mock.patch( 'faker.providers.internet.Provider.image_placeholder_services', {'https://dummyimage.com/{width}x{height}'}, ) def test_image_url(self): my_width = 500 my_height = 1024 url = self.factory.image_url(my_width, my_height) assert 'https://dummyimage.com/{}x{}'.format(my_width, my_height) == url url = self.factory.image_url() assert 'https://dummyimage.com/' in url def test_hostname(self): hostname_1_level = self.factory.hostname(levels=1) hostname_parts = hostname_1_level.split(".") assert hostname_1_level self.assertIsInstance(hostname_1_level, six.string_types) assert len(hostname_parts) == 3 hostname_0_level = self.factory.hostname(levels=0) assert hostname_0_level self.assertIsInstance(hostname_0_level, six.string_types) class TestInternetProviderUrl(unittest.TestCase): """ Test internet url generation """ def setUp(self): self.factory = Faker() @staticmethod def is_correct_scheme(url, schemes): return any(url.startswith('{}://'.format(scheme)) for scheme in schemes) def test_url_default_schemes(self): for _ in range(100): url = self.factory.url() assert self.is_correct_scheme(url, ['http', 'https']) def test_url_custom_schemes(self): schemes_sets = [ ['usb'], ['ftp', 'file'], ['usb', 'telnet', 'http'], ] for _, schemes in zip(range(100), cycle(schemes_sets)): url = self.factory.url(schemes=schemes) assert self.is_correct_scheme(url, schemes) def test_url_empty_schemes_list_generate_schemeless_urls(self): for _ in range(100): url = self.factory.url(schemes=[]) assert not url.startswith('http') assert url.startswith('://') class TestJaJP(unittest.TestCase): """ Tests internet in the ja_JP locale """ def setUp(self): self.factory = Faker('ja') def test_internet(self): names = JaProvider.last_romanized_names domain_word = self.factory.domain_word() self.assertIsInstance(domain_word, six.string_types) assert any(domain_word == text.slugify(name) for name in names) domain_name = self.factory.domain_name() deep_domain_name = self.factory.domain_name(3) self.assertIsInstance(domain_name, six.string_types) self.assertIsInstance(deep_domain_name, six.string_types) assert deep_domain_name.count('.') == 3 with pytest.raises(ValueError): self.factory.domain_name(-1) user_name = self.factory.user_name() self.assertIsInstance(user_name, six.string_types) tld = self.factory.tld() self.assertIsInstance(tld, six.string_types) class TestZhCN(unittest.TestCase): def setUp(self): self.factory = Faker(locale='zh_CN') def test_email(self): email = self.factory.email() validate_email(email, check_deliverability=False) def test_domain_word(self): domain_word = self.factory.domain_word() assert len(domain_word) > 1 @mock.patch( 'faker.providers.internet.Provider.tld', lambda x: 'cn', ) def test_domain_name(self): domain_name_1_level = self.factory.domain_name(levels=1) domain_parts = domain_name_1_level.split(".") assert len(domain_parts) == 2 assert domain_parts[-1] == 'cn' domain_name_2_level = self.factory.domain_name(levels=2) domain_parts = domain_name_2_level.split(".") assert len(domain_parts) == 3 assert domain_parts[-1] == 'cn' assert domain_parts[1] in ['ac', 'com', 'edu', 'gov', 'mil', 'net', 'org', 'ah', 'bj', 'cq', 'fj', 'gd', 'gs', 'gz', 'gx', 'ha', 'hb', 'he', 'hi', 'hk', 'hl', 'hn', 'jl', 'js', 'jx', 'ln', 'mo', 'nm', 'nx', 'qh', 'sc', 'sd', 'sh', 'sn', 'sx', 'tj', 'xj', 'xz', 'yn', 'zj'] class TestZhTW(unittest.TestCase): def setUp(self): self.factory = Faker(locale='zh_TW') def test_email(self): email = self.factory.email() validate_email(email, check_deliverability=False) class TestHuHU(unittest.TestCase): """ Tests internet module in the hu_HU locale. """ def setUp(self): self.factory = Faker('hu_HU') def test_internet(self): domain_name = self.factory.domain_name() self.assertIsInstance(domain_name, six.string_types) tld = self.factory.tld() self.assertIsInstance(tld, six.string_types) email = self.factory.email() self.assertIsInstance(email, six.string_types) class TestPlPL(unittest.TestCase): def setUp(self): self.factory = Faker('pl_PL') self.provider = self.factory.provider('faker.providers.internet') def test_free_email_domain(self): domain = self.factory.free_email_domain() assert domain in self.provider.free_email_domains def test_tld(self): tld = self.factory.tld() assert tld in self.provider.tlds class TestNlNl(unittest.TestCase): def setUp(self): self.factory = Faker('nl_NL') self.provider = self.factory.provider('faker.providers.internet') @mock.patch( 'faker.providers.internet.Provider.user_name', lambda x: 'fabiënné', ) def test_ascii_safe_email(self): email = self.factory.ascii_safe_email() validate_email(email, check_deliverability=False) assert email.split('@')[0] == 'fabienne' @mock.patch( 'faker.providers.internet.Provider.user_name', lambda x: 'fabiënné', ) def test_ascii_free_email(self): email = self.factory.ascii_free_email() validate_email(email, check_deliverability=False) assert email.split('@')[0] == 'fabienne' @mock.patch( 'faker.providers.internet.Provider.user_name', lambda x: 'fabiënné', ) def test_ascii_company_email(self): email = self.factory.ascii_company_email() validate_email(email, check_deliverability=False) assert email.split('@')[0] == 'fabienne' class TestArAa(unittest.TestCase): def setUp(self): self.factory = Faker('ar_AA') self.provider = self.factory.provider('faker.providers.internet') @mock.patch( 'faker.providers.internet.Provider.user_name', lambda x: 'اصيل', ) def test_ascii_safe_email(self): email = self.factory.ascii_safe_email() validate_email(email, check_deliverability=False) assert email.split('@')[0] == 'asyl' @mock.patch( 'faker.providers.internet.Provider.user_name', lambda x: 'اصيل', ) def test_ascii_free_email(self): email = self.factory.ascii_free_email() validate_email(email, check_deliverability=False) assert email.split('@')[0] == 'asyl' @mock.patch( 'faker.providers.internet.Provider.user_name', lambda x: 'اصيل', ) def test_ascii_company_email(self): email = self.factory.ascii_company_email() validate_email(email, check_deliverability=False) assert email.split('@')[0] == 'asyl' class TestPtBR(unittest.TestCase): def setUp(self): self.factory = Faker('pt_BR') self.provider = self.factory.provider('faker.providers.internet') @mock.patch( 'faker.providers.internet.Provider.user_name', lambda x: 'VitóriaMagalhães', ) def test_ascii_safe_email(self): email = self.factory.ascii_safe_email() validate_email(email, check_deliverability=False) assert email.split('@')[0] == 'vitoriamagalhaes' @mock.patch( 'faker.providers.internet.Provider.user_name', lambda x: 'JoãoSimões', ) def test_ascii_free_email(self): email = self.factory.ascii_free_email() validate_email(email, check_deliverability=False) assert email.split('@')[0] == 'joaosimoes' @mock.patch( 'faker.providers.internet.Provider.user_name', lambda x: 'AndréCauã', ) def test_ascii_company_email(self): email = self.factory.ascii_company_email() validate_email(email, check_deliverability=False) assert email.split('@')[0] == 'andrecaua'

31.6609

80

0.628962

from __future__ import unicode_literals from itertools import cycle import unittest import mock import pytest import six from email_validator import validate_email from faker import Faker from faker.providers.person.ja_JP import Provider as JaProvider from faker.utils import text class TestInternetProvider(unittest.TestCase): def setUp(self): self.factory = Faker() def test_email(self): email = self.factory.email(domain='example.com') assert email.split('@')[1] == 'example.com' @mock.patch( 'faker.providers.internet.Provider.image_placeholder_services', {'https://dummyimage.com/{width}x{height}'}, ) def test_image_url(self): my_width = 500 my_height = 1024 url = self.factory.image_url(my_width, my_height) assert 'https://dummyimage.com/{}x{}'.format(my_width, my_height) == url url = self.factory.image_url() assert 'https://dummyimage.com/' in url def test_hostname(self): hostname_1_level = self.factory.hostname(levels=1) hostname_parts = hostname_1_level.split(".") assert hostname_1_level self.assertIsInstance(hostname_1_level, six.string_types) assert len(hostname_parts) == 3 hostname_0_level = self.factory.hostname(levels=0) assert hostname_0_level self.assertIsInstance(hostname_0_level, six.string_types) class TestInternetProviderUrl(unittest.TestCase): def setUp(self): self.factory = Faker() @staticmethod def is_correct_scheme(url, schemes): return any(url.startswith('{}://'.format(scheme)) for scheme in schemes) def test_url_default_schemes(self): for _ in range(100): url = self.factory.url() assert self.is_correct_scheme(url, ['http', 'https']) def test_url_custom_schemes(self): schemes_sets = [ ['usb'], ['ftp', 'file'], ['usb', 'telnet', 'http'], ] for _, schemes in zip(range(100), cycle(schemes_sets)): url = self.factory.url(schemes=schemes) assert self.is_correct_scheme(url, schemes) def test_url_empty_schemes_list_generate_schemeless_urls(self): for _ in range(100): url = self.factory.url(schemes=[]) assert not url.startswith('http') assert url.startswith('://') class TestJaJP(unittest.TestCase): def setUp(self): self.factory = Faker('ja') def test_internet(self): names = JaProvider.last_romanized_names domain_word = self.factory.domain_word() self.assertIsInstance(domain_word, six.string_types) assert any(domain_word == text.slugify(name) for name in names) domain_name = self.factory.domain_name() deep_domain_name = self.factory.domain_name(3) self.assertIsInstance(domain_name, six.string_types) self.assertIsInstance(deep_domain_name, six.string_types) assert deep_domain_name.count('.') == 3 with pytest.raises(ValueError): self.factory.domain_name(-1) user_name = self.factory.user_name() self.assertIsInstance(user_name, six.string_types) tld = self.factory.tld() self.assertIsInstance(tld, six.string_types) class TestZhCN(unittest.TestCase): def setUp(self): self.factory = Faker(locale='zh_CN') def test_email(self): email = self.factory.email() validate_email(email, check_deliverability=False) def test_domain_word(self): domain_word = self.factory.domain_word() assert len(domain_word) > 1 @mock.patch( 'faker.providers.internet.Provider.tld', lambda x: 'cn', ) def test_domain_name(self): domain_name_1_level = self.factory.domain_name(levels=1) domain_parts = domain_name_1_level.split(".") assert len(domain_parts) == 2 assert domain_parts[-1] == 'cn' domain_name_2_level = self.factory.domain_name(levels=2) domain_parts = domain_name_2_level.split(".") assert len(domain_parts) == 3 assert domain_parts[-1] == 'cn' assert domain_parts[1] in ['ac', 'com', 'edu', 'gov', 'mil', 'net', 'org', 'ah', 'bj', 'cq', 'fj', 'gd', 'gs', 'gz', 'gx', 'ha', 'hb', 'he', 'hi', 'hk', 'hl', 'hn', 'jl', 'js', 'jx', 'ln', 'mo', 'nm', 'nx', 'qh', 'sc', 'sd', 'sh', 'sn', 'sx', 'tj', 'xj', 'xz', 'yn', 'zj'] class TestZhTW(unittest.TestCase): def setUp(self): self.factory = Faker(locale='zh_TW') def test_email(self): email = self.factory.email() validate_email(email, check_deliverability=False) class TestHuHU(unittest.TestCase): def setUp(self): self.factory = Faker('hu_HU') def test_internet(self): domain_name = self.factory.domain_name() self.assertIsInstance(domain_name, six.string_types) tld = self.factory.tld() self.assertIsInstance(tld, six.string_types) email = self.factory.email() self.assertIsInstance(email, six.string_types) class TestPlPL(unittest.TestCase): def setUp(self): self.factory = Faker('pl_PL') self.provider = self.factory.provider('faker.providers.internet') def test_free_email_domain(self): domain = self.factory.free_email_domain() assert domain in self.provider.free_email_domains def test_tld(self): tld = self.factory.tld() assert tld in self.provider.tlds class TestNlNl(unittest.TestCase): def setUp(self): self.factory = Faker('nl_NL') self.provider = self.factory.provider('faker.providers.internet') @mock.patch( 'faker.providers.internet.Provider.user_name', lambda x: 'fabiënné', ) def test_ascii_safe_email(self): email = self.factory.ascii_safe_email() validate_email(email, check_deliverability=False) assert email.split('@')[0] == 'fabienne' @mock.patch( 'faker.providers.internet.Provider.user_name', lambda x: 'fabiënné', ) def test_ascii_free_email(self): email = self.factory.ascii_free_email() validate_email(email, check_deliverability=False) assert email.split('@')[0] == 'fabienne' @mock.patch( 'faker.providers.internet.Provider.user_name', lambda x: 'fabiënné', ) def test_ascii_company_email(self): email = self.factory.ascii_company_email() validate_email(email, check_deliverability=False) assert email.split('@')[0] == 'fabienne' class TestArAa(unittest.TestCase): def setUp(self): self.factory = Faker('ar_AA') self.provider = self.factory.provider('faker.providers.internet') @mock.patch( 'faker.providers.internet.Provider.user_name', lambda x: 'اصيل', ) def test_ascii_safe_email(self): email = self.factory.ascii_safe_email() validate_email(email, check_deliverability=False) assert email.split('@')[0] == 'asyl' @mock.patch( 'faker.providers.internet.Provider.user_name', lambda x: 'اصيل', ) def test_ascii_free_email(self): email = self.factory.ascii_free_email() validate_email(email, check_deliverability=False) assert email.split('@')[0] == 'asyl' @mock.patch( 'faker.providers.internet.Provider.user_name', lambda x: 'اصيل', ) def test_ascii_company_email(self): email = self.factory.ascii_company_email() validate_email(email, check_deliverability=False) assert email.split('@')[0] == 'asyl' class TestPtBR(unittest.TestCase): def setUp(self): self.factory = Faker('pt_BR') self.provider = self.factory.provider('faker.providers.internet') @mock.patch( 'faker.providers.internet.Provider.user_name', lambda x: 'VitóriaMagalhães', ) def test_ascii_safe_email(self): email = self.factory.ascii_safe_email() validate_email(email, check_deliverability=False) assert email.split('@')[0] == 'vitoriamagalhaes' @mock.patch( 'faker.providers.internet.Provider.user_name', lambda x: 'JoãoSimões', ) def test_ascii_free_email(self): email = self.factory.ascii_free_email() validate_email(email, check_deliverability=False) assert email.split('@')[0] == 'joaosimoes' @mock.patch( 'faker.providers.internet.Provider.user_name', lambda x: 'AndréCauã', ) def test_ascii_company_email(self): email = self.factory.ascii_company_email() validate_email(email, check_deliverability=False) assert email.split('@')[0] == 'andrecaua'

true

f7318f9b6f7017b7889c28a18c24f5841345d651

2,324

py

Python

project-chat/chatApp/migrations/0001_initial.py

Torkvamedo/smx

a5aef4f430f56ac67100c505902f55e18fba5978

[ "Unlicense" ]

null

project-chat/chatApp/migrations/0001_initial.py

Torkvamedo/smx

a5aef4f430f56ac67100c505902f55e18fba5978

[ "Unlicense" ]

null

project-chat/chatApp/migrations/0001_initial.py

Torkvamedo/smx

a5aef4f430f56ac67100c505902f55e18fba5978

[ "Unlicense" ]

null

# -*- coding: utf-8 -*- # Generated by Django 1.11.2 on 2017-06-17 19:23 from __future__ import unicode_literals from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Ban', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('date', models.DateTimeField(auto_now=True)), ], ), migrations.CreateModel( name='ChatUser', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=30)), ('login', models.CharField(max_length=30, unique=True)), ('password', models.CharField(max_length=30)), ], ), migrations.CreateModel( name='Message', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('body', models.CharField(max_length=1024)), ('date', models.DateTimeField(auto_now=True)), ('receiver_id', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='receiver', to='chatApp.ChatUser')), ('sender_id', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='sender', to='chatApp.ChatUser')), ], ), migrations.CreateModel( name='Role', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('role_name', models.CharField(max_length=20)), ], ), migrations.AddField( model_name='chatuser', name='role_id', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='chatApp.Role'), ), migrations.AddField( model_name='ban', name='user_id', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='chatApp.ChatUser'), ), ]

38.098361

144

0.577883

from __future__ import unicode_literals from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Ban', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('date', models.DateTimeField(auto_now=True)), ], ), migrations.CreateModel( name='ChatUser', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=30)), ('login', models.CharField(max_length=30, unique=True)), ('password', models.CharField(max_length=30)), ], ), migrations.CreateModel( name='Message', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('body', models.CharField(max_length=1024)), ('date', models.DateTimeField(auto_now=True)), ('receiver_id', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='receiver', to='chatApp.ChatUser')), ('sender_id', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='sender', to='chatApp.ChatUser')), ], ), migrations.CreateModel( name='Role', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('role_name', models.CharField(max_length=20)), ], ), migrations.AddField( model_name='chatuser', name='role_id', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='chatApp.Role'), ), migrations.AddField( model_name='ban', name='user_id', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='chatApp.ChatUser'), ), ]

true

f731902b159345554e51c2ec9585cd8075fabbd5

846

py

Python

setup.py

kenlowrie/avscript

7e3550adc46a01785ea89cdcd9ddaa5ac35bdbdd

[ "Apache-2.0" ]

1

2021-09-30T19:29:30.000Z

setup.py

kenlowrie/avscript

7e3550adc46a01785ea89cdcd9ddaa5ac35bdbdd

[ "Apache-2.0" ]

7

2018-07-16T22:52:55.000Z

2020-05-20T23:48:36.000Z

setup.py

kenlowrie/avscript

7e3550adc46a01785ea89cdcd9ddaa5ac35bdbdd

[ "Apache-2.0" ]

1

2018-05-24T22:58:44.000Z

from setuptools import setup from sys import version_info setup(name='avscript', version='0.3.8', description='Audio/Visual Script Parser', url='https://github.com/kenlowrie/avscript', author='Ken Lowrie', author_email='ken@kenlowrie.com', license='Apache', packages=['avscript', 'avscript.avs'], install_requires=['kenl380.pylib'], entry_points = { 'console_scripts': ['avscript=avscript.avscript_md:av_parse_file', 'avscript{}=avscript.avscript_md:av_parse_file'.format(version_info.major), 'mkavscript=avscript.mkavscript_md:mkavscript_md', 'mkavscript{}=avscript.mkavscript_md:mkavscript_md'.format(version_info.major), ], }, include_package_data=True, zip_safe=False)

38.454545

107

0.630024

from setuptools import setup from sys import version_info setup(name='avscript', version='0.3.8', description='Audio/Visual Script Parser', url='https://github.com/kenlowrie/avscript', author='Ken Lowrie', author_email='ken@kenlowrie.com', license='Apache', packages=['avscript', 'avscript.avs'], install_requires=['kenl380.pylib'], entry_points = { 'console_scripts': ['avscript=avscript.avscript_md:av_parse_file', 'avscript{}=avscript.avscript_md:av_parse_file'.format(version_info.major), 'mkavscript=avscript.mkavscript_md:mkavscript_md', 'mkavscript{}=avscript.mkavscript_md:mkavscript_md'.format(version_info.major), ], }, include_package_data=True, zip_safe=False)

true

f73190671897a2b17be262536826df2b282c304f

24,050

py

Python

test/galaxy_selenium/navigates_galaxy.py

Galaxyinternship/Galaxy

204be086a8c16d6684584cefa9053ed7c86a1784

[ "CC-BY-3.0" ]

null

test/galaxy_selenium/navigates_galaxy.py

Galaxyinternship/Galaxy

204be086a8c16d6684584cefa9053ed7c86a1784

[ "CC-BY-3.0" ]

null

test/galaxy_selenium/navigates_galaxy.py

Galaxyinternship/Galaxy

204be086a8c16d6684584cefa9053ed7c86a1784

[ "CC-BY-3.0" ]

null

"""A mixing that extends a HasDriver class with Galaxy-specific utilities. Implementer must provide a self.build_url method to target Galaxy. """ from __future__ import print_function import contextlib import random import string import time from functools import partial, wraps import requests import yaml from .data import NAVIGATION_DATA from .has_driver import exception_indicates_stale_element, HasDriver from . import sizzle # Test case data DEFAULT_PASSWORD = '123456' class NullTourCallback(object): def handle_step(self, step, step_index): pass def retry_call_during_transitions(f, attempts=5, sleep=.1): previous_attempts = 0 while True: try: return f() except Exception as e: if previous_attempts > attempts: raise if not exception_indicates_stale_element(e): raise time.sleep(sleep) previous_attempts += 1 def retry_during_transitions(f, attempts=5, sleep=.1): @wraps(f) def _retry(*args, **kwds): retry_call_during_transitions(partial(f, *args, **kwds), attempts=attempts, sleep=sleep) return _retry class NavigatesGalaxy(HasDriver): default_password = DEFAULT_PASSWORD def get(self, url=""): full_url = self.build_url(url) return self.driver.get(full_url) @property def navigation_data(self): return NAVIGATION_DATA def home(self): self.get() self.wait_for_selector_visible("#masthead") self.wait_for_selector_visible("#current-history-panel") def switch_to_main_panel(self): self.driver.switch_to.frame(self.navigation_data["selectors"]["frames"]["main"]) @contextlib.contextmanager def main_panel(self): try: self.switch_to_main_panel() yield finally: self.driver.switch_to.default_content def api_get(self, endpoint, data={}, raw=False): full_url = self.build_url("api/" + endpoint, for_selenium=False) response = requests.get(full_url, data=data, cookies=self.selenium_to_requests_cookies()) if raw: return response else: return response.json() def get_galaxy_session(self): for cookie in self.driver.get_cookies(): if cookie["name"] == "galaxysession": return cookie["value"] def selenium_to_requests_cookies(self): return { 'galaxysession': self.get_galaxy_session() } def history_panel_name_element(self): name_selector = self.test_data["historyPanel"]["selectors"]["history"]["name"] return self.wait_for_selector(name_selector) def current_history(self): history = self.api_get("histories")[0] return history def current_history_id(self): return self.current_history()["id"] def current_history_contents(self): current_history_id = self.current_history_id() history_contents = self.api_get("histories/%s/contents" % current_history_id) return history_contents def latest_history_item(self): history_contents = self.current_history_contents() assert len(history_contents) > 0 return history_contents[-1] def wait_for_history(self, timeout=30, assert_ok=True): def history_becomes_terminal(driver): current_history_id = self.current_history_id() state = self.api_get("histories/%s" % current_history_id)["state"] if state not in ["running", "queued", "new", "ready"]: return state else: return None final_state = self.wait(timeout).until(history_becomes_terminal) if assert_ok: assert final_state == "ok", final_state return final_state def history_panel_wait_for_hid_ok(self, hid, timeout=60): self.history_panel_wait_for_hid_state(hid, 'ok', timeout=timeout) def history_panel_wait_for_hid_visible(self, hid, timeout=60): current_history_id = self.current_history_id() def history_has_hid(driver): contents = self.api_get("histories/%s/contents" % current_history_id) return any([d for d in contents if d["hid"] == hid]) self.wait(timeout).until(history_has_hid) contents = self.api_get("histories/%s/contents" % current_history_id) history_item = [d for d in contents if d["hid"] == hid][0] history_item_selector = "#%s-%s" % (history_item["history_content_type"], history_item["id"]) self.wait_for_selector_visible(history_item_selector) return history_item_selector def history_panel_wait_for_hid_hidden(self, hid, timeout=60): current_history_id = self.current_history_id() contents = self.api_get("histories/%s/contents" % current_history_id) history_item = [d for d in contents if d["hid"] == hid][0] history_item_selector = "#%s-%s" % (history_item["history_content_type"], history_item["id"]) self.wait_for_selector_absent(history_item_selector) return history_item_selector def history_panel_wait_for_hid_state(self, hid, state, timeout=60): history_item_selector = self.history_panel_wait_for_hid_visible(hid, timeout=timeout) history_item_selector_state = "%s.state-%s" % (history_item_selector, state) try: self.wait_for_selector_visible(history_item_selector_state) except self.TimeoutException as e: history_item = self.driver.find_element_by_css_selector(history_item_selector) current_state = "UNKNOWN" classes = history_item.get_attribute("class").split(" ") for clazz in classes: if clazz.startswith("state-"): current_state = clazz[len("state-"):] template = "Failed waiting on history item %d state to change to [%s] current state [%s]. " message = template % (hid, state, current_state) raise self.prepend_timeout_message(e, message) def get_logged_in_user(self): return self.api_get("users/current") def is_logged_in(self): return "email" in self.get_logged_in_user() def _get_random_name(self, prefix=None, suffix=None, len=10): return '%s%s%s' % ( prefix or '', ''.join(random.choice(string.ascii_lowercase + string.digits) for _ in range(len)), suffix or '', ) def _get_random_email(self, username=None, domain=None): username = username or 'test' domain = domain or 'test.test' return self._get_random_name(prefix=username, suffix="@" + domain) def submit_login(self, email, password=None): if password is None: password = self.default_password login_info = { 'login': email, 'password': password, } self.click_masthead_user() self.click_label(self.navigation_data["labels"]["masthead"]["userMenu"]["login"]) with self.main_panel(): form = self.wait_for_selector(self.navigation_data["selectors"]["loginPage"]["form"]) self.fill(form, login_info) self.click_submit(form) def register(self, email=None, password=None, username=None, confirm=None, assert_valid=True): if email is None: email = self._get_random_email() if password is None: password = self.default_password if confirm is None: confirm = password if username is None: username = email.split("@")[0] self.home() self.click_masthead_user() self.click_label(self.navigation_data["labels"]["masthead"]["userMenu"]["register"]) with self.main_panel(): register_form_id = self.navigation_data["selectors"]["registrationPage"]["form"] form = self.wait_for_id(register_form_id) self.fill(form, dict( email=email, password=password, username=username, confirm=confirm )) self.click_xpath(self.navigation_data["selectors"]["registrationPage"]["submit_xpath"]) if assert_valid: self.home() self.click_masthead_user() user_email_element = self.wait_for_xpath_visible(self.navigation_data["selectors"]["masthead"]["userMenu"]["userEmail_xpath"]) text = user_email_element.text assert email in text assert self.get_logged_in_user()["email"] == email # Hide masthead menu click self.click_center() def click_center(self): action_chains = self.action_chains() center_element = self.driver.find_element_by_css_selector("#center") action_chains.move_to_element(center_element).click().perform() def perform_upload(self, test_path, ext=None, genome=None, ext_all=None, genome_all=None): self.home() upload_button = self.wait_for_selector_clickable(".upload-button") upload_button.click() if ext_all is not None: self.wait_for_selector_visible('.upload-footer-extension') self.select2_set_value(".upload-footer-extension", ext_all) if genome_all is not None: self.wait_for_selector_visible('.upload-footer-genome') self.select2_set_value(".upload-footer-genome", genome_all) local_upload_button = self.wait_for_selector_clickable("button#btn-local") local_upload_button.click() file_upload = self.wait_for_selector('input[type="file"]') file_upload.send_keys(test_path) if ext is not None: self.wait_for_selector_visible('.upload-extension') self.select2_set_value(".upload-extension", ext) if genome is not None: self.wait_for_selector_visible('.upload-genome') self.select2_set_value(".upload-genome", genome) start_button = self.wait_for_selector_clickable("button#btn-start") start_button.click() close_button = self.wait_for_selector_clickable("button#btn-close") close_button.click() def workflow_index_open(self): self.home() self.click_masthead_workflow() def workflow_index_table_elements(self): self.wait_for_selector_visible(".manage-table tbody") table_elements = self.driver.find_elements_by_css_selector(".manage-table tbody > tr") # drop header return table_elements[1:] def workflow_index_click_option(self, option_title, workflow_index=0): table_elements = self.workflow_index_table_elements() workflow_row = table_elements[workflow_index] workflow_button = workflow_row.find_element_by_css_selector(".menubutton") workflow_button.click() menu_element = self.wait_for_selector_visible(".popmenu-wrapper .dropdown-menu") menu_options = menu_element.find_elements_by_css_selector("li a") found_option = False for menu_option in menu_options: if option_title in menu_option.text: menu_option.click() found_option = True break if not found_option: raise AssertionError("Failed to find workflow action option with title [%s]" % option_title) def workflow_run_submit(self): button = self.wait_for_selector(".ui-form-header button") button.click() def tool_open(self, tool_id): link_element = self.wait_for_selector('a[href$="tool_runner?tool_id=%s"]' % tool_id) link_element.click() def tool_parameter_div(self, expanded_parameter_id): return self.wait_for_selector("div.ui-form-element[tour_id$='%s']" % expanded_parameter_id) def tool_set_value(self, expanded_parameter_id, value, expected_type=None, test_data_resolver=None): div_element = self.tool_parameter_div(expanded_parameter_id) assert div_element if expected_type == "data": div_selector = "div.ui-form-element[tour_id$='%s']" % expanded_parameter_id self.select2_set_value(div_selector, value) else: input_element = div_element.find_element_by_css_selector("input") # Clear default value input_element.clear() input_element.send_keys(value) def tool_execute(self): execute_button = self.wait_for_selector("button#execute") execute_button.click() def click_masthead_user(self): self.click_xpath(self.navigation_data["selectors"]["masthead"]["user"]) def click_masthead_workflow(self): self.click_xpath(self.navigation_data["selectors"]["masthead"]["workflow"]) def click_button_new_workflow(self): self.click_selector(self.navigation_data["selectors"]["workflows"]["new_button"]) def click_history_options(self): history_options_button_selector = self.test_data["historyOptions"]["selectors"]["button"] history_options_element = self.wait_for_selector(history_options_button_selector) assert history_options_element.is_displayed() history_options_button_icon_selector = self.test_data["historyOptions"]["selectors"]["buttonIcon"] history_options_button_icon_element = self.wait_for_selector(history_options_button_icon_selector) assert history_options_button_icon_element.is_displayed() history_options_element.click() def click_history_option(self, option_label): # Open menu self.click_history_options() # Click labelled option menu_selector = self.history_options_menu_selector() menu_element = self.wait_for_selector(menu_selector) menu_selection_element = menu_element.find_element_by_xpath('//ul[@id="history-options-button-menu"]/li/a[text()[contains(.,"%s")]]' % option_label) menu_selection_element.click() def history_options_menu_selector(self): menu_selector = self.test_data["historyOptions"]["selectors"]["menu"] return menu_selector @retry_during_transitions def history_panel_refresh_click(self): refresh_item = self.wait_for_selector_clickable("#history-refresh-button") refresh_item.click() def history_panel_multi_operations_selector(self): return self.test_data["historyPanel"]["selectors"]["history"]["multiOperationsIcon"] def history_panel_multi_operations_show(self): operations_selector = self.history_panel_multi_operations_selector() operations_element = self.wait_for_selector_clickable(operations_selector) operations_element.click() @retry_during_transitions def history_panel_muli_operation_select_hid(self, hid): item_selector = self.history_panel_item_selector(hid, wait=True) operation_radio_selector = "%s .selector" % item_selector element = self.wait_for_selector_clickable(operation_radio_selector) element.click() def history_panel_multi_operation_action_selector(self): return self.test_data["historyPanel"]["selectors"]["history"]["multiOperationsActionBtn"] def history_panel_multi_operation_action_click(self, action): time.sleep(5) button_element = self.wait_for_selector_clickable(self.history_panel_multi_operation_action_selector()) button_element.click() menu_element = self.wait_for_selector_visible(".list-action-menu.open") action_element = menu_element.find_element_by_link_text(action) action_element.click() def history_panel_item_selector(self, hid, wait=False): current_history_id = self.current_history_id() contents = self.api_get("histories/%s/contents" % current_history_id) try: history_item = [d for d in contents if d["hid"] == hid][0] except IndexError: raise Exception("Could not find history item with hid [%s] in contents [%s]" % (hid, contents)) history_item_selector = "#%s-%s" % (history_item["history_content_type"], history_item["id"]) if wait: self.wait_for_selector_visible(history_item_selector) return history_item_selector def modal_body_selector(self): return ".modal-body" def history_panel_item_body_selector(self, hid, wait=False): selector = "%s %s" % (self.history_panel_item_selector(hid), self.test_data["historyPanel"]["selectors"]["hda"]["body"]) if wait: self.wait_for_selector_visible(selector) return selector def hda_div_selector(self, hda_id): return "#dataset-%s" % hda_id def hda_body_selector(self, hda_id): return "%s %s" % (self.hda_div_selector(hda_id), self.test_data["historyPanel"]["selectors"]["hda"]["body"]) def hda_click_primary_action_button(self, hid, button_key): self.history_panel_click_item_title(hid=hid, wait=True) body_selector = self.history_panel_item_body_selector(hid=hid, wait=True) buttons_selector = body_selector + " " + self.test_data["historyPanel"]["selectors"]["hda"]["primaryActionButtons"] self.wait_for_selector_visible(buttons_selector) button_def = self.test_data["historyPanel"]["hdaPrimaryActionButtons"][button_key] button_selector = button_def["selector"] button_item = self.wait_for_selector_visible("%s %s" % (buttons_selector, button_selector)) return button_item.click() def history_panel_click_item_title(self, **kwds): if "hda_id" in kwds: item_selector = self.hda_div_selector(kwds["hda_id"]) else: item_selector = self.history_panel_item_selector(kwds["hid"]) title_selector = "%s .title" % item_selector title_element = self.wait_for_selector(title_selector) title_element.click() if kwds.get("wait", False): # Find a better way to wait for transition time.sleep(.5) def click_hda_title(self, hda_id, wait=False): # TODO: Replace with calls to history_panel_click_item_title. return self.history_panel_click_item_title(hda_id=hda_id, wait=wait) def collection_builder_set_name(self, name): name_element = self.wait_for_selector_visible("input.collection-name") name_element.send_keys(name) def collection_builder_hide_originals(self): hide_element = self.wait_for_selector_clickable("input.hide-originals") hide_element.click() def collection_builder_create(self): create_element = self.wait_for_selector_clickable("button.create-collection") create_element.click() def logout_if_needed(self): if self.is_logged_in(): self.home() self.click_masthead_user() self.click_label(self.navigation_data["labels"]["masthead"]["userMenu"]["logout"]) self.click_label('go to the home page') assert not self.is_logged_in() def run_tour(self, path, skip_steps=[], sleep_on_steps={}, tour_callback=None): if tour_callback is None: tour_callback = NullTourCallback() self.home() with open(path, "r") as f: tour_dict = yaml.load(f) steps = tour_dict["steps"] for i, step in enumerate(steps): title = step.get("title", None) skip = False if skip_steps: for skip_step in skip_steps: if title == skip_step: skip = True if title in sleep_on_steps: time.sleep(sleep_on_steps[title]) if skip: continue self.run_tour_step(step, i, tour_callback) def tour_wait_for_clickable_element(self, selector): wait = self.wait() element = wait.until(sizzle.sizzle_selector_clickable(selector)) return element def tour_wait_for_element_present(self, selector): wait = self.wait() element = wait.until(sizzle.sizzle_presence_of_selector(selector)) return element def get_tooltip_text(self, element, sleep=0, click_away=True): tooltip_selector = self.test_data["selectors"]["tooltipBalloon"] self.wait_for_selector_absent(tooltip_selector) action_chains = self.action_chains() action_chains.move_to_element(element) action_chains.perform() if sleep > 0: time.sleep(sleep) tooltip_element = self.wait_for_selector_visible(tooltip_selector) text = tooltip_element.text if click_away: self.click_center() return text def assert_tooltip_text(self, element, expected, sleep=0, click_away=True): text = self.get_tooltip_text(element, sleep=sleep, click_away=click_away) assert text == expected, "Tooltip text [%s] was not expected text [%s]." % (text, expected) def assert_error_message(self, contains=None): return self._assert_message("error", contains=contains) def assert_warning_message(self, contains=None): return self._assert_message("warning", contains=contains) def _assert_message(self, type, contains=None): element = self.wait_for_selector(self.test_data["selectors"]["messages"][type]) assert element, "No error message found, one expected." if contains is not None: assert contains in element.text def assert_no_error_message(self): self.assert_selector_absent(self.test_data["selectors"]["messages"]["error"]) def run_tour_step(self, step, step_index, tour_callback): preclick = step.get("preclick", []) for preclick_selector in preclick: print("(Pre)Clicking %s" % preclick_selector) element = self.tour_wait_for_clickable_element(preclick_selector) element.click() element_str = step.get("element", None) if element_str is not None: print("Waiting for element %s" % element_str) element = self.tour_wait_for_element_present(element_str) assert element is not None textinsert = step.get("textinsert", None) if textinsert is not None: element.send_keys(textinsert) tour_callback.handle_step(step, step_index) postclick = step.get("postclick", []) for postclick_selector in postclick: print("(Post)Clicking %s" % postclick_selector) element = self.tour_wait_for_clickable_element(postclick_selector) element.click() def select2_set_value(self, container_selector, value, with_click=True): # There are two hacky was to select things from the select2 widget - # with_click=True: This simulates the mouse click after the suggestion contains # only the selected value. # with_click=False: This presses enter on the selection. Not sure # why. # with_click seems to work in all situtations - the enter methods # doesn't seem to work with the tool form for some reason. container_elem = self.wait_for_selector(container_selector) text_element = container_elem.find_element_by_css_selector("input[type='text']") text_element.send_keys(value) # Wait for select2 options to load and then click to add this one. drop_elem = self.wait_for_selector_visible("#select2-drop") # Sleep seems to be needed - at least for send_enter. time.sleep(.5) if not with_click: # Wait for select2 options to load and then click to add this one. self.send_enter(text_element) else: select_elem = drop_elem.find_elements_by_css_selector(".select2-result-label")[0] action_chains = self.action_chains() action_chains.move_to_element(select_elem).click().perform() self.wait_for_selector_absent_or_hidden("#select2-drop")

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from __future__ import print_function import contextlib import random import string import time from functools import partial, wraps import requests import yaml from .data import NAVIGATION_DATA from .has_driver import exception_indicates_stale_element, HasDriver from . import sizzle DEFAULT_PASSWORD = '123456' class NullTourCallback(object): def handle_step(self, step, step_index): pass def retry_call_during_transitions(f, attempts=5, sleep=.1): previous_attempts = 0 while True: try: return f() except Exception as e: if previous_attempts > attempts: raise if not exception_indicates_stale_element(e): raise time.sleep(sleep) previous_attempts += 1 def retry_during_transitions(f, attempts=5, sleep=.1): @wraps(f) def _retry(*args, **kwds): retry_call_during_transitions(partial(f, *args, **kwds), attempts=attempts, sleep=sleep) return _retry class NavigatesGalaxy(HasDriver): default_password = DEFAULT_PASSWORD def get(self, url=""): full_url = self.build_url(url) return self.driver.get(full_url) @property def navigation_data(self): return NAVIGATION_DATA def home(self): self.get() self.wait_for_selector_visible("#masthead") self.wait_for_selector_visible("#current-history-panel") def switch_to_main_panel(self): self.driver.switch_to.frame(self.navigation_data["selectors"]["frames"]["main"]) @contextlib.contextmanager def main_panel(self): try: self.switch_to_main_panel() yield finally: self.driver.switch_to.default_content def api_get(self, endpoint, data={}, raw=False): full_url = self.build_url("api/" + endpoint, for_selenium=False) response = requests.get(full_url, data=data, cookies=self.selenium_to_requests_cookies()) if raw: return response else: return response.json() def get_galaxy_session(self): for cookie in self.driver.get_cookies(): if cookie["name"] == "galaxysession": return cookie["value"] def selenium_to_requests_cookies(self): return { 'galaxysession': self.get_galaxy_session() } def history_panel_name_element(self): name_selector = self.test_data["historyPanel"]["selectors"]["history"]["name"] return self.wait_for_selector(name_selector) def current_history(self): history = self.api_get("histories")[0] return history def current_history_id(self): return self.current_history()["id"] def current_history_contents(self): current_history_id = self.current_history_id() history_contents = self.api_get("histories/%s/contents" % current_history_id) return history_contents def latest_history_item(self): history_contents = self.current_history_contents() assert len(history_contents) > 0 return history_contents[-1] def wait_for_history(self, timeout=30, assert_ok=True): def history_becomes_terminal(driver): current_history_id = self.current_history_id() state = self.api_get("histories/%s" % current_history_id)["state"] if state not in ["running", "queued", "new", "ready"]: return state else: return None final_state = self.wait(timeout).until(history_becomes_terminal) if assert_ok: assert final_state == "ok", final_state return final_state def history_panel_wait_for_hid_ok(self, hid, timeout=60): self.history_panel_wait_for_hid_state(hid, 'ok', timeout=timeout) def history_panel_wait_for_hid_visible(self, hid, timeout=60): current_history_id = self.current_history_id() def history_has_hid(driver): contents = self.api_get("histories/%s/contents" % current_history_id) return any([d for d in contents if d["hid"] == hid]) self.wait(timeout).until(history_has_hid) contents = self.api_get("histories/%s/contents" % current_history_id) history_item = [d for d in contents if d["hid"] == hid][0] history_item_selector = "#%s-%s" % (history_item["history_content_type"], history_item["id"]) self.wait_for_selector_visible(history_item_selector) return history_item_selector def history_panel_wait_for_hid_hidden(self, hid, timeout=60): current_history_id = self.current_history_id() contents = self.api_get("histories/%s/contents" % current_history_id) history_item = [d for d in contents if d["hid"] == hid][0] history_item_selector = "#%s-%s" % (history_item["history_content_type"], history_item["id"]) self.wait_for_selector_absent(history_item_selector) return history_item_selector def history_panel_wait_for_hid_state(self, hid, state, timeout=60): history_item_selector = self.history_panel_wait_for_hid_visible(hid, timeout=timeout) history_item_selector_state = "%s.state-%s" % (history_item_selector, state) try: self.wait_for_selector_visible(history_item_selector_state) except self.TimeoutException as e: history_item = self.driver.find_element_by_css_selector(history_item_selector) current_state = "UNKNOWN" classes = history_item.get_attribute("class").split(" ") for clazz in classes: if clazz.startswith("state-"): current_state = clazz[len("state-"):] template = "Failed waiting on history item %d state to change to [%s] current state [%s]. " message = template % (hid, state, current_state) raise self.prepend_timeout_message(e, message) def get_logged_in_user(self): return self.api_get("users/current") def is_logged_in(self): return "email" in self.get_logged_in_user() def _get_random_name(self, prefix=None, suffix=None, len=10): return '%s%s%s' % ( prefix or '', ''.join(random.choice(string.ascii_lowercase + string.digits) for _ in range(len)), suffix or '', ) def _get_random_email(self, username=None, domain=None): username = username or 'test' domain = domain or 'test.test' return self._get_random_name(prefix=username, suffix="@" + domain) def submit_login(self, email, password=None): if password is None: password = self.default_password login_info = { 'login': email, 'password': password, } self.click_masthead_user() self.click_label(self.navigation_data["labels"]["masthead"]["userMenu"]["login"]) with self.main_panel(): form = self.wait_for_selector(self.navigation_data["selectors"]["loginPage"]["form"]) self.fill(form, login_info) self.click_submit(form) def register(self, email=None, password=None, username=None, confirm=None, assert_valid=True): if email is None: email = self._get_random_email() if password is None: password = self.default_password if confirm is None: confirm = password if username is None: username = email.split("@")[0] self.home() self.click_masthead_user() self.click_label(self.navigation_data["labels"]["masthead"]["userMenu"]["register"]) with self.main_panel(): register_form_id = self.navigation_data["selectors"]["registrationPage"]["form"] form = self.wait_for_id(register_form_id) self.fill(form, dict( email=email, password=password, username=username, confirm=confirm )) self.click_xpath(self.navigation_data["selectors"]["registrationPage"]["submit_xpath"]) if assert_valid: self.home() self.click_masthead_user() user_email_element = self.wait_for_xpath_visible(self.navigation_data["selectors"]["masthead"]["userMenu"]["userEmail_xpath"]) text = user_email_element.text assert email in text assert self.get_logged_in_user()["email"] == email self.click_center() def click_center(self): action_chains = self.action_chains() center_element = self.driver.find_element_by_css_selector("#center") action_chains.move_to_element(center_element).click().perform() def perform_upload(self, test_path, ext=None, genome=None, ext_all=None, genome_all=None): self.home() upload_button = self.wait_for_selector_clickable(".upload-button") upload_button.click() if ext_all is not None: self.wait_for_selector_visible('.upload-footer-extension') self.select2_set_value(".upload-footer-extension", ext_all) if genome_all is not None: self.wait_for_selector_visible('.upload-footer-genome') self.select2_set_value(".upload-footer-genome", genome_all) local_upload_button = self.wait_for_selector_clickable("button#btn-local") local_upload_button.click() file_upload = self.wait_for_selector('input[type="file"]') file_upload.send_keys(test_path) if ext is not None: self.wait_for_selector_visible('.upload-extension') self.select2_set_value(".upload-extension", ext) if genome is not None: self.wait_for_selector_visible('.upload-genome') self.select2_set_value(".upload-genome", genome) start_button = self.wait_for_selector_clickable("button#btn-start") start_button.click() close_button = self.wait_for_selector_clickable("button#btn-close") close_button.click() def workflow_index_open(self): self.home() self.click_masthead_workflow() def workflow_index_table_elements(self): self.wait_for_selector_visible(".manage-table tbody") table_elements = self.driver.find_elements_by_css_selector(".manage-table tbody > tr") return table_elements[1:] def workflow_index_click_option(self, option_title, workflow_index=0): table_elements = self.workflow_index_table_elements() workflow_row = table_elements[workflow_index] workflow_button = workflow_row.find_element_by_css_selector(".menubutton") workflow_button.click() menu_element = self.wait_for_selector_visible(".popmenu-wrapper .dropdown-menu") menu_options = menu_element.find_elements_by_css_selector("li a") found_option = False for menu_option in menu_options: if option_title in menu_option.text: menu_option.click() found_option = True break if not found_option: raise AssertionError("Failed to find workflow action option with title [%s]" % option_title) def workflow_run_submit(self): button = self.wait_for_selector(".ui-form-header button") button.click() def tool_open(self, tool_id): link_element = self.wait_for_selector('a[href$="tool_runner?tool_id=%s"]' % tool_id) link_element.click() def tool_parameter_div(self, expanded_parameter_id): return self.wait_for_selector("div.ui-form-element[tour_id$='%s']" % expanded_parameter_id) def tool_set_value(self, expanded_parameter_id, value, expected_type=None, test_data_resolver=None): div_element = self.tool_parameter_div(expanded_parameter_id) assert div_element if expected_type == "data": div_selector = "div.ui-form-element[tour_id$='%s']" % expanded_parameter_id self.select2_set_value(div_selector, value) else: input_element = div_element.find_element_by_css_selector("input") input_element.clear() input_element.send_keys(value) def tool_execute(self): execute_button = self.wait_for_selector("button#execute") execute_button.click() def click_masthead_user(self): self.click_xpath(self.navigation_data["selectors"]["masthead"]["user"]) def click_masthead_workflow(self): self.click_xpath(self.navigation_data["selectors"]["masthead"]["workflow"]) def click_button_new_workflow(self): self.click_selector(self.navigation_data["selectors"]["workflows"]["new_button"]) def click_history_options(self): history_options_button_selector = self.test_data["historyOptions"]["selectors"]["button"] history_options_element = self.wait_for_selector(history_options_button_selector) assert history_options_element.is_displayed() history_options_button_icon_selector = self.test_data["historyOptions"]["selectors"]["buttonIcon"] history_options_button_icon_element = self.wait_for_selector(history_options_button_icon_selector) assert history_options_button_icon_element.is_displayed() history_options_element.click() def click_history_option(self, option_label): self.click_history_options() menu_selector = self.history_options_menu_selector() menu_element = self.wait_for_selector(menu_selector) menu_selection_element = menu_element.find_element_by_xpath('//ul[@id="history-options-button-menu"]/li/a[text()[contains(.,"%s")]]' % option_label) menu_selection_element.click() def history_options_menu_selector(self): menu_selector = self.test_data["historyOptions"]["selectors"]["menu"] return menu_selector @retry_during_transitions def history_panel_refresh_click(self): refresh_item = self.wait_for_selector_clickable("#history-refresh-button") refresh_item.click() def history_panel_multi_operations_selector(self): return self.test_data["historyPanel"]["selectors"]["history"]["multiOperationsIcon"] def history_panel_multi_operations_show(self): operations_selector = self.history_panel_multi_operations_selector() operations_element = self.wait_for_selector_clickable(operations_selector) operations_element.click() @retry_during_transitions def history_panel_muli_operation_select_hid(self, hid): item_selector = self.history_panel_item_selector(hid, wait=True) operation_radio_selector = "%s .selector" % item_selector element = self.wait_for_selector_clickable(operation_radio_selector) element.click() def history_panel_multi_operation_action_selector(self): return self.test_data["historyPanel"]["selectors"]["history"]["multiOperationsActionBtn"] def history_panel_multi_operation_action_click(self, action): time.sleep(5) button_element = self.wait_for_selector_clickable(self.history_panel_multi_operation_action_selector()) button_element.click() menu_element = self.wait_for_selector_visible(".list-action-menu.open") action_element = menu_element.find_element_by_link_text(action) action_element.click() def history_panel_item_selector(self, hid, wait=False): current_history_id = self.current_history_id() contents = self.api_get("histories/%s/contents" % current_history_id) try: history_item = [d for d in contents if d["hid"] == hid][0] except IndexError: raise Exception("Could not find history item with hid [%s] in contents [%s]" % (hid, contents)) history_item_selector = "#%s-%s" % (history_item["history_content_type"], history_item["id"]) if wait: self.wait_for_selector_visible(history_item_selector) return history_item_selector def modal_body_selector(self): return ".modal-body" def history_panel_item_body_selector(self, hid, wait=False): selector = "%s %s" % (self.history_panel_item_selector(hid), self.test_data["historyPanel"]["selectors"]["hda"]["body"]) if wait: self.wait_for_selector_visible(selector) return selector def hda_div_selector(self, hda_id): return "#dataset-%s" % hda_id def hda_body_selector(self, hda_id): return "%s %s" % (self.hda_div_selector(hda_id), self.test_data["historyPanel"]["selectors"]["hda"]["body"]) def hda_click_primary_action_button(self, hid, button_key): self.history_panel_click_item_title(hid=hid, wait=True) body_selector = self.history_panel_item_body_selector(hid=hid, wait=True) buttons_selector = body_selector + " " + self.test_data["historyPanel"]["selectors"]["hda"]["primaryActionButtons"] self.wait_for_selector_visible(buttons_selector) button_def = self.test_data["historyPanel"]["hdaPrimaryActionButtons"][button_key] button_selector = button_def["selector"] button_item = self.wait_for_selector_visible("%s %s" % (buttons_selector, button_selector)) return button_item.click() def history_panel_click_item_title(self, **kwds): if "hda_id" in kwds: item_selector = self.hda_div_selector(kwds["hda_id"]) else: item_selector = self.history_panel_item_selector(kwds["hid"]) title_selector = "%s .title" % item_selector title_element = self.wait_for_selector(title_selector) title_element.click() if kwds.get("wait", False): time.sleep(.5) def click_hda_title(self, hda_id, wait=False): return self.history_panel_click_item_title(hda_id=hda_id, wait=wait) def collection_builder_set_name(self, name): name_element = self.wait_for_selector_visible("input.collection-name") name_element.send_keys(name) def collection_builder_hide_originals(self): hide_element = self.wait_for_selector_clickable("input.hide-originals") hide_element.click() def collection_builder_create(self): create_element = self.wait_for_selector_clickable("button.create-collection") create_element.click() def logout_if_needed(self): if self.is_logged_in(): self.home() self.click_masthead_user() self.click_label(self.navigation_data["labels"]["masthead"]["userMenu"]["logout"]) self.click_label('go to the home page') assert not self.is_logged_in() def run_tour(self, path, skip_steps=[], sleep_on_steps={}, tour_callback=None): if tour_callback is None: tour_callback = NullTourCallback() self.home() with open(path, "r") as f: tour_dict = yaml.load(f) steps = tour_dict["steps"] for i, step in enumerate(steps): title = step.get("title", None) skip = False if skip_steps: for skip_step in skip_steps: if title == skip_step: skip = True if title in sleep_on_steps: time.sleep(sleep_on_steps[title]) if skip: continue self.run_tour_step(step, i, tour_callback) def tour_wait_for_clickable_element(self, selector): wait = self.wait() element = wait.until(sizzle.sizzle_selector_clickable(selector)) return element def tour_wait_for_element_present(self, selector): wait = self.wait() element = wait.until(sizzle.sizzle_presence_of_selector(selector)) return element def get_tooltip_text(self, element, sleep=0, click_away=True): tooltip_selector = self.test_data["selectors"]["tooltipBalloon"] self.wait_for_selector_absent(tooltip_selector) action_chains = self.action_chains() action_chains.move_to_element(element) action_chains.perform() if sleep > 0: time.sleep(sleep) tooltip_element = self.wait_for_selector_visible(tooltip_selector) text = tooltip_element.text if click_away: self.click_center() return text def assert_tooltip_text(self, element, expected, sleep=0, click_away=True): text = self.get_tooltip_text(element, sleep=sleep, click_away=click_away) assert text == expected, "Tooltip text [%s] was not expected text [%s]." % (text, expected) def assert_error_message(self, contains=None): return self._assert_message("error", contains=contains) def assert_warning_message(self, contains=None): return self._assert_message("warning", contains=contains) def _assert_message(self, type, contains=None): element = self.wait_for_selector(self.test_data["selectors"]["messages"][type]) assert element, "No error message found, one expected." if contains is not None: assert contains in element.text def assert_no_error_message(self): self.assert_selector_absent(self.test_data["selectors"]["messages"]["error"]) def run_tour_step(self, step, step_index, tour_callback): preclick = step.get("preclick", []) for preclick_selector in preclick: print("(Pre)Clicking %s" % preclick_selector) element = self.tour_wait_for_clickable_element(preclick_selector) element.click() element_str = step.get("element", None) if element_str is not None: print("Waiting for element %s" % element_str) element = self.tour_wait_for_element_present(element_str) assert element is not None textinsert = step.get("textinsert", None) if textinsert is not None: element.send_keys(textinsert) tour_callback.handle_step(step, step_index) postclick = step.get("postclick", []) for postclick_selector in postclick: print("(Post)Clicking %s" % postclick_selector) element = self.tour_wait_for_clickable_element(postclick_selector) element.click() def select2_set_value(self, container_selector, value, with_click=True): container_elem = self.wait_for_selector(container_selector) text_element = container_elem.find_element_by_css_selector("input[type='text']") text_element.send_keys(value) # Wait for select2 options to load and then click to add this one. drop_elem = self.wait_for_selector_visible("#select2-drop") # Sleep seems to be needed - at least for send_enter. time.sleep(.5) if not with_click: # Wait for select2 options to load and then click to add this one. self.send_enter(text_element) else: select_elem = drop_elem.find_elements_by_css_selector(".select2-result-label")[0] action_chains = self.action_chains() action_chains.move_to_element(select_elem).click().perform() self.wait_for_selector_absent_or_hidden("#select2-drop")

true

f7319258f8ed765fde12cec23303820fd0ad703d

14,348

py

Python

Lib/warnings.py

adamwen829/cpython

0f1c7c760c6b2804f5d05cae9ca045d1fdf3d667

[ "PSF-2.0" ]

2

2017-05-05T02:07:59.000Z

2017-08-18T09:24:48.000Z

Lib/warnings.py

adamwen829/cpython

0f1c7c760c6b2804f5d05cae9ca045d1fdf3d667

[ "PSF-2.0" ]

null

Lib/warnings.py

adamwen829/cpython

0f1c7c760c6b2804f5d05cae9ca045d1fdf3d667

[ "PSF-2.0" ]

3

2016-04-21T07:58:27.000Z

2016-05-06T21:34:44.000Z

"""Python part of the warnings subsystem.""" import sys __all__ = ["warn", "warn_explicit", "showwarning", "formatwarning", "filterwarnings", "simplefilter", "resetwarnings", "catch_warnings"] def showwarning(message, category, filename, lineno, file=None, line=None): """Hook to write a warning to a file; replace if you like.""" if file is None: file = sys.stderr try: file.write(formatwarning(message, category, filename, lineno, line)) except OSError: pass # the file (probably stderr) is invalid - this warning gets lost. def formatwarning(message, category, filename, lineno, line=None): """Function to format a warning the standard way.""" import linecache s = "%s:%s: %s: %s\n" % (filename, lineno, category.__name__, message) line = linecache.getline(filename, lineno) if line is None else line if line: line = line.strip() s += " %s\n" % line return s def filterwarnings(action, message="", category=Warning, module="", lineno=0, append=False): """Insert an entry into the list of warnings filters (at the front). 'action' -- one of "error", "ignore", "always", "default", "module", or "once" 'message' -- a regex that the warning message must match 'category' -- a class that the warning must be a subclass of 'module' -- a regex that the module name must match 'lineno' -- an integer line number, 0 matches all warnings 'append' -- if true, append to the list of filters """ import re assert action in ("error", "ignore", "always", "default", "module", "once"), "invalid action: %r" % (action,) assert isinstance(message, str), "message must be a string" assert isinstance(category, type), "category must be a class" assert issubclass(category, Warning), "category must be a Warning subclass" assert isinstance(module, str), "module must be a string" assert isinstance(lineno, int) and lineno >= 0, \ "lineno must be an int >= 0" item = (action, re.compile(message, re.I), category, re.compile(module), lineno) if append: filters.append(item) else: filters.insert(0, item) _filters_mutated() def simplefilter(action, category=Warning, lineno=0, append=False): """Insert a simple entry into the list of warnings filters (at the front). A simple filter matches all modules and messages. 'action' -- one of "error", "ignore", "always", "default", "module", or "once" 'category' -- a class that the warning must be a subclass of 'lineno' -- an integer line number, 0 matches all warnings 'append' -- if true, append to the list of filters """ assert action in ("error", "ignore", "always", "default", "module", "once"), "invalid action: %r" % (action,) assert isinstance(lineno, int) and lineno >= 0, \ "lineno must be an int >= 0" item = (action, None, category, None, lineno) if append: filters.append(item) else: filters.insert(0, item) _filters_mutated() def resetwarnings(): """Clear the list of warning filters, so that no filters are active.""" filters[:] = [] _filters_mutated() class _OptionError(Exception): """Exception used by option processing helpers.""" pass # Helper to process -W options passed via sys.warnoptions def _processoptions(args): for arg in args: try: _setoption(arg) except _OptionError as msg: print("Invalid -W option ignored:", msg, file=sys.stderr) # Helper for _processoptions() def _setoption(arg): import re parts = arg.split(':') if len(parts) > 5: raise _OptionError("too many fields (max 5): %r" % (arg,)) while len(parts) < 5: parts.append('') action, message, category, module, lineno = [s.strip() for s in parts] action = _getaction(action) message = re.escape(message) category = _getcategory(category) module = re.escape(module) if module: module = module + '$' if lineno: try: lineno = int(lineno) if lineno < 0: raise ValueError except (ValueError, OverflowError): raise _OptionError("invalid lineno %r" % (lineno,)) else: lineno = 0 filterwarnings(action, message, category, module, lineno) # Helper for _setoption() def _getaction(action): if not action: return "default" if action == "all": return "always" # Alias for a in ('default', 'always', 'ignore', 'module', 'once', 'error'): if a.startswith(action): return a raise _OptionError("invalid action: %r" % (action,)) # Helper for _setoption() def _getcategory(category): import re if not category: return Warning if re.match("^[a-zA-Z0-9_]+$", category): try: cat = eval(category) except NameError: raise _OptionError("unknown warning category: %r" % (category,)) else: i = category.rfind(".") module = category[:i] klass = category[i+1:] try: m = __import__(module, None, None, [klass]) except ImportError: raise _OptionError("invalid module name: %r" % (module,)) try: cat = getattr(m, klass) except AttributeError: raise _OptionError("unknown warning category: %r" % (category,)) if not issubclass(cat, Warning): raise _OptionError("invalid warning category: %r" % (category,)) return cat # Code typically replaced by _warnings def warn(message, category=None, stacklevel=1): """Issue a warning, or maybe ignore it or raise an exception.""" # Check if message is already a Warning object if isinstance(message, Warning): category = message.__class__ # Check category argument if category is None: category = UserWarning if not (isinstance(category, type) and issubclass(category, Warning)): raise TypeError("category must be a Warning subclass, " "not '{:s}'".format(type(category).__name__)) # Get context information try: caller = sys._getframe(stacklevel) except ValueError: globals = sys.__dict__ lineno = 1 else: globals = caller.f_globals lineno = caller.f_lineno if '__name__' in globals: module = globals['__name__'] else: module = "<string>" filename = globals.get('__file__') if filename: fnl = filename.lower() if fnl.endswith((".pyc", ".pyo")): filename = filename[:-1] else: if module == "__main__": try: filename = sys.argv[0] except AttributeError: # embedded interpreters don't have sys.argv, see bug #839151 filename = '__main__' if not filename: filename = module registry = globals.setdefault("__warningregistry__", {}) warn_explicit(message, category, filename, lineno, module, registry, globals) def warn_explicit(message, category, filename, lineno, module=None, registry=None, module_globals=None): lineno = int(lineno) if module is None: module = filename or "<unknown>" if module[-3:].lower() == ".py": module = module[:-3] # XXX What about leading pathname? if registry is None: registry = {} if registry.get('version', 0) != _filters_version: registry.clear() registry['version'] = _filters_version if isinstance(message, Warning): text = str(message) category = message.__class__ else: text = message message = category(message) key = (text, category, lineno) # Quick test for common case if registry.get(key): return # Search the filters for item in filters: action, msg, cat, mod, ln = item if ((msg is None or msg.match(text)) and issubclass(category, cat) and (mod is None or mod.match(module)) and (ln == 0 or lineno == ln)): break else: action = defaultaction # Early exit actions if action == "ignore": registry[key] = 1 return # Prime the linecache for formatting, in case the # "file" is actually in a zipfile or something. import linecache linecache.getlines(filename, module_globals) if action == "error": raise message # Other actions if action == "once": registry[key] = 1 oncekey = (text, category) if onceregistry.get(oncekey): return onceregistry[oncekey] = 1 elif action == "always": pass elif action == "module": registry[key] = 1 altkey = (text, category, 0) if registry.get(altkey): return registry[altkey] = 1 elif action == "default": registry[key] = 1 else: # Unrecognized actions are errors raise RuntimeError( "Unrecognized action (%r) in warnings.filters:\n %s" % (action, item)) if not callable(showwarning): raise TypeError("warnings.showwarning() must be set to a " "function or method") # Print message and context showwarning(message, category, filename, lineno) class WarningMessage(object): """Holds the result of a single showwarning() call.""" _WARNING_DETAILS = ("message", "category", "filename", "lineno", "file", "line") def __init__(self, message, category, filename, lineno, file=None, line=None): local_values = locals() for attr in self._WARNING_DETAILS: setattr(self, attr, local_values[attr]) self._category_name = category.__name__ if category else None def __str__(self): return ("{message : %r, category : %r, filename : %r, lineno : %s, " "line : %r}" % (self.message, self._category_name, self.filename, self.lineno, self.line)) class catch_warnings(object): """A context manager that copies and restores the warnings filter upon exiting the context. The 'record' argument specifies whether warnings should be captured by a custom implementation of warnings.showwarning() and be appended to a list returned by the context manager. Otherwise None is returned by the context manager. The objects appended to the list are arguments whose attributes mirror the arguments to showwarning(). The 'module' argument is to specify an alternative module to the module named 'warnings' and imported under that name. This argument is only useful when testing the warnings module itself. """ def __init__(self, *, record=False, module=None): """Specify whether to record warnings and if an alternative module should be used other than sys.modules['warnings']. For compatibility with Python 3.0, please consider all arguments to be keyword-only. """ self._record = record self._module = sys.modules['warnings'] if module is None else module self._entered = False def __repr__(self): args = [] if self._record: args.append("record=True") if self._module is not sys.modules['warnings']: args.append("module=%r" % self._module) name = type(self).__name__ return "%s(%s)" % (name, ", ".join(args)) def __enter__(self): if self._entered: raise RuntimeError("Cannot enter %r twice" % self) self._entered = True self._filters = self._module.filters self._module.filters = self._filters[:] self._module._filters_mutated() self._showwarning = self._module.showwarning if self._record: log = [] def showwarning(*args, **kwargs): log.append(WarningMessage(*args, **kwargs)) self._module.showwarning = showwarning return log else: return None def __exit__(self, *exc_info): if not self._entered: raise RuntimeError("Cannot exit %r without entering first" % self) self._module.filters = self._filters self._module._filters_mutated() self._module.showwarning = self._showwarning # filters contains a sequence of filter 5-tuples # The components of the 5-tuple are: # - an action: error, ignore, always, default, module, or once # - a compiled regex that must match the warning message # - a class representing the warning category # - a compiled regex that must match the module that is being warned # - a line number for the line being warning, or 0 to mean any line # If either if the compiled regexs are None, match anything. _warnings_defaults = False try: from _warnings import (filters, _defaultaction, _onceregistry, warn, warn_explicit, _filters_mutated) defaultaction = _defaultaction onceregistry = _onceregistry _warnings_defaults = True except ImportError: filters = [] defaultaction = "default" onceregistry = {} _filters_version = 1 def _filters_mutated(): global _filters_version _filters_version += 1 # Module initialization _processoptions(sys.warnoptions) if not _warnings_defaults: silence = [ImportWarning, PendingDeprecationWarning] silence.append(DeprecationWarning) for cls in silence: simplefilter("ignore", category=cls) bytes_warning = sys.flags.bytes_warning if bytes_warning > 1: bytes_action = "error" elif bytes_warning: bytes_action = "default" else: bytes_action = "ignore" simplefilter(bytes_action, category=BytesWarning, append=1) # resource usage warnings are enabled by default in pydebug mode if hasattr(sys, 'gettotalrefcount'): resource_action = "always" else: resource_action = "ignore" simplefilter(resource_action, category=ResourceWarning, append=1) del _warnings_defaults

34.995122

79

0.617299

import sys __all__ = ["warn", "warn_explicit", "showwarning", "formatwarning", "filterwarnings", "simplefilter", "resetwarnings", "catch_warnings"] def showwarning(message, category, filename, lineno, file=None, line=None): if file is None: file = sys.stderr try: file.write(formatwarning(message, category, filename, lineno, line)) except OSError: pass def formatwarning(message, category, filename, lineno, line=None): import linecache s = "%s:%s: %s: %s\n" % (filename, lineno, category.__name__, message) line = linecache.getline(filename, lineno) if line is None else line if line: line = line.strip() s += " %s\n" % line return s def filterwarnings(action, message="", category=Warning, module="", lineno=0, append=False): import re assert action in ("error", "ignore", "always", "default", "module", "once"), "invalid action: %r" % (action,) assert isinstance(message, str), "message must be a string" assert isinstance(category, type), "category must be a class" assert issubclass(category, Warning), "category must be a Warning subclass" assert isinstance(module, str), "module must be a string" assert isinstance(lineno, int) and lineno >= 0, \ "lineno must be an int >= 0" item = (action, re.compile(message, re.I), category, re.compile(module), lineno) if append: filters.append(item) else: filters.insert(0, item) _filters_mutated() def simplefilter(action, category=Warning, lineno=0, append=False): assert action in ("error", "ignore", "always", "default", "module", "once"), "invalid action: %r" % (action,) assert isinstance(lineno, int) and lineno >= 0, \ "lineno must be an int >= 0" item = (action, None, category, None, lineno) if append: filters.append(item) else: filters.insert(0, item) _filters_mutated() def resetwarnings(): filters[:] = [] _filters_mutated() class _OptionError(Exception): pass def _processoptions(args): for arg in args: try: _setoption(arg) except _OptionError as msg: print("Invalid -W option ignored:", msg, file=sys.stderr) def _setoption(arg): import re parts = arg.split(':') if len(parts) > 5: raise _OptionError("too many fields (max 5): %r" % (arg,)) while len(parts) < 5: parts.append('') action, message, category, module, lineno = [s.strip() for s in parts] action = _getaction(action) message = re.escape(message) category = _getcategory(category) module = re.escape(module) if module: module = module + '$' if lineno: try: lineno = int(lineno) if lineno < 0: raise ValueError except (ValueError, OverflowError): raise _OptionError("invalid lineno %r" % (lineno,)) else: lineno = 0 filterwarnings(action, message, category, module, lineno) def _getaction(action): if not action: return "default" if action == "all": return "always" for a in ('default', 'always', 'ignore', 'module', 'once', 'error'): if a.startswith(action): return a raise _OptionError("invalid action: %r" % (action,)) def _getcategory(category): import re if not category: return Warning if re.match("^[a-zA-Z0-9_]+$", category): try: cat = eval(category) except NameError: raise _OptionError("unknown warning category: %r" % (category,)) else: i = category.rfind(".") module = category[:i] klass = category[i+1:] try: m = __import__(module, None, None, [klass]) except ImportError: raise _OptionError("invalid module name: %r" % (module,)) try: cat = getattr(m, klass) except AttributeError: raise _OptionError("unknown warning category: %r" % (category,)) if not issubclass(cat, Warning): raise _OptionError("invalid warning category: %r" % (category,)) return cat def warn(message, category=None, stacklevel=1): if isinstance(message, Warning): category = message.__class__ if category is None: category = UserWarning if not (isinstance(category, type) and issubclass(category, Warning)): raise TypeError("category must be a Warning subclass, " "not '{:s}'".format(type(category).__name__)) try: caller = sys._getframe(stacklevel) except ValueError: globals = sys.__dict__ lineno = 1 else: globals = caller.f_globals lineno = caller.f_lineno if '__name__' in globals: module = globals['__name__'] else: module = "<string>" filename = globals.get('__file__') if filename: fnl = filename.lower() if fnl.endswith((".pyc", ".pyo")): filename = filename[:-1] else: if module == "__main__": try: filename = sys.argv[0] except AttributeError: filename = '__main__' if not filename: filename = module registry = globals.setdefault("__warningregistry__", {}) warn_explicit(message, category, filename, lineno, module, registry, globals) def warn_explicit(message, category, filename, lineno, module=None, registry=None, module_globals=None): lineno = int(lineno) if module is None: module = filename or "<unknown>" if module[-3:].lower() == ".py": module = module[:-3] # XXX What about leading pathname? if registry is None: registry = {} if registry.get('version', 0) != _filters_version: registry.clear() registry['version'] = _filters_version if isinstance(message, Warning): text = str(message) category = message.__class__ else: text = message message = category(message) key = (text, category, lineno) # Quick test for common case if registry.get(key): return # Search the filters for item in filters: action, msg, cat, mod, ln = item if ((msg is None or msg.match(text)) and issubclass(category, cat) and (mod is None or mod.match(module)) and (ln == 0 or lineno == ln)): break else: action = defaultaction # Early exit actions if action == "ignore": registry[key] = 1 return # Prime the linecache for formatting, in case the # "file" is actually in a zipfile or something. import linecache linecache.getlines(filename, module_globals) if action == "error": raise message # Other actions if action == "once": registry[key] = 1 oncekey = (text, category) if onceregistry.get(oncekey): return onceregistry[oncekey] = 1 elif action == "always": pass elif action == "module": registry[key] = 1 altkey = (text, category, 0) if registry.get(altkey): return registry[altkey] = 1 elif action == "default": registry[key] = 1 else: # Unrecognized actions are errors raise RuntimeError( "Unrecognized action (%r) in warnings.filters:\n %s" % (action, item)) if not callable(showwarning): raise TypeError("warnings.showwarning() must be set to a " "function or method") # Print message and context showwarning(message, category, filename, lineno) class WarningMessage(object): _WARNING_DETAILS = ("message", "category", "filename", "lineno", "file", "line") def __init__(self, message, category, filename, lineno, file=None, line=None): local_values = locals() for attr in self._WARNING_DETAILS: setattr(self, attr, local_values[attr]) self._category_name = category.__name__ if category else None def __str__(self): return ("{message : %r, category : %r, filename : %r, lineno : %s, " "line : %r}" % (self.message, self._category_name, self.filename, self.lineno, self.line)) class catch_warnings(object): def __init__(self, *, record=False, module=None): self._record = record self._module = sys.modules['warnings'] if module is None else module self._entered = False def __repr__(self): args = [] if self._record: args.append("record=True") if self._module is not sys.modules['warnings']: args.append("module=%r" % self._module) name = type(self).__name__ return "%s(%s)" % (name, ", ".join(args)) def __enter__(self): if self._entered: raise RuntimeError("Cannot enter %r twice" % self) self._entered = True self._filters = self._module.filters self._module.filters = self._filters[:] self._module._filters_mutated() self._showwarning = self._module.showwarning if self._record: log = [] def showwarning(*args, **kwargs): log.append(WarningMessage(*args, **kwargs)) self._module.showwarning = showwarning return log else: return None def __exit__(self, *exc_info): if not self._entered: raise RuntimeError("Cannot exit %r without entering first" % self) self._module.filters = self._filters self._module._filters_mutated() self._module.showwarning = self._showwarning # filters contains a sequence of filter 5-tuples # The components of the 5-tuple are: # - an action: error, ignore, always, default, module, or once # - a compiled regex that must match the warning message # - a class representing the warning category # - a compiled regex that must match the module that is being warned # - a line number for the line being warning, or 0 to mean any line # If either if the compiled regexs are None, match anything. _warnings_defaults = False try: from _warnings import (filters, _defaultaction, _onceregistry, warn, warn_explicit, _filters_mutated) defaultaction = _defaultaction onceregistry = _onceregistry _warnings_defaults = True except ImportError: filters = [] defaultaction = "default" onceregistry = {} _filters_version = 1 def _filters_mutated(): global _filters_version _filters_version += 1 # Module initialization _processoptions(sys.warnoptions) if not _warnings_defaults: silence = [ImportWarning, PendingDeprecationWarning] silence.append(DeprecationWarning) for cls in silence: simplefilter("ignore", category=cls) bytes_warning = sys.flags.bytes_warning if bytes_warning > 1: bytes_action = "error" elif bytes_warning: bytes_action = "default" else: bytes_action = "ignore" simplefilter(bytes_action, category=BytesWarning, append=1) # resource usage warnings are enabled by default in pydebug mode if hasattr(sys, 'gettotalrefcount'): resource_action = "always" else: resource_action = "ignore" simplefilter(resource_action, category=ResourceWarning, append=1) del _warnings_defaults

true

f731926d323d02ff6850d4318c6001330ae3775c

737

py

Python

benchmarks/__init__.py

cedric05/dothttp

ae68f2c3dbb8f1053a0feeab0ac829aa5d3a2a6e

[ "Apache-2.0" ]

10

2021-02-09T17:21:49.000Z

2021-12-05T19:10:53.000Z

benchmarks/__init__.py

cedric05/dothttp

ae68f2c3dbb8f1053a0feeab0ac829aa5d3a2a6e

[ "Apache-2.0" ]

60

2021-02-07T17:20:47.000Z

2022-03-12T06:26:23.000Z

benchmarks/__init__.py

cedric05/dothttp

ae68f2c3dbb8f1053a0feeab0ac829aa5d3a2a6e

[ "Apache-2.0" ]

null

#!/usr/bin/env python3 import os from dothttp import Config from dothttp.request_base import RequestCompiler def run_model(): """ main use case is to check benchmarks for loading from file to textx model modifying http.tx would increase or lower performance. """ filename = os.path.join(os.path.dirname(__file__), "../examples/example.http") envs = [] target = "1" nocookie = False curl = False properties = [] config = Config(file=filename, env=envs, properties=properties, curl=curl, property_file=None, debug=True, no_cookie=nocookie, format=False, info=False, target=target) comp = RequestCompiler(config) comp.load() comp.load_def() return

28.346154

110

0.671642

import os from dothttp import Config from dothttp.request_base import RequestCompiler def run_model(): filename = os.path.join(os.path.dirname(__file__), "../examples/example.http") envs = [] target = "1" nocookie = False curl = False properties = [] config = Config(file=filename, env=envs, properties=properties, curl=curl, property_file=None, debug=True, no_cookie=nocookie, format=False, info=False, target=target) comp = RequestCompiler(config) comp.load() comp.load_def() return

true

f73192bc3cb337f8236fb50957755b303a00785e

6,757

py

Python

cheating/client.py

totu/challenge-fi-2021

7e41581e3dd5a61b838ecb7aadfde501b94802c5

[ "Unlicense" ]

null

cheating/client.py

totu/challenge-fi-2021

7e41581e3dd5a61b838ecb7aadfde501b94802c5

[ "Unlicense" ]

null

cheating/client.py

totu/challenge-fi-2021

7e41581e3dd5a61b838ecb7aadfde501b94802c5

[ "Unlicense" ]

null

import pygame from pygame.locals import * from _thread import * import os import random import socket import json import math from gamelogic import objects, world, common SOCK_COUNT = 2 pygame.init() W, H = 800, 437 win = pygame.display.set_mode((W, H)) pygame.display.set_caption("Projekt Kyyber 2021 Client") bg_orig = [ pygame.image.load(os.path.join("images/bg1/", str(x) + ".png")).convert_alpha() for x in range(1, 8) ] # bg_orig = [pygame.image.load(os.path.join('images/bg2/', str(x) + '.png')).convert_alpha() for x in range(1,7)] bg = [] BGW = int(bg_orig[0].get_width() * (H / float(bg_orig[0].get_height()))) for i in bg_orig: bg.append(pygame.transform.scale(i, (BGW, H))) bg.reverse() clock = pygame.time.Clock() camera_pos = 0 gameoverfade = 0 def redrawWindow(): global camera_pos, gameoverfade largeFont = pygame.font.SysFont("comicsans", 30) hugeFont = pygame.font.SysFont("comicsans", 90) gameover = False for player in gamestate.players: if player.id == my_id: camera_pos = player.x - W / 2 camera_pos = min(camera_pos, world.WORLD_SIZE - W) camera_pos = max(camera_pos, 0) text = largeFont.render( "AMMO: {}, RELOAD: {}, HP: {}".format( player.ammo, player.reloadleft, player.hp ), 1, (255, 255, 255), ) break else: text = hugeFont.render("TRY HARDER!", 1, (255, 255, 255)) gameover = True for j, layer in enumerate(bg): for i in range(0, W * 2, BGW): camera_pos_bg = (camera_pos * (float(j) / float(len(bg)))) % BGW win.blit(bg[j], (i - camera_pos_bg, 0)) for player in gamestate.players: player.draw(win, camera_pos, my_id) sorted_enemies = sorted(gamestate.enemies, key=lambda i: i.y_pos) sorted_enemies.reverse() # Closest ones to front for enemy in sorted_enemies: enemy.draw(win, camera_pos) for boss in gamestate.bosses: boss.draw(win, camera_pos) for projectile in gamestate.projectiles: projectile.draw(win, camera_pos) if gameover: veil = pygame.Surface((W, H)) veil.fill((0, 0, 0)) veil.set_alpha(gameoverfade) gameoverfade += 0.1 if gameoverfade > 255: gameoverfade = 255 win.blit(veil, (0, 0)) win.blit(text, (W / 2 - text.get_width() / 2, H / 2 - text.get_height() / 2)) else: win.blit(text, (20, 400)) for i, achievement in enumerate(achievements): win.blit( largeFont.render( str(achievement) + ": " + str(achievements[achievement]), 1, (255, 255, 255), ), (10, 10 + 30 * i), ) pygame.display.update() def update_gamestate_thread(): global gamestate while True: clock.tick(world.SPEED) gamestate = common.physics(gamestate) redrawWindow() gamestate = objects.gamestate() achievements = [] my_id = 0 sendevent = [] start_new_thread(update_gamestate_thread, ()) sockets = set() main = None for i in range(SOCK_COUNT): sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) sockets.add(sock) serveraddr = "127.0.0.1" serveraddr = "challenge.fi" # Join remote server for sock in sockets: if main is None: main = sock sock.connect((serveraddr, 9999)) data = main.recv(1024).strip() my_id = json.loads(data)["player_id"] while True: me = None for player in gamestate.players: if player.id == my_id: me = player if me: sendevent.append( [ ['shoot', 4900, 100, 0.38] ]) keys = pygame.key.get_pressed() for event in pygame.event.get(): if event.type == pygame.QUIT: pygame.quit() if keys[pygame.K_x]: eka = me.x #+ math.cos(me.mouseDir(camera_pos)) * 60 toka = me.y + 60 #- math.sin(me.mouseDir(camera_pos)) * 60 mouse = me.mouseDir(camera_pos) sendevent.append( [ "shoot", eka, toka, mouse ] ) print(f"{eka}, {toka}, {mouse}") if keys[pygame.K_r]: eka = 4900 sendevent.append( # ['shoot', 4931.029795212544, 260.67432742841345, 1.7208623491309805] #['shoot', 4754.656506303356, 100, 0] ['shoot', 4900, 100, 0.38] #205.57772405728508, 297.39211224788403, 0.38633721482131445] ) if event.type == pygame.MOUSEBUTTONDOWN and event.button == 1: eka = me.x - 20 + math.cos(me.mouseDir(camera_pos)) * 60 print(eka) toka = me.y - math.sin(me.mouseDir(camera_pos)) * 60 mouse = me.mouseDir(camera_pos) sendevent.append( [ "shoot", eka, toka, mouse ] ) print(f"{eka}, {toka}, {mouse}") if keys[pygame.K_SPACE]: sendevent.append(["jump"]) if keys[pygame.K_m]: sendevent.append(["jump"]) sendevent.append(["reload"]) sendevent.append(["jump"]) if keys[pygame.K_a] and not me.dir == 1: sendevent.append(["left"]) elif keys[pygame.K_d] and not me.dir == -1: sendevent.append(["right"]) elif not me.dir == 0: sendevent.append(["stop"]) common.parse_clientevents(my_id, json.dumps(sendevent), gamestate) # for boss in gamestate.bosses: # print(f"{boss.id} {boss.x},{boss.y}, hp:{boss.hp}") for sock in sockets: sock.sendall(bytes(json.dumps(sendevent), "utf-8")) # if gamestate.projectiles: # for projectile in gamestate.projectiles: # print(f"{projectile.x},{projectile.y} d:{projectile.dir} r:{projectile.moveremaining}") if sendevent: print(sendevent) print(f"{me.x},{me.y}") sendevent = [] for boss in gamestate.bosses: print(boss.x-me.x, boss.y-me.y, boss.hp) # receive packets until a valid json can be formed response = b"" while True: chunk = main.recv(1000) response += chunk try: gamestate_dict, achievements = json.loads(response) gamestate = common.update_gamestate_from_dict( gamestate, gamestate_dict ) break except Exception as e: pass

31.282407

113

0.547432

import pygame from pygame.locals import * from _thread import * import os import random import socket import json import math from gamelogic import objects, world, common SOCK_COUNT = 2 pygame.init() W, H = 800, 437 win = pygame.display.set_mode((W, H)) pygame.display.set_caption("Projekt Kyyber 2021 Client") bg_orig = [ pygame.image.load(os.path.join("images/bg1/", str(x) + ".png")).convert_alpha() for x in range(1, 8) ] bg = [] BGW = int(bg_orig[0].get_width() * (H / float(bg_orig[0].get_height()))) for i in bg_orig: bg.append(pygame.transform.scale(i, (BGW, H))) bg.reverse() clock = pygame.time.Clock() camera_pos = 0 gameoverfade = 0 def redrawWindow(): global camera_pos, gameoverfade largeFont = pygame.font.SysFont("comicsans", 30) hugeFont = pygame.font.SysFont("comicsans", 90) gameover = False for player in gamestate.players: if player.id == my_id: camera_pos = player.x - W / 2 camera_pos = min(camera_pos, world.WORLD_SIZE - W) camera_pos = max(camera_pos, 0) text = largeFont.render( "AMMO: {}, RELOAD: {}, HP: {}".format( player.ammo, player.reloadleft, player.hp ), 1, (255, 255, 255), ) break else: text = hugeFont.render("TRY HARDER!", 1, (255, 255, 255)) gameover = True for j, layer in enumerate(bg): for i in range(0, W * 2, BGW): camera_pos_bg = (camera_pos * (float(j) / float(len(bg)))) % BGW win.blit(bg[j], (i - camera_pos_bg, 0)) for player in gamestate.players: player.draw(win, camera_pos, my_id) sorted_enemies = sorted(gamestate.enemies, key=lambda i: i.y_pos) sorted_enemies.reverse() for enemy in sorted_enemies: enemy.draw(win, camera_pos) for boss in gamestate.bosses: boss.draw(win, camera_pos) for projectile in gamestate.projectiles: projectile.draw(win, camera_pos) if gameover: veil = pygame.Surface((W, H)) veil.fill((0, 0, 0)) veil.set_alpha(gameoverfade) gameoverfade += 0.1 if gameoverfade > 255: gameoverfade = 255 win.blit(veil, (0, 0)) win.blit(text, (W / 2 - text.get_width() / 2, H / 2 - text.get_height() / 2)) else: win.blit(text, (20, 400)) for i, achievement in enumerate(achievements): win.blit( largeFont.render( str(achievement) + ": " + str(achievements[achievement]), 1, (255, 255, 255), ), (10, 10 + 30 * i), ) pygame.display.update() def update_gamestate_thread(): global gamestate while True: clock.tick(world.SPEED) gamestate = common.physics(gamestate) redrawWindow() gamestate = objects.gamestate() achievements = [] my_id = 0 sendevent = [] start_new_thread(update_gamestate_thread, ()) sockets = set() main = None for i in range(SOCK_COUNT): sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) sockets.add(sock) serveraddr = "127.0.0.1" serveraddr = "challenge.fi" for sock in sockets: if main is None: main = sock sock.connect((serveraddr, 9999)) data = main.recv(1024).strip() my_id = json.loads(data)["player_id"] while True: me = None for player in gamestate.players: if player.id == my_id: me = player if me: sendevent.append( [ ['shoot', 4900, 100, 0.38] ]) keys = pygame.key.get_pressed() for event in pygame.event.get(): if event.type == pygame.QUIT: pygame.quit() if keys[pygame.K_x]: eka = me.x toka = me.y + 60 mouse = me.mouseDir(camera_pos) sendevent.append( [ "shoot", eka, toka, mouse ] ) print(f"{eka}, {toka}, {mouse}") if keys[pygame.K_r]: eka = 4900 sendevent.append( ['shoot', 4900, 100, 0.38] ) if event.type == pygame.MOUSEBUTTONDOWN and event.button == 1: eka = me.x - 20 + math.cos(me.mouseDir(camera_pos)) * 60 print(eka) toka = me.y - math.sin(me.mouseDir(camera_pos)) * 60 mouse = me.mouseDir(camera_pos) sendevent.append( [ "shoot", eka, toka, mouse ] ) print(f"{eka}, {toka}, {mouse}") if keys[pygame.K_SPACE]: sendevent.append(["jump"]) if keys[pygame.K_m]: sendevent.append(["jump"]) sendevent.append(["reload"]) sendevent.append(["jump"]) if keys[pygame.K_a] and not me.dir == 1: sendevent.append(["left"]) elif keys[pygame.K_d] and not me.dir == -1: sendevent.append(["right"]) elif not me.dir == 0: sendevent.append(["stop"]) common.parse_clientevents(my_id, json.dumps(sendevent), gamestate) for sock in sockets: sock.sendall(bytes(json.dumps(sendevent), "utf-8")) if sendevent: print(sendevent) print(f"{me.x},{me.y}") sendevent = [] for boss in gamestate.bosses: print(boss.x-me.x, boss.y-me.y, boss.hp) response = b"" while True: chunk = main.recv(1000) response += chunk try: gamestate_dict, achievements = json.loads(response) gamestate = common.update_gamestate_from_dict( gamestate, gamestate_dict ) break except Exception as e: pass

true

f73192e61c63b58e1cc57017f11c01d7531dc764

3,051

py

Python

pyaz/pyaz_utils.py

py-az-cli/py-az-cli

9a7dc44e360c096a5a2f15595353e9dad88a9792

[ "MIT" ]

null

pyaz/pyaz_utils.py

py-az-cli/py-az-cli

9a7dc44e360c096a5a2f15595353e9dad88a9792

[ "MIT" ]

null

pyaz/pyaz_utils.py

py-az-cli/py-az-cli

9a7dc44e360c096a5a2f15595353e9dad88a9792

[ "MIT" ]

1

2022-02-03T09:12:01.000Z

"""Utility functions for the pyaz generated code to use.""" import json import logging import shlex import shutil import subprocess from typing import Dict def _call_az(command: str, parameters: Dict) -> object: """ Call an az command (supplied as a string, and parameters as dictionary). Calls az cli via a subprocess Returns the az cli json converted to python object Example: ` _call_az("az group create", locals()) ` """ # format the parameters into a list params = _get_params(parameters) # split commands into a list commands = command.split() # add the params to the commands commands.extend(params) full_command = " ".join(commands) print(f"Executing command: {full_command}") logging.info("Executing command: %s", full_command) # split full command using shlex rules commands = shlex.split(full_command) # strip off az and replace it with full path to az to accomodate Windows commands.pop(0) commands.insert(0, shutil.which("az")) output = subprocess.run( commands, shell=False, stdout=subprocess.PIPE, stderr=subprocess.PIPE, check=True, ) stdout = output.stdout.decode("utf-8") stderr = output.stderr.decode("utf-8") if stdout: try: return json.loads(stdout) except: # pylint: disable=bare-except return stdout elif stderr: raise Exception(stderr) def _get_cli_param_name(name: str) -> str: """ Convert parameter name back to cli format from pythonic version. - Strips trailing underscore from keywords - Converts remaining underscores to dashes - Adds leading dashes """ if name[-1] == "_": name = name[0:-1] name = name.replace("_", "-") name = f"--{name}" return name def _get_params(params: Dict) -> list: """ Given the built-in locals dictionary returns a formatted list of parameters. The list returned contains the az cli formatted parameter names and values """ # return params output = [] # loop through locals and append list of parameters and their values # as long as the parameter has a value for param in params: if params[param]: # if value is a boolean then don't append value, just param, used for flags if isinstance(params[param], bool): output.append(_get_cli_param_name(param)) else: output.append(_get_cli_param_name(param)) # special case to handle tags, need to apply shlex.split # to handle case where there are multiple tags if param == "tags": param_values = shlex.split(params[param]) param_values = [f'"{value}"' for value in param_values] output.extend(param_values) else: # wrap parameter value in quotes output.append(f'"{params[param]}"') return output

28.514019

87

0.621763

import json import logging import shlex import shutil import subprocess from typing import Dict def _call_az(command: str, parameters: Dict) -> object: params = _get_params(parameters) commands = command.split() commands.extend(params) full_command = " ".join(commands) print(f"Executing command: {full_command}") logging.info("Executing command: %s", full_command) commands = shlex.split(full_command) commands.pop(0) commands.insert(0, shutil.which("az")) output = subprocess.run( commands, shell=False, stdout=subprocess.PIPE, stderr=subprocess.PIPE, check=True, ) stdout = output.stdout.decode("utf-8") stderr = output.stderr.decode("utf-8") if stdout: try: return json.loads(stdout) except: return stdout elif stderr: raise Exception(stderr) def _get_cli_param_name(name: str) -> str: if name[-1] == "_": name = name[0:-1] name = name.replace("_", "-") name = f"--{name}" return name def _get_params(params: Dict) -> list: output = [] for param in params: if params[param]: if isinstance(params[param], bool): output.append(_get_cli_param_name(param)) else: output.append(_get_cli_param_name(param)) # special case to handle tags, need to apply shlex.split # to handle case where there are multiple tags if param == "tags": param_values = shlex.split(params[param]) param_values = [f'"{value}"' for value in param_values] output.extend(param_values) else: # wrap parameter value in quotes output.append(f'"{params[param]}"') return output

true

f731932dfc82782e68c3b57938cdc030d69e56ef

53,591

py

Python

electrum/wallet_db.py

L-47/qtum-electrum

dd1b0a8b6ef6e96015a6210de36b23949eaad359

[ "MIT" ]

1

2020-07-21T18:37:59.000Z

electrum/wallet_db.py

L-47/qtum-electrum

dd1b0a8b6ef6e96015a6210de36b23949eaad359

[ "MIT" ]

null

electrum/wallet_db.py

L-47/qtum-electrum

dd1b0a8b6ef6e96015a6210de36b23949eaad359

[ "MIT" ]

null

#!/usr/bin/env python # # Electrum - lightweight Bitcoin client # Copyright (C) 2015 Thomas Voegtlin # # Permission is hereby granted, free of charge, to any person # obtaining a copy of this software and associated documentation files # (the "Software"), to deal in the Software without restriction, # including without limitation the rights to use, copy, modify, merge, # publish, distribute, sublicense, and/or sell copies of the Software, # and to permit persons to whom the Software is furnished to do so, # subject to the following conditions: # # The above copyright notice and this permission notice shall be # included in all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, # EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF # MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND # NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS # BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN # ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN # CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. import os import ast import json import copy import threading from collections import defaultdict from functools import reduce from typing import Dict, Optional, List, Tuple, Set, Iterable, NamedTuple, Sequence, TYPE_CHECKING, Union import binascii from . import util, bitcoin from .util import profiler, WalletFileException, multisig_type, TxMinedInfo, bfh from .invoices import PR_TYPE_ONCHAIN, Invoice from .keystore import bip44_derivation from .transaction import Transaction, TxOutpoint, tx_from_any, PartialTransaction, PartialTxOutput from .logging import Logger from .lnutil import LOCAL, REMOTE, FeeUpdate, UpdateAddHtlc, LocalConfig, RemoteConfig, Keypair, OnlyPubkeyKeypair, RevocationStore, ChannelBackupStorage from .lnutil import ChannelConstraints, Outpoint, ShachainElement from .json_db import StoredDict, JsonDB, locked, modifier from .plugin import run_hook, plugin_loaders from .paymentrequest import PaymentRequest from .submarine_swaps import SwapData from .bitcoin import Token, Delegation if TYPE_CHECKING: from .storage import WalletStorage # seed_version is now used for the version of the wallet file OLD_SEED_VERSION = 4 # electrum versions < 2.0 NEW_SEED_VERSION = 11 # electrum versions >= 2.0 FINAL_SEED_VERSION = 32 # electrum >= 2.7 will set this to prevent # old versions from overwriting new format class TxFeesValue(NamedTuple): fee: Optional[int] = None is_calculated_by_us: bool = False num_inputs: Optional[int] = None class WalletDB(JsonDB): def __init__(self, raw, *, manual_upgrades: bool): JsonDB.__init__(self, {}) self._manual_upgrades = manual_upgrades self._called_after_upgrade_tasks = False if raw: # loading existing db self.load_data(raw) self.load_plugins() else: # creating new db self.put('seed_version', FINAL_SEED_VERSION) self._after_upgrade_tasks() def load_data(self, s): try: self.data = json.loads(s) except: try: d = ast.literal_eval(s) labels = d.get('labels', {}) except Exception as e: raise WalletFileException("Cannot read wallet file. (parsing failed)") self.data = {} for key, value in d.items(): try: json.dumps(key) json.dumps(value) except: self.logger.info(f'Failed to convert label to json format: {key}') continue self.data[key] = value if not isinstance(self.data, dict): raise WalletFileException("Malformed wallet file (not dict)") if not self._manual_upgrades and self.requires_split(): raise WalletFileException("This wallet has multiple accounts and must be split") if not self.requires_upgrade(): self._after_upgrade_tasks() elif not self._manual_upgrades: self.upgrade() def requires_split(self): d = self.get('accounts', {}) return len(d) > 1 def get_split_accounts(self): result = [] # backward compatibility with old wallets d = self.get('accounts', {}) if len(d) < 2: return wallet_type = self.get('wallet_type') if wallet_type == 'old': assert len(d) == 2 data1 = copy.deepcopy(self.data) data1['accounts'] = {'0': d['0']} data1['suffix'] = 'deterministic' data2 = copy.deepcopy(self.data) data2['accounts'] = {'/x': d['/x']} data2['seed'] = None data2['seed_version'] = None data2['master_public_key'] = None data2['wallet_type'] = 'imported' data2['suffix'] = 'imported' result = [data1, data2] elif wallet_type in ['bip44', 'trezor', 'keepkey', 'ledger', 'btchip', 'digitalbitbox', 'safe_t']: mpk = self.get('master_public_keys') for k in d.keys(): i = int(k) x = d[k] if x.get("pending"): continue xpub = mpk["x/%d'"%i] new_data = copy.deepcopy(self.data) # save account, derivation and xpub at index 0 new_data['accounts'] = {'0': x} new_data['master_public_keys'] = {"x/0'": xpub} new_data['derivation'] = bip44_derivation(k) new_data['suffix'] = k result.append(new_data) else: raise WalletFileException("This wallet has multiple accounts and must be split") return result def requires_upgrade(self): return self.get_seed_version() < FINAL_SEED_VERSION @profiler def upgrade(self): self.logger.info('upgrading wallet format') if self._called_after_upgrade_tasks: # we need strict ordering between upgrade() and after_upgrade_tasks() raise Exception("'after_upgrade_tasks' must NOT be called before 'upgrade'") self._convert_imported() self._convert_wallet_type() self._convert_account() self._convert_version_13_b() self._convert_version_14() self._convert_version_15() self._convert_version_16() self._convert_version_17() self._convert_version_18() self._convert_version_19() self._convert_version_20() self._convert_version_21() self._convert_version_22() self._convert_version_23() self._convert_version_24() self._convert_version_25() self._convert_version_26() self._convert_version_27() self._convert_version_28() self._convert_version_29() self._convert_version_30() self._convert_version_31() self._convert_version_32() self.put('seed_version', FINAL_SEED_VERSION) # just to be sure self._after_upgrade_tasks() def _after_upgrade_tasks(self): self._called_after_upgrade_tasks = True self._load_transactions() def _convert_wallet_type(self): if not self._is_upgrade_method_needed(0, 13): return wallet_type = self.get('wallet_type') if wallet_type == 'btchip': wallet_type = 'ledger' if self.get('keystore') or self.get('x1/') or wallet_type=='imported': return False assert not self.requires_split() seed_version = self.get_seed_version() seed = self.get('seed') xpubs = self.get('master_public_keys') xprvs = self.get('master_private_keys', {}) mpk = self.get('master_public_key') keypairs = self.get('keypairs') key_type = self.get('key_type') if seed_version == OLD_SEED_VERSION or wallet_type == 'old': d = { 'type': 'old', 'seed': seed, 'mpk': mpk, } self.put('wallet_type', 'standard') self.put('keystore', d) elif key_type == 'imported': d = { 'type': 'imported', 'keypairs': keypairs, } self.put('wallet_type', 'standard') self.put('keystore', d) elif wallet_type in ['xpub', 'standard']: xpub = xpubs["x/"] xprv = xprvs.get("x/") d = { 'type': 'bip32', 'xpub': xpub, 'xprv': xprv, 'seed': seed, } self.put('wallet_type', 'standard') self.put('keystore', d) elif wallet_type in ['bip44']: xpub = xpubs["x/0'"] xprv = xprvs.get("x/0'") d = { 'type': 'bip32', 'xpub': xpub, 'xprv': xprv, } self.put('wallet_type', 'standard') self.put('keystore', d) elif wallet_type in ['trezor', 'keepkey', 'ledger', 'digitalbitbox', 'safe_t']: xpub = xpubs["x/0'"] derivation = self.get('derivation', bip44_derivation(0)) d = { 'type': 'hardware', 'hw_type': wallet_type, 'xpub': xpub, 'derivation': derivation, } self.put('wallet_type', 'standard') self.put('keystore', d) elif (wallet_type == '2fa') or multisig_type(wallet_type): for key in xpubs.keys(): d = { 'type': 'bip32', 'xpub': xpubs[key], 'xprv': xprvs.get(key), } if key == 'x1/' and seed: d['seed'] = seed self.put(key, d) else: raise WalletFileException('Unable to tell wallet type. Is this even a wallet file?') # remove junk self.put('master_public_key', None) self.put('master_public_keys', None) self.put('master_private_keys', None) self.put('derivation', None) self.put('seed', None) self.put('keypairs', None) self.put('key_type', None) def _convert_version_13_b(self): # version 13 is ambiguous, and has an earlier and a later structure if not self._is_upgrade_method_needed(0, 13): return if self.get('wallet_type') == 'standard': if self.get('keystore').get('type') == 'imported': pubkeys = self.get('keystore').get('keypairs').keys() d = {'change': []} receiving_addresses = [] for pubkey in pubkeys: addr = bitcoin.pubkey_to_address('p2pkh', pubkey) receiving_addresses.append(addr) d['receiving'] = receiving_addresses self.put('addresses', d) self.put('pubkeys', None) self.put('seed_version', 13) def _convert_version_14(self): # convert imported wallets for 3.0 if not self._is_upgrade_method_needed(13, 13): return if self.get('wallet_type') =='imported': addresses = self.get('addresses') if type(addresses) is list: addresses = dict([(x, None) for x in addresses]) self.put('addresses', addresses) elif self.get('wallet_type') == 'standard': if self.get('keystore').get('type')=='imported': addresses = set(self.get('addresses').get('receiving')) pubkeys = self.get('keystore').get('keypairs').keys() assert len(addresses) == len(pubkeys) d = {} for pubkey in pubkeys: addr = bitcoin.pubkey_to_address('p2pkh', pubkey) assert addr in addresses d[addr] = { 'pubkey': pubkey, 'redeem_script': None, 'type': 'p2pkh' } self.put('addresses', d) self.put('pubkeys', None) self.put('wallet_type', 'imported') self.put('seed_version', 14) def _convert_version_15(self): if not self._is_upgrade_method_needed(14, 14): return if self.get('seed_type') == 'segwit': # should not get here; get_seed_version should have caught this raise Exception('unsupported derivation (development segwit, v14)') self.put('seed_version', 15) def _convert_version_16(self): # fixes issue #3193 for Imported_Wallets with addresses # also, previous versions allowed importing any garbage as an address # which we now try to remove, see pr #3191 if not self._is_upgrade_method_needed(15, 15): return def remove_address(addr): def remove_from_dict(dict_name): d = self.get(dict_name, None) if d is not None: d.pop(addr, None) self.put(dict_name, d) def remove_from_list(list_name): lst = self.get(list_name, None) if lst is not None: s = set(lst) s -= {addr} self.put(list_name, list(s)) # note: we don't remove 'addr' from self.get('addresses') remove_from_dict('addr_history') remove_from_dict('labels') remove_from_dict('payment_requests') remove_from_list('frozen_addresses') if self.get('wallet_type') == 'imported': addresses = self.get('addresses') assert isinstance(addresses, dict) addresses_new = dict() for address, details in addresses.items(): if not bitcoin.is_address(address): remove_address(address) continue if details is None: addresses_new[address] = {} else: addresses_new[address] = details self.put('addresses', addresses_new) self.put('seed_version', 16) def _convert_version_17(self): # delete pruned_txo; construct spent_outpoints if not self._is_upgrade_method_needed(16, 16): return self.put('pruned_txo', None) transactions = self.get('transactions', {}) # txid -> raw_tx spent_outpoints = defaultdict(dict) for txid, raw_tx in transactions.items(): tx = Transaction(raw_tx) for txin in tx.inputs(): if txin.is_coinbase_input(): continue prevout_hash = txin.prevout.txid.hex() prevout_n = txin.prevout.out_idx spent_outpoints[prevout_hash][str(prevout_n)] = txid self.put('spent_outpoints', spent_outpoints) tokens = self.get('tokens', {}) # contractAddr_bindAddr -> [name, symbol, decimals, balance] new_tokens = {} for key, value in tokens.items(): contract_addr, bind_addr = key.split('_') new_token = Token(contract_addr, bind_addr, value[0], value[1], value[2], value[3]) new_tokens[new_token.get_key()] = new_token self.put('tokens', new_tokens) self.put('seed_version', 17) def _convert_version_18(self): # delete verified_tx3 as its structure changed if not self._is_upgrade_method_needed(17, 17): return self.put('verified_tx3', None) self.put('seed_version', 18) def _convert_version_19(self): # delete tx_fees as its structure changed if not self._is_upgrade_method_needed(18, 18): return self.put('tx_fees', None) self.put('seed_version', 19) def _convert_version_20(self): # store 'derivation' (prefix) and 'root_fingerprint' in all xpub-based keystores. # store explicit None values if we cannot retroactively determine them if not self._is_upgrade_method_needed(19, 19): return from .bip32 import BIP32Node, convert_bip32_intpath_to_strpath # note: This upgrade method reimplements bip32.root_fp_and_der_prefix_from_xkey. # This is done deliberately, to avoid introducing that method as a dependency to this upgrade. for ks_name in ('keystore', *['x{}/'.format(i) for i in range(1, 16)]): ks = self.get(ks_name, None) if ks is None: continue xpub = ks.get('xpub', None) if xpub is None: continue bip32node = BIP32Node.from_xkey(xpub) # derivation prefix derivation_prefix = ks.get('derivation', None) if derivation_prefix is None: assert bip32node.depth >= 0, bip32node.depth if bip32node.depth == 0: derivation_prefix = 'm' elif bip32node.depth == 1: child_number_int = int.from_bytes(bip32node.child_number, 'big') derivation_prefix = convert_bip32_intpath_to_strpath([child_number_int]) ks['derivation'] = derivation_prefix # root fingerprint root_fingerprint = ks.get('ckcc_xfp', None) if root_fingerprint is not None: root_fingerprint = root_fingerprint.to_bytes(4, byteorder="little", signed=False).hex().lower() if root_fingerprint is None: if bip32node.depth == 0: root_fingerprint = bip32node.calc_fingerprint_of_this_node().hex().lower() elif bip32node.depth == 1: root_fingerprint = bip32node.fingerprint.hex() ks['root_fingerprint'] = root_fingerprint ks.pop('ckcc_xfp', None) self.put(ks_name, ks) self.put('seed_version', 20) def _convert_version_21(self): if not self._is_upgrade_method_needed(20, 20): return channels = self.get('channels') if channels: for channel in channels: channel['state'] = 'OPENING' self.put('channels', channels) self.put('seed_version', 21) def _convert_version_22(self): # construct prevouts_by_scripthash if not self._is_upgrade_method_needed(21, 21): return from .bitcoin import script_to_scripthash transactions = self.get('transactions', {}) # txid -> raw_tx prevouts_by_scripthash = defaultdict(list) for txid, raw_tx in transactions.items(): tx = Transaction(raw_tx) for idx, txout in enumerate(tx.outputs()): outpoint = f"{txid}:{idx}" scripthash = script_to_scripthash(txout.scriptpubkey.hex()) prevouts_by_scripthash[scripthash].append((outpoint, txout.value)) self.put('prevouts_by_scripthash', prevouts_by_scripthash) self.put('seed_version', 22) def _convert_version_23(self): if not self._is_upgrade_method_needed(22, 22): return channels = self.get('channels', []) LOCAL = 1 REMOTE = -1 for c in channels: # move revocation store from remote_config r = c['remote_config'].pop('revocation_store') c['revocation_store'] = r # convert fee updates log = c.get('log', {}) for sub in LOCAL, REMOTE: l = log[str(sub)]['fee_updates'] d = {} for i, fu in enumerate(l): d[str(i)] = { 'rate':fu['rate'], 'ctn_local':fu['ctns'][str(LOCAL)], 'ctn_remote':fu['ctns'][str(REMOTE)] } log[str(int(sub))]['fee_updates'] = d self.data['channels'] = channels self.data['seed_version'] = 23 def _convert_version_24(self): if not self._is_upgrade_method_needed(23, 23): return channels = self.get('channels', []) for c in channels: # convert revocation store to dict r = c['revocation_store'] d = {} for i in range(49): v = r['buckets'][i] if v is not None: d[str(i)] = v r['buckets'] = d c['revocation_store'] = r # convert channels to dict self.data['channels'] = { x['channel_id']: x for x in channels } # convert txi & txo txi = self.get('txi', {}) for tx_hash, d in txi.items(): d2 = {} for addr, l in d.items(): d2[addr] = {} for ser, v in l: d2[addr][ser] = v txi[tx_hash] = d2 self.data['txi'] = txi txo = self.get('txo', {}) for tx_hash, d in txo.items(): d2 = {} for addr, l in d.items(): d2[addr] = {} for n, v, cb in l: d2[addr][str(n)] = (v, cb) txo[tx_hash] = d2 self.data['txo'] = txo self.data['seed_version'] = 24 def _convert_version_25(self): if not self._is_upgrade_method_needed(24, 24): return # add 'type' field to onchain requests requests = self.data.get('payment_requests', {}) for k, r in list(requests.items()): if r.get('address') == k: requests[k] = { 'address': r['address'], 'amount': r.get('amount'), 'exp': r.get('exp'), 'id': r.get('id'), 'memo': r.get('memo'), 'time': r.get('time'), 'type': PR_TYPE_ONCHAIN, } # convert bip70 invoices invoices = self.data.get('invoices', {}) for k, r in list(invoices.items()): data = r.get("hex") if data: pr = PaymentRequest(bytes.fromhex(data)) if pr.id != k: continue invoices[k] = { 'type': PR_TYPE_ONCHAIN, 'amount': pr.get_amount(), 'bip70': data, 'exp': pr.get_expiration_date() - pr.get_time(), 'id': pr.id, 'message': pr.get_memo(), 'outputs': [x.to_legacy_tuple() for x in pr.get_outputs()], 'time': pr.get_time(), 'requestor': pr.get_requestor(), } self.data['seed_version'] = 25 def _convert_version_26(self): if not self._is_upgrade_method_needed(25, 25): return channels = self.data.get('channels', {}) channel_timestamps = self.data.pop('lightning_channel_timestamps', {}) for channel_id, c in channels.items(): item = channel_timestamps.get(channel_id) if item: funding_txid, funding_height, funding_timestamp, closing_txid, closing_height, closing_timestamp = item if funding_txid: c['funding_height'] = funding_txid, funding_height, funding_timestamp if closing_txid: c['closing_height'] = closing_txid, closing_height, closing_timestamp self.data['seed_version'] = 26 def _convert_version_27(self): if not self._is_upgrade_method_needed(26, 26): return channels = self.data.get('channels', {}) for channel_id, c in channels.items(): c['local_config']['htlc_minimum_msat'] = 1 self.data['seed_version'] = 27 def _convert_version_28(self): if not self._is_upgrade_method_needed(27, 27): return channels = self.data.get('channels', {}) for channel_id, c in channels.items(): c['local_config']['channel_seed'] = None self.data['seed_version'] = 28 def _convert_version_29(self): if not self._is_upgrade_method_needed(28, 28): return requests = self.data.get('payment_requests', {}) invoices = self.data.get('invoices', {}) for d in [invoices, requests]: for key, r in list(d.items()): _type = r.get('type', 0) item = { 'type': _type, 'message': r.get('message') or r.get('memo', ''), 'amount': r.get('amount'), 'exp': r.get('exp') or 0, 'time': r.get('time', 0), } if _type == PR_TYPE_ONCHAIN: address = r.pop('address', None) if address: outputs = [(0, address, r.get('amount'))] else: outputs = r.get('outputs') item.update({ 'outputs': outputs, 'id': r.get('id'), 'bip70': r.get('bip70'), 'requestor': r.get('requestor'), }) else: item.update({ 'rhash': r['rhash'], 'invoice': r['invoice'], }) d[key] = item self.data['seed_version'] = 29 def _convert_version_30(self): if not self._is_upgrade_method_needed(29, 29): return from .invoices import PR_TYPE_ONCHAIN, PR_TYPE_LN requests = self.data.get('payment_requests', {}) invoices = self.data.get('invoices', {}) for d in [invoices, requests]: for key, item in list(d.items()): _type = item['type'] if _type == PR_TYPE_ONCHAIN: item['amount_sat'] = item.pop('amount') elif _type == PR_TYPE_LN: amount_sat = item.pop('amount') item['amount_msat'] = 1000 * amount_sat if amount_sat is not None else None item.pop('exp') item.pop('message') item.pop('rhash') item.pop('time') else: raise Exception(f"unknown invoice type: {_type}") self.data['seed_version'] = 30 def _convert_version_31(self): if not self._is_upgrade_method_needed(30, 30): return from .invoices import PR_TYPE_ONCHAIN requests = self.data.get('payment_requests', {}) invoices = self.data.get('invoices', {}) for d in [invoices, requests]: for key, item in list(d.items()): if item['type'] == PR_TYPE_ONCHAIN: item['amount_sat'] = item['amount_sat'] or 0 item['exp'] = item['exp'] or 0 item['time'] = item['time'] or 0 self.data['seed_version'] = 31 def _convert_version_32(self): if not self._is_upgrade_method_needed(31, 31): return from .invoices import PR_TYPE_ONCHAIN invoices_old = self.data.get('invoices', {}) invoices_new = {k: item for k, item in invoices_old.items() if not (item['type'] == PR_TYPE_ONCHAIN and item['outputs'] is None)} self.data['invoices'] = invoices_new self.data['seed_version'] = 32 def _convert_imported(self): if not self._is_upgrade_method_needed(0, 13): return # '/x' is the internal ID for imported accounts d = self.get('accounts', {}).get('/x', {}).get('imported',{}) if not d: return False addresses = [] keypairs = {} for addr, v in d.items(): pubkey, privkey = v if privkey: keypairs[pubkey] = privkey else: addresses.append(addr) if addresses and keypairs: raise WalletFileException('mixed addresses and privkeys') elif addresses: self.put('addresses', addresses) self.put('accounts', None) elif keypairs: self.put('wallet_type', 'standard') self.put('key_type', 'imported') self.put('keypairs', keypairs) self.put('accounts', None) else: raise WalletFileException('no addresses or privkeys') def _convert_account(self): if not self._is_upgrade_method_needed(0, 13): return self.put('accounts', None) def _is_upgrade_method_needed(self, min_version, max_version): assert min_version <= max_version cur_version = self.get_seed_version() if cur_version > max_version: return False elif cur_version < min_version: raise WalletFileException( 'storage upgrade: unexpected version {} (should be {}-{})' .format(cur_version, min_version, max_version)) else: return True @locked def get_seed_version(self): seed_version = self.get('seed_version') if not seed_version: seed_version = OLD_SEED_VERSION if len(self.get('master_public_key','')) == 128 else NEW_SEED_VERSION if seed_version > FINAL_SEED_VERSION: raise WalletFileException('This version of Electrum is too old to open this wallet.\n' '(highest supported storage version: {}, version of this file: {})' .format(FINAL_SEED_VERSION, seed_version)) if seed_version==14 and self.get('seed_type') == 'segwit': self._raise_unsupported_version(seed_version) if seed_version >=12: return seed_version if seed_version not in [OLD_SEED_VERSION, NEW_SEED_VERSION]: self._raise_unsupported_version(seed_version) return seed_version def _raise_unsupported_version(self, seed_version): msg = f"Your wallet has an unsupported seed version: {seed_version}." if seed_version in [5, 7, 8, 9, 10, 14]: msg += "\n\nTo open this wallet, try 'git checkout seed_v%d'"%seed_version if seed_version == 6: # version 1.9.8 created v6 wallets when an incorrect seed was entered in the restore dialog msg += '\n\nThis file was created because of a bug in version 1.9.8.' if self.get('master_public_keys') is None and self.get('master_private_keys') is None and self.get('imported_keys') is None: # pbkdf2 (at that time an additional dependency) was not included with the binaries, and wallet creation aborted. msg += "\nIt does not contain any keys, and can safely be removed." else: # creation was complete if electrum was run from source msg += "\nPlease open this file with Electrum 1.9.8, and move your coins to a new wallet." raise WalletFileException(msg) @locked def get_txi_addresses(self, tx_hash: str) -> List[str]: """Returns list of is_mine addresses that appear as inputs in tx.""" assert isinstance(tx_hash, str) return list(self.txi.get(tx_hash, {}).keys()) @locked def get_txo_addresses(self, tx_hash: str) -> List[str]: """Returns list of is_mine addresses that appear as outputs in tx.""" assert isinstance(tx_hash, str) return list(self.txo.get(tx_hash, {}).keys()) @locked def get_txi_addr(self, tx_hash: str, address: str) -> Iterable[Tuple[str, int]]: """Returns an iterable of (prev_outpoint, value).""" assert isinstance(tx_hash, str) assert isinstance(address, str) d = self.txi.get(tx_hash, {}).get(address, {}) return list(d.items()) @locked def get_txo_addr(self, tx_hash: str, address: str) -> Iterable[Tuple[int, int, bool]]: """Returns an iterable of (output_index, value, is_coinbase).""" assert isinstance(tx_hash, str) assert isinstance(address, str) d = self.txo.get(tx_hash, {}).get(address, {}) return [(int(n), v, cb) for (n, (v, cb)) in d.items()] @modifier def add_txi_addr(self, tx_hash: str, addr: str, ser: str, v: int) -> None: assert isinstance(tx_hash, str) assert isinstance(addr, str) assert isinstance(ser, str) assert isinstance(v, int) if tx_hash not in self.txi: self.txi[tx_hash] = {} d = self.txi[tx_hash] if addr not in d: d[addr] = {} d[addr][ser] = v @modifier def add_txo_addr(self, tx_hash: str, addr: str, n: Union[int, str], v: int, is_coinbase: bool) -> None: n = str(n) assert isinstance(tx_hash, str) assert isinstance(addr, str) assert isinstance(n, str) assert isinstance(v, int) assert isinstance(is_coinbase, bool) if tx_hash not in self.txo: self.txo[tx_hash] = {} d = self.txo[tx_hash] if addr not in d: d[addr] = {} d[addr][n] = (v, is_coinbase) @locked def list_txi(self) -> Sequence[str]: return list(self.txi.keys()) @locked def list_txo(self) -> Sequence[str]: return list(self.txo.keys()) @modifier def remove_txi(self, tx_hash: str) -> None: assert isinstance(tx_hash, str) self.txi.pop(tx_hash, None) @modifier def remove_txo(self, tx_hash: str) -> None: assert isinstance(tx_hash, str) self.txo.pop(tx_hash, None) @locked def list_spent_outpoints(self) -> Sequence[Tuple[str, str]]: return [(h, n) for h in self.spent_outpoints.keys() for n in self.get_spent_outpoints(h) ] @locked def get_spent_outpoints(self, prevout_hash: str) -> Sequence[str]: assert isinstance(prevout_hash, str) return list(self.spent_outpoints.get(prevout_hash, {}).keys()) @locked def get_spent_outpoint(self, prevout_hash: str, prevout_n: Union[int, str]) -> Optional[str]: assert isinstance(prevout_hash, str) prevout_n = str(prevout_n) return self.spent_outpoints.get(prevout_hash, {}).get(prevout_n) @modifier def remove_spent_outpoint(self, prevout_hash: str, prevout_n: Union[int, str]) -> None: assert isinstance(prevout_hash, str) prevout_n = str(prevout_n) self.spent_outpoints[prevout_hash].pop(prevout_n, None) if not self.spent_outpoints[prevout_hash]: self.spent_outpoints.pop(prevout_hash) @modifier def set_spent_outpoint(self, prevout_hash: str, prevout_n: Union[int, str], tx_hash: str) -> None: assert isinstance(prevout_hash, str) assert isinstance(tx_hash, str) prevout_n = str(prevout_n) if prevout_hash not in self.spent_outpoints: self.spent_outpoints[prevout_hash] = {} self.spent_outpoints[prevout_hash][prevout_n] = tx_hash @modifier def add_prevout_by_scripthash(self, scripthash: str, *, prevout: TxOutpoint, value: int) -> None: assert isinstance(scripthash, str) assert isinstance(prevout, TxOutpoint) assert isinstance(value, int) if scripthash not in self._prevouts_by_scripthash: self._prevouts_by_scripthash[scripthash] = set() self._prevouts_by_scripthash[scripthash].add((prevout.to_str(), value)) @modifier def remove_prevout_by_scripthash(self, scripthash: str, *, prevout: TxOutpoint, value: int) -> None: assert isinstance(scripthash, str) assert isinstance(prevout, TxOutpoint) assert isinstance(value, int) self._prevouts_by_scripthash[scripthash].discard((prevout.to_str(), value)) if not self._prevouts_by_scripthash[scripthash]: self._prevouts_by_scripthash.pop(scripthash) @locked def get_prevouts_by_scripthash(self, scripthash: str) -> Set[Tuple[TxOutpoint, int]]: assert isinstance(scripthash, str) prevouts_and_values = self._prevouts_by_scripthash.get(scripthash, set()) return {(TxOutpoint.from_str(prevout), value) for prevout, value in prevouts_and_values} @modifier def add_transaction(self, tx_hash: str, tx: Transaction) -> None: assert isinstance(tx_hash, str) assert isinstance(tx, Transaction), tx # note that tx might be a PartialTransaction if not tx_hash: raise Exception("trying to add tx to db without txid") if tx_hash != tx.txid(): raise Exception(f"trying to add tx to db with inconsistent txid: {tx_hash} != {tx.txid()}") # don't allow overwriting complete tx with partial tx tx_we_already_have = self.transactions.get(tx_hash, None) if tx_we_already_have is None or isinstance(tx_we_already_have, PartialTransaction): self.transactions[tx_hash] = tx @modifier def remove_transaction(self, tx_hash: str) -> Optional[Transaction]: assert isinstance(tx_hash, str) return self.transactions.pop(tx_hash, None) @locked def get_transaction(self, tx_hash: Optional[str]) -> Optional[Transaction]: if tx_hash is None: return None assert isinstance(tx_hash, str) return self.transactions.get(tx_hash) @locked def list_transactions(self) -> Sequence[str]: return list(self.transactions.keys()) @locked def get_history(self) -> Sequence[str]: return list(self.history.keys()) def is_addr_in_history(self, addr: str) -> bool: # does not mean history is non-empty! assert isinstance(addr, str) return addr in self.history @locked def get_addr_history(self, addr: str) -> Sequence[Tuple[str, int]]: assert isinstance(addr, str) return self.history.get(addr, []) @modifier def set_addr_history(self, addr: str, hist) -> None: assert isinstance(addr, str) self.history[addr] = hist @modifier def remove_addr_history(self, addr: str) -> None: assert isinstance(addr, str) self.history.pop(addr, None) @locked def list_verified_tx(self) -> Sequence[str]: return list(self.verified_tx.keys()) @locked def get_verified_tx(self, txid: str) -> Optional[TxMinedInfo]: assert isinstance(txid, str) if txid not in self.verified_tx: return None height, timestamp, txpos, header_hash = self.verified_tx[txid] return TxMinedInfo(height=height, conf=None, timestamp=timestamp, txpos=txpos, header_hash=header_hash) @modifier def add_verified_tx(self, txid: str, info: TxMinedInfo): assert isinstance(txid, str) assert isinstance(info, TxMinedInfo) self.verified_tx[txid] = (info.height, info.timestamp, info.txpos, info.header_hash) @modifier def remove_verified_tx(self, txid: str): assert isinstance(txid, str) self.verified_tx.pop(txid, None) def is_in_verified_tx(self, txid: str) -> bool: assert isinstance(txid, str) return txid in self.verified_tx @modifier def add_tx_fee_from_server(self, txid: str, fee_sat: Optional[int]) -> None: assert isinstance(txid, str) # note: when called with (fee_sat is None), rm currently saved value if txid not in self.tx_fees: self.tx_fees[txid] = TxFeesValue() tx_fees_value = self.tx_fees[txid] if tx_fees_value.is_calculated_by_us: return self.tx_fees[txid] = tx_fees_value._replace(fee=fee_sat, is_calculated_by_us=False) @modifier def add_tx_fee_we_calculated(self, txid: str, fee_sat: Optional[int]) -> None: assert isinstance(txid, str) if fee_sat is None: return assert isinstance(fee_sat, int) if txid not in self.tx_fees: self.tx_fees[txid] = TxFeesValue() self.tx_fees[txid] = self.tx_fees[txid]._replace(fee=fee_sat, is_calculated_by_us=True) @locked def get_tx_fee(self, txid: str, *, trust_server: bool = False) -> Optional[int]: assert isinstance(txid, str) """Returns tx_fee.""" tx_fees_value = self.tx_fees.get(txid) if tx_fees_value is None: return None if not trust_server and not tx_fees_value.is_calculated_by_us: return None return tx_fees_value.fee @modifier def add_num_inputs_to_tx(self, txid: str, num_inputs: int) -> None: assert isinstance(txid, str) assert isinstance(num_inputs, int) if txid not in self.tx_fees: self.tx_fees[txid] = TxFeesValue() self.tx_fees[txid] = self.tx_fees[txid]._replace(num_inputs=num_inputs) @locked def get_num_all_inputs_of_tx(self, txid: str) -> Optional[int]: assert isinstance(txid, str) tx_fees_value = self.tx_fees.get(txid) if tx_fees_value is None: return None return tx_fees_value.num_inputs @locked def get_num_ismine_inputs_of_tx(self, txid: str) -> int: assert isinstance(txid, str) txins = self.txi.get(txid, {}) return sum([len(tupls) for addr, tupls in txins.items()]) @modifier def remove_tx_fee(self, txid: str) -> None: assert isinstance(txid, str) self.tx_fees.pop(txid, None) @locked def get_dict(self, name): # Warning: interacts un-intuitively with 'put': certain parts # of 'data' will have pointers saved as separate variables. if name not in self.data: self.data[name] = {} return self.data[name] @locked def num_change_addresses(self) -> int: return len(self.change_addresses) @locked def num_receiving_addresses(self) -> int: return len(self.receiving_addresses) @locked def get_change_addresses(self, *, slice_start=None, slice_stop=None) -> List[str]: # note: slicing makes a shallow copy return self.change_addresses[slice_start:slice_stop] @locked def get_receiving_addresses(self, *, slice_start=None, slice_stop=None) -> List[str]: # note: slicing makes a shallow copy return self.receiving_addresses[slice_start:slice_stop] @modifier def add_change_address(self, addr: str) -> None: assert isinstance(addr, str) self._addr_to_addr_index[addr] = (1, len(self.change_addresses)) self.change_addresses.append(addr) @modifier def add_receiving_address(self, addr: str) -> None: assert isinstance(addr, str) self._addr_to_addr_index[addr] = (0, len(self.receiving_addresses)) self.receiving_addresses.append(addr) @locked def get_address_index(self, address: str) -> Optional[Sequence[int]]: assert isinstance(address, str) return self._addr_to_addr_index.get(address) @modifier def add_imported_address(self, addr: str, d: dict) -> None: assert isinstance(addr, str) self.imported_addresses[addr] = d @modifier def remove_imported_address(self, addr: str) -> None: assert isinstance(addr, str) self.imported_addresses.pop(addr) @locked def has_imported_address(self, addr: str) -> bool: assert isinstance(addr, str) return addr in self.imported_addresses @locked def get_imported_addresses(self) -> Sequence[str]: return list(sorted(self.imported_addresses.keys())) @locked def get_imported_address(self, addr: str) -> Optional[dict]: assert isinstance(addr, str) return self.imported_addresses.get(addr) def load_addresses(self, wallet_type): """ called from Abstract_Wallet.__init__ """ if wallet_type == 'imported': self.imported_addresses = self.get_dict('addresses') # type: Dict[str, dict] else: self.get_dict('addresses') for name in ['receiving', 'change']: if name not in self.data['addresses']: self.data['addresses'][name] = [] self.change_addresses = self.data['addresses']['change'] self.receiving_addresses = self.data['addresses']['receiving'] self._addr_to_addr_index = {} # type: Dict[str, Sequence[int]] # key: address, value: (is_change, index) for i, addr in enumerate(self.receiving_addresses): self._addr_to_addr_index[addr] = (0, i) for i, addr in enumerate(self.change_addresses): self._addr_to_addr_index[addr] = (1, i) @profiler def _load_transactions(self): self.data = StoredDict(self.data, self, []) # references in self.data # TODO make all these private # txid -> address -> prev_outpoint -> value self.txi = self.get_dict('txi') # type: Dict[str, Dict[str, Dict[str, int]]] # txid -> address -> output_index -> (value, is_coinbase) self.txo = self.get_dict('txo') # type: Dict[str, Dict[str, Dict[str, Tuple[int, bool]]]] self.transactions = self.get_dict('transactions') # type: Dict[str, Transaction] self.spent_outpoints = self.get_dict('spent_outpoints') # txid -> output_index -> next_txid self.history = self.get_dict('addr_history') # address -> list of (txid, height) self.verified_tx = self.get_dict('verified_tx3') # txid -> (height, timestamp, txpos, header_hash) self.tx_fees = self.get_dict('tx_fees') # type: Dict[str, TxFeesValue] # scripthash -> set of (outpoint, value) self._prevouts_by_scripthash = self.get_dict('prevouts_by_scripthash') # type: Dict[str, Set[Tuple[str, int]]] # remove unreferenced tx for tx_hash in list(self.transactions.keys()): if not self.get_txi_addresses(tx_hash) and not self.get_txo_addresses(tx_hash): self.logger.info(f"removing unreferenced tx: {tx_hash}") self.transactions.pop(tx_hash) # remove unreferenced outpoints for prevout_hash in self.spent_outpoints.keys(): d = self.spent_outpoints[prevout_hash] for prevout_n, spending_txid in list(d.items()): if spending_txid not in self.transactions: self.logger.info("removing unreferenced spent outpoint") d.pop(prevout_n) self.tokens = self.get_dict('tokens') # contract_addr + '_' + b58addr -> list(txid, height, log_index) self.token_history = self.get_dict('addr_token_history') # txid -> tx receipt self.tx_receipt = self.get_dict('tx_receipt') # txid -> raw tx self.token_txs = self.get_dict('token_txs') self.smart_contracts = self.get_dict('smart_contracts') self.delegations = self.get_dict('delegations') if self.token_history: token_hist_txids = [x2[0] for x2 in reduce(lambda x1, y1: x1+y1, self.token_history.values())] else: token_hist_txids = [] for tx_hash, raw in self.token_txs.items(): if tx_hash in token_hist_txids: tx = Transaction(raw) self.token_txs[tx_hash] = tx @modifier def set_token(self, token: Token): self.tokens[token.get_key()] = token @modifier def delete_token(self, key: str): self.tokens.pop(key, None) self.token_history.pop(key, None) @locked def get_token(self, key: str) -> Optional[Token]: return Token(*self.tokens.get(key)) @locked def list_tokens(self) -> list: return list(self.tokens.keys()) @modifier def set_token_history(self, key: str, hist: list): self.token_history[key] = hist @modifier def delete_token_history(self, key: str): self.token_history.pop(key, None) @locked def get_token_history(self, key: str) -> list: return self.token_history.get(key, []) @locked def list_token_histories(self) -> list: return list(self.token_history.keys()) @modifier def set_token_tx(self, txid: str, raw: str): self.token_txs[txid] = raw @modifier def delete_token_tx(self, txid: str): self.token_txs.pop(txid, None) @locked def get_token_tx(self, txid: str): return self.token_txs.get(txid) @locked def list_token_txs(self) -> list: return list(self.token_txs.keys()) @modifier def set_tx_receipt(self, txid: str, receipt: list): self.tx_receipt[txid] = receipt @modifier def delete_tx_receipt(self, txid: str): return self.tx_receipt.pop(txid, None) @locked def get_tx_receipt(self, txid: str) -> list: return self.tx_receipt.get(txid, []) @locked def list_tx_receipts(self) -> list: return list(self.tx_receipt.keys()) @modifier def set_delegation(self, dele: Delegation): self.delegations[dele.addr] = [dele.staker, dele.fee] @modifier def delete_delegation(self, addr: str): self.delegations.pop(addr, None) @locked def get_delegation(self, addr: str) -> Optional[Delegation]: dele = self.delegations.get(addr, []) if len(dele) != 2: return None return Delegation(addr=addr, staker=dele[0], fee=dele[1]) @locked def list_delegations(self) -> Sequence[str]: return list(self.delegations.keys()) @modifier def clear_history(self): self.txi.clear() self.txo.clear() self.spent_outpoints.clear() self.transactions.clear() self.history.clear() self.verified_tx.clear() self.tx_fees.clear() self.token_txs.clear() self.token_history.clear() self.tx_receipt.clear() self._prevouts_by_scripthash.clear() def _convert_dict(self, path, key, v): if key == 'transactions': # note: for performance, "deserialize=False" so that we will deserialize these on-demand v = dict((k, tx_from_any(x, deserialize=False)) for k, x in v.items()) if key == 'invoices': v = dict((k, Invoice.from_json(x)) for k, x in v.items()) if key == 'payment_requests': v = dict((k, Invoice.from_json(x)) for k, x in v.items()) elif key == 'adds': v = dict((k, UpdateAddHtlc.from_tuple(*x)) for k, x in v.items()) elif key == 'fee_updates': v = dict((k, FeeUpdate(**x)) for k, x in v.items()) elif key == 'submarine_swaps': v = dict((k, SwapData(**x)) for k, x in v.items()) elif key == 'channel_backups': v = dict((k, ChannelBackupStorage(**x)) for k, x in v.items()) elif key == 'tx_fees': v = dict((k, TxFeesValue(*x)) for k, x in v.items()) elif key == 'prevouts_by_scripthash': v = dict((k, {(prevout, value) for (prevout, value) in x}) for k, x in v.items()) elif key == 'buckets': v = dict((k, ShachainElement(bfh(x[0]), int(x[1]))) for k, x in v.items()) elif key == 'data_loss_protect_remote_pcp': v = dict((k, bfh(x)) for k, x in v.items()) return v def _convert_value(self, path, key, v): if key == 'local_config': v = LocalConfig(**v) elif key == 'remote_config': v = RemoteConfig(**v) elif key == 'constraints': v = ChannelConstraints(**v) elif key == 'funding_outpoint': v = Outpoint(**v) return v def write(self, storage: 'WalletStorage'): with self.lock: self._write(storage) def _write(self, storage: 'WalletStorage'): if threading.currentThread().isDaemon(): self.logger.warning('daemon thread cannot write db') return if not self.modified(): return storage.write(self.dump()) self.set_modified(False) def is_ready_to_be_used_by_wallet(self): return not self.requires_upgrade() and self._called_after_upgrade_tasks def split_accounts(self, root_path): from .storage import WalletStorage out = [] result = self.get_split_accounts() for data in result: path = root_path + '.' + data['suffix'] storage = WalletStorage(path) db = WalletDB(json.dumps(data), manual_upgrades=False) db._called_after_upgrade_tasks = False db.upgrade() db.write(storage) out.append(path) return out def get_action(self): action = run_hook('get_action', self) return action def load_plugins(self): wallet_type = self.get('wallet_type') if wallet_type in plugin_loaders: plugin_loaders[wallet_type]() def set_keystore_encryption(self, enable): self.put('use_encryption', enable)

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0.582038

import os import ast import json import copy import threading from collections import defaultdict from functools import reduce from typing import Dict, Optional, List, Tuple, Set, Iterable, NamedTuple, Sequence, TYPE_CHECKING, Union import binascii from . import util, bitcoin from .util import profiler, WalletFileException, multisig_type, TxMinedInfo, bfh from .invoices import PR_TYPE_ONCHAIN, Invoice from .keystore import bip44_derivation from .transaction import Transaction, TxOutpoint, tx_from_any, PartialTransaction, PartialTxOutput from .logging import Logger from .lnutil import LOCAL, REMOTE, FeeUpdate, UpdateAddHtlc, LocalConfig, RemoteConfig, Keypair, OnlyPubkeyKeypair, RevocationStore, ChannelBackupStorage from .lnutil import ChannelConstraints, Outpoint, ShachainElement from .json_db import StoredDict, JsonDB, locked, modifier from .plugin import run_hook, plugin_loaders from .paymentrequest import PaymentRequest from .submarine_swaps import SwapData from .bitcoin import Token, Delegation if TYPE_CHECKING: from .storage import WalletStorage OLD_SEED_VERSION = 4 NEW_SEED_VERSION = 11 FINAL_SEED_VERSION = 32 class TxFeesValue(NamedTuple): fee: Optional[int] = None is_calculated_by_us: bool = False num_inputs: Optional[int] = None class WalletDB(JsonDB): def __init__(self, raw, *, manual_upgrades: bool): JsonDB.__init__(self, {}) self._manual_upgrades = manual_upgrades self._called_after_upgrade_tasks = False if raw: self.load_data(raw) self.load_plugins() else: self.put('seed_version', FINAL_SEED_VERSION) self._after_upgrade_tasks() def load_data(self, s): try: self.data = json.loads(s) except: try: d = ast.literal_eval(s) labels = d.get('labels', {}) except Exception as e: raise WalletFileException("Cannot read wallet file. (parsing failed)") self.data = {} for key, value in d.items(): try: json.dumps(key) json.dumps(value) except: self.logger.info(f'Failed to convert label to json format: {key}') continue self.data[key] = value if not isinstance(self.data, dict): raise WalletFileException("Malformed wallet file (not dict)") if not self._manual_upgrades and self.requires_split(): raise WalletFileException("This wallet has multiple accounts and must be split") if not self.requires_upgrade(): self._after_upgrade_tasks() elif not self._manual_upgrades: self.upgrade() def requires_split(self): d = self.get('accounts', {}) return len(d) > 1 def get_split_accounts(self): result = [] d = self.get('accounts', {}) if len(d) < 2: return wallet_type = self.get('wallet_type') if wallet_type == 'old': assert len(d) == 2 data1 = copy.deepcopy(self.data) data1['accounts'] = {'0': d['0']} data1['suffix'] = 'deterministic' data2 = copy.deepcopy(self.data) data2['accounts'] = {'/x': d['/x']} data2['seed'] = None data2['seed_version'] = None data2['master_public_key'] = None data2['wallet_type'] = 'imported' data2['suffix'] = 'imported' result = [data1, data2] elif wallet_type in ['bip44', 'trezor', 'keepkey', 'ledger', 'btchip', 'digitalbitbox', 'safe_t']: mpk = self.get('master_public_keys') for k in d.keys(): i = int(k) x = d[k] if x.get("pending"): continue xpub = mpk["x/%d'"%i] new_data = copy.deepcopy(self.data) # save account, derivation and xpub at index 0 new_data['accounts'] = {'0': x} new_data['master_public_keys'] = {"x/0'": xpub} new_data['derivation'] = bip44_derivation(k) new_data['suffix'] = k result.append(new_data) else: raise WalletFileException("This wallet has multiple accounts and must be split") return result def requires_upgrade(self): return self.get_seed_version() < FINAL_SEED_VERSION @profiler def upgrade(self): self.logger.info('upgrading wallet format') if self._called_after_upgrade_tasks: raise Exception("'after_upgrade_tasks' must NOT be called before 'upgrade'") self._convert_imported() self._convert_wallet_type() self._convert_account() self._convert_version_13_b() self._convert_version_14() self._convert_version_15() self._convert_version_16() self._convert_version_17() self._convert_version_18() self._convert_version_19() self._convert_version_20() self._convert_version_21() self._convert_version_22() self._convert_version_23() self._convert_version_24() self._convert_version_25() self._convert_version_26() self._convert_version_27() self._convert_version_28() self._convert_version_29() self._convert_version_30() self._convert_version_31() self._convert_version_32() self.put('seed_version', FINAL_SEED_VERSION) self._after_upgrade_tasks() def _after_upgrade_tasks(self): self._called_after_upgrade_tasks = True self._load_transactions() def _convert_wallet_type(self): if not self._is_upgrade_method_needed(0, 13): return wallet_type = self.get('wallet_type') if wallet_type == 'btchip': wallet_type = 'ledger' if self.get('keystore') or self.get('x1/') or wallet_type=='imported': return False assert not self.requires_split() seed_version = self.get_seed_version() seed = self.get('seed') xpubs = self.get('master_public_keys') xprvs = self.get('master_private_keys', {}) mpk = self.get('master_public_key') keypairs = self.get('keypairs') key_type = self.get('key_type') if seed_version == OLD_SEED_VERSION or wallet_type == 'old': d = { 'type': 'old', 'seed': seed, 'mpk': mpk, } self.put('wallet_type', 'standard') self.put('keystore', d) elif key_type == 'imported': d = { 'type': 'imported', 'keypairs': keypairs, } self.put('wallet_type', 'standard') self.put('keystore', d) elif wallet_type in ['xpub', 'standard']: xpub = xpubs["x/"] xprv = xprvs.get("x/") d = { 'type': 'bip32', 'xpub': xpub, 'xprv': xprv, 'seed': seed, } self.put('wallet_type', 'standard') self.put('keystore', d) elif wallet_type in ['bip44']: xpub = xpubs["x/0'"] xprv = xprvs.get("x/0'") d = { 'type': 'bip32', 'xpub': xpub, 'xprv': xprv, } self.put('wallet_type', 'standard') self.put('keystore', d) elif wallet_type in ['trezor', 'keepkey', 'ledger', 'digitalbitbox', 'safe_t']: xpub = xpubs["x/0'"] derivation = self.get('derivation', bip44_derivation(0)) d = { 'type': 'hardware', 'hw_type': wallet_type, 'xpub': xpub, 'derivation': derivation, } self.put('wallet_type', 'standard') self.put('keystore', d) elif (wallet_type == '2fa') or multisig_type(wallet_type): for key in xpubs.keys(): d = { 'type': 'bip32', 'xpub': xpubs[key], 'xprv': xprvs.get(key), } if key == 'x1/' and seed: d['seed'] = seed self.put(key, d) else: raise WalletFileException('Unable to tell wallet type. Is this even a wallet file?') # remove junk self.put('master_public_key', None) self.put('master_public_keys', None) self.put('master_private_keys', None) self.put('derivation', None) self.put('seed', None) self.put('keypairs', None) self.put('key_type', None) def _convert_version_13_b(self): # version 13 is ambiguous, and has an earlier and a later structure if not self._is_upgrade_method_needed(0, 13): return if self.get('wallet_type') == 'standard': if self.get('keystore').get('type') == 'imported': pubkeys = self.get('keystore').get('keypairs').keys() d = {'change': []} receiving_addresses = [] for pubkey in pubkeys: addr = bitcoin.pubkey_to_address('p2pkh', pubkey) receiving_addresses.append(addr) d['receiving'] = receiving_addresses self.put('addresses', d) self.put('pubkeys', None) self.put('seed_version', 13) def _convert_version_14(self): # convert imported wallets for 3.0 if not self._is_upgrade_method_needed(13, 13): return if self.get('wallet_type') =='imported': addresses = self.get('addresses') if type(addresses) is list: addresses = dict([(x, None) for x in addresses]) self.put('addresses', addresses) elif self.get('wallet_type') == 'standard': if self.get('keystore').get('type')=='imported': addresses = set(self.get('addresses').get('receiving')) pubkeys = self.get('keystore').get('keypairs').keys() assert len(addresses) == len(pubkeys) d = {} for pubkey in pubkeys: addr = bitcoin.pubkey_to_address('p2pkh', pubkey) assert addr in addresses d[addr] = { 'pubkey': pubkey, 'redeem_script': None, 'type': 'p2pkh' } self.put('addresses', d) self.put('pubkeys', None) self.put('wallet_type', 'imported') self.put('seed_version', 14) def _convert_version_15(self): if not self._is_upgrade_method_needed(14, 14): return if self.get('seed_type') == 'segwit': # should not get here; get_seed_version should have caught this raise Exception('unsupported derivation (development segwit, v14)') self.put('seed_version', 15) def _convert_version_16(self): # fixes issue #3193 for Imported_Wallets with addresses # also, previous versions allowed importing any garbage as an address # which we now try to remove, see pr #3191 if not self._is_upgrade_method_needed(15, 15): return def remove_address(addr): def remove_from_dict(dict_name): d = self.get(dict_name, None) if d is not None: d.pop(addr, None) self.put(dict_name, d) def remove_from_list(list_name): lst = self.get(list_name, None) if lst is not None: s = set(lst) s -= {addr} self.put(list_name, list(s)) # note: we don't remove 'addr' from self.get('addresses') remove_from_dict('addr_history') remove_from_dict('labels') remove_from_dict('payment_requests') remove_from_list('frozen_addresses') if self.get('wallet_type') == 'imported': addresses = self.get('addresses') assert isinstance(addresses, dict) addresses_new = dict() for address, details in addresses.items(): if not bitcoin.is_address(address): remove_address(address) continue if details is None: addresses_new[address] = {} else: addresses_new[address] = details self.put('addresses', addresses_new) self.put('seed_version', 16) def _convert_version_17(self): if not self._is_upgrade_method_needed(16, 16): return self.put('pruned_txo', None) transactions = self.get('transactions', {}) spent_outpoints = defaultdict(dict) for txid, raw_tx in transactions.items(): tx = Transaction(raw_tx) for txin in tx.inputs(): if txin.is_coinbase_input(): continue prevout_hash = txin.prevout.txid.hex() prevout_n = txin.prevout.out_idx spent_outpoints[prevout_hash][str(prevout_n)] = txid self.put('spent_outpoints', spent_outpoints) tokens = self.get('tokens', {}) new_tokens = {} for key, value in tokens.items(): contract_addr, bind_addr = key.split('_') new_token = Token(contract_addr, bind_addr, value[0], value[1], value[2], value[3]) new_tokens[new_token.get_key()] = new_token self.put('tokens', new_tokens) self.put('seed_version', 17) def _convert_version_18(self): if not self._is_upgrade_method_needed(17, 17): return self.put('verified_tx3', None) self.put('seed_version', 18) def _convert_version_19(self): if not self._is_upgrade_method_needed(18, 18): return self.put('tx_fees', None) self.put('seed_version', 19) def _convert_version_20(self): if not self._is_upgrade_method_needed(19, 19): return from .bip32 import BIP32Node, convert_bip32_intpath_to_strpath for ks_name in ('keystore', *['x{}/'.format(i) for i in range(1, 16)]): ks = self.get(ks_name, None) if ks is None: continue xpub = ks.get('xpub', None) if xpub is None: continue bip32node = BIP32Node.from_xkey(xpub) derivation_prefix = ks.get('derivation', None) if derivation_prefix is None: assert bip32node.depth >= 0, bip32node.depth if bip32node.depth == 0: derivation_prefix = 'm' elif bip32node.depth == 1: child_number_int = int.from_bytes(bip32node.child_number, 'big') derivation_prefix = convert_bip32_intpath_to_strpath([child_number_int]) ks['derivation'] = derivation_prefix root_fingerprint = ks.get('ckcc_xfp', None) if root_fingerprint is not None: root_fingerprint = root_fingerprint.to_bytes(4, byteorder="little", signed=False).hex().lower() if root_fingerprint is None: if bip32node.depth == 0: root_fingerprint = bip32node.calc_fingerprint_of_this_node().hex().lower() elif bip32node.depth == 1: root_fingerprint = bip32node.fingerprint.hex() ks['root_fingerprint'] = root_fingerprint ks.pop('ckcc_xfp', None) self.put(ks_name, ks) self.put('seed_version', 20) def _convert_version_21(self): if not self._is_upgrade_method_needed(20, 20): return channels = self.get('channels') if channels: for channel in channels: channel['state'] = 'OPENING' self.put('channels', channels) self.put('seed_version', 21) def _convert_version_22(self): if not self._is_upgrade_method_needed(21, 21): return from .bitcoin import script_to_scripthash transactions = self.get('transactions', {}) prevouts_by_scripthash = defaultdict(list) for txid, raw_tx in transactions.items(): tx = Transaction(raw_tx) for idx, txout in enumerate(tx.outputs()): outpoint = f"{txid}:{idx}" scripthash = script_to_scripthash(txout.scriptpubkey.hex()) prevouts_by_scripthash[scripthash].append((outpoint, txout.value)) self.put('prevouts_by_scripthash', prevouts_by_scripthash) self.put('seed_version', 22) def _convert_version_23(self): if not self._is_upgrade_method_needed(22, 22): return channels = self.get('channels', []) LOCAL = 1 REMOTE = -1 for c in channels: r = c['remote_config'].pop('revocation_store') c['revocation_store'] = r log = c.get('log', {}) for sub in LOCAL, REMOTE: l = log[str(sub)]['fee_updates'] d = {} for i, fu in enumerate(l): d[str(i)] = { 'rate':fu['rate'], 'ctn_local':fu['ctns'][str(LOCAL)], 'ctn_remote':fu['ctns'][str(REMOTE)] } log[str(int(sub))]['fee_updates'] = d self.data['channels'] = channels self.data['seed_version'] = 23 def _convert_version_24(self): if not self._is_upgrade_method_needed(23, 23): return channels = self.get('channels', []) for c in channels: r = c['revocation_store'] d = {} for i in range(49): v = r['buckets'][i] if v is not None: d[str(i)] = v r['buckets'] = d c['revocation_store'] = r self.data['channels'] = { x['channel_id']: x for x in channels } txi = self.get('txi', {}) for tx_hash, d in txi.items(): d2 = {} for addr, l in d.items(): d2[addr] = {} for ser, v in l: d2[addr][ser] = v txi[tx_hash] = d2 self.data['txi'] = txi txo = self.get('txo', {}) for tx_hash, d in txo.items(): d2 = {} for addr, l in d.items(): d2[addr] = {} for n, v, cb in l: d2[addr][str(n)] = (v, cb) txo[tx_hash] = d2 self.data['txo'] = txo self.data['seed_version'] = 24 def _convert_version_25(self): if not self._is_upgrade_method_needed(24, 24): return requests = self.data.get('payment_requests', {}) for k, r in list(requests.items()): if r.get('address') == k: requests[k] = { 'address': r['address'], 'amount': r.get('amount'), 'exp': r.get('exp'), 'id': r.get('id'), 'memo': r.get('memo'), 'time': r.get('time'), 'type': PR_TYPE_ONCHAIN, } invoices = self.data.get('invoices', {}) for k, r in list(invoices.items()): data = r.get("hex") if data: pr = PaymentRequest(bytes.fromhex(data)) if pr.id != k: continue invoices[k] = { 'type': PR_TYPE_ONCHAIN, 'amount': pr.get_amount(), 'bip70': data, 'exp': pr.get_expiration_date() - pr.get_time(), 'id': pr.id, 'message': pr.get_memo(), 'outputs': [x.to_legacy_tuple() for x in pr.get_outputs()], 'time': pr.get_time(), 'requestor': pr.get_requestor(), } self.data['seed_version'] = 25 def _convert_version_26(self): if not self._is_upgrade_method_needed(25, 25): return channels = self.data.get('channels', {}) channel_timestamps = self.data.pop('lightning_channel_timestamps', {}) for channel_id, c in channels.items(): item = channel_timestamps.get(channel_id) if item: funding_txid, funding_height, funding_timestamp, closing_txid, closing_height, closing_timestamp = item if funding_txid: c['funding_height'] = funding_txid, funding_height, funding_timestamp if closing_txid: c['closing_height'] = closing_txid, closing_height, closing_timestamp self.data['seed_version'] = 26 def _convert_version_27(self): if not self._is_upgrade_method_needed(26, 26): return channels = self.data.get('channels', {}) for channel_id, c in channels.items(): c['local_config']['htlc_minimum_msat'] = 1 self.data['seed_version'] = 27 def _convert_version_28(self): if not self._is_upgrade_method_needed(27, 27): return channels = self.data.get('channels', {}) for channel_id, c in channels.items(): c['local_config']['channel_seed'] = None self.data['seed_version'] = 28 def _convert_version_29(self): if not self._is_upgrade_method_needed(28, 28): return requests = self.data.get('payment_requests', {}) invoices = self.data.get('invoices', {}) for d in [invoices, requests]: for key, r in list(d.items()): _type = r.get('type', 0) item = { 'type': _type, 'message': r.get('message') or r.get('memo', ''), 'amount': r.get('amount'), 'exp': r.get('exp') or 0, 'time': r.get('time', 0), } if _type == PR_TYPE_ONCHAIN: address = r.pop('address', None) if address: outputs = [(0, address, r.get('amount'))] else: outputs = r.get('outputs') item.update({ 'outputs': outputs, 'id': r.get('id'), 'bip70': r.get('bip70'), 'requestor': r.get('requestor'), }) else: item.update({ 'rhash': r['rhash'], 'invoice': r['invoice'], }) d[key] = item self.data['seed_version'] = 29 def _convert_version_30(self): if not self._is_upgrade_method_needed(29, 29): return from .invoices import PR_TYPE_ONCHAIN, PR_TYPE_LN requests = self.data.get('payment_requests', {}) invoices = self.data.get('invoices', {}) for d in [invoices, requests]: for key, item in list(d.items()): _type = item['type'] if _type == PR_TYPE_ONCHAIN: item['amount_sat'] = item.pop('amount') elif _type == PR_TYPE_LN: amount_sat = item.pop('amount') item['amount_msat'] = 1000 * amount_sat if amount_sat is not None else None item.pop('exp') item.pop('message') item.pop('rhash') item.pop('time') else: raise Exception(f"unknown invoice type: {_type}") self.data['seed_version'] = 30 def _convert_version_31(self): if not self._is_upgrade_method_needed(30, 30): return from .invoices import PR_TYPE_ONCHAIN requests = self.data.get('payment_requests', {}) invoices = self.data.get('invoices', {}) for d in [invoices, requests]: for key, item in list(d.items()): if item['type'] == PR_TYPE_ONCHAIN: item['amount_sat'] = item['amount_sat'] or 0 item['exp'] = item['exp'] or 0 item['time'] = item['time'] or 0 self.data['seed_version'] = 31 def _convert_version_32(self): if not self._is_upgrade_method_needed(31, 31): return from .invoices import PR_TYPE_ONCHAIN invoices_old = self.data.get('invoices', {}) invoices_new = {k: item for k, item in invoices_old.items() if not (item['type'] == PR_TYPE_ONCHAIN and item['outputs'] is None)} self.data['invoices'] = invoices_new self.data['seed_version'] = 32 def _convert_imported(self): if not self._is_upgrade_method_needed(0, 13): return d = self.get('accounts', {}).get('/x', {}).get('imported',{}) if not d: return False addresses = [] keypairs = {} for addr, v in d.items(): pubkey, privkey = v if privkey: keypairs[pubkey] = privkey else: addresses.append(addr) if addresses and keypairs: raise WalletFileException('mixed addresses and privkeys') elif addresses: self.put('addresses', addresses) self.put('accounts', None) elif keypairs: self.put('wallet_type', 'standard') self.put('key_type', 'imported') self.put('keypairs', keypairs) self.put('accounts', None) else: raise WalletFileException('no addresses or privkeys') def _convert_account(self): if not self._is_upgrade_method_needed(0, 13): return self.put('accounts', None) def _is_upgrade_method_needed(self, min_version, max_version): assert min_version <= max_version cur_version = self.get_seed_version() if cur_version > max_version: return False elif cur_version < min_version: raise WalletFileException( 'storage upgrade: unexpected version {} (should be {}-{})' .format(cur_version, min_version, max_version)) else: return True @locked def get_seed_version(self): seed_version = self.get('seed_version') if not seed_version: seed_version = OLD_SEED_VERSION if len(self.get('master_public_key','')) == 128 else NEW_SEED_VERSION if seed_version > FINAL_SEED_VERSION: raise WalletFileException('This version of Electrum is too old to open this wallet.\n' '(highest supported storage version: {}, version of this file: {})' .format(FINAL_SEED_VERSION, seed_version)) if seed_version==14 and self.get('seed_type') == 'segwit': self._raise_unsupported_version(seed_version) if seed_version >=12: return seed_version if seed_version not in [OLD_SEED_VERSION, NEW_SEED_VERSION]: self._raise_unsupported_version(seed_version) return seed_version def _raise_unsupported_version(self, seed_version): msg = f"Your wallet has an unsupported seed version: {seed_version}." if seed_version in [5, 7, 8, 9, 10, 14]: msg += "\n\nTo open this wallet, try 'git checkout seed_v%d'"%seed_version if seed_version == 6: msg += '\n\nThis file was created because of a bug in version 1.9.8.' if self.get('master_public_keys') is None and self.get('master_private_keys') is None and self.get('imported_keys') is None: msg += "\nIt does not contain any keys, and can safely be removed." else: msg += "\nPlease open this file with Electrum 1.9.8, and move your coins to a new wallet." raise WalletFileException(msg) @locked def get_txi_addresses(self, tx_hash: str) -> List[str]: assert isinstance(tx_hash, str) return list(self.txi.get(tx_hash, {}).keys()) @locked def get_txo_addresses(self, tx_hash: str) -> List[str]: assert isinstance(tx_hash, str) return list(self.txo.get(tx_hash, {}).keys()) @locked def get_txi_addr(self, tx_hash: str, address: str) -> Iterable[Tuple[str, int]]: assert isinstance(tx_hash, str) assert isinstance(address, str) d = self.txi.get(tx_hash, {}).get(address, {}) return list(d.items()) @locked def get_txo_addr(self, tx_hash: str, address: str) -> Iterable[Tuple[int, int, bool]]: assert isinstance(tx_hash, str) assert isinstance(address, str) d = self.txo.get(tx_hash, {}).get(address, {}) return [(int(n), v, cb) for (n, (v, cb)) in d.items()] @modifier def add_txi_addr(self, tx_hash: str, addr: str, ser: str, v: int) -> None: assert isinstance(tx_hash, str) assert isinstance(addr, str) assert isinstance(ser, str) assert isinstance(v, int) if tx_hash not in self.txi: self.txi[tx_hash] = {} d = self.txi[tx_hash] if addr not in d: d[addr] = {} d[addr][ser] = v @modifier def add_txo_addr(self, tx_hash: str, addr: str, n: Union[int, str], v: int, is_coinbase: bool) -> None: n = str(n) assert isinstance(tx_hash, str) assert isinstance(addr, str) assert isinstance(n, str) assert isinstance(v, int) assert isinstance(is_coinbase, bool) if tx_hash not in self.txo: self.txo[tx_hash] = {} d = self.txo[tx_hash] if addr not in d: d[addr] = {} d[addr][n] = (v, is_coinbase) @locked def list_txi(self) -> Sequence[str]: return list(self.txi.keys()) @locked def list_txo(self) -> Sequence[str]: return list(self.txo.keys()) @modifier def remove_txi(self, tx_hash: str) -> None: assert isinstance(tx_hash, str) self.txi.pop(tx_hash, None) @modifier def remove_txo(self, tx_hash: str) -> None: assert isinstance(tx_hash, str) self.txo.pop(tx_hash, None) @locked def list_spent_outpoints(self) -> Sequence[Tuple[str, str]]: return [(h, n) for h in self.spent_outpoints.keys() for n in self.get_spent_outpoints(h) ] @locked def get_spent_outpoints(self, prevout_hash: str) -> Sequence[str]: assert isinstance(prevout_hash, str) return list(self.spent_outpoints.get(prevout_hash, {}).keys()) @locked def get_spent_outpoint(self, prevout_hash: str, prevout_n: Union[int, str]) -> Optional[str]: assert isinstance(prevout_hash, str) prevout_n = str(prevout_n) return self.spent_outpoints.get(prevout_hash, {}).get(prevout_n) @modifier def remove_spent_outpoint(self, prevout_hash: str, prevout_n: Union[int, str]) -> None: assert isinstance(prevout_hash, str) prevout_n = str(prevout_n) self.spent_outpoints[prevout_hash].pop(prevout_n, None) if not self.spent_outpoints[prevout_hash]: self.spent_outpoints.pop(prevout_hash) @modifier def set_spent_outpoint(self, prevout_hash: str, prevout_n: Union[int, str], tx_hash: str) -> None: assert isinstance(prevout_hash, str) assert isinstance(tx_hash, str) prevout_n = str(prevout_n) if prevout_hash not in self.spent_outpoints: self.spent_outpoints[prevout_hash] = {} self.spent_outpoints[prevout_hash][prevout_n] = tx_hash @modifier def add_prevout_by_scripthash(self, scripthash: str, *, prevout: TxOutpoint, value: int) -> None: assert isinstance(scripthash, str) assert isinstance(prevout, TxOutpoint) assert isinstance(value, int) if scripthash not in self._prevouts_by_scripthash: self._prevouts_by_scripthash[scripthash] = set() self._prevouts_by_scripthash[scripthash].add((prevout.to_str(), value)) @modifier def remove_prevout_by_scripthash(self, scripthash: str, *, prevout: TxOutpoint, value: int) -> None: assert isinstance(scripthash, str) assert isinstance(prevout, TxOutpoint) assert isinstance(value, int) self._prevouts_by_scripthash[scripthash].discard((prevout.to_str(), value)) if not self._prevouts_by_scripthash[scripthash]: self._prevouts_by_scripthash.pop(scripthash) @locked def get_prevouts_by_scripthash(self, scripthash: str) -> Set[Tuple[TxOutpoint, int]]: assert isinstance(scripthash, str) prevouts_and_values = self._prevouts_by_scripthash.get(scripthash, set()) return {(TxOutpoint.from_str(prevout), value) for prevout, value in prevouts_and_values} @modifier def add_transaction(self, tx_hash: str, tx: Transaction) -> None: assert isinstance(tx_hash, str) assert isinstance(tx, Transaction), tx if not tx_hash: raise Exception("trying to add tx to db without txid") if tx_hash != tx.txid(): raise Exception(f"trying to add tx to db with inconsistent txid: {tx_hash} != {tx.txid()}") tx_we_already_have = self.transactions.get(tx_hash, None) if tx_we_already_have is None or isinstance(tx_we_already_have, PartialTransaction): self.transactions[tx_hash] = tx @modifier def remove_transaction(self, tx_hash: str) -> Optional[Transaction]: assert isinstance(tx_hash, str) return self.transactions.pop(tx_hash, None) @locked def get_transaction(self, tx_hash: Optional[str]) -> Optional[Transaction]: if tx_hash is None: return None assert isinstance(tx_hash, str) return self.transactions.get(tx_hash) @locked def list_transactions(self) -> Sequence[str]: return list(self.transactions.keys()) @locked def get_history(self) -> Sequence[str]: return list(self.history.keys()) def is_addr_in_history(self, addr: str) -> bool: # does not mean history is non-empty! assert isinstance(addr, str) return addr in self.history @locked def get_addr_history(self, addr: str) -> Sequence[Tuple[str, int]]: assert isinstance(addr, str) return self.history.get(addr, []) @modifier def set_addr_history(self, addr: str, hist) -> None: assert isinstance(addr, str) self.history[addr] = hist @modifier def remove_addr_history(self, addr: str) -> None: assert isinstance(addr, str) self.history.pop(addr, None) @locked def list_verified_tx(self) -> Sequence[str]: return list(self.verified_tx.keys()) @locked def get_verified_tx(self, txid: str) -> Optional[TxMinedInfo]: assert isinstance(txid, str) if txid not in self.verified_tx: return None height, timestamp, txpos, header_hash = self.verified_tx[txid] return TxMinedInfo(height=height, conf=None, timestamp=timestamp, txpos=txpos, header_hash=header_hash) @modifier def add_verified_tx(self, txid: str, info: TxMinedInfo): assert isinstance(txid, str) assert isinstance(info, TxMinedInfo) self.verified_tx[txid] = (info.height, info.timestamp, info.txpos, info.header_hash) @modifier def remove_verified_tx(self, txid: str): assert isinstance(txid, str) self.verified_tx.pop(txid, None) def is_in_verified_tx(self, txid: str) -> bool: assert isinstance(txid, str) return txid in self.verified_tx @modifier def add_tx_fee_from_server(self, txid: str, fee_sat: Optional[int]) -> None: assert isinstance(txid, str) # note: when called with (fee_sat is None), rm currently saved value if txid not in self.tx_fees: self.tx_fees[txid] = TxFeesValue() tx_fees_value = self.tx_fees[txid] if tx_fees_value.is_calculated_by_us: return self.tx_fees[txid] = tx_fees_value._replace(fee=fee_sat, is_calculated_by_us=False) @modifier def add_tx_fee_we_calculated(self, txid: str, fee_sat: Optional[int]) -> None: assert isinstance(txid, str) if fee_sat is None: return assert isinstance(fee_sat, int) if txid not in self.tx_fees: self.tx_fees[txid] = TxFeesValue() self.tx_fees[txid] = self.tx_fees[txid]._replace(fee=fee_sat, is_calculated_by_us=True) @locked def get_tx_fee(self, txid: str, *, trust_server: bool = False) -> Optional[int]: assert isinstance(txid, str) tx_fees_value = self.tx_fees.get(txid) if tx_fees_value is None: return None if not trust_server and not tx_fees_value.is_calculated_by_us: return None return tx_fees_value.fee @modifier def add_num_inputs_to_tx(self, txid: str, num_inputs: int) -> None: assert isinstance(txid, str) assert isinstance(num_inputs, int) if txid not in self.tx_fees: self.tx_fees[txid] = TxFeesValue() self.tx_fees[txid] = self.tx_fees[txid]._replace(num_inputs=num_inputs) @locked def get_num_all_inputs_of_tx(self, txid: str) -> Optional[int]: assert isinstance(txid, str) tx_fees_value = self.tx_fees.get(txid) if tx_fees_value is None: return None return tx_fees_value.num_inputs @locked def get_num_ismine_inputs_of_tx(self, txid: str) -> int: assert isinstance(txid, str) txins = self.txi.get(txid, {}) return sum([len(tupls) for addr, tupls in txins.items()]) @modifier def remove_tx_fee(self, txid: str) -> None: assert isinstance(txid, str) self.tx_fees.pop(txid, None) @locked def get_dict(self, name): # Warning: interacts un-intuitively with 'put': certain parts # of 'data' will have pointers saved as separate variables. if name not in self.data: self.data[name] = {} return self.data[name] @locked def num_change_addresses(self) -> int: return len(self.change_addresses) @locked def num_receiving_addresses(self) -> int: return len(self.receiving_addresses) @locked def get_change_addresses(self, *, slice_start=None, slice_stop=None) -> List[str]: # note: slicing makes a shallow copy return self.change_addresses[slice_start:slice_stop] @locked def get_receiving_addresses(self, *, slice_start=None, slice_stop=None) -> List[str]: # note: slicing makes a shallow copy return self.receiving_addresses[slice_start:slice_stop] @modifier def add_change_address(self, addr: str) -> None: assert isinstance(addr, str) self._addr_to_addr_index[addr] = (1, len(self.change_addresses)) self.change_addresses.append(addr) @modifier def add_receiving_address(self, addr: str) -> None: assert isinstance(addr, str) self._addr_to_addr_index[addr] = (0, len(self.receiving_addresses)) self.receiving_addresses.append(addr) @locked def get_address_index(self, address: str) -> Optional[Sequence[int]]: assert isinstance(address, str) return self._addr_to_addr_index.get(address) @modifier def add_imported_address(self, addr: str, d: dict) -> None: assert isinstance(addr, str) self.imported_addresses[addr] = d @modifier def remove_imported_address(self, addr: str) -> None: assert isinstance(addr, str) self.imported_addresses.pop(addr) @locked def has_imported_address(self, addr: str) -> bool: assert isinstance(addr, str) return addr in self.imported_addresses @locked def get_imported_addresses(self) -> Sequence[str]: return list(sorted(self.imported_addresses.keys())) @locked def get_imported_address(self, addr: str) -> Optional[dict]: assert isinstance(addr, str) return self.imported_addresses.get(addr) def load_addresses(self, wallet_type): if wallet_type == 'imported': self.imported_addresses = self.get_dict('addresses') # type: Dict[str, dict] else: self.get_dict('addresses') for name in ['receiving', 'change']: if name not in self.data['addresses']: self.data['addresses'][name] = [] self.change_addresses = self.data['addresses']['change'] self.receiving_addresses = self.data['addresses']['receiving'] self._addr_to_addr_index = {} # type: Dict[str, Sequence[int]] # key: address, value: (is_change, index) for i, addr in enumerate(self.receiving_addresses): self._addr_to_addr_index[addr] = (0, i) for i, addr in enumerate(self.change_addresses): self._addr_to_addr_index[addr] = (1, i) @profiler def _load_transactions(self): self.data = StoredDict(self.data, self, []) # references in self.data # TODO make all these private # txid -> address -> prev_outpoint -> value self.txi = self.get_dict('txi') # type: Dict[str, Dict[str, Dict[str, int]]] # txid -> address -> output_index -> (value, is_coinbase) self.txo = self.get_dict('txo') # type: Dict[str, Dict[str, Dict[str, Tuple[int, bool]]]] self.transactions = self.get_dict('transactions') # type: Dict[str, Transaction] self.spent_outpoints = self.get_dict('spent_outpoints') # txid -> output_index -> next_txid self.history = self.get_dict('addr_history') # address -> list of (txid, height) self.verified_tx = self.get_dict('verified_tx3') # txid -> (height, timestamp, txpos, header_hash) self.tx_fees = self.get_dict('tx_fees') # type: Dict[str, TxFeesValue] # scripthash -> set of (outpoint, value) self._prevouts_by_scripthash = self.get_dict('prevouts_by_scripthash') # type: Dict[str, Set[Tuple[str, int]]] # remove unreferenced tx for tx_hash in list(self.transactions.keys()): if not self.get_txi_addresses(tx_hash) and not self.get_txo_addresses(tx_hash): self.logger.info(f"removing unreferenced tx: {tx_hash}") self.transactions.pop(tx_hash) # remove unreferenced outpoints for prevout_hash in self.spent_outpoints.keys(): d = self.spent_outpoints[prevout_hash] for prevout_n, spending_txid in list(d.items()): if spending_txid not in self.transactions: self.logger.info("removing unreferenced spent outpoint") d.pop(prevout_n) self.tokens = self.get_dict('tokens') # contract_addr + '_' + b58addr -> list(txid, height, log_index) self.token_history = self.get_dict('addr_token_history') # txid -> tx receipt self.tx_receipt = self.get_dict('tx_receipt') # txid -> raw tx self.token_txs = self.get_dict('token_txs') self.smart_contracts = self.get_dict('smart_contracts') self.delegations = self.get_dict('delegations') if self.token_history: token_hist_txids = [x2[0] for x2 in reduce(lambda x1, y1: x1+y1, self.token_history.values())] else: token_hist_txids = [] for tx_hash, raw in self.token_txs.items(): if tx_hash in token_hist_txids: tx = Transaction(raw) self.token_txs[tx_hash] = tx @modifier def set_token(self, token: Token): self.tokens[token.get_key()] = token @modifier def delete_token(self, key: str): self.tokens.pop(key, None) self.token_history.pop(key, None) @locked def get_token(self, key: str) -> Optional[Token]: return Token(*self.tokens.get(key)) @locked def list_tokens(self) -> list: return list(self.tokens.keys()) @modifier def set_token_history(self, key: str, hist: list): self.token_history[key] = hist @modifier def delete_token_history(self, key: str): self.token_history.pop(key, None) @locked def get_token_history(self, key: str) -> list: return self.token_history.get(key, []) @locked def list_token_histories(self) -> list: return list(self.token_history.keys()) @modifier def set_token_tx(self, txid: str, raw: str): self.token_txs[txid] = raw @modifier def delete_token_tx(self, txid: str): self.token_txs.pop(txid, None) @locked def get_token_tx(self, txid: str): return self.token_txs.get(txid) @locked def list_token_txs(self) -> list: return list(self.token_txs.keys()) @modifier def set_tx_receipt(self, txid: str, receipt: list): self.tx_receipt[txid] = receipt @modifier def delete_tx_receipt(self, txid: str): return self.tx_receipt.pop(txid, None) @locked def get_tx_receipt(self, txid: str) -> list: return self.tx_receipt.get(txid, []) @locked def list_tx_receipts(self) -> list: return list(self.tx_receipt.keys()) @modifier def set_delegation(self, dele: Delegation): self.delegations[dele.addr] = [dele.staker, dele.fee] @modifier def delete_delegation(self, addr: str): self.delegations.pop(addr, None) @locked def get_delegation(self, addr: str) -> Optional[Delegation]: dele = self.delegations.get(addr, []) if len(dele) != 2: return None return Delegation(addr=addr, staker=dele[0], fee=dele[1]) @locked def list_delegations(self) -> Sequence[str]: return list(self.delegations.keys()) @modifier def clear_history(self): self.txi.clear() self.txo.clear() self.spent_outpoints.clear() self.transactions.clear() self.history.clear() self.verified_tx.clear() self.tx_fees.clear() self.token_txs.clear() self.token_history.clear() self.tx_receipt.clear() self._prevouts_by_scripthash.clear() def _convert_dict(self, path, key, v): if key == 'transactions': # note: for performance, "deserialize=False" so that we will deserialize these on-demand v = dict((k, tx_from_any(x, deserialize=False)) for k, x in v.items()) if key == 'invoices': v = dict((k, Invoice.from_json(x)) for k, x in v.items()) if key == 'payment_requests': v = dict((k, Invoice.from_json(x)) for k, x in v.items()) elif key == 'adds': v = dict((k, UpdateAddHtlc.from_tuple(*x)) for k, x in v.items()) elif key == 'fee_updates': v = dict((k, FeeUpdate(**x)) for k, x in v.items()) elif key == 'submarine_swaps': v = dict((k, SwapData(**x)) for k, x in v.items()) elif key == 'channel_backups': v = dict((k, ChannelBackupStorage(**x)) for k, x in v.items()) elif key == 'tx_fees': v = dict((k, TxFeesValue(*x)) for k, x in v.items()) elif key == 'prevouts_by_scripthash': v = dict((k, {(prevout, value) for (prevout, value) in x}) for k, x in v.items()) elif key == 'buckets': v = dict((k, ShachainElement(bfh(x[0]), int(x[1]))) for k, x in v.items()) elif key == 'data_loss_protect_remote_pcp': v = dict((k, bfh(x)) for k, x in v.items()) return v def _convert_value(self, path, key, v): if key == 'local_config': v = LocalConfig(**v) elif key == 'remote_config': v = RemoteConfig(**v) elif key == 'constraints': v = ChannelConstraints(**v) elif key == 'funding_outpoint': v = Outpoint(**v) return v def write(self, storage: 'WalletStorage'): with self.lock: self._write(storage) def _write(self, storage: 'WalletStorage'): if threading.currentThread().isDaemon(): self.logger.warning('daemon thread cannot write db') return if not self.modified(): return storage.write(self.dump()) self.set_modified(False) def is_ready_to_be_used_by_wallet(self): return not self.requires_upgrade() and self._called_after_upgrade_tasks def split_accounts(self, root_path): from .storage import WalletStorage out = [] result = self.get_split_accounts() for data in result: path = root_path + '.' + data['suffix'] storage = WalletStorage(path) db = WalletDB(json.dumps(data), manual_upgrades=False) db._called_after_upgrade_tasks = False db.upgrade() db.write(storage) out.append(path) return out def get_action(self): action = run_hook('get_action', self) return action def load_plugins(self): wallet_type = self.get('wallet_type') if wallet_type in plugin_loaders: plugin_loaders[wallet_type]() def set_keystore_encryption(self, enable): self.put('use_encryption', enable)

true

f7319439ecbf5ae98691f76abcedaa8bb60c23b1

4,513

py

Python

lib/bindings/samples/server/API/social_api.py

tlalexander/stitchEm

cdff821ad2c500703e6cb237ec61139fce7bf11c

[ "MIT" ]

182

2019-04-19T12:38:30.000Z

2022-03-20T16:48:20.000Z

lib/bindings/samples/server/API/social_api.py

doymcc/stitchEm

20693a55fa522d7a196b92635e7a82df9917c2e2

[ "MIT" ]

107

2019-04-23T10:49:35.000Z

2022-03-02T18:12:28.000Z

lib/bindings/samples/server/API/social_api.py

doymcc/stitchEm

20693a55fa522d7a196b92635e7a82df9917c2e2

[ "MIT" ]

59

2019-06-04T11:27:25.000Z

2022-03-17T23:49:49.000Z

from concurrent.futures import ThreadPoolExecutor from tornado.concurrent import run_on_executor from API.handlers import APIHandler from API.schema import api import errors from social.social_factory import SOCIAL_NETWORKS class SocialAPI(APIHandler): """REST interface related to social networks """ executor = ThreadPoolExecutor(1) def __init__(self, extra): """Init """ super(SocialAPI, self).__init__(extra) """ self.server = extra["server"] self.project_manager = extra["project_manager"] self.output_manager = extra["output_manager"] """ @api(name="MakeLink", endpoint="social.make_link", description="Link the box to a user account on a social network", parameters={ "type": "object", "properties": { "social_network": { "type": "string", "enum": ["facebook"] }, "token": { "type": "string" } }, "required": ["social_network", "token"] } ) @run_on_executor def make_link(self, parameters): social_network_name = parameters.get("social_network") token = parameters.get("token") if social_network_name not in SOCIAL_NETWORKS: raise errors.InvalidParameter("the social network {} is not implemented".format(social_network_name)) if not SOCIAL_NETWORKS[social_network_name].is_Disconnected(): raise errors.InvalidParameter("cannot link to social network {} as it is in state {}".format( social_network_name, SOCIAL_NETWORKS[social_network_name].state)) SOCIAL_NETWORKS[social_network_name].t_make_connection(token) @api(name="MakeLink", endpoint="social.unmake_link", description="Remove the link between the box and a user account on a social network", parameters={ "type": "object", "properties": { "social_network": { "type": "string", "enum": ["facebook"] }, }, "required": ["social_network"] } ) @run_on_executor def unmake_link(self, parameters): social_network_name = parameters.get("social_network") if social_network_name not in SOCIAL_NETWORKS: raise errors.InvalidParameter("the social network {} is not implemented".format(social_network_name)) if not SOCIAL_NETWORKS[social_network_name].is_Connected(): raise errors.InvalidParameter("cannot unlink from social network {} as it is in state {}".format( social_network_name, SOCIAL_NETWORKS[social_network_name].state)) SOCIAL_NETWORKS[social_network_name].t_remove_connection() @api(name="Call", endpoint="social.call", description="Make a call to a social network", parameters={ "type": "object", "properties": { "social_network": { "type": "string", "enum": ["facebook"] }, "endpoint" : { "type": "string" }, "parameters" : { "type": "object" } }, "required": ["social_network", "endpoint"] }, result={ "type": "object" } ) @run_on_executor def call(self, parameters): social_network_name = parameters.get("social_network") endpoint = parameters.get("endpoint") call_parameters = parameters.get("parameters") if social_network_name not in SOCIAL_NETWORKS: raise errors.InvalidParameter("the social network {} is not implemented".format(social_network_name)) if not SOCIAL_NETWORKS[social_network_name].is_Connected(): raise errors.InvalidParameter("cannot make API call to social network {} as it is in state {}".format( social_network_name, SOCIAL_NETWORKS[social_network_name].state)) result = SOCIAL_NETWORKS[social_network_name].call_api(endpoint, call_parameters) return result if result is not None else {}

39.587719

114

0.561489

from concurrent.futures import ThreadPoolExecutor from tornado.concurrent import run_on_executor from API.handlers import APIHandler from API.schema import api import errors from social.social_factory import SOCIAL_NETWORKS class SocialAPI(APIHandler): executor = ThreadPoolExecutor(1) def __init__(self, extra): super(SocialAPI, self).__init__(extra) @api(name="MakeLink", endpoint="social.make_link", description="Link the box to a user account on a social network", parameters={ "type": "object", "properties": { "social_network": { "type": "string", "enum": ["facebook"] }, "token": { "type": "string" } }, "required": ["social_network", "token"] } ) @run_on_executor def make_link(self, parameters): social_network_name = parameters.get("social_network") token = parameters.get("token") if social_network_name not in SOCIAL_NETWORKS: raise errors.InvalidParameter("the social network {} is not implemented".format(social_network_name)) if not SOCIAL_NETWORKS[social_network_name].is_Disconnected(): raise errors.InvalidParameter("cannot link to social network {} as it is in state {}".format( social_network_name, SOCIAL_NETWORKS[social_network_name].state)) SOCIAL_NETWORKS[social_network_name].t_make_connection(token) @api(name="MakeLink", endpoint="social.unmake_link", description="Remove the link between the box and a user account on a social network", parameters={ "type": "object", "properties": { "social_network": { "type": "string", "enum": ["facebook"] }, }, "required": ["social_network"] } ) @run_on_executor def unmake_link(self, parameters): social_network_name = parameters.get("social_network") if social_network_name not in SOCIAL_NETWORKS: raise errors.InvalidParameter("the social network {} is not implemented".format(social_network_name)) if not SOCIAL_NETWORKS[social_network_name].is_Connected(): raise errors.InvalidParameter("cannot unlink from social network {} as it is in state {}".format( social_network_name, SOCIAL_NETWORKS[social_network_name].state)) SOCIAL_NETWORKS[social_network_name].t_remove_connection() @api(name="Call", endpoint="social.call", description="Make a call to a social network", parameters={ "type": "object", "properties": { "social_network": { "type": "string", "enum": ["facebook"] }, "endpoint" : { "type": "string" }, "parameters" : { "type": "object" } }, "required": ["social_network", "endpoint"] }, result={ "type": "object" } ) @run_on_executor def call(self, parameters): social_network_name = parameters.get("social_network") endpoint = parameters.get("endpoint") call_parameters = parameters.get("parameters") if social_network_name not in SOCIAL_NETWORKS: raise errors.InvalidParameter("the social network {} is not implemented".format(social_network_name)) if not SOCIAL_NETWORKS[social_network_name].is_Connected(): raise errors.InvalidParameter("cannot make API call to social network {} as it is in state {}".format( social_network_name, SOCIAL_NETWORKS[social_network_name].state)) result = SOCIAL_NETWORKS[social_network_name].call_api(endpoint, call_parameters) return result if result is not None else {}

true

f7319447be1c583c56c689293955c02433bab7f1

4,941

py

Python

docs/source/conf.py

Geofroy/meerkat_dev

ebff9d16b3edc6efdc580f940ca8d60e733d9da1

[ "MIT" ]

null

docs/source/conf.py

Geofroy/meerkat_dev

ebff9d16b3edc6efdc580f940ca8d60e733d9da1

[ "MIT" ]

4

2017-11-06T13:11:03.000Z

2018-05-04T14:57:18.000Z

docs/source/conf.py

meerkat-code/meerkat_dev

c3f4bef39a72aa99a460ed1a0022c7ea594e037c

[ "MIT" ]

null

# -*- coding: utf-8 -*- # # Configuration file for the Sphinx documentation builder. # # This file does only contain a selection of the most common options. For a # full list see the documentation: # http://www.sphinx-doc.org/en/stable/config import os import sys # -- Path setup -------------------------------------------------------------- # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. # sys.path.insert(0, os.path.abspath('../../')) # -- Project information ----------------------------------------------------- project = 'meerkat_dev' copyright = '2018, Meerkat Developers' author = 'Jonathan Berry' # The short X.Y version version = '' # The full version, including alpha/beta/rc tags release = '' # -- General configuration --------------------------------------------------- # If your documentation needs a minimal Sphinx version, state it here. # # needs_sphinx = '1.0' # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ 'sphinx.ext.autodoc', 'sphinxcontrib.autohttp.flask', 'sphinxcontrib.napoleon' ] # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix(es) of source filenames. # You can specify multiple suffix as a list of string: # # source_suffix = ['.rst', '.md'] source_suffix = '.rst' # The master toctree document. master_doc = 'index' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. language = None # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This pattern also affects html_static_path and html_extra_path . exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' # -- Options for HTML output ------------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # html_theme = 'alabaster' # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. # # html_theme_options = {} # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] # Custom sidebar templates, must be a dictionary that maps document names # to template names. # # The default sidebars (for documents that don't match any pattern) are # defined by theme itself. Builtin themes are using these templates by # default: ``['localtoc.html', 'relations.html', 'sourcelink.html', # 'searchbox.html']``. # # html_sidebars = {} # -- Options for HTMLHelp output --------------------------------------------- # Output file base name for HTML help builder. htmlhelp_basename = 'meerkat_authdoc' # -- Options for LaTeX output ------------------------------------------------ latex_elements = { # The paper size ('letterpaper' or 'a4paper'). # # 'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). # # 'pointsize': '10pt', # Additional stuff for the LaTeX preamble. # # 'preamble': '', # Latex figure (float) alignment # # 'figure_align': 'htbp', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ (master_doc, 'meerkat_dev.tex', 'meerkat\\_dev Documentation', 'Jonathan Berry', 'manual'), ] # -- Options for manual page output ------------------------------------------ # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ (master_doc, 'meerkat_dev', 'meerkat_dev Documentation', [author], 1) ] # -- Options for Texinfo output ---------------------------------------------- # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ (master_doc, 'meerkat_dev', 'meerkat_dev Documentation', author, 'meerkat_dev', 'One line description of project.', 'Miscellaneous'), ] # -- Extension configuration -------------------------------------------------

30.5

79

0.65088

import os import sys project = 'meerkat_dev' copyright = '2018, Meerkat Developers' author = 'Jonathan Berry' version = '' release = '' extensions = [ 'sphinx.ext.autodoc', 'sphinxcontrib.autohttp.flask', 'sphinxcontrib.napoleon' ] templates_path = ['_templates'] source_suffix = '.rst' master_doc = 'index' language = None exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] pygments_style = 'sphinx' html_theme = 'alabaster' html_static_path = ['_static'] # defined by theme itself. Builtin themes are using these templates by # default: ``['localtoc.html', 'relations.html', 'sourcelink.html', # 'searchbox.html']``. # # html_sidebars = {} # -- Options for HTMLHelp output --------------------------------------------- # Output file base name for HTML help builder. htmlhelp_basename = 'meerkat_authdoc' # -- Options for LaTeX output ------------------------------------------------ latex_elements = { # The paper size ('letterpaper' or 'a4paper'). # # 'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). # # 'pointsize': '10pt', # Additional stuff for the LaTeX preamble. # # 'preamble': '', # Latex figure (float) alignment # # 'figure_align': 'htbp', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ (master_doc, 'meerkat_dev.tex', 'meerkat\\_dev Documentation', 'Jonathan Berry', 'manual'), ] # -- Options for manual page output ------------------------------------------ # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ (master_doc, 'meerkat_dev', 'meerkat_dev Documentation', [author], 1) ] # -- Options for Texinfo output ---------------------------------------------- # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ (master_doc, 'meerkat_dev', 'meerkat_dev Documentation', author, 'meerkat_dev', 'One line description of project.', 'Miscellaneous'), ] # -- Extension configuration -------------------------------------------------

true

f731958aa03e65b62779fd052badafaa8d11eb1d

8,117

py

Python

07_train/privacy/tensorflow_privacy/privacy/membership_inference_attack/membership_inference_attack.py

ichen20/oreilly_book

8098d8096d9decca6aa5afbb267b9f05ce0570f2

[ "Apache-2.0" ]

2,327

2020-03-01T09:47:34.000Z

2021-11-25T12:38:42.000Z

07_train/privacy/tensorflow_privacy/privacy/membership_inference_attack/membership_inference_attack.py

ichen20/oreilly_book

8098d8096d9decca6aa5afbb267b9f05ce0570f2

[ "Apache-2.0" ]

209

2020-03-01T17:14:12.000Z

2021-11-08T20:35:42.000Z

07_train/privacy/tensorflow_privacy/privacy/membership_inference_attack/membership_inference_attack.py

ichen20/oreilly_book

8098d8096d9decca6aa5afbb267b9f05ce0570f2

[ "Apache-2.0" ]

686

2020-03-03T17:24:51.000Z

2021-11-25T23:39:12.000Z

# Copyright 2020, The TensorFlow Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Lint as: python3 """Code that runs membership inference attacks based on the model outputs. This file belongs to the new API for membership inference attacks. This file will be renamed to membership_inference_attack.py after the old API is removed. """ from typing import Iterable import numpy as np from sklearn import metrics from tensorflow_privacy.privacy.membership_inference_attack import models from tensorflow_privacy.privacy.membership_inference_attack.data_structures import AttackInputData from tensorflow_privacy.privacy.membership_inference_attack.data_structures import AttackResults from tensorflow_privacy.privacy.membership_inference_attack.data_structures import AttackType from tensorflow_privacy.privacy.membership_inference_attack.data_structures import \ PrivacyReportMetadata from tensorflow_privacy.privacy.membership_inference_attack.data_structures import RocCurve from tensorflow_privacy.privacy.membership_inference_attack.data_structures import SingleAttackResult from tensorflow_privacy.privacy.membership_inference_attack.data_structures import SingleSliceSpec from tensorflow_privacy.privacy.membership_inference_attack.data_structures import SlicingSpec from tensorflow_privacy.privacy.membership_inference_attack.dataset_slicing import get_single_slice_specs from tensorflow_privacy.privacy.membership_inference_attack.dataset_slicing import get_slice def _get_slice_spec(data: AttackInputData) -> SingleSliceSpec: if hasattr(data, 'slice_spec'): return data.slice_spec return SingleSliceSpec() def _run_trained_attack(attack_input: AttackInputData, attack_type: AttackType, balance_attacker_training: bool = True): """Classification attack done by ML models.""" attacker = None if attack_type == AttackType.LOGISTIC_REGRESSION: attacker = models.LogisticRegressionAttacker() elif attack_type == AttackType.MULTI_LAYERED_PERCEPTRON: attacker = models.MultilayerPerceptronAttacker() elif attack_type == AttackType.RANDOM_FOREST: attacker = models.RandomForestAttacker() elif attack_type == AttackType.K_NEAREST_NEIGHBORS: attacker = models.KNearestNeighborsAttacker() else: raise NotImplementedError('Attack type %s not implemented yet.' % attack_type) prepared_attacker_data = models.create_attacker_data( attack_input, balance=balance_attacker_training) attacker.train_model(prepared_attacker_data.features_train, prepared_attacker_data.is_training_labels_train) # Run the attacker on (permuted) test examples. predictions_test = attacker.predict(prepared_attacker_data.features_test) # Generate ROC curves with predictions. fpr, tpr, thresholds = metrics.roc_curve( prepared_attacker_data.is_training_labels_test, predictions_test) roc_curve = RocCurve(tpr=tpr, fpr=fpr, thresholds=thresholds) return SingleAttackResult( slice_spec=_get_slice_spec(attack_input), attack_type=attack_type, roc_curve=roc_curve) def _run_threshold_attack(attack_input: AttackInputData): fpr, tpr, thresholds = metrics.roc_curve( np.concatenate((np.zeros(attack_input.get_train_size()), np.ones(attack_input.get_test_size()))), np.concatenate( (attack_input.get_loss_train(), attack_input.get_loss_test()))) roc_curve = RocCurve(tpr=tpr, fpr=fpr, thresholds=thresholds) return SingleAttackResult( slice_spec=_get_slice_spec(attack_input), attack_type=AttackType.THRESHOLD_ATTACK, roc_curve=roc_curve) def _run_threshold_entropy_attack(attack_input: AttackInputData): fpr, tpr, thresholds = metrics.roc_curve( np.concatenate((np.zeros(attack_input.get_train_size()), np.ones(attack_input.get_test_size()))), np.concatenate( (attack_input.get_entropy_train(), attack_input.get_entropy_test()))) roc_curve = RocCurve(tpr=tpr, fpr=fpr, thresholds=thresholds) return SingleAttackResult( slice_spec=_get_slice_spec(attack_input), attack_type=AttackType.THRESHOLD_ENTROPY_ATTACK, roc_curve=roc_curve) def _run_attack(attack_input: AttackInputData, attack_type: AttackType, balance_attacker_training: bool = True): attack_input.validate() if attack_type.is_trained_attack: return _run_trained_attack(attack_input, attack_type, balance_attacker_training) if attack_type == AttackType.THRESHOLD_ENTROPY_ATTACK: return _run_threshold_entropy_attack(attack_input) return _run_threshold_attack(attack_input) def run_attacks(attack_input: AttackInputData, slicing_spec: SlicingSpec = None, attack_types: Iterable[AttackType] = ( AttackType.THRESHOLD_ATTACK,), privacy_report_metadata: PrivacyReportMetadata = None, balance_attacker_training: bool = True) -> AttackResults: """Runs membership inference attacks on a classification model. It runs attacks specified by attack_types on each attack_input slice which is specified by slicing_spec. Args: attack_input: input data for running an attack slicing_spec: specifies attack_input slices to run attack on attack_types: attacks to run privacy_report_metadata: the metadata of the model under attack. balance_attacker_training: Whether the training and test sets for the membership inference attacker should have a balanced (roughly equal) number of samples from the training and test sets used to develop the model under attack. Returns: the attack result. """ attack_input.validate() attack_results = [] if slicing_spec is None: slicing_spec = SlicingSpec(entire_dataset=True) input_slice_specs = get_single_slice_specs(slicing_spec, attack_input.num_classes) for single_slice_spec in input_slice_specs: attack_input_slice = get_slice(attack_input, single_slice_spec) for attack_type in attack_types: attack_results.append( _run_attack(attack_input_slice, attack_type, balance_attacker_training)) privacy_report_metadata = _compute_missing_privacy_report_metadata( privacy_report_metadata, attack_input) return AttackResults( single_attack_results=attack_results, privacy_report_metadata=privacy_report_metadata) def _compute_missing_privacy_report_metadata( metadata: PrivacyReportMetadata, attack_input: AttackInputData) -> PrivacyReportMetadata: """Populates metadata fields if they are missing.""" if metadata is None: metadata = PrivacyReportMetadata() if metadata.accuracy_train is None: metadata.accuracy_train = _get_accuracy(attack_input.logits_train, attack_input.labels_train) if metadata.accuracy_test is None: metadata.accuracy_test = _get_accuracy(attack_input.logits_test, attack_input.labels_test) if metadata.loss_train is None: metadata.loss_train = np.average(attack_input.get_loss_train()) if metadata.loss_test is None: metadata.loss_test = np.average(attack_input.get_loss_test()) return metadata def _get_accuracy(logits, labels): """Computes the accuracy if it is missing.""" if logits is None or labels is None: return None return metrics.accuracy_score(labels, np.argmax(logits, axis=1))

41.203046

105

0.756684

from typing import Iterable import numpy as np from sklearn import metrics from tensorflow_privacy.privacy.membership_inference_attack import models from tensorflow_privacy.privacy.membership_inference_attack.data_structures import AttackInputData from tensorflow_privacy.privacy.membership_inference_attack.data_structures import AttackResults from tensorflow_privacy.privacy.membership_inference_attack.data_structures import AttackType from tensorflow_privacy.privacy.membership_inference_attack.data_structures import \ PrivacyReportMetadata from tensorflow_privacy.privacy.membership_inference_attack.data_structures import RocCurve from tensorflow_privacy.privacy.membership_inference_attack.data_structures import SingleAttackResult from tensorflow_privacy.privacy.membership_inference_attack.data_structures import SingleSliceSpec from tensorflow_privacy.privacy.membership_inference_attack.data_structures import SlicingSpec from tensorflow_privacy.privacy.membership_inference_attack.dataset_slicing import get_single_slice_specs from tensorflow_privacy.privacy.membership_inference_attack.dataset_slicing import get_slice def _get_slice_spec(data: AttackInputData) -> SingleSliceSpec: if hasattr(data, 'slice_spec'): return data.slice_spec return SingleSliceSpec() def _run_trained_attack(attack_input: AttackInputData, attack_type: AttackType, balance_attacker_training: bool = True): attacker = None if attack_type == AttackType.LOGISTIC_REGRESSION: attacker = models.LogisticRegressionAttacker() elif attack_type == AttackType.MULTI_LAYERED_PERCEPTRON: attacker = models.MultilayerPerceptronAttacker() elif attack_type == AttackType.RANDOM_FOREST: attacker = models.RandomForestAttacker() elif attack_type == AttackType.K_NEAREST_NEIGHBORS: attacker = models.KNearestNeighborsAttacker() else: raise NotImplementedError('Attack type %s not implemented yet.' % attack_type) prepared_attacker_data = models.create_attacker_data( attack_input, balance=balance_attacker_training) attacker.train_model(prepared_attacker_data.features_train, prepared_attacker_data.is_training_labels_train) predictions_test = attacker.predict(prepared_attacker_data.features_test) fpr, tpr, thresholds = metrics.roc_curve( prepared_attacker_data.is_training_labels_test, predictions_test) roc_curve = RocCurve(tpr=tpr, fpr=fpr, thresholds=thresholds) return SingleAttackResult( slice_spec=_get_slice_spec(attack_input), attack_type=attack_type, roc_curve=roc_curve) def _run_threshold_attack(attack_input: AttackInputData): fpr, tpr, thresholds = metrics.roc_curve( np.concatenate((np.zeros(attack_input.get_train_size()), np.ones(attack_input.get_test_size()))), np.concatenate( (attack_input.get_loss_train(), attack_input.get_loss_test()))) roc_curve = RocCurve(tpr=tpr, fpr=fpr, thresholds=thresholds) return SingleAttackResult( slice_spec=_get_slice_spec(attack_input), attack_type=AttackType.THRESHOLD_ATTACK, roc_curve=roc_curve) def _run_threshold_entropy_attack(attack_input: AttackInputData): fpr, tpr, thresholds = metrics.roc_curve( np.concatenate((np.zeros(attack_input.get_train_size()), np.ones(attack_input.get_test_size()))), np.concatenate( (attack_input.get_entropy_train(), attack_input.get_entropy_test()))) roc_curve = RocCurve(tpr=tpr, fpr=fpr, thresholds=thresholds) return SingleAttackResult( slice_spec=_get_slice_spec(attack_input), attack_type=AttackType.THRESHOLD_ENTROPY_ATTACK, roc_curve=roc_curve) def _run_attack(attack_input: AttackInputData, attack_type: AttackType, balance_attacker_training: bool = True): attack_input.validate() if attack_type.is_trained_attack: return _run_trained_attack(attack_input, attack_type, balance_attacker_training) if attack_type == AttackType.THRESHOLD_ENTROPY_ATTACK: return _run_threshold_entropy_attack(attack_input) return _run_threshold_attack(attack_input) def run_attacks(attack_input: AttackInputData, slicing_spec: SlicingSpec = None, attack_types: Iterable[AttackType] = ( AttackType.THRESHOLD_ATTACK,), privacy_report_metadata: PrivacyReportMetadata = None, balance_attacker_training: bool = True) -> AttackResults: attack_input.validate() attack_results = [] if slicing_spec is None: slicing_spec = SlicingSpec(entire_dataset=True) input_slice_specs = get_single_slice_specs(slicing_spec, attack_input.num_classes) for single_slice_spec in input_slice_specs: attack_input_slice = get_slice(attack_input, single_slice_spec) for attack_type in attack_types: attack_results.append( _run_attack(attack_input_slice, attack_type, balance_attacker_training)) privacy_report_metadata = _compute_missing_privacy_report_metadata( privacy_report_metadata, attack_input) return AttackResults( single_attack_results=attack_results, privacy_report_metadata=privacy_report_metadata) def _compute_missing_privacy_report_metadata( metadata: PrivacyReportMetadata, attack_input: AttackInputData) -> PrivacyReportMetadata: if metadata is None: metadata = PrivacyReportMetadata() if metadata.accuracy_train is None: metadata.accuracy_train = _get_accuracy(attack_input.logits_train, attack_input.labels_train) if metadata.accuracy_test is None: metadata.accuracy_test = _get_accuracy(attack_input.logits_test, attack_input.labels_test) if metadata.loss_train is None: metadata.loss_train = np.average(attack_input.get_loss_train()) if metadata.loss_test is None: metadata.loss_test = np.average(attack_input.get_loss_test()) return metadata def _get_accuracy(logits, labels): if logits is None or labels is None: return None return metrics.accuracy_score(labels, np.argmax(logits, axis=1))

true

f73196366aa14046b85680925a5d5d15177b1086

1,779

py

Python

chainer/snap2model.py

fujibo/poseHG

e582a6ca1badc9a894b8b7e2a5e0acf9eb348c5c

[ "BSD-3-Clause" ]

null

chainer/snap2model.py

fujibo/poseHG

e582a6ca1badc9a894b8b7e2a5e0acf9eb348c5c

[ "BSD-3-Clause" ]

null

chainer/snap2model.py

fujibo/poseHG

e582a6ca1badc9a894b8b7e2a5e0acf9eb348c5c

[ "BSD-3-Clause" ]

1

2018-06-30T07:07:25.000Z

import numpy as np import tempfile def snap2model_parser(path_snapshot, path_model=None): """convert snapshot to model :param path_snapshot: str :param path_model: str, default None :return: file descriptor (path_model is None) or None (otherwise) """ snapshot = np.load(path_snapshot) model = dict() for key in snapshot.keys(): parse = key.split('/') if parse[0] == 'updater' and parse[1] == 'optimizer:main': if parse[2] == 'model': model_key = '/'.join(parse[3:-1]) model[model_key] = snapshot[key] if path_model is None: outfile = tempfile.TemporaryFile() np.savez(outfile, **model) outfile.seek(0) return outfile else: np.savez(path_model, **model) return None def snap2model_trainer(path_snapshot, path_model=None): import chainer from dataset import MPIIDataset from train import TrainChain from net import StackedHG train_data = MPIIDataset(split='train') model = StackedHG(16) train_chain = TrainChain(model) optimizer = chainer.optimizers.RMSprop(lr=2.5e-4) optimizer.setup(train_chain) # original batch size 6 train_iter = chainer.iterators.SerialIterator(train_data, 1, repeat=True, shuffle=True) updater = chainer.training.StandardUpdater(train_iter, optimizer, device=-1) trainer = chainer.training.Trainer(updater, (100, 'epoch'), out='') chainer.serializers.load_npz(path_snapshot, trainer) if path_model is None: outfile = tempfile.TemporaryFile() chainer.serializers.save_npz(outfile, model) outfile.seek(0) return outfile else: chainer.serializers.save_npz(path_model, model) return None

29.65

91

0.658797

import numpy as np import tempfile def snap2model_parser(path_snapshot, path_model=None): snapshot = np.load(path_snapshot) model = dict() for key in snapshot.keys(): parse = key.split('/') if parse[0] == 'updater' and parse[1] == 'optimizer:main': if parse[2] == 'model': model_key = '/'.join(parse[3:-1]) model[model_key] = snapshot[key] if path_model is None: outfile = tempfile.TemporaryFile() np.savez(outfile, **model) outfile.seek(0) return outfile else: np.savez(path_model, **model) return None def snap2model_trainer(path_snapshot, path_model=None): import chainer from dataset import MPIIDataset from train import TrainChain from net import StackedHG train_data = MPIIDataset(split='train') model = StackedHG(16) train_chain = TrainChain(model) optimizer = chainer.optimizers.RMSprop(lr=2.5e-4) optimizer.setup(train_chain) train_iter = chainer.iterators.SerialIterator(train_data, 1, repeat=True, shuffle=True) updater = chainer.training.StandardUpdater(train_iter, optimizer, device=-1) trainer = chainer.training.Trainer(updater, (100, 'epoch'), out='') chainer.serializers.load_npz(path_snapshot, trainer) if path_model is None: outfile = tempfile.TemporaryFile() chainer.serializers.save_npz(outfile, model) outfile.seek(0) return outfile else: chainer.serializers.save_npz(path_model, model) return None

true

f73196906eb86fee01bdd6f96402a0ac464b613b

1,524

py

Python

symengine/__init__.py

Meldanya/symengine.py

ec3ce7aef387878abd10a3991e4c3610c98cc70d

[ "MIT" ]

null

symengine/__init__.py

Meldanya/symengine.py

ec3ce7aef387878abd10a3991e4c3610c98cc70d

[ "MIT" ]

1

2015-10-17T13:22:48.000Z

2015-10-18T11:43:13.000Z

symengine/__init__.py

Meldanya/symengine.py

ec3ce7aef387878abd10a3991e4c3610c98cc70d

[ "MIT" ]

null

from .lib.symengine_wrapper import ( have_mpfr, have_mpc, have_flint, have_piranha, have_llvm, I, E, pi, oo, zoo, nan, Symbol, Dummy, S, sympify, SympifyError, Integer, Rational, Float, Number, RealNumber, RealDouble, ComplexDouble, add, Add, Mul, Pow, function_symbol, Max, Min, DenseMatrix, Matrix, ImmutableMatrix, ImmutableDenseMatrix, MutableDenseMatrix, MatrixBase, Basic, DictBasic, symarray, series, diff, zeros, eye, diag, ones, Derivative, Subs, expand, has_symbol, UndefFunction, Function, latex, have_numpy, true, false, Equality, Unequality, GreaterThan, LessThan, StrictGreaterThan, StrictLessThan, Eq, Ne, Ge, Le, Gt, Lt, And, Or, Not, Nand, Nor, Xor, Xnor, perfect_power, integer_nthroot, isprime, sqrt_mod, Expr, cse, count_ops, ccode, Piecewise, Contains, Interval, FiniteSet, FunctionSymbol as AppliedUndef, golden_ratio as GoldenRatio, catalan as Catalan, eulergamma as EulerGamma ) from .utilities import var, symbols from .functions import * from .printing import init_printing if have_mpfr: from .lib.symengine_wrapper import RealMPFR if have_mpc: from .lib.symengine_wrapper import ComplexMPC if have_numpy: from .lib.symengine_wrapper import (Lambdify, LambdifyCSE) def lambdify(args, exprs, **kwargs): return Lambdify(args, *exprs, **kwargs) __version__ = "0.4.0" def test(): import pytest import os return not pytest.cmdline.main( [os.path.dirname(os.path.abspath(__file__))])

33.866667

93

0.723097

from .lib.symengine_wrapper import ( have_mpfr, have_mpc, have_flint, have_piranha, have_llvm, I, E, pi, oo, zoo, nan, Symbol, Dummy, S, sympify, SympifyError, Integer, Rational, Float, Number, RealNumber, RealDouble, ComplexDouble, add, Add, Mul, Pow, function_symbol, Max, Min, DenseMatrix, Matrix, ImmutableMatrix, ImmutableDenseMatrix, MutableDenseMatrix, MatrixBase, Basic, DictBasic, symarray, series, diff, zeros, eye, diag, ones, Derivative, Subs, expand, has_symbol, UndefFunction, Function, latex, have_numpy, true, false, Equality, Unequality, GreaterThan, LessThan, StrictGreaterThan, StrictLessThan, Eq, Ne, Ge, Le, Gt, Lt, And, Or, Not, Nand, Nor, Xor, Xnor, perfect_power, integer_nthroot, isprime, sqrt_mod, Expr, cse, count_ops, ccode, Piecewise, Contains, Interval, FiniteSet, FunctionSymbol as AppliedUndef, golden_ratio as GoldenRatio, catalan as Catalan, eulergamma as EulerGamma ) from .utilities import var, symbols from .functions import * from .printing import init_printing if have_mpfr: from .lib.symengine_wrapper import RealMPFR if have_mpc: from .lib.symengine_wrapper import ComplexMPC if have_numpy: from .lib.symengine_wrapper import (Lambdify, LambdifyCSE) def lambdify(args, exprs, **kwargs): return Lambdify(args, *exprs, **kwargs) __version__ = "0.4.0" def test(): import pytest import os return not pytest.cmdline.main( [os.path.dirname(os.path.abspath(__file__))])

true

f731975854283587ddab9488f9c9c75d42aa8ed2

7,107

py

Python

databricks/koalas/missing/series.py

dennyglee/koalas

9781c1ece43b1fa2dce78a0813602e03db0b1fa3

[ "Apache-2.0" ]

1

2019-05-29T05:46:19.000Z

databricks/koalas/missing/series.py

dennyglee/koalas

9781c1ece43b1fa2dce78a0813602e03db0b1fa3

[ "Apache-2.0" ]

null

databricks/koalas/missing/series.py

dennyglee/koalas

9781c1ece43b1fa2dce78a0813602e03db0b1fa3

[ "Apache-2.0" ]

null

# # Copyright (C) 2019 Databricks, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from databricks.koalas.missing import _unsupported_function, _unsupported_property, common def unsupported_function(method_name, deprecated=False, reason=""): return _unsupported_function(class_name='pd.Series', method_name=method_name, deprecated=deprecated, reason=reason) def unsupported_property(property_name, deprecated=False, reason=""): return _unsupported_property(class_name='pd.Series', property_name=property_name, deprecated=deprecated, reason=reason) class _MissingPandasLikeSeries(object): # Properties axes = unsupported_property('axes') iat = unsupported_property('iat') # Deprecated properties blocks = unsupported_property('blocks', deprecated=True) ftypes = unsupported_property('ftypes', deprecated=True) ftype = unsupported_property('ftype', deprecated=True) is_copy = unsupported_property('is_copy', deprecated=True) ix = unsupported_property('ix', deprecated=True) asobject = unsupported_property('asobject', deprecated=True) strides = unsupported_property('strides', deprecated=True) imag = unsupported_property('imag', deprecated=True) itemsize = unsupported_property('itemsize', deprecated=True) data = unsupported_property('data', deprecated=True) base = unsupported_property('base', deprecated=True) flags = unsupported_property('flags', deprecated=True) # Functions align = unsupported_function('align') argsort = unsupported_function('argsort') asfreq = unsupported_function('asfreq') asof = unsupported_function('asof') at_time = unsupported_function('at_time') autocorr = unsupported_function('autocorr') between_time = unsupported_function('between_time') bfill = unsupported_function('bfill') combine = unsupported_function('combine') combine_first = unsupported_function('combine_first') cov = unsupported_function('cov') divmod = unsupported_function('divmod') dot = unsupported_function('dot') droplevel = unsupported_function('droplevel') duplicated = unsupported_function('duplicated') ewm = unsupported_function('ewm') factorize = unsupported_function('factorize') ffill = unsupported_function('ffill') filter = unsupported_function('filter') first = unsupported_function('first') get = unsupported_function('get') infer_objects = unsupported_function('infer_objects') interpolate = unsupported_function('interpolate') items = unsupported_function('items') iteritems = unsupported_function('iteritems') last = unsupported_function('last') last_valid_index = unsupported_function('last_valid_index') mad = unsupported_function('mad') pct_change = unsupported_function('pct_change') prod = unsupported_function('prod') product = unsupported_function('product') ravel = unsupported_function('ravel') rdivmod = unsupported_function('rdivmod') reindex = unsupported_function('reindex') reindex_like = unsupported_function('reindex_like') rename_axis = unsupported_function('rename_axis') reorder_levels = unsupported_function('reorder_levels') repeat = unsupported_function('repeat') resample = unsupported_function('resample') searchsorted = unsupported_function('searchsorted') sem = unsupported_function('sem') set_axis = unsupported_function('set_axis') slice_shift = unsupported_function('slice_shift') squeeze = unsupported_function('squeeze') swapaxes = unsupported_function('swapaxes') swaplevel = unsupported_function('swaplevel') tail = unsupported_function('tail') take = unsupported_function('take') to_hdf = unsupported_function('to_hdf') to_period = unsupported_function('to_period') to_sql = unsupported_function('to_sql') to_timestamp = unsupported_function('to_timestamp') tshift = unsupported_function('tshift') tz_convert = unsupported_function('tz_convert') tz_localize = unsupported_function('tz_localize') unstack = unsupported_function('unstack') view = unsupported_function('view') # Deprecated functions as_blocks = unsupported_function('as_blocks', deprecated=True) as_matrix = unsupported_function('as_matrix', deprecated=True) clip_lower = unsupported_function('clip_lower', deprecated=True) clip_upper = unsupported_function('clip_upper', deprecated=True) compress = unsupported_function('compress', deprecated=True) convert_objects = unsupported_function('convert_objects', deprecated=True) get_ftype_counts = unsupported_function('get_ftype_counts', deprecated=True) get_value = unsupported_function('get_value', deprecated=True) nonzero = unsupported_function('nonzero', deprecated=True) reindex_axis = unsupported_function('reindex_axis', deprecated=True) select = unsupported_function('select', deprecated=True) set_value = unsupported_function('set_value', deprecated=True) valid = unsupported_function('valid', deprecated=True) get_values = unsupported_function('get_values', deprecated=True) to_dense = unsupported_function('to_dense', deprecated=True) to_sparse = unsupported_function('to_sparse', deprecated=True) to_msgpack = unsupported_function('to_msgpack', deprecated=True) compound = unsupported_function('compound', deprecated=True) put = unsupported_function('put', deprecated=True) item = unsupported_function('item', deprecated=True) ptp = unsupported_function('ptp', deprecated=True) argmax = unsupported_function('argmax', deprecated=True) argmin = unsupported_function('argmin', deprecated=True) # Properties we won't support. values = common.values(unsupported_property) array = common.array(unsupported_property) real = unsupported_property( 'real', reason="If you want to collect your data as an NumPy array, use 'to_numpy()' instead.") nbytes = unsupported_property( 'nbytes', reason="'nbytes' requires to compute whole dataset. You can calculate manually it, " "with its 'itemsize', by explicitly executing its count. Use Spark's web UI " "to monitor disk and memory usage of your application in general.") # Functions we won't support. memory_usage = common.memory_usage(unsupported_function) to_pickle = common.to_pickle(unsupported_function) to_xarray = common.to_xarray(unsupported_function) __iter__ = common.__iter__(unsupported_function)

47.066225

95

0.74307

from databricks.koalas.missing import _unsupported_function, _unsupported_property, common def unsupported_function(method_name, deprecated=False, reason=""): return _unsupported_function(class_name='pd.Series', method_name=method_name, deprecated=deprecated, reason=reason) def unsupported_property(property_name, deprecated=False, reason=""): return _unsupported_property(class_name='pd.Series', property_name=property_name, deprecated=deprecated, reason=reason) class _MissingPandasLikeSeries(object): axes = unsupported_property('axes') iat = unsupported_property('iat') blocks = unsupported_property('blocks', deprecated=True) ftypes = unsupported_property('ftypes', deprecated=True) ftype = unsupported_property('ftype', deprecated=True) is_copy = unsupported_property('is_copy', deprecated=True) ix = unsupported_property('ix', deprecated=True) asobject = unsupported_property('asobject', deprecated=True) strides = unsupported_property('strides', deprecated=True) imag = unsupported_property('imag', deprecated=True) itemsize = unsupported_property('itemsize', deprecated=True) data = unsupported_property('data', deprecated=True) base = unsupported_property('base', deprecated=True) flags = unsupported_property('flags', deprecated=True) align = unsupported_function('align') argsort = unsupported_function('argsort') asfreq = unsupported_function('asfreq') asof = unsupported_function('asof') at_time = unsupported_function('at_time') autocorr = unsupported_function('autocorr') between_time = unsupported_function('between_time') bfill = unsupported_function('bfill') combine = unsupported_function('combine') combine_first = unsupported_function('combine_first') cov = unsupported_function('cov') divmod = unsupported_function('divmod') dot = unsupported_function('dot') droplevel = unsupported_function('droplevel') duplicated = unsupported_function('duplicated') ewm = unsupported_function('ewm') factorize = unsupported_function('factorize') ffill = unsupported_function('ffill') filter = unsupported_function('filter') first = unsupported_function('first') get = unsupported_function('get') infer_objects = unsupported_function('infer_objects') interpolate = unsupported_function('interpolate') items = unsupported_function('items') iteritems = unsupported_function('iteritems') last = unsupported_function('last') last_valid_index = unsupported_function('last_valid_index') mad = unsupported_function('mad') pct_change = unsupported_function('pct_change') prod = unsupported_function('prod') product = unsupported_function('product') ravel = unsupported_function('ravel') rdivmod = unsupported_function('rdivmod') reindex = unsupported_function('reindex') reindex_like = unsupported_function('reindex_like') rename_axis = unsupported_function('rename_axis') reorder_levels = unsupported_function('reorder_levels') repeat = unsupported_function('repeat') resample = unsupported_function('resample') searchsorted = unsupported_function('searchsorted') sem = unsupported_function('sem') set_axis = unsupported_function('set_axis') slice_shift = unsupported_function('slice_shift') squeeze = unsupported_function('squeeze') swapaxes = unsupported_function('swapaxes') swaplevel = unsupported_function('swaplevel') tail = unsupported_function('tail') take = unsupported_function('take') to_hdf = unsupported_function('to_hdf') to_period = unsupported_function('to_period') to_sql = unsupported_function('to_sql') to_timestamp = unsupported_function('to_timestamp') tshift = unsupported_function('tshift') tz_convert = unsupported_function('tz_convert') tz_localize = unsupported_function('tz_localize') unstack = unsupported_function('unstack') view = unsupported_function('view') as_blocks = unsupported_function('as_blocks', deprecated=True) as_matrix = unsupported_function('as_matrix', deprecated=True) clip_lower = unsupported_function('clip_lower', deprecated=True) clip_upper = unsupported_function('clip_upper', deprecated=True) compress = unsupported_function('compress', deprecated=True) convert_objects = unsupported_function('convert_objects', deprecated=True) get_ftype_counts = unsupported_function('get_ftype_counts', deprecated=True) get_value = unsupported_function('get_value', deprecated=True) nonzero = unsupported_function('nonzero', deprecated=True) reindex_axis = unsupported_function('reindex_axis', deprecated=True) select = unsupported_function('select', deprecated=True) set_value = unsupported_function('set_value', deprecated=True) valid = unsupported_function('valid', deprecated=True) get_values = unsupported_function('get_values', deprecated=True) to_dense = unsupported_function('to_dense', deprecated=True) to_sparse = unsupported_function('to_sparse', deprecated=True) to_msgpack = unsupported_function('to_msgpack', deprecated=True) compound = unsupported_function('compound', deprecated=True) put = unsupported_function('put', deprecated=True) item = unsupported_function('item', deprecated=True) ptp = unsupported_function('ptp', deprecated=True) argmax = unsupported_function('argmax', deprecated=True) argmin = unsupported_function('argmin', deprecated=True) values = common.values(unsupported_property) array = common.array(unsupported_property) real = unsupported_property( 'real', reason="If you want to collect your data as an NumPy array, use 'to_numpy()' instead.") nbytes = unsupported_property( 'nbytes', reason="'nbytes' requires to compute whole dataset. You can calculate manually it, " "with its 'itemsize', by explicitly executing its count. Use Spark's web UI " "to monitor disk and memory usage of your application in general.") memory_usage = common.memory_usage(unsupported_function) to_pickle = common.to_pickle(unsupported_function) to_xarray = common.to_xarray(unsupported_function) __iter__ = common.__iter__(unsupported_function)

true

f73197e08ced9d0164ad4b85df778656c785b84a

3,279

py

Python

cube_root.py

scottwedge/CSC110

8d1e39de183e3ea477dac993d0fa9b721feca1ca

[ "MIT" ]

null

cube_root.py

scottwedge/CSC110

8d1e39de183e3ea477dac993d0fa9b721feca1ca

[ "MIT" ]

1

2020-09-04T02:04:33.000Z

cube_root.py

scottwedge/CSC110

8d1e39de183e3ea477dac993d0fa9b721feca1ca

[ "MIT" ]

1

2020-09-04T01:51:55.000Z

# Sample program illustrating programmer-defined functions # along with code that calls the functions # # The 'cube_root' function uses a 'return' statement. Its # only job is to calculate and "return" the cube root of a # number. It does not print anything. Notice that statements # that "call" the 'cube_root' function need to USE the value # returned. They can do this by a) saving it in a variable, # b) using it in a 'print' statement, or c) using the value in # ANY general expression. Imagine that the value returned by # the function REPLACES the function call wherever it occurs. # This is EXACTLY the same way you use built-in functions like # 'input()', 'abs()', 'round()', etc. # # The 'show_table' function does NOT use a 'return' statement. # It's job is to PRINT a table. Different functions may be # used in different ways. # # CSC 110 # Winter 2013 # The cube_root function calculates and RETURNS the cube root # of a number. If the value of 'x' is negative, a negative # real number is returned. def cube_root(x): if x < 0: result = -( (-x)**(1.0/3.0) ) else: result = x ** (1.0/3.0) return result # Main program def main(): print('Let\'s examine the cube roots of some numbers.\n') # CALL the function and save the value returned in a variable: num = 27 root = cube_root(num) # The ARGUMENT is a variable print('Cube root of ' + str(num) + ' is ' + format(root, '.1f')) root = cube_root(num + 98) # The argument is an expression print(root) # Use the function call directly in a 'print' statement: print('Cube root of ' + str(num) + ' is ' + format(cube_root(num), '.1f')) # Use multiple function calls in an expression: print('The answer is', cube_root(8) + cube_root(1000) / 2) print('The answer (rounded) is', round(cube_root(8) + cube_root(1000) / 2)) # Here is a table of some cube roots: print('\n n cube_root(n)') # print header row num = 8 print(format(num, '8.1f'), format(cube_root(num), '10.3f')) num = 31 print(format(num, '8.1f'), format(cube_root(num), '10.3f')) num = 1727 print(format(num, '8.1f'), format(cube_root(num), '10.3f')) num = 1728 print(format(num, '8.1f'), format(cube_root(num), '10.3f')) num = 1729 print(format(num, '8.1f'), format(cube_root(num), '10.3f')) # here is a table of cube roots print("using loop") x = [8,31,1727,1728,1729] for num in x: print(format(num, '8.1f'), format(cube_root(num), '10.3f')) # Here are a couple of longer tables: show_table(0, 1000, 10) show_table(42875, 1000000, 20) # This function shows a table of cube roots. # The first two parameters are the minimum and maximum values for 'n'. # The third parameter is the number of rows to show in the table. def show_table(minN, maxN, rows): # Calculate the step size. There are (rows - 1) intervals: step = (maxN - minN) / (rows - 1.0) print('\n n cube_root(n)') # print header row # Loop 'rows' times to print the rows in the table: for i in range(rows): n = minN + i * step # calculate the value of 'n' for row 'i' print(format(n, '12.3f'), format(cube_root(n), '8.3f')) # Run the program main()

35.641304

79

0.643489

# used in different ways. # # CSC 110 # Winter 2013 # The cube_root function calculates and RETURNS the cube root # of a number. If the value of 'x' is negative, a negative # real number is returned. def cube_root(x): if x < 0: result = -( (-x)**(1.0/3.0) ) else: result = x ** (1.0/3.0) return result # Main program def main(): print('Let\'s examine the cube roots of some numbers.\n') num = 27 root = cube_root(num) print('Cube root of ' + str(num) + ' is ' + format(root, '.1f')) root = cube_root(num + 98) print(root) print('Cube root of ' + str(num) + ' is ' + format(cube_root(num), '.1f')) print('The answer is', cube_root(8) + cube_root(1000) / 2) print('The answer (rounded) is', round(cube_root(8) + cube_root(1000) / 2)) print('\n n cube_root(n)') num = 8 print(format(num, '8.1f'), format(cube_root(num), '10.3f')) num = 31 print(format(num, '8.1f'), format(cube_root(num), '10.3f')) num = 1727 print(format(num, '8.1f'), format(cube_root(num), '10.3f')) num = 1728 print(format(num, '8.1f'), format(cube_root(num), '10.3f')) num = 1729 print(format(num, '8.1f'), format(cube_root(num), '10.3f')) print("using loop") x = [8,31,1727,1728,1729] for num in x: print(format(num, '8.1f'), format(cube_root(num), '10.3f')) show_table(0, 1000, 10) show_table(42875, 1000000, 20) def show_table(minN, maxN, rows): step = (maxN - minN) / (rows - 1.0) print('\n n cube_root(n)') for i in range(rows): n = minN + i * step print(format(n, '12.3f'), format(cube_root(n), '8.3f')) main()

true

f73198cc95d621ef4641d770730e34b065a75e7a

1,470

py

Python

MultiMediaAnalyse/Task2/main.py

wsh-nie/Assignments

e1f800ca9abe54218f9b695dce843ec96a99710c

[ "MIT" ]

null

MultiMediaAnalyse/Task2/main.py

wsh-nie/Assignments

e1f800ca9abe54218f9b695dce843ec96a99710c

[ "MIT" ]

null

MultiMediaAnalyse/Task2/main.py

wsh-nie/Assignments

e1f800ca9abe54218f9b695dce843ec96a99710c

[ "MIT" ]

null

import cv2 def SIFT(imgname1, imgname2): sift = cv2.xfeatures2d.SIFT_create() img1 = cv2.imread(imgname1) img2 = cv2.imread(imgname2) kp1, des1 = sift.detectAndCompute(img1, None) kp2, des2 = sift.detectAndCompute(img2, None) FLANN_INDEX_KDTREE = 0 index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5) search_params = dict(checks=50) flann = cv2.FlannBasedMatcher(index_params, search_params) matches = flann.knnMatch(des1,des2,k=2) good = [] for m,n in matches: if m.distance < 0.70*n.distance: good.append([m]) img3 = cv2.drawMatchesKnn(img1,kp1,img2,kp2,good,None,flags=2) return img3 def ORB(imgname1, imgname2): orb = cv2.ORB_create() img1 = cv2.imread(imgname1) img2 = cv2.imread(imgname2) kp1, des1 = orb.detectAndCompute(img1,None) kp2, des2 = orb.detectAndCompute(img2,None) bf = cv2.BFMatcher() matches = bf.knnMatch(des1,des2, k=2) good = [] for m,n in matches: if m.distance < 0.8*n.distance: good.append([m]) img3 = cv2.drawMatchesKnn(img1,kp1,img2,kp2,good,None,flags=2) return img3 if __name__ == '__main__': imgname1 = '1.jpg' imgname2 = '2.jpg' img3 = SIFT(imgname1, imgname2) img4 = ORB(imgname1, imgname2) cv2.imshow("SIFT", img3) cv2.imwrite("sift.jpg",img3) cv2.imshow("ORB", img4) cv2.imwrite("orb.jpg",img4) cv2.waitKey(0) cv2.destroyAllWindows()

25.344828

66

0.646259

import cv2 def SIFT(imgname1, imgname2): sift = cv2.xfeatures2d.SIFT_create() img1 = cv2.imread(imgname1) img2 = cv2.imread(imgname2) kp1, des1 = sift.detectAndCompute(img1, None) kp2, des2 = sift.detectAndCompute(img2, None) FLANN_INDEX_KDTREE = 0 index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5) search_params = dict(checks=50) flann = cv2.FlannBasedMatcher(index_params, search_params) matches = flann.knnMatch(des1,des2,k=2) good = [] for m,n in matches: if m.distance < 0.70*n.distance: good.append([m]) img3 = cv2.drawMatchesKnn(img1,kp1,img2,kp2,good,None,flags=2) return img3 def ORB(imgname1, imgname2): orb = cv2.ORB_create() img1 = cv2.imread(imgname1) img2 = cv2.imread(imgname2) kp1, des1 = orb.detectAndCompute(img1,None) kp2, des2 = orb.detectAndCompute(img2,None) bf = cv2.BFMatcher() matches = bf.knnMatch(des1,des2, k=2) good = [] for m,n in matches: if m.distance < 0.8*n.distance: good.append([m]) img3 = cv2.drawMatchesKnn(img1,kp1,img2,kp2,good,None,flags=2) return img3 if __name__ == '__main__': imgname1 = '1.jpg' imgname2 = '2.jpg' img3 = SIFT(imgname1, imgname2) img4 = ORB(imgname1, imgname2) cv2.imshow("SIFT", img3) cv2.imwrite("sift.jpg",img3) cv2.imshow("ORB", img4) cv2.imwrite("orb.jpg",img4) cv2.waitKey(0) cv2.destroyAllWindows()

true

f73198d4417ffcba32dc97836964e3ca95693c56

3,900

py

Python

romp/lib/loss_funcs/params_loss.py

vltmedia/ROMP

1d2d96bd39f67a0a86ce7e397e3af856b3c5ee00

[ "MIT" ]

385

2021-03-30T15:40:31.000Z

2022-03-31T21:52:52.000Z

romp/lib/loss_funcs/params_loss.py

vltmedia/ROMP

1d2d96bd39f67a0a86ce7e397e3af856b3c5ee00

[ "MIT" ]

154

2021-03-31T11:28:27.000Z

2022-03-31T08:00:45.000Z

romp/lib/loss_funcs/params_loss.py

vltmedia/ROMP

1d2d96bd39f67a0a86ce7e397e3af856b3c5ee00

[ "MIT" ]

102

2021-04-15T06:43:00.000Z

2022-03-31T12:40:08.000Z

from __future__ import absolute_import from __future__ import division from __future__ import print_function import torch import torch.nn as nn import time import pickle import numpy as np import config import constants from config import args from utils import batch_rodrigues, rotation_matrix_to_angle_axis def batch_l2_loss(real,predict): loss_batch = torch.norm(real-predict, p=2, dim=1) return loss_batch.mean() def batch_l2_loss_param(real,predict): # convert to rot mat, multiple angular maps to the same rotation with Pi as a period. batch_size = real.shape[0] real = batch_rodrigues(real.reshape(-1,3)).contiguous()#(N*J)*3 -> (N*J)*3*3 predict = batch_rodrigues(predict.reshape(-1,3)).contiguous()#(N*J)*3 -> (N*J)*3*3 loss = torch.norm((real-predict).view(-1,9), p=2, dim=-1)#self.sl1loss(real,predict)# loss = loss.reshape(batch_size, -1).mean(-1) return loss def _calc_MPJAE(rel_pose_pred,rel_pose_real): global_pose_rotmat_pred = trans_relative_rot_to_global_rotmat(rel_pose_pred, with_global_rot=True) global_pose_rotmat_real = trans_relative_rot_to_global_rotmat(rel_pose_real, with_global_rot=True) MPJAE_error = _calc_joint_angle_error(global_pose_rotmat_pred, global_pose_rotmat_real).cpu().numpy() return MPJAE_error def trans_relative_rot_to_global_rotmat(params, with_global_rot=False): ''' calculate absolute rotation matrix in the global coordinate frame of K body parts. The rotation is the map from the local bone coordinate frame to the global one. K= 9 parts in the following order: root (JOINT 0) , left hip (JOINT 1), right hip (JOINT 2), left knee (JOINT 4), right knee (JOINT 5), left shoulder (JOINT 16), right shoulder (JOINT 17), left elbow (JOINT 18), right elbow (JOINT 19). parent kinetic tree [-1, 0, 0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 9, 9, 12, 13, 14, 16, 17, 18, 19, 20, 21] ''' batch_size, param_num = params.shape[0], params.shape[1]//3 pose_rotmat = batch_rodrigues(params.reshape(-1,3)).view(batch_size, param_num, 3, 3).contiguous() if with_global_rot: sellect_joints = np.array([0,1,2,4,5,16,17,18,19],dtype=np.int) results = [pose_rotmat[:, 0]] for idx in range(param_num-1): i_val = int(idx + 1) joint_rot = pose_rotmat[:, i_val] parent = constants.kintree_parents[i_val] glob_transf_mat = torch.matmul(results[parent], joint_rot) results.append(glob_transf_mat) else: sellect_joints = np.array([1,2,4,5,16,17,18,19],dtype=np.int)-1 results = [torch.eye(3,3)[None].cuda().repeat(batch_size,1,1)] for i_val in range(param_num-1): #i_val = int(idx + 1) joint_rot = pose_rotmat[:, i_val] parent = constants.kintree_parents[i_val+1] glob_transf_mat = torch.matmul(results[parent], joint_rot) results.append(glob_transf_mat) global_rotmat = torch.stack(results, axis=1)[:, sellect_joints].contiguous() return global_rotmat def _calc_joint_angle_error(pred_mat, gt_mat, return_axis_angle=False): """ Compute the geodesic distance between the two input matrices. :param pred_mat: predicted rotation matrices. Shape: ( Seq, 9g, 3, 3) :param gt_mat: ground truth rotation matrices. Shape: ( Seq, 9, 3, 3) :return: Mean geodesic distance between input matrices. """ # Reshape the matrices into B x 3 x 3 arrays r1 = pred_mat.reshape(-1,3,3) r2 = gt_mat.reshape(-1,3,3) # Transpose gt matrices r2t = r2.permute(0,2,1) r = torch.matmul(r1, r2t) # Convert rotation matrix to axis angle representation and find the angle axis_angles = rotation_matrix_to_angle_axis(r) angles = torch.norm(axis_angles, dim=-1)*(180./np.pi) if return_axis_angle: return angles,axis_angles return angles

42.857143

120

0.691538

from __future__ import absolute_import from __future__ import division from __future__ import print_function import torch import torch.nn as nn import time import pickle import numpy as np import config import constants from config import args from utils import batch_rodrigues, rotation_matrix_to_angle_axis def batch_l2_loss(real,predict): loss_batch = torch.norm(real-predict, p=2, dim=1) return loss_batch.mean() def batch_l2_loss_param(real,predict): batch_size = real.shape[0] real = batch_rodrigues(real.reshape(-1,3)).contiguous() predict = batch_rodrigues(predict.reshape(-1,3)).contiguous() loss = torch.norm((real-predict).view(-1,9), p=2, dim=-1) loss = loss.reshape(batch_size, -1).mean(-1) return loss def _calc_MPJAE(rel_pose_pred,rel_pose_real): global_pose_rotmat_pred = trans_relative_rot_to_global_rotmat(rel_pose_pred, with_global_rot=True) global_pose_rotmat_real = trans_relative_rot_to_global_rotmat(rel_pose_real, with_global_rot=True) MPJAE_error = _calc_joint_angle_error(global_pose_rotmat_pred, global_pose_rotmat_real).cpu().numpy() return MPJAE_error def trans_relative_rot_to_global_rotmat(params, with_global_rot=False): batch_size, param_num = params.shape[0], params.shape[1]//3 pose_rotmat = batch_rodrigues(params.reshape(-1,3)).view(batch_size, param_num, 3, 3).contiguous() if with_global_rot: sellect_joints = np.array([0,1,2,4,5,16,17,18,19],dtype=np.int) results = [pose_rotmat[:, 0]] for idx in range(param_num-1): i_val = int(idx + 1) joint_rot = pose_rotmat[:, i_val] parent = constants.kintree_parents[i_val] glob_transf_mat = torch.matmul(results[parent], joint_rot) results.append(glob_transf_mat) else: sellect_joints = np.array([1,2,4,5,16,17,18,19],dtype=np.int)-1 results = [torch.eye(3,3)[None].cuda().repeat(batch_size,1,1)] for i_val in range(param_num-1): joint_rot = pose_rotmat[:, i_val] parent = constants.kintree_parents[i_val+1] glob_transf_mat = torch.matmul(results[parent], joint_rot) results.append(glob_transf_mat) global_rotmat = torch.stack(results, axis=1)[:, sellect_joints].contiguous() return global_rotmat def _calc_joint_angle_error(pred_mat, gt_mat, return_axis_angle=False): r1 = pred_mat.reshape(-1,3,3) r2 = gt_mat.reshape(-1,3,3) r2t = r2.permute(0,2,1) r = torch.matmul(r1, r2t) axis_angles = rotation_matrix_to_angle_axis(r) angles = torch.norm(axis_angles, dim=-1)*(180./np.pi) if return_axis_angle: return angles,axis_angles return angles

true

f7319a140d0f0b8ef8a302e45f0ea738b572ba7a

63,805

py

Python

ironic/drivers/modules/drac/raid.py

inmotionhosting/ironic

1c7b5f82592e23ab66dddca56e0b059d3cb0710b

[ "Apache-2.0" ]

1

2021-02-27T02:48:59.000Z

ironic/drivers/modules/drac/raid.py

inmotionhosting/ironic

1c7b5f82592e23ab66dddca56e0b059d3cb0710b

[ "Apache-2.0" ]

null

ironic/drivers/modules/drac/raid.py

inmotionhosting/ironic

1c7b5f82592e23ab66dddca56e0b059d3cb0710b

[ "Apache-2.0" ]

null

# # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. """ DRAC RAID specific methods """ from collections import defaultdict import math from futurist import periodics from ironic_lib import metrics_utils from oslo_log import log as logging from oslo_utils import importutils from oslo_utils import units from ironic.common import exception from ironic.common.i18n import _ from ironic.common import raid as raid_common from ironic.conductor import task_manager from ironic.conductor import utils as manager_utils from ironic.conf import CONF from ironic.drivers import base from ironic.drivers.modules import deploy_utils from ironic.drivers.modules.drac import common as drac_common from ironic.drivers.modules.drac import job as drac_job drac_exceptions = importutils.try_import('dracclient.exceptions') drac_constants = importutils.try_import('dracclient.constants') LOG = logging.getLogger(__name__) METRICS = metrics_utils.get_metrics_logger(__name__) _CURRENT_RAID_CONTROLLER_MODE = "RAIDCurrentControllerMode" _REQUESTED_RAID_CONTROLLER_MODE = "RAIDRequestedControllerMode" _EHBA_MODE = "Enhanced HBA" _RAID_MODE = "RAID" RAID_LEVELS = { '0': { 'min_disks': 1, 'max_disks': 1000, 'type': 'simple', 'overhead': 0 }, '1': { 'min_disks': 2, 'max_disks': 2, 'type': 'simple', 'overhead': 1 }, '5': { 'min_disks': 3, 'max_disks': 1000, 'type': 'simple', 'overhead': 1 }, '6': { 'min_disks': 4, 'max_disks': 1000, 'type': 'simple', 'overhead': 2 }, '1+0': { 'type': 'spanned', 'span_type': '1' }, '5+0': { 'type': 'spanned', 'span_type': '5' }, '6+0': { 'type': 'spanned', 'span_type': '6' } } def list_raid_controllers(node): """List the RAID controllers of the node. :param node: an ironic node object. :returns: a list of RAIDController objects from dracclient. :raises: DracOperationError on an error from python-dracclient. """ client = drac_common.get_drac_client(node) try: return client.list_raid_controllers() except drac_exceptions.BaseClientException as exc: LOG.error('DRAC driver failed to get the list of RAID controllers ' 'for node %(node_uuid)s. Reason: %(error)s.', {'node_uuid': node.uuid, 'error': exc}) raise exception.DracOperationError(error=exc) def list_virtual_disks(node): """List the virtual disks of the node. :param node: an ironic node object. :returns: a list of VirtualDisk objects from dracclient. :raises: DracOperationError on an error from python-dracclient. """ client = drac_common.get_drac_client(node) try: return client.list_virtual_disks() except drac_exceptions.BaseClientException as exc: LOG.error('DRAC driver failed to get the list of virtual disks ' 'for node %(node_uuid)s. Reason: %(error)s.', {'node_uuid': node.uuid, 'error': exc}) raise exception.DracOperationError(error=exc) def list_physical_disks(node): """List the physical disks of the node. :param node: an ironic node object. :returns: a list of PhysicalDisk objects from dracclient. :raises: DracOperationError on an error from python-dracclient. """ client = drac_common.get_drac_client(node) try: return client.list_physical_disks() except drac_exceptions.BaseClientException as exc: LOG.error('DRAC driver failed to get the list of physical disks ' 'for node %(node_uuid)s. Reason: %(error)s.', {'node_uuid': node.uuid, 'error': exc}) raise exception.DracOperationError(error=exc) def _is_raid_controller(node, raid_controller_fqdd, raid_controllers=None): """Find out if object's fqdd is for a raid controller or not :param node: an ironic node object :param raid_controller_fqdd: The object's fqdd we are testing to see if it is a raid controller or not. :param raid_controllers: A list of RAIDControllers used to check for the presence of BOSS cards. If None, the iDRAC will be queried for the list of controllers. :returns: boolean, True if the device is a RAID controller, False if not. """ client = drac_common.get_drac_client(node) try: return client.is_raid_controller(raid_controller_fqdd, raid_controllers) except drac_exceptions.BaseClientException as exc: LOG.error('Unable to determine if controller %(raid_controller_fqdd)s ' 'on node %(node_uuid)s is a RAID controller. ' 'Reason: %(error)s. ', {'raid_controller_fqdd': raid_controller_fqdd, 'node_uuid': node.uuid, 'error': exc}) raise exception.DracOperationError(error=exc) def _validate_job_queue(node, raid_controller=None): """Validate that there are no pending jobs for this controller. :param node: an ironic node object. :param raid_controller: id of the RAID controller. """ kwargs = {} if raid_controller: kwargs["name_prefix"] = "Config:RAID:%s" % raid_controller drac_job.validate_job_queue(node, **kwargs) def create_virtual_disk(node, raid_controller, physical_disks, raid_level, size_mb, disk_name=None, span_length=None, span_depth=None): """Create a single virtual disk on a RAID controller. The created virtual disk will be in pending state. The DRAC card will do the actual configuration once the changes are applied by calling the ``commit_config`` method. :param node: an ironic node object. :param raid_controller: id of the RAID controller. :param physical_disks: ids of the physical disks. :param raid_level: RAID level of the virtual disk. :param size_mb: size of the virtual disk. :param disk_name: name of the virtual disk. (optional) :param span_depth: Number of spans in virtual disk. (optional) :param span_length: Number of disks per span. (optional) :returns: a dictionary containing the commit_needed key with a boolean value indicating whether a config job must be created for the values to be applied. :raises: DracOperationError on an error from python-dracclient. """ # This causes config to fail, because the boot mode is set via a config # job. _validate_job_queue(node, raid_controller) client = drac_common.get_drac_client(node) try: return client.create_virtual_disk(raid_controller, physical_disks, raid_level, size_mb, disk_name, span_length, span_depth) except drac_exceptions.BaseClientException as exc: LOG.error('DRAC driver failed to create virtual disk for node ' '%(node_uuid)s. Reason: %(error)s.', {'node_uuid': node.uuid, 'error': exc}) raise exception.DracOperationError(error=exc) def delete_virtual_disk(node, virtual_disk): """Delete a single virtual disk on a RAID controller. The deleted virtual disk will be in pending state. The DRAC card will do the actual configuration once the changes are applied by calling the ``commit_config`` method. :param node: an ironic node object. :param virtual_disk: id of the virtual disk. :returns: a dictionary containing the commit_needed key with a boolean value indicating whether a config job must be created for the values to be applied. :raises: DracOperationError on an error from python-dracclient. """ # NOTE(mgoddard): Cannot specify raid_controller as we don't know it. _validate_job_queue(node) client = drac_common.get_drac_client(node) try: return client.delete_virtual_disk(virtual_disk) except drac_exceptions.BaseClientException as exc: LOG.error('DRAC driver failed to delete virtual disk ' '%(virtual_disk_fqdd)s for node %(node_uuid)s. ' 'Reason: %(error)s.', {'virtual_disk_fqdd': virtual_disk, 'node_uuid': node.uuid, 'error': exc}) raise exception.DracOperationError(error=exc) def _reset_raid_config(node, raid_controller): """Delete all virtual disk and unassign all hotspares physical disk :param node: an ironic node object. :param raid_controller: id of the RAID controller. :returns: a dictionary containing - The is_commit_required needed key with a boolean value indicating whether a config job must be created for the values to be applied. - The is_reboot_required key with a RebootRequired enumerated value indicating whether the server must be rebooted to reset configuration. :raises: DracOperationError on an error from python-dracclient. """ try: _validate_job_queue(node, raid_controller) client = drac_common.get_drac_client(node) return client.reset_raid_config(raid_controller) except drac_exceptions.BaseClientException as exc: LOG.error('DRAC driver failed to delete all virtual disk ' 'and unassign all hotspares ' 'on %(raid_controller_fqdd)s ' 'for node %(node_uuid)s. ' 'Reason: %(error)s.', {'raid_controller_fqdd': raid_controller, 'node_uuid': node.uuid, 'error': exc}) raise exception.DracOperationError(error=exc) def clear_foreign_config(node, raid_controller): """Free up the foreign drives. :param node: an ironic node object. :param raid_controller: id of the RAID controller. :returns: a dictionary containing - The is_commit_required needed key with a boolean value indicating whether a config job must be created for the values to be applied. - The is_reboot_required key with a RebootRequired enumerated value indicating whether the server must be rebooted to clear foreign configuration. :raises: DracOperationError on an error from python-dracclient. """ try: _validate_job_queue(node, raid_controller) client = drac_common.get_drac_client(node) return client.clear_foreign_config(raid_controller) except drac_exceptions.BaseClientException as exc: LOG.error('DRAC driver failed to free foreign driver ' 'on %(raid_controller_fqdd)s ' 'for node %(node_uuid)s. ' 'Reason: %(error)s.', {'raid_controller_fqdd': raid_controller, 'node_uuid': node.uuid, 'error': exc}) raise exception.DracOperationError(error=exc) def set_raid_settings(node, controller_fqdd, settings): """Sets the RAID configuration It sets the pending_value parameter for each of the attributes passed in. For the values to be applied, a config job must be created. :param node: an ironic node object. :param controller_fqdd: the ID of the RAID controller. :param settings: a dictionary containing the proposed values, with each key being the name of attribute and the value being the proposed value. :returns: a dictionary containing: - The is_commit_required key with a boolean value indicating whether a config job must be created for the values to be applied. - The is_reboot_required key with a RebootRequired enumerated value indicating whether the server must be rebooted for the values to be applied. Possible values are true and false. :raises: DRACOperationFailed on error reported back by the DRAC interface """ try: drac_job.validate_job_queue(node) client = drac_common.get_drac_client(node) return client.set_raid_settings(controller_fqdd, settings) except drac_exceptions.BaseClientException as exc: LOG.error('DRAC driver failed to set raid settings ' 'on %(raid_controller_fqdd)s ' 'for node %(node_uuid)s. ' 'Reason: %(error)s.', {'raid_controller_fqdd': controller_fqdd, 'node_uuid': node.uuid, 'error': exc}) raise exception.DracOperationError(error=exc) def list_raid_settings(node): """List the RAID configuration settings :param node: an ironic node object. :returns: a dictionary with the RAID settings using InstanceID as the key. The attributes are RAIDEnumerableAttribute, RAIDStringAttribute and RAIDIntegerAttribute objects. :raises: DRACOperationFailed on error reported back by the DRAC interface """ try: drac_job.validate_job_queue(node) client = drac_common.get_drac_client(node) return client.list_raid_settings() except drac_exceptions.BaseClientException as exc: LOG.error('DRAC driver failed to list raid settings ' 'for node %(node_uuid)s. ' 'Reason: %(error)s.', {'node_uuid': node.uuid, 'error': exc}) raise exception.DracOperationError(error=exc) def change_physical_disk_state(node, mode=None, controllers_to_physical_disk_ids=None): """Convert disks RAID status This method converts the requested physical disks from RAID to JBOD or vice versa. It does this by only converting the disks that are not already in the correct state. :param node: an ironic node object. :param mode: the mode to change the disks either to RAID or JBOD. :param controllers_to_physical_disk_ids: Dictionary of controllers and corresponding disk ids to convert to the requested mode. :return: a dictionary containing: - conversion_results, a dictionary that maps controller ids to the conversion results for that controller. The conversion results are a dict that contains: - The is_commit_required key with the value always set to True indicating that a config job must be created to complete disk conversion. - The is_reboot_required key with a RebootRequired enumerated value indicating whether the server must be rebooted to complete disk conversion. :raises: DRACOperationError on an error from python-dracclient. """ try: drac_job.validate_job_queue(node) client = drac_common.get_drac_client(node) return client.change_physical_disk_state( mode, controllers_to_physical_disk_ids) except drac_exceptions.BaseClientException as exc: LOG.error('DRAC driver failed to change physical drives ' 'to %(mode)s mode for node %(node_uuid)s. ' 'Reason: %(error)s.', {'mode': mode, 'node_uuid': node.uuid, 'error': exc}) raise exception.DracOperationError(error=exc) def commit_config(node, raid_controller, reboot=False, realtime=False): """Apply all pending changes on a RAID controller. :param node: an ironic node object. :param raid_controller: id of the RAID controller. :param reboot: indicates whether a reboot job should be automatically created with the config job. (optional, defaults to False) :param realtime: indicates RAID controller supports realtime. (optional, defaults to False) :returns: id of the created job :raises: DracOperationError on an error from python-dracclient. """ client = drac_common.get_drac_client(node) try: return client.commit_pending_raid_changes( raid_controller=raid_controller, reboot=reboot, realtime=realtime) except drac_exceptions.BaseClientException as exc: LOG.error('DRAC driver failed to commit pending RAID config for' ' controller %(raid_controller_fqdd)s on node ' '%(node_uuid)s. Reason: %(error)s.', {'raid_controller_fqdd': raid_controller, 'node_uuid': node.uuid, 'error': exc}) raise exception.DracOperationError(error=exc) def _change_physical_disk_mode(node, mode=None, controllers_to_physical_disk_ids=None, substep="completed"): """Physical drives conversion from RAID to JBOD or vice-versa. :param node: an ironic node object. :param mode: the mode to change the disks either to RAID or JBOD. :param controllers_to_physical_disk_ids: Dictionary of controllers and corresponding disk ids to convert to the requested mode. :returns: states.CLEANWAIT if deletion is in progress asynchronously or None if it is completed. """ change_disk_state = change_physical_disk_state( node, mode, controllers_to_physical_disk_ids) controllers = list() conversion_results = change_disk_state['conversion_results'] for controller_id, result in conversion_results.items(): controller = {'raid_controller': controller_id, 'is_reboot_required': result['is_reboot_required'], 'is_commit_required': result['is_commit_required']} controllers.append(controller) return _commit_to_controllers( node, controllers, substep=substep) def abandon_config(node, raid_controller): """Deletes all pending changes on a RAID controller. :param node: an ironic node object. :param raid_controller: id of the RAID controller. :raises: DracOperationError on an error from python-dracclient. """ client = drac_common.get_drac_client(node) try: client.abandon_pending_raid_changes(raid_controller) except drac_exceptions.BaseClientException as exc: LOG.error('DRAC driver failed to delete pending RAID config ' 'for controller %(raid_controller_fqdd)s on node ' '%(node_uuid)s. Reason: %(error)s.', {'raid_controller_fqdd': raid_controller, 'node_uuid': node.uuid, 'error': exc}) raise exception.DracOperationError(error=exc) def _calculate_spans(raid_level, disks_count): """Calculates number of spans for a RAID level given a physical disk count :param raid_level: RAID level of the virtual disk. :param disk_count: number of physical disks used for the virtual disk. :returns: number of spans. """ if raid_level in ['0', '1', '5', '6']: return 1 elif raid_level in ['5+0', '6+0']: return 2 elif raid_level in ['1+0']: return disks_count >> 1 else: reason = (_('Cannot calculate spans for RAID level "%s"') % raid_level) raise exception.DracOperationError(error=reason) def _usable_disks_count(raid_level, disks_count): """Calculates the number of disks usable for a RAID level ...given a physical disk count :param raid_level: RAID level of the virtual disk. :param disk_count: number of physical disks used for the virtual disk. :returns: number of disks. """ if raid_level in ['0', '1', '5', '6']: return disks_count elif raid_level in ['5+0', '6+0', '1+0']: # largest even number less than disk_count return (disks_count >> 1) << 1 else: reason = (_('RAID level %(raid_level)s is not supported by the ' 'driver. Supported RAID levels: %(supported_raid_levels)s') % {'raid_level': raid_level, 'supported_raid_levels': list(RAID_LEVELS)}) raise exception.DracOperationError(error=reason) def _raid_level_min_disks(raid_level, spans_count=1): try: raid_level_info = RAID_LEVELS[raid_level] except KeyError: reason = (_('RAID level %(raid_level)s is not supported by the ' 'driver. Supported RAID levels: %(supported_raid_levels)s') % {'raid_level': raid_level, 'supported_raid_levels': list(RAID_LEVELS)}) raise exception.DracOperationError(error=reason) if raid_level_info['type'] == 'spanned': if spans_count <= 1: reason = _('Spanned RAID volumes cannot contain a single span') raise exception.DracOperationError(error=reason) span_type = raid_level_info['span_type'] raid_level_info = RAID_LEVELS[span_type] return raid_level_info['min_disks'] * spans_count def _raid_level_max_disks(raid_level, spans_count=1): try: raid_level_info = RAID_LEVELS[raid_level] except KeyError: reason = (_('RAID level %(raid_level)s is not supported by the ' 'driver. Supported RAID levels: %(supported_raid_levels)s') % {'raid_level': raid_level, 'supported_raid_levels': list(RAID_LEVELS)}) raise exception.DracOperationError(error=reason) if raid_level_info['type'] == 'spanned': if spans_count <= 1: reason = _('Spanned RAID volumes cannot contain a single span') raise exception.DracOperationError(error=reason) span_type = raid_level_info['span_type'] raid_level_info = RAID_LEVELS[span_type] return raid_level_info['max_disks'] * spans_count def _raid_level_overhead(raid_level, spans_count=1): try: raid_level_info = RAID_LEVELS[raid_level] except KeyError: reason = (_('RAID level %(raid_level)s is not supported by the ' 'driver. Supported RAID levels: %(supported_raid_levels)s') % {'raid_level': raid_level, 'supported_raid_levels': list(RAID_LEVELS)}) raise exception.DracOperationError(error=reason) if raid_level_info['type'] == 'spanned': if spans_count <= 1: reason = _('Spanned RAID volumes cannot contain a single span') raise exception.DracOperationError(error=reason) span_type = raid_level_info['span_type'] raid_level_info = RAID_LEVELS[span_type] return raid_level_info['overhead'] * spans_count def _max_volume_size_mb(raid_level, physical_disks, free_space_mb, spans_count=1, stripe_size_kb=64 * units.Ki): # restrict the size to the smallest available space free_spaces = [free_space_mb[disk] for disk in physical_disks] size_kb = min(free_spaces) * units.Ki # NOTE(ifarkas): using math.floor so we get a volume size that does not # exceed the available space stripes_per_disk = int(math.floor(float(size_kb) / stripe_size_kb)) disks_count = len(physical_disks) overhead_disks_count = _raid_level_overhead(raid_level, spans_count) return int(stripes_per_disk * stripe_size_kb * (disks_count - overhead_disks_count) / units.Ki) def _volume_usage_per_disk_mb(logical_disk, physical_disks, spans_count=1, stripe_size_kb=64 * units.Ki): disks_count = len(physical_disks) overhead_disks_count = _raid_level_overhead(logical_disk['raid_level'], spans_count) volume_size_kb = logical_disk['size_mb'] * units.Ki # NOTE(ifarkas): using math.ceil so we get the largest disk usage # possible, so we can avoid over-committing stripes_per_volume = math.ceil(float(volume_size_kb) / stripe_size_kb) stripes_per_disk = math.ceil( float(stripes_per_volume) / (disks_count - overhead_disks_count)) return int(stripes_per_disk * stripe_size_kb / units.Ki) def _find_configuration(logical_disks, physical_disks, pending_delete): """Find RAID configuration. This method transforms the RAID configuration defined in Ironic to a format that is required by dracclient. This includes matching the physical disks to RAID volumes when it's not pre-defined, or in general calculating missing properties. :param logical_disks: list of logical disk definitions. :param physical_disks: list of physical disk definitions. :param pending_delete: Whether there is a pending deletion of virtual disks that should be accounted for. """ # shared physical disks of RAID volumes size_gb='MAX' should be # deprioritized during the matching process to reserve as much space as # possible. Reserved means it won't be used during matching. volumes_with_reserved_physical_disks = [ volume for volume in logical_disks if ('physical_disks' in volume and volume['size_mb'] == 'MAX' and volume.get('share_physical_disks', False))] reserved_physical_disks = [ disk for disk in physical_disks for volume in volumes_with_reserved_physical_disks if disk.id in volume['physical_disks']] # we require each logical disk contain only homogeneous physical disks, so # sort them by type physical_disks_by_type = {} reserved_physical_disks_by_type = {} free_space_mb = {} for disk in physical_disks: # calculate free disk space free_space_mb[disk] = _get_disk_free_size_mb(disk, pending_delete) disk_type = (disk.controller, disk.media_type, disk.interface_type, disk.size_mb) if disk_type not in physical_disks_by_type: physical_disks_by_type[disk_type] = [] reserved_physical_disks_by_type[disk_type] = [] if disk in reserved_physical_disks: reserved_physical_disks_by_type[disk_type].append(disk) else: physical_disks_by_type[disk_type].append(disk) # exclude non-shared physical disks (predefined by the user) from # physical_disks_by_type because they are not going to be used during # matching for volume in logical_disks: if ('physical_disks' in volume and not volume.get('share_physical_disks', False)): for disk in physical_disks: if disk.id in volume['physical_disks']: disk_type = (disk.controller, disk.media_type, disk.interface_type, disk.size_mb) if disk in physical_disks_by_type[disk_type]: physical_disks_by_type[disk_type].remove(disk) processed_volumes = [] # step 1 - process volumes with predefined disks and exact size for volume in [volume for volume in logical_disks if ('physical_disks' in volume and volume['size_mb'] != 'MAX')]: _calculate_volume_props(volume, physical_disks, free_space_mb) processed_volumes.append(volume) # step 2 - process volumes without predefined disks volumes_without_disks = [disk for disk in logical_disks if 'physical_disks' not in disk] if volumes_without_disks: result, free_space_mb = ( _assign_disks_to_volume(volumes_without_disks, physical_disks_by_type, free_space_mb, pending_delete)) if not result: # try again using the reserved physical disks in addition for disk_type, disks in physical_disks_by_type.items(): physical_disks_by_type[disk_type] += ( reserved_physical_disks_by_type[disk_type]) result, free_space_mb = ( _assign_disks_to_volume(volumes_without_disks, physical_disks_by_type, free_space_mb, pending_delete)) if not result: error_msg = _('failed to find matching physical disks for all ' 'logical disks') LOG.error('DRAC driver failed to create RAID ' 'configuration. Reason: %(error)s.', {'error': error_msg}) raise exception.DracOperationError(error=error_msg) processed_volumes += volumes_without_disks # step 3 - process volumes with predefined disks and size_mb == 'MAX' for volume in [volume for volume in logical_disks if ('physical_disks' in volume and volume['size_mb'] == 'MAX')]: _calculate_volume_props(volume, physical_disks, free_space_mb) processed_volumes.append(volume) return processed_volumes def _calculate_volume_props(logical_disk, physical_disks, free_space_mb): selected_disks = [disk for disk in physical_disks if disk.id in logical_disk['physical_disks']] spans_count = _calculate_spans( logical_disk['raid_level'], len(selected_disks)) if len(selected_disks) % spans_count != 0: error_msg = _('invalid number of physical disks was provided') raise exception.DracOperationError(error=error_msg) disks_per_span = int(len(selected_disks) / spans_count) # Best practice is to not pass span_length and span_depth when creating a # RAID10. The iDRAC will dynamically calculate these values using maximum # values obtained from the RAID controller. logical_disk['span_depth'] = None logical_disk['span_length'] = None if logical_disk['raid_level'] != '1+0': logical_disk['span_depth'] = spans_count logical_disk['span_length'] = disks_per_span max_volume_size_mb = _max_volume_size_mb( logical_disk['raid_level'], selected_disks, free_space_mb, spans_count=spans_count) if logical_disk['size_mb'] == 'MAX': if max_volume_size_mb == 0: error_msg = _("size set to 'MAX' but could not allocate physical " "disk space") raise exception.DracOperationError(error=error_msg) logical_disk['size_mb'] = max_volume_size_mb elif max_volume_size_mb < logical_disk['size_mb']: if max_volume_size_mb == 0: error_msg = _('not enough physical disk space for the logical ' 'disk') raise exception.DracOperationError(error=error_msg) disk_usage = _volume_usage_per_disk_mb(logical_disk, selected_disks, spans_count=spans_count) for disk in selected_disks: if free_space_mb[disk] < disk_usage: error_msg = _('not enough free space on physical disks for the ' 'logical disk') raise exception.DracOperationError(error=error_msg) else: free_space_mb[disk] -= disk_usage if 'controller' not in logical_disk: logical_disk['controller'] = selected_disks[0].controller def _assign_disks_to_volume(logical_disks, physical_disks_by_type, free_space_mb, pending_delete): logical_disk = logical_disks.pop(0) raid_level = logical_disk['raid_level'] # iterate over all possible configurations for (controller, disk_type, interface_type, size_mb), disks in physical_disks_by_type.items(): if ('disk_type' in logical_disk and logical_disk['disk_type'] != disk_type): continue if ('interface_type' in logical_disk and logical_disk['interface_type'] != interface_type): continue # filter out disks without free disk space disks = [disk for disk in disks if free_space_mb[disk] > 0] # sort disks by free size which is important if we have max disks limit # on a volume disks = sorted( disks, key=lambda disk: free_space_mb[disk]) # filter out disks already in use if sharing is disabled if ('share_physical_disks' not in logical_disk or not logical_disk['share_physical_disks']): initial_free_size_mb = { disk: _get_disk_free_size_mb(disk, pending_delete) for disk in disks } disks = [disk for disk in disks if initial_free_size_mb[disk] == free_space_mb[disk]] max_spans = _calculate_spans(raid_level, len(disks)) min_spans = min([2, max_spans]) min_disks = _raid_level_min_disks(raid_level, spans_count=min_spans) max_disks = _raid_level_max_disks(raid_level, spans_count=max_spans) candidate_max_disks = min([max_disks, len(disks)]) for disks_count in range(min_disks, candidate_max_disks + 1): if ('number_of_physical_disks' in logical_disk and (logical_disk['number_of_physical_disks'] != disks_count)): continue # skip invalid disks_count if disks_count != _usable_disks_count(logical_disk['raid_level'], disks_count): continue selected_disks = disks[0:disks_count] candidate_volume = logical_disk.copy() candidate_free_space_mb = free_space_mb.copy() candidate_volume['physical_disks'] = [disk.id for disk in selected_disks] try: _calculate_volume_props(candidate_volume, selected_disks, candidate_free_space_mb) except exception.DracOperationError: continue if len(logical_disks) > 0: result, candidate_free_space_mb = ( _assign_disks_to_volume(logical_disks, physical_disks_by_type, candidate_free_space_mb, pending_delete)) if result: logical_disks.append(candidate_volume) return (True, candidate_free_space_mb) else: logical_disks.append(candidate_volume) return (True, candidate_free_space_mb) else: # put back the logical_disk to queue logical_disks.insert(0, logical_disk) return (False, free_space_mb) def _filter_logical_disks(logical_disks, include_root_volume, include_nonroot_volumes): filtered_disks = [] for disk in logical_disks: if include_root_volume and disk.get('is_root_volume'): filtered_disks.append(disk) if include_nonroot_volumes and not disk.get('is_root_volume'): filtered_disks.append(disk) return filtered_disks def _create_config_job(node, controller, reboot=False, realtime=False, raid_config_job_ids=[], raid_config_parameters=[]): job_id = commit_config(node, raid_controller=controller, reboot=reboot, realtime=realtime) raid_config_job_ids.append(job_id) if controller not in raid_config_parameters: raid_config_parameters.append(controller) LOG.info('Change has been committed to RAID controller ' '%(controller)s on node %(node)s. ' 'DRAC job id: %(job_id)s', {'controller': controller, 'node': node.uuid, 'job_id': job_id}) return {'raid_config_job_ids': raid_config_job_ids, 'raid_config_parameters': raid_config_parameters} def _validate_volume_size(node, logical_disks): new_physical_disks = list_physical_disks(node) free_space_mb = {} new_processed_volumes = [] for disk in new_physical_disks: free_space_mb[disk] = disk.free_size_mb for logical_disk in logical_disks: selected_disks = [disk for disk in new_physical_disks if disk.id in logical_disk['physical_disks']] spans_count = _calculate_spans( logical_disk['raid_level'], len(selected_disks)) new_max_vol_size_mb = _max_volume_size_mb( logical_disk['raid_level'], selected_disks, free_space_mb, spans_count=spans_count) if logical_disk['size_mb'] > new_max_vol_size_mb: logical_disk['size_mb'] = new_max_vol_size_mb LOG.info("Logical size does not match so calculating volume " "properties for current logical_disk") _calculate_volume_props( logical_disk, new_physical_disks, free_space_mb) new_processed_volumes.append(logical_disk) if new_processed_volumes: return new_processed_volumes return logical_disks def _switch_to_raid_mode(node, controller_fqdd): """Convert the controller mode from Enhanced HBA to RAID mode :param node: an ironic node object :param controller_fqdd: the ID of the RAID controller. :returns: a dictionary containing - The raid_controller key with a ID of the RAID controller value. - The is_commit_required needed key with a boolean value indicating whether a config job must be created for the values to be applied. - The is_reboot_required key with a RebootRequired enumerated value indicating whether the server must be rebooted to switch the controller mode to RAID. """ # wait for pending jobs to complete drac_job.wait_for_job_completion(node) raid_attr = "{}:{}".format(controller_fqdd, _REQUESTED_RAID_CONTROLLER_MODE) settings = {raid_attr: _RAID_MODE} settings_results = set_raid_settings( node, controller_fqdd, settings) controller = { 'raid_controller': controller_fqdd, 'is_reboot_required': settings_results['is_reboot_required'], 'is_commit_required': settings_results['is_commit_required']} return controller def _commit_to_controllers(node, controllers, substep="completed"): """Commit changes to RAID controllers on the node. :param node: an ironic node object :param controllers: a list of dictionary containing - The raid_controller key with raid controller fqdd value indicating on which raid configuration job needs to be perform. - The is_commit_required needed key with a boolean value indicating whether a config job must be created. - The is_reboot_required key with a RebootRequired enumerated value indicating whether the server must be rebooted only if raid controller does not support realtime. :param substep: contain sub cleaning or deploy step which executes any raid configuration job if set after cleaning or deploy step. (default to completed) :returns: states.CLEANWAIT (cleaning) or states.DEPLOYWAIT (deployment) if configuration is in progress asynchronously or None if it is completed. """ # remove controller which does not require configuration job controllers = [controller for controller in controllers if controller['is_commit_required']] if not controllers: LOG.debug('No changes on any of the controllers on node %s', node.uuid) driver_internal_info = node.driver_internal_info driver_internal_info['raid_config_substep'] = substep driver_internal_info['raid_config_parameters'] = [] node.driver_internal_info = driver_internal_info node.save() return driver_internal_info = node.driver_internal_info driver_internal_info['raid_config_substep'] = substep driver_internal_info['raid_config_parameters'] = [] if 'raid_config_job_ids' not in driver_internal_info: driver_internal_info['raid_config_job_ids'] = [] optional = drac_constants.RebootRequired.optional # all realtime controllers all_realtime = all( (cntlr['is_reboot_required'] == optional) and not(cntlr.get('is_ehba_mode')) for cntlr in controllers) # check any controller with ehba mode any_ehba_controllers = any( cntrl.get('is_ehba_mode') is True for cntrl in controllers) raid_config_job_ids = [] raid_config_parameters = [] if all_realtime: for controller in controllers: realtime_controller = controller['raid_controller'] job_details = _create_config_job( node, controller=realtime_controller, reboot=False, realtime=True, raid_config_job_ids=raid_config_job_ids, raid_config_parameters=raid_config_parameters) elif any_ehba_controllers: commit_to_ehba_controllers = [] for controller in controllers: if controller.get('is_ehba_mode'): job_details = _create_config_job( node, controller=controller['raid_controller'], reboot=False, realtime=True, raid_config_job_ids=raid_config_job_ids, raid_config_parameters=raid_config_parameters) ehba_controller = _switch_to_raid_mode( node, controller['raid_controller']) commit_to_ehba_controllers.append( ehba_controller['raid_controller']) else: job_details = _create_config_job( node, controller=controller['raid_controller'], reboot=False, realtime=False, raid_config_job_ids=raid_config_job_ids, raid_config_parameters=raid_config_parameters) for controller in commit_to_ehba_controllers: LOG.debug("Create job with Reboot to apply configuration " "changes for ehba controllers") job_details = _create_config_job( node, controller=controller, reboot=(controller == commit_to_ehba_controllers[-1]), realtime=False, raid_config_job_ids=raid_config_job_ids, raid_config_parameters=raid_config_parameters) else: for controller in controllers: mix_controller = controller['raid_controller'] reboot = (controller == controllers[-1]) job_details = _create_config_job( node, controller=mix_controller, reboot=reboot, realtime=False, raid_config_job_ids=raid_config_job_ids, raid_config_parameters=raid_config_parameters) driver_internal_info['raid_config_job_ids'].extend(job_details[ 'raid_config_job_ids']) driver_internal_info['raid_config_parameters'].extend(job_details[ 'raid_config_parameters']) node.driver_internal_info = driver_internal_info # Signal whether the node has been rebooted, that we do not need to execute # the step again, and that this completion of this step is triggered # through async polling. # NOTE(mgoddard): set_async_step_flags calls node.save(). deploy_utils.set_async_step_flags( node, reboot=not all_realtime, skip_current_step=True, polling=True) return deploy_utils.get_async_step_return_state(node) def _create_virtual_disks(task, node): logical_disks_to_create = node.driver_internal_info[ 'logical_disks_to_create'] # Check valid properties attached to voiume after drives conversion isVolValidationNeeded = node.driver_internal_info[ 'volume_validation'] if isVolValidationNeeded: logical_disks_to_create = _validate_volume_size( node, logical_disks_to_create) controllers = list() for logical_disk in logical_disks_to_create: controller = dict() controller_cap = create_virtual_disk( node, raid_controller=logical_disk['controller'], physical_disks=logical_disk['physical_disks'], raid_level=logical_disk['raid_level'], size_mb=logical_disk['size_mb'], disk_name=logical_disk.get('name'), span_length=logical_disk.get('span_length'), span_depth=logical_disk.get('span_depth')) controller['raid_controller'] = logical_disk['controller'] controller['is_reboot_required'] = controller_cap[ 'is_reboot_required'] controller['is_commit_required'] = controller_cap[ 'is_commit_required'] if controller not in controllers: controllers.append(controller) return _commit_to_controllers(node, controllers) def _controller_in_hba_mode(raid_settings, controller_fqdd): controller_mode = raid_settings.get( '{}:{}'.format(controller_fqdd, _CURRENT_RAID_CONTROLLER_MODE)) return _EHBA_MODE in controller_mode.current_value def _controller_supports_ehba_mode(settings, controller_fqdd): raid_cntrl_attr = "{}:{}".format(controller_fqdd, _CURRENT_RAID_CONTROLLER_MODE) current_cntrl_mode = settings.get(raid_cntrl_attr) if not current_cntrl_mode: return False else: return _EHBA_MODE in current_cntrl_mode.possible_values def _get_disk_free_size_mb(disk, pending_delete): """Return the size of free space on the disk in MB. :param disk: a PhysicalDisk object. :param pending_delete: Whether there is a pending deletion of all virtual disks. """ return disk.size_mb if pending_delete else disk.free_size_mb class DracWSManRAID(base.RAIDInterface): def get_properties(self): """Return the properties of the interface.""" return drac_common.COMMON_PROPERTIES @base.deploy_step(priority=0, argsinfo=base.RAID_APPLY_CONFIGURATION_ARGSINFO) def apply_configuration(self, task, raid_config, create_root_volume=True, create_nonroot_volumes=False, delete_existing=True): return super(DracWSManRAID, self).apply_configuration( task, raid_config, create_root_volume=create_root_volume, create_nonroot_volumes=create_nonroot_volumes, delete_existing=delete_existing) @METRICS.timer('DracRAID.create_configuration') @base.clean_step(priority=0, abortable=False, argsinfo={ 'create_root_volume': { 'description': ( 'This specifies whether to create the root volume. ' 'Defaults to `True`.' ), 'required': False }, 'create_nonroot_volumes': { 'description': ( 'This specifies whether to create the non-root volumes. ' 'Defaults to `True`.' ), 'required': False }, "delete_existing": { "description": ( "Setting this to 'True' indicates to delete existing RAID " "configuration prior to creating the new configuration. " "Default value is 'False'." ), "required": False, } }) def create_configuration(self, task, create_root_volume=True, create_nonroot_volumes=True, delete_existing=False): """Create the RAID configuration. This method creates the RAID configuration on the given node. :param task: a TaskManager instance containing the node to act on. :param create_root_volume: If True, a root volume is created during RAID configuration. Otherwise, no root volume is created. Default is True. :param create_nonroot_volumes: If True, non-root volumes are created. If False, no non-root volumes are created. Default is True. :param delete_existing: Setting this to True indicates to delete RAID configuration prior to creating the new configuration. Default is False. :returns: states.CLEANWAIT (cleaning) or states.DEPLOYWAIT (deployment) if creation is in progress asynchronously or None if it is completed. :raises: MissingParameterValue, if node.target_raid_config is missing or empty. :raises: DracOperationError on an error from python-dracclient. """ node = task.node logical_disks = node.target_raid_config['logical_disks'] for disk in logical_disks: if disk['size_gb'] == 'MAX' and 'physical_disks' not in disk: raise exception.InvalidParameterValue( _("create_configuration called with invalid " "target_raid_configuration for node %(node_id)s. " "'physical_disks' is missing from logical_disk while " "'size_gb'='MAX' was requested: " "%(logical_disk)s") % {'node_id': node.uuid, 'logical_disk': disk}) if disk['size_gb'] == 'MAX': disk['size_mb'] = 'MAX' else: disk['size_mb'] = disk['size_gb'] * units.Ki del disk['size_gb'] if delete_existing: self._delete_configuration_no_commit(task) physical_disks = list_physical_disks(node) logical_disks = _find_configuration(logical_disks, physical_disks, pending_delete=delete_existing) logical_disks_to_create = _filter_logical_disks( logical_disks, create_root_volume, create_nonroot_volumes) controllers_to_physical_disk_ids = defaultdict(list) for logical_disk in logical_disks_to_create: # Not applicable to JBOD logical disks. if logical_disk['raid_level'] == 'JBOD': continue for physical_disk_name in logical_disk['physical_disks']: controllers_to_physical_disk_ids[ logical_disk['controller']].append( physical_disk_name) # adding logical_disks to driver_internal_info to create virtual disks driver_internal_info = node.driver_internal_info driver_internal_info[ "logical_disks_to_create"] = logical_disks_to_create commit_results = None if logical_disks_to_create: LOG.debug( "Converting physical disks configured to back RAID " "logical disks to RAID mode for node %(node_uuid)s ", {"node_uuid": node.uuid}) raid_mode = drac_constants.RaidStatus.raid commit_results = _change_physical_disk_mode( node, raid_mode, controllers_to_physical_disk_ids, substep="create_virtual_disks") volume_validation = True if commit_results else False driver_internal_info['volume_validation'] = volume_validation node.driver_internal_info = driver_internal_info node.save() if commit_results: return commit_results else: LOG.debug("Controller does not support drives conversion " "so creating virtual disks") return _create_virtual_disks(task, node) @METRICS.timer('DracRAID.delete_configuration') @base.clean_step(priority=0) @base.deploy_step(priority=0) def delete_configuration(self, task): """Delete the RAID configuration. :param task: a TaskManager instance containing the node to act on. :returns: states.CLEANWAIT (cleaning) or states.DEPLOYWAIT (deployment) if deletion is in progress asynchronously or None if it is completed. :raises: DracOperationError on an error from python-dracclient. """ controllers = self._delete_configuration_no_commit(task) return _commit_to_controllers(task.node, controllers, substep="delete_foreign_config") @METRICS.timer('DracRAID.get_logical_disks') def get_logical_disks(self, task): """Get the RAID configuration of the node. :param task: a TaskManager instance containing the node to act on. :returns: A dictionary of properties. :raises: DracOperationError on an error from python-dracclient. """ node = task.node logical_disks = [] for disk in list_virtual_disks(node): logical_disk = { 'id': disk.id, 'controller': disk.controller, 'size_gb': int(disk.size_mb / units.Ki), 'raid_level': disk.raid_level } if disk.name is not None: logical_disk['name'] = disk.name logical_disks.append(logical_disk) return {'logical_disks': logical_disks} @METRICS.timer('DracRAID._query_raid_config_job_status') @periodics.periodic( spacing=CONF.drac.query_raid_config_job_status_interval) def _query_raid_config_job_status(self, manager, context): """Periodic task to check the progress of running RAID config jobs.""" filters = {'reserved': False, 'maintenance': False} fields = ['driver_internal_info'] node_list = manager.iter_nodes(fields=fields, filters=filters) for (node_uuid, driver, conductor_group, driver_internal_info) in node_list: try: lock_purpose = 'checking async raid configuration jobs' with task_manager.acquire(context, node_uuid, purpose=lock_purpose, shared=True) as task: if not isinstance(task.driver.raid, DracWSManRAID): continue job_ids = driver_internal_info.get('raid_config_job_ids') if not job_ids: continue self._check_node_raid_jobs(task) except exception.NodeNotFound: LOG.info("During query_raid_config_job_status, node " "%(node)s was not found and presumed deleted by " "another process.", {'node': node_uuid}) except exception.NodeLocked: LOG.info("During query_raid_config_job_status, node " "%(node)s was already locked by another process. " "Skip.", {'node': node_uuid}) @METRICS.timer('DracRAID._check_node_raid_jobs') def _check_node_raid_jobs(self, task): """Check the progress of running RAID config jobs of a node.""" node = task.node raid_config_job_ids = node.driver_internal_info['raid_config_job_ids'] finished_job_ids = [] for config_job_id in raid_config_job_ids: config_job = drac_job.get_job(node, job_id=config_job_id) if config_job is None or config_job.status == 'Completed': finished_job_ids.append(config_job_id) elif config_job.status == 'Failed': finished_job_ids.append(config_job_id) self._set_raid_config_job_failure(node) if not finished_job_ids: return task.upgrade_lock() self._delete_cached_config_job_id(node, finished_job_ids) if not node.driver_internal_info.get('raid_config_job_failure', False): if 'raid_config_substep' in node.driver_internal_info: substep = node.driver_internal_info['raid_config_substep'] if substep == 'delete_foreign_config': foreign_drives = self._execute_foreign_drives(task, node) if foreign_drives is None: return self._convert_drives(task, node) elif substep == 'physical_disk_conversion': self._convert_drives(task, node) elif substep == "create_virtual_disks": return _create_virtual_disks(task, node) elif substep == 'completed': self._complete_raid_substep(task, node) else: self._complete_raid_substep(task, node) else: self._clear_raid_substep(node) self._clear_raid_config_job_failure(node) self._set_failed(task, config_job) def _execute_foreign_drives(self, task, node): controllers = list() jobs_required = False for controller_id in node.driver_internal_info[ 'raid_config_parameters']: controller_cap = clear_foreign_config( node, controller_id) controller = { 'raid_controller': controller_id, 'is_reboot_required': controller_cap['is_reboot_required'], 'is_commit_required': controller_cap['is_commit_required']} controllers.append(controller) jobs_required = jobs_required or controller_cap[ 'is_commit_required'] if not jobs_required: LOG.info( "No foreign drives detected, so " "resume %s", "cleaning" if node.clean_step else "deployment") return None else: return _commit_to_controllers( node, controllers, substep='physical_disk_conversion') def _complete_raid_substep(self, task, node): self._clear_raid_substep(node) self._resume(task) def _convert_drives(self, task, node): jbod = drac_constants.RaidStatus.jbod drives_results = _change_physical_disk_mode( node, mode=jbod) if drives_results is None: LOG.debug("Controller does not support drives " "conversion on %(node_uuid)s", {'node_uuid': node.uuid}) self._complete_raid_substep(task, node) def _clear_raid_substep(self, node): driver_internal_info = node.driver_internal_info driver_internal_info.pop('raid_config_substep', None) driver_internal_info.pop('raid_config_parameters', None) node.driver_internal_info = driver_internal_info node.save() def _set_raid_config_job_failure(self, node): driver_internal_info = node.driver_internal_info driver_internal_info['raid_config_job_failure'] = True node.driver_internal_info = driver_internal_info node.save() def _clear_raid_config_job_failure(self, node): driver_internal_info = node.driver_internal_info del driver_internal_info['raid_config_job_failure'] node.driver_internal_info = driver_internal_info node.save() def _delete_cached_config_job_id(self, node, finished_config_job_ids=None): if finished_config_job_ids is None: finished_config_job_ids = [] driver_internal_info = node.driver_internal_info unfinished_job_ids = [job_id for job_id in driver_internal_info['raid_config_job_ids'] if job_id not in finished_config_job_ids] driver_internal_info['raid_config_job_ids'] = unfinished_job_ids node.driver_internal_info = driver_internal_info node.save() def _set_failed(self, task, config_job): error_msg = (_("Failed config job: %(config_job_id)s. " "Message: '%(message)s'.") % {'config_job_id': config_job.id, 'message': config_job.message}) log_msg = ("RAID configuration job failed for node %(node)s. " "%(error)s" % {'node': task.node.uuid, 'error': error_msg}) if task.node.clean_step: manager_utils.cleaning_error_handler(task, error_msg) else: manager_utils.deploying_error_handler(task, log_msg, error_msg) def _resume(self, task): raid_common.update_raid_info( task.node, self.get_logical_disks(task)) if task.node.clean_step: manager_utils.notify_conductor_resume_clean(task) else: manager_utils.notify_conductor_resume_deploy(task) def _delete_configuration_no_commit(self, task): """Delete existing RAID configuration without committing the change. :param task: A TaskManager instance. :returns: A set of names of RAID controllers which need RAID changes to be committed. """ node = task.node controllers = list() drac_raid_controllers = list_raid_controllers(node) drac_raid_settings = list_raid_settings(node) for cntrl in drac_raid_controllers: if _is_raid_controller(node, cntrl.id, drac_raid_controllers): controller = dict() if _controller_supports_ehba_mode( drac_raid_settings, cntrl.id) and _controller_in_hba_mode( drac_raid_settings, cntrl.id): controller['is_ehba_mode'] = True controller_cap = _reset_raid_config(node, cntrl.id) controller["raid_controller"] = cntrl.id controller["is_reboot_required"] = controller_cap[ "is_reboot_required"] controller["is_commit_required"] = controller_cap[ "is_commit_required"] controllers.append(controller) return controllers class DracRAID(DracWSManRAID): """Class alias of class DracWSManRAID. This class provides ongoing support of the deprecated 'idrac' RAID interface implementation entrypoint. All bug fixes and new features should be implemented in its base class, DracWSManRAID. That makes them available to both the deprecated 'idrac' and new 'idrac-wsman' entrypoints. Such changes should not be made to this class. """ def __init__(self): super(DracRAID, self).__init__() LOG.warning("RAID interface 'idrac' is deprecated and may be removed " "in a future release. Use 'idrac-wsman' instead.")

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from collections import defaultdict import math from futurist import periodics from ironic_lib import metrics_utils from oslo_log import log as logging from oslo_utils import importutils from oslo_utils import units from ironic.common import exception from ironic.common.i18n import _ from ironic.common import raid as raid_common from ironic.conductor import task_manager from ironic.conductor import utils as manager_utils from ironic.conf import CONF from ironic.drivers import base from ironic.drivers.modules import deploy_utils from ironic.drivers.modules.drac import common as drac_common from ironic.drivers.modules.drac import job as drac_job drac_exceptions = importutils.try_import('dracclient.exceptions') drac_constants = importutils.try_import('dracclient.constants') LOG = logging.getLogger(__name__) METRICS = metrics_utils.get_metrics_logger(__name__) _CURRENT_RAID_CONTROLLER_MODE = "RAIDCurrentControllerMode" _REQUESTED_RAID_CONTROLLER_MODE = "RAIDRequestedControllerMode" _EHBA_MODE = "Enhanced HBA" _RAID_MODE = "RAID" RAID_LEVELS = { '0': { 'min_disks': 1, 'max_disks': 1000, 'type': 'simple', 'overhead': 0 }, '1': { 'min_disks': 2, 'max_disks': 2, 'type': 'simple', 'overhead': 1 }, '5': { 'min_disks': 3, 'max_disks': 1000, 'type': 'simple', 'overhead': 1 }, '6': { 'min_disks': 4, 'max_disks': 1000, 'type': 'simple', 'overhead': 2 }, '1+0': { 'type': 'spanned', 'span_type': '1' }, '5+0': { 'type': 'spanned', 'span_type': '5' }, '6+0': { 'type': 'spanned', 'span_type': '6' } } def list_raid_controllers(node): client = drac_common.get_drac_client(node) try: return client.list_raid_controllers() except drac_exceptions.BaseClientException as exc: LOG.error('DRAC driver failed to get the list of RAID controllers ' 'for node %(node_uuid)s. Reason: %(error)s.', {'node_uuid': node.uuid, 'error': exc}) raise exception.DracOperationError(error=exc) def list_virtual_disks(node): client = drac_common.get_drac_client(node) try: return client.list_virtual_disks() except drac_exceptions.BaseClientException as exc: LOG.error('DRAC driver failed to get the list of virtual disks ' 'for node %(node_uuid)s. Reason: %(error)s.', {'node_uuid': node.uuid, 'error': exc}) raise exception.DracOperationError(error=exc) def list_physical_disks(node): client = drac_common.get_drac_client(node) try: return client.list_physical_disks() except drac_exceptions.BaseClientException as exc: LOG.error('DRAC driver failed to get the list of physical disks ' 'for node %(node_uuid)s. Reason: %(error)s.', {'node_uuid': node.uuid, 'error': exc}) raise exception.DracOperationError(error=exc) def _is_raid_controller(node, raid_controller_fqdd, raid_controllers=None): client = drac_common.get_drac_client(node) try: return client.is_raid_controller(raid_controller_fqdd, raid_controllers) except drac_exceptions.BaseClientException as exc: LOG.error('Unable to determine if controller %(raid_controller_fqdd)s ' 'on node %(node_uuid)s is a RAID controller. ' 'Reason: %(error)s. ', {'raid_controller_fqdd': raid_controller_fqdd, 'node_uuid': node.uuid, 'error': exc}) raise exception.DracOperationError(error=exc) def _validate_job_queue(node, raid_controller=None): kwargs = {} if raid_controller: kwargs["name_prefix"] = "Config:RAID:%s" % raid_controller drac_job.validate_job_queue(node, **kwargs) def create_virtual_disk(node, raid_controller, physical_disks, raid_level, size_mb, disk_name=None, span_length=None, span_depth=None): _validate_job_queue(node, raid_controller) client = drac_common.get_drac_client(node) try: return client.create_virtual_disk(raid_controller, physical_disks, raid_level, size_mb, disk_name, span_length, span_depth) except drac_exceptions.BaseClientException as exc: LOG.error('DRAC driver failed to create virtual disk for node ' '%(node_uuid)s. Reason: %(error)s.', {'node_uuid': node.uuid, 'error': exc}) raise exception.DracOperationError(error=exc) def delete_virtual_disk(node, virtual_disk): _validate_job_queue(node) client = drac_common.get_drac_client(node) try: return client.delete_virtual_disk(virtual_disk) except drac_exceptions.BaseClientException as exc: LOG.error('DRAC driver failed to delete virtual disk ' '%(virtual_disk_fqdd)s for node %(node_uuid)s. ' 'Reason: %(error)s.', {'virtual_disk_fqdd': virtual_disk, 'node_uuid': node.uuid, 'error': exc}) raise exception.DracOperationError(error=exc) def _reset_raid_config(node, raid_controller): try: _validate_job_queue(node, raid_controller) client = drac_common.get_drac_client(node) return client.reset_raid_config(raid_controller) except drac_exceptions.BaseClientException as exc: LOG.error('DRAC driver failed to delete all virtual disk ' 'and unassign all hotspares ' 'on %(raid_controller_fqdd)s ' 'for node %(node_uuid)s. ' 'Reason: %(error)s.', {'raid_controller_fqdd': raid_controller, 'node_uuid': node.uuid, 'error': exc}) raise exception.DracOperationError(error=exc) def clear_foreign_config(node, raid_controller): try: _validate_job_queue(node, raid_controller) client = drac_common.get_drac_client(node) return client.clear_foreign_config(raid_controller) except drac_exceptions.BaseClientException as exc: LOG.error('DRAC driver failed to free foreign driver ' 'on %(raid_controller_fqdd)s ' 'for node %(node_uuid)s. ' 'Reason: %(error)s.', {'raid_controller_fqdd': raid_controller, 'node_uuid': node.uuid, 'error': exc}) raise exception.DracOperationError(error=exc) def set_raid_settings(node, controller_fqdd, settings): try: drac_job.validate_job_queue(node) client = drac_common.get_drac_client(node) return client.set_raid_settings(controller_fqdd, settings) except drac_exceptions.BaseClientException as exc: LOG.error('DRAC driver failed to set raid settings ' 'on %(raid_controller_fqdd)s ' 'for node %(node_uuid)s. ' 'Reason: %(error)s.', {'raid_controller_fqdd': controller_fqdd, 'node_uuid': node.uuid, 'error': exc}) raise exception.DracOperationError(error=exc) def list_raid_settings(node): try: drac_job.validate_job_queue(node) client = drac_common.get_drac_client(node) return client.list_raid_settings() except drac_exceptions.BaseClientException as exc: LOG.error('DRAC driver failed to list raid settings ' 'for node %(node_uuid)s. ' 'Reason: %(error)s.', {'node_uuid': node.uuid, 'error': exc}) raise exception.DracOperationError(error=exc) def change_physical_disk_state(node, mode=None, controllers_to_physical_disk_ids=None): try: drac_job.validate_job_queue(node) client = drac_common.get_drac_client(node) return client.change_physical_disk_state( mode, controllers_to_physical_disk_ids) except drac_exceptions.BaseClientException as exc: LOG.error('DRAC driver failed to change physical drives ' 'to %(mode)s mode for node %(node_uuid)s. ' 'Reason: %(error)s.', {'mode': mode, 'node_uuid': node.uuid, 'error': exc}) raise exception.DracOperationError(error=exc) def commit_config(node, raid_controller, reboot=False, realtime=False): client = drac_common.get_drac_client(node) try: return client.commit_pending_raid_changes( raid_controller=raid_controller, reboot=reboot, realtime=realtime) except drac_exceptions.BaseClientException as exc: LOG.error('DRAC driver failed to commit pending RAID config for' ' controller %(raid_controller_fqdd)s on node ' '%(node_uuid)s. Reason: %(error)s.', {'raid_controller_fqdd': raid_controller, 'node_uuid': node.uuid, 'error': exc}) raise exception.DracOperationError(error=exc) def _change_physical_disk_mode(node, mode=None, controllers_to_physical_disk_ids=None, substep="completed"): change_disk_state = change_physical_disk_state( node, mode, controllers_to_physical_disk_ids) controllers = list() conversion_results = change_disk_state['conversion_results'] for controller_id, result in conversion_results.items(): controller = {'raid_controller': controller_id, 'is_reboot_required': result['is_reboot_required'], 'is_commit_required': result['is_commit_required']} controllers.append(controller) return _commit_to_controllers( node, controllers, substep=substep) def abandon_config(node, raid_controller): client = drac_common.get_drac_client(node) try: client.abandon_pending_raid_changes(raid_controller) except drac_exceptions.BaseClientException as exc: LOG.error('DRAC driver failed to delete pending RAID config ' 'for controller %(raid_controller_fqdd)s on node ' '%(node_uuid)s. Reason: %(error)s.', {'raid_controller_fqdd': raid_controller, 'node_uuid': node.uuid, 'error': exc}) raise exception.DracOperationError(error=exc) def _calculate_spans(raid_level, disks_count): if raid_level in ['0', '1', '5', '6']: return 1 elif raid_level in ['5+0', '6+0']: return 2 elif raid_level in ['1+0']: return disks_count >> 1 else: reason = (_('Cannot calculate spans for RAID level "%s"') % raid_level) raise exception.DracOperationError(error=reason) def _usable_disks_count(raid_level, disks_count): if raid_level in ['0', '1', '5', '6']: return disks_count elif raid_level in ['5+0', '6+0', '1+0']: # largest even number less than disk_count return (disks_count >> 1) << 1 else: reason = (_('RAID level %(raid_level)s is not supported by the ' 'driver. Supported RAID levels: %(supported_raid_levels)s') % {'raid_level': raid_level, 'supported_raid_levels': list(RAID_LEVELS)}) raise exception.DracOperationError(error=reason) def _raid_level_min_disks(raid_level, spans_count=1): try: raid_level_info = RAID_LEVELS[raid_level] except KeyError: reason = (_('RAID level %(raid_level)s is not supported by the ' 'driver. Supported RAID levels: %(supported_raid_levels)s') % {'raid_level': raid_level, 'supported_raid_levels': list(RAID_LEVELS)}) raise exception.DracOperationError(error=reason) if raid_level_info['type'] == 'spanned': if spans_count <= 1: reason = _('Spanned RAID volumes cannot contain a single span') raise exception.DracOperationError(error=reason) span_type = raid_level_info['span_type'] raid_level_info = RAID_LEVELS[span_type] return raid_level_info['min_disks'] * spans_count def _raid_level_max_disks(raid_level, spans_count=1): try: raid_level_info = RAID_LEVELS[raid_level] except KeyError: reason = (_('RAID level %(raid_level)s is not supported by the ' 'driver. Supported RAID levels: %(supported_raid_levels)s') % {'raid_level': raid_level, 'supported_raid_levels': list(RAID_LEVELS)}) raise exception.DracOperationError(error=reason) if raid_level_info['type'] == 'spanned': if spans_count <= 1: reason = _('Spanned RAID volumes cannot contain a single span') raise exception.DracOperationError(error=reason) span_type = raid_level_info['span_type'] raid_level_info = RAID_LEVELS[span_type] return raid_level_info['max_disks'] * spans_count def _raid_level_overhead(raid_level, spans_count=1): try: raid_level_info = RAID_LEVELS[raid_level] except KeyError: reason = (_('RAID level %(raid_level)s is not supported by the ' 'driver. Supported RAID levels: %(supported_raid_levels)s') % {'raid_level': raid_level, 'supported_raid_levels': list(RAID_LEVELS)}) raise exception.DracOperationError(error=reason) if raid_level_info['type'] == 'spanned': if spans_count <= 1: reason = _('Spanned RAID volumes cannot contain a single span') raise exception.DracOperationError(error=reason) span_type = raid_level_info['span_type'] raid_level_info = RAID_LEVELS[span_type] return raid_level_info['overhead'] * spans_count def _max_volume_size_mb(raid_level, physical_disks, free_space_mb, spans_count=1, stripe_size_kb=64 * units.Ki): # restrict the size to the smallest available space free_spaces = [free_space_mb[disk] for disk in physical_disks] size_kb = min(free_spaces) * units.Ki # NOTE(ifarkas): using math.floor so we get a volume size that does not # exceed the available space stripes_per_disk = int(math.floor(float(size_kb) / stripe_size_kb)) disks_count = len(physical_disks) overhead_disks_count = _raid_level_overhead(raid_level, spans_count) return int(stripes_per_disk * stripe_size_kb * (disks_count - overhead_disks_count) / units.Ki) def _volume_usage_per_disk_mb(logical_disk, physical_disks, spans_count=1, stripe_size_kb=64 * units.Ki): disks_count = len(physical_disks) overhead_disks_count = _raid_level_overhead(logical_disk['raid_level'], spans_count) volume_size_kb = logical_disk['size_mb'] * units.Ki # NOTE(ifarkas): using math.ceil so we get the largest disk usage # possible, so we can avoid over-committing stripes_per_volume = math.ceil(float(volume_size_kb) / stripe_size_kb) stripes_per_disk = math.ceil( float(stripes_per_volume) / (disks_count - overhead_disks_count)) return int(stripes_per_disk * stripe_size_kb / units.Ki) def _find_configuration(logical_disks, physical_disks, pending_delete): # shared physical disks of RAID volumes size_gb='MAX' should be # deprioritized during the matching process to reserve as much space as # possible. Reserved means it won't be used during matching. volumes_with_reserved_physical_disks = [ volume for volume in logical_disks if ('physical_disks' in volume and volume['size_mb'] == 'MAX' and volume.get('share_physical_disks', False))] reserved_physical_disks = [ disk for disk in physical_disks for volume in volumes_with_reserved_physical_disks if disk.id in volume['physical_disks']] physical_disks_by_type = {} reserved_physical_disks_by_type = {} free_space_mb = {} for disk in physical_disks: free_space_mb[disk] = _get_disk_free_size_mb(disk, pending_delete) disk_type = (disk.controller, disk.media_type, disk.interface_type, disk.size_mb) if disk_type not in physical_disks_by_type: physical_disks_by_type[disk_type] = [] reserved_physical_disks_by_type[disk_type] = [] if disk in reserved_physical_disks: reserved_physical_disks_by_type[disk_type].append(disk) else: physical_disks_by_type[disk_type].append(disk) for volume in logical_disks: if ('physical_disks' in volume and not volume.get('share_physical_disks', False)): for disk in physical_disks: if disk.id in volume['physical_disks']: disk_type = (disk.controller, disk.media_type, disk.interface_type, disk.size_mb) if disk in physical_disks_by_type[disk_type]: physical_disks_by_type[disk_type].remove(disk) processed_volumes = [] for volume in [volume for volume in logical_disks if ('physical_disks' in volume and volume['size_mb'] != 'MAX')]: _calculate_volume_props(volume, physical_disks, free_space_mb) processed_volumes.append(volume) volumes_without_disks = [disk for disk in logical_disks if 'physical_disks' not in disk] if volumes_without_disks: result, free_space_mb = ( _assign_disks_to_volume(volumes_without_disks, physical_disks_by_type, free_space_mb, pending_delete)) if not result: for disk_type, disks in physical_disks_by_type.items(): physical_disks_by_type[disk_type] += ( reserved_physical_disks_by_type[disk_type]) result, free_space_mb = ( _assign_disks_to_volume(volumes_without_disks, physical_disks_by_type, free_space_mb, pending_delete)) if not result: error_msg = _('failed to find matching physical disks for all ' 'logical disks') LOG.error('DRAC driver failed to create RAID ' 'configuration. Reason: %(error)s.', {'error': error_msg}) raise exception.DracOperationError(error=error_msg) processed_volumes += volumes_without_disks for volume in [volume for volume in logical_disks if ('physical_disks' in volume and volume['size_mb'] == 'MAX')]: _calculate_volume_props(volume, physical_disks, free_space_mb) processed_volumes.append(volume) return processed_volumes def _calculate_volume_props(logical_disk, physical_disks, free_space_mb): selected_disks = [disk for disk in physical_disks if disk.id in logical_disk['physical_disks']] spans_count = _calculate_spans( logical_disk['raid_level'], len(selected_disks)) if len(selected_disks) % spans_count != 0: error_msg = _('invalid number of physical disks was provided') raise exception.DracOperationError(error=error_msg) disks_per_span = int(len(selected_disks) / spans_count) logical_disk['span_depth'] = None logical_disk['span_length'] = None if logical_disk['raid_level'] != '1+0': logical_disk['span_depth'] = spans_count logical_disk['span_length'] = disks_per_span max_volume_size_mb = _max_volume_size_mb( logical_disk['raid_level'], selected_disks, free_space_mb, spans_count=spans_count) if logical_disk['size_mb'] == 'MAX': if max_volume_size_mb == 0: error_msg = _("size set to 'MAX' but could not allocate physical " "disk space") raise exception.DracOperationError(error=error_msg) logical_disk['size_mb'] = max_volume_size_mb elif max_volume_size_mb < logical_disk['size_mb']: if max_volume_size_mb == 0: error_msg = _('not enough physical disk space for the logical ' 'disk') raise exception.DracOperationError(error=error_msg) disk_usage = _volume_usage_per_disk_mb(logical_disk, selected_disks, spans_count=spans_count) for disk in selected_disks: if free_space_mb[disk] < disk_usage: error_msg = _('not enough free space on physical disks for the ' 'logical disk') raise exception.DracOperationError(error=error_msg) else: free_space_mb[disk] -= disk_usage if 'controller' not in logical_disk: logical_disk['controller'] = selected_disks[0].controller def _assign_disks_to_volume(logical_disks, physical_disks_by_type, free_space_mb, pending_delete): logical_disk = logical_disks.pop(0) raid_level = logical_disk['raid_level'] for (controller, disk_type, interface_type, size_mb), disks in physical_disks_by_type.items(): if ('disk_type' in logical_disk and logical_disk['disk_type'] != disk_type): continue if ('interface_type' in logical_disk and logical_disk['interface_type'] != interface_type): continue disks = [disk for disk in disks if free_space_mb[disk] > 0] disks = sorted( disks, key=lambda disk: free_space_mb[disk]) if ('share_physical_disks' not in logical_disk or not logical_disk['share_physical_disks']): initial_free_size_mb = { disk: _get_disk_free_size_mb(disk, pending_delete) for disk in disks } disks = [disk for disk in disks if initial_free_size_mb[disk] == free_space_mb[disk]] max_spans = _calculate_spans(raid_level, len(disks)) min_spans = min([2, max_spans]) min_disks = _raid_level_min_disks(raid_level, spans_count=min_spans) max_disks = _raid_level_max_disks(raid_level, spans_count=max_spans) candidate_max_disks = min([max_disks, len(disks)]) for disks_count in range(min_disks, candidate_max_disks + 1): if ('number_of_physical_disks' in logical_disk and (logical_disk['number_of_physical_disks'] != disks_count)): continue if disks_count != _usable_disks_count(logical_disk['raid_level'], disks_count): continue selected_disks = disks[0:disks_count] candidate_volume = logical_disk.copy() candidate_free_space_mb = free_space_mb.copy() candidate_volume['physical_disks'] = [disk.id for disk in selected_disks] try: _calculate_volume_props(candidate_volume, selected_disks, candidate_free_space_mb) except exception.DracOperationError: continue if len(logical_disks) > 0: result, candidate_free_space_mb = ( _assign_disks_to_volume(logical_disks, physical_disks_by_type, candidate_free_space_mb, pending_delete)) if result: logical_disks.append(candidate_volume) return (True, candidate_free_space_mb) else: logical_disks.append(candidate_volume) return (True, candidate_free_space_mb) else: logical_disks.insert(0, logical_disk) return (False, free_space_mb) def _filter_logical_disks(logical_disks, include_root_volume, include_nonroot_volumes): filtered_disks = [] for disk in logical_disks: if include_root_volume and disk.get('is_root_volume'): filtered_disks.append(disk) if include_nonroot_volumes and not disk.get('is_root_volume'): filtered_disks.append(disk) return filtered_disks def _create_config_job(node, controller, reboot=False, realtime=False, raid_config_job_ids=[], raid_config_parameters=[]): job_id = commit_config(node, raid_controller=controller, reboot=reboot, realtime=realtime) raid_config_job_ids.append(job_id) if controller not in raid_config_parameters: raid_config_parameters.append(controller) LOG.info('Change has been committed to RAID controller ' '%(controller)s on node %(node)s. ' 'DRAC job id: %(job_id)s', {'controller': controller, 'node': node.uuid, 'job_id': job_id}) return {'raid_config_job_ids': raid_config_job_ids, 'raid_config_parameters': raid_config_parameters} def _validate_volume_size(node, logical_disks): new_physical_disks = list_physical_disks(node) free_space_mb = {} new_processed_volumes = [] for disk in new_physical_disks: free_space_mb[disk] = disk.free_size_mb for logical_disk in logical_disks: selected_disks = [disk for disk in new_physical_disks if disk.id in logical_disk['physical_disks']] spans_count = _calculate_spans( logical_disk['raid_level'], len(selected_disks)) new_max_vol_size_mb = _max_volume_size_mb( logical_disk['raid_level'], selected_disks, free_space_mb, spans_count=spans_count) if logical_disk['size_mb'] > new_max_vol_size_mb: logical_disk['size_mb'] = new_max_vol_size_mb LOG.info("Logical size does not match so calculating volume " "properties for current logical_disk") _calculate_volume_props( logical_disk, new_physical_disks, free_space_mb) new_processed_volumes.append(logical_disk) if new_processed_volumes: return new_processed_volumes return logical_disks def _switch_to_raid_mode(node, controller_fqdd): drac_job.wait_for_job_completion(node) raid_attr = "{}:{}".format(controller_fqdd, _REQUESTED_RAID_CONTROLLER_MODE) settings = {raid_attr: _RAID_MODE} settings_results = set_raid_settings( node, controller_fqdd, settings) controller = { 'raid_controller': controller_fqdd, 'is_reboot_required': settings_results['is_reboot_required'], 'is_commit_required': settings_results['is_commit_required']} return controller def _commit_to_controllers(node, controllers, substep="completed"): controllers = [controller for controller in controllers if controller['is_commit_required']] if not controllers: LOG.debug('No changes on any of the controllers on node %s', node.uuid) driver_internal_info = node.driver_internal_info driver_internal_info['raid_config_substep'] = substep driver_internal_info['raid_config_parameters'] = [] node.driver_internal_info = driver_internal_info node.save() return driver_internal_info = node.driver_internal_info driver_internal_info['raid_config_substep'] = substep driver_internal_info['raid_config_parameters'] = [] if 'raid_config_job_ids' not in driver_internal_info: driver_internal_info['raid_config_job_ids'] = [] optional = drac_constants.RebootRequired.optional all_realtime = all( (cntlr['is_reboot_required'] == optional) and not(cntlr.get('is_ehba_mode')) for cntlr in controllers) any_ehba_controllers = any( cntrl.get('is_ehba_mode') is True for cntrl in controllers) raid_config_job_ids = [] raid_config_parameters = [] if all_realtime: for controller in controllers: realtime_controller = controller['raid_controller'] job_details = _create_config_job( node, controller=realtime_controller, reboot=False, realtime=True, raid_config_job_ids=raid_config_job_ids, raid_config_parameters=raid_config_parameters) elif any_ehba_controllers: commit_to_ehba_controllers = [] for controller in controllers: if controller.get('is_ehba_mode'): job_details = _create_config_job( node, controller=controller['raid_controller'], reboot=False, realtime=True, raid_config_job_ids=raid_config_job_ids, raid_config_parameters=raid_config_parameters) ehba_controller = _switch_to_raid_mode( node, controller['raid_controller']) commit_to_ehba_controllers.append( ehba_controller['raid_controller']) else: job_details = _create_config_job( node, controller=controller['raid_controller'], reboot=False, realtime=False, raid_config_job_ids=raid_config_job_ids, raid_config_parameters=raid_config_parameters) for controller in commit_to_ehba_controllers: LOG.debug("Create job with Reboot to apply configuration " "changes for ehba controllers") job_details = _create_config_job( node, controller=controller, reboot=(controller == commit_to_ehba_controllers[-1]), realtime=False, raid_config_job_ids=raid_config_job_ids, raid_config_parameters=raid_config_parameters) else: for controller in controllers: mix_controller = controller['raid_controller'] reboot = (controller == controllers[-1]) job_details = _create_config_job( node, controller=mix_controller, reboot=reboot, realtime=False, raid_config_job_ids=raid_config_job_ids, raid_config_parameters=raid_config_parameters) driver_internal_info['raid_config_job_ids'].extend(job_details[ 'raid_config_job_ids']) driver_internal_info['raid_config_parameters'].extend(job_details[ 'raid_config_parameters']) node.driver_internal_info = driver_internal_info deploy_utils.set_async_step_flags( node, reboot=not all_realtime, skip_current_step=True, polling=True) return deploy_utils.get_async_step_return_state(node) def _create_virtual_disks(task, node): logical_disks_to_create = node.driver_internal_info[ 'logical_disks_to_create'] isVolValidationNeeded = node.driver_internal_info[ 'volume_validation'] if isVolValidationNeeded: logical_disks_to_create = _validate_volume_size( node, logical_disks_to_create) controllers = list() for logical_disk in logical_disks_to_create: controller = dict() controller_cap = create_virtual_disk( node, raid_controller=logical_disk['controller'], physical_disks=logical_disk['physical_disks'], raid_level=logical_disk['raid_level'], size_mb=logical_disk['size_mb'], disk_name=logical_disk.get('name'), span_length=logical_disk.get('span_length'), span_depth=logical_disk.get('span_depth')) controller['raid_controller'] = logical_disk['controller'] controller['is_reboot_required'] = controller_cap[ 'is_reboot_required'] controller['is_commit_required'] = controller_cap[ 'is_commit_required'] if controller not in controllers: controllers.append(controller) return _commit_to_controllers(node, controllers) def _controller_in_hba_mode(raid_settings, controller_fqdd): controller_mode = raid_settings.get( '{}:{}'.format(controller_fqdd, _CURRENT_RAID_CONTROLLER_MODE)) return _EHBA_MODE in controller_mode.current_value def _controller_supports_ehba_mode(settings, controller_fqdd): raid_cntrl_attr = "{}:{}".format(controller_fqdd, _CURRENT_RAID_CONTROLLER_MODE) current_cntrl_mode = settings.get(raid_cntrl_attr) if not current_cntrl_mode: return False else: return _EHBA_MODE in current_cntrl_mode.possible_values def _get_disk_free_size_mb(disk, pending_delete): return disk.size_mb if pending_delete else disk.free_size_mb class DracWSManRAID(base.RAIDInterface): def get_properties(self): return drac_common.COMMON_PROPERTIES @base.deploy_step(priority=0, argsinfo=base.RAID_APPLY_CONFIGURATION_ARGSINFO) def apply_configuration(self, task, raid_config, create_root_volume=True, create_nonroot_volumes=False, delete_existing=True): return super(DracWSManRAID, self).apply_configuration( task, raid_config, create_root_volume=create_root_volume, create_nonroot_volumes=create_nonroot_volumes, delete_existing=delete_existing) @METRICS.timer('DracRAID.create_configuration') @base.clean_step(priority=0, abortable=False, argsinfo={ 'create_root_volume': { 'description': ( 'This specifies whether to create the root volume. ' 'Defaults to `True`.' ), 'required': False }, 'create_nonroot_volumes': { 'description': ( 'This specifies whether to create the non-root volumes. ' 'Defaults to `True`.' ), 'required': False }, "delete_existing": { "description": ( "Setting this to 'True' indicates to delete existing RAID " "configuration prior to creating the new configuration. " "Default value is 'False'." ), "required": False, } }) def create_configuration(self, task, create_root_volume=True, create_nonroot_volumes=True, delete_existing=False): node = task.node logical_disks = node.target_raid_config['logical_disks'] for disk in logical_disks: if disk['size_gb'] == 'MAX' and 'physical_disks' not in disk: raise exception.InvalidParameterValue( _("create_configuration called with invalid " "target_raid_configuration for node %(node_id)s. " "'physical_disks' is missing from logical_disk while " "'size_gb'='MAX' was requested: " "%(logical_disk)s") % {'node_id': node.uuid, 'logical_disk': disk}) if disk['size_gb'] == 'MAX': disk['size_mb'] = 'MAX' else: disk['size_mb'] = disk['size_gb'] * units.Ki del disk['size_gb'] if delete_existing: self._delete_configuration_no_commit(task) physical_disks = list_physical_disks(node) logical_disks = _find_configuration(logical_disks, physical_disks, pending_delete=delete_existing) logical_disks_to_create = _filter_logical_disks( logical_disks, create_root_volume, create_nonroot_volumes) controllers_to_physical_disk_ids = defaultdict(list) for logical_disk in logical_disks_to_create: if logical_disk['raid_level'] == 'JBOD': continue for physical_disk_name in logical_disk['physical_disks']: controllers_to_physical_disk_ids[ logical_disk['controller']].append( physical_disk_name) driver_internal_info = node.driver_internal_info driver_internal_info[ "logical_disks_to_create"] = logical_disks_to_create commit_results = None if logical_disks_to_create: LOG.debug( "Converting physical disks configured to back RAID " "logical disks to RAID mode for node %(node_uuid)s ", {"node_uuid": node.uuid}) raid_mode = drac_constants.RaidStatus.raid commit_results = _change_physical_disk_mode( node, raid_mode, controllers_to_physical_disk_ids, substep="create_virtual_disks") volume_validation = True if commit_results else False driver_internal_info['volume_validation'] = volume_validation node.driver_internal_info = driver_internal_info node.save() if commit_results: return commit_results else: LOG.debug("Controller does not support drives conversion " "so creating virtual disks") return _create_virtual_disks(task, node) @METRICS.timer('DracRAID.delete_configuration') @base.clean_step(priority=0) @base.deploy_step(priority=0) def delete_configuration(self, task): controllers = self._delete_configuration_no_commit(task) return _commit_to_controllers(task.node, controllers, substep="delete_foreign_config") @METRICS.timer('DracRAID.get_logical_disks') def get_logical_disks(self, task): node = task.node logical_disks = [] for disk in list_virtual_disks(node): logical_disk = { 'id': disk.id, 'controller': disk.controller, 'size_gb': int(disk.size_mb / units.Ki), 'raid_level': disk.raid_level } if disk.name is not None: logical_disk['name'] = disk.name logical_disks.append(logical_disk) return {'logical_disks': logical_disks} @METRICS.timer('DracRAID._query_raid_config_job_status') @periodics.periodic( spacing=CONF.drac.query_raid_config_job_status_interval) def _query_raid_config_job_status(self, manager, context): filters = {'reserved': False, 'maintenance': False} fields = ['driver_internal_info'] node_list = manager.iter_nodes(fields=fields, filters=filters) for (node_uuid, driver, conductor_group, driver_internal_info) in node_list: try: lock_purpose = 'checking async raid configuration jobs' with task_manager.acquire(context, node_uuid, purpose=lock_purpose, shared=True) as task: if not isinstance(task.driver.raid, DracWSManRAID): continue job_ids = driver_internal_info.get('raid_config_job_ids') if not job_ids: continue self._check_node_raid_jobs(task) except exception.NodeNotFound: LOG.info("During query_raid_config_job_status, node " "%(node)s was not found and presumed deleted by " "another process.", {'node': node_uuid}) except exception.NodeLocked: LOG.info("During query_raid_config_job_status, node " "%(node)s was already locked by another process. " "Skip.", {'node': node_uuid}) @METRICS.timer('DracRAID._check_node_raid_jobs') def _check_node_raid_jobs(self, task): node = task.node raid_config_job_ids = node.driver_internal_info['raid_config_job_ids'] finished_job_ids = [] for config_job_id in raid_config_job_ids: config_job = drac_job.get_job(node, job_id=config_job_id) if config_job is None or config_job.status == 'Completed': finished_job_ids.append(config_job_id) elif config_job.status == 'Failed': finished_job_ids.append(config_job_id) self._set_raid_config_job_failure(node) if not finished_job_ids: return task.upgrade_lock() self._delete_cached_config_job_id(node, finished_job_ids) if not node.driver_internal_info.get('raid_config_job_failure', False): if 'raid_config_substep' in node.driver_internal_info: substep = node.driver_internal_info['raid_config_substep'] if substep == 'delete_foreign_config': foreign_drives = self._execute_foreign_drives(task, node) if foreign_drives is None: return self._convert_drives(task, node) elif substep == 'physical_disk_conversion': self._convert_drives(task, node) elif substep == "create_virtual_disks": return _create_virtual_disks(task, node) elif substep == 'completed': self._complete_raid_substep(task, node) else: self._complete_raid_substep(task, node) else: self._clear_raid_substep(node) self._clear_raid_config_job_failure(node) self._set_failed(task, config_job) def _execute_foreign_drives(self, task, node): controllers = list() jobs_required = False for controller_id in node.driver_internal_info[ 'raid_config_parameters']: controller_cap = clear_foreign_config( node, controller_id) controller = { 'raid_controller': controller_id, 'is_reboot_required': controller_cap['is_reboot_required'], 'is_commit_required': controller_cap['is_commit_required']} controllers.append(controller) jobs_required = jobs_required or controller_cap[ 'is_commit_required'] if not jobs_required: LOG.info( "No foreign drives detected, so " "resume %s", "cleaning" if node.clean_step else "deployment") return None else: return _commit_to_controllers( node, controllers, substep='physical_disk_conversion') def _complete_raid_substep(self, task, node): self._clear_raid_substep(node) self._resume(task) def _convert_drives(self, task, node): jbod = drac_constants.RaidStatus.jbod drives_results = _change_physical_disk_mode( node, mode=jbod) if drives_results is None: LOG.debug("Controller does not support drives " "conversion on %(node_uuid)s", {'node_uuid': node.uuid}) self._complete_raid_substep(task, node) def _clear_raid_substep(self, node): driver_internal_info = node.driver_internal_info driver_internal_info.pop('raid_config_substep', None) driver_internal_info.pop('raid_config_parameters', None) node.driver_internal_info = driver_internal_info node.save() def _set_raid_config_job_failure(self, node): driver_internal_info = node.driver_internal_info driver_internal_info['raid_config_job_failure'] = True node.driver_internal_info = driver_internal_info node.save() def _clear_raid_config_job_failure(self, node): driver_internal_info = node.driver_internal_info del driver_internal_info['raid_config_job_failure'] node.driver_internal_info = driver_internal_info node.save() def _delete_cached_config_job_id(self, node, finished_config_job_ids=None): if finished_config_job_ids is None: finished_config_job_ids = [] driver_internal_info = node.driver_internal_info unfinished_job_ids = [job_id for job_id in driver_internal_info['raid_config_job_ids'] if job_id not in finished_config_job_ids] driver_internal_info['raid_config_job_ids'] = unfinished_job_ids node.driver_internal_info = driver_internal_info node.save() def _set_failed(self, task, config_job): error_msg = (_("Failed config job: %(config_job_id)s. " "Message: '%(message)s'.") % {'config_job_id': config_job.id, 'message': config_job.message}) log_msg = ("RAID configuration job failed for node %(node)s. " "%(error)s" % {'node': task.node.uuid, 'error': error_msg}) if task.node.clean_step: manager_utils.cleaning_error_handler(task, error_msg) else: manager_utils.deploying_error_handler(task, log_msg, error_msg) def _resume(self, task): raid_common.update_raid_info( task.node, self.get_logical_disks(task)) if task.node.clean_step: manager_utils.notify_conductor_resume_clean(task) else: manager_utils.notify_conductor_resume_deploy(task) def _delete_configuration_no_commit(self, task): node = task.node controllers = list() drac_raid_controllers = list_raid_controllers(node) drac_raid_settings = list_raid_settings(node) for cntrl in drac_raid_controllers: if _is_raid_controller(node, cntrl.id, drac_raid_controllers): controller = dict() if _controller_supports_ehba_mode( drac_raid_settings, cntrl.id) and _controller_in_hba_mode( drac_raid_settings, cntrl.id): controller['is_ehba_mode'] = True controller_cap = _reset_raid_config(node, cntrl.id) controller["raid_controller"] = cntrl.id controller["is_reboot_required"] = controller_cap[ "is_reboot_required"] controller["is_commit_required"] = controller_cap[ "is_commit_required"] controllers.append(controller) return controllers class DracRAID(DracWSManRAID): def __init__(self): super(DracRAID, self).__init__() LOG.warning("RAID interface 'idrac' is deprecated and may be removed " "in a future release. Use 'idrac-wsman' instead.")

true

f7319a3273ea4a13ac90d44d92e546e36cb45026

20,249

py

Python

Decompiler/BattleActionScript.py

AGraber/EDDecompiler

6e00c7c6ba8a12cea7722fcef34ed8ff4ac6bc66

[ "MIT" ]

5

2021-05-23T19:34:38.000Z

2021-12-05T05:57:36.000Z

Decompiler/BattleActionScript.py

AGraber/EDDecompiler

6e00c7c6ba8a12cea7722fcef34ed8ff4ac6bc66

[ "MIT" ]

null

Decompiler/BattleActionScript.py

AGraber/EDDecompiler

6e00c7c6ba8a12cea7722fcef34ed8ff4ac6bc66

[ "MIT" ]

1

2021-05-06T05:50:14.000Z

from Assembler.Assembler2s import * from Base.EDAOBase import * import Instruction.ActionOpTableEDAO as edao import BattleMonsterStatus as MSFile INVALID_ACTION_OFFSET = 0xFFFF EMPTY_ACTION = INVALID_ACTION_OFFSET class CharacterPositionFactor: def __init__(self, fs = None): if fs == None: return self.X = fs.ReadByte() self.Y = fs.ReadByte() class BattleActionScriptInfo: ActionFileType_Normal = 0 ActionFileType_Arts = 1 ActionFileType_Item = 2 def __init__(self): self.ChrPosFactorOffset = 0 self.ActionListOffset = 0 self.UnknownTableOffset = 0 self.ActionStartOffset = 0 self.PreloadChipList = [] self.ModelFileList = [] self.UnknownTableList = [] self.ActionList = [] self.ChrPosFactor = [] self.CraftActions = [] self.GlobalLabelTable = {} self.ChrName = None self.ASFileName = '' self.ActionFileType = self.ActionFileType_Normal def open(self, asname): fs = fileio.FileStream() fs.Open(asname) self.ASFileName = asname asname = os.path.basename(asname).lower() if asname == 'as90000.dat': self.ActionFileType = self.ActionFileType_Arts elif asname == 'as90001.dat': self.ActionFileType = self.ActionFileType_Item else: self.ActionFileType = self.ActionFileType_Normal self.ActionListOffset = fs.ReadUShort() if self.ActionFileType == self.ActionFileType_Normal: self.ChrPosFactorOffset = fs.ReadUShort() self.UnknownTableOffset = fs.ReadUShort() while True: index = fs.ReadULong() if index == 0xFFFFFFFF: break self.PreloadChipList.append(ChipFileIndex(index)) fs.seek(self.ChrPosFactorOffset) for i in range(8): self.ChrPosFactor.append(CharacterPositionFactor(fs)) minoffset = 0xFFFFFFFF fs.seek(self.ActionListOffset) while True: if fs.tell() >= minoffset: break offset = fs.ReadUShort() if offset == 0: break minoffset = min(minoffset, offset) self.ActionList.append(offset) if len(self.ActionList) == 0: raise Exception('action number == 0') self.CraftActions = self.DisassembleCraftActions(fs) return for i in range(0x69, fs.Length): if i not in offsetlist: print('%X' % i) #input() input() def GetBuiltinNames(self): if self.ActionFileType == self.ActionFileType_Arts: BuiltinArtsNames = [] try: offsetlist = [] t_magic = os.path.abspath(os.path.dirname(os.path.abspath(self.ASFileName)) + '\\..\\..\\text\\t_magic._dt') if not os.path.exists(t_magic): if t_magic.endswith('\\patch\\text\\t_magic._dt'): t_magic = t_magic.replace('\\patch\\text\\t_magic._dt', '\\data\\text\\t_magic._dt') elif t_magic.endswith('\\patch2\\text\\t_magic._dt'): t_magic = t_magic.replace('\\patch2\\text\\t_magic._dt', '\\data\\text\\t_magic._dt') magic = fileio.FileStream() magic.Open(t_magic) for i in range(len(self.ActionList)): offsetlist.append(magic.ReadUShort()) BuiltinArtsNames.append('') NameConflict = {} for i in range(len(offsetlist)): offset = offsetlist[i] if i != len(offsetlist) - 1 and offsetlist[i + 1] - offset < 0x1C: continue #print('%X' % offset) magic.seek(offset + 0x18) offset = magic.ReadUShort() if offset == 0: continue magic.seek(offset) name = magic.ReadMultiByte().replace(' ', '') if name == '': continue if name not in NameConflict: NameConflict[name] = 1 else: NameConflict[name] += 1 name += '_%d' % NameConflict[name] BuiltinArtsNames[i] = name except: BuiltinArtsNames = [] return BuiltinArtsNames elif self.ActionFileType == self.ActionFileType_Normal: BuiltinCraftNames = \ [ 'SysCraft_Init', # 00 0 'SysCraft_Stand', # 01 1 'SysCraft_Move', # 02 2 'SysCraft_UnderAttack', # 03 3 'SysCraft_Dead', # 04 4 'SysCraft_NormalAttack', # 05 5 'SysCraft_ArtsAria', # 06 6 'SysCraft_ArtsCast', # 07 7 'SysCraft_Win', # 08 8 'SysCraft_EnterBattle', # 09 9 'SysCraft_UseItem', # 0A 10 'SysCraft_Stun', # 0B 11 'SysCraft_Unknown2', # 0C 12 'SysCraft_Reserve1', # 0D 13 'SysCraft_Reserve2', # 0E 14 'SysCraft_Counter', # 0F 15 '', # 10 16 '', # 11 17 '', # 12 18 '', # 13 19 '', # 14 20 '', # 15 21 '', # 16 22 '', # 17 23 '', # 18 24 '', # 19 25 '', # 1A 26 '', # 1B 27 '', # 1C 28 '', # 1D 29 'SysCraft_TeamRushInit', # 1E 30 'SysCraft_TeamRushAction', # 1F 31 ] return BuiltinCraftNames return [] def DiasmInstructionCallback(self, data): return def DisassembleCraftActions(self, fs): CraftNameMap = {} msfile = None try: msfile = MSFile.BattleMonsterStatus() msfile.open(os.path.dirname(self.ASFileName) + '\\ms' + os.path.basename(self.ASFileName)[2:]) self.ChrName = None if msfile.Name == '' or msfile.Name == ' ' else msfile.Name except: msfile = None BuiltinCraftNames = self.GetBuiltinNames() disasm = Disassembler(edao.edao_as_op_table, self.DiasmInstructionCallback) index = -1 codeblocks = [] blockoffsetmap = {} for func in self.ActionList: index += 1 if func == INVALID_ACTION_OFFSET: codeblocks.append(CodeBlock(INVALID_ACTION_OFFSET)) continue if func in blockoffsetmap: codeblocks.append(blockoffsetmap[func]) continue fs.seek(func) data = Disassembler.DisasmData() data.Stream = fs data.GlobalLabelTable = self.GlobalLabelTable block = disasm.DisasmBlock2(data) if index >= len(BuiltinCraftNames) or BuiltinCraftNames[index] == '': name = 'Craft_%X_%d_%X' % (index, index, block.Offset) if msfile != None: craft = msfile.FindCraftByActionIndex(index) if craft != None: if craft.Name != '' and craft.Name != ' ': name += '_' + craft.Name.replace(' ', '_').replace('　', '_').replace('·', '') block.Name = name else: block.Name = BuiltinCraftNames[index] codeblocks.append(block) blockoffsetmap[func] = block return codeblocks def FormatCodeBlocks(self): disasm = Disassembler(edao.edao_as_op_table) blocks = [] blockoffsetmap = {} for block in sorted(self.CraftActions, key=lambda x: x.Offset): if block.Offset == INVALID_ACTION_OFFSET: continue if block.Offset in blockoffsetmap: continue blockoffsetmap[block.Offset] = True data = Disassembler.FormatData() data.Block = block data.Block.Instructions = sorted(data.Block.Instructions, key=lambda x: x.Offset) data.GlobalLabelTable = self.GlobalLabelTable name = GetValidLabelName(block.Name) if not name.startswith('Craft_'): name = 'Craft_' + name blocks.append(['def %s(): pass' % name]) blocks.append(disasm.FormatCodeBlock2(data)) #for x in disasmtbl: print('%08X' % x) #input() return blocks def SaveToFile(self, filename): lines = [] #lines.append('from %s import *' % os.path.splitext(os.path.basename(__file__))[0]) lines.append('from ActionHelper import *') lines.append('') lines.append('SetCodePage("%s")' % edao.CODE_PAGE) lines.append('') name = os.path.splitext(os.path.basename(filename))[0] name = os.path.splitext(name)[0] if self.ActionFileType == self.ActionFileType_Arts: lines.append('CreateArtsAction("%s")' % (name + '.dat')) else: tmp = [] for pos in self.ChrPosFactor: tmp.append('(%d, %d)' % (pos.X, pos.Y)) lines.append('CreateBattleAction("%s", (%s))' % (name + '.dat', ', '.join(tmp))) lines.append('') lines.append('AddPreloadChip((') index = 0 for chip in self.PreloadChipList: x = ljust_cn(' "%s",' % chip.Name(), 30) x += ' # %02X %d' % (index, index) lines.append(x) index += 1 lines.append('))') lines.append('') lines.append('CraftAction((') index = 0 for craft in self.CraftActions: name = ('"%s"'% craft.Name) if craft.Offset != INVALID_ACTION_OFFSET else 'EMPTY_ACTION' lines.append( ljust_cn(' %s,' % name, 40) + ('# %02X %d' % (index, index))) index += 1 lines.append('))') lines.append('') blocks = self.FormatCodeBlocks() for block in blocks: lines += block lines.append('SaveToFile()') lines.append('') txt = '\r\n'.join(lines) lines = txt.replace('\r\n', '\n').replace('\r', '\n').split('\n') for i in range(2, len(lines)): if lines[i] != '': lines[i] = ' %s' % lines[i] lines.insert(2, 'def main():') lines.append('Try(main)') lines.append('') if self.ChrName != None: lines.insert(2, '# %s' % self.ChrName) lines.insert(3, '') fs = open(filename, 'wb') fs.write(''.encode('utf_8_sig')) fs.write('\r\n'.join(lines).encode('UTF8')) ############################################################################################ # support functions ############################################################################################ class BattleActionScriptInfoPort(BattleActionScriptInfo): def __init__(self): super().__init__() self.FileName = '' self.Labels = {} # map<name, offset> self.DelayFixLabels = [] # list of LabelEntry self.PrevousHandlerData = None self.fs = None actionfile = None def label(labelname): pos = actionfile.fs.tell() if actionfile.PrevousHandlerData is not None: pos += actionfile.PrevousHandlerData.FileStream.tell() plog('%08X: %s' % (pos, labelname)) if labelname in actionfile.Labels and actionfile.Labels[labelname] != pos: raise Exception('label name conflict: %s' % labelname) actionfile.Labels[labelname] = pos def getlabel(name): return actionfile.Labels[name] def CreateBattleAction(filename, ChrPosFactorList = None, ModelFileList = None, UnknownTableList = None): if not IsTupleOrList(ChrPosFactorList): raise Exception('ChrPosFactorList must be list') global actionfile actionfile = BattleActionScriptInfoPort() start_argv = 1 global CODE_PAGE cp = CODE_PAGE if sys.argv[1].startswith('--cp='): cp = sys.argv[1][5:] start_argv = 2 elif sys.argv[1].startswith('--cppy='): cppy = os.path.abspath(sys.argv[1][7:]) ccode = importlib.machinery.SourceFileLoader(os.path.basename(cppy).split('.')[0], cppy).load_module() ccode.register() cp = ccode.get_name() start_argv = 2 if cp == 'NotSet': cp = 'gbk' CODE_PAGE = cp edao.CODE_PAGE = cp edao.edao_as_op_table.CodePage = cp if len(sys.argv) > start_argv: filename = os.path.join(sys.argv[start_argv], filename) actionfile.fs = fileio.FileStream() actionfile.fs.Open(filename, 'wb+') actionfile.FileName = filename for factor in ChrPosFactorList: f = CharacterPositionFactor() f.X = factor[0] f.Y = factor[1] actionfile.ChrPosFactor.append(f) if IsTupleOrList(ModelFileList): actionfile.ModelFileList = ModelFileList if IsTupleOrList(UnknownTableList): actionfile.UnknownTableList = UnknownTableList asname = os.path.basename(filename).lower() if asname == 'as90000.dat': actionfile.ActionFileType = actionfile.ActionFileType_Arts elif asname == 'as90001.dat': actionfile.ActionFileType = actionfile.ActionFileType_Item else: actionfile.ActionFileType = actionfile.ActionFileType_Normal def CreateArtsAction(filename): global actionfile actionfile = BattleActionScriptInfoPort() actionfile.fs = fileio.FileStream() actionfile.fs.Open(filename, 'wb+') actionfile.ActionFileType = BattleActionScriptInfoPort.ActionFileType_Arts actionfile.ActionListOffset = 2 actionfile.fs.WriteUShort(actionfile.ActionListOffset) def AddPreloadChip(ChipFileList): if not IsTupleOrList(ChipFileList): raise Exception('ChipFileList must be list') actionfile.ChipFileList = list(ChipFileList) def CraftAction(CraftNameList): if not IsTupleOrList(CraftNameList): raise Exception('CraftNameList must be list') actionfile.ActionList = list(CraftNameList) fs = actionfile.fs if actionfile.ActionFileType == actionfile.ActionFileType_Normal: fs.seek(6) for chip in actionfile.ChipFileList: fs.WriteULong(ChipFileIndex(chip).Index()) fs.WriteULong(0xFFFFFFFF) for model in actionfile.ModelFileList: fs.WriteMultiByte(model, "cp932") fs.WriteByte(0) fs.WriteByte(0) if len(actionfile.UnknownTableList) > 0: actionfile.UnknownTableOffset = fs.tell() for factor in actionfile.UnknownTableList: fs.WriteUShort(factor) else: fs.seek(2) actionfile.ActionListOffset = fs.tell() for craft in CraftNameList: if craft != INVALID_ACTION_OFFSET: actionfile.DelayFixLabels.append(LabelEntry(craft, fs.tell())) fs.WriteUShort(INVALID_ACTION_OFFSET) fs.write(b'\x00\x00') actionfile.ChrPosFactorOffset = fs.tell() for factor in actionfile.ChrPosFactor: fs.WriteByte(factor.X) fs.WriteByte(factor.Y) actionfile.ActionStartOffset = fs.tell() fs.seek(0) fs.WriteUShort(actionfile.ActionListOffset) if actionfile.ActionFileType == actionfile.ActionFileType_Normal: fs.WriteUShort(actionfile.ChrPosFactorOffset) fs.WriteUShort(actionfile.UnknownTableOffset) fs.seek(actionfile.ActionStartOffset) for op, inst in edao.edao_as_op_table.items(): func = [] func.append('def %s(*args):' % inst.OpName) func.append(' return OpCodeHandler(0x%02X, args)' % inst.OpCode) func.append('') exec('\r\n'.join(func)) opx = 'AS_%02X' % inst.OpCode if inst.OpName != opx: func[0] = 'def %s(*args):' % opx exec('\r\n'.join(func)) def AssembleForExec(expr): return eval(expr) def OpCodeHandler(op, args): entry = edao.edao_as_op_table[op] data = HandlerData(HANDLER_REASON_ASSEMBLE) data.Instruction = Instruction(op) data.Arguments = list(args) data.TableEntry = entry data.Assemble = AssembleForExec data.Instruction.OperandFormat = entry.Operand UsePrevous = bool(actionfile.PrevousHandlerData != None) if UsePrevous: data.FileStream = actionfile.PrevousHandlerData.FileStream data.Instruction.Labels = actionfile.PrevousHandlerData.Instruction.Labels else: data.FileStream = fileio.FileStream(b'') actionfile.PrevousHandlerData = data #print(entry.OpName) inst = OpCodeHandlerPrivate(data) if UsePrevous: return inst actionfile.PrevousHandlerData = None offset = actionfile.fs.tell() for lb in inst.Labels: actionfile.DelayFixLabels.append(LabelEntry(lb.Label, lb.Offset + offset)) data.FileStream.seek(0) actionfile.fs.write(data.FileStream.read()) return inst def SaveToFile(): fs = actionfile.fs for lb in actionfile.DelayFixLabels: fs.seek(lb.Offset) fs.WriteUShort(getlabel(lb.Label)) ''' if has_target: jump label Jc(0x16, 0x1, 0x0, "loc_A4A") ''' def procfile(file, cp=None): if cp: edao.CODE_PAGE = cp edao.edao_op_table.CodePage = cp console.setTitle(os.path.basename(file)) #print('disasm %s' % file) asdat = BattleActionScriptInfo() asdat.open(file) outfile = os.path.splitext(file)[0] + ".py" plog('SAVE %s' % outfile) asdat.SaveToFile(outfile) return outfile if __name__ == '__main__': # iterlib.forEachFileMP(procfile, sys.argv[1:], 'as*.dat') cp = 'gbk' start_argv = 1 if sys.argv[1].startswith('--cp='): cp = sys.argv[1][5:] start_argv = 2 elif sys.argv[1].startswith('--cppy='): cppy = os.path.abspath(sys.argv[1][7:]) ccode = importlib.machinery.SourceFileLoader(os.path.basename(cppy).split('.')[0], cppy).load_module() ccode.register() cp = ccode.get_name() start_argv = 2 edao.CODE_PAGE = cp edao.edao_as_op_table.CodePage = cp files = iterlib.forEachGetFiles(sys.argv[start_argv:], 'as*.dat') #Log.OpenLog(sys.argv[start_argv] + '\..\log.txt') for file in files: plog('START %s' % file) procfile(file) plog('FINISHED %s' % file) #Log.CloseLog()

31.056748

125

0.521754

from Assembler.Assembler2s import * from Base.EDAOBase import * import Instruction.ActionOpTableEDAO as edao import BattleMonsterStatus as MSFile INVALID_ACTION_OFFSET = 0xFFFF EMPTY_ACTION = INVALID_ACTION_OFFSET class CharacterPositionFactor: def __init__(self, fs = None): if fs == None: return self.X = fs.ReadByte() self.Y = fs.ReadByte() class BattleActionScriptInfo: ActionFileType_Normal = 0 ActionFileType_Arts = 1 ActionFileType_Item = 2 def __init__(self): self.ChrPosFactorOffset = 0 self.ActionListOffset = 0 self.UnknownTableOffset = 0 self.ActionStartOffset = 0 self.PreloadChipList = [] self.ModelFileList = [] self.UnknownTableList = [] self.ActionList = [] self.ChrPosFactor = [] self.CraftActions = [] self.GlobalLabelTable = {} self.ChrName = None self.ASFileName = '' self.ActionFileType = self.ActionFileType_Normal def open(self, asname): fs = fileio.FileStream() fs.Open(asname) self.ASFileName = asname asname = os.path.basename(asname).lower() if asname == 'as90000.dat': self.ActionFileType = self.ActionFileType_Arts elif asname == 'as90001.dat': self.ActionFileType = self.ActionFileType_Item else: self.ActionFileType = self.ActionFileType_Normal self.ActionListOffset = fs.ReadUShort() if self.ActionFileType == self.ActionFileType_Normal: self.ChrPosFactorOffset = fs.ReadUShort() self.UnknownTableOffset = fs.ReadUShort() while True: index = fs.ReadULong() if index == 0xFFFFFFFF: break self.PreloadChipList.append(ChipFileIndex(index)) fs.seek(self.ChrPosFactorOffset) for i in range(8): self.ChrPosFactor.append(CharacterPositionFactor(fs)) minoffset = 0xFFFFFFFF fs.seek(self.ActionListOffset) while True: if fs.tell() >= minoffset: break offset = fs.ReadUShort() if offset == 0: break minoffset = min(minoffset, offset) self.ActionList.append(offset) if len(self.ActionList) == 0: raise Exception('action number == 0') self.CraftActions = self.DisassembleCraftActions(fs) return for i in range(0x69, fs.Length): if i not in offsetlist: print('%X' % i) input() def GetBuiltinNames(self): if self.ActionFileType == self.ActionFileType_Arts: BuiltinArtsNames = [] try: offsetlist = [] t_magic = os.path.abspath(os.path.dirname(os.path.abspath(self.ASFileName)) + '\\..\\..\\text\\t_magic._dt') if not os.path.exists(t_magic): if t_magic.endswith('\\patch\\text\\t_magic._dt'): t_magic = t_magic.replace('\\patch\\text\\t_magic._dt', '\\data\\text\\t_magic._dt') elif t_magic.endswith('\\patch2\\text\\t_magic._dt'): t_magic = t_magic.replace('\\patch2\\text\\t_magic._dt', '\\data\\text\\t_magic._dt') magic = fileio.FileStream() magic.Open(t_magic) for i in range(len(self.ActionList)): offsetlist.append(magic.ReadUShort()) BuiltinArtsNames.append('') NameConflict = {} for i in range(len(offsetlist)): offset = offsetlist[i] if i != len(offsetlist) - 1 and offsetlist[i + 1] - offset < 0x1C: continue magic.seek(offset + 0x18) offset = magic.ReadUShort() if offset == 0: continue magic.seek(offset) name = magic.ReadMultiByte().replace(' ', '') if name == '': continue if name not in NameConflict: NameConflict[name] = 1 else: NameConflict[name] += 1 name += '_%d' % NameConflict[name] BuiltinArtsNames[i] = name except: BuiltinArtsNames = [] return BuiltinArtsNames elif self.ActionFileType == self.ActionFileType_Normal: BuiltinCraftNames = \ [ 'SysCraft_Init', 'SysCraft_Stand', 'SysCraft_Move', 'SysCraft_UnderAttack', 'SysCraft_Dead', 'SysCraft_NormalAttack', 'SysCraft_ArtsAria', 'SysCraft_ArtsCast', 'SysCraft_Win', 'SysCraft_EnterBattle', 'SysCraft_UseItem', 'SysCraft_Stun', 'SysCraft_Unknown2', 'SysCraft_Reserve1', 'SysCraft_Reserve2', 'SysCraft_Counter', '', '', '', '', '', '', '', '', '', '', '', '', '', '', 'SysCraft_TeamRushInit', 'SysCraft_TeamRushAction', ] return BuiltinCraftNames return [] def DiasmInstructionCallback(self, data): return def DisassembleCraftActions(self, fs): CraftNameMap = {} msfile = None try: msfile = MSFile.BattleMonsterStatus() msfile.open(os.path.dirname(self.ASFileName) + '\\ms' + os.path.basename(self.ASFileName)[2:]) self.ChrName = None if msfile.Name == '' or msfile.Name == ' ' else msfile.Name except: msfile = None BuiltinCraftNames = self.GetBuiltinNames() disasm = Disassembler(edao.edao_as_op_table, self.DiasmInstructionCallback) index = -1 codeblocks = [] blockoffsetmap = {} for func in self.ActionList: index += 1 if func == INVALID_ACTION_OFFSET: codeblocks.append(CodeBlock(INVALID_ACTION_OFFSET)) continue if func in blockoffsetmap: codeblocks.append(blockoffsetmap[func]) continue fs.seek(func) data = Disassembler.DisasmData() data.Stream = fs data.GlobalLabelTable = self.GlobalLabelTable block = disasm.DisasmBlock2(data) if index >= len(BuiltinCraftNames) or BuiltinCraftNames[index] == '': name = 'Craft_%X_%d_%X' % (index, index, block.Offset) if msfile != None: craft = msfile.FindCraftByActionIndex(index) if craft != None: if craft.Name != '' and craft.Name != ' ': name += '_' + craft.Name.replace(' ', '_').replace('　', '_').replace('·', '') block.Name = name else: block.Name = BuiltinCraftNames[index] codeblocks.append(block) blockoffsetmap[func] = block return codeblocks def FormatCodeBlocks(self): disasm = Disassembler(edao.edao_as_op_table) blocks = [] blockoffsetmap = {} for block in sorted(self.CraftActions, key=lambda x: x.Offset): if block.Offset == INVALID_ACTION_OFFSET: continue if block.Offset in blockoffsetmap: continue blockoffsetmap[block.Offset] = True data = Disassembler.FormatData() data.Block = block data.Block.Instructions = sorted(data.Block.Instructions, key=lambda x: x.Offset) data.GlobalLabelTable = self.GlobalLabelTable name = GetValidLabelName(block.Name) if not name.startswith('Craft_'): name = 'Craft_' + name blocks.append(['def %s(): pass' % name]) blocks.append(disasm.FormatCodeBlock2(data)) return blocks def SaveToFile(self, filename): lines = [] lines.append('from ActionHelper import *') lines.append('') lines.append('SetCodePage("%s")' % edao.CODE_PAGE) lines.append('') name = os.path.splitext(os.path.basename(filename))[0] name = os.path.splitext(name)[0] if self.ActionFileType == self.ActionFileType_Arts: lines.append('CreateArtsAction("%s")' % (name + '.dat')) else: tmp = [] for pos in self.ChrPosFactor: tmp.append('(%d, %d)' % (pos.X, pos.Y)) lines.append('CreateBattleAction("%s", (%s))' % (name + '.dat', ', '.join(tmp))) lines.append('') lines.append('AddPreloadChip((') index = 0 for chip in self.PreloadChipList: x = ljust_cn(' "%s",' % chip.Name(), 30) x += ' # %02X %d' % (index, index) lines.append(x) index += 1 lines.append('))') lines.append('') lines.append('CraftAction((') index = 0 for craft in self.CraftActions: name = ('"%s"'% craft.Name) if craft.Offset != INVALID_ACTION_OFFSET else 'EMPTY_ACTION' lines.append( ljust_cn(' %s,' % name, 40) + ('# %02X %d' % (index, index))) index += 1 lines.append('))') lines.append('') blocks = self.FormatCodeBlocks() for block in blocks: lines += block lines.append('SaveToFile()') lines.append('') txt = '\r\n'.join(lines) lines = txt.replace('\r\n', '\n').replace('\r', '\n').split('\n') for i in range(2, len(lines)): if lines[i] != '': lines[i] = ' %s' % lines[i] lines.insert(2, 'def main():') lines.append('Try(main)') lines.append('') if self.ChrName != None: lines.insert(2, '# %s' % self.ChrName) lines.insert(3, '') fs = open(filename, 'wb') fs.write(''.encode('utf_8_sig')) fs.write('\r\n'.join(lines).encode('UTF8'))

true

f7319b025a15cca212a3758b38a253304ad2ede4

2,382

py

Python

src/streamlink/plugins/huya.py

melmorabity/streamlink

24c59a23103922977991acc28741a323d8efa7a1

[ "BSD-2-Clause" ]

null

src/streamlink/plugins/huya.py

melmorabity/streamlink

24c59a23103922977991acc28741a323d8efa7a1

[ "BSD-2-Clause" ]

null

src/streamlink/plugins/huya.py

melmorabity/streamlink

24c59a23103922977991acc28741a323d8efa7a1

[ "BSD-2-Clause" ]

null

import base64 import logging import re from html import unescape as html_unescape from streamlink.plugin import Plugin, pluginmatcher from streamlink.plugin.api import validate from streamlink.stream import HTTPStream from streamlink.utils.parse import parse_json log = logging.getLogger(__name__) @pluginmatcher(re.compile( r'https?://(?:www\.)?huya\.com/(?P<channel>[^/]+)' )) class Huya(Plugin): _re_stream = re.compile(r'"stream"\s?:\s?"([^"]+)"') _schema_data = validate.Schema( { # 'status': int, # 'msg': validate.any(None, str), 'data': [{ 'gameStreamInfoList': [{ 'sCdnType': str, 'sStreamName': str, 'sFlvUrl': str, 'sFlvUrlSuffix': str, 'sFlvAntiCode': validate.all(str, validate.transform(lambda v: html_unescape(v))), # 'sHlsUrl': str, # 'sHlsUrlSuffix': str, # 'sHlsAntiCode': validate.all(str, validate.transform(lambda v: html_unescape(v))), validate.optional('iIsMultiStream'): int, 'iPCPriorityRate': int, }] }], # 'vMultiStreamInfo': [{ # 'sDisplayName': str, # 'iBitRate': int, # }], }, validate.get('data'), validate.get(0), validate.get('gameStreamInfoList'), ) QUALITY_WEIGHTS = {} @classmethod def stream_weight(cls, key): weight = cls.QUALITY_WEIGHTS.get(key) if weight: return weight, 'huya' return Plugin.stream_weight(key) def _get_streams(self): res = self.session.http.get(self.url) data = self._re_stream.search(res.text) if not data: return data = parse_json(base64.b64decode(data.group(1)), schema=self._schema_data) for info in data: log.trace(f'{info!r}') flv_url = f'{info["sFlvUrl"]}/{info["sStreamName"]}.{info["sFlvUrlSuffix"]}?{info["sFlvAntiCode"]}' name = f'source_{info["sCdnType"].lower()}' self.QUALITY_WEIGHTS[name] = info['iPCPriorityRate'] yield name, HTTPStream(self.session, flv_url) log.debug(f'QUALITY_WEIGHTS: {self.QUALITY_WEIGHTS!r}') __plugin__ = Huya

31.76

111

0.554156

import base64 import logging import re from html import unescape as html_unescape from streamlink.plugin import Plugin, pluginmatcher from streamlink.plugin.api import validate from streamlink.stream import HTTPStream from streamlink.utils.parse import parse_json log = logging.getLogger(__name__) @pluginmatcher(re.compile( r'https?://(?:www\.)?huya\.com/(?P<channel>[^/]+)' )) class Huya(Plugin): _re_stream = re.compile(r'"stream"\s?:\s?"([^"]+)"') _schema_data = validate.Schema( { # 'status': int, # 'msg': validate.any(None, str), 'data': [{ 'gameStreamInfoList': [{ 'sCdnType': str, 'sStreamName': str, 'sFlvUrl': str, 'sFlvUrlSuffix': str, 'sFlvAntiCode': validate.all(str, validate.transform(lambda v: html_unescape(v))), # 'sHlsUrl': str, # 'sHlsUrlSuffix': str, # 'sHlsAntiCode': validate.all(str, validate.transform(lambda v: html_unescape(v))), validate.optional('iIsMultiStream'): int, 'iPCPriorityRate': int, }] }], # 'vMultiStreamInfo': [{ # 'sDisplayName': str, # 'iBitRate': int, # }], }, validate.get('data'), validate.get(0), validate.get('gameStreamInfoList'), ) QUALITY_WEIGHTS = {} @classmethod def stream_weight(cls, key): weight = cls.QUALITY_WEIGHTS.get(key) if weight: return weight, 'huya' return Plugin.stream_weight(key) def _get_streams(self): res = self.session.http.get(self.url) data = self._re_stream.search(res.text) if not data: return data = parse_json(base64.b64decode(data.group(1)), schema=self._schema_data) for info in data: log.trace(f'{info!r}') flv_url = f'{info["sFlvUrl"]}/{info["sStreamName"]}.{info["sFlvUrlSuffix"]}?{info["sFlvAntiCode"]}' name = f'source_{info["sCdnType"].lower()}' self.QUALITY_WEIGHTS[name] = info['iPCPriorityRate'] yield name, HTTPStream(self.session, flv_url) log.debug(f'QUALITY_WEIGHTS: {self.QUALITY_WEIGHTS!r}') __plugin__ = Huya

true

f7319b3ce9b7c689f47a1cdeca8dd710ca242644

6,175

py

Python

salt/netapi/rest_cherrypy/event_processor.py

styro/salt

d087d94dca02ca8bf53a6c21b94944bc7957522c

[ "Apache-2.0" ]

3

2016-09-03T06:26:42.000Z

2019-06-30T13:04:53.000Z

salt/netapi/rest_cherrypy/event_processor.py

styro/salt

d087d94dca02ca8bf53a6c21b94944bc7957522c

[ "Apache-2.0" ]

null

salt/netapi/rest_cherrypy/event_processor.py

styro/salt

d087d94dca02ca8bf53a6c21b94944bc7957522c

[ "Apache-2.0" ]

1

2021-12-02T15:30:00.000Z

# encoding: utf-8 from __future__ import absolute_import import json import logging import salt.ext.six as six import salt.netapi logger = logging.getLogger(__name__) class SaltInfo(object): ''' Class to handle processing and publishing of "real time" Salt upates. ''' def __init__(self, handler): ''' handler is expected to be the server side end of a websocket connection. ''' self.handler = handler # These represent a "real time" view into Salt's jobs. self.jobs = {} # This represents a "real time" view of minions connected to Salt. self.minions = {} def publish_minions(self): ''' Publishes minions as a list of dicts. ''' minions = [] for minion, minion_info in six.iteritems(self.minions): curr_minion = {} curr_minion.update(minion_info) curr_minion.update({'id': minion}) minions.append(curr_minion) ret = {'minions': minions} self.handler.send(json.dumps(ret), False) def publish(self, key, data): ''' Publishes the data to the event stream. ''' publish_data = {key: data} self.handler.send(json.dumps(publish_data), False) def process_minion_update(self, event_data): ''' Associate grains data with a minion and publish minion update ''' tag = event_data['tag'] event_info = event_data['data'] _, _, _, _, mid = tag.split('/') if not self.minions.get(mid, None): self.minions[mid] = {} minion = self.minions[mid] minion.update({'grains': event_info['return']}) self.publish_minions() def process_ret_job_event(self, event_data): ''' Process a /ret event returned by Salt for a particular minion. These events contain the returned results from a particular execution. ''' tag = event_data['tag'] event_info = event_data['data'] _, _, jid, _, mid = tag.split('/') job = self.jobs.setdefault(jid, {}) minion = job.setdefault('minions', {}).setdefault(mid, {}) minion.update({'return': event_info['return']}) minion.update({'retcode': event_info['retcode']}) minion.update({'success': event_info['success']}) job_complete = all([minion['success'] for mid, minion in six.iteritems(job['minions'])]) if job_complete: job['state'] = 'complete' self.publish('jobs', self.jobs) def process_new_job_event(self, event_data): ''' Creates a new job with properties from the event data like jid, function, args, timestamp. Also sets the initial state to started. Minions that are participating in this job are also noted. ''' job = None tag = event_data['tag'] event_info = event_data['data'] minions = {} for mid in event_info['minions']: minions[mid] = {'success': False} job = { 'jid': event_info['jid'], 'start_time': event_info['_stamp'], 'minions': minions, # is a dictionary keyed by mids 'fun': event_info['fun'], 'tgt': event_info['tgt'], 'tgt_type': event_info['tgt_type'], 'state': 'running', } self.jobs[event_info['jid']] = job self.publish('jobs', self.jobs) def process_key_event(self, event_data): ''' Tag: salt/key Data: {'_stamp': '2014-05-20T22:45:04.345583', 'act': 'delete', 'id': 'compute.home', 'result': True} ''' tag = event_data['tag'] event_info = event_data['data'] if event_info['act'] == 'delete': self.minions.pop(event_info['id'], None) elif event_info['act'] == 'accept': self.minions.setdefault(event_info['id'], {}) self.publish_minions() def process_presence_events(self, event_data, token, opts): ''' Check if any minions have connected or dropped. Send a message to the client if they have. ''' tag = event_data['tag'] event_info = event_data['data'] minions_detected = event_info['present'] curr_minions = self.minions.keys() changed = False # check if any connections were dropped dropped_minions = set(curr_minions) - set(minions_detected) for minion in dropped_minions: changed = True self.minions.pop(minion, None) # check if any new connections were made new_minions = set(minions_detected) - set(curr_minions) tgt = ','.join(new_minions) if tgt: changed = True client = salt.netapi.NetapiClient(opts) client.run( { 'fun': 'grains.items', 'tgt': tgt, 'expr_type': 'list', 'mode': 'client', 'client': 'local', 'async': 'local_async', 'token': token, }) if changed: self.publish_minions() def process(self, salt_data, token, opts): ''' Process events and publish data ''' parts = salt_data['tag'].split('/') if len(parts) < 2: return # TBD: Simplify these conditional expressions if parts[1] == 'job': if parts[3] == 'new': self.process_new_job_event(salt_data) if salt_data['data']['fun'] == 'grains.items': self.minions = {} elif parts[3] == 'ret': self.process_ret_job_event(salt_data) if salt_data['data']['fun'] == 'grains.items': self.process_minion_update(salt_data) if parts[1] == 'key': self.process_key_event(salt_data) if parts[1] == 'presence': self.process_presence_events(salt_data, token, opts)

29.830918

78

0.545263

from __future__ import absolute_import import json import logging import salt.ext.six as six import salt.netapi logger = logging.getLogger(__name__) class SaltInfo(object): def __init__(self, handler): self.handler = handler self.jobs = {} # This represents a "real time" view of minions connected to Salt. self.minions = {} def publish_minions(self): minions = [] for minion, minion_info in six.iteritems(self.minions): curr_minion = {} curr_minion.update(minion_info) curr_minion.update({'id': minion}) minions.append(curr_minion) ret = {'minions': minions} self.handler.send(json.dumps(ret), False) def publish(self, key, data): publish_data = {key: data} self.handler.send(json.dumps(publish_data), False) def process_minion_update(self, event_data): tag = event_data['tag'] event_info = event_data['data'] _, _, _, _, mid = tag.split('/') if not self.minions.get(mid, None): self.minions[mid] = {} minion = self.minions[mid] minion.update({'grains': event_info['return']}) self.publish_minions() def process_ret_job_event(self, event_data): tag = event_data['tag'] event_info = event_data['data'] _, _, jid, _, mid = tag.split('/') job = self.jobs.setdefault(jid, {}) minion = job.setdefault('minions', {}).setdefault(mid, {}) minion.update({'return': event_info['return']}) minion.update({'retcode': event_info['retcode']}) minion.update({'success': event_info['success']}) job_complete = all([minion['success'] for mid, minion in six.iteritems(job['minions'])]) if job_complete: job['state'] = 'complete' self.publish('jobs', self.jobs) def process_new_job_event(self, event_data): job = None tag = event_data['tag'] event_info = event_data['data'] minions = {} for mid in event_info['minions']: minions[mid] = {'success': False} job = { 'jid': event_info['jid'], 'start_time': event_info['_stamp'], 'minions': minions, # is a dictionary keyed by mids 'fun': event_info['fun'], 'tgt': event_info['tgt'], 'tgt_type': event_info['tgt_type'], 'state': 'running', } self.jobs[event_info['jid']] = job self.publish('jobs', self.jobs) def process_key_event(self, event_data): tag = event_data['tag'] event_info = event_data['data'] if event_info['act'] == 'delete': self.minions.pop(event_info['id'], None) elif event_info['act'] == 'accept': self.minions.setdefault(event_info['id'], {}) self.publish_minions() def process_presence_events(self, event_data, token, opts): tag = event_data['tag'] event_info = event_data['data'] minions_detected = event_info['present'] curr_minions = self.minions.keys() changed = False # check if any connections were dropped dropped_minions = set(curr_minions) - set(minions_detected) for minion in dropped_minions: changed = True self.minions.pop(minion, None) # check if any new connections were made new_minions = set(minions_detected) - set(curr_minions) tgt = ','.join(new_minions) if tgt: changed = True client = salt.netapi.NetapiClient(opts) client.run( { 'fun': 'grains.items', 'tgt': tgt, 'expr_type': 'list', 'mode': 'client', 'client': 'local', 'async': 'local_async', 'token': token, }) if changed: self.publish_minions() def process(self, salt_data, token, opts): parts = salt_data['tag'].split('/') if len(parts) < 2: return # TBD: Simplify these conditional expressions if parts[1] == 'job': if parts[3] == 'new': self.process_new_job_event(salt_data) if salt_data['data']['fun'] == 'grains.items': self.minions = {} elif parts[3] == 'ret': self.process_ret_job_event(salt_data) if salt_data['data']['fun'] == 'grains.items': self.process_minion_update(salt_data) if parts[1] == 'key': self.process_key_event(salt_data) if parts[1] == 'presence': self.process_presence_events(salt_data, token, opts)

true

f7319b609eb62f710cfd393ed8f31297d6216369

2,636

py

Python

equality/wallet/puzzles/prefarm/spend_prefarm.py

grayfallstown/equality-blockchain

019425b703f6b013e441481ac43389a80415f2f1

[ "Apache-2.0" ]

10

2021-07-04T15:14:12.000Z

2021-10-17T14:52:56.000Z

equality/wallet/puzzles/prefarm/spend_prefarm.py

grayfallstown/equality-blockchain

019425b703f6b013e441481ac43389a80415f2f1

[ "Apache-2.0" ]

11

2021-07-04T19:31:36.000Z

2022-01-11T02:46:23.000Z

equality/wallet/puzzles/prefarm/spend_prefarm.py

grayfallstown/equality-blockchain

019425b703f6b013e441481ac43389a80415f2f1

[ "Apache-2.0" ]

11

2021-07-04T21:49:17.000Z

2021-10-04T17:45:38.000Z

import asyncio from blspy import G2Element from clvm_tools import binutils from equality.consensus.block_rewards import calculate_base_farmer_reward, calculate_pool_reward from equality.rpc.full_node_rpc_client import FullNodeRpcClient from equality.types.blockchain_format.program import Program from equality.types.coin_solution import CoinSolution from equality.types.spend_bundle import SpendBundle from equality.util.bech32m import decode_puzzle_hash from equality.util.config import load_config from equality.util.default_root import DEFAULT_ROOT_PATH from equality.util.ints import uint32, uint16 async def main() -> None: rpc_port: uint16 = uint16(8555) self_hostname = "localhost" path = DEFAULT_ROOT_PATH config = load_config(path, "config.yaml") client = await FullNodeRpcClient.create(self_hostname, rpc_port, path, config) try: farmer_prefarm = (await client.get_block_record_by_height(1)).reward_claims_incorporated[1] pool_prefarm = (await client.get_block_record_by_height(1)).reward_claims_incorporated[0] pool_amounts = int(calculate_pool_reward(uint32(0)) / 2) farmer_amounts = int(calculate_base_farmer_reward(uint32(0)) / 2) print(farmer_prefarm.amount, farmer_amounts) assert farmer_amounts == farmer_prefarm.amount // 2 assert pool_amounts == pool_prefarm.amount // 2 address1 = "xeq1rdatypul5c642jkeh4yp933zu3hw8vv8tfup8ta6zfampnyhjnusxdgns6" # Key 1 address2 = "xeq1duvy5ur5eyj7lp5geetfg84cj2d7xgpxt7pya3lr2y6ke3696w9qvda66e" # Key 2 ph1 = decode_puzzle_hash(address1) ph2 = decode_puzzle_hash(address2) p_farmer_2 = Program.to( binutils.assemble(f"(q . ((51 0x{ph1.hex()} {farmer_amounts}) (51 0x{ph2.hex()} {farmer_amounts})))") ) p_pool_2 = Program.to( binutils.assemble(f"(q . ((51 0x{ph1.hex()} {pool_amounts}) (51 0x{ph2.hex()} {pool_amounts})))") ) p_solution = Program.to(binutils.assemble("()")) sb_farmer = SpendBundle([CoinSolution(farmer_prefarm, p_farmer_2, p_solution)], G2Element()) sb_pool = SpendBundle([CoinSolution(pool_prefarm, p_pool_2, p_solution)], G2Element()) print(sb_pool, sb_farmer) res = await client.push_tx(sb_farmer) # res = await client.push_tx(sb_pool) print(res) up = await client.get_coin_records_by_puzzle_hash(farmer_prefarm.puzzle_hash, True) uf = await client.get_coin_records_by_puzzle_hash(pool_prefarm.puzzle_hash, True) print(up) print(uf) finally: client.close() asyncio.run(main())

41.1875

113

0.725721

import asyncio from blspy import G2Element from clvm_tools import binutils from equality.consensus.block_rewards import calculate_base_farmer_reward, calculate_pool_reward from equality.rpc.full_node_rpc_client import FullNodeRpcClient from equality.types.blockchain_format.program import Program from equality.types.coin_solution import CoinSolution from equality.types.spend_bundle import SpendBundle from equality.util.bech32m import decode_puzzle_hash from equality.util.config import load_config from equality.util.default_root import DEFAULT_ROOT_PATH from equality.util.ints import uint32, uint16 async def main() -> None: rpc_port: uint16 = uint16(8555) self_hostname = "localhost" path = DEFAULT_ROOT_PATH config = load_config(path, "config.yaml") client = await FullNodeRpcClient.create(self_hostname, rpc_port, path, config) try: farmer_prefarm = (await client.get_block_record_by_height(1)).reward_claims_incorporated[1] pool_prefarm = (await client.get_block_record_by_height(1)).reward_claims_incorporated[0] pool_amounts = int(calculate_pool_reward(uint32(0)) / 2) farmer_amounts = int(calculate_base_farmer_reward(uint32(0)) / 2) print(farmer_prefarm.amount, farmer_amounts) assert farmer_amounts == farmer_prefarm.amount // 2 assert pool_amounts == pool_prefarm.amount // 2 address1 = "xeq1rdatypul5c642jkeh4yp933zu3hw8vv8tfup8ta6zfampnyhjnusxdgns6" address2 = "xeq1duvy5ur5eyj7lp5geetfg84cj2d7xgpxt7pya3lr2y6ke3696w9qvda66e" ph1 = decode_puzzle_hash(address1) ph2 = decode_puzzle_hash(address2) p_farmer_2 = Program.to( binutils.assemble(f"(q . ((51 0x{ph1.hex()} {farmer_amounts}) (51 0x{ph2.hex()} {farmer_amounts})))") ) p_pool_2 = Program.to( binutils.assemble(f"(q . ((51 0x{ph1.hex()} {pool_amounts}) (51 0x{ph2.hex()} {pool_amounts})))") ) p_solution = Program.to(binutils.assemble("()")) sb_farmer = SpendBundle([CoinSolution(farmer_prefarm, p_farmer_2, p_solution)], G2Element()) sb_pool = SpendBundle([CoinSolution(pool_prefarm, p_pool_2, p_solution)], G2Element()) print(sb_pool, sb_farmer) res = await client.push_tx(sb_farmer) print(res) up = await client.get_coin_records_by_puzzle_hash(farmer_prefarm.puzzle_hash, True) uf = await client.get_coin_records_by_puzzle_hash(pool_prefarm.puzzle_hash, True) print(up) print(uf) finally: client.close() asyncio.run(main())

true

f7319cdf9d302bcce6db295df0177ccd74b1ba86

457

py

Python

server/src/views/tests/test_delete.py

budtmo/GIoT

4cfdeacbe53b2bee10613519d86fa1bb0d9057b4

[ "Apache-2.0" ]

null

server/src/views/tests/test_delete.py

budtmo/GIoT

4cfdeacbe53b2bee10613519d86fa1bb0d9057b4

[ "Apache-2.0" ]

null

server/src/views/tests/test_delete.py

budtmo/GIoT

4cfdeacbe53b2bee10613519d86fa1bb0d9057b4

[ "Apache-2.0" ]

null

from src.views.tests import BaseTest class TestDeleteDevice(BaseTest): """Tests to delete device from the list.""" def test_delete_device(self): self.register_device() res = self.test_app.delete('/device/{id}'.format(id=1)) self.assertEqual(res.status_code, 204) def test_delete_non_existing_device(self): res = self.test_app.delete('/device/{id}'.format(id=5)) self.assertEqual(res.status_code, 404)

30.466667

63

0.682713

from src.views.tests import BaseTest class TestDeleteDevice(BaseTest): def test_delete_device(self): self.register_device() res = self.test_app.delete('/device/{id}'.format(id=1)) self.assertEqual(res.status_code, 204) def test_delete_non_existing_device(self): res = self.test_app.delete('/device/{id}'.format(id=5)) self.assertEqual(res.status_code, 404)

true

f7319ce68a4eb20de0f2eff9b0d27c720c5108bd

596

py

Python

sherlockpipe/search_zones/SearchZone.py

LuisCerdenoMota/SHERLOCK

5fb52795d3ab44e27bc7dbc6f2c2e6c214995ba1

[ "MIT" ]

1

2021-01-14T16:44:48.000Z

sherlockpipe/search_zones/SearchZone.py

martindevora/SHERLOCK

5e7492552cbce29e960684a44fd6ad875c8cf60e

[ "MIT" ]

null

sherlockpipe/search_zones/SearchZone.py

martindevora/SHERLOCK

5e7492552cbce29e960684a44fd6ad875c8cf60e

[ "MIT" ]

null

from abc import ABC, abstractmethod from sherlockpipe.star.starinfo import StarInfo class SearchZone(ABC): """ Abstract class to be implemented for calculating minimum and maximum search periods for an input star. """ def __init__(self): pass @abstractmethod def calculate_period_range(self, star_info: StarInfo): """ Calculates the minimum and maximum periods for the given star_info @param star_info: the star where the range should be calculated @return: a tuple of minimum_period and maximum_period """ pass

28.380952

106

0.692953

from abc import ABC, abstractmethod from sherlockpipe.star.starinfo import StarInfo class SearchZone(ABC): def __init__(self): pass @abstractmethod def calculate_period_range(self, star_info: StarInfo): pass

true

f7319d88946b78f26293dd74a23f8a820010f76c

4,775

py

Python

.environment/lib/python3.8/site-packages/docplex/mp/engine_factory.py

LuisMi1245/QPath-and-Snakes

48f784da67d9720c955890a28543c9863e02a455

[ "MIT" ]

null

.environment/lib/python3.8/site-packages/docplex/mp/engine_factory.py

LuisMi1245/QPath-and-Snakes

48f784da67d9720c955890a28543c9863e02a455

[ "MIT" ]

null

.environment/lib/python3.8/site-packages/docplex/mp/engine_factory.py

LuisMi1245/QPath-and-Snakes

48f784da67d9720c955890a28543c9863e02a455

[ "MIT" ]

null

# -------------------------------------------------------------------------- # Source file provided under Apache License, Version 2.0, January 2004, # http://www.apache.org/licenses/ # (c) Copyright IBM Corp. 2015, 2016 # -------------------------------------------------------------------------- # gendoc: ignore from docplex.mp.engine import NoSolveEngine, ZeroSolveEngine from docplex.mp.utils import is_string from docplex.mp.error_handler import docplex_fatal class EngineFactory(object): """ A factory class that manages creation of solver instances. """ _default_engine_map = {"nosolve": NoSolveEngine, "zero": ZeroSolveEngine } def __init__(self, env=None): self._engine_types_by_agent = self._default_engine_map.copy() # no cplex engine type yet? if env is not None: self._resolve_cplex(env) def _get_engine_type_from_agent(self, agent, default_engine, default_engine_name): if agent is None: return default_engine elif is_string(agent): agent_key = agent.lower() engine_type = self._engine_types_by_agent.get(agent_key) if engine_type: return engine_type elif 'cplex' == agent_key: print('* warning: CPLEX runtime not found in path, using {0} instead'.format(default_engine_name)) return self._engine_types_by_agent.get(default_engine_name) elif '.' in agent: # assuming a qualified name, e.g. com.ibm.docplex.quantum.QuantumEngine from docplex.mp.internal.mloader import import_class try: agent_class = import_class(agent) return agent_class except ValueError as ve: print( "Cannot load agent class {0}, expecting 'cplex' or valid class path, error: {1}".format( agent, str(ve))) raise ve else: docplex_fatal("Unexpected agent name: {0}, expecting 'cplex' or valid class path", agent) else: # try a class type try: # noinspection PyUnresolvedReferences from inspect import isclass if isclass(agent): return agent except ImportError: if type(agent) == type: return agent # agent cannot be mapped to any class. docplex_fatal("* unexpected agent: {0!r} -expecting 'cplex', class or class name", agent) def _is_cplex_resolved(self): return hasattr(self, "_cplex_engine_type") def _resolve_cplex(self, env): # INTERNAL if env is None: docplex_fatal("need an environment to resolve cplex, got None") if not self._is_cplex_resolved(): if env.has_cplex: env.check_cplex_version() from docplex.mp.cplex_engine import CplexEngine self._cplex_engine_type = CplexEngine # noinspection PyTypeChecker self._engine_types_by_agent["cplex"] = CplexEngine else: self._cplex_engine_type = None def _ensure_cplex_resolved(self, env): if not self._is_cplex_resolved(): self._resolve_cplex(env) assert self._is_cplex_resolved() def new_engine(self, agent, env, model, context=None): self._ensure_cplex_resolved(env) # compute a default engine and kwargs to use.. kwargs = {} if self._cplex_engine_type: # default is CPLEX if we have it default_engine_type = self._cplex_engine_type default_engine_name = 'cplex' else: default_engine_type = NoSolveEngine default_engine_name = 'nosolve' if context is not None: kwargs['context'] = context engine_type = self._get_engine_type_from_agent(agent=agent, default_engine=default_engine_type, default_engine_name=default_engine_name) assert engine_type is not None try: return engine_type(model, **kwargs) except TypeError: docplex_fatal("agent: {0!s} failed to create instance from model, kwargs.", agent) def extend(self, new_agent, new_engine): # INTERNAL assert new_engine is not None self._engine_types_by_agent[new_agent] = new_engine

39.46281

115

0.553927

from docplex.mp.engine import NoSolveEngine, ZeroSolveEngine from docplex.mp.utils import is_string from docplex.mp.error_handler import docplex_fatal class EngineFactory(object): _default_engine_map = {"nosolve": NoSolveEngine, "zero": ZeroSolveEngine } def __init__(self, env=None): self._engine_types_by_agent = self._default_engine_map.copy() if env is not None: self._resolve_cplex(env) def _get_engine_type_from_agent(self, agent, default_engine, default_engine_name): if agent is None: return default_engine elif is_string(agent): agent_key = agent.lower() engine_type = self._engine_types_by_agent.get(agent_key) if engine_type: return engine_type elif 'cplex' == agent_key: print('* warning: CPLEX runtime not found in path, using {0} instead'.format(default_engine_name)) return self._engine_types_by_agent.get(default_engine_name) elif '.' in agent: from docplex.mp.internal.mloader import import_class try: agent_class = import_class(agent) return agent_class except ValueError as ve: print( "Cannot load agent class {0}, expecting 'cplex' or valid class path, error: {1}".format( agent, str(ve))) raise ve else: docplex_fatal("Unexpected agent name: {0}, expecting 'cplex' or valid class path", agent) else: try: from inspect import isclass if isclass(agent): return agent except ImportError: if type(agent) == type: return agent docplex_fatal("* unexpected agent: {0!r} -expecting 'cplex', class or class name", agent) def _is_cplex_resolved(self): return hasattr(self, "_cplex_engine_type") def _resolve_cplex(self, env): if env is None: docplex_fatal("need an environment to resolve cplex, got None") if not self._is_cplex_resolved(): if env.has_cplex: env.check_cplex_version() from docplex.mp.cplex_engine import CplexEngine self._cplex_engine_type = CplexEngine self._engine_types_by_agent["cplex"] = CplexEngine else: self._cplex_engine_type = None def _ensure_cplex_resolved(self, env): if not self._is_cplex_resolved(): self._resolve_cplex(env) assert self._is_cplex_resolved() def new_engine(self, agent, env, model, context=None): self._ensure_cplex_resolved(env) kwargs = {} if self._cplex_engine_type: default_engine_type = self._cplex_engine_type default_engine_name = 'cplex' else: default_engine_type = NoSolveEngine default_engine_name = 'nosolve' if context is not None: kwargs['context'] = context engine_type = self._get_engine_type_from_agent(agent=agent, default_engine=default_engine_type, default_engine_name=default_engine_name) assert engine_type is not None try: return engine_type(model, **kwargs) except TypeError: docplex_fatal("agent: {0!s} failed to create instance from model, kwargs.", agent) def extend(self, new_agent, new_engine): assert new_engine is not None self._engine_types_by_agent[new_agent] = new_engine

true

f7319da39f77cefd4e939a349a31747373e82aa7

4,711

py

Python

simple_api/django_object/django_object.py

karlosss/simple_api

03f87035c648f161d5e7a59b24f4e04bd34399f1

[ "MIT" ]

2

2020-11-13T14:00:06.000Z

2020-12-19T11:50:22.000Z

simple_api/django_object/django_object.py

karlosss/simple_api

03f87035c648f161d5e7a59b24f4e04bd34399f1

[ "MIT" ]

5

2021-02-04T14:27:43.000Z

2021-06-04T23:22:24.000Z

simple_api/django_object/django_object.py

karlosss/simple_api

03f87035c648f161d5e7a59b24f4e04bd34399f1

[ "MIT" ]

1

2021-01-06T13:54:38.000Z

from copy import deepcopy from simple_api.django_object.actions import DetailAction, ListAction, CreateAction, UpdateAction, DeleteAction from simple_api.django_object.datatypes import create_associated_list_type from simple_api.django_object.filters import generate_filters from simple_api.django_object.converter import determine_simple_api_fields from simple_api.django_object.utils import get_pk_field from simple_api.object.datatypes import StringType from simple_api.object.object import Object, ObjectMeta from simple_api.object.registry import object_storage from simple_api.django_object.registry import model_django_object_storage from simple_api.utils import ClassStub class DjangoObjectMeta(type): base_class = "simple_api.django_object.django_object.DjangoObject" def __new__(mcs, name, bases, attrs, **kwargs): cls = super().__new__(mcs, name, bases, attrs) if kwargs.get("skip", False) or object_storage.key_for_class(attrs["__module__"], name) == mcs.base_class: return cls object_stub = ClassStub(name=cls.__name__, bases=(Object,)) # set the module of the generated Object class to match the module of the user class object_stub.add_attr("__module__", cls.__module__) assert cls.model is not None, "`model` must be set." # if the class is meant to resolve relations, store it for the particular model if cls.class_for_related: model_django_object_storage.store(cls.model, cls) cls.pk_field_name, cls.pk_field = get_pk_field(cls.model) object_stub.add_attr("pk_field", cls.pk_field_name) # make sure the primary key is included, otherwise `ModelObjectAction`s would just not work if cls.only_fields and cls.pk_field_name not in cls.only_fields: cls.only_fields = cls.only_fields + (cls.pk_field_name,) elif cls.exclude_fields and cls.pk_field_name in cls.exclude_fields: cls.exclude_fields = (f for f in cls.exclude_fields if f != cls.pk_field_name) fields, input_fields, output_fields, field_validators = determine_simple_api_fields( cls.model, cls.only_fields, cls.exclude_fields, cls.custom_fields, cls.input_custom_fields, cls.output_custom_fields, ) output_fields["__str__"] = StringType(resolver=lambda *a, **kw: kw["parent_val"]()) for f in input_fields: assert f not in fields, "Redefinition of `{}` field.".format(f) cls.in_fields = {**fields, **input_fields} for f in output_fields: assert f not in fields, "Redefinition of `{}` field.".format(f) cls.out_fields = {**fields, **output_fields} object_stub.add_attr("fields", fields) object_stub.add_attr("input_fields", input_fields) object_stub.add_attr("output_fields", output_fields) # create filters and List type for potential listing actions cls.filter_type = ObjectMeta("{}Filters".format(cls.__name__), (Object,), {"fields": generate_filters(cls), "hidden": True}) object_stub.add_attr("filter_type", cls.filter_type) create_associated_list_type(cls) actions = {} if cls.detail_action is not None: actions["detail"] = deepcopy(cls.detail_action) if cls.list_action is not None: actions["list"] = deepcopy(cls.list_action) if cls.create_action is not None: actions["create"] = deepcopy(cls.create_action) if cls.update_action is not None: actions["update"] = deepcopy(cls.update_action) if cls.delete_action is not None: actions["delete"] = deepcopy(cls.delete_action) actions.update(cls.custom_actions) converted_actions = {} for action_name, action in actions.items(): action.set_parent_class(cls) action.set_name(action_name) converted_actions[action_name] = action.to_action() object_stub.add_attr("actions", converted_actions) cls._object = object_stub.build(ObjectMeta) return cls class DjangoObject(metaclass=DjangoObjectMeta): model = None auto_pk = True class_for_related = True only_fields = None exclude_fields = None custom_fields = {} input_custom_fields = {} output_custom_fields = {} detail_action = DetailAction() list_action = ListAction() create_action = CreateAction() update_action = UpdateAction() delete_action = DeleteAction() custom_actions = {} @classmethod def to_object(cls): return cls._object

39.923729

115

0.68478

from copy import deepcopy from simple_api.django_object.actions import DetailAction, ListAction, CreateAction, UpdateAction, DeleteAction from simple_api.django_object.datatypes import create_associated_list_type from simple_api.django_object.filters import generate_filters from simple_api.django_object.converter import determine_simple_api_fields from simple_api.django_object.utils import get_pk_field from simple_api.object.datatypes import StringType from simple_api.object.object import Object, ObjectMeta from simple_api.object.registry import object_storage from simple_api.django_object.registry import model_django_object_storage from simple_api.utils import ClassStub class DjangoObjectMeta(type): base_class = "simple_api.django_object.django_object.DjangoObject" def __new__(mcs, name, bases, attrs, **kwargs): cls = super().__new__(mcs, name, bases, attrs) if kwargs.get("skip", False) or object_storage.key_for_class(attrs["__module__"], name) == mcs.base_class: return cls object_stub = ClassStub(name=cls.__name__, bases=(Object,)) object_stub.add_attr("__module__", cls.__module__) assert cls.model is not None, "`model` must be set." if cls.class_for_related: model_django_object_storage.store(cls.model, cls) cls.pk_field_name, cls.pk_field = get_pk_field(cls.model) object_stub.add_attr("pk_field", cls.pk_field_name) if cls.only_fields and cls.pk_field_name not in cls.only_fields: cls.only_fields = cls.only_fields + (cls.pk_field_name,) elif cls.exclude_fields and cls.pk_field_name in cls.exclude_fields: cls.exclude_fields = (f for f in cls.exclude_fields if f != cls.pk_field_name) fields, input_fields, output_fields, field_validators = determine_simple_api_fields( cls.model, cls.only_fields, cls.exclude_fields, cls.custom_fields, cls.input_custom_fields, cls.output_custom_fields, ) output_fields["__str__"] = StringType(resolver=lambda *a, **kw: kw["parent_val"]()) for f in input_fields: assert f not in fields, "Redefinition of `{}` field.".format(f) cls.in_fields = {**fields, **input_fields} for f in output_fields: assert f not in fields, "Redefinition of `{}` field.".format(f) cls.out_fields = {**fields, **output_fields} object_stub.add_attr("fields", fields) object_stub.add_attr("input_fields", input_fields) object_stub.add_attr("output_fields", output_fields) cls.filter_type = ObjectMeta("{}Filters".format(cls.__name__), (Object,), {"fields": generate_filters(cls), "hidden": True}) object_stub.add_attr("filter_type", cls.filter_type) create_associated_list_type(cls) actions = {} if cls.detail_action is not None: actions["detail"] = deepcopy(cls.detail_action) if cls.list_action is not None: actions["list"] = deepcopy(cls.list_action) if cls.create_action is not None: actions["create"] = deepcopy(cls.create_action) if cls.update_action is not None: actions["update"] = deepcopy(cls.update_action) if cls.delete_action is not None: actions["delete"] = deepcopy(cls.delete_action) actions.update(cls.custom_actions) converted_actions = {} for action_name, action in actions.items(): action.set_parent_class(cls) action.set_name(action_name) converted_actions[action_name] = action.to_action() object_stub.add_attr("actions", converted_actions) cls._object = object_stub.build(ObjectMeta) return cls class DjangoObject(metaclass=DjangoObjectMeta): model = None auto_pk = True class_for_related = True only_fields = None exclude_fields = None custom_fields = {} input_custom_fields = {} output_custom_fields = {} detail_action = DetailAction() list_action = ListAction() create_action = CreateAction() update_action = UpdateAction() delete_action = DeleteAction() custom_actions = {} @classmethod def to_object(cls): return cls._object

true

f7319df270d89691854f3a1b9a62c1231d3c7bfe

8,177

py

Python

python/paddle/fluid/tests/unittests/xpu/test_expand_v2_op_xpu.py

L-Net-1992/Paddle

4d0ca02ba56760b456f3d4b42a538555b9b6c307

[ "Apache-2.0" ]

11

2016-08-29T07:43:26.000Z

2016-08-29T07:51:24.000Z

python/paddle/fluid/tests/unittests/xpu/test_expand_v2_op_xpu.py

L-Net-1992/Paddle

4d0ca02ba56760b456f3d4b42a538555b9b6c307

[ "Apache-2.0" ]

null

python/paddle/fluid/tests/unittests/xpu/test_expand_v2_op_xpu.py

L-Net-1992/Paddle

4d0ca02ba56760b456f3d4b42a538555b9b6c307

[ "Apache-2.0" ]

1

2021-12-09T08:59:17.000Z

# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import print_function import unittest import sys import numpy as np sys.path.append("..") from op_test import OpTest from op_test_xpu import XPUOpTest import paddle.fluid as fluid from paddle.fluid import Program, program_guard import paddle from xpu.get_test_cover_info import create_test_class, get_xpu_op_support_types, XPUOpTestWrapper paddle.enable_static() np.random.seed(10) # CANN Op Support X: float32, int32, int64 # Situation 1: shape is a list(without tensor) class XPUTestExpandV2Op(XPUOpTestWrapper): def __init__(self): self.op_name = 'expand_v2' self.use_dynamic_create_class = False class TestExpandV2XPUOp(XPUOpTest): def setUp(self): self.init_dtype() self.set_xpu() self.op_type = "expand_v2" self.place = paddle.XPUPlace(0) self.init_data() self.inputs = { 'X': np.random.random(self.ori_shape).astype(self.dtype) } self.attrs = {'shape': self.shape} output = np.tile(self.inputs['X'], self.expand_times) self.outputs = {'Out': output} def init_dtype(self): self.dtype = self.in_type def set_xpu(self): self.__class__.use_xpu = True self.__class__.no_need_check_grad = True def init_data(self): self.ori_shape = [100] self.shape = [100] self.expand_times = [1] def test_check_output(self): self.check_output_with_place(self.place) class TestExpandV2OpRank2_DimExpanding(TestExpandV2XPUOp): def init_data(self): self.ori_shape = [120] self.shape = [2, 120] self.expand_times = [2, 1] class TestExpandV2OpRank2(TestExpandV2XPUOp): def init_data(self): self.ori_shape = [1, 140] self.shape = [12, 140] self.expand_times = [12, 1] class TestExpandV2OpRank3_Corner(TestExpandV2XPUOp): def init_data(self): self.ori_shape = (2, 10, 5) self.shape = (2, 10, 5) self.expand_times = (1, 1, 1) class TestExpandV2OpRank4(TestExpandV2XPUOp): def init_data(self): self.ori_shape = (2, 4, 5, 7) self.shape = (-1, -1, -1, -1) self.expand_times = (1, 1, 1, 1) class TestExpandV2OpRank5(TestExpandV2XPUOp): def init_data(self): self.ori_shape = (2, 4, 1, 15) self.shape = (2, -1, 4, -1) self.expand_times = (1, 1, 4, 1) class TestExpandV2OpRank6(TestExpandV2XPUOp): def init_data(self): self.ori_shape = (4, 1, 30) self.shape = (2, -1, 4, 30) self.expand_times = (2, 1, 4, 1) # Situation 2: shape is a list(with tensor) class TestExpandV2OpXPURank1_tensor_attr(TestExpandV2XPUOp): def setUp(self): self.set_xpu() self.place = paddle.XPUPlace(0) self.op_type = "expand_v2" self.init_data() self.dtype = np.float32 expand_shapes_tensor = [] for index, ele in enumerate(self.expand_shape): expand_shapes_tensor.append(("x" + str(index), np.ones( (1)).astype('int32') * ele)) self.inputs = { 'X': np.random.random(self.ori_shape).astype(self.dtype), 'expand_shapes_tensor': expand_shapes_tensor, } self.attrs = {"shape": self.infer_expand_shape} output = np.tile(self.inputs['X'], self.expand_times) self.outputs = {'Out': output} def init_data(self): self.ori_shape = [100] self.expand_times = [1] self.expand_shape = [100] self.infer_expand_shape = [-1] class TestExpandV2OpRank2_Corner_tensor_attr( TestExpandV2OpXPURank1_tensor_attr): def init_data(self): self.ori_shape = [12, 14] self.expand_times = [1, 1] self.expand_shape = [12, 14] self.infer_expand_shape = [12, -1] # Situation 3: shape is a tensor class TestExpandV2XPUOp_tensor(TestExpandV2XPUOp): def setUp(self): self.set_xpu() self.place = paddle.XPUPlace(0) self.op_type = "expand_v2" self.init_data() self.dtype = np.float32 self.inputs = { 'X': np.random.random(self.ori_shape).astype(self.dtype), 'Shape': np.array(self.expand_shape).astype("int32"), } self.attrs = {} output = np.tile(self.inputs['X'], self.expand_times) self.outputs = {'Out': output} def init_data(self): self.ori_shape = [100] self.expand_times = [2, 1] self.expand_shape = [2, 100] # Situation 5: input x is int32 # skip grad check for int32 class TestExpandV2OpInteger(XPUOpTest): def init_type(self): self.dtype = 'int32' def setUp(self): self.set_xpu() self.init_type() self.place = paddle.XPUPlace(0) self.op_type = "expand_v2" self.inputs = { 'X': np.random.randint(10, size=(2, 4, 20)).astype(self.dtype) } self.attrs = {'shape': [2, 4, 20]} output = np.tile(self.inputs['X'], (1, 1, 1)) self.outputs = {'Out': output} def set_xpu(self): self.__class__.use_xpu = True def test_check_output(self): self.check_output_with_place(self.place) def test_check_grad(self): pass # Test python API class TestExpandV2API(unittest.TestCase): def test_static(self): with fluid.program_guard(fluid.Program(), fluid.Program()): input = np.random.random([12, 14]).astype("float32") x = fluid.layers.data(name='x', shape=[12, 14], append_batch_size=False, dtype="float32") positive_2 = fluid.layers.fill_constant([1], "int32", 12) expand_shape = fluid.layers.data(name="expand_shape", shape=[2], append_batch_size=False, dtype="int32") out_1 = paddle.expand(x, shape=[12, 14]) out_2 = paddle.expand(x, shape=[positive_2, 14]) out_3 = paddle.expand(x, shape=expand_shape) g0 = fluid.backward.calc_gradient(out_2, x) exe = fluid.Executor(place=paddle.XPUPlace(0)) res_1, res_2, res_3 = exe.run(fluid.default_main_program(), feed={ "x": input, "expand_shape": np.array([12, 14]).astype("int32") }, fetch_list=[out_1, out_2, out_3]) assert np.array_equal(res_1, np.tile(input, (1, 1))) assert np.array_equal(res_2, np.tile(input, (1, 1))) assert np.array_equal(res_3, np.tile(input, (1, 1))) support_types = get_xpu_op_support_types('expand_v2') for stype in support_types: create_test_class(globals(), XPUTestExpandV2Op, stype) if __name__ == "__main__": unittest.main()

33.239837

97

0.559863

from __future__ import print_function import unittest import sys import numpy as np sys.path.append("..") from op_test import OpTest from op_test_xpu import XPUOpTest import paddle.fluid as fluid from paddle.fluid import Program, program_guard import paddle from xpu.get_test_cover_info import create_test_class, get_xpu_op_support_types, XPUOpTestWrapper paddle.enable_static() np.random.seed(10) class XPUTestExpandV2Op(XPUOpTestWrapper): def __init__(self): self.op_name = 'expand_v2' self.use_dynamic_create_class = False class TestExpandV2XPUOp(XPUOpTest): def setUp(self): self.init_dtype() self.set_xpu() self.op_type = "expand_v2" self.place = paddle.XPUPlace(0) self.init_data() self.inputs = { 'X': np.random.random(self.ori_shape).astype(self.dtype) } self.attrs = {'shape': self.shape} output = np.tile(self.inputs['X'], self.expand_times) self.outputs = {'Out': output} def init_dtype(self): self.dtype = self.in_type def set_xpu(self): self.__class__.use_xpu = True self.__class__.no_need_check_grad = True def init_data(self): self.ori_shape = [100] self.shape = [100] self.expand_times = [1] def test_check_output(self): self.check_output_with_place(self.place) class TestExpandV2OpRank2_DimExpanding(TestExpandV2XPUOp): def init_data(self): self.ori_shape = [120] self.shape = [2, 120] self.expand_times = [2, 1] class TestExpandV2OpRank2(TestExpandV2XPUOp): def init_data(self): self.ori_shape = [1, 140] self.shape = [12, 140] self.expand_times = [12, 1] class TestExpandV2OpRank3_Corner(TestExpandV2XPUOp): def init_data(self): self.ori_shape = (2, 10, 5) self.shape = (2, 10, 5) self.expand_times = (1, 1, 1) class TestExpandV2OpRank4(TestExpandV2XPUOp): def init_data(self): self.ori_shape = (2, 4, 5, 7) self.shape = (-1, -1, -1, -1) self.expand_times = (1, 1, 1, 1) class TestExpandV2OpRank5(TestExpandV2XPUOp): def init_data(self): self.ori_shape = (2, 4, 1, 15) self.shape = (2, -1, 4, -1) self.expand_times = (1, 1, 4, 1) class TestExpandV2OpRank6(TestExpandV2XPUOp): def init_data(self): self.ori_shape = (4, 1, 30) self.shape = (2, -1, 4, 30) self.expand_times = (2, 1, 4, 1) class TestExpandV2OpXPURank1_tensor_attr(TestExpandV2XPUOp): def setUp(self): self.set_xpu() self.place = paddle.XPUPlace(0) self.op_type = "expand_v2" self.init_data() self.dtype = np.float32 expand_shapes_tensor = [] for index, ele in enumerate(self.expand_shape): expand_shapes_tensor.append(("x" + str(index), np.ones( (1)).astype('int32') * ele)) self.inputs = { 'X': np.random.random(self.ori_shape).astype(self.dtype), 'expand_shapes_tensor': expand_shapes_tensor, } self.attrs = {"shape": self.infer_expand_shape} output = np.tile(self.inputs['X'], self.expand_times) self.outputs = {'Out': output} def init_data(self): self.ori_shape = [100] self.expand_times = [1] self.expand_shape = [100] self.infer_expand_shape = [-1] class TestExpandV2OpRank2_Corner_tensor_attr( TestExpandV2OpXPURank1_tensor_attr): def init_data(self): self.ori_shape = [12, 14] self.expand_times = [1, 1] self.expand_shape = [12, 14] self.infer_expand_shape = [12, -1] class TestExpandV2XPUOp_tensor(TestExpandV2XPUOp): def setUp(self): self.set_xpu() self.place = paddle.XPUPlace(0) self.op_type = "expand_v2" self.init_data() self.dtype = np.float32 self.inputs = { 'X': np.random.random(self.ori_shape).astype(self.dtype), 'Shape': np.array(self.expand_shape).astype("int32"), } self.attrs = {} output = np.tile(self.inputs['X'], self.expand_times) self.outputs = {'Out': output} def init_data(self): self.ori_shape = [100] self.expand_times = [2, 1] self.expand_shape = [2, 100] class TestExpandV2OpInteger(XPUOpTest): def init_type(self): self.dtype = 'int32' def setUp(self): self.set_xpu() self.init_type() self.place = paddle.XPUPlace(0) self.op_type = "expand_v2" self.inputs = { 'X': np.random.randint(10, size=(2, 4, 20)).astype(self.dtype) } self.attrs = {'shape': [2, 4, 20]} output = np.tile(self.inputs['X'], (1, 1, 1)) self.outputs = {'Out': output} def set_xpu(self): self.__class__.use_xpu = True def test_check_output(self): self.check_output_with_place(self.place) def test_check_grad(self): pass class TestExpandV2API(unittest.TestCase): def test_static(self): with fluid.program_guard(fluid.Program(), fluid.Program()): input = np.random.random([12, 14]).astype("float32") x = fluid.layers.data(name='x', shape=[12, 14], append_batch_size=False, dtype="float32") positive_2 = fluid.layers.fill_constant([1], "int32", 12) expand_shape = fluid.layers.data(name="expand_shape", shape=[2], append_batch_size=False, dtype="int32") out_1 = paddle.expand(x, shape=[12, 14]) out_2 = paddle.expand(x, shape=[positive_2, 14]) out_3 = paddle.expand(x, shape=expand_shape) g0 = fluid.backward.calc_gradient(out_2, x) exe = fluid.Executor(place=paddle.XPUPlace(0)) res_1, res_2, res_3 = exe.run(fluid.default_main_program(), feed={ "x": input, "expand_shape": np.array([12, 14]).astype("int32") }, fetch_list=[out_1, out_2, out_3]) assert np.array_equal(res_1, np.tile(input, (1, 1))) assert np.array_equal(res_2, np.tile(input, (1, 1))) assert np.array_equal(res_3, np.tile(input, (1, 1))) support_types = get_xpu_op_support_types('expand_v2') for stype in support_types: create_test_class(globals(), XPUTestExpandV2Op, stype) if __name__ == "__main__": unittest.main()

true

f7319df5d2cfd54a36d3021fcea5ac6d789dee56

7,191

py

Python

mapillary_tools/commands/process.py

kudarisenmon/mapillary_tools

ef3201847ece95a0bbeb95187e38a0a84309a2b7

[ "BSD-2-Clause" ]

null

mapillary_tools/commands/process.py

kudarisenmon/mapillary_tools

ef3201847ece95a0bbeb95187e38a0a84309a2b7

[ "BSD-2-Clause" ]

null

mapillary_tools/commands/process.py

kudarisenmon/mapillary_tools

ef3201847ece95a0bbeb95187e38a0a84309a2b7

[ "BSD-2-Clause" ]

null

import inspect from mapillary_tools.process_user_properties import process_user_properties from mapillary_tools.process_import_meta_properties import process_import_meta_properties from mapillary_tools.process_geotag_properties import process_geotag_properties from mapillary_tools.process_sequence_properties import process_sequence_properties from mapillary_tools.process_upload_params import process_upload_params from mapillary_tools.insert_MAPJson import insert_MAPJson class Command: name = 'process' help = 'Main tool : Process image meta data and insert it in image EXIF.' def add_basic_arguments(self, parser): parser.add_argument( '--rerun', help='rerun the processing', action='store_true', required=False) # user properties # user name for the import parser.add_argument("--user_name", help="user name", required=True) # organization level parameters parser.add_argument( '--organization_username', help="Specify organization user name", default=None, required=False) parser.add_argument( '--organization_key', help="Specify organization key", default=None, required=False) parser.add_argument('--private', help="Specify whether the import is private", action='store_true', default=False, required=False) parser.add_argument( '--skip_subfolders', help='Skip all subfolders and import only the images in the given directory path.', action='store_true', default=False, required=False) def add_advanced_arguments(self, parser): # master upload parser.add_argument('--master_upload', help='Process images with a master key, note: only used by Mapillary employees', action='store_true', default=False, required=False) #import meta parser.add_argument( "--device_make", help="Specify device manufacturer. Note this input has precedence over the input read from the import source file.", default=None, required=False) parser.add_argument( "--device_model", help="Specify device model. Note this input has precedence over the input read from the import source file.", default=None, required=False) parser.add_argument( '--add_file_name', help="Add original file name to EXIF. Note this input has precedence over the input read from the import source file.", action='store_true', required=False) parser.add_argument( '--add_import_date', help="Add import date.", action='store_true', required=False) parser.add_argument('--orientation', help='Specify the image orientation in degrees. Note this might result in image rotation. Note this input has precedence over the input read from the import source file.', choices=[0, 90, 180, 270], type=int, default=None, required=False) parser.add_argument( "--GPS_accuracy", help="GPS accuracy in meters. Note this input has precedence over the input read from the import source file.", default=None, required=False) # geotagging parser.add_argument('--geotag_source', help='Provide the source of date/time and gps information needed for geotagging.', action='store', choices=['exif', 'gpx', 'gopro_video', 'nmea'], default="exif", required=False) parser.add_argument( '--geotag_source_path', help='Provide the path to the file source of date/time and gps information needed for geotagging.', action='store', default=None, required=False) parser.add_argument( '--local_time', help='Assume image timestamps are in your local time', action='store_true', default=False, required=False) parser.add_argument('--sub_second_interval', help='Sub second time between shots. Used to set image times with sub-second precision', type=float, default=0.0, required=False) parser.add_argument('--offset_time', default=0., type=float, help='time offset between the camera and the gps device, in seconds.', required=False) parser.add_argument('--offset_angle', default=0., type=float, help='offset camera angle (90 for right facing, 180 for rear facing, -90 for left facing)', required=False) parser.add_argument("--use_gps_start_time", help="Use GPS trace starting time in case of derivating timestamp from filename.", action="store_true", default=False, required=False) # sequence parser.add_argument('--cutoff_distance', default=600., type=float, help='maximum gps distance in meters within a sequence', required=False) parser.add_argument('--cutoff_time', default=60., type=float, help='maximum time interval in seconds within a sequence', required=False) parser.add_argument('--interpolate_directions', help='perform interploation of directions', action='store_true', required=False) parser.add_argument('--flag_duplicates', help='flag duplicates', action='store_true', required=False) parser.add_argument('--duplicate_distance', help='max distance for two images to be considered duplicates in meters', type=float, default=0.1, required=False) parser.add_argument( '--duplicate_angle', help='max angle for two images to be considered duplicates in degrees', type=float, default=5, required=False) # EXIF insert parser.add_argument('--skip_EXIF_insert', help='Skip inserting the extracted data into image EXIF.', action='store_true', default=False, required=False) parser.add_argument('--keep_original', help='Do not overwrite original images, instead save the processed images in a new directory by adding suffix "_processed" to the import_path.', action='store_true', default=False, required=False) def run(self, args): vars_args = vars(args) process_user_properties(**({k: v for k, v in vars_args.iteritems() if k in inspect.getargspec(process_user_properties).args})) process_import_meta_properties( **({k: v for k, v in vars_args.iteritems() if k in inspect.getargspec(process_import_meta_properties).args})) process_geotag_properties( **({k: v for k, v in vars_args.iteritems() if k in inspect.getargspec(process_geotag_properties).args})) process_sequence_properties( **({k: v for k, v in vars_args.iteritems() if k in inspect.getargspec(process_sequence_properties).args})) process_upload_params(**({k: v for k, v in vars_args.iteritems() if k in inspect.getargspec(process_upload_params).args})) insert_MAPJson(**({k: v for k, v in vars_args.iteritems() if k in inspect.getargspec(insert_MAPJson).args})) print("Process done.")

67.205607

216

0.667779

import inspect from mapillary_tools.process_user_properties import process_user_properties from mapillary_tools.process_import_meta_properties import process_import_meta_properties from mapillary_tools.process_geotag_properties import process_geotag_properties from mapillary_tools.process_sequence_properties import process_sequence_properties from mapillary_tools.process_upload_params import process_upload_params from mapillary_tools.insert_MAPJson import insert_MAPJson class Command: name = 'process' help = 'Main tool : Process image meta data and insert it in image EXIF.' def add_basic_arguments(self, parser): parser.add_argument( '--rerun', help='rerun the processing', action='store_true', required=False) parser.add_argument("--user_name", help="user name", required=True) parser.add_argument( '--organization_username', help="Specify organization user name", default=None, required=False) parser.add_argument( '--organization_key', help="Specify organization key", default=None, required=False) parser.add_argument('--private', help="Specify whether the import is private", action='store_true', default=False, required=False) parser.add_argument( '--skip_subfolders', help='Skip all subfolders and import only the images in the given directory path.', action='store_true', default=False, required=False) def add_advanced_arguments(self, parser): parser.add_argument('--master_upload', help='Process images with a master key, note: only used by Mapillary employees', action='store_true', default=False, required=False) parser.add_argument( "--device_make", help="Specify device manufacturer. Note this input has precedence over the input read from the import source file.", default=None, required=False) parser.add_argument( "--device_model", help="Specify device model. Note this input has precedence over the input read from the import source file.", default=None, required=False) parser.add_argument( '--add_file_name', help="Add original file name to EXIF. Note this input has precedence over the input read from the import source file.", action='store_true', required=False) parser.add_argument( '--add_import_date', help="Add import date.", action='store_true', required=False) parser.add_argument('--orientation', help='Specify the image orientation in degrees. Note this might result in image rotation. Note this input has precedence over the input read from the import source file.', choices=[0, 90, 180, 270], type=int, default=None, required=False) parser.add_argument( "--GPS_accuracy", help="GPS accuracy in meters. Note this input has precedence over the input read from the import source file.", default=None, required=False) parser.add_argument('--geotag_source', help='Provide the source of date/time and gps information needed for geotagging.', action='store', choices=['exif', 'gpx', 'gopro_video', 'nmea'], default="exif", required=False) parser.add_argument( '--geotag_source_path', help='Provide the path to the file source of date/time and gps information needed for geotagging.', action='store', default=None, required=False) parser.add_argument( '--local_time', help='Assume image timestamps are in your local time', action='store_true', default=False, required=False) parser.add_argument('--sub_second_interval', help='Sub second time between shots. Used to set image times with sub-second precision', type=float, default=0.0, required=False) parser.add_argument('--offset_time', default=0., type=float, help='time offset between the camera and the gps device, in seconds.', required=False) parser.add_argument('--offset_angle', default=0., type=float, help='offset camera angle (90 for right facing, 180 for rear facing, -90 for left facing)', required=False) parser.add_argument("--use_gps_start_time", help="Use GPS trace starting time in case of derivating timestamp from filename.", action="store_true", default=False, required=False) parser.add_argument('--cutoff_distance', default=600., type=float, help='maximum gps distance in meters within a sequence', required=False) parser.add_argument('--cutoff_time', default=60., type=float, help='maximum time interval in seconds within a sequence', required=False) parser.add_argument('--interpolate_directions', help='perform interploation of directions', action='store_true', required=False) parser.add_argument('--flag_duplicates', help='flag duplicates', action='store_true', required=False) parser.add_argument('--duplicate_distance', help='max distance for two images to be considered duplicates in meters', type=float, default=0.1, required=False) parser.add_argument( '--duplicate_angle', help='max angle for two images to be considered duplicates in degrees', type=float, default=5, required=False) parser.add_argument('--skip_EXIF_insert', help='Skip inserting the extracted data into image EXIF.', action='store_true', default=False, required=False) parser.add_argument('--keep_original', help='Do not overwrite original images, instead save the processed images in a new directory by adding suffix "_processed" to the import_path.', action='store_true', default=False, required=False) def run(self, args): vars_args = vars(args) process_user_properties(**({k: v for k, v in vars_args.iteritems() if k in inspect.getargspec(process_user_properties).args})) process_import_meta_properties( **({k: v for k, v in vars_args.iteritems() if k in inspect.getargspec(process_import_meta_properties).args})) process_geotag_properties( **({k: v for k, v in vars_args.iteritems() if k in inspect.getargspec(process_geotag_properties).args})) process_sequence_properties( **({k: v for k, v in vars_args.iteritems() if k in inspect.getargspec(process_sequence_properties).args})) process_upload_params(**({k: v for k, v in vars_args.iteritems() if k in inspect.getargspec(process_upload_params).args})) insert_MAPJson(**({k: v for k, v in vars_args.iteritems() if k in inspect.getargspec(insert_MAPJson).args})) print("Process done.")

true

f7319e3e7b4ea41930c29754506d03bd52eac137

1,166

py

Python

envs/cell_place_gym/native/acp_state.py

Yvette1993/spinningup

5094cf291fa24cf93d58b4507dab56dafe73dac1

[ "MIT" ]

null

envs/cell_place_gym/native/acp_state.py

Yvette1993/spinningup

5094cf291fa24cf93d58b4507dab56dafe73dac1

[ "MIT" ]

null

envs/cell_place_gym/native/acp_state.py

Yvette1993/spinningup

5094cf291fa24cf93d58b4507dab56dafe73dac1

[ "MIT" ]

null

import numpy as np from cell_place_gym.native.acp_placement import * class acp_placement_state (object): def __init__ (self, place): self.place = place self.design = place.design l_inst_count = len (self.design.instances) # Non-Placed Nets Matrix self.c_matrix = np.zeros (shape = (l_inst_count, l_inst_count)) # Placed Nets Matrix self.n_matrix = np.zeros (shape = (l_inst_count, l_inst_count)) def get_state (self): nets = self.design.nets self.c_matrix [:,:] = 0 self.n_matrix [:,:] = 0 for n in nets: src = n.net_source src_id = src.get_inst_id () src_placed = src.is_placed () src_position = src.get_position () for dst in n.net_dests: dst_id = dst.get_inst_id () dst_placed = dst.is_placed () dst_position = dst.get_position () if src_placed and dst_placed: self.n_matrix [src_id][dst_id] = 1 else: self.n_matrix [src_id][dst_id] = 1 return

34.294118

71

0.542882

import numpy as np from cell_place_gym.native.acp_placement import * class acp_placement_state (object): def __init__ (self, place): self.place = place self.design = place.design l_inst_count = len (self.design.instances) self.c_matrix = np.zeros (shape = (l_inst_count, l_inst_count)) self.n_matrix = np.zeros (shape = (l_inst_count, l_inst_count)) def get_state (self): nets = self.design.nets self.c_matrix [:,:] = 0 self.n_matrix [:,:] = 0 for n in nets: src = n.net_source src_id = src.get_inst_id () src_placed = src.is_placed () src_position = src.get_position () for dst in n.net_dests: dst_id = dst.get_inst_id () dst_placed = dst.is_placed () dst_position = dst.get_position () if src_placed and dst_placed: self.n_matrix [src_id][dst_id] = 1 else: self.n_matrix [src_id][dst_id] = 1 return

true

f7319e483d3b8d072192a8a4e21ee30c26c57e8a

2,638

py

Python

homeassistant/components/sonos/entity.py

DavidDeSloovere/core

909a20b36d4df6724c955c2ae28cb82fe6d50c2e

[ "Apache-2.0" ]

1

2019-08-19T18:18:50.000Z

homeassistant/components/sonos/entity.py

jagadeeshvenkatesh/core

1bd982668449815fee2105478569f8e4b5670add

[ "Apache-2.0" ]

70

2020-08-05T07:20:00.000Z

2022-03-31T06:01:46.000Z

homeassistant/components/sonos/entity.py

jagadeeshvenkatesh/core

1bd982668449815fee2105478569f8e4b5670add

[ "Apache-2.0" ]

1

2021-10-29T22:10:26.000Z

"""Entity representing a Sonos player.""" from __future__ import annotations import logging from pysonos.core import SoCo import homeassistant.helpers.device_registry as dr from homeassistant.helpers.dispatcher import ( async_dispatcher_connect, async_dispatcher_send, ) from homeassistant.helpers.entity import DeviceInfo, Entity from .const import ( DOMAIN, SONOS_ENTITY_CREATED, SONOS_ENTITY_UPDATE, SONOS_STATE_UPDATED, ) from .speaker import SonosSpeaker _LOGGER = logging.getLogger(__name__) class SonosEntity(Entity): """Representation of a Sonos entity.""" def __init__(self, speaker: SonosSpeaker) -> None: """Initialize a SonosEntity.""" self.speaker = speaker async def async_added_to_hass(self) -> None: """Handle common setup when added to hass.""" await self.speaker.async_seen() self.async_on_remove( async_dispatcher_connect( self.hass, f"{SONOS_ENTITY_UPDATE}-{self.soco.uid}", self.async_update, # pylint: disable=no-member ) ) self.async_on_remove( async_dispatcher_connect( self.hass, f"{SONOS_STATE_UPDATED}-{self.soco.uid}", self.async_write_ha_state, ) ) @property def soco(self) -> SoCo: """Return the speaker SoCo instance.""" return self.speaker.soco @property def device_info(self) -> DeviceInfo: """Return information about the device.""" return { "identifiers": {(DOMAIN, self.soco.uid)}, "name": self.speaker.zone_name, "model": self.speaker.model_name.replace("Sonos ", ""), "sw_version": self.speaker.version, "connections": {(dr.CONNECTION_NETWORK_MAC, self.speaker.mac_address)}, "manufacturer": "Sonos", "suggested_area": self.speaker.zone_name, } @property def available(self) -> bool: """Return whether this device is available.""" return self.speaker.available @property def should_poll(self) -> bool: """Return that we should not be polled (we handle that internally).""" return False class SonosSensorEntity(SonosEntity): """Representation of a Sonos sensor entity.""" async def async_added_to_hass(self) -> None: """Handle common setup when added to hass.""" await super().async_added_to_hass() async_dispatcher_send( self.hass, f"{SONOS_ENTITY_CREATED}-{self.soco.uid}", self.platform.domain )

29.311111

86

0.628886

from __future__ import annotations import logging from pysonos.core import SoCo import homeassistant.helpers.device_registry as dr from homeassistant.helpers.dispatcher import ( async_dispatcher_connect, async_dispatcher_send, ) from homeassistant.helpers.entity import DeviceInfo, Entity from .const import ( DOMAIN, SONOS_ENTITY_CREATED, SONOS_ENTITY_UPDATE, SONOS_STATE_UPDATED, ) from .speaker import SonosSpeaker _LOGGER = logging.getLogger(__name__) class SonosEntity(Entity): def __init__(self, speaker: SonosSpeaker) -> None: self.speaker = speaker async def async_added_to_hass(self) -> None: await self.speaker.async_seen() self.async_on_remove( async_dispatcher_connect( self.hass, f"{SONOS_ENTITY_UPDATE}-{self.soco.uid}", self.async_update, ) ) self.async_on_remove( async_dispatcher_connect( self.hass, f"{SONOS_STATE_UPDATED}-{self.soco.uid}", self.async_write_ha_state, ) ) @property def soco(self) -> SoCo: return self.speaker.soco @property def device_info(self) -> DeviceInfo: return { "identifiers": {(DOMAIN, self.soco.uid)}, "name": self.speaker.zone_name, "model": self.speaker.model_name.replace("Sonos ", ""), "sw_version": self.speaker.version, "connections": {(dr.CONNECTION_NETWORK_MAC, self.speaker.mac_address)}, "manufacturer": "Sonos", "suggested_area": self.speaker.zone_name, } @property def available(self) -> bool: return self.speaker.available @property def should_poll(self) -> bool: return False class SonosSensorEntity(SonosEntity): async def async_added_to_hass(self) -> None: await super().async_added_to_hass() async_dispatcher_send( self.hass, f"{SONOS_ENTITY_CREATED}-{self.soco.uid}", self.platform.domain )

true

f7319eef72e8d23633c8d8b5b9c928a276dda952

912

py

Python

vimfiles/bundle/vim-python/submodules/pylint/tests/functional/n/not_in_loop.py

ciskoinch8/vimrc

5bf77a7e7bc70fac5173ab2e9ea05d7dda3e52b8

[ "MIT" ]

463

2015-01-15T08:17:42.000Z

2022-03-28T15:10:20.000Z

vimfiles/bundle/vim-python/submodules/pylint/tests/functional/n/not_in_loop.py

ciskoinch8/vimrc

5bf77a7e7bc70fac5173ab2e9ea05d7dda3e52b8

[ "MIT" ]

52

2015-01-06T02:43:59.000Z

2022-03-14T11:15:21.000Z

vimfiles/bundle/vim-python/submodules/pylint/tests/functional/n/not_in_loop.py

ciskoinch8/vimrc

5bf77a7e7bc70fac5173ab2e9ea05d7dda3e52b8

[ "MIT" ]

249

2015-01-07T22:49:49.000Z

2022-03-18T02:32:06.000Z

"""Test that not-in-loop is detected properly.""" # pylint: disable=missing-docstring, invalid-name, too-few-public-methods # pylint: disable=useless-else-on-loop, using-constant-test, useless-object-inheritance # pylint: disable=no-else-continue while True: def ala(): continue # [not-in-loop] while True: pass else: continue # [not-in-loop] def lala(): continue # [not-in-loop] while True: class A(object): continue # [not-in-loop] for _ in range(10): pass else: continue # [not-in-loop] for _ in range(42): pass else: break # [not-in-loop] if True: continue # [not-in-loop] else: break # [not-in-loop] for _ in range(10): for _ in range(20): pass else: continue while True: while True: break else: break break else: pass for _ in range(1): continue for _ in range(42): break

16.285714

87

0.612939

while True: def ala(): continue while True: pass else: continue def lala(): continue while True: class A(object): continue for _ in range(10): pass else: continue for _ in range(42): pass else: break if True: continue else: break for _ in range(10): for _ in range(20): pass else: continue while True: while True: break else: break break else: pass for _ in range(1): continue for _ in range(42): break

true

f7319f7dedc4d4bfa733e08b15bd1217c7611f0d

860

py

Python

wagtail_localize/migrations/0004_one_source_per_objectlocale.py

KalobTaulien/wagtail-localize

e513d18dea5f76f6941f1acf55f945150de767eb

[ "BSD-3-Clause" ]

123

2019-11-21T12:55:04.000Z

2022-03-23T08:08:47.000Z

wagtail_localize/migrations/0004_one_source_per_objectlocale.py

KalobTaulien/wagtail-localize

e513d18dea5f76f6941f1acf55f945150de767eb

[ "BSD-3-Clause" ]

334

2019-11-20T10:40:08.000Z

2022-03-27T17:33:01.000Z

wagtail_localize/migrations/0004_one_source_per_objectlocale.py

KalobTaulien/wagtail-localize

e513d18dea5f76f6941f1acf55f945150de767eb

[ "BSD-3-Clause" ]

41

2020-01-16T17:24:52.000Z

2022-03-28T13:09:59.000Z

# Generated by Django 3.0.8 on 2020-08-05 09:33 from django.db import migrations, models import django.db.models.deletion import django.utils.timezone class Migration(migrations.Migration): dependencies = [ ('wagtail_localize', '0003_delete_translation_sources'), ] operations = [ migrations.AddField( model_name='translationsource', name='last_updated_at', field=models.DateTimeField(default=django.utils.timezone.now), preserve_default=False, ), migrations.AlterField( model_name='translationsource', name='created_at', field=models.DateTimeField(auto_now_add=True), ), migrations.AlterUniqueTogether( name='translationsource', unique_together={('object', 'locale')}, ), ]

27.741935

74

0.626744

from django.db import migrations, models import django.db.models.deletion import django.utils.timezone class Migration(migrations.Migration): dependencies = [ ('wagtail_localize', '0003_delete_translation_sources'), ] operations = [ migrations.AddField( model_name='translationsource', name='last_updated_at', field=models.DateTimeField(default=django.utils.timezone.now), preserve_default=False, ), migrations.AlterField( model_name='translationsource', name='created_at', field=models.DateTimeField(auto_now_add=True), ), migrations.AlterUniqueTogether( name='translationsource', unique_together={('object', 'locale')}, ), ]

true

f731a03ce0e6675d8bd15187040163106262826b

23,980

py

Python

awx/main/models/notifications.py

sganesh999/awx

783a6dc5111e1757982bf7f5e1bb4bb197d4f199

[ "Apache-2.0" ]

null

awx/main/models/notifications.py

sganesh999/awx

783a6dc5111e1757982bf7f5e1bb4bb197d4f199

[ "Apache-2.0" ]

null

awx/main/models/notifications.py

sganesh999/awx

783a6dc5111e1757982bf7f5e1bb4bb197d4f199

[ "Apache-2.0" ]

null

# Copyright (c) 2016 Ansible, Inc. # All Rights Reserved. from copy import deepcopy import datetime import logging import json from django.db import models from django.conf import settings from django.core.mail.message import EmailMessage from django.db import connection from django.utils.translation import ugettext_lazy as _ from django.utils.encoding import smart_str, force_text from jinja2 import sandbox from jinja2.exceptions import TemplateSyntaxError, UndefinedError, SecurityError # AWX from awx.api.versioning import reverse from awx.main.models.base import CommonModelNameNotUnique, CreatedModifiedModel, prevent_search from awx.main.utils import encrypt_field, decrypt_field, set_environ from awx.main.notifications.email_backend import CustomEmailBackend from awx.main.notifications.slack_backend import SlackBackend from awx.main.notifications.twilio_backend import TwilioBackend from awx.main.notifications.pagerduty_backend import PagerDutyBackend from awx.main.notifications.hipchat_backend import HipChatBackend from awx.main.notifications.webhook_backend import WebhookBackend from awx.main.notifications.mattermost_backend import MattermostBackend from awx.main.notifications.grafana_backend import GrafanaBackend from awx.main.notifications.rocketchat_backend import RocketChatBackend from awx.main.notifications.irc_backend import IrcBackend from awx.main.fields import JSONField logger = logging.getLogger('awx.main.models.notifications') __all__ = ['NotificationTemplate', 'Notification'] class NotificationTemplate(CommonModelNameNotUnique): NOTIFICATION_TYPES = [('email', _('Email'), CustomEmailBackend), ('slack', _('Slack'), SlackBackend), ('twilio', _('Twilio'), TwilioBackend), ('pagerduty', _('Pagerduty'), PagerDutyBackend), ('grafana', _('Grafana'), GrafanaBackend), ('hipchat', _('HipChat'), HipChatBackend), ('webhook', _('Webhook'), WebhookBackend), ('mattermost', _('Mattermost'), MattermostBackend), ('rocketchat', _('Rocket.Chat'), RocketChatBackend), ('irc', _('IRC'), IrcBackend)] NOTIFICATION_TYPE_CHOICES = sorted([(x[0], x[1]) for x in NOTIFICATION_TYPES]) CLASS_FOR_NOTIFICATION_TYPE = dict([(x[0], x[2]) for x in NOTIFICATION_TYPES]) class Meta: app_label = 'main' unique_together = ('organization', 'name') ordering = ("name",) organization = models.ForeignKey( 'Organization', blank=False, null=True, on_delete=models.CASCADE, related_name='notification_templates', ) notification_type = models.CharField( max_length = 32, choices=NOTIFICATION_TYPE_CHOICES, ) notification_configuration = prevent_search(JSONField(blank=False)) def default_messages(): return {'started': None, 'success': None, 'error': None, 'workflow_approval': None} messages = JSONField( null=True, blank=True, default=default_messages, help_text=_('Optional custom messages for notification template.')) def has_message(self, condition): potential_template = self.messages.get(condition, {}) if potential_template == {}: return False if potential_template.get('message', {}) == {}: return False return True def get_message(self, condition): return self.messages.get(condition, {}) def get_absolute_url(self, request=None): return reverse('api:notification_template_detail', kwargs={'pk': self.pk}, request=request) @property def notification_class(self): return self.CLASS_FOR_NOTIFICATION_TYPE[self.notification_type] def save(self, *args, **kwargs): new_instance = not bool(self.pk) update_fields = kwargs.get('update_fields', []) # preserve existing notification messages if not overwritten by new messages if not new_instance: old_nt = NotificationTemplate.objects.get(pk=self.id) old_messages = old_nt.messages new_messages = self.messages def merge_messages(local_old_messages, local_new_messages, local_event): if local_new_messages.get(local_event, {}) and local_old_messages.get(local_event, {}): local_old_event_msgs = local_old_messages[local_event] local_new_event_msgs = local_new_messages[local_event] for msg_type in ['message', 'body']: if msg_type not in local_new_event_msgs and local_old_event_msgs.get(msg_type, None): local_new_event_msgs[msg_type] = local_old_event_msgs[msg_type] if old_messages is not None and new_messages is not None: for event in ('started', 'success', 'error', 'workflow_approval'): if not new_messages.get(event, {}) and old_messages.get(event, {}): new_messages[event] = old_messages[event] continue if event == 'workflow_approval' and old_messages.get('workflow_approval', None): new_messages.setdefault('workflow_approval', {}) for subevent in ('running', 'approved', 'timed_out', 'denied'): old_wfa_messages = old_messages['workflow_approval'] new_wfa_messages = new_messages['workflow_approval'] if not new_wfa_messages.get(subevent, {}) and old_wfa_messages.get(subevent, {}): new_wfa_messages[subevent] = old_wfa_messages[subevent] continue if old_wfa_messages: merge_messages(old_wfa_messages, new_wfa_messages, subevent) else: merge_messages(old_messages, new_messages, event) new_messages.setdefault(event, None) for field in filter(lambda x: self.notification_class.init_parameters[x]['type'] == "password", self.notification_class.init_parameters): if self.notification_configuration[field].startswith("$encrypted$"): continue if new_instance: value = self.notification_configuration[field] setattr(self, '_saved_{}_{}'.format("config", field), value) self.notification_configuration[field] = '' else: encrypted = encrypt_field(self, 'notification_configuration', subfield=field) self.notification_configuration[field] = encrypted if 'notification_configuration' not in update_fields: update_fields.append('notification_configuration') super(NotificationTemplate, self).save(*args, **kwargs) if new_instance: update_fields = [] for field in filter(lambda x: self.notification_class.init_parameters[x]['type'] == "password", self.notification_class.init_parameters): saved_value = getattr(self, '_saved_{}_{}'.format("config", field), '') self.notification_configuration[field] = saved_value if 'notification_configuration' not in update_fields: update_fields.append('notification_configuration') self.save(update_fields=update_fields) @property def recipients(self): return self.notification_configuration[self.notification_class.recipient_parameter] def generate_notification(self, msg, body): notification = Notification(notification_template=self, notification_type=self.notification_type, recipients=smart_str(self.recipients), subject=msg, body=body) notification.save() return notification def send(self, subject, body): for field in filter(lambda x: self.notification_class.init_parameters[x]['type'] == "password", self.notification_class.init_parameters): if field in self.notification_configuration: self.notification_configuration[field] = decrypt_field(self, 'notification_configuration', subfield=field) recipients = self.notification_configuration.pop(self.notification_class.recipient_parameter) if not isinstance(recipients, list): recipients = [recipients] sender = self.notification_configuration.pop(self.notification_class.sender_parameter, None) notification_configuration = deepcopy(self.notification_configuration) for field, params in self.notification_class.init_parameters.items(): if field not in notification_configuration: if 'default' in params: notification_configuration[field] = params['default'] backend_obj = self.notification_class(**notification_configuration) notification_obj = EmailMessage(subject, backend_obj.format_body(body), sender, recipients) with set_environ(**settings.AWX_TASK_ENV): return backend_obj.send_messages([notification_obj]) def display_notification_configuration(self): field_val = self.notification_configuration.copy() for field in self.notification_class.init_parameters: if field in field_val and force_text(field_val[field]).startswith('$encrypted$'): field_val[field] = '$encrypted$' return field_val class Notification(CreatedModifiedModel): ''' A notification event emitted when a NotificationTemplate is run ''' NOTIFICATION_STATE_CHOICES = [ ('pending', _('Pending')), ('successful', _('Successful')), ('failed', _('Failed')), ] class Meta: app_label = 'main' ordering = ('pk',) notification_template = models.ForeignKey( 'NotificationTemplate', related_name='notifications', on_delete=models.CASCADE, editable=False ) status = models.CharField( max_length=20, choices=NOTIFICATION_STATE_CHOICES, default='pending', editable=False, ) error = models.TextField( blank=True, default='', editable=False, ) notifications_sent = models.IntegerField( default=0, editable=False, ) notification_type = models.CharField( max_length = 32, choices=NotificationTemplate.NOTIFICATION_TYPE_CHOICES, ) recipients = models.TextField( blank=True, default='', editable=False, ) subject = models.TextField( blank=True, default='', editable=False, ) body = JSONField(blank=True) def get_absolute_url(self, request=None): return reverse('api:notification_detail', kwargs={'pk': self.pk}, request=request) class JobNotificationMixin(object): STATUS_TO_TEMPLATE_TYPE = {'succeeded': 'success', 'running': 'started', 'failed': 'error'} # Tree of fields that can be safely referenced in a notification message JOB_FIELDS_WHITELIST = ['id', 'type', 'url', 'created', 'modified', 'name', 'description', 'job_type', 'playbook', 'forks', 'limit', 'verbosity', 'job_tags', 'force_handlers', 'skip_tags', 'start_at_task', 'timeout', 'use_fact_cache', 'launch_type', 'status', 'failed', 'started', 'finished', 'elapsed', 'job_explanation', 'execution_node', 'controller_node', 'allow_simultaneous', 'scm_revision', 'diff_mode', 'job_slice_number', 'job_slice_count', 'custom_virtualenv', 'approval_status', 'approval_node_name', 'workflow_url', {'host_status_counts': ['skipped', 'ok', 'changed', 'failed', 'failures', 'dark' 'processed', 'rescued', 'ignored']}, {'summary_fields': [{'inventory': ['id', 'name', 'description', 'has_active_failures', 'total_hosts', 'hosts_with_active_failures', 'total_groups', 'has_inventory_sources', 'total_inventory_sources', 'inventory_sources_with_failures', 'organization_id', 'kind']}, {'project': ['id', 'name', 'description', 'status', 'scm_type']}, {'job_template': ['id', 'name', 'description']}, {'unified_job_template': ['id', 'name', 'description', 'unified_job_type']}, {'instance_group': ['name', 'id']}, {'created_by': ['id', 'username', 'first_name', 'last_name']}, {'labels': ['count', 'results']}]}] @classmethod def context_stub(cls): """Returns a stub context that can be used for validating notification messages. Context has the same structure as the context that will actually be used to render a notification message.""" context = {'job': {'allow_simultaneous': False, 'controller_node': 'foo_controller', 'created': datetime.datetime(2018, 11, 13, 6, 4, 0, 0, tzinfo=datetime.timezone.utc), 'custom_virtualenv': 'my_venv', 'description': 'Sample job description', 'diff_mode': False, 'elapsed': 0.403018, 'execution_node': 'awx', 'failed': False, 'finished': False, 'force_handlers': False, 'forks': 0, 'host_status_counts': {'skipped': 1, 'ok': 5, 'changed': 3, 'failures': 0, 'dark': 0, 'failed': False, 'processed': 0, 'rescued': 0}, 'id': 42, 'job_explanation': 'Sample job explanation', 'job_slice_count': 1, 'job_slice_number': 0, 'job_tags': '', 'job_type': 'run', 'launch_type': 'workflow', 'limit': 'bar_limit', 'modified': datetime.datetime(2018, 12, 13, 6, 4, 0, 0, tzinfo=datetime.timezone.utc), 'name': 'Stub JobTemplate', 'playbook': 'ping.yml', 'scm_revision': '', 'skip_tags': '', 'start_at_task': '', 'started': '2019-07-29T17:38:14.137461Z', 'status': 'running', 'summary_fields': {'created_by': {'first_name': '', 'id': 1, 'last_name': '', 'username': 'admin'}, 'instance_group': {'id': 1, 'name': 'tower'}, 'inventory': {'description': 'Sample inventory description', 'has_active_failures': False, 'has_inventory_sources': False, 'hosts_with_active_failures': 0, 'id': 17, 'inventory_sources_with_failures': 0, 'kind': '', 'name': 'Stub Inventory', 'organization_id': 121, 'total_groups': 0, 'total_hosts': 1, 'total_inventory_sources': 0}, 'job_template': {'description': 'Sample job template description', 'id': 39, 'name': 'Stub JobTemplate'}, 'labels': {'count': 0, 'results': []}, 'project': {'description': 'Sample project description', 'id': 38, 'name': 'Stub project', 'scm_type': 'git', 'status': 'successful'}, 'unified_job_template': {'description': 'Sample unified job template description', 'id': 39, 'name': 'Stub Job Template', 'unified_job_type': 'job'}}, 'timeout': 0, 'type': 'job', 'url': '/api/v2/jobs/13/', 'use_fact_cache': False, 'verbosity': 0}, 'job_friendly_name': 'Job', 'url': 'https://towerhost/#/jobs/playbook/1010', 'approval_status': 'approved', 'approval_node_name': 'Approve Me', 'workflow_url': 'https://towerhost/#/workflows/1010', 'job_metadata': """{'url': 'https://towerhost/$/jobs/playbook/13', 'traceback': '', 'status': 'running', 'started': '2019-08-07T21:46:38.362630+00:00', 'project': 'Stub project', 'playbook': 'ping.yml', 'name': 'Stub Job Template', 'limit': '', 'inventory': 'Stub Inventory', 'id': 42, 'hosts': {}, 'friendly_name': 'Job', 'finished': False, 'credential': 'Stub credential', 'created_by': 'admin'}"""} return context def context(self, serialized_job): """Returns a dictionary that can be used for rendering notification messages. The context will contain whitelisted content retrieved from a serialized job object (see JobNotificationMixin.JOB_FIELDS_WHITELIST), the job's friendly name, and a url to the job run.""" job_context = {'host_status_counts': {}} summary = None if hasattr(self, 'job_host_summaries'): summary = self.job_host_summaries.first() if summary: from awx.api.serializers import JobHostSummarySerializer summary_data = JobHostSummarySerializer(summary).to_representation(summary) job_context['host_status_counts'] = summary_data context = { 'job': job_context, 'job_friendly_name': self.get_notification_friendly_name(), 'url': self.get_ui_url(), 'job_metadata': json.dumps(self.notification_data(), indent=4) } def build_context(node, fields, whitelisted_fields): for safe_field in whitelisted_fields: if type(safe_field) is dict: field, whitelist_subnode = safe_field.copy().popitem() # ensure content present in job serialization if field not in fields: continue subnode = fields[field] node[field] = {} build_context(node[field], subnode, whitelist_subnode) else: # ensure content present in job serialization if safe_field not in fields: continue node[safe_field] = fields[safe_field] build_context(context['job'], serialized_job, self.JOB_FIELDS_WHITELIST) return context def get_notification_templates(self): raise RuntimeError("Define me") def get_notification_friendly_name(self): raise RuntimeError("Define me") def notification_data(self): raise RuntimeError("Define me") def build_notification_message(self, nt, status): env = sandbox.ImmutableSandboxedEnvironment() from awx.api.serializers import UnifiedJobSerializer job_serialization = UnifiedJobSerializer(self).to_representation(self) context = self.context(job_serialization) msg_template = body_template = None msg = body = '' # Use custom template if available if nt.messages: template = nt.messages.get(self.STATUS_TO_TEMPLATE_TYPE[status], {}) or {} msg_template = template.get('message', None) body_template = template.get('body', None) # If custom template not provided, look up default template default_template = nt.notification_class.default_messages[self.STATUS_TO_TEMPLATE_TYPE[status]] if not msg_template: msg_template = default_template.get('message', None) if not body_template: body_template = default_template.get('body', None) if msg_template: try: msg = env.from_string(msg_template).render(**context) except (TemplateSyntaxError, UndefinedError, SecurityError): msg = '' if body_template: try: body = env.from_string(body_template).render(**context) except (TemplateSyntaxError, UndefinedError, SecurityError): body = '' return (msg, body) def send_notification_templates(self, status): from awx.main.tasks import send_notifications # avoid circular import if status not in ['running', 'succeeded', 'failed']: raise ValueError(_("status must be either running, succeeded or failed")) try: notification_templates = self.get_notification_templates() except Exception: logger.warn("No notification template defined for emitting notification") return if not notification_templates: return for nt in set(notification_templates.get(self.STATUS_TO_TEMPLATE_TYPE[status], [])): (msg, body) = self.build_notification_message(nt, status) # Use kwargs to force late-binding # https://stackoverflow.com/a/3431699/10669572 def send_it(local_nt=nt, local_msg=msg, local_body=body): def _func(): send_notifications.delay([local_nt.generate_notification(local_msg, local_body).id], job_id=self.id) return _func connection.on_commit(send_it())

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from copy import deepcopy import datetime import logging import json from django.db import models from django.conf import settings from django.core.mail.message import EmailMessage from django.db import connection from django.utils.translation import ugettext_lazy as _ from django.utils.encoding import smart_str, force_text from jinja2 import sandbox from jinja2.exceptions import TemplateSyntaxError, UndefinedError, SecurityError from awx.api.versioning import reverse from awx.main.models.base import CommonModelNameNotUnique, CreatedModifiedModel, prevent_search from awx.main.utils import encrypt_field, decrypt_field, set_environ from awx.main.notifications.email_backend import CustomEmailBackend from awx.main.notifications.slack_backend import SlackBackend from awx.main.notifications.twilio_backend import TwilioBackend from awx.main.notifications.pagerduty_backend import PagerDutyBackend from awx.main.notifications.hipchat_backend import HipChatBackend from awx.main.notifications.webhook_backend import WebhookBackend from awx.main.notifications.mattermost_backend import MattermostBackend from awx.main.notifications.grafana_backend import GrafanaBackend from awx.main.notifications.rocketchat_backend import RocketChatBackend from awx.main.notifications.irc_backend import IrcBackend from awx.main.fields import JSONField logger = logging.getLogger('awx.main.models.notifications') __all__ = ['NotificationTemplate', 'Notification'] class NotificationTemplate(CommonModelNameNotUnique): NOTIFICATION_TYPES = [('email', _('Email'), CustomEmailBackend), ('slack', _('Slack'), SlackBackend), ('twilio', _('Twilio'), TwilioBackend), ('pagerduty', _('Pagerduty'), PagerDutyBackend), ('grafana', _('Grafana'), GrafanaBackend), ('hipchat', _('HipChat'), HipChatBackend), ('webhook', _('Webhook'), WebhookBackend), ('mattermost', _('Mattermost'), MattermostBackend), ('rocketchat', _('Rocket.Chat'), RocketChatBackend), ('irc', _('IRC'), IrcBackend)] NOTIFICATION_TYPE_CHOICES = sorted([(x[0], x[1]) for x in NOTIFICATION_TYPES]) CLASS_FOR_NOTIFICATION_TYPE = dict([(x[0], x[2]) for x in NOTIFICATION_TYPES]) class Meta: app_label = 'main' unique_together = ('organization', 'name') ordering = ("name",) organization = models.ForeignKey( 'Organization', blank=False, null=True, on_delete=models.CASCADE, related_name='notification_templates', ) notification_type = models.CharField( max_length = 32, choices=NOTIFICATION_TYPE_CHOICES, ) notification_configuration = prevent_search(JSONField(blank=False)) def default_messages(): return {'started': None, 'success': None, 'error': None, 'workflow_approval': None} messages = JSONField( null=True, blank=True, default=default_messages, help_text=_('Optional custom messages for notification template.')) def has_message(self, condition): potential_template = self.messages.get(condition, {}) if potential_template == {}: return False if potential_template.get('message', {}) == {}: return False return True def get_message(self, condition): return self.messages.get(condition, {}) def get_absolute_url(self, request=None): return reverse('api:notification_template_detail', kwargs={'pk': self.pk}, request=request) @property def notification_class(self): return self.CLASS_FOR_NOTIFICATION_TYPE[self.notification_type] def save(self, *args, **kwargs): new_instance = not bool(self.pk) update_fields = kwargs.get('update_fields', []) if not new_instance: old_nt = NotificationTemplate.objects.get(pk=self.id) old_messages = old_nt.messages new_messages = self.messages def merge_messages(local_old_messages, local_new_messages, local_event): if local_new_messages.get(local_event, {}) and local_old_messages.get(local_event, {}): local_old_event_msgs = local_old_messages[local_event] local_new_event_msgs = local_new_messages[local_event] for msg_type in ['message', 'body']: if msg_type not in local_new_event_msgs and local_old_event_msgs.get(msg_type, None): local_new_event_msgs[msg_type] = local_old_event_msgs[msg_type] if old_messages is not None and new_messages is not None: for event in ('started', 'success', 'error', 'workflow_approval'): if not new_messages.get(event, {}) and old_messages.get(event, {}): new_messages[event] = old_messages[event] continue if event == 'workflow_approval' and old_messages.get('workflow_approval', None): new_messages.setdefault('workflow_approval', {}) for subevent in ('running', 'approved', 'timed_out', 'denied'): old_wfa_messages = old_messages['workflow_approval'] new_wfa_messages = new_messages['workflow_approval'] if not new_wfa_messages.get(subevent, {}) and old_wfa_messages.get(subevent, {}): new_wfa_messages[subevent] = old_wfa_messages[subevent] continue if old_wfa_messages: merge_messages(old_wfa_messages, new_wfa_messages, subevent) else: merge_messages(old_messages, new_messages, event) new_messages.setdefault(event, None) for field in filter(lambda x: self.notification_class.init_parameters[x]['type'] == "password", self.notification_class.init_parameters): if self.notification_configuration[field].startswith("$encrypted$"): continue if new_instance: value = self.notification_configuration[field] setattr(self, '_saved_{}_{}'.format("config", field), value) self.notification_configuration[field] = '' else: encrypted = encrypt_field(self, 'notification_configuration', subfield=field) self.notification_configuration[field] = encrypted if 'notification_configuration' not in update_fields: update_fields.append('notification_configuration') super(NotificationTemplate, self).save(*args, **kwargs) if new_instance: update_fields = [] for field in filter(lambda x: self.notification_class.init_parameters[x]['type'] == "password", self.notification_class.init_parameters): saved_value = getattr(self, '_saved_{}_{}'.format("config", field), '') self.notification_configuration[field] = saved_value if 'notification_configuration' not in update_fields: update_fields.append('notification_configuration') self.save(update_fields=update_fields) @property def recipients(self): return self.notification_configuration[self.notification_class.recipient_parameter] def generate_notification(self, msg, body): notification = Notification(notification_template=self, notification_type=self.notification_type, recipients=smart_str(self.recipients), subject=msg, body=body) notification.save() return notification def send(self, subject, body): for field in filter(lambda x: self.notification_class.init_parameters[x]['type'] == "password", self.notification_class.init_parameters): if field in self.notification_configuration: self.notification_configuration[field] = decrypt_field(self, 'notification_configuration', subfield=field) recipients = self.notification_configuration.pop(self.notification_class.recipient_parameter) if not isinstance(recipients, list): recipients = [recipients] sender = self.notification_configuration.pop(self.notification_class.sender_parameter, None) notification_configuration = deepcopy(self.notification_configuration) for field, params in self.notification_class.init_parameters.items(): if field not in notification_configuration: if 'default' in params: notification_configuration[field] = params['default'] backend_obj = self.notification_class(**notification_configuration) notification_obj = EmailMessage(subject, backend_obj.format_body(body), sender, recipients) with set_environ(**settings.AWX_TASK_ENV): return backend_obj.send_messages([notification_obj]) def display_notification_configuration(self): field_val = self.notification_configuration.copy() for field in self.notification_class.init_parameters: if field in field_val and force_text(field_val[field]).startswith('$encrypted$'): field_val[field] = '$encrypted$' return field_val class Notification(CreatedModifiedModel): NOTIFICATION_STATE_CHOICES = [ ('pending', _('Pending')), ('successful', _('Successful')), ('failed', _('Failed')), ] class Meta: app_label = 'main' ordering = ('pk',) notification_template = models.ForeignKey( 'NotificationTemplate', related_name='notifications', on_delete=models.CASCADE, editable=False ) status = models.CharField( max_length=20, choices=NOTIFICATION_STATE_CHOICES, default='pending', editable=False, ) error = models.TextField( blank=True, default='', editable=False, ) notifications_sent = models.IntegerField( default=0, editable=False, ) notification_type = models.CharField( max_length = 32, choices=NotificationTemplate.NOTIFICATION_TYPE_CHOICES, ) recipients = models.TextField( blank=True, default='', editable=False, ) subject = models.TextField( blank=True, default='', editable=False, ) body = JSONField(blank=True) def get_absolute_url(self, request=None): return reverse('api:notification_detail', kwargs={'pk': self.pk}, request=request) class JobNotificationMixin(object): STATUS_TO_TEMPLATE_TYPE = {'succeeded': 'success', 'running': 'started', 'failed': 'error'} JOB_FIELDS_WHITELIST = ['id', 'type', 'url', 'created', 'modified', 'name', 'description', 'job_type', 'playbook', 'forks', 'limit', 'verbosity', 'job_tags', 'force_handlers', 'skip_tags', 'start_at_task', 'timeout', 'use_fact_cache', 'launch_type', 'status', 'failed', 'started', 'finished', 'elapsed', 'job_explanation', 'execution_node', 'controller_node', 'allow_simultaneous', 'scm_revision', 'diff_mode', 'job_slice_number', 'job_slice_count', 'custom_virtualenv', 'approval_status', 'approval_node_name', 'workflow_url', {'host_status_counts': ['skipped', 'ok', 'changed', 'failed', 'failures', 'dark' 'processed', 'rescued', 'ignored']}, {'summary_fields': [{'inventory': ['id', 'name', 'description', 'has_active_failures', 'total_hosts', 'hosts_with_active_failures', 'total_groups', 'has_inventory_sources', 'total_inventory_sources', 'inventory_sources_with_failures', 'organization_id', 'kind']}, {'project': ['id', 'name', 'description', 'status', 'scm_type']}, {'job_template': ['id', 'name', 'description']}, {'unified_job_template': ['id', 'name', 'description', 'unified_job_type']}, {'instance_group': ['name', 'id']}, {'created_by': ['id', 'username', 'first_name', 'last_name']}, {'labels': ['count', 'results']}]}] @classmethod def context_stub(cls): context = {'job': {'allow_simultaneous': False, 'controller_node': 'foo_controller', 'created': datetime.datetime(2018, 11, 13, 6, 4, 0, 0, tzinfo=datetime.timezone.utc), 'custom_virtualenv': 'my_venv', 'description': 'Sample job description', 'diff_mode': False, 'elapsed': 0.403018, 'execution_node': 'awx', 'failed': False, 'finished': False, 'force_handlers': False, 'forks': 0, 'host_status_counts': {'skipped': 1, 'ok': 5, 'changed': 3, 'failures': 0, 'dark': 0, 'failed': False, 'processed': 0, 'rescued': 0}, 'id': 42, 'job_explanation': 'Sample job explanation', 'job_slice_count': 1, 'job_slice_number': 0, 'job_tags': '', 'job_type': 'run', 'launch_type': 'workflow', 'limit': 'bar_limit', 'modified': datetime.datetime(2018, 12, 13, 6, 4, 0, 0, tzinfo=datetime.timezone.utc), 'name': 'Stub JobTemplate', 'playbook': 'ping.yml', 'scm_revision': '', 'skip_tags': '', 'start_at_task': '', 'started': '2019-07-29T17:38:14.137461Z', 'status': 'running', 'summary_fields': {'created_by': {'first_name': '', 'id': 1, 'last_name': '', 'username': 'admin'}, 'instance_group': {'id': 1, 'name': 'tower'}, 'inventory': {'description': 'Sample inventory description', 'has_active_failures': False, 'has_inventory_sources': False, 'hosts_with_active_failures': 0, 'id': 17, 'inventory_sources_with_failures': 0, 'kind': '', 'name': 'Stub Inventory', 'organization_id': 121, 'total_groups': 0, 'total_hosts': 1, 'total_inventory_sources': 0}, 'job_template': {'description': 'Sample job template description', 'id': 39, 'name': 'Stub JobTemplate'}, 'labels': {'count': 0, 'results': []}, 'project': {'description': 'Sample project description', 'id': 38, 'name': 'Stub project', 'scm_type': 'git', 'status': 'successful'}, 'unified_job_template': {'description': 'Sample unified job template description', 'id': 39, 'name': 'Stub Job Template', 'unified_job_type': 'job'}}, 'timeout': 0, 'type': 'job', 'url': '/api/v2/jobs/13/', 'use_fact_cache': False, 'verbosity': 0}, 'job_friendly_name': 'Job', 'url': 'https://towerhost/#/jobs/playbook/1010', 'approval_status': 'approved', 'approval_node_name': 'Approve Me', 'workflow_url': 'https://towerhost/#/workflows/1010', 'job_metadata': """{'url': 'https://towerhost/$/jobs/playbook/13', 'traceback': '', 'status': 'running', 'started': '2019-08-07T21:46:38.362630+00:00', 'project': 'Stub project', 'playbook': 'ping.yml', 'name': 'Stub Job Template', 'limit': '', 'inventory': 'Stub Inventory', 'id': 42, 'hosts': {}, 'friendly_name': 'Job', 'finished': False, 'credential': 'Stub credential', 'created_by': 'admin'}"""} return context def context(self, serialized_job): job_context = {'host_status_counts': {}} summary = None if hasattr(self, 'job_host_summaries'): summary = self.job_host_summaries.first() if summary: from awx.api.serializers import JobHostSummarySerializer summary_data = JobHostSummarySerializer(summary).to_representation(summary) job_context['host_status_counts'] = summary_data context = { 'job': job_context, 'job_friendly_name': self.get_notification_friendly_name(), 'url': self.get_ui_url(), 'job_metadata': json.dumps(self.notification_data(), indent=4) } def build_context(node, fields, whitelisted_fields): for safe_field in whitelisted_fields: if type(safe_field) is dict: field, whitelist_subnode = safe_field.copy().popitem() if field not in fields: continue subnode = fields[field] node[field] = {} build_context(node[field], subnode, whitelist_subnode) else: if safe_field not in fields: continue node[safe_field] = fields[safe_field] build_context(context['job'], serialized_job, self.JOB_FIELDS_WHITELIST) return context def get_notification_templates(self): raise RuntimeError("Define me") def get_notification_friendly_name(self): raise RuntimeError("Define me") def notification_data(self): raise RuntimeError("Define me") def build_notification_message(self, nt, status): env = sandbox.ImmutableSandboxedEnvironment() from awx.api.serializers import UnifiedJobSerializer job_serialization = UnifiedJobSerializer(self).to_representation(self) context = self.context(job_serialization) msg_template = body_template = None msg = body = '' if nt.messages: template = nt.messages.get(self.STATUS_TO_TEMPLATE_TYPE[status], {}) or {} msg_template = template.get('message', None) body_template = template.get('body', None) default_template = nt.notification_class.default_messages[self.STATUS_TO_TEMPLATE_TYPE[status]] if not msg_template: msg_template = default_template.get('message', None) if not body_template: body_template = default_template.get('body', None) if msg_template: try: msg = env.from_string(msg_template).render(**context) except (TemplateSyntaxError, UndefinedError, SecurityError): msg = '' if body_template: try: body = env.from_string(body_template).render(**context) except (TemplateSyntaxError, UndefinedError, SecurityError): body = '' return (msg, body) def send_notification_templates(self, status): from awx.main.tasks import send_notifications if status not in ['running', 'succeeded', 'failed']: raise ValueError(_("status must be either running, succeeded or failed")) try: notification_templates = self.get_notification_templates() except Exception: logger.warn("No notification template defined for emitting notification") return if not notification_templates: return for nt in set(notification_templates.get(self.STATUS_TO_TEMPLATE_TYPE[status], [])): (msg, body) = self.build_notification_message(nt, status) def send_it(local_nt=nt, local_msg=msg, local_body=body): def _func(): send_notifications.delay([local_nt.generate_notification(local_msg, local_body).id], job_id=self.id) return _func connection.on_commit(send_it())

true

f731a055821f656efbcb5c83ad2f7ff8e59dd572

818

py

Python

manage.py

neosergio/WisdomBox

f41bd828f5e264c7ad05262b29c8f02cf904b54a

[ "MIT" ]

null

manage.py

neosergio/WisdomBox

f41bd828f5e264c7ad05262b29c8f02cf904b54a

[ "MIT" ]

2

2017-02-09T14:52:43.000Z

2017-02-10T19:31:38.000Z

manage.py

neosergio/WisdomBox

f41bd828f5e264c7ad05262b29c8f02cf904b54a

[ "MIT" ]

null

#!/usr/bin/env python import os import sys if __name__ == "__main__": os.environ.setdefault("DJANGO_SETTINGS_MODULE", "wisdom.settings") try: from django.core.management import execute_from_command_line except ImportError: # The above import may fail for some other reason. Ensure that the # issue is really that Django is missing to avoid masking other # exceptions on Python 2. try: import django # noqa: F401 except ImportError: raise ImportError( "Couldn't import Django. Are you sure it's installed and " "available on your PYTHONPATH environment variable? Did you " "forget to activate a virtual environment?" ) raise execute_from_command_line(sys.argv)

35.565217

77

0.640587

import os import sys if __name__ == "__main__": os.environ.setdefault("DJANGO_SETTINGS_MODULE", "wisdom.settings") try: from django.core.management import execute_from_command_line except ImportError: try: import django except ImportError: raise ImportError( "Couldn't import Django. Are you sure it's installed and " "available on your PYTHONPATH environment variable? Did you " "forget to activate a virtual environment?" ) raise execute_from_command_line(sys.argv)

true

f731a0e7d3248ef87117117d791e52aa967f0a12

2,465

py

Python

forecaster.py

Adri1bo/HEMS_API

dca26e55696f9f2e36f29968a8c3a90871d6bc16

[ "MIT" ]

1

2021-02-17T08:47:47.000Z

forecaster.py

Adri1bo/HEMS_API

dca26e55696f9f2e36f29968a8c3a90871d6bc16

[ "MIT" ]

null

forecaster.py

Adri1bo/HEMS_API

dca26e55696f9f2e36f29968a8c3a90871d6bc16

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Sun Jun 7 17:58:57 2020 @author: adria.bove """ from BBDD import BBDD import analytics import pandas as pd import matplotlib.pyplot as plt import numpy as np import datetime as dt class consumption: def __init__(self, nom): self.nom_BBDD=nom def forecaster(self,timestamp): self.timestamp=str(timestamp) # get the df this_BBDD=BBDD(self.nom_BBDD) a=pd.Series(dtype='float64') k=0 list_weekdays=[(dt.datetime.now()+dt.timedelta(days=i+k)).weekday() for i in range(5)] for weekday in list_weekdays: df=this_BBDD.extract_weekday(weekday) #send it to the mean_day function que farà el dia tipus a=a.append(self.mean_day(df,k)) k=k+1 del(a[0]) a['dt']=a.index a = a.reset_index(drop=True) self.store_data(a,'loads_forecast') return a def mean_day(self,df,k): #amb el weekday he de fer millor de date_range i fer-lo sobre el valor del timer df['timer'] = df.dt.apply(self.massive_rounder,groupsize=1,groups=int(60)) df = df.rename(columns={'P_load [kW]': 'P_load'}) df.P_load=pd.to_numeric(df.P_load) mean_DAY=df.groupby('timer').P_load.mean() mean_DAY=mean_DAY.to_frame() start_date=dt.datetime.combine(dt.date.today(), dt.datetime.min.time())#+dt.timedelta(days=1) mean_DAY['dta']=mean_DAY.index mean_DAY.dta=mean_DAY.dta.apply(lambda x: dt.timedelta(minutes=x) + start_date + dt.timedelta(days=k)) mean_DAY.index=mean_DAY.dta del(mean_DAY['dta']) new_mean_DAY=mean_DAY.resample(self.timestamp+'T').pad() return new_mean_DAY def massive_rounder(self, element, groupsize, groups): for i in range(groups): if element.time().minute < (groupsize*(range(groups)[i]+1)): return range(groups)[i] + element.time().hour*groups def store_data(self,data,name): this_BBDD=BBDD(name) this_BBDD.store_data(data) if __name__ == '__main__': consumption_forecast=consumption('grid_BBDD') b=consumption_forecast.forecaster(timestamp=15) plt.plot(-b.P_load)

30.8125

111

0.577688

from BBDD import BBDD import analytics import pandas as pd import matplotlib.pyplot as plt import numpy as np import datetime as dt class consumption: def __init__(self, nom): self.nom_BBDD=nom def forecaster(self,timestamp): self.timestamp=str(timestamp) this_BBDD=BBDD(self.nom_BBDD) a=pd.Series(dtype='float64') k=0 list_weekdays=[(dt.datetime.now()+dt.timedelta(days=i+k)).weekday() for i in range(5)] for weekday in list_weekdays: df=this_BBDD.extract_weekday(weekday) a=a.append(self.mean_day(df,k)) k=k+1 del(a[0]) a['dt']=a.index a = a.reset_index(drop=True) self.store_data(a,'loads_forecast') return a def mean_day(self,df,k): df['timer'] = df.dt.apply(self.massive_rounder,groupsize=1,groups=int(60)) df = df.rename(columns={'P_load [kW]': 'P_load'}) df.P_load=pd.to_numeric(df.P_load) mean_DAY=df.groupby('timer').P_load.mean() mean_DAY=mean_DAY.to_frame() start_date=dt.datetime.combine(dt.date.today(), dt.datetime.min.time()) mean_DAY['dta']=mean_DAY.index mean_DAY.dta=mean_DAY.dta.apply(lambda x: dt.timedelta(minutes=x) + start_date + dt.timedelta(days=k)) mean_DAY.index=mean_DAY.dta del(mean_DAY['dta']) new_mean_DAY=mean_DAY.resample(self.timestamp+'T').pad() return new_mean_DAY def massive_rounder(self, element, groupsize, groups): for i in range(groups): if element.time().minute < (groupsize*(range(groups)[i]+1)): return range(groups)[i] + element.time().hour*groups def store_data(self,data,name): this_BBDD=BBDD(name) this_BBDD.store_data(data) if __name__ == '__main__': consumption_forecast=consumption('grid_BBDD') b=consumption_forecast.forecaster(timestamp=15) plt.plot(-b.P_load)

true

f731a16e0fc215d4c3817488810ef1839361e054

49,894

py

Python

cogs/TwitchAlert.py

SnowyJaguar1034/KoalaBot

840a5e30476492c60157687804a2445903279207

[ "MIT" ]

null

cogs/TwitchAlert.py

SnowyJaguar1034/KoalaBot

840a5e30476492c60157687804a2445903279207

[ "MIT" ]

13

2021-07-02T04:11:14.000Z

2022-03-25T04:08:55.000Z

cogs/TwitchAlert.py

SnowyJaguar1034/KoalaBot

840a5e30476492c60157687804a2445903279207

[ "MIT" ]

null

#!/usr/bin/env python """ Koala Bot Base Cog code and additional base cog functions Commented using reStructuredText (reST) """ # Futures # Built-in/Generic Imports import os import time import re import aiohttp import logging from concurrent.futures import ThreadPoolExecutor logging.basicConfig(filename='TwitchAlert.log') # Own modules import KoalaBot from utils.KoalaColours import * from utils.KoalaUtils import error_embed, is_channel_in_guild, extract_id from utils import KoalaDBManager # Libs from discord.ext import commands, tasks from dotenv import load_dotenv import asyncio # Constants load_dotenv() DEFAULT_MESSAGE = "" TWITCH_ICON = "https://cdn3.iconfinder.com/data/icons/social-messaging-ui-color-shapes-2-free" \ "/128/social-twitch-circle-512.png" TWITCH_CLIENT_ID = os.environ.get('TWITCH_TOKEN') TWITCH_SECRET = os.environ.get('TWITCH_SECRET') TWITCH_USERNAME_REGEX = "^[a-z0-9][a-z0-9_]{3,24}$" LOOP_CHECK_LIVE_DELAY = 1 TEAMS_LOOP_CHECK_LIVE_DELAY = 1 REFRESH_TEAMS_DELAY = 5 # Variables def twitch_is_enabled(ctx): """ A command used to check if the guild has enabled twitch alert e.g. @commands.check(KoalaBot.is_admin) :param ctx: The context of the message :return: True if admin or test, False otherwise """ try: result = KoalaBot.check_guild_has_ext(ctx, "TwitchAlert") except PermissionError: result = False return result class TwitchAlert(commands.Cog): """ A discord.py cog for alerting when someone goes live on twitch """ def __init__(self, bot, database_manager=None): """ Initialises local variables :param bot: The bot client for this cog """ if not database_manager: database_manager = KoalaBot.database_manager self.bot = bot database_manager.create_base_tables() database_manager.insert_extension("TwitchAlert", 0, True, True) self.ta_database_manager = TwitchAlertDBManager(database_manager, bot) self.ta_database_manager.create_tables() self.loop_thread = None self.loop_team_thread = None self.running = False self.stop_loop = False @commands.command(name="twitchEditMsg", aliases=["edit_default_message"]) @commands.check(KoalaBot.is_admin) @commands.check(twitch_is_enabled) async def edit_default_message(self, ctx, raw_channel_id, *default_live_message): """ Edit the default message put in a Twitch Alert Notification :param ctx: The discord context of the command :param raw_channel_id: The channel ID where the twitch alert is being used :param default_live_message: The default live message of users within this Twitch Alert, leave empty for program default :return: """ try: channel_id = extract_id(raw_channel_id) except TypeError: channel_id = ctx.message.channel.id default_live_message = (raw_channel_id,) + default_live_message if not is_channel_in_guild(self.bot, ctx.message.guild.id, channel_id): await ctx.send(embed=error_embed("The channel ID provided is either invalid, or not in this server.")) return # Assigning default message if provided if default_live_message is not None and default_live_message != (None,): default_message = " ".join(default_live_message) if len(default_message) > 1000: await ctx.send(embed=error_embed( "custom_message is too long, try something with less than 1000 characters")) return else: default_message = None # Creates a new Twitch Alert with the used guild ID and default message if provided default_message = self.ta_database_manager.new_ta(ctx.message.guild.id, channel_id, default_message, replace=True) # Returns an embed with information altered new_embed = discord.Embed(title="Default Message Edited", colour=KOALA_GREEN, description=f"Guild: {ctx.message.guild.id}\n" f"Channel: {channel_id}\n" f"Default Message: {default_message}") await ctx.send(embed=new_embed) @commands.command(name="twitchViewMsg", aliases=["view_default_message"]) @commands.check(KoalaBot.is_admin) @commands.check(twitch_is_enabled) async def view_default_message(self, ctx, raw_channel_id=None): """ Shows the current default message for Twitch Alerts :param ctx: The discord context of the command :param raw_channel_id: The channel ID where the twitch alert is being used leave empty for program default :return: """ if raw_channel_id is None: channel_id = ctx.message.channel.id else: channel_id = extract_id(raw_channel_id) if not is_channel_in_guild(self.bot, ctx.message.guild.id, channel_id): await ctx.send(embed=error_embed("The channel ID provided is either invalid, or not in this server.")) return # Creates a new Twitch Alert with the used guild ID and default message if provided default_message = self.ta_database_manager.get_default_message(channel_id)[0][0] # Returns an embed with information altered new_embed = discord.Embed(title="Default Message", colour=KOALA_GREEN, description=f"Guild: {ctx.message.guild.id}\n" f"Channel: {channel_id}\n" f"Default Message: {default_message}") # new_embed.set_footer(text=f"Twitch Alert ID: {new_id}") await ctx.send(embed=new_embed) @commands.command(name="twitchAdd", aliases=['add_user_to_twitch_alert']) @commands.check(KoalaBot.is_admin) @commands.check(twitch_is_enabled) async def add_user_to_twitch_alert(self, ctx, raw_channel_id, twitch_username=None, *custom_live_message): """ Add a Twitch user to a Twitch Alert :param ctx: The discord context of the command :param raw_channel_id: The channel ID where the twitch alert is being used :param twitch_username: The Twitch Username of the user being added (lowercase) :param custom_live_message: the custom live message for this user's alert :return: """ try: channel_id = extract_id(raw_channel_id) except TypeError: custom_live_message = (twitch_username,) + custom_live_message twitch_username = raw_channel_id channel_id = ctx.message.channel.id if twitch_username is None: raise discord.errors.InvalidArgument("twitch_username is a required argument that is missing.") elif not re.search(TWITCH_USERNAME_REGEX, twitch_username): raise discord.errors.InvalidArgument( "The given twitch_username is not a valid username (please use lowercase)") # Check the channel specified is in this guild if not is_channel_in_guild(self.bot, ctx.message.guild.id, channel_id): await ctx.send(embed=error_embed("The channel ID provided is either invalid, or not in this server.")) return default_message = self.ta_database_manager.new_ta(ctx.message.guild.id, channel_id) # Setting the custom message as required if custom_live_message is not None and custom_live_message != (None,): custom_message = " ".join(custom_live_message) default_message = custom_message if len(default_message) > 1000: await ctx.send(embed=error_embed( "custom_message is too long, try something with less than 1000 characters")) return else: custom_message = None self.ta_database_manager.add_user_to_ta(channel_id, twitch_username, custom_message, ctx.message.guild.id) # Response Message new_embed = discord.Embed(title="Added User to Twitch Alert", colour=KOALA_GREEN, description=f"Channel: {channel_id}\n" f"User: {twitch_username}\n" f"Message: {default_message}") await ctx.send(embed=new_embed) @commands.command(name="twitchRemove", aliases=['remove_user_from_twitch_alert']) @commands.check(KoalaBot.is_admin) @commands.check(twitch_is_enabled) async def remove_user_from_twitch_alert(self, ctx, raw_channel_id, twitch_username=None): """ Removes a user from a Twitch Alert :param ctx: the discord context :param raw_channel_id: The discord channel ID of the Twitch Alert :param twitch_username: The username of the user to be removed :return: """ try: channel_id = extract_id(raw_channel_id) except TypeError: twitch_username = raw_channel_id channel_id = ctx.message.channel.id if twitch_username is None: raise discord.errors.InvalidArgument("twitch_username is a required argument that is missing.") # Check the channel specified is in this guild if not is_channel_in_guild(self.bot, ctx.message.guild.id, channel_id): await ctx.send(embed=error_embed("The channel ID provided is either invalid, or not in this server.")) return await self.ta_database_manager.remove_user_from_ta(channel_id, twitch_username) # Response Message new_embed = discord.Embed(title="Removed User from Twitch Alert", colour=KOALA_GREEN, description=f"Channel: {channel_id}\n" f"User: {twitch_username}") await ctx.send(embed=new_embed) @commands.command(name="twitchAddTeam", aliases=["add_team_to_twitch_alert"]) @commands.check(KoalaBot.is_admin) @commands.check(twitch_is_enabled) async def add_team_to_twitch_alert(self, ctx, raw_channel_id, team_name=None, *custom_live_message): """ Add a Twitch team to a Twitch Alert :param ctx: The discord context of the command :param raw_channel_id: The channel ID where the twitch alert is being used :param team_name: The Twitch team being added (lowercase) :param custom_live_message: the custom live message for this team's alert :return: """ try: channel_id = extract_id(raw_channel_id) except TypeError: custom_live_message = (team_name,) + custom_live_message team_name = raw_channel_id channel_id = ctx.message.channel.id if team_name is None: raise discord.errors.InvalidArgument("team_name is a required argument that is missing.") elif not re.search(TWITCH_USERNAME_REGEX, team_name): raise discord.errors.InvalidArgument( "The given team_name is not a valid twitch team name (please use lowercase)") # Check the channel specified is in this guild if not is_channel_in_guild(self.bot, ctx.message.guild.id, channel_id): await ctx.send(embed=error_embed("The channel ID provided is either invalid, or not in this server.")) return self.ta_database_manager.new_ta(ctx.message.guild.id, channel_id) # Setting the custom message as required if custom_live_message is not None and custom_live_message != (None,): default_message = " ".join(custom_live_message) if len(default_message) > 1000: await ctx.send(embed=error_embed( "custom_message is too long, try something with less than 1000 characters")) return else: default_message = DEFAULT_MESSAGE self.ta_database_manager.add_team_to_ta(channel_id, team_name, default_message, ctx.message.guild.id) # Response Message new_embed = discord.Embed(title="Added Team to Twitch Alert", colour=KOALA_GREEN, description=f"Channel: {channel_id}\n" f"Team: {team_name}\n" f"Message: {default_message}") # new_embed.set_footer(text=f"Twitch Alert ID: {channel_id}") await ctx.send(embed=new_embed) @commands.command(name="twitchRemoveTeam", aliases=["remove_team_from_twitch_alert"]) @commands.check(KoalaBot.is_admin) @commands.check(twitch_is_enabled) async def remove_team_from_twitch_alert(self, ctx, raw_channel_id, team_name=None): """ Removes a team from a Twitch Alert :param ctx: the discord context :param raw_channel_id: The discord channel ID of the Twitch Alert :param team_name: The Twitch team being added (lowercase) :return: """ try: channel_id = extract_id(raw_channel_id) except TypeError: team_name = raw_channel_id channel_id = ctx.message.channel.id if team_name is None: raise discord.errors.InvalidArgument("team_name is a required argument that is missing.") # Check the channel specified is in this guild if not is_channel_in_guild(self.bot, ctx.message.guild.id, channel_id): await ctx.send(embed=error_embed("The channel ID provided is either invalid, or not in this server.")) return await self.ta_database_manager.remove_team_from_ta(channel_id, team_name) # Response Message new_embed = discord.Embed(title="Removed Team from Twitch Alert", colour=KOALA_GREEN, description=f"Channel: {channel_id}\n" f"Team: {team_name}") await ctx.send(embed=new_embed) @commands.command(name="twitchList", aliases=["list_twitch_alert"]) @commands.check(KoalaBot.is_admin) @commands.check(twitch_is_enabled) async def list_twitch_alert(self, ctx, raw_channel_id=None): """ Shows all current TwitchAlert users and teams in a channel :param ctx: :param raw_channel_id: :return: """ if raw_channel_id is None: channel_id = ctx.message.channel.id else: channel_id = extract_id(raw_channel_id) if not is_channel_in_guild(self.bot, ctx.message.guild.id, channel_id): await ctx.send(embed=error_embed("The channel ID provided is either invalid, or not in this server.")) return embed = discord.Embed() embed.title = "Twitch Alerts" embed.colour = KOALA_GREEN embed.set_footer(text=f"Channel ID: {channel_id}") results = self.ta_database_manager.get_users_in_ta(channel_id) if results: users = "" for result in results: users += f"{result[0]}\n" embed.add_field(name=":bust_in_silhouette: Users", value=users) else: embed.add_field(name=":bust_in_silhouette: Users", value="None") results = self.ta_database_manager.get_teams_in_ta(channel_id) if results: teams = "" for result in results: teams += f"{result[0]}\n" embed.add_field(name=":busts_in_silhouette: Teams", value=teams) else: embed.add_field(name=":busts_in_silhouette: Teams", value="None") await ctx.send(embed=embed) @commands.Cog.listener() async def on_ready(self): """ When the bot is started up, the loop begins :return: """ if not self.running: self.start_loops() def start_loops(self): self.loop_update_teams.start() self.loop_check_team_live.start() self.loop_check_live.start() self.running = True def end_loops(self): self.loop_update_teams.cancel() self.loop_check_team_live.cancel() self.loop_check_live.cancel() self.running = False @tasks.loop(minutes=LOOP_CHECK_LIVE_DELAY) async def loop_check_live(self): """ A loop that continually checks the live status of users and sends alerts when online, removing them when offline :return: """ start = time.time() # logging.info("TwitchAlert: User Loop Started") sql_find_users = "SELECT twitch_username " \ "FROM UserInTwitchAlert " \ "JOIN TwitchAlerts TA on UserInTwitchAlert.channel_id = TA.channel_id " \ "JOIN (SELECT extension_id, guild_id FROM GuildExtensions " \ "WHERE extension_id = 'TwitchAlert' OR extension_id = 'All') GE on TA.guild_id = GE.guild_id;" users = self.ta_database_manager.database_manager.db_execute_select(sql_find_users) usernames = [] for user in users: if not re.search(TWITCH_USERNAME_REGEX, user[0]): sql_remove_invalid_user = "DELETE FROM UserInTwitchAlert WHERE twitch_username = ?" self.ta_database_manager.database_manager.db_execute_commit(sql_remove_invalid_user, args=[user[0]]) else: usernames.append(user[0]) # user_streams = self.ta_database_manager.twitch_handler.get_streams_data(usernames) if not usernames: return user_streams = await self.ta_database_manager.twitch_handler.get_streams_data(usernames) if user_streams is None: return # Deals with online streams for streams_details in user_streams: try: if streams_details.get('type') == "live": current_username = str.lower(streams_details.get("user_name")) usernames.remove(current_username) sql_find_message_id = \ "SELECT UserInTwitchAlert.channel_id, message_id, custom_message, default_message " \ "FROM UserInTwitchAlert " \ "JOIN TwitchAlerts TA on UserInTwitchAlert.channel_id = TA.channel_id " \ "JOIN (SELECT extension_id, guild_id FROM GuildExtensions " \ "WHERE extension_id = 'TwitchAlert' " \ " OR extension_id = 'All') GE on TA.guild_id = GE.guild_id " \ "WHERE twitch_username = ?;" results = self.ta_database_manager.database_manager.db_execute_select( sql_find_message_id, args=[current_username]) new_message_embed = None for result in results: channel_id = result[0] message_id = result[1] custom_message = result[2] channel_default_message = result[3] channel = self.bot.get_channel(id=channel_id) try: # If no Alert is posted if message_id is None: if new_message_embed is None: if custom_message is not None: message = custom_message else: message = channel_default_message new_message_embed = await self.create_alert_embed(streams_details, message) if new_message_embed is not None and channel is not None: new_message = await channel.send(embed=new_message_embed) sql_update_message_id = """ UPDATE UserInTwitchAlert SET message_id = ? WHERE channel_id = ? AND twitch_username = ?""" self.ta_database_manager.database_manager.db_execute_commit( sql_update_message_id, args=[new_message.id, result[0], current_username]) except discord.errors.Forbidden as err: logging.warning(f"TwitchAlert: {err} Name: {channel} ID: {channel.id}") sql_remove_invalid_channel = "DELETE FROM TwitchAlerts WHERE channel_id = ?" self.ta_database_manager.database_manager.db_execute_commit(sql_remove_invalid_channel, args=[channel.id]) except Exception as err: logging.error(f"TwitchAlert: User Loop error {err}") # Deals with remaining offline streams await self.ta_database_manager.delete_all_offline_streams(False, usernames) time_diff = time.time() - start if time_diff > 5: logging.warning(f"TwitchAlert: User Loop Finished in > 5s | {time_diff}s") async def create_alert_embed(self, stream_data, message): """ Creates and sends an alert message :param stream_data: The twitch stream data to have in the message :param message: The custom message to be added as a description :return: The discord message id of the sent message """ user_details = await self.ta_database_manager.twitch_handler.get_user_data( stream_data.get("user_name")) game_details = await self.ta_database_manager.twitch_handler.get_game_data( stream_data.get("game_id")) return create_live_embed(stream_data, user_details, game_details, message) @tasks.loop(minutes=REFRESH_TEAMS_DELAY) async def loop_update_teams(self): start = time.time() # logging.info("TwitchAlert: Started Update Teams") await self.ta_database_manager.update_all_teams_members() time_diff = time.time() - start if time_diff > 5: logging.warning(f"TwitchAlert: Teams updated in > 5s | {time_diff}s") @tasks.loop(minutes=TEAMS_LOOP_CHECK_LIVE_DELAY) async def loop_check_team_live(self): """ A loop to repeatedly send messages if a member of a team is live, and remove it when they are not :return: """ start = time.time() # logging.info("TwitchAlert: Team Loop Started") sql_select_team_users = "SELECT twitch_username, twitch_team_name " \ "FROM UserInTwitchTeam " \ "JOIN TeamInTwitchAlert TITA " \ " ON UserInTwitchTeam.team_twitch_alert_id = TITA.team_twitch_alert_id " \ "JOIN TwitchAlerts TA on TITA.channel_id = TA.channel_id " \ "JOIN (SELECT extension_id, guild_id FROM GuildExtensions " \ "WHERE extension_id = 'TwitchAlert' " \ " OR extension_id = 'All') GE on TA.guild_id = GE.guild_id " users_and_teams = self.ta_database_manager.database_manager.db_execute_select(sql_select_team_users) usernames = [] for user in users_and_teams: if not re.search(TWITCH_USERNAME_REGEX, user[1]): sql_remove_invalid_user = "DELETE FROM TeamInTwitchAlert WHERE twitch_team_name = ?" self.ta_database_manager.database_manager.db_execute_commit(sql_remove_invalid_user, args=[user[1]]) else: usernames.append(user[0]) if not usernames: return streams_data = await self.ta_database_manager.twitch_handler.get_streams_data(usernames) if streams_data is None: return # Deals with online streams for stream_data in streams_data: try: if stream_data.get('type') == "live": current_username = str.lower(stream_data.get("user_name")) usernames.remove(current_username) sql_find_message_id = """ SELECT TITA.channel_id, UserInTwitchTeam.message_id, TITA.team_twitch_alert_id, custom_message, default_message FROM UserInTwitchTeam JOIN TeamInTwitchAlert TITA on UserInTwitchTeam.team_twitch_alert_id = TITA.team_twitch_alert_id JOIN TwitchAlerts TA on TITA.channel_id = TA.channel_id JOIN (SELECT extension_id, guild_id FROM GuildExtensions WHERE extension_id = 'TwitchAlert' OR extension_id = 'All') GE ON TA.guild_id = GE.guild_id WHERE twitch_username = ?""" results = self.ta_database_manager.database_manager.db_execute_select( sql_find_message_id, args=[current_username]) new_message_embed = None for result in results: channel_id = result[0] message_id = result[1] team_twitch_alert_id = result[2] custom_message = result[3] channel_default_message = result[4] channel = self.bot.get_channel(id=channel_id) try: # If no Alert is posted if message_id is None: if new_message_embed is None: if custom_message is not None: message = custom_message else: message = channel_default_message new_message_embed = await self.create_alert_embed(stream_data, message) if new_message_embed is not None and channel is not None: new_message = await channel.send(embed=new_message_embed) sql_update_message_id = """ UPDATE UserInTwitchTeam SET message_id = ? WHERE team_twitch_alert_id = ? AND twitch_username = ?""" self.ta_database_manager.database_manager.db_execute_commit( sql_update_message_id, args=[new_message.id, team_twitch_alert_id, current_username]) except discord.errors.Forbidden as err: logging.warning(f"TwitchAlert: {err} Name: {channel} ID: {channel.id}") sql_remove_invalid_channel = "DELETE FROM TwitchAlerts WHERE channel_id = ?" self.ta_database_manager.database_manager.db_execute_commit(sql_remove_invalid_channel, args=[channel.id]) except Exception as err: logging.error(f"TwitchAlert: Team Loop error {err}") # Deals with remaining offline streams await self.ta_database_manager.delete_all_offline_streams(True, usernames) time_diff = time.time() - start if time_diff > 5: logging.warning(f"TwitchAlert: Teams Loop Finished in > 5s | {time_diff}s") def create_live_embed(stream_info, user_info, game_info, message): """ Creates an embed for the go live announcement :param stream_info: The stream data from the Twitch API :param user_info: The user data for this streamer from the Twitch API :param game_info: The game data for this game from the Twitch API :param message: The custom message to be added as a description :return: The embed created """ embed = discord.Embed(colour=KOALA_GREEN) if message is not None and message != "": embed.description = message embed.set_author(name=stream_info.get("user_name") + " is now streaming!", icon_url=TWITCH_ICON) embed.title = "https://twitch.tv/" + str.lower(stream_info.get("user_name")) embed.add_field(name="Stream Title", value=stream_info.get("title")) if game_info is None: embed.add_field(name="Playing", value="No Category") else: embed.add_field(name="Playing", value=game_info.get("name")) embed.set_thumbnail(url=user_info.get("profile_image_url")) return embed class TwitchAPIHandler: """ A wrapper to interact with the twitch API """ def __init__(self, client_id: str, client_secret: str): self.client_id = client_id self.client_secret = client_secret self.params = {'client_id': self.client_id, 'client_secret': self.client_secret, 'grant_type': 'client_credentials'} self.token = {} @property def base_headers(self): return { 'Authorization': f'Bearer {self.token.get("access_token")}', 'Client-ID': self.client_id } async def get_new_twitch_oauth(self): """ Get a new OAuth2 token from twitch using client_id and client_secret :return: The new OAuth2 token """ async with aiohttp.ClientSession(timeout=aiohttp.ClientTimeout(60)) as client: async with client.post('https://id.twitch.tv/oauth2/token', params=self.params) as response: if response.status > 399: logging.critical(f'TwitchAlert: Error {response.status} while getting Oauth token') self.token = {} response_json = await response.json() try: response_json['expires_in'] += time.time() except KeyError: # probably shouldn't need this, but catch just in case logging.warning('TwitchAlert: Failed to set token expiration time') self.token = response_json return self.token async def requests_get(self, url, headers=None, params=None): """ Gets a response from a curl get request to the given url using headers of this object :param headers: the Headers required for the request, will use self.headers by default :param url: The URL to send the request to :param params: The parameters of the request :return: The response of the request """ if self.token.get('expires_in', 0) <= time.time() + 1 or not self.token: await self.get_new_twitch_oauth() async with aiohttp.ClientSession(timeout=aiohttp.ClientTimeout(60)) as client: async with client.get(url=url, headers=headers if headers else self.base_headers, params=params) as \ response: if response.status == 401: logging.info(f"TwitchAlert: {response.status}, getting new oauth and retrying") await self.get_new_twitch_oauth() return await self.requests_get(url, headers, params) elif response.status > 399: logging.warning(f'TwitchAlert: {response.status} while getting requesting URL:{url}') return await response.json() async def get_streams_data(self, usernames): """ Gets all stream information from a list of given usernames :param usernames: The list of usernames :return: The JSON data of the request """ url = 'https://api.twitch.tv/helix/streams?' next_hundred_users = usernames[:100] usernames = usernames[100:] result = (await self.requests_get(url + "user_login=" + "&user_login=".join(next_hundred_users))).get("data") while usernames: next_hundred_users = usernames[:100] usernames = usernames[100:] result += (await self.requests_get(url + "user_login=" + "&user_login=".join(next_hundred_users))).get( "data") return result async def get_user_data(self, username): """ Gets the user information of a given user :param username: The display twitch username of the user :return: The JSON information of the user's data """ url = 'https://api.twitch.tv/helix/users?login=' + username return (await self.requests_get(url)).get("data")[0] async def get_game_data(self, game_id): """ Gets the game information of a given game :param game_id: The twitch game ID of a game :return: The JSON information of the game's data """ if game_id != "": url = 'https://api.twitch.tv/helix/games?id=' + game_id game_data = await self.requests_get(url) return game_data.get("data")[0] else: return None async def get_team_users(self, team_id): """ Gets the users data about a given team :param team_id: The team name of the twitch team :return: the JSON information of the users """ url = 'https://api.twitch.tv/helix/teams?name=' + team_id return ( await self.requests_get(url)).get("data")[0].get("users") class TwitchAlertDBManager: """ A class for interacting with the Koala twitch database """ def __init__(self, database_manager: KoalaDBManager.KoalaDBManager, bot_client: discord.client): """ Initialises local variables :param database_manager: :param bot_client: """ self.database_manager = database_manager self.twitch_handler = TwitchAPIHandler(TWITCH_CLIENT_ID, TWITCH_SECRET) self.bot = bot_client def get_parent_database_manager(self): """ A getter for the database manager of this object :return: """ return self.database_manager def create_tables(self): """ Creates all the tables associated with the twitch alert extension :return: """ # TwitchAlerts sql_create_twitch_alerts_table = """ CREATE TABLE IF NOT EXISTS TwitchAlerts ( guild_id integer NOT NULL, channel_id integer NOT NULL, default_message text NOT NULL, PRIMARY KEY (guild_id, channel_id), CONSTRAINT fk_guild FOREIGN KEY (guild_id) REFERENCES GuildExtensions (guild_id) ON DELETE CASCADE );""" # UserInTwitchAlert sql_create_user_in_twitch_alert_table = """ CREATE TABLE IF NOT EXISTS UserInTwitchAlert ( channel_id integer NOT NULL, twitch_username text NOT NULL, custom_message text, message_id integer, PRIMARY KEY (channel_id, twitch_username), CONSTRAINT fk_channel FOREIGN KEY (channel_id) REFERENCES TwitchAlerts (channel_id) ON DELETE CASCADE );""" # TeamInTwitchAlert sql_create_team_in_twitch_alert_table = """ CREATE TABLE IF NOT EXISTS TeamInTwitchAlert ( team_twitch_alert_id integer PRIMARY KEY AUTOINCREMENT, channel_id integer NOT NULL, twitch_team_name text NOT NULL, custom_message text, CONSTRAINT fk_channel FOREIGN KEY (channel_id) REFERENCES TwitchAlerts (channel_id) ON DELETE CASCADE );""" # UserInTwitchTeam sql_create_user_in_twitch_team_table = """ CREATE TABLE IF NOT EXISTS UserInTwitchTeam ( team_twitch_alert_id text NOT NULL, twitch_username text NOT NULL, message_id integer, PRIMARY KEY (team_twitch_alert_id, twitch_username), CONSTRAINT fk_twitch_team_alert FOREIGN KEY (team_twitch_alert_id) REFERENCES TeamInTwitchAlert (team_twitch_alert_id) ON DELETE CASCADE );""" # Create Tables self.database_manager.db_execute_commit(sql_create_twitch_alerts_table) self.database_manager.db_execute_commit(sql_create_user_in_twitch_alert_table) self.database_manager.db_execute_commit(sql_create_team_in_twitch_alert_table) self.database_manager.db_execute_commit(sql_create_user_in_twitch_team_table) def new_ta(self, guild_id, channel_id, default_message=None, replace=False): """ Creates a new Twitch Alert and gives the ID associated with it :param guild_id: The discord guild ID where the Twitch Alert is located :param channel_id: The discord channel ID of the twitch Alert :param default_message: The default message of users in the Twitch Alert :param replace: True if the new ta should replace the current if exists :return: The new default_message """ sql_find_ta = "SELECT default_message FROM TwitchAlerts WHERE channel_id=?" message = self.database_manager.db_execute_select(sql_find_ta, args=[channel_id]) if message and not replace: return message[0][0] # Sets the default message if not provided if default_message is None: default_message = DEFAULT_MESSAGE # Insert new Twitch Alert to database if replace: sql_insert_twitch_alert = """ REPLACE INTO TwitchAlerts(guild_id, channel_id, default_message) VALUES(?,?,?) """ else: sql_insert_twitch_alert = """ INSERT INTO TwitchAlerts(guild_id, channel_id, default_message) VALUES(?,?,?) """ self.database_manager.db_execute_commit(sql_insert_twitch_alert, args=[guild_id, channel_id, default_message]) return default_message def get_default_message(self, channel_id): """ Get the set default message for the twitch alert :param channel_id: The discord channel ID of the twitch Alert :return: The current default_message """ sql_find_ta = "SELECT default_message FROM TwitchAlerts WHERE channel_id= ?" return self.database_manager.db_execute_select(sql_find_ta, args=[channel_id]) def add_user_to_ta(self, channel_id, twitch_username, custom_message, guild_id=None): """ Add a twitch user to a given Twitch Alert :param channel_id: The discord channel ID of the twitch Alert :param twitch_username: The Twitch username of the user to be added :param custom_message: The custom Message of the user's live notification. None = use default Twitch Alert message :param guild_id: The guild ID of the channel :return: :raises: KeyError if channel ID is not defined in TwitchAlerts and guild_id is not provided """ self.new_ta(guild_id, channel_id) if custom_message: sql_insert_user_twitch_alert = """ INSERT INTO UserInTwitchAlert(channel_id, twitch_username, custom_message) VALUES(?, ?, ?) """ self.database_manager.db_execute_commit( sql_insert_user_twitch_alert, args=[channel_id, str.lower(twitch_username), custom_message]) else: sql_insert_user_twitch_alert = """ INSERT INTO UserInTwitchAlert(channel_id, twitch_username) VALUES(?, ?) """ self.database_manager.db_execute_commit( sql_insert_user_twitch_alert, args=[channel_id, str.lower(twitch_username)]) async def remove_user_from_ta(self, channel_id, twitch_username): """ Removes a user from a given Twitch Alert :param channel_id: The discord channel ID of the twitch Alert :param twitch_username: The Twitch username of the user to be added :return: """ sql_get_message_id = "SELECT message_id " \ "FROM UserInTwitchAlert " \ "WHERE twitch_username = ? " \ "AND channel_id = ? " message_id = self.database_manager.db_execute_select(sql_get_message_id, args=[twitch_username, channel_id])[0][0] if message_id is not None: await self.delete_message(message_id, channel_id) sql_remove_entry = """DELETE FROM UserInTwitchAlert WHERE twitch_username = ? AND channel_id = ?""" self.database_manager.db_execute_commit(sql_remove_entry, args=[twitch_username, channel_id]) async def delete_message(self, message_id, channel_id): """ Deletes a given discord message :param message_id: discord message ID of the message to delete :param channel_id: discord channel ID which has the message :return: """ try: channel = self.bot.get_channel(int(channel_id)) if channel is None: logging.warning(f"TwitchAlert: Channel ID {channel_id} does not exist, removing from database") sql_remove_invalid_channel = "DELETE FROM TwitchAlerts WHERE channel_id = ?" self.database_manager.db_execute_commit(sql_remove_invalid_channel, args=[channel_id]) return message = await channel.fetch_message(message_id) await message.delete() except discord.errors.NotFound as err: logging.warning(f"TwitchAlert: Message ID {message_id} does not exist, skipping \nError: {err}") except discord.errors.Forbidden as err: logging.warning(f"TwitchAlert: {err} Channel ID: {channel_id}") sql_remove_invalid_channel = "DELETE FROM TwitchAlerts WHERE channel_id = ?" self.database_manager.db_execute_commit(sql_remove_invalid_channel, args=[channel_id]) def get_users_in_ta(self, channel_id): """ Returns all users in a given Twitch Alert :param channel_id: The channel ID of the Twitch Alert :return: The sql results of the users """ sql_get_users = "SELECT twitch_username FROM UserInTwitchAlert WHERE channel_id = ?" return self.database_manager.db_execute_select(sql_get_users, args=[channel_id]) def get_teams_in_ta(self, channel_id): """ Returns all teams in a given Twitch Alert :param channel_id: The channel ID of the Twitch Alert :return: The sql results of the teams """ sql_get_teams = "SELECT twitch_team_name FROM TeamInTwitchAlert WHERE channel_id = ?" return self.database_manager.db_execute_select(sql_get_teams, args=[channel_id]) def add_team_to_ta(self, channel_id, twitch_team, custom_message, guild_id=None): """ Add a twitch team to a given Twitch Alert :param channel_id: The discord channel ID of the twitch Alert :param twitch_team: The Twitch team to be added :param custom_message: The custom Message of the team's live notification. None = use default Twitch Alert message :param guild_id: The guild ID of the channel :return: :raises: KeyError if channel ID is not defined in TwitchAlerts and guild_id is not provided """ self.new_ta(guild_id, channel_id) if custom_message: sql_insert_team_twitch_alert = """ INSERT INTO TeamInTwitchAlert(channel_id, twitch_team_name, custom_message) VALUES(?, ?, ?) """ self.database_manager.db_execute_commit( sql_insert_team_twitch_alert, args=[channel_id, str.lower(twitch_team), custom_message]) else: sql_insert_team_twitch_alert = """ INSERT INTO TeamInTwitchAlert(channel_id, twitch_team_name) VALUES(?, ?) """ self.database_manager.db_execute_commit( sql_insert_team_twitch_alert, args=[channel_id, str.lower(twitch_team)]) async def remove_team_from_ta(self, channel_id, team_name): """ Removes a team from a given twitch alert :param channel_id: The channel ID of the Twitch Alert :param team_name: The team name of the team to be removed :return: """ sql_get_team_alert_id = "SELECT team_twitch_alert_id " \ "FROM TeamInTwitchAlert " \ "WHERE twitch_team_name = ? " \ " AND channel_id = ?" result = self.database_manager.db_execute_select(sql_get_team_alert_id, args=[team_name, channel_id]) if not result: raise AttributeError("Team name not found") team_alert_id = result[0][0] sql_get_message_id = """SELECT UserInTwitchTeam.message_id FROM UserInTwitchTeam WHERE team_twitch_alert_id = ?""" message_ids = self.database_manager.db_execute_select(sql_get_message_id, args=[team_alert_id]) if message_ids is not None: for message_id in message_ids: if message_id[0] is not None: await self.delete_message(message_id[0], channel_id) sql_remove_users = """DELETE FROM UserInTwitchTeam WHERE team_twitch_alert_id = ?""" sql_remove_team = """DELETE FROM TeamInTwitchAlert WHERE team_twitch_alert_id = ?""" self.database_manager.db_execute_commit(sql_remove_users, args=[team_alert_id]) self.database_manager.db_execute_commit(sql_remove_team, args=[team_alert_id]) async def update_team_members(self, twitch_team_id, team_name): """ Users in a team are updated to ensure they are assigned to the correct team :param twitch_team_id: the team twitch alert id :param team_name: the name of the team :return: """ if re.search(TWITCH_USERNAME_REGEX, team_name): users = await self.twitch_handler.get_team_users(team_name) for user in users: sql_add_user = """INSERT OR IGNORE INTO UserInTwitchTeam(team_twitch_alert_id, twitch_username) VALUES(?, ?)""" try: self.database_manager.db_execute_commit(sql_add_user, args=[twitch_team_id, user.get("user_login")], pass_errors=True) except KoalaDBManager.sqlite3.IntegrityError as err: logging.error(f"Twitch Alert: 1034: {err}") pass async def update_all_teams_members(self): """ Updates all teams with the current team members :return: """ sql_get_teams = """SELECT team_twitch_alert_id, twitch_team_name FROM TeamInTwitchAlert""" teams_info = self.database_manager.db_execute_select(sql_get_teams) for team_info in teams_info: await self.update_team_members(team_info[0], team_info[1]) async def delete_all_offline_streams(self, team: bool, usernames): """ A method that deletes all currently offline streams :param team: True if the users are from teams, false if individuals :param usernames: The usernames of the team members :return: """ if team: sql_select_offline_streams_with_message_ids = f""" SELECT channel_id, message_id FROM UserInTwitchTeam JOIN TeamInTwitchAlert TITA on UserInTwitchTeam.team_twitch_alert_id = TITA.team_twitch_alert_id WHERE message_id NOT NULL AND twitch_username in ({','.join(['?'] * len(usernames))})""" sql_update_offline_streams = f""" UPDATE UserInTwitchTeam SET message_id = NULL WHERE twitch_username in ({','.join(['?'] * len(usernames))})""" else: sql_select_offline_streams_with_message_ids = f""" SELECT channel_id, message_id FROM UserInTwitchAlert WHERE message_id NOT NULL AND twitch_username in ({','.join(['?'] * len(usernames))})""" sql_update_offline_streams = f""" UPDATE UserInTwitchAlert SET message_id = NULL WHERE twitch_username in ({','.join(['?'] * len(usernames))})""" results = self.database_manager.db_execute_select( sql_select_offline_streams_with_message_ids, usernames) for result in results: await self.delete_message(result[1], result[0]) self.database_manager.db_execute_commit(sql_update_offline_streams, usernames) def setup(bot: KoalaBot) -> None: """ Load this cog to the KoalaBot. :param bot: the bot client for KoalaBot """ if TWITCH_SECRET is None or TWITCH_CLIENT_ID is None: logging.error("TwitchAlert not started. API keys not found in environment.") print("TwitchAlert not started. API keys not found in environment.") KoalaBot.database_manager.insert_extension("TwitchAlert", 0, False, False) else: bot.add_cog(TwitchAlert(bot)) logging.info("TwitchAlert is ready.") print("TwitchAlert is ready.")

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import os import time import re import aiohttp import logging from concurrent.futures import ThreadPoolExecutor logging.basicConfig(filename='TwitchAlert.log') import KoalaBot from utils.KoalaColours import * from utils.KoalaUtils import error_embed, is_channel_in_guild, extract_id from utils import KoalaDBManager from discord.ext import commands, tasks from dotenv import load_dotenv import asyncio load_dotenv() DEFAULT_MESSAGE = "" TWITCH_ICON = "https://cdn3.iconfinder.com/data/icons/social-messaging-ui-color-shapes-2-free" \ "/128/social-twitch-circle-512.png" TWITCH_CLIENT_ID = os.environ.get('TWITCH_TOKEN') TWITCH_SECRET = os.environ.get('TWITCH_SECRET') TWITCH_USERNAME_REGEX = "^[a-z0-9][a-z0-9_]{3,24}$" LOOP_CHECK_LIVE_DELAY = 1 TEAMS_LOOP_CHECK_LIVE_DELAY = 1 REFRESH_TEAMS_DELAY = 5 def twitch_is_enabled(ctx): try: result = KoalaBot.check_guild_has_ext(ctx, "TwitchAlert") except PermissionError: result = False return result class TwitchAlert(commands.Cog): def __init__(self, bot, database_manager=None): if not database_manager: database_manager = KoalaBot.database_manager self.bot = bot database_manager.create_base_tables() database_manager.insert_extension("TwitchAlert", 0, True, True) self.ta_database_manager = TwitchAlertDBManager(database_manager, bot) self.ta_database_manager.create_tables() self.loop_thread = None self.loop_team_thread = None self.running = False self.stop_loop = False @commands.command(name="twitchEditMsg", aliases=["edit_default_message"]) @commands.check(KoalaBot.is_admin) @commands.check(twitch_is_enabled) async def edit_default_message(self, ctx, raw_channel_id, *default_live_message): try: channel_id = extract_id(raw_channel_id) except TypeError: channel_id = ctx.message.channel.id default_live_message = (raw_channel_id,) + default_live_message if not is_channel_in_guild(self.bot, ctx.message.guild.id, channel_id): await ctx.send(embed=error_embed("The channel ID provided is either invalid, or not in this server.")) return if default_live_message is not None and default_live_message != (None,): default_message = " ".join(default_live_message) if len(default_message) > 1000: await ctx.send(embed=error_embed( "custom_message is too long, try something with less than 1000 characters")) return else: default_message = None default_message = self.ta_database_manager.new_ta(ctx.message.guild.id, channel_id, default_message, replace=True) new_embed = discord.Embed(title="Default Message Edited", colour=KOALA_GREEN, description=f"Guild: {ctx.message.guild.id}\n" f"Channel: {channel_id}\n" f"Default Message: {default_message}") await ctx.send(embed=new_embed) @commands.command(name="twitchViewMsg", aliases=["view_default_message"]) @commands.check(KoalaBot.is_admin) @commands.check(twitch_is_enabled) async def view_default_message(self, ctx, raw_channel_id=None): if raw_channel_id is None: channel_id = ctx.message.channel.id else: channel_id = extract_id(raw_channel_id) if not is_channel_in_guild(self.bot, ctx.message.guild.id, channel_id): await ctx.send(embed=error_embed("The channel ID provided is either invalid, or not in this server.")) return default_message = self.ta_database_manager.get_default_message(channel_id)[0][0] new_embed = discord.Embed(title="Default Message", colour=KOALA_GREEN, description=f"Guild: {ctx.message.guild.id}\n" f"Channel: {channel_id}\n" f"Default Message: {default_message}") await ctx.send(embed=new_embed) @commands.command(name="twitchAdd", aliases=['add_user_to_twitch_alert']) @commands.check(KoalaBot.is_admin) @commands.check(twitch_is_enabled) async def add_user_to_twitch_alert(self, ctx, raw_channel_id, twitch_username=None, *custom_live_message): try: channel_id = extract_id(raw_channel_id) except TypeError: custom_live_message = (twitch_username,) + custom_live_message twitch_username = raw_channel_id channel_id = ctx.message.channel.id if twitch_username is None: raise discord.errors.InvalidArgument("twitch_username is a required argument that is missing.") elif not re.search(TWITCH_USERNAME_REGEX, twitch_username): raise discord.errors.InvalidArgument( "The given twitch_username is not a valid username (please use lowercase)") if not is_channel_in_guild(self.bot, ctx.message.guild.id, channel_id): await ctx.send(embed=error_embed("The channel ID provided is either invalid, or not in this server.")) return default_message = self.ta_database_manager.new_ta(ctx.message.guild.id, channel_id) if custom_live_message is not None and custom_live_message != (None,): custom_message = " ".join(custom_live_message) default_message = custom_message if len(default_message) > 1000: await ctx.send(embed=error_embed( "custom_message is too long, try something with less than 1000 characters")) return else: custom_message = None self.ta_database_manager.add_user_to_ta(channel_id, twitch_username, custom_message, ctx.message.guild.id) new_embed = discord.Embed(title="Added User to Twitch Alert", colour=KOALA_GREEN, description=f"Channel: {channel_id}\n" f"User: {twitch_username}\n" f"Message: {default_message}") await ctx.send(embed=new_embed) @commands.command(name="twitchRemove", aliases=['remove_user_from_twitch_alert']) @commands.check(KoalaBot.is_admin) @commands.check(twitch_is_enabled) async def remove_user_from_twitch_alert(self, ctx, raw_channel_id, twitch_username=None): try: channel_id = extract_id(raw_channel_id) except TypeError: twitch_username = raw_channel_id channel_id = ctx.message.channel.id if twitch_username is None: raise discord.errors.InvalidArgument("twitch_username is a required argument that is missing.") if not is_channel_in_guild(self.bot, ctx.message.guild.id, channel_id): await ctx.send(embed=error_embed("The channel ID provided is either invalid, or not in this server.")) return await self.ta_database_manager.remove_user_from_ta(channel_id, twitch_username) new_embed = discord.Embed(title="Removed User from Twitch Alert", colour=KOALA_GREEN, description=f"Channel: {channel_id}\n" f"User: {twitch_username}") await ctx.send(embed=new_embed) @commands.command(name="twitchAddTeam", aliases=["add_team_to_twitch_alert"]) @commands.check(KoalaBot.is_admin) @commands.check(twitch_is_enabled) async def add_team_to_twitch_alert(self, ctx, raw_channel_id, team_name=None, *custom_live_message): try: channel_id = extract_id(raw_channel_id) except TypeError: custom_live_message = (team_name,) + custom_live_message team_name = raw_channel_id channel_id = ctx.message.channel.id if team_name is None: raise discord.errors.InvalidArgument("team_name is a required argument that is missing.") elif not re.search(TWITCH_USERNAME_REGEX, team_name): raise discord.errors.InvalidArgument( "The given team_name is not a valid twitch team name (please use lowercase)") if not is_channel_in_guild(self.bot, ctx.message.guild.id, channel_id): await ctx.send(embed=error_embed("The channel ID provided is either invalid, or not in this server.")) return self.ta_database_manager.new_ta(ctx.message.guild.id, channel_id) if custom_live_message is not None and custom_live_message != (None,): default_message = " ".join(custom_live_message) if len(default_message) > 1000: await ctx.send(embed=error_embed( "custom_message is too long, try something with less than 1000 characters")) return else: default_message = DEFAULT_MESSAGE self.ta_database_manager.add_team_to_ta(channel_id, team_name, default_message, ctx.message.guild.id) new_embed = discord.Embed(title="Added Team to Twitch Alert", colour=KOALA_GREEN, description=f"Channel: {channel_id}\n" f"Team: {team_name}\n" f"Message: {default_message}") await ctx.send(embed=new_embed) @commands.command(name="twitchRemoveTeam", aliases=["remove_team_from_twitch_alert"]) @commands.check(KoalaBot.is_admin) @commands.check(twitch_is_enabled) async def remove_team_from_twitch_alert(self, ctx, raw_channel_id, team_name=None): try: channel_id = extract_id(raw_channel_id) except TypeError: team_name = raw_channel_id channel_id = ctx.message.channel.id if team_name is None: raise discord.errors.InvalidArgument("team_name is a required argument that is missing.") if not is_channel_in_guild(self.bot, ctx.message.guild.id, channel_id): await ctx.send(embed=error_embed("The channel ID provided is either invalid, or not in this server.")) return await self.ta_database_manager.remove_team_from_ta(channel_id, team_name) new_embed = discord.Embed(title="Removed Team from Twitch Alert", colour=KOALA_GREEN, description=f"Channel: {channel_id}\n" f"Team: {team_name}") await ctx.send(embed=new_embed) @commands.command(name="twitchList", aliases=["list_twitch_alert"]) @commands.check(KoalaBot.is_admin) @commands.check(twitch_is_enabled) async def list_twitch_alert(self, ctx, raw_channel_id=None): if raw_channel_id is None: channel_id = ctx.message.channel.id else: channel_id = extract_id(raw_channel_id) if not is_channel_in_guild(self.bot, ctx.message.guild.id, channel_id): await ctx.send(embed=error_embed("The channel ID provided is either invalid, or not in this server.")) return embed = discord.Embed() embed.title = "Twitch Alerts" embed.colour = KOALA_GREEN embed.set_footer(text=f"Channel ID: {channel_id}") results = self.ta_database_manager.get_users_in_ta(channel_id) if results: users = "" for result in results: users += f"{result[0]}\n" embed.add_field(name=":bust_in_silhouette: Users", value=users) else: embed.add_field(name=":bust_in_silhouette: Users", value="None") results = self.ta_database_manager.get_teams_in_ta(channel_id) if results: teams = "" for result in results: teams += f"{result[0]}\n" embed.add_field(name=":busts_in_silhouette: Teams", value=teams) else: embed.add_field(name=":busts_in_silhouette: Teams", value="None") await ctx.send(embed=embed) @commands.Cog.listener() async def on_ready(self): if not self.running: self.start_loops() def start_loops(self): self.loop_update_teams.start() self.loop_check_team_live.start() self.loop_check_live.start() self.running = True def end_loops(self): self.loop_update_teams.cancel() self.loop_check_team_live.cancel() self.loop_check_live.cancel() self.running = False @tasks.loop(minutes=LOOP_CHECK_LIVE_DELAY) async def loop_check_live(self): start = time.time() sql_find_users = "SELECT twitch_username " \ "FROM UserInTwitchAlert " \ "JOIN TwitchAlerts TA on UserInTwitchAlert.channel_id = TA.channel_id " \ "JOIN (SELECT extension_id, guild_id FROM GuildExtensions " \ "WHERE extension_id = 'TwitchAlert' OR extension_id = 'All') GE on TA.guild_id = GE.guild_id;" users = self.ta_database_manager.database_manager.db_execute_select(sql_find_users) usernames = [] for user in users: if not re.search(TWITCH_USERNAME_REGEX, user[0]): sql_remove_invalid_user = "DELETE FROM UserInTwitchAlert WHERE twitch_username = ?" self.ta_database_manager.database_manager.db_execute_commit(sql_remove_invalid_user, args=[user[0]]) else: usernames.append(user[0]) if not usernames: return user_streams = await self.ta_database_manager.twitch_handler.get_streams_data(usernames) if user_streams is None: return for streams_details in user_streams: try: if streams_details.get('type') == "live": current_username = str.lower(streams_details.get("user_name")) usernames.remove(current_username) sql_find_message_id = \ "SELECT UserInTwitchAlert.channel_id, message_id, custom_message, default_message " \ "FROM UserInTwitchAlert " \ "JOIN TwitchAlerts TA on UserInTwitchAlert.channel_id = TA.channel_id " \ "JOIN (SELECT extension_id, guild_id FROM GuildExtensions " \ "WHERE extension_id = 'TwitchAlert' " \ " OR extension_id = 'All') GE on TA.guild_id = GE.guild_id " \ "WHERE twitch_username = ?;" results = self.ta_database_manager.database_manager.db_execute_select( sql_find_message_id, args=[current_username]) new_message_embed = None for result in results: channel_id = result[0] message_id = result[1] custom_message = result[2] channel_default_message = result[3] channel = self.bot.get_channel(id=channel_id) try: if message_id is None: if new_message_embed is None: if custom_message is not None: message = custom_message else: message = channel_default_message new_message_embed = await self.create_alert_embed(streams_details, message) if new_message_embed is not None and channel is not None: new_message = await channel.send(embed=new_message_embed) sql_update_message_id = """ UPDATE UserInTwitchAlert SET message_id = ? WHERE channel_id = ? AND twitch_username = ?""" self.ta_database_manager.database_manager.db_execute_commit( sql_update_message_id, args=[new_message.id, result[0], current_username]) except discord.errors.Forbidden as err: logging.warning(f"TwitchAlert: {err} Name: {channel} ID: {channel.id}") sql_remove_invalid_channel = "DELETE FROM TwitchAlerts WHERE channel_id = ?" self.ta_database_manager.database_manager.db_execute_commit(sql_remove_invalid_channel, args=[channel.id]) except Exception as err: logging.error(f"TwitchAlert: User Loop error {err}") await self.ta_database_manager.delete_all_offline_streams(False, usernames) time_diff = time.time() - start if time_diff > 5: logging.warning(f"TwitchAlert: User Loop Finished in > 5s | {time_diff}s") async def create_alert_embed(self, stream_data, message): user_details = await self.ta_database_manager.twitch_handler.get_user_data( stream_data.get("user_name")) game_details = await self.ta_database_manager.twitch_handler.get_game_data( stream_data.get("game_id")) return create_live_embed(stream_data, user_details, game_details, message) @tasks.loop(minutes=REFRESH_TEAMS_DELAY) async def loop_update_teams(self): start = time.time() await self.ta_database_manager.update_all_teams_members() time_diff = time.time() - start if time_diff > 5: logging.warning(f"TwitchAlert: Teams updated in > 5s | {time_diff}s") @tasks.loop(minutes=TEAMS_LOOP_CHECK_LIVE_DELAY) async def loop_check_team_live(self): start = time.time() sql_select_team_users = "SELECT twitch_username, twitch_team_name " \ "FROM UserInTwitchTeam " \ "JOIN TeamInTwitchAlert TITA " \ " ON UserInTwitchTeam.team_twitch_alert_id = TITA.team_twitch_alert_id " \ "JOIN TwitchAlerts TA on TITA.channel_id = TA.channel_id " \ "JOIN (SELECT extension_id, guild_id FROM GuildExtensions " \ "WHERE extension_id = 'TwitchAlert' " \ " OR extension_id = 'All') GE on TA.guild_id = GE.guild_id " users_and_teams = self.ta_database_manager.database_manager.db_execute_select(sql_select_team_users) usernames = [] for user in users_and_teams: if not re.search(TWITCH_USERNAME_REGEX, user[1]): sql_remove_invalid_user = "DELETE FROM TeamInTwitchAlert WHERE twitch_team_name = ?" self.ta_database_manager.database_manager.db_execute_commit(sql_remove_invalid_user, args=[user[1]]) else: usernames.append(user[0]) if not usernames: return streams_data = await self.ta_database_manager.twitch_handler.get_streams_data(usernames) if streams_data is None: return for stream_data in streams_data: try: if stream_data.get('type') == "live": current_username = str.lower(stream_data.get("user_name")) usernames.remove(current_username) sql_find_message_id = """ SELECT TITA.channel_id, UserInTwitchTeam.message_id, TITA.team_twitch_alert_id, custom_message, default_message FROM UserInTwitchTeam JOIN TeamInTwitchAlert TITA on UserInTwitchTeam.team_twitch_alert_id = TITA.team_twitch_alert_id JOIN TwitchAlerts TA on TITA.channel_id = TA.channel_id JOIN (SELECT extension_id, guild_id FROM GuildExtensions WHERE extension_id = 'TwitchAlert' OR extension_id = 'All') GE ON TA.guild_id = GE.guild_id WHERE twitch_username = ?""" results = self.ta_database_manager.database_manager.db_execute_select( sql_find_message_id, args=[current_username]) new_message_embed = None for result in results: channel_id = result[0] message_id = result[1] team_twitch_alert_id = result[2] custom_message = result[3] channel_default_message = result[4] channel = self.bot.get_channel(id=channel_id) try: if message_id is None: if new_message_embed is None: if custom_message is not None: message = custom_message else: message = channel_default_message new_message_embed = await self.create_alert_embed(stream_data, message) if new_message_embed is not None and channel is not None: new_message = await channel.send(embed=new_message_embed) sql_update_message_id = """ UPDATE UserInTwitchTeam SET message_id = ? WHERE team_twitch_alert_id = ? AND twitch_username = ?""" self.ta_database_manager.database_manager.db_execute_commit( sql_update_message_id, args=[new_message.id, team_twitch_alert_id, current_username]) except discord.errors.Forbidden as err: logging.warning(f"TwitchAlert: {err} Name: {channel} ID: {channel.id}") sql_remove_invalid_channel = "DELETE FROM TwitchAlerts WHERE channel_id = ?" self.ta_database_manager.database_manager.db_execute_commit(sql_remove_invalid_channel, args=[channel.id]) except Exception as err: logging.error(f"TwitchAlert: Team Loop error {err}") await self.ta_database_manager.delete_all_offline_streams(True, usernames) time_diff = time.time() - start if time_diff > 5: logging.warning(f"TwitchAlert: Teams Loop Finished in > 5s | {time_diff}s") def create_live_embed(stream_info, user_info, game_info, message): embed = discord.Embed(colour=KOALA_GREEN) if message is not None and message != "": embed.description = message embed.set_author(name=stream_info.get("user_name") + " is now streaming!", icon_url=TWITCH_ICON) embed.title = "https://twitch.tv/" + str.lower(stream_info.get("user_name")) embed.add_field(name="Stream Title", value=stream_info.get("title")) if game_info is None: embed.add_field(name="Playing", value="No Category") else: embed.add_field(name="Playing", value=game_info.get("name")) embed.set_thumbnail(url=user_info.get("profile_image_url")) return embed class TwitchAPIHandler: def __init__(self, client_id: str, client_secret: str): self.client_id = client_id self.client_secret = client_secret self.params = {'client_id': self.client_id, 'client_secret': self.client_secret, 'grant_type': 'client_credentials'} self.token = {} @property def base_headers(self): return { 'Authorization': f'Bearer {self.token.get("access_token")}', 'Client-ID': self.client_id } async def get_new_twitch_oauth(self): async with aiohttp.ClientSession(timeout=aiohttp.ClientTimeout(60)) as client: async with client.post('https://id.twitch.tv/oauth2/token', params=self.params) as response: if response.status > 399: logging.critical(f'TwitchAlert: Error {response.status} while getting Oauth token') self.token = {} response_json = await response.json() try: response_json['expires_in'] += time.time() except KeyError: logging.warning('TwitchAlert: Failed to set token expiration time') self.token = response_json return self.token async def requests_get(self, url, headers=None, params=None): if self.token.get('expires_in', 0) <= time.time() + 1 or not self.token: await self.get_new_twitch_oauth() async with aiohttp.ClientSession(timeout=aiohttp.ClientTimeout(60)) as client: async with client.get(url=url, headers=headers if headers else self.base_headers, params=params) as \ response: if response.status == 401: logging.info(f"TwitchAlert: {response.status}, getting new oauth and retrying") await self.get_new_twitch_oauth() return await self.requests_get(url, headers, params) elif response.status > 399: logging.warning(f'TwitchAlert: {response.status} while getting requesting URL:{url}') return await response.json() async def get_streams_data(self, usernames): url = 'https://api.twitch.tv/helix/streams?' next_hundred_users = usernames[:100] usernames = usernames[100:] result = (await self.requests_get(url + "user_login=" + "&user_login=".join(next_hundred_users))).get("data") while usernames: next_hundred_users = usernames[:100] usernames = usernames[100:] result += (await self.requests_get(url + "user_login=" + "&user_login=".join(next_hundred_users))).get( "data") return result async def get_user_data(self, username): url = 'https://api.twitch.tv/helix/users?login=' + username return (await self.requests_get(url)).get("data")[0] async def get_game_data(self, game_id): if game_id != "": url = 'https://api.twitch.tv/helix/games?id=' + game_id game_data = await self.requests_get(url) return game_data.get("data")[0] else: return None async def get_team_users(self, team_id): url = 'https://api.twitch.tv/helix/teams?name=' + team_id return ( await self.requests_get(url)).get("data")[0].get("users") class TwitchAlertDBManager: def __init__(self, database_manager: KoalaDBManager.KoalaDBManager, bot_client: discord.client): self.database_manager = database_manager self.twitch_handler = TwitchAPIHandler(TWITCH_CLIENT_ID, TWITCH_SECRET) self.bot = bot_client def get_parent_database_manager(self): return self.database_manager def create_tables(self): # TwitchAlerts sql_create_twitch_alerts_table = """ CREATE TABLE IF NOT EXISTS TwitchAlerts ( guild_id integer NOT NULL, channel_id integer NOT NULL, default_message text NOT NULL, PRIMARY KEY (guild_id, channel_id), CONSTRAINT fk_guild FOREIGN KEY (guild_id) REFERENCES GuildExtensions (guild_id) ON DELETE CASCADE );""" # UserInTwitchAlert sql_create_user_in_twitch_alert_table = """ CREATE TABLE IF NOT EXISTS UserInTwitchAlert ( channel_id integer NOT NULL, twitch_username text NOT NULL, custom_message text, message_id integer, PRIMARY KEY (channel_id, twitch_username), CONSTRAINT fk_channel FOREIGN KEY (channel_id) REFERENCES TwitchAlerts (channel_id) ON DELETE CASCADE );""" # TeamInTwitchAlert sql_create_team_in_twitch_alert_table = """ CREATE TABLE IF NOT EXISTS TeamInTwitchAlert ( team_twitch_alert_id integer PRIMARY KEY AUTOINCREMENT, channel_id integer NOT NULL, twitch_team_name text NOT NULL, custom_message text, CONSTRAINT fk_channel FOREIGN KEY (channel_id) REFERENCES TwitchAlerts (channel_id) ON DELETE CASCADE );""" # UserInTwitchTeam sql_create_user_in_twitch_team_table = """ CREATE TABLE IF NOT EXISTS UserInTwitchTeam ( team_twitch_alert_id text NOT NULL, twitch_username text NOT NULL, message_id integer, PRIMARY KEY (team_twitch_alert_id, twitch_username), CONSTRAINT fk_twitch_team_alert FOREIGN KEY (team_twitch_alert_id) REFERENCES TeamInTwitchAlert (team_twitch_alert_id) ON DELETE CASCADE );""" # Create Tables self.database_manager.db_execute_commit(sql_create_twitch_alerts_table) self.database_manager.db_execute_commit(sql_create_user_in_twitch_alert_table) self.database_manager.db_execute_commit(sql_create_team_in_twitch_alert_table) self.database_manager.db_execute_commit(sql_create_user_in_twitch_team_table) def new_ta(self, guild_id, channel_id, default_message=None, replace=False): sql_find_ta = "SELECT default_message FROM TwitchAlerts WHERE channel_id=?" message = self.database_manager.db_execute_select(sql_find_ta, args=[channel_id]) if message and not replace: return message[0][0] # Sets the default message if not provided if default_message is None: default_message = DEFAULT_MESSAGE # Insert new Twitch Alert to database if replace: sql_insert_twitch_alert = """ REPLACE INTO TwitchAlerts(guild_id, channel_id, default_message) VALUES(?,?,?) """ else: sql_insert_twitch_alert = """ INSERT INTO TwitchAlerts(guild_id, channel_id, default_message) VALUES(?,?,?) """ self.database_manager.db_execute_commit(sql_insert_twitch_alert, args=[guild_id, channel_id, default_message]) return default_message def get_default_message(self, channel_id): sql_find_ta = "SELECT default_message FROM TwitchAlerts WHERE channel_id= ?" return self.database_manager.db_execute_select(sql_find_ta, args=[channel_id]) def add_user_to_ta(self, channel_id, twitch_username, custom_message, guild_id=None): self.new_ta(guild_id, channel_id) if custom_message: sql_insert_user_twitch_alert = """ INSERT INTO UserInTwitchAlert(channel_id, twitch_username, custom_message) VALUES(?, ?, ?) """ self.database_manager.db_execute_commit( sql_insert_user_twitch_alert, args=[channel_id, str.lower(twitch_username), custom_message]) else: sql_insert_user_twitch_alert = """ INSERT INTO UserInTwitchAlert(channel_id, twitch_username) VALUES(?, ?) """ self.database_manager.db_execute_commit( sql_insert_user_twitch_alert, args=[channel_id, str.lower(twitch_username)]) async def remove_user_from_ta(self, channel_id, twitch_username): sql_get_message_id = "SELECT message_id " \ "FROM UserInTwitchAlert " \ "WHERE twitch_username = ? " \ "AND channel_id = ? " message_id = self.database_manager.db_execute_select(sql_get_message_id, args=[twitch_username, channel_id])[0][0] if message_id is not None: await self.delete_message(message_id, channel_id) sql_remove_entry = """DELETE FROM UserInTwitchAlert WHERE twitch_username = ? AND channel_id = ?""" self.database_manager.db_execute_commit(sql_remove_entry, args=[twitch_username, channel_id]) async def delete_message(self, message_id, channel_id): try: channel = self.bot.get_channel(int(channel_id)) if channel is None: logging.warning(f"TwitchAlert: Channel ID {channel_id} does not exist, removing from database") sql_remove_invalid_channel = "DELETE FROM TwitchAlerts WHERE channel_id = ?" self.database_manager.db_execute_commit(sql_remove_invalid_channel, args=[channel_id]) return message = await channel.fetch_message(message_id) await message.delete() except discord.errors.NotFound as err: logging.warning(f"TwitchAlert: Message ID {message_id} does not exist, skipping \nError: {err}") except discord.errors.Forbidden as err: logging.warning(f"TwitchAlert: {err} Channel ID: {channel_id}") sql_remove_invalid_channel = "DELETE FROM TwitchAlerts WHERE channel_id = ?" self.database_manager.db_execute_commit(sql_remove_invalid_channel, args=[channel_id]) def get_users_in_ta(self, channel_id): sql_get_users = "SELECT twitch_username FROM UserInTwitchAlert WHERE channel_id = ?" return self.database_manager.db_execute_select(sql_get_users, args=[channel_id]) def get_teams_in_ta(self, channel_id): sql_get_teams = "SELECT twitch_team_name FROM TeamInTwitchAlert WHERE channel_id = ?" return self.database_manager.db_execute_select(sql_get_teams, args=[channel_id]) def add_team_to_ta(self, channel_id, twitch_team, custom_message, guild_id=None): self.new_ta(guild_id, channel_id) if custom_message: sql_insert_team_twitch_alert = """ INSERT INTO TeamInTwitchAlert(channel_id, twitch_team_name, custom_message) VALUES(?, ?, ?) """ self.database_manager.db_execute_commit( sql_insert_team_twitch_alert, args=[channel_id, str.lower(twitch_team), custom_message]) else: sql_insert_team_twitch_alert = """ INSERT INTO TeamInTwitchAlert(channel_id, twitch_team_name) VALUES(?, ?) """ self.database_manager.db_execute_commit( sql_insert_team_twitch_alert, args=[channel_id, str.lower(twitch_team)]) async def remove_team_from_ta(self, channel_id, team_name): sql_get_team_alert_id = "SELECT team_twitch_alert_id " \ "FROM TeamInTwitchAlert " \ "WHERE twitch_team_name = ? " \ " AND channel_id = ?" result = self.database_manager.db_execute_select(sql_get_team_alert_id, args=[team_name, channel_id]) if not result: raise AttributeError("Team name not found") team_alert_id = result[0][0] sql_get_message_id = """SELECT UserInTwitchTeam.message_id FROM UserInTwitchTeam WHERE team_twitch_alert_id = ?""" message_ids = self.database_manager.db_execute_select(sql_get_message_id, args=[team_alert_id]) if message_ids is not None: for message_id in message_ids: if message_id[0] is not None: await self.delete_message(message_id[0], channel_id) sql_remove_users = """DELETE FROM UserInTwitchTeam WHERE team_twitch_alert_id = ?""" sql_remove_team = """DELETE FROM TeamInTwitchAlert WHERE team_twitch_alert_id = ?""" self.database_manager.db_execute_commit(sql_remove_users, args=[team_alert_id]) self.database_manager.db_execute_commit(sql_remove_team, args=[team_alert_id]) async def update_team_members(self, twitch_team_id, team_name): if re.search(TWITCH_USERNAME_REGEX, team_name): users = await self.twitch_handler.get_team_users(team_name) for user in users: sql_add_user = """INSERT OR IGNORE INTO UserInTwitchTeam(team_twitch_alert_id, twitch_username) VALUES(?, ?)""" try: self.database_manager.db_execute_commit(sql_add_user, args=[twitch_team_id, user.get("user_login")], pass_errors=True) except KoalaDBManager.sqlite3.IntegrityError as err: logging.error(f"Twitch Alert: 1034: {err}") pass async def update_all_teams_members(self): sql_get_teams = """SELECT team_twitch_alert_id, twitch_team_name FROM TeamInTwitchAlert""" teams_info = self.database_manager.db_execute_select(sql_get_teams) for team_info in teams_info: await self.update_team_members(team_info[0], team_info[1]) async def delete_all_offline_streams(self, team: bool, usernames): if team: sql_select_offline_streams_with_message_ids = f""" SELECT channel_id, message_id FROM UserInTwitchTeam JOIN TeamInTwitchAlert TITA on UserInTwitchTeam.team_twitch_alert_id = TITA.team_twitch_alert_id WHERE message_id NOT NULL AND twitch_username in ({','.join(['?'] * len(usernames))})""" sql_update_offline_streams = f""" UPDATE UserInTwitchTeam SET message_id = NULL WHERE twitch_username in ({','.join(['?'] * len(usernames))})""" else: sql_select_offline_streams_with_message_ids = f""" SELECT channel_id, message_id FROM UserInTwitchAlert WHERE message_id NOT NULL AND twitch_username in ({','.join(['?'] * len(usernames))})""" sql_update_offline_streams = f""" UPDATE UserInTwitchAlert SET message_id = NULL WHERE twitch_username in ({','.join(['?'] * len(usernames))})""" results = self.database_manager.db_execute_select( sql_select_offline_streams_with_message_ids, usernames) for result in results: await self.delete_message(result[1], result[0]) self.database_manager.db_execute_commit(sql_update_offline_streams, usernames) def setup(bot: KoalaBot) -> None: if TWITCH_SECRET is None or TWITCH_CLIENT_ID is None: logging.error("TwitchAlert not started. API keys not found in environment.") print("TwitchAlert not started. API keys not found in environment.") KoalaBot.database_manager.insert_extension("TwitchAlert", 0, False, False) else: bot.add_cog(TwitchAlert(bot)) logging.info("TwitchAlert is ready.") print("TwitchAlert is ready.")

true

f731a1ae7561e1b6b771c407eeb69465e2d4177b

3,929

py

Python

openstack/tests/unit/cloud/test_limits.py

NeCTAR-RC/openstacksdk

60a24f6c4717a1f9a0e545c9a07e68afaedc5a27

[ "Apache-2.0" ]

99

2018-03-28T15:41:45.000Z

2022-01-23T17:22:13.000Z

openstack/tests/unit/cloud/test_limits.py

NeCTAR-RC/openstacksdk

60a24f6c4717a1f9a0e545c9a07e68afaedc5a27

[ "Apache-2.0" ]

5

2018-05-25T16:54:23.000Z

2021-11-21T02:27:16.000Z

openstack/tests/unit/cloud/test_limits.py

NeCTAR-RC/openstacksdk

60a24f6c4717a1f9a0e545c9a07e68afaedc5a27

[ "Apache-2.0" ]

104

2018-04-06T14:33:54.000Z

2022-03-01T01:58:09.000Z

# Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. from openstack.tests.unit import base class TestLimits(base.TestCase): def test_get_compute_limits(self): self.register_uris([ dict(method='GET', uri=self.get_mock_url( 'compute', 'public', append=['limits']), json={ "limits": { "absolute": { "maxImageMeta": 128, "maxPersonality": 5, "maxPersonalitySize": 10240, "maxSecurityGroupRules": 20, "maxSecurityGroups": 10, "maxServerMeta": 128, "maxTotalCores": 20, "maxTotalFloatingIps": 10, "maxTotalInstances": 10, "maxTotalKeypairs": 100, "maxTotalRAMSize": 51200, "maxServerGroups": 10, "maxServerGroupMembers": 10, "totalCoresUsed": 0, "totalInstancesUsed": 0, "totalRAMUsed": 0, "totalSecurityGroupsUsed": 0, "totalFloatingIpsUsed": 0, "totalServerGroupsUsed": 0 }, "rate": [] } }), ]) self.cloud.get_compute_limits() self.assert_calls() def test_other_get_compute_limits(self): project = self.mock_for_keystone_projects(project_count=1, list_get=True)[0] self.register_uris([ dict(method='GET', uri=self.get_mock_url( 'compute', 'public', append=['limits'], qs_elements=[ 'tenant_id={id}'.format(id=project.project_id) ]), json={ "limits": { "absolute": { "maxImageMeta": 128, "maxPersonality": 5, "maxPersonalitySize": 10240, "maxSecurityGroupRules": 20, "maxSecurityGroups": 10, "maxServerMeta": 128, "maxTotalCores": 20, "maxTotalFloatingIps": 10, "maxTotalInstances": 10, "maxTotalKeypairs": 100, "maxTotalRAMSize": 51200, "maxServerGroups": 10, "maxServerGroupMembers": 10, "totalCoresUsed": 0, "totalInstancesUsed": 0, "totalRAMUsed": 0, "totalSecurityGroupsUsed": 0, "totalFloatingIpsUsed": 0, "totalServerGroupsUsed": 0 }, "rate": [] } }), ]) self.cloud.get_compute_limits(project.project_id) self.assert_calls()

40.927083

75

0.423772

from openstack.tests.unit import base class TestLimits(base.TestCase): def test_get_compute_limits(self): self.register_uris([ dict(method='GET', uri=self.get_mock_url( 'compute', 'public', append=['limits']), json={ "limits": { "absolute": { "maxImageMeta": 128, "maxPersonality": 5, "maxPersonalitySize": 10240, "maxSecurityGroupRules": 20, "maxSecurityGroups": 10, "maxServerMeta": 128, "maxTotalCores": 20, "maxTotalFloatingIps": 10, "maxTotalInstances": 10, "maxTotalKeypairs": 100, "maxTotalRAMSize": 51200, "maxServerGroups": 10, "maxServerGroupMembers": 10, "totalCoresUsed": 0, "totalInstancesUsed": 0, "totalRAMUsed": 0, "totalSecurityGroupsUsed": 0, "totalFloatingIpsUsed": 0, "totalServerGroupsUsed": 0 }, "rate": [] } }), ]) self.cloud.get_compute_limits() self.assert_calls() def test_other_get_compute_limits(self): project = self.mock_for_keystone_projects(project_count=1, list_get=True)[0] self.register_uris([ dict(method='GET', uri=self.get_mock_url( 'compute', 'public', append=['limits'], qs_elements=[ 'tenant_id={id}'.format(id=project.project_id) ]), json={ "limits": { "absolute": { "maxImageMeta": 128, "maxPersonality": 5, "maxPersonalitySize": 10240, "maxSecurityGroupRules": 20, "maxSecurityGroups": 10, "maxServerMeta": 128, "maxTotalCores": 20, "maxTotalFloatingIps": 10, "maxTotalInstances": 10, "maxTotalKeypairs": 100, "maxTotalRAMSize": 51200, "maxServerGroups": 10, "maxServerGroupMembers": 10, "totalCoresUsed": 0, "totalInstancesUsed": 0, "totalRAMUsed": 0, "totalSecurityGroupsUsed": 0, "totalFloatingIpsUsed": 0, "totalServerGroupsUsed": 0 }, "rate": [] } }), ]) self.cloud.get_compute_limits(project.project_id) self.assert_calls()

true

f731a2121c3590ca3b044f3b321708cb81eb5991

9,878

py

Python

GlutWrapper.py

kosystem/PythonGlutWrapper

e619b62927d6875f1bd3d2da8b5f2291487c8920

[ "MIT" ]

null

GlutWrapper.py

kosystem/PythonGlutWrapper

e619b62927d6875f1bd3d2da8b5f2291487c8920

[ "MIT" ]

null

GlutWrapper.py

kosystem/PythonGlutWrapper

e619b62927d6875f1bd3d2da8b5f2291487c8920

[ "MIT" ]

null

from OpenGL.GL import * from OpenGL.GLUT import * from OpenGL.GLU import * import sys from math import * import time ESCAPE = '\033' class Camera(object): """docstring for Camera""" def __init__(self): self.lock_x = 0 self.lock_y = 0 self.lock_z = 0 self.distance = 300 self.pan = 0.0 self.tilt = 0.0 class MouseState(object): """docstring for MouseState""" def __init__(self): self.button = 0 self.pressed = 0 self.x = 0 self.y = 0 class GlutWrapper(object): """docstring for GlutWrapper""" def __init__(self): self.windowWidth = 640 self.windowHeight = 480 self.windowPositionX = 100 self.windowPositionY = 100 self.title = b"Glut Wrapper" self.camera = Camera() self.mouseState = MouseState() self.frameElapsed = 0.0 self.displayElapsed = 0.0 self.elapsedTime = 0.0 self.frameTime = 1.0/20.0 def startFramework(self): glutInit(sys.argv) glutInitDisplayMode(GLUT_RGB | GLUT_DEPTH) glutInitWindowPosition(self.windowPositionX, self.windowPositionY) glutInitWindowSize(self.windowWidth, self.windowHeight) glutCreateWindow(self.title) glutDisplayFunc(self.displayFramework) glutReshapeFunc(self.reshape) glutIdleFunc(self.idle) glutMouseFunc(self.mouse) glutMotionFunc(self.motion) glutPassiveMotionFunc(self.passiveMotion) # glutMouseWheelFunc(self.mouseWheel) glutKeyboardFunc(self.keyboard) glutKeyboardUpFunc(self.keyboardUp) glutSpecialFunc(self.special) glutSpecialUpFunc(self.specialUp) self.initialize() self.load() glutMainLoop() def displayFramework(self): glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT) self.setLights() self.setCamera() self.display(self.getDisplayElapsed()) glutSwapBuffers() def setCamera(self): width = glutGet(GLUT_WINDOW_WIDTH) height = glutGet(GLUT_WINDOW_HEIGHT) glViewport(0, 0, width, height) glMatrixMode(GL_PROJECTION) glLoadIdentity() gluPerspective(30.0, float(width) / height, 0.5, 10000) glMatrixMode(GL_MODELVIEW) glLoadIdentity() distance = self.camera.distance tilt = self.camera.tilt pan = self.camera.pan lock_x = self.camera.lock_x lock_y = self.camera.lock_y lock_z = self.camera.lock_z gluLookAt( distance*sin(pan)*cos(tilt) + lock_x, distance*sin(tilt) + lock_y, distance*cos(pan)*cos(tilt) + lock_z, lock_x, lock_y, lock_z, 0.0, 1.0, 0.0) def setLights(self): light1_position = (0.0, 1.0, 1.0, 0.0) light2_position = (0.0, -1.0, -1.0, 0.0) white_light = (1.0, 1.0, 1.0, 1.0) lmodel_ambient = (0.2, 0.2, 0.2, 1.0) ambient_light = (0.4, 0.4, 0.4, 1.0) glLight(GL_LIGHT0, GL_POSITION, light1_position) glLight(GL_LIGHT0, GL_AMBIENT, ambient_light) glLight(GL_LIGHT0, GL_DIFFUSE, white_light) glLight(GL_LIGHT0, GL_SPECULAR, white_light) glLight(GL_LIGHT1, GL_POSITION, light2_position) glLight(GL_LIGHT1, GL_AMBIENT, lmodel_ambient) glLight(GL_LIGHT1, GL_DIFFUSE, ambient_light) glLight(GL_LIGHT1, GL_SPECULAR, lmodel_ambient) # glLightModel(GL_LIGHT_MODEL_AMBIENT, lmodel_ambient) def getFrameElapsed(self): now = time.time() if self.frameElapsed == 0.0: self.frameElapsed = now elapsed = now - self.frameElapsed self.frameElapsed = now return elapsed def getDisplayElapsed(self): now = time.time() if self.displayElapsed == 0.0: self.displayElapsed = now elapsed = now - self.displayElapsed self.displayElapsed = now return elapsed # User overwite --------------------------------------- def display(self, deltaTime): glMaterial(GL_FRONT, GL_AMBIENT, (0.8, 0.6, 0.5, 1.0)) glMaterial(GL_FRONT, GL_DIFFUSE, (0.8, 0.6, 0.5, 1.0)) glutSolidTeapot(50) def idle(self): self.elapsedTime += self.getFrameElapsed() if self.elapsedTime >= self.frameTime: glutPostRedisplay() self.elapsedTime -= self.frameTime def reshape(self, w, h): glViewport(0, 0, w, h) def initialize(self): glClearColor(0.4, 0.5, 0.5, 1.0) glEnable(GL_LIGHTING) glEnable(GL_LIGHT0) glEnable(GL_LIGHT1) glShadeModel(GL_SMOOTH) glEnable(GL_DEPTH_TEST) def load(self): # NOTE: model data load pass # User interface ----------------------------------- def mouse(self, button, state, x, y): #print "MousePress: button: %d, x: %d, y:%d" % (button, x, y) pass def motion(self, x, y): #print "MouseMove: x: %d, y: %d" % (x, y) pass def passiveMotion(self, x, y): self.mouseState.x = x self.mouseState.y = y def keyboard(self, key, x, y): #print "KeyboardPress: %s" % key if key == ESCAPE: sys.exit() def keyboardUp(self, key, x, y): #print "KeyboardUp: %s" % key pass def special(self, key, x, y): #print "SpecialKeyPress: %s" % key pass def specialUp(self, key, x, y): #print "SpecialKeyUp: %s" % key pass # Basic Draw ---------------------------------------------- def drawAxis(self, length): lighting = glGetBoolean(GL_LIGHTING) light0 = glGetBoolean(GL_LIGHT0) light1 = glGetBoolean(GL_LIGHT1) color = glGetFloatv(GL_CURRENT_COLOR) depth = glGetBoolean(GL_DEPTH_TEST) glEnable(GL_DEPTH_TEST) glDisable(GL_LIGHTING) glDisable(GL_LIGHT0) glLineWidth(1.0) glBegin(GL_LINES) glColor(1.0, 0.0, 0.0, 1.0) glVertex(0.0, 0.0, 0.0) glVertex(length, 0.0, 0.0) glColor(0.0, 1.0, 0.0, 1.0) glVertex(0.0, 0.0, 0.0) glVertex(0.0, length, 0.0) glColor(0.0, 0.0, 1.0, 1.0) glVertex(0.0, 0.0, 0.0) glVertex(0.0, 0.0, length) glEnd() if lighting: glEnable(GL_LIGHTING) if light0: glEnable(GL_LIGHT0) if light1: glEnable(GL_LIGHT1) if not depth: glDisable(GL_DEPTH_TEST) glColor(color) def drawHorizon(self, x, y, xTick, yTick): lighting = glGetBoolean(GL_LIGHTING) light0 = glGetBoolean(GL_LIGHT0) light1 = glGetBoolean(GL_LIGHT1) color = glGetFloatv(GL_CURRENT_COLOR) depth = glGetBoolean(GL_DEPTH_TEST) glEnable(GL_DEPTH_TEST) glDisable(GL_LIGHTING) glDisable(GL_LIGHT0) glLineWidth(1.0) glBegin(GL_LINES) glColor(0.7, 0.7, 0.7, 1.0) for xi in range(-x, x+xTick, xTick): glVertex(xi, 0.0, -y) glVertex(xi, 0.0, y) for yi in range(-y, y+yTick, yTick): glVertex(-x, 0.0, yi) glVertex(x, 0.0, yi) glEnd() if lighting: glEnable(GL_LIGHTING) if light0: glEnable(GL_LIGHT0) if light1: glEnable(GL_LIGHT1) if not depth: glDisable(GL_DEPTH_TEST) glColor(color) def overlayString(self, string, x, y, color=(1, 1, 1)): lighting = glGetBoolean(GL_LIGHTING) light0 = glGetBoolean(GL_LIGHT0) light1 = glGetBoolean(GL_LIGHT1) currentcolor = glGetFloatv(GL_CURRENT_COLOR) depth = glGetBoolean(GL_DEPTH_TEST) glEnable(GL_DEPTH_TEST) glDisable(GL_LIGHTING) glDisable(GL_LIGHT0) glDisable(GL_LIGHT1) glLineWidth(1.0) glMatrixMode(GL_PROJECTION) # glPushMatrix() glLoadIdentity() glOrtho(0.0, 2.0, 2.0, 0.0, -1.0, 1.0) glMatrixMode(GL_MODELVIEW) # glPushMatrix() glLoadIdentity() # glPushAttrib(GL_ENABLE_BIT) glDisable(GL_DEPTH_TEST) glDisable(GL_CULL_FACE) width = glutGet(GLUT_WINDOW_WIDTH) height = glutGet(GLUT_WINDOW_HEIGHT)/2 glColor(color) if x >= 0: positionX = x/width*2.0 else: positionX = (width + x)/width*2.0 if y >= 0: positionY = (y + 10.0)/height*2.0 else: positionY = (height + y)/height*2.0 glRasterPos3f(positionX, positionY, 0.0) for x in string: glutBitmapCharacter(GLUT_BITMAP_HELVETICA_12, ord(x)) # glPopAttrib() # glPopMatrix() glMatrixMode(GL_PROJECTION) # glPopMatrix() glMatrixMode(GL_MODELVIEW) if lighting: glEnable(GL_LIGHTING) if light0: glEnable(GL_LIGHT0) if light1: glEnable(GL_LIGHT1) if depth: glEnable(GL_DEPTH_TEST) glColor(currentcolor) def drawBlock(self, w, h, d): glPushMatrix() glScale(w/100, h/100, d/100 ) glutSolidCube(100) glPopMatrix() def drawSquer(self, w, h): glPushMatrix() glBegin(GL_QUADS) glVertex(w, h, 0) glVertex(-w, h, 0) glVertex(-w, -h, 0) glVertex(w, -h, 0) glEnd() glPopMatrix() def setColor(self, color): glColor(color[0], color[1], color[2]) glMaterial(GL_FRONT, GL_AMBIENT, color) glMaterial(GL_FRONT, GL_DIFFUSE, color) if __name__ == '__main__': #print "Hit ESC key to quit." gl = GlutWrapper() gl.title = b"Tracer" gl.startFramework()

27.983003

74

0.571877

from OpenGL.GL import * from OpenGL.GLUT import * from OpenGL.GLU import * import sys from math import * import time ESCAPE = '\033' class Camera(object): def __init__(self): self.lock_x = 0 self.lock_y = 0 self.lock_z = 0 self.distance = 300 self.pan = 0.0 self.tilt = 0.0 class MouseState(object): def __init__(self): self.button = 0 self.pressed = 0 self.x = 0 self.y = 0 class GlutWrapper(object): def __init__(self): self.windowWidth = 640 self.windowHeight = 480 self.windowPositionX = 100 self.windowPositionY = 100 self.title = b"Glut Wrapper" self.camera = Camera() self.mouseState = MouseState() self.frameElapsed = 0.0 self.displayElapsed = 0.0 self.elapsedTime = 0.0 self.frameTime = 1.0/20.0 def startFramework(self): glutInit(sys.argv) glutInitDisplayMode(GLUT_RGB | GLUT_DEPTH) glutInitWindowPosition(self.windowPositionX, self.windowPositionY) glutInitWindowSize(self.windowWidth, self.windowHeight) glutCreateWindow(self.title) glutDisplayFunc(self.displayFramework) glutReshapeFunc(self.reshape) glutIdleFunc(self.idle) glutMouseFunc(self.mouse) glutMotionFunc(self.motion) glutPassiveMotionFunc(self.passiveMotion) glutKeyboardFunc(self.keyboard) glutKeyboardUpFunc(self.keyboardUp) glutSpecialFunc(self.special) glutSpecialUpFunc(self.specialUp) self.initialize() self.load() glutMainLoop() def displayFramework(self): glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT) self.setLights() self.setCamera() self.display(self.getDisplayElapsed()) glutSwapBuffers() def setCamera(self): width = glutGet(GLUT_WINDOW_WIDTH) height = glutGet(GLUT_WINDOW_HEIGHT) glViewport(0, 0, width, height) glMatrixMode(GL_PROJECTION) glLoadIdentity() gluPerspective(30.0, float(width) / height, 0.5, 10000) glMatrixMode(GL_MODELVIEW) glLoadIdentity() distance = self.camera.distance tilt = self.camera.tilt pan = self.camera.pan lock_x = self.camera.lock_x lock_y = self.camera.lock_y lock_z = self.camera.lock_z gluLookAt( distance*sin(pan)*cos(tilt) + lock_x, distance*sin(tilt) + lock_y, distance*cos(pan)*cos(tilt) + lock_z, lock_x, lock_y, lock_z, 0.0, 1.0, 0.0) def setLights(self): light1_position = (0.0, 1.0, 1.0, 0.0) light2_position = (0.0, -1.0, -1.0, 0.0) white_light = (1.0, 1.0, 1.0, 1.0) lmodel_ambient = (0.2, 0.2, 0.2, 1.0) ambient_light = (0.4, 0.4, 0.4, 1.0) glLight(GL_LIGHT0, GL_POSITION, light1_position) glLight(GL_LIGHT0, GL_AMBIENT, ambient_light) glLight(GL_LIGHT0, GL_DIFFUSE, white_light) glLight(GL_LIGHT0, GL_SPECULAR, white_light) glLight(GL_LIGHT1, GL_POSITION, light2_position) glLight(GL_LIGHT1, GL_AMBIENT, lmodel_ambient) glLight(GL_LIGHT1, GL_DIFFUSE, ambient_light) glLight(GL_LIGHT1, GL_SPECULAR, lmodel_ambient) def getFrameElapsed(self): now = time.time() if self.frameElapsed == 0.0: self.frameElapsed = now elapsed = now - self.frameElapsed self.frameElapsed = now return elapsed def getDisplayElapsed(self): now = time.time() if self.displayElapsed == 0.0: self.displayElapsed = now elapsed = now - self.displayElapsed self.displayElapsed = now return elapsed def display(self, deltaTime): glMaterial(GL_FRONT, GL_AMBIENT, (0.8, 0.6, 0.5, 1.0)) glMaterial(GL_FRONT, GL_DIFFUSE, (0.8, 0.6, 0.5, 1.0)) glutSolidTeapot(50) def idle(self): self.elapsedTime += self.getFrameElapsed() if self.elapsedTime >= self.frameTime: glutPostRedisplay() self.elapsedTime -= self.frameTime def reshape(self, w, h): glViewport(0, 0, w, h) def initialize(self): glClearColor(0.4, 0.5, 0.5, 1.0) glEnable(GL_LIGHTING) glEnable(GL_LIGHT0) glEnable(GL_LIGHT1) glShadeModel(GL_SMOOTH) glEnable(GL_DEPTH_TEST) def load(self): pass def mouse(self, button, state, x, y): pass def motion(self, x, y): pass def passiveMotion(self, x, y): self.mouseState.x = x self.mouseState.y = y def keyboard(self, key, x, y): if key == ESCAPE: sys.exit() def keyboardUp(self, key, x, y): pass def special(self, key, x, y): pass def specialUp(self, key, x, y): pass def drawAxis(self, length): lighting = glGetBoolean(GL_LIGHTING) light0 = glGetBoolean(GL_LIGHT0) light1 = glGetBoolean(GL_LIGHT1) color = glGetFloatv(GL_CURRENT_COLOR) depth = glGetBoolean(GL_DEPTH_TEST) glEnable(GL_DEPTH_TEST) glDisable(GL_LIGHTING) glDisable(GL_LIGHT0) glLineWidth(1.0) glBegin(GL_LINES) glColor(1.0, 0.0, 0.0, 1.0) glVertex(0.0, 0.0, 0.0) glVertex(length, 0.0, 0.0) glColor(0.0, 1.0, 0.0, 1.0) glVertex(0.0, 0.0, 0.0) glVertex(0.0, length, 0.0) glColor(0.0, 0.0, 1.0, 1.0) glVertex(0.0, 0.0, 0.0) glVertex(0.0, 0.0, length) glEnd() if lighting: glEnable(GL_LIGHTING) if light0: glEnable(GL_LIGHT0) if light1: glEnable(GL_LIGHT1) if not depth: glDisable(GL_DEPTH_TEST) glColor(color) def drawHorizon(self, x, y, xTick, yTick): lighting = glGetBoolean(GL_LIGHTING) light0 = glGetBoolean(GL_LIGHT0) light1 = glGetBoolean(GL_LIGHT1) color = glGetFloatv(GL_CURRENT_COLOR) depth = glGetBoolean(GL_DEPTH_TEST) glEnable(GL_DEPTH_TEST) glDisable(GL_LIGHTING) glDisable(GL_LIGHT0) glLineWidth(1.0) glBegin(GL_LINES) glColor(0.7, 0.7, 0.7, 1.0) for xi in range(-x, x+xTick, xTick): glVertex(xi, 0.0, -y) glVertex(xi, 0.0, y) for yi in range(-y, y+yTick, yTick): glVertex(-x, 0.0, yi) glVertex(x, 0.0, yi) glEnd() if lighting: glEnable(GL_LIGHTING) if light0: glEnable(GL_LIGHT0) if light1: glEnable(GL_LIGHT1) if not depth: glDisable(GL_DEPTH_TEST) glColor(color) def overlayString(self, string, x, y, color=(1, 1, 1)): lighting = glGetBoolean(GL_LIGHTING) light0 = glGetBoolean(GL_LIGHT0) light1 = glGetBoolean(GL_LIGHT1) currentcolor = glGetFloatv(GL_CURRENT_COLOR) depth = glGetBoolean(GL_DEPTH_TEST) glEnable(GL_DEPTH_TEST) glDisable(GL_LIGHTING) glDisable(GL_LIGHT0) glDisable(GL_LIGHT1) glLineWidth(1.0) glMatrixMode(GL_PROJECTION) glLoadIdentity() glOrtho(0.0, 2.0, 2.0, 0.0, -1.0, 1.0) glMatrixMode(GL_MODELVIEW) glLoadIdentity() glDisable(GL_DEPTH_TEST) glDisable(GL_CULL_FACE) width = glutGet(GLUT_WINDOW_WIDTH) height = glutGet(GLUT_WINDOW_HEIGHT)/2 glColor(color) if x >= 0: positionX = x/width*2.0 else: positionX = (width + x)/width*2.0 if y >= 0: positionY = (y + 10.0)/height*2.0 else: positionY = (height + y)/height*2.0 glRasterPos3f(positionX, positionY, 0.0) for x in string: glutBitmapCharacter(GLUT_BITMAP_HELVETICA_12, ord(x)) glMatrixMode(GL_PROJECTION) glMatrixMode(GL_MODELVIEW) if lighting: glEnable(GL_LIGHTING) if light0: glEnable(GL_LIGHT0) if light1: glEnable(GL_LIGHT1) if depth: glEnable(GL_DEPTH_TEST) glColor(currentcolor) def drawBlock(self, w, h, d): glPushMatrix() glScale(w/100, h/100, d/100 ) glutSolidCube(100) glPopMatrix() def drawSquer(self, w, h): glPushMatrix() glBegin(GL_QUADS) glVertex(w, h, 0) glVertex(-w, h, 0) glVertex(-w, -h, 0) glVertex(w, -h, 0) glEnd() glPopMatrix() def setColor(self, color): glColor(color[0], color[1], color[2]) glMaterial(GL_FRONT, GL_AMBIENT, color) glMaterial(GL_FRONT, GL_DIFFUSE, color) if __name__ == '__main__': gl = GlutWrapper() gl.title = b"Tracer" gl.startFramework()

true

f731a2862378f2a67c66b8effa16a59cf7ae26f1

867

py

Python

py/pe/pe8.py

kittttttan/pe

6f87e4527793198c393700fedbdd52274fec5b44

[ "MIT" ]

null

py/pe/pe8.py

kittttttan/pe

6f87e4527793198c393700fedbdd52274fec5b44

[ "MIT" ]

null

py/pe/pe8.py

kittttttan/pe

6f87e4527793198c393700fedbdd52274fec5b44

[ "MIT" ]

1

2016-09-01T22:47:28.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- """ Discover the largest product of five consecutive digits in the 1000-digit number. """ def pe8(fname="../../res/pe8.txt", n=5): """ Discover the largest product of five consecutive digits in the 1000-digit number. >>> pe8() 40824 """ with open(fname, 'r') as f: s = f.read() s = s.replace('\n', '') ls = len(s) if ls < n: raise ValueError m = 0 for x in range(ls - n + 1): t = 1 for y in range(n): t *= int(s[x + y]) if m < t: m = t return(m) if __name__ == "__main__": import doctest doctest.testmod() try: while True: s = input('> ') n = int(s) print(pe8(n=n)) except (SyntaxError, EOFError, KeyboardInterrupt, NameError): pass

20.642857

65

0.500577

def pe8(fname="../../res/pe8.txt", n=5): with open(fname, 'r') as f: s = f.read() s = s.replace('\n', '') ls = len(s) if ls < n: raise ValueError m = 0 for x in range(ls - n + 1): t = 1 for y in range(n): t *= int(s[x + y]) if m < t: m = t return(m) if __name__ == "__main__": import doctest doctest.testmod() try: while True: s = input('> ') n = int(s) print(pe8(n=n)) except (SyntaxError, EOFError, KeyboardInterrupt, NameError): pass

true

f731a3593f56de78afad7e6ff95b896326f0909e

2,885

py

Python

ExcelPixelator.py

nibble-4bits/excel-pixel-art

793c04c69f5035034dc42e26948b3e6390c81668

[ "MIT" ]

2

2021-01-02T02:46:30.000Z

2021-06-30T00:37:06.000Z

ExcelPixelator.py

nibble-4bits/excel-pixel-art

793c04c69f5035034dc42e26948b3e6390c81668

[ "MIT" ]

null

ExcelPixelator.py

nibble-4bits/excel-pixel-art

793c04c69f5035034dc42e26948b3e6390c81668

[ "MIT" ]

1

2021-06-30T00:37:13.000Z

from openpyxl import Workbook from openpyxl.utils import get_column_letter from openpyxl.styles import PatternFill from PIL import Image class ExcelPixelator: def __init__(self, input_path, output_path, file_name, cell_size, pixel_size): self.image = Image.open(input_path).convert('RGB') self.output_path = output_path self.file_name = file_name self.cell_size = cell_size # size of the cell in pixels self.pixel_size = pixel_size self.__is_pixelsize_common_factor() def create_pixel_art(self): default_excel_font_size = 16 wb = Workbook() ws = wb.create_sheet('Pixel-Art') wb.remove(wb['Sheet']) # remove default worksheet width, height = self.image.size pixel_map = self.get_pixel_map(width, height) for row in range(len(pixel_map)): ws.row_dimensions[row + 1].height = self.cell_size * 10 / default_excel_font_size for col in range(len(pixel_map[row])): curr_col = get_column_letter(col + 1) ws.column_dimensions[curr_col].width = self.cell_size / 9 rgbTuple = pixel_map[row][col] fill_color = self.__rgbToHex(rgbTuple) ws[f'{curr_col}{row + 1}'].fill = PatternFill(start_color=fill_color, end_color=fill_color, fill_type='solid') wb.save(f'{self.output_path}/{self.file_name}.xlsx') wb.close() def get_pixel_map(self, w, h): pixel_map = [[0 for x in range(w // self.pixel_size)] for y in range(h // self.pixel_size)] squares = w * h // self.pixel_size ** 2 i, j = 0, 0 for sq in range(squares): rAvg, gAvg, bAvg = 0, 0, 0 row_start = (sq * self.pixel_size // h) * self.pixel_size row_end = row_start + self.pixel_size col_start = sq * self.pixel_size % w col_end = col_start + self.pixel_size for row in range(row_start, row_end): for col in range(col_start, col_end): r, g, b = self.image.getpixel((col, row)) rAvg += r gAvg += g bAvg += b rAvg //= self.pixel_size ** 2 gAvg //= self.pixel_size ** 2 bAvg //= self.pixel_size ** 2 pixel_map[i][j] = (rAvg, gAvg, bAvg) i = i + 1 if j >= (w // self.pixel_size) - 1 else i j = (j + 1) % (w // self.pixel_size) return pixel_map def __rgbToHex(self, rgbTuple): return ('%02x%02x%02x' % rgbTuple).upper() def __is_pixelsize_common_factor(self): width, height = self.image.size if width % self.pixel_size != 0 or height % self.pixel_size != 0: print('ERROR: Pixel size must be a number divisible exactly by both the image width and height') exit(1)

38.986486

126

0.586828

from openpyxl import Workbook from openpyxl.utils import get_column_letter from openpyxl.styles import PatternFill from PIL import Image class ExcelPixelator: def __init__(self, input_path, output_path, file_name, cell_size, pixel_size): self.image = Image.open(input_path).convert('RGB') self.output_path = output_path self.file_name = file_name self.cell_size = cell_size self.pixel_size = pixel_size self.__is_pixelsize_common_factor() def create_pixel_art(self): default_excel_font_size = 16 wb = Workbook() ws = wb.create_sheet('Pixel-Art') wb.remove(wb['Sheet']) width, height = self.image.size pixel_map = self.get_pixel_map(width, height) for row in range(len(pixel_map)): ws.row_dimensions[row + 1].height = self.cell_size * 10 / default_excel_font_size for col in range(len(pixel_map[row])): curr_col = get_column_letter(col + 1) ws.column_dimensions[curr_col].width = self.cell_size / 9 rgbTuple = pixel_map[row][col] fill_color = self.__rgbToHex(rgbTuple) ws[f'{curr_col}{row + 1}'].fill = PatternFill(start_color=fill_color, end_color=fill_color, fill_type='solid') wb.save(f'{self.output_path}/{self.file_name}.xlsx') wb.close() def get_pixel_map(self, w, h): pixel_map = [[0 for x in range(w // self.pixel_size)] for y in range(h // self.pixel_size)] squares = w * h // self.pixel_size ** 2 i, j = 0, 0 for sq in range(squares): rAvg, gAvg, bAvg = 0, 0, 0 row_start = (sq * self.pixel_size // h) * self.pixel_size row_end = row_start + self.pixel_size col_start = sq * self.pixel_size % w col_end = col_start + self.pixel_size for row in range(row_start, row_end): for col in range(col_start, col_end): r, g, b = self.image.getpixel((col, row)) rAvg += r gAvg += g bAvg += b rAvg //= self.pixel_size ** 2 gAvg //= self.pixel_size ** 2 bAvg //= self.pixel_size ** 2 pixel_map[i][j] = (rAvg, gAvg, bAvg) i = i + 1 if j >= (w // self.pixel_size) - 1 else i j = (j + 1) % (w // self.pixel_size) return pixel_map def __rgbToHex(self, rgbTuple): return ('%02x%02x%02x' % rgbTuple).upper() def __is_pixelsize_common_factor(self): width, height = self.image.size if width % self.pixel_size != 0 or height % self.pixel_size != 0: print('ERROR: Pixel size must be a number divisible exactly by both the image width and height') exit(1)

true

f731a40b7f102fd430b7e6b6d58a594d1b5f7c7b

3,806

py

Python

scripts/spectral_clustering_demo.py

vipavlovic/pyprobml

59a2edc682d0163955db5e2f27491ad772b60141

[ "MIT" ]

4,895

2016-08-17T22:28:34.000Z

2022-03-31T17:07:15.000Z

scripts/spectral_clustering_demo.py

vipavlovic/pyprobml

59a2edc682d0163955db5e2f27491ad772b60141

[ "MIT" ]

446

2016-09-17T14:35:29.000Z

2022-03-31T19:59:33.000Z

scripts/spectral_clustering_demo.py

vipavlovic/pyprobml

59a2edc682d0163955db5e2f27491ad772b60141

[ "MIT" ]

1,160

2016-08-18T23:19:27.000Z

2022-03-31T12:44:07.000Z

import superimport import itertools import matplotlib.pyplot as plt import numpy as np from scipy.linalg import eigh from sklearn.cluster import KMeans from sklearn.metrics.pairwise import rbf_kernel import pyprobml_utils as pml plt.style.use('classic') def spectral_clustering_demo(): np.random.seed(0) num_clusters = 2 for data_type, data in (('circle', sample_circle(num_clusters)), ('spiral', sample_spiral())): kmeans = KMeans(n_clusters=num_clusters, random_state=0) kmeans.fit(data) assignments = kmeans.predict(data) plot_data(data, assignments, 'k-means clustering', data_type) sigma = 0.1 gamma = 1 / (2 * sigma ** 2) W = rbf_kernel(data, gamma=gamma) d = np.sum(W, 1, keepdims=True) sqrt_d = np.sqrt(d) normalized_W = (W / sqrt_d) / sqrt_d.T paranoid_assert(W, normalized_W, False) # We select the largest eigen values of normalized_W, rather # than the smallest eigenvalues of I - normalized_W. The two # problems are equivalent. The eigen values can be converted # between the two problems via `1 - eigen_values`. The eigen # vectors are the same between both problems. eigen_values, eigen_vectors = eigh(normalized_W, # Get only the top num_clusters eigenvalues eigvals=(data.shape[0] - num_clusters, data.shape[0]-1)) eigen_vectors = eigen_vectors / np.linalg.norm(eigen_vectors, axis=1, keepdims=True) kmeans.fit(eigen_vectors) assignments = kmeans.predict(eigen_vectors) plot_data(data, assignments, 'spectral clustering', data_type) plt.show() def paranoid_assert(W, normalized_W, enable): if not enable: return D = np.diag(np.sum(W, 1)) L = D - W D_inv_sqrt = np.diag(1 / np.diag(np.sqrt(D))) np.testing.assert_almost_equal(np.sum(L, 1), 0, err_msg="Rows of Laplacian must sum to 0.") np.testing.assert_allclose(normalized_W, D_inv_sqrt * W * D_inv_sqrt, rtol=0, atol=1) def sample_circle(num_clusters): points_per_cluster = 500 bandwidth = 0.1 data = np.zeros((num_clusters * points_per_cluster, 2)) for k, n in itertools.product(range(num_clusters), range(points_per_cluster)): theta = 2 * np.pi * np.random.uniform() rho = k + 1 + np.random.randn() * bandwidth x, y = pol2cart(theta, rho) idx = k * points_per_cluster + n data[idx, 0] = x data[idx, 1] = y data = data.reshape((num_clusters * points_per_cluster, 2)) return data def pol2cart(theta, rho): x = rho * np.cos(theta) y = rho * np.sin(theta) return(x, y) def sample_spiral(): # Only 2 clusters in this case. This is hard-coded. points_per_cluster = 500 bandwidth = 0.1 data = np.empty((points_per_cluster, 2)) w = np.arange(1, points_per_cluster + 1).astype(np.float32) / points_per_cluster data[:,0] = (4 * w + 1) * np.cos(2*np.pi * w) + np.random.randn(points_per_cluster) * bandwidth data[:,1] = (4 * w + 1) * np.sin(2*np.pi * w) + np.random.randn(points_per_cluster) * bandwidth data = np.vstack((data, -data)) return data def plot_data(data, assignments, title, data_type): fig = plt.figure() ax = fig.add_subplot(1,1,1) ax.plot(data[assignments == 0, 0], data[assignments == 0, 1], 'o', color='r') ax.plot(data[assignments == 1, 0], data[assignments == 1, 1], 'o', color='b') ax.set_xlabel('x') ax.set_ylabel('y') ax.axis('square') ax.grid(True) ax.set_title(title) plt.tight_layout() pml.savefig(f"{data_type}_{title.replace(' ', '_')}.pdf") if __name__ == '__main__': spectral_clustering_demo()

35.90566

99

0.633211

import superimport import itertools import matplotlib.pyplot as plt import numpy as np from scipy.linalg import eigh from sklearn.cluster import KMeans from sklearn.metrics.pairwise import rbf_kernel import pyprobml_utils as pml plt.style.use('classic') def spectral_clustering_demo(): np.random.seed(0) num_clusters = 2 for data_type, data in (('circle', sample_circle(num_clusters)), ('spiral', sample_spiral())): kmeans = KMeans(n_clusters=num_clusters, random_state=0) kmeans.fit(data) assignments = kmeans.predict(data) plot_data(data, assignments, 'k-means clustering', data_type) sigma = 0.1 gamma = 1 / (2 * sigma ** 2) W = rbf_kernel(data, gamma=gamma) d = np.sum(W, 1, keepdims=True) sqrt_d = np.sqrt(d) normalized_W = (W / sqrt_d) / sqrt_d.T paranoid_assert(W, normalized_W, False) eigen_values, eigen_vectors = eigh(normalized_W, eigvals=(data.shape[0] - num_clusters, data.shape[0]-1)) eigen_vectors = eigen_vectors / np.linalg.norm(eigen_vectors, axis=1, keepdims=True) kmeans.fit(eigen_vectors) assignments = kmeans.predict(eigen_vectors) plot_data(data, assignments, 'spectral clustering', data_type) plt.show() def paranoid_assert(W, normalized_W, enable): if not enable: return D = np.diag(np.sum(W, 1)) L = D - W D_inv_sqrt = np.diag(1 / np.diag(np.sqrt(D))) np.testing.assert_almost_equal(np.sum(L, 1), 0, err_msg="Rows of Laplacian must sum to 0.") np.testing.assert_allclose(normalized_W, D_inv_sqrt * W * D_inv_sqrt, rtol=0, atol=1) def sample_circle(num_clusters): points_per_cluster = 500 bandwidth = 0.1 data = np.zeros((num_clusters * points_per_cluster, 2)) for k, n in itertools.product(range(num_clusters), range(points_per_cluster)): theta = 2 * np.pi * np.random.uniform() rho = k + 1 + np.random.randn() * bandwidth x, y = pol2cart(theta, rho) idx = k * points_per_cluster + n data[idx, 0] = x data[idx, 1] = y data = data.reshape((num_clusters * points_per_cluster, 2)) return data def pol2cart(theta, rho): x = rho * np.cos(theta) y = rho * np.sin(theta) return(x, y) def sample_spiral(): points_per_cluster = 500 bandwidth = 0.1 data = np.empty((points_per_cluster, 2)) w = np.arange(1, points_per_cluster + 1).astype(np.float32) / points_per_cluster data[:,0] = (4 * w + 1) * np.cos(2*np.pi * w) + np.random.randn(points_per_cluster) * bandwidth data[:,1] = (4 * w + 1) * np.sin(2*np.pi * w) + np.random.randn(points_per_cluster) * bandwidth data = np.vstack((data, -data)) return data def plot_data(data, assignments, title, data_type): fig = plt.figure() ax = fig.add_subplot(1,1,1) ax.plot(data[assignments == 0, 0], data[assignments == 0, 1], 'o', color='r') ax.plot(data[assignments == 1, 0], data[assignments == 1, 1], 'o', color='b') ax.set_xlabel('x') ax.set_ylabel('y') ax.axis('square') ax.grid(True) ax.set_title(title) plt.tight_layout() pml.savefig(f"{data_type}_{title.replace(' ', '_')}.pdf") if __name__ == '__main__': spectral_clustering_demo()

true

f731a4104b7ef8d6f39fe8aa8ecf2aad84c9136c

5,770

py

Python

run_fits.py

ruizca/pseudoXspec

a8ad2ca3744fe73e5ace78aedbabc49e32e2202a

[ "MIT" ]

null

run_fits.py

ruizca/pseudoXspec

a8ad2ca3744fe73e5ace78aedbabc49e32e2202a

[ "MIT" ]

null

run_fits.py

ruizca/pseudoXspec

a8ad2ca3744fe73e5ace78aedbabc49e32e2202a

[ "MIT" ]

null

#!/usr/bin/env python """ Created on Fri Jun 19 10:46:32 2015 @author: ruizca """ import argparse import logging import subprocess from itertools import count from pathlib import Path from astropy.coordinates.sky_coordinate import SkyCoord from astropy.table import Table from astropy.units import UnitTypeError from gdpyc import GasMap from tqdm.contrib import tzip logging.basicConfig(level=logging.INFO) def get_last_source_fit(last_source_file): try: with last_source_file.open("r") as fp: first_source = int(fp.readline()) except FileNotFoundError: first_source = 0 return first_source def update_last_source_fit(last_source, last_source_file): with last_source_file.open("w") as fp: fp.write(str(last_source)) def check_results_folder(results_folder): if results_folder.exists(): raise FileExistsError(f"results_folder '{results_folder}' already exists!") else: results_folder.mkdir() def _get_redshift(sample, zcol): if not zcol: zcol = "DEFAULT_REDSHIFT" sample[zcol] = 0.0 return sample[zcol] def _set_coords(ra, dec, unit): try: coords = SkyCoord(ra, dec) except UnitTypeError: coords = SkyCoord(ra, dec, unit=unit) return coords def _get_nhgal(sample, nhcol, racol, deccol, unit="deg"): if not nhcol: nhcol = "NHGAL" coords = _set_coords(sample[racol], sample[deccol], unit) sample[nhcol] = GasMap.nh(coords, nhmap="LAB") return sample[nhcol] def get_sources_data(sample_file, racol, deccol, zcol=None, nhcol=None, first_source=0): sample = Table.read(sample_file) sample = sample[first_source:] obsid = sample["OBS_ID"] detid = sample["DETID"] z = _get_redshift(sample, zcol) nhgal = _get_nhgal(sample, nhcol, racol, deccol) return obsid, detid, z, nhgal def stack_spectra(obsid, detid, spec_folder): # Find spectra of interest for this detection obs_path = spec_folder.joinpath(obsid) spec_files = obs_path.glob(f"{detid}_SRSPEC_*.pha") # Create stack file for existing spectra in the observation stack_file = Path(f"spec_{detid}.lis") with stack_file.open("w") as fp: for spec in spec_files: fp.write(spec.resolve().as_posix() + "\n") return stack_file def remove_stack_spectra(stack_file): try: stack_file.unlink() except FileNotFoundError: logging.warning("No stack file!") def fit_detection(z, nh, obsid, detid, results_folder, spectra_folder, fixgamma=True): task = "./fit_Xspec.py" args = [ "--redshift", f"{z:f}", "--nh", str(nh), "--obsid", obsid, "--detid", str(detid), "--output_folder", results_folder.as_posix(), "--spectra_folder", spectra_folder.as_posix(), ] if fixgamma: args += ["--fixGamma"] logging.debug(" ".join([task] + args)) subprocess.check_output([task] + args, stderr=subprocess.STDOUT) def main(args): spec_folder = Path(args.spec_folder) results_folder = Path(args.results_folder) lastsource_file = Path(args.file_lastsource) first_source = get_last_source_fit(lastsource_file) if first_source == 0: check_results_folder(results_folder) obsids, detids, redshifts, nhgals = get_sources_data( args.sources_table, args.racol, args.deccol, args.zcol, args.nhcol, first_source ) for obsid, detid, z, nh, current_source in tzip( obsids, detids, redshifts, nhgals, count(first_source) ): try: fit_detection(z, nh, obsid, detid, results_folder, spec_folder, args.fixgamma) update_last_source_fit(current_source + 1, lastsource_file) except Exception as e: logging.error(e) logging.error(f"Something went wrong fitting detection {detid}") if __name__ == "__main__": # Parser for shell parameters parser = argparse.ArgumentParser(description="Fitting X-ray pseudospectra") parser.add_argument( "--catalogue", dest="sources_table", action="store", default=None, help="Full route to the detections catalogue.", ) parser.add_argument( "--spec_folder", dest="spec_folder", action="store", default="./data/spectra/", help="Folder of the pseudospectra.", ) parser.add_argument( "--results_folder", dest="results_folder", action="store", default="./fit_results/", help="Folder for saving the fit results.", ) parser.add_argument( "--racol", dest="racol", action="store", default="XMM_RA", help="Name of the RA column in the catalogue.", ) parser.add_argument( "--deccol", dest="deccol", action="store", default="XMM_DEC", help="Name of the Dec column in the catalogue.", ) parser.add_argument( "--zcol", dest="zcol", action="store", default=None, help="Name of the redshift column in the catalogue.", ) parser.add_argument( "--nhcol", dest="nhcol", action="store", default=None, help="Name of the Galactic NH column in the catalogue.", ) parser.add_argument( "--lsf", dest="file_lastsource", action="store", default="last_source.dat", help="File to store the last fitted source.", ) parser.add_argument( "--fixGamma", dest="fixgamma", action="store_true", default=False, help="Fit with a fixed photon index (1.9).", ) main(parser.parse_args())

25.874439

90

0.629116

import argparse import logging import subprocess from itertools import count from pathlib import Path from astropy.coordinates.sky_coordinate import SkyCoord from astropy.table import Table from astropy.units import UnitTypeError from gdpyc import GasMap from tqdm.contrib import tzip logging.basicConfig(level=logging.INFO) def get_last_source_fit(last_source_file): try: with last_source_file.open("r") as fp: first_source = int(fp.readline()) except FileNotFoundError: first_source = 0 return first_source def update_last_source_fit(last_source, last_source_file): with last_source_file.open("w") as fp: fp.write(str(last_source)) def check_results_folder(results_folder): if results_folder.exists(): raise FileExistsError(f"results_folder '{results_folder}' already exists!") else: results_folder.mkdir() def _get_redshift(sample, zcol): if not zcol: zcol = "DEFAULT_REDSHIFT" sample[zcol] = 0.0 return sample[zcol] def _set_coords(ra, dec, unit): try: coords = SkyCoord(ra, dec) except UnitTypeError: coords = SkyCoord(ra, dec, unit=unit) return coords def _get_nhgal(sample, nhcol, racol, deccol, unit="deg"): if not nhcol: nhcol = "NHGAL" coords = _set_coords(sample[racol], sample[deccol], unit) sample[nhcol] = GasMap.nh(coords, nhmap="LAB") return sample[nhcol] def get_sources_data(sample_file, racol, deccol, zcol=None, nhcol=None, first_source=0): sample = Table.read(sample_file) sample = sample[first_source:] obsid = sample["OBS_ID"] detid = sample["DETID"] z = _get_redshift(sample, zcol) nhgal = _get_nhgal(sample, nhcol, racol, deccol) return obsid, detid, z, nhgal def stack_spectra(obsid, detid, spec_folder): obs_path = spec_folder.joinpath(obsid) spec_files = obs_path.glob(f"{detid}_SRSPEC_*.pha") stack_file = Path(f"spec_{detid}.lis") with stack_file.open("w") as fp: for spec in spec_files: fp.write(spec.resolve().as_posix() + "\n") return stack_file def remove_stack_spectra(stack_file): try: stack_file.unlink() except FileNotFoundError: logging.warning("No stack file!") def fit_detection(z, nh, obsid, detid, results_folder, spectra_folder, fixgamma=True): task = "./fit_Xspec.py" args = [ "--redshift", f"{z:f}", "--nh", str(nh), "--obsid", obsid, "--detid", str(detid), "--output_folder", results_folder.as_posix(), "--spectra_folder", spectra_folder.as_posix(), ] if fixgamma: args += ["--fixGamma"] logging.debug(" ".join([task] + args)) subprocess.check_output([task] + args, stderr=subprocess.STDOUT) def main(args): spec_folder = Path(args.spec_folder) results_folder = Path(args.results_folder) lastsource_file = Path(args.file_lastsource) first_source = get_last_source_fit(lastsource_file) if first_source == 0: check_results_folder(results_folder) obsids, detids, redshifts, nhgals = get_sources_data( args.sources_table, args.racol, args.deccol, args.zcol, args.nhcol, first_source ) for obsid, detid, z, nh, current_source in tzip( obsids, detids, redshifts, nhgals, count(first_source) ): try: fit_detection(z, nh, obsid, detid, results_folder, spec_folder, args.fixgamma) update_last_source_fit(current_source + 1, lastsource_file) except Exception as e: logging.error(e) logging.error(f"Something went wrong fitting detection {detid}") if __name__ == "__main__": parser = argparse.ArgumentParser(description="Fitting X-ray pseudospectra") parser.add_argument( "--catalogue", dest="sources_table", action="store", default=None, help="Full route to the detections catalogue.", ) parser.add_argument( "--spec_folder", dest="spec_folder", action="store", default="./data/spectra/", help="Folder of the pseudospectra.", ) parser.add_argument( "--results_folder", dest="results_folder", action="store", default="./fit_results/", help="Folder for saving the fit results.", ) parser.add_argument( "--racol", dest="racol", action="store", default="XMM_RA", help="Name of the RA column in the catalogue.", ) parser.add_argument( "--deccol", dest="deccol", action="store", default="XMM_DEC", help="Name of the Dec column in the catalogue.", ) parser.add_argument( "--zcol", dest="zcol", action="store", default=None, help="Name of the redshift column in the catalogue.", ) parser.add_argument( "--nhcol", dest="nhcol", action="store", default=None, help="Name of the Galactic NH column in the catalogue.", ) parser.add_argument( "--lsf", dest="file_lastsource", action="store", default="last_source.dat", help="File to store the last fitted source.", ) parser.add_argument( "--fixGamma", dest="fixgamma", action="store_true", default=False, help="Fit with a fixed photon index (1.9).", ) main(parser.parse_args())

true

f731a56cb0c8590006223c1be11722be665011fc

1,998

py

Python

nitro-python/nssrc/com/citrix/netscaler/nitro/resource/config/dns/dnssoarec_args.py

culbertm/NSttyPython

ff9f6aedae3fb8495342cd0fc4247c819cf47397

[ "Apache-2.0" ]

null

nitro-python/nssrc/com/citrix/netscaler/nitro/resource/config/dns/dnssoarec_args.py

culbertm/NSttyPython

ff9f6aedae3fb8495342cd0fc4247c819cf47397

[ "Apache-2.0" ]

null

nitro-python/nssrc/com/citrix/netscaler/nitro/resource/config/dns/dnssoarec_args.py

culbertm/NSttyPython

ff9f6aedae3fb8495342cd0fc4247c819cf47397

[ "Apache-2.0" ]

null

# # Copyright (c) 2008-2016 Citrix Systems, Inc. # # Licensed under the Apache License, Version 2.0 (the "License") # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # class dnssoarec_args : r""" Provides additional arguments required for fetching the dnssoarec resource. """ def __init__(self) : self._type = None self._nodeid = None @property def type(self) : r"""Type of records to display. Available settings function as follows: * ADNS - Display all authoritative address records. * PROXY - Display all proxy address records. * ALL - Display all address records. Possible values = ALL, ADNS, PROXY. """ try : return self._type except Exception as e: raise e @type.setter def type(self, type) : r"""Type of records to display. Available settings function as follows: * ADNS - Display all authoritative address records. * PROXY - Display all proxy address records. * ALL - Display all address records. Possible values = ALL, ADNS, PROXY """ try : self._type = type except Exception as e: raise e @property def nodeid(self) : r"""Unique number that identifies the cluster node. Minimum value = 0 Maximum value = 31. """ try : return self._nodeid except Exception as e: raise e @nodeid.setter def nodeid(self, nodeid) : r"""Unique number that identifies the cluster node. Minimum value = 0 Maximum value = 31 """ try : self._nodeid = nodeid except Exception as e: raise e class Type: ALL = "ALL" ADNS = "ADNS" PROXY = "PROXY"

27.75

101

0.698699

class dnssoarec_args : def __init__(self) : self._type = None self._nodeid = None @property def type(self) : try : return self._type except Exception as e: raise e @type.setter def type(self, type) : try : self._type = type except Exception as e: raise e @property def nodeid(self) : try : return self._nodeid except Exception as e: raise e @nodeid.setter def nodeid(self, nodeid) : try : self._nodeid = nodeid except Exception as e: raise e class Type: ALL = "ALL" ADNS = "ADNS" PROXY = "PROXY"

true

f731a5771e4fe79a045785785375a014bc97a2e2

717

py

Python

bot_v_bot.py

garybake/man_alphagb

e69c954e3f42dd556f9b5b7b9edcbf2856389d2f

[ "MIT" ]

null

bot_v_bot.py

garybake/man_alphagb

e69c954e3f42dd556f9b5b7b9edcbf2856389d2f

[ "MIT" ]

null

bot_v_bot.py

garybake/man_alphagb

e69c954e3f42dd556f9b5b7b9edcbf2856389d2f

[ "MIT" ]

null

import time # from dlgo.agent import naive from dlgo.agent import naive from dlgo import gotypes from dlgo import goboard_slow as goboard from dlgo.utils import print_board, print_move def main(): board_size = 9 game = goboard.GameState.new_game(board_size) bots = { gotypes.Player.black: naive.RandomBot(), gotypes.Player.white: naive.RandomBot() } while not game.is_over(): time.sleep(0.3) # print(chr(27) + "[2J") # print_board(game.board) bot_move = bots[game.next_player].select_move(game) print_move(game.next_player, bot_move) game = game.apply_move(bot_move) if __name__ == '__main__': main() # x = naive.RandomBot()

25.607143

59

0.666667

import time from dlgo.agent import naive from dlgo import gotypes from dlgo import goboard_slow as goboard from dlgo.utils import print_board, print_move def main(): board_size = 9 game = goboard.GameState.new_game(board_size) bots = { gotypes.Player.black: naive.RandomBot(), gotypes.Player.white: naive.RandomBot() } while not game.is_over(): time.sleep(0.3) bot_move = bots[game.next_player].select_move(game) print_move(game.next_player, bot_move) game = game.apply_move(bot_move) if __name__ == '__main__': main()

true

f731a5baeb0a5f98871e45535782937905736879

187,564

bzl

Python

go_third_party.bzl

enp0s3/project-infra

a53795f5df0dbb22c94ba04da47e9ba2fdfe60ed

[ "Apache-2.0" ]

16

2017-07-30T10:51:51.000Z

2021-09-02T17:58:03.000Z

go_third_party.bzl

enp0s3/project-infra

a53795f5df0dbb22c94ba04da47e9ba2fdfe60ed

[ "Apache-2.0" ]

1,946

2017-07-03T13:00:30.000Z

2022-03-31T18:55:19.000Z

go_third_party.bzl

enp0s3/project-infra

a53795f5df0dbb22c94ba04da47e9ba2fdfe60ed

[ "Apache-2.0" ]

103

2017-07-03T09:53:36.000Z

2022-03-22T20:12:00.000Z

load("@bazel_gazelle//:deps.bzl", "go_repository") def go_deps(): go_repository( name = "ag_pack_amqp", build_file_proto_mode = "disable", importpath = "pack.ag/amqp", sum = "h1:cuNDWLUTbKRtEZwhB0WQBXf9pGbm87pUBXQhvcFxBWg=", version = "v0.11.2", ) go_repository( name = "cc_mvdan_interfacer", build_file_proto_mode = "disable", importpath = "mvdan.cc/interfacer", sum = "h1:WX1yoOaKQfddO/mLzdV4wptyWgoH/6hwLs7QHTixo0I=", version = "v0.0.0-20180901003855-c20040233aed", ) go_repository( name = "cc_mvdan_lint", build_file_proto_mode = "disable", importpath = "mvdan.cc/lint", sum = "h1:DxJ5nJdkhDlLok9K6qO+5290kphDJbHOQO1DFFFTeBo=", version = "v0.0.0-20170908181259-adc824a0674b", ) go_repository( name = "cc_mvdan_unparam", build_file_proto_mode = "disable", importpath = "mvdan.cc/unparam", sum = "h1:kAREL6MPwpsk1/PQPFD3Eg7WAQR5mPTWZJaBiG5LDbY=", version = "v0.0.0-20200501210554-b37ab49443f7", ) go_repository( name = "cc_mvdan_xurls_v2", build_file_proto_mode = "disable", importpath = "mvdan.cc/xurls/v2", sum = "h1:r1zSOSNS/kqtpmATyMMMvaZ4/djsesbYz5kr0+qMRWc=", version = "v2.0.0", ) go_repository( name = "co_honnef_go_tools", build_file_proto_mode = "disable", importpath = "honnef.co/go/tools", sum = "h1:UoveltGrhghAA7ePc+e+QYDHXrBps2PqFZiHkGR/xK8=", version = "v0.0.1-2020.1.4", ) go_repository( name = "com_github_agnivade_levenshtein", build_file_proto_mode = "disable", importpath = "github.com/agnivade/levenshtein", sum = "h1:3oJU7J3FGFmyhn8KHjmVaZCN5hxTr7GxgRue+sxIXdQ=", version = "v1.0.1", ) go_repository( name = "com_github_alcortesm_tgz", build_file_proto_mode = "disable", importpath = "github.com/alcortesm/tgz", sum = "h1:uSoVVbwJiQipAclBbw+8quDsfcvFjOpI5iCf4p/cqCs=", version = "v0.0.0-20161220082320-9c5fe88206d7", ) go_repository( name = "com_github_alecthomas_kingpin", build_file_proto_mode = "disable", importpath = "github.com/alecthomas/kingpin", sum = "h1:5svnBTFgJjZvGKyYBtMB0+m5wvrbUHiqye8wRJMlnYI=", version = "v2.2.6+incompatible", ) go_repository( name = "com_github_alecthomas_template", build_file_proto_mode = "disable", importpath = "github.com/alecthomas/template", sum = "h1:JYp7IbQjafoB+tBA3gMyHYHrpOtNuDiK/uB5uXxq5wM=", version = "v0.0.0-20190718012654-fb15b899a751", ) go_repository( name = "com_github_alecthomas_units", build_file_proto_mode = "disable", importpath = "github.com/alecthomas/units", sum = "h1:UQZhZ2O0vMHr2cI+DC1Mbh0TJxzA3RcLoMsFw+aXw7E=", version = "v0.0.0-20190924025748-f65c72e2690d", ) go_repository( name = "com_github_andreyvit_diff", build_file_proto_mode = "disable", importpath = "github.com/andreyvit/diff", sum = "h1:bvNMNQO63//z+xNgfBlViaCIJKLlCJ6/fmUseuG0wVQ=", version = "v0.0.0-20170406064948-c7f18ee00883", ) go_repository( name = "com_github_andybalholm_brotli", build_file_proto_mode = "disable", importpath = "github.com/andybalholm/brotli", sum = "h1:bZ28Hqta7TFAK3Q08CMvv8y3/8ATaEqv2nGoc6yff6c=", version = "v0.0.0-20190621154722-5f990b63d2d6", ) go_repository( name = "com_github_andygrunwald_go_gerrit", build_file_proto_mode = "disable", importpath = "github.com/andygrunwald/go-gerrit", sum = "h1:7gokoTWteZhP1t2f0OzrFFXlyL8o0+b0r4ZaRV9PXOs=", version = "v0.0.0-20210709065208-9d38b0be0268", ) go_repository( name = "com_github_andygrunwald_go_jira", build_file_proto_mode = "disable", importpath = "github.com/andygrunwald/go-jira", sum = "h1:vvIImGgX32bHfoiyUwkNo+/YrPnRczNarvhLOncP6dE=", version = "v1.13.0", ) go_repository( name = "com_github_anmitsu_go_shlex", build_file_proto_mode = "disable", importpath = "github.com/anmitsu/go-shlex", sum = "h1:kFOfPq6dUM1hTo4JG6LR5AXSUEsOjtdm0kw0FtQtMJA=", version = "v0.0.0-20161002113705-648efa622239", ) go_repository( name = "com_github_antihax_optional", build_file_proto_mode = "disable", importpath = "github.com/antihax/optional", sum = "h1:uZuxRZCz65cG1o6K/xUqImNcYKtmk9ylqaH0itMSvzA=", version = "v0.0.0-20180407024304-ca021399b1a6", ) go_repository( name = "com_github_apache_thrift", build_file_proto_mode = "disable", importpath = "github.com/apache/thrift", sum = "h1:pODnxUFNcjP9UTLZGTdeh+j16A8lJbRvD3rOtrk/7bs=", version = "v0.12.0", ) go_repository( name = "com_github_apex_log", build_file_proto_mode = "disable", importpath = "github.com/apex/log", sum = "h1:1fyfbPvUwD10nMoh3hY6MXzvZShJQn9/ck7ATgAt5pA=", version = "v1.3.0", ) go_repository( name = "com_github_apex_logs", build_file_proto_mode = "disable", importpath = "github.com/apex/logs", sum = "h1:KmEBVwfDUOTFcBO8cfkJYwdQ5487UZSN+GteOGPmiro=", version = "v0.0.4", ) go_repository( name = "com_github_aphistic_golf", build_file_proto_mode = "disable", importpath = "github.com/aphistic/golf", sum = "h1:2KLQMJ8msqoPHIPDufkxVcoTtcmE5+1sL9950m4R9Pk=", version = "v0.0.0-20180712155816-02c07f170c5a", ) go_repository( name = "com_github_aphistic_sweet", build_file_proto_mode = "disable", importpath = "github.com/aphistic/sweet", sum = "h1:I4z+fAUqvKfvZV/CHi5dV0QuwbmIvYYFDjG0Ss5QpAs=", version = "v0.2.0", ) go_repository( name = "com_github_armon_circbuf", build_file_proto_mode = "disable", importpath = "github.com/armon/circbuf", sum = "h1:QEF07wC0T1rKkctt1RINW/+RMTVmiwxETico2l3gxJA=", version = "v0.0.0-20150827004946-bbbad097214e", ) go_repository( name = "com_github_armon_consul_api", build_file_proto_mode = "disable", importpath = "github.com/armon/consul-api", sum = "h1:G1bPvciwNyF7IUmKXNt9Ak3m6u9DE1rF+RmtIkBpVdA=", version = "v0.0.0-20180202201655-eb2c6b5be1b6", ) go_repository( name = "com_github_armon_go_metrics", build_file_proto_mode = "disable", importpath = "github.com/armon/go-metrics", sum = "h1:8GUt8eRujhVEGZFFEjBj46YV4rDjvGrNxb0KMWYkL2I=", version = "v0.0.0-20180917152333-f0300d1749da", ) go_repository( name = "com_github_armon_go_radix", build_file_proto_mode = "disable", importpath = "github.com/armon/go-radix", sum = "h1:BUAU3CGlLvorLI26FmByPp2eC2qla6E1Tw+scpcg/to=", version = "v0.0.0-20180808171621-7fddfc383310", ) go_repository( name = "com_github_armon_go_socks5", build_file_proto_mode = "disable", importpath = "github.com/armon/go-socks5", sum = "h1:0CwZNZbxp69SHPdPJAN/hZIm0C4OItdklCFmMRWYpio=", version = "v0.0.0-20160902184237-e75332964ef5", ) go_repository( name = "com_github_asaskevich_govalidator", build_file_proto_mode = "disable", importpath = "github.com/asaskevich/govalidator", sum = "h1:zV3ejI06GQ59hwDQAvmK1qxOQGB3WuVTRoY0okPTAv0=", version = "v0.0.0-20200108200545-475eaeb16496", ) go_repository( name = "com_github_aws_aws_k8s_tester", build_file_proto_mode = "disable", importpath = "github.com/aws/aws-k8s-tester", sum = "h1:Zr5NWiRK5fhmRIlhrsTwrY8yB488FyN6iulci2D7VaI=", version = "v1.0.0", ) go_repository( name = "com_github_aws_aws_sdk_go", build_file_proto_mode = "disable", importpath = "github.com/aws/aws-sdk-go", sum = "h1:cyZp8TvUbH9rrShdrwULtCj4pB5szddrw9aKHUsw1Ic=", version = "v1.37.22", ) go_repository( name = "com_github_aybabtme_rgbterm", build_file_proto_mode = "disable", importpath = "github.com/aybabtme/rgbterm", sum = "h1:WWB576BN5zNSZc/M9d/10pqEx5VHNhaQ/yOVAkmj5Yo=", version = "v0.0.0-20170906152045-cc83f3b3ce59", ) go_repository( name = "com_github_azure_azure_amqp_common_go_v2", build_file_proto_mode = "disable", importpath = "github.com/Azure/azure-amqp-common-go/v2", sum = "h1:+QbFgmWCnPzdaRMfsI0Yb6GrRdBj5jVL8N3EXuEUcBQ=", version = "v2.1.0", ) go_repository( name = "com_github_azure_azure_pipeline_go", build_file_proto_mode = "disable", importpath = "github.com/Azure/azure-pipeline-go", sum = "h1:6oiIS9yaG6XCCzhgAgKFfIWyo4LLCiDhZot6ltoThhY=", version = "v0.2.2", ) go_repository( name = "com_github_azure_azure_sdk_for_go", build_file_proto_mode = "disable", importpath = "github.com/Azure/azure-sdk-for-go", sum = "h1:m4oQOm3HXtQh2Ipata+pLSS1kGUD/7ikkvNq81XM/7s=", version = "v46.3.0+incompatible", ) go_repository( name = "com_github_azure_azure_service_bus_go", build_file_proto_mode = "disable", importpath = "github.com/Azure/azure-service-bus-go", sum = "h1:G1qBLQvHCFDv9pcpgwgFkspzvnGknJRR0PYJ9ytY/JA=", version = "v0.9.1", ) go_repository( name = "com_github_azure_azure_storage_blob_go", build_file_proto_mode = "disable", importpath = "github.com/Azure/azure-storage-blob-go", sum = "h1:53qhf0Oxa0nOjgbDeeYPUeyiNmafAFEY95rZLK0Tj6o=", version = "v0.8.0", ) go_repository( name = "com_github_azure_go_ansiterm", build_file_proto_mode = "disable", importpath = "github.com/Azure/go-ansiterm", sum = "h1:w+iIsaOQNcT7OZ575w+acHgRric5iCyQh+xv+KJ4HB8=", version = "v0.0.0-20170929234023-d6e3b3328b78", ) go_repository( name = "com_github_azure_go_autorest", build_file_proto_mode = "disable", importpath = "github.com/Azure/go-autorest", replace = "github.com/Azure/go-autorest", sum = "h1:V5VMDjClD3GiElqLWO7mz2MxNAK/vTfRHdAubSIPRgs=", version = "v14.2.0+incompatible", ) go_repository( name = "com_github_azure_go_autorest_autorest", build_file_proto_mode = "disable", importpath = "github.com/Azure/go-autorest/autorest", sum = "h1:gI8ytXbxMfI+IVbI9mP2JGCTXIuhHLgRlvQ9X4PsnHE=", version = "v0.11.12", ) go_repository( name = "com_github_azure_go_autorest_autorest_adal", build_file_proto_mode = "disable", importpath = "github.com/Azure/go-autorest/autorest/adal", sum = "h1:Y3bBUV4rTuxenJJs41HU3qmqsb+auo+a3Lz+PlJPpL0=", version = "v0.9.5", ) go_repository( name = "com_github_azure_go_autorest_autorest_azure_auth", build_file_proto_mode = "disable", importpath = "github.com/Azure/go-autorest/autorest/azure/auth", sum = "h1:iM6UAvjR97ZIeR93qTcwpKNMpV+/FTWjwEbuPD495Tk=", version = "v0.4.2", ) go_repository( name = "com_github_azure_go_autorest_autorest_azure_cli", build_file_proto_mode = "disable", importpath = "github.com/Azure/go-autorest/autorest/azure/cli", sum = "h1:LXl088ZQlP0SBppGFsRZonW6hSvwgL5gRByMbvUbx8U=", version = "v0.3.1", ) go_repository( name = "com_github_azure_go_autorest_autorest_date", build_file_proto_mode = "disable", importpath = "github.com/Azure/go-autorest/autorest/date", sum = "h1:7gUk1U5M/CQbp9WoqinNzJar+8KY+LPI6wiWrP/myHw=", version = "v0.3.0", ) go_repository( name = "com_github_azure_go_autorest_autorest_mocks", build_file_proto_mode = "disable", importpath = "github.com/Azure/go-autorest/autorest/mocks", sum = "h1:K0laFcLE6VLTOwNgSxaGbUcLPuGXlNkbVvq4cW4nIHk=", version = "v0.4.1", ) go_repository( name = "com_github_azure_go_autorest_autorest_to", build_file_proto_mode = "disable", importpath = "github.com/Azure/go-autorest/autorest/to", sum = "h1:oXVqrxakqqV1UZdSazDOPOLvOIz+XA683u8EctwboHk=", version = "v0.4.0", ) go_repository( name = "com_github_azure_go_autorest_autorest_validation", build_file_proto_mode = "disable", importpath = "github.com/Azure/go-autorest/autorest/validation", sum = "h1:3I9AAI63HfcLtphd9g39ruUwRI+Ca+z/f36KHPFRUss=", version = "v0.3.0", ) go_repository( name = "com_github_azure_go_autorest_logger", build_file_proto_mode = "disable", importpath = "github.com/Azure/go-autorest/logger", sum = "h1:e4RVHVZKC5p6UANLJHkM4OfR1UKZPj8Wt8Pcx+3oqrE=", version = "v0.2.0", ) go_repository( name = "com_github_azure_go_autorest_tracing", build_file_proto_mode = "disable", importpath = "github.com/Azure/go-autorest/tracing", sum = "h1:TYi4+3m5t6K48TGI9AUdb+IzbnSxvnvUMfuitfgcfuo=", version = "v0.6.0", ) go_repository( name = "com_github_beorn7_perks", build_file_proto_mode = "disable", importpath = "github.com/beorn7/perks", sum = "h1:VlbKKnNfV8bJzeqoa4cOKqO6bYr3WgKZxO8Z16+hsOM=", version = "v1.0.1", ) go_repository( name = "com_github_bgentry_speakeasy", build_file_proto_mode = "disable", importpath = "github.com/bgentry/speakeasy", sum = "h1:ByYyxL9InA1OWqxJqqp2A5pYHUrCiAL6K3J+LKSsQkY=", version = "v0.1.0", ) go_repository( name = "com_github_bitly_go_simplejson", build_file_proto_mode = "disable", importpath = "github.com/bitly/go-simplejson", sum = "h1:6IH+V8/tVMab511d5bn4M7EwGXZf9Hj6i2xSwkNEM+Y=", version = "v0.5.0", ) go_repository( name = "com_github_bketelsen_crypt", build_file_proto_mode = "disable", importpath = "github.com/bketelsen/crypt", sum = "h1:+0HFd5KSZ/mm3JmhmrDukiId5iR6w4+BdFtfSy4yWIc=", version = "v0.0.3-0.20200106085610-5cbc8cc4026c", ) go_repository( name = "com_github_blakesmith_ar", build_file_proto_mode = "disable", importpath = "github.com/blakesmith/ar", sum = "h1:m935MPodAbYS46DG4pJSv7WO+VECIWUQ7OJYSoTrMh4=", version = "v0.0.0-20190502131153-809d4375e1fb", ) go_repository( name = "com_github_blang_semver", build_file_proto_mode = "disable", importpath = "github.com/blang/semver", sum = "h1:cQNTCjp13qL8KC3Nbxr/y2Bqb63oX6wdnnjpJbkM4JQ=", version = "v3.5.1+incompatible", ) go_repository( name = "com_github_bmizerany_assert", build_file_proto_mode = "disable", importpath = "github.com/bmizerany/assert", sum = "h1:DDGfHa7BWjL4YnC6+E63dPcxHo2sUxDIu8g3QgEJdRY=", version = "v0.0.0-20160611221934-b7ed37b82869", ) go_repository( name = "com_github_bmizerany_perks", build_file_proto_mode = "disable", importpath = "github.com/bmizerany/perks", sum = "h1:AP/Y7sqYicnjGDfD5VcY4CIfh1hRXBUavxrvELjTiOE=", version = "v0.0.0-20141205001514-d9a9656a3a4b", ) go_repository( name = "com_github_bombsimon_wsl_v2", build_file_proto_mode = "disable", importpath = "github.com/bombsimon/wsl/v2", sum = "h1:/DdSteYCq4lPX+LqDg7mdoxm14UxzZPoDT0taYc3DTU=", version = "v2.2.0", ) go_repository( name = "com_github_bombsimon_wsl_v3", build_file_proto_mode = "disable", importpath = "github.com/bombsimon/wsl/v3", sum = "h1:E5SRssoBgtVFPcYWUOFJEcgaySgdtTNYzsSKDOY7ss8=", version = "v3.1.0", ) go_repository( name = "com_github_bshuster_repo_logrus_logstash_hook", build_file_proto_mode = "disable", importpath = "github.com/bshuster-repo/logrus-logstash-hook", sum = "h1:pgAtgj+A31JBVtEHu2uHuEx0n+2ukqUJnS2vVe5pQNA=", version = "v0.4.1", ) go_repository( name = "com_github_bugsnag_bugsnag_go", build_file_proto_mode = "disable", importpath = "github.com/bugsnag/bugsnag-go", sum = "h1:rFt+Y/IK1aEZkEHchZRSq9OQbsSzIT/OrI8YFFmRIng=", version = "v0.0.0-20141110184014-b1d153021fcd", ) go_repository( name = "com_github_bugsnag_osext", build_file_proto_mode = "disable", importpath = "github.com/bugsnag/osext", sum = "h1:otBG+dV+YK+Soembjv71DPz3uX/V/6MMlSyD9JBQ6kQ=", version = "v0.0.0-20130617224835-0dd3f918b21b", ) go_repository( name = "com_github_bugsnag_panicwrap", build_file_proto_mode = "disable", importpath = "github.com/bugsnag/panicwrap", sum = "h1:nvj0OLI3YqYXer/kZD8Ri1aaunCxIEsOst1BVJswV0o=", version = "v0.0.0-20151223152923-e2c28503fcd0", ) go_repository( name = "com_github_burntsushi_toml", build_file_proto_mode = "disable", importpath = "github.com/BurntSushi/toml", sum = "h1:WXkYYl6Yr3qBf1K79EBnL4mak0OimBfB0XUf9Vl28OQ=", version = "v0.3.1", ) go_repository( name = "com_github_burntsushi_xgb", build_file_proto_mode = "disable", importpath = "github.com/BurntSushi/xgb", sum = "h1:1BDTz0u9nC3//pOCMdNH+CiXJVYJh5UQNCOBG7jbELc=", version = "v0.0.0-20160522181843-27f122750802", ) go_repository( name = "com_github_bwmarrin_snowflake", build_file_proto_mode = "disable", importpath = "github.com/bwmarrin/snowflake", sum = "h1:dRbqXFjM10uA3wdrVZ8Kh19uhciRMOroUYJ7qAqDLhY=", version = "v0.0.0", ) go_repository( name = "com_github_caarlos0_ctrlc", build_file_proto_mode = "disable", importpath = "github.com/caarlos0/ctrlc", sum = "h1:2DtF8GSIcajgffDFJzyG15vO+1PuBWOMUdFut7NnXhw=", version = "v1.0.0", ) go_repository( name = "com_github_campoy_unique", build_file_proto_mode = "disable", importpath = "github.com/campoy/unique", sum = "h1:V9a67dfYqPLAvzk5hMQOXYJlZ4SLIXgyKIE+ZiHzgGQ=", version = "v0.0.0-20180121183637-88950e537e7e", ) go_repository( name = "com_github_cavaliercoder_go_cpio", build_file_proto_mode = "disable", importpath = "github.com/cavaliercoder/go-cpio", sum = "h1:hHg27A0RSSp2Om9lubZpiMgVbvn39bsUmW9U5h0twqc=", version = "v0.0.0-20180626203310-925f9528c45e", ) go_repository( name = "com_github_census_instrumentation_opencensus_proto", build_file_proto_mode = "disable", importpath = "github.com/census-instrumentation/opencensus-proto", sum = "h1:glEXhBS5PSLLv4IXzLA5yPRVX4bilULVyxxbrfOtDAk=", version = "v0.2.1", ) go_repository( name = "com_github_cespare_xxhash", build_file_proto_mode = "disable", importpath = "github.com/cespare/xxhash", sum = "h1:a6HrQnmkObjyL+Gs60czilIUGqrzKutQD6XZog3p+ko=", version = "v1.1.0", ) go_repository( name = "com_github_cespare_xxhash_v2", build_file_proto_mode = "disable", importpath = "github.com/cespare/xxhash/v2", sum = "h1:6MnRN8NT7+YBpUIWxHtefFZOKTAPgGjpQSxqLNn0+qY=", version = "v2.1.1", ) go_repository( name = "com_github_chai2010_gettext_go", build_file_proto_mode = "disable", importpath = "github.com/chai2010/gettext-go", sum = "h1:7aWHqerlJ41y6FOsEUvknqgXnGmJyJSbjhAWq5pO4F8=", version = "v0.0.0-20160711120539-c6fed771bfd5", ) go_repository( name = "com_github_chzyer_logex", build_file_proto_mode = "disable", importpath = "github.com/chzyer/logex", sum = "h1:Swpa1K6QvQznwJRcfTfQJmTE72DqScAa40E+fbHEXEE=", version = "v1.1.10", ) go_repository( name = "com_github_chzyer_readline", build_file_proto_mode = "disable", importpath = "github.com/chzyer/readline", sum = "h1:fY5BOSpyZCqRo5OhCuC+XN+r/bBCmeuuJtjz+bCNIf8=", version = "v0.0.0-20180603132655-2972be24d48e", ) go_repository( name = "com_github_chzyer_test", build_file_proto_mode = "disable", importpath = "github.com/chzyer/test", sum = "h1:q763qf9huN11kDQavWsoZXJNW3xEE4JJyHa5Q25/sd8=", version = "v0.0.0-20180213035817-a1ea475d72b1", ) go_repository( name = "com_github_cihub_seelog", build_file_proto_mode = "disable", importpath = "github.com/cihub/seelog", sum = "h1:kHaBemcxl8o/pQ5VM1c8PVE1PubbNx3mjUr09OqWGCs=", version = "v0.0.0-20170130134532-f561c5e57575", ) go_repository( name = "com_github_clarketm_json", build_file_proto_mode = "disable", importpath = "github.com/clarketm/json", sum = "h1:0JketcMdLC16WGnRGJiNmTXuQznDEQaiknxSPRBxg+k=", version = "v1.13.4", ) go_repository( name = "com_github_client9_misspell", build_file_proto_mode = "disable", importpath = "github.com/client9/misspell", sum = "h1:ta993UF76GwbvJcIo3Y68y/M3WxlpEHPWIGDkJYwzJI=", version = "v0.3.4", ) go_repository( name = "com_github_cloudevents_sdk_go", build_file_proto_mode = "disable", importpath = "github.com/cloudevents/sdk-go", sum = "h1:gS5I0s2qPmdc4GBPlUmzZU7RH30BaiOdcRJ1RkXnPrc=", version = "v1.0.0", ) go_repository( name = "com_github_cloudevents_sdk_go_v2", build_file_proto_mode = "disable", importpath = "github.com/cloudevents/sdk-go/v2", sum = "h1:AUdGJwaSUnA+VvepKqgjy6XDkPcf0hf/3L7icEs1ibs=", version = "v2.0.0", ) go_repository( name = "com_github_cloudflare_cloudflare_go", build_file_proto_mode = "disable", importpath = "github.com/cloudflare/cloudflare-go", sum = "h1:bhMGoNhAg21DuqJjU9jQepRRft6vYfo6pejT3NN4V6A=", version = "v0.13.2", ) go_repository( name = "com_github_cncf_udpa_go", build_file_proto_mode = "disable", importpath = "github.com/cncf/udpa/go", sum = "h1:WBZRG4aNOuI15bLRrCgN8fCq8E5Xuty6jGbmSNEvSsU=", version = "v0.0.0-20191209042840-269d4d468f6f", ) go_repository( name = "com_github_cockroachdb_datadriven", build_file_proto_mode = "disable", importpath = "github.com/cockroachdb/datadriven", sum = "h1:OaNxuTZr7kxeODyLWsRMC+OD03aFUH+mW6r2d+MWa5Y=", version = "v0.0.0-20190809214429-80d97fb3cbaa", ) go_repository( name = "com_github_containerd_cgroups", build_file_proto_mode = "disable", importpath = "github.com/containerd/cgroups", sum = "h1:tSNMc+rJDfmYntojat8lljbt1mgKNpTxUZJsSzJ9Y1s=", version = "v0.0.0-20190919134610-bf292b21730f", ) go_repository( name = "com_github_containerd_console", build_file_proto_mode = "disable", importpath = "github.com/containerd/console", sum = "h1:uict5mhHFTzKLUCufdSLym7z/J0CbBJT59lYbP9wtbg=", version = "v0.0.0-20180822173158-c12b1e7919c1", ) go_repository( name = "com_github_containerd_containerd", build_file_proto_mode = "disable", importpath = "github.com/containerd/containerd", sum = "h1:LoIzb5y9x5l8VKAlyrbusNPXqBY0+kviRloxFUMFwKc=", version = "v1.3.3", ) go_repository( name = "com_github_containerd_continuity", build_file_proto_mode = "disable", importpath = "github.com/containerd/continuity", sum = "h1:kIFnQBO7rQ0XkMe6xEwbybYHBEaWmh/f++laI6Emt7M=", version = "v0.0.0-20200107194136-26c1120b8d41", ) go_repository( name = "com_github_containerd_fifo", build_file_proto_mode = "disable", importpath = "github.com/containerd/fifo", sum = "h1:PUD50EuOMkXVcpBIA/R95d56duJR9VxhwncsFbNnxW4=", version = "v0.0.0-20190226154929-a9fb20d87448", ) go_repository( name = "com_github_containerd_go_runc", build_file_proto_mode = "disable", importpath = "github.com/containerd/go-runc", sum = "h1:esQOJREg8nw8aXj6uCN5dfW5cKUBiEJ/+nni1Q/D/sw=", version = "v0.0.0-20180907222934-5a6d9f37cfa3", ) go_repository( name = "com_github_containerd_ttrpc", build_file_proto_mode = "disable", importpath = "github.com/containerd/ttrpc", sum = "h1:dlfGmNcE3jDAecLqwKPMNX6nk2qh1c1Vg1/YTzpOOF4=", version = "v0.0.0-20190828154514-0e0f228740de", ) go_repository( name = "com_github_containerd_typeurl", build_file_proto_mode = "disable", importpath = "github.com/containerd/typeurl", sum = "h1:JNn81o/xG+8NEo3bC/vx9pbi/g2WI8mtP2/nXzu297Y=", version = "v0.0.0-20180627222232-a93fcdb778cd", ) go_repository( name = "com_github_coreos_bbolt", build_file_proto_mode = "disable", importpath = "github.com/coreos/bbolt", sum = "h1:n6AiVyVRKQFNb6mJlwESEvvLoDyiTzXX7ORAUlkeBdY=", version = "v1.3.3", ) go_repository( name = "com_github_coreos_etcd", build_file_proto_mode = "disable", importpath = "github.com/coreos/etcd", sum = "h1:f/Z3EoDSx1yjaIjLQGo1diYUlQYSBrrAQ5vP8NjwXwo=", version = "v3.3.17+incompatible", ) go_repository( name = "com_github_coreos_go_etcd", build_file_proto_mode = "disable", importpath = "github.com/coreos/go-etcd", sum = "h1:bXhRBIXoTm9BYHS3gE0TtQuyNZyeEMux2sDi4oo5YOo=", version = "v2.0.0+incompatible", ) go_repository( name = "com_github_coreos_go_oidc", build_file_proto_mode = "disable", importpath = "github.com/coreos/go-oidc", sum = "h1:sdJrfw8akMnCuUlaZU3tE/uYXFgfqom8DBE9so9EBsM=", version = "v2.1.0+incompatible", ) go_repository( name = "com_github_coreos_go_semver", build_file_proto_mode = "disable", importpath = "github.com/coreos/go-semver", sum = "h1:wkHLiw0WNATZnSG7epLsujiMCgPAc9xhjJ4tgnAxmfM=", version = "v0.3.0", ) go_repository( name = "com_github_coreos_go_systemd", build_file_proto_mode = "disable", importpath = "github.com/coreos/go-systemd", sum = "h1:Wf6HqHfScWJN9/ZjdUKyjop4mf3Qdd+1TvvltAvM3m8=", version = "v0.0.0-20190321100706-95778dfbb74e", ) go_repository( name = "com_github_coreos_pkg", build_file_proto_mode = "disable", importpath = "github.com/coreos/pkg", sum = "h1:lBNOc5arjvs8E5mO2tbpBpLoyyu8B6e44T7hJy6potg=", version = "v0.0.0-20180928190104-399ea9e2e55f", ) go_repository( name = "com_github_cpu_goacmedns", build_file_proto_mode = "disable", importpath = "github.com/cpu/goacmedns", sum = "h1:QOeMpIEsIdm1LSASSswjaTf8CXmzcrgy5OeCfHjppA4=", version = "v0.0.3", ) go_repository( name = "com_github_cpuguy83_go_md2man", build_file_proto_mode = "disable", importpath = "github.com/cpuguy83/go-md2man", sum = "h1:BSKMNlYxDvnunlTymqtgONjNnaRV1sTpcovwwjF22jk=", version = "v1.0.10", ) go_repository( name = "com_github_cpuguy83_go_md2man_v2", build_file_proto_mode = "disable", importpath = "github.com/cpuguy83/go-md2man/v2", sum = "h1:EoUDS0afbrsXAZ9YQ9jdu/mZ2sXgT1/2yyNng4PGlyM=", version = "v2.0.0", ) go_repository( name = "com_github_creack_pty", build_file_proto_mode = "disable", importpath = "github.com/creack/pty", sum = "h1:07n33Z8lZxZ2qwegKbObQohDhXDQxiMMz1NOUGYlesw=", version = "v1.1.11", ) go_repository( name = "com_github_cyphar_filepath_securejoin", build_file_proto_mode = "disable", importpath = "github.com/cyphar/filepath-securejoin", sum = "h1:jCwT2GTP+PY5nBz3c/YL5PAIbusElVrPujOBSCj8xRg=", version = "v0.2.2", ) go_repository( name = "com_github_datadog_zstd", build_file_proto_mode = "disable", importpath = "github.com/DataDog/zstd", sum = "h1:3oxKN3wbHibqx897utPC2LTQU4J+IHWWJO+glkAkpFM=", version = "v1.4.1", ) go_repository( name = "com_github_davecgh_go_spew", build_file_proto_mode = "disable", importpath = "github.com/davecgh/go-spew", sum = "h1:vj9j/u1bqnvCEfJOwUhtlOARqs3+rkHYY13jYWTU97c=", version = "v1.1.1", ) go_repository( name = "com_github_daviddengcn_go_colortext", build_file_proto_mode = "disable", importpath = "github.com/daviddengcn/go-colortext", sum = "h1:uVsMphB1eRx7xB1njzL3fuMdWRN8HtVzoUOItHMwv5c=", version = "v0.0.0-20160507010035-511bcaf42ccd", ) go_repository( name = "com_github_deislabs_oras", build_file_proto_mode = "disable", importpath = "github.com/deislabs/oras", sum = "h1:If674KraJVpujYR00rzdi0QAmW4BxzMJPVAZJKuhQ0c=", version = "v0.8.1", ) go_repository( name = "com_github_denisenkom_go_mssqldb", build_file_proto_mode = "disable", importpath = "github.com/denisenkom/go-mssqldb", sum = "h1:83Wprp6ROGeiHFAP8WJdI2RoxALQYgdllERc3N5N2DM=", version = "v0.0.0-20191124224453-732737034ffd", ) go_repository( name = "com_github_denverdino_aliyungo", build_file_proto_mode = "disable", importpath = "github.com/denverdino/aliyungo", sum = "h1:p6poVbjHDkKa+wtC8frBMwQtT3BmqGYBjzMwJ63tuR4=", version = "v0.0.0-20190125010748-a747050bb1ba", ) go_repository( name = "com_github_devigned_tab", build_file_proto_mode = "disable", importpath = "github.com/devigned/tab", sum = "h1:3mD6Kb1mUOYeLpJvTVSDwSg5ZsfSxfvxGRTxRsJsITA=", version = "v0.1.1", ) go_repository( name = "com_github_dgrijalva_jwt_go", build_file_proto_mode = "disable", importpath = "github.com/dgrijalva/jwt-go", sum = "h1:7qlOGliEKZXTDg6OTjfoBKDXWrumCAMpl/TFQ4/5kLM=", version = "v3.2.0+incompatible", ) go_repository( name = "com_github_dgrijalva_jwt_go_v4", build_file_proto_mode = "disable", importpath = "github.com/dgrijalva/jwt-go/v4", sum = "h1:CaO/zOnF8VvUfEbhRatPcwKVWamvbYd8tQGRWacE9kU=", version = "v4.0.0-preview1", ) go_repository( name = "com_github_dgryski_go_gk", build_file_proto_mode = "disable", importpath = "github.com/dgryski/go-gk", sum = "h1:XOPLOMn/zT4jIgxfxSsoXPxkrzz0FaCHwp33x5POJ+Q=", version = "v0.0.0-20200319235926-a69029f61654", ) go_repository( name = "com_github_dgryski_go_sip13", build_file_proto_mode = "disable", importpath = "github.com/dgryski/go-sip13", sum = "h1:RMLoZVzv4GliuWafOuPuQDKSm1SJph7uCRnnS61JAn4=", version = "v0.0.0-20181026042036-e10d5fee7954", ) go_repository( name = "com_github_digitalocean_godo", build_file_proto_mode = "disable", importpath = "github.com/digitalocean/godo", sum = "h1:IMElzMUpO1dVR8qjSg53+5vDkOLzMbhJt4yTAq7NGCQ=", version = "v1.44.0", ) go_repository( name = "com_github_dimchansky_utfbom", build_file_proto_mode = "disable", importpath = "github.com/dimchansky/utfbom", sum = "h1:FcM3g+nofKgUteL8dm/UpdRXNC9KmADgTpLKsu0TRo4=", version = "v1.1.0", ) go_repository( name = "com_github_djarvur_go_err113", build_file_proto_mode = "disable", importpath = "github.com/Djarvur/go-err113", sum = "h1:uCRZZOdMQ0TZPHYTdYpoC0bLYJKPEHPUJ8MeAa51lNU=", version = "v0.1.0", ) go_repository( name = "com_github_djherbis_atime", build_file_proto_mode = "disable", importpath = "github.com/djherbis/atime", sum = "h1:ySLvBAM0EvOGaX7TI4dAM5lWj+RdJUCKtGSEHN8SGBg=", version = "v1.0.0", ) go_repository( name = "com_github_dnaeon_go_vcr", build_file_proto_mode = "disable", importpath = "github.com/dnaeon/go-vcr", sum = "h1:r8L/HqC0Hje5AXMu1ooW8oyQyOFv4GxqpL0nRP7SLLY=", version = "v1.0.1", ) go_repository( name = "com_github_docker_cli", build_file_proto_mode = "disable", importpath = "github.com/docker/cli", sum = "h1:AbI1uj9w4yt6TvfKHfRu7G55KuQe7NCvWPQRKDoXggE=", version = "v0.0.0-20200210162036-a4bedce16568", ) go_repository( name = "com_github_docker_distribution", build_file_proto_mode = "disable", importpath = "github.com/docker/distribution", sum = "h1:a5mlkVzth6W5A4fOsS3D2EO5BUmsJpcB+cRlLU7cSug=", version = "v2.7.1+incompatible", ) go_repository( name = "com_github_docker_docker", build_file_proto_mode = "disable", importpath = "github.com/docker/docker", sum = "h1:IkZjBSIc8hBjLpqeAbeE5mca5mNgeatLHBy3GO78BWo=", version = "v1.13.1", ) go_repository( name = "com_github_docker_docker_credential_helpers", build_file_proto_mode = "disable", importpath = "github.com/docker/docker-credential-helpers", sum = "h1:zI2p9+1NQYdnG6sMU26EX4aVGlqbInSQxQXLvzJ4RPQ=", version = "v0.6.3", ) go_repository( name = "com_github_docker_go_connections", build_file_proto_mode = "disable", importpath = "github.com/docker/go-connections", sum = "h1:El9xVISelRB7BuFusrZozjnkIM5YnzCViNKohAFqRJQ=", version = "v0.4.0", ) go_repository( name = "com_github_docker_go_metrics", build_file_proto_mode = "disable", importpath = "github.com/docker/go-metrics", sum = "h1:yWHOI+vFjEsAakUTSrtqc/SAHrhSkmn48pqjidZX3QA=", version = "v0.0.0-20180209012529-399ea8c73916", ) go_repository( name = "com_github_docker_go_units", build_file_proto_mode = "disable", importpath = "github.com/docker/go-units", sum = "h1:3uh0PgVws3nIA0Q+MwDC8yjEPf9zjRfZZWXZYDct3Tw=", version = "v0.4.0", ) go_repository( name = "com_github_docker_libtrust", build_file_proto_mode = "disable", importpath = "github.com/docker/libtrust", sum = "h1:ZClxb8laGDf5arXfYcAtECDFgAgHklGI8CxgjHnXKJ4=", version = "v0.0.0-20150114040149-fa567046d9b1", ) go_repository( name = "com_github_docker_spdystream", build_file_proto_mode = "disable", importpath = "github.com/docker/spdystream", sum = "h1:cenwrSVm+Z7QLSV/BsnenAOcDXdX4cMv4wP0B/5QbPg=", version = "v0.0.0-20160310174837-449fdfce4d96", ) go_repository( name = "com_github_docopt_docopt_go", build_file_proto_mode = "disable", importpath = "github.com/docopt/docopt-go", sum = "h1:bWDMxwH3px2JBh6AyO7hdCn/PkvCZXii8TGj7sbtEbQ=", version = "v0.0.0-20180111231733-ee0de3bc6815", ) go_repository( name = "com_github_dsnet_compress", build_file_proto_mode = "disable", importpath = "github.com/dsnet/compress", sum = "h1:PlZu0n3Tuv04TzpfPbrnI0HW/YwodEXDS+oPKahKF0Q=", version = "v0.0.1", ) go_repository( name = "com_github_dsnet_golib", build_file_proto_mode = "disable", importpath = "github.com/dsnet/golib", sum = "h1:tFh1tRc4CA31yP6qDcu+Trax5wW5GuMxvkIba07qVLY=", version = "v0.0.0-20171103203638-1ea166775780", ) go_repository( name = "com_github_dustin_go_humanize", build_file_proto_mode = "disable", importpath = "github.com/dustin/go-humanize", sum = "h1:VSnTsYCnlFHaM2/igO1h6X3HA71jcobQuxemgkq4zYo=", version = "v1.0.0", ) go_repository( name = "com_github_eapache_go_resiliency", build_file_proto_mode = "disable", importpath = "github.com/eapache/go-resiliency", sum = "h1:v7g92e/KSN71Rq7vSThKaWIq68fL4YHvWyiUKorFR1Q=", version = "v1.2.0", ) go_repository( name = "com_github_eapache_go_xerial_snappy", build_file_proto_mode = "disable", importpath = "github.com/eapache/go-xerial-snappy", sum = "h1:YEetp8/yCZMuEPMUDHG0CW/brkkEp8mzqk2+ODEitlw=", version = "v0.0.0-20180814174437-776d5712da21", ) go_repository( name = "com_github_eapache_queue", build_file_proto_mode = "disable", importpath = "github.com/eapache/queue", sum = "h1:YOEu7KNc61ntiQlcEeUIoDTJ2o8mQznoNvUhiigpIqc=", version = "v1.1.0", ) go_repository( name = "com_github_elazarl_goproxy", build_file_proto_mode = "disable", importpath = "github.com/elazarl/goproxy", sum = "h1:yUdfgN0XgIJw7foRItutHYUIhlcKzcSf5vDpdhQAKTc=", version = "v0.0.0-20180725130230-947c36da3153", ) go_repository( name = "com_github_emicklei_go_restful", build_file_proto_mode = "disable", importpath = "github.com/emicklei/go-restful", sum = "h1:spTtZBk5DYEvbxMVutUuTyh1Ao2r4iyvLdACqsl/Ljk=", version = "v2.9.5+incompatible", ) go_repository( name = "com_github_emirpasic_gods", build_file_proto_mode = "disable", importpath = "github.com/emirpasic/gods", sum = "h1:QAUIPSaCu4G+POclxeqb3F+WPpdKqFGlw36+yOzGlrg=", version = "v1.12.0", ) go_repository( name = "com_github_envoyproxy_go_control_plane", build_file_proto_mode = "disable", importpath = "github.com/envoyproxy/go-control-plane", sum = "h1:rEvIZUSZ3fx39WIi3JkQqQBitGwpELBIYWeBVh6wn+E=", version = "v0.9.4", ) go_repository( name = "com_github_envoyproxy_protoc_gen_validate", build_file_proto_mode = "disable", importpath = "github.com/envoyproxy/protoc-gen-validate", sum = "h1:EQciDnbrYxy13PgWoY8AqoxGiPrpgBZ1R8UNe3ddc+A=", version = "v0.1.0", ) go_repository( name = "com_github_erikstmartin_go_testdb", build_file_proto_mode = "disable", importpath = "github.com/erikstmartin/go-testdb", sum = "h1:Yzb9+7DPaBjB8zlTR87/ElzFsnQfuHnVUVqpZZIcV5Y=", version = "v0.0.0-20160219214506-8d10e4a1bae5", ) go_repository( name = "com_github_evanphx_json_patch", build_file_proto_mode = "disable", importpath = "github.com/evanphx/json-patch", sum = "h1:glyUF9yIYtMHzn8xaKw5rMhdWcwsYV8dZHIq5567/xs=", version = "v4.11.0+incompatible", ) go_repository( name = "com_github_exponent_io_jsonpath", build_file_proto_mode = "disable", importpath = "github.com/exponent-io/jsonpath", sum = "h1:105gxyaGwCFad8crR9dcMQWvV9Hvulu6hwUh4tWPJnM=", version = "v0.0.0-20151013193312-d6023ce2651d", ) go_repository( name = "com_github_fatih_camelcase", build_file_proto_mode = "disable", importpath = "github.com/fatih/camelcase", sum = "h1:hxNvNX/xYBp0ovncs8WyWZrOrpBNub/JfaMvbURyft8=", version = "v1.0.0", ) go_repository( name = "com_github_fatih_color", build_file_proto_mode = "disable", importpath = "github.com/fatih/color", sum = "h1:8xPHl4/q1VyqGIPif1F+1V3Y3lSmrq01EabUW3CoW5s=", version = "v1.9.0", ) go_repository( name = "com_github_fatih_structs", build_file_proto_mode = "disable", importpath = "github.com/fatih/structs", sum = "h1:Q7juDM0QtcnhCpeyLGQKyg4TOIghuNXrkL32pHAUMxo=", version = "v1.1.0", ) go_repository( name = "com_github_felixge_fgprof", build_file_proto_mode = "disable", importpath = "github.com/felixge/fgprof", sum = "h1:E6FUJ2Mlv043ipLOCFqo8+cHo9MhQ203E2cdEK/isEs=", version = "v0.9.1", ) go_repository( name = "com_github_flynn_go_shlex", build_file_proto_mode = "disable", importpath = "github.com/flynn/go-shlex", sum = "h1:BHsljHzVlRcyQhjrss6TZTdY2VfCqZPbv5k3iBFa2ZQ=", version = "v0.0.0-20150515145356-3f9db97f8568", ) go_repository( name = "com_github_form3tech_oss_jwt_go", build_file_proto_mode = "disable", importpath = "github.com/form3tech-oss/jwt-go", sum = "h1:TcekIExNqud5crz4xD2pavyTgWiPvpYe4Xau31I0PRk=", version = "v3.2.2+incompatible", ) go_repository( name = "com_github_fortytw2_leaktest", build_file_proto_mode = "disable", importpath = "github.com/fortytw2/leaktest", sum = "h1:u8491cBMTQ8ft8aeV+adlcytMZylmA5nnwwkRZjI8vw=", version = "v1.3.0", ) go_repository( name = "com_github_frankban_quicktest", build_file_proto_mode = "disable", importpath = "github.com/frankban/quicktest", sum = "h1:PvpJR0Uq8SdX+zagCMsarBMlhz6ysGTf1+pRmCsRXqY=", version = "v1.8.1", ) go_repository( name = "com_github_fsnotify_fsnotify", build_file_proto_mode = "disable", importpath = "github.com/fsnotify/fsnotify", sum = "h1:hsms1Qyu0jgnwNXIxa+/V/PDsU6CfLf6CNO8H7IWoS4=", version = "v1.4.9", ) go_repository( name = "com_github_fsouza_fake_gcs_server", build_file_proto_mode = "disable", importpath = "github.com/fsouza/fake-gcs-server", sum = "h1:3bRRh/rQnB2XbrMolHAj9oX/PFiWVQFVVfPR5y2pxb8=", version = "v1.19.4", ) go_repository( name = "com_github_fvbommel_sortorder", build_file_proto_mode = "disable", importpath = "github.com/fvbommel/sortorder", sum = "h1:dSnXLt4mJYH25uDDGa3biZNQsozaUWDSWeKJ0qqFfzE=", version = "v1.0.1", ) go_repository( name = "com_github_garyburd_redigo", build_file_proto_mode = "disable", importpath = "github.com/garyburd/redigo", sum = "h1:LofdAjjjqCSXMwLGgOgnE+rdPuvX9DxCqaHwKy7i/ko=", version = "v0.0.0-20150301180006-535138d7bcd7", ) go_repository( name = "com_github_ghodss_yaml", build_file_proto_mode = "disable", importpath = "github.com/ghodss/yaml", sum = "h1:wQHKEahhL6wmXdzwWG11gIVCkOv05bNOh+Rxn0yngAk=", version = "v1.0.0", ) go_repository( name = "com_github_gliderlabs_ssh", build_file_proto_mode = "disable", importpath = "github.com/gliderlabs/ssh", sum = "h1:6zsha5zo/TWhRhwqCD3+EarCAgZ2yN28ipRnGPnwkI0=", version = "v0.2.2", ) go_repository( name = "com_github_globalsign_mgo", build_file_proto_mode = "disable", importpath = "github.com/globalsign/mgo", sum = "h1:DujepqpGd1hyOd7aW59XpK7Qymp8iy83xq74fLr21is=", version = "v0.0.0-20181015135952-eeefdecb41b8", ) go_repository( name = "com_github_go_bindata_go_bindata_v3", build_file_proto_mode = "disable", importpath = "github.com/go-bindata/go-bindata/v3", sum = "h1:F0nVttLC3ws0ojc7p60veTurcOm//D4QBODNM7EGrCI=", version = "v3.1.3", ) go_repository( name = "com_github_go_critic_go_critic", build_file_proto_mode = "disable", importpath = "github.com/go-critic/go-critic", sum = "h1:sGEEdiuvLV0OC7/yC6MnK3K6LCPBplspK45B0XVdFAc=", version = "v0.4.3", ) go_repository( name = "com_github_go_git_gcfg", build_file_proto_mode = "disable", importpath = "github.com/go-git/gcfg", sum = "h1:Q5ViNfGF8zFgyJWPqYwA7qGFoMTEiBmdlkcfRmpIMa4=", version = "v1.5.0", ) go_repository( name = "com_github_go_git_go_billy_v5", build_file_proto_mode = "disable", importpath = "github.com/go-git/go-billy/v5", sum = "h1:4pl5BV4o7ZG/lterP4S6WzJ6xr49Ba5ET9ygheTYahk=", version = "v5.1.0", ) go_repository( name = "com_github_go_git_go_git_fixtures_v4", build_file_proto_mode = "disable", importpath = "github.com/go-git/go-git-fixtures/v4", sum = "h1:PbKy9zOy4aAKrJ5pibIRpVO2BXnK1Tlcg+caKI7Ox5M=", version = "v4.0.2-0.20200613231340-f56387b50c12", ) go_repository( name = "com_github_go_git_go_git_v5", build_file_proto_mode = "disable", importpath = "github.com/go-git/go-git/v5", sum = "h1:8WKMtJR2j8RntEXR/uvTKagfEt4GYlwQ7mntE4+0GWc=", version = "v5.3.0", ) go_repository( name = "com_github_go_gl_glfw", build_file_proto_mode = "disable", importpath = "github.com/go-gl/glfw", sum = "h1:QbL/5oDUmRBzO9/Z7Seo6zf912W/a6Sr4Eu0G/3Jho0=", version = "v0.0.0-20190409004039-e6da0acd62b1", ) go_repository( name = "com_github_go_gl_glfw_v3_3_glfw", build_file_proto_mode = "disable", importpath = "github.com/go-gl/glfw/v3.3/glfw", sum = "h1:WtGNWLvXpe6ZudgnXrq0barxBImvnnJoMEhXAzcbM0I=", version = "v0.0.0-20200222043503-6f7a984d4dc4", ) go_repository( name = "com_github_go_ini_ini", build_file_proto_mode = "disable", importpath = "github.com/go-ini/ini", sum = "h1:0wVcG9udk2C3TGgmdIGKK9ScOZHZB5nbG+gwji9fhhc=", version = "v1.55.0", ) go_repository( name = "com_github_go_kit_kit", build_file_proto_mode = "disable", importpath = "github.com/go-kit/kit", sum = "h1:wDJmvq38kDhkVxi50ni9ykkdUr1PKgqKOoi01fa0Mdk=", version = "v0.9.0", ) go_repository( name = "com_github_go_kit_log", build_file_proto_mode = "disable", importpath = "github.com/go-kit/log", sum = "h1:DGJh0Sm43HbOeYDNnVZFl8BvcYVvjD5bqYJvp0REbwQ=", version = "v0.1.0", ) go_repository( name = "com_github_go_ldap_ldap", build_file_proto_mode = "disable", importpath = "github.com/go-ldap/ldap", sum = "h1:kD5HQcAzlQ7yrhfn+h+MSABeAy/jAJhvIJ/QDllP44g=", version = "v3.0.2+incompatible", ) go_repository( name = "com_github_go_lintpack_lintpack", build_file_proto_mode = "disable", importpath = "github.com/go-lintpack/lintpack", sum = "h1:DI5mA3+eKdWeJ40nU4d6Wc26qmdG8RCi/btYq0TuRN0=", version = "v0.5.2", ) go_repository( name = "com_github_go_logfmt_logfmt", build_file_proto_mode = "disable", importpath = "github.com/go-logfmt/logfmt", sum = "h1:TrB8swr/68K7m9CcGut2g3UOihhbcbiMAYiuTXdEih4=", version = "v0.5.0", ) go_repository( name = "com_github_go_logr_logr", build_file_proto_mode = "disable", importpath = "github.com/go-logr/logr", sum = "h1:K7/B1jt6fIBQVd4Owv2MqGQClcgf0R266+7C/QjRcLc=", version = "v0.4.0", ) go_repository( name = "com_github_go_logr_zapr", build_file_proto_mode = "disable", importpath = "github.com/go-logr/zapr", sum = "h1:uc1uML3hRYL9/ZZPdgHS/n8Nzo+eaYL/Efxkkamf7OM=", version = "v0.4.0", ) go_repository( name = "com_github_go_ole_go_ole", build_file_proto_mode = "disable", importpath = "github.com/go-ole/go-ole", sum = "h1:2lOsA72HgjxAuMlKpFiCbHTvu44PIVkZ5hqm3RSdI/E=", version = "v1.2.1", ) go_repository( name = "com_github_go_openapi_analysis", build_file_proto_mode = "disable", importpath = "github.com/go-openapi/analysis", sum = "h1:8b2ZgKfKIUTVQpTb77MoRDIMEIwvDVw40o3aOXdfYzI=", version = "v0.19.5", ) go_repository( name = "com_github_go_openapi_errors", build_file_proto_mode = "disable", importpath = "github.com/go-openapi/errors", sum = "h1:a2kIyV3w+OS3S97zxUndRVD46+FhGOUBDFY7nmu4CsY=", version = "v0.19.2", ) go_repository( name = "com_github_go_openapi_jsonpointer", build_file_proto_mode = "disable", importpath = "github.com/go-openapi/jsonpointer", sum = "h1:gihV7YNZK1iK6Tgwwsxo2rJbD1GTbdm72325Bq8FI3w=", version = "v0.19.3", ) go_repository( name = "com_github_go_openapi_jsonreference", build_file_proto_mode = "disable", importpath = "github.com/go-openapi/jsonreference", sum = "h1:5cxNfTy0UVC3X8JL5ymxzyoUZmo8iZb+jeTWn7tUa8o=", version = "v0.19.3", ) go_repository( name = "com_github_go_openapi_loads", build_file_proto_mode = "disable", importpath = "github.com/go-openapi/loads", sum = "h1:5I4CCSqoWzT+82bBkNIvmLc0UOsoKKQ4Fz+3VxOB7SY=", version = "v0.19.4", ) go_repository( name = "com_github_go_openapi_runtime", build_file_proto_mode = "disable", importpath = "github.com/go-openapi/runtime", sum = "h1:csnOgcgAiuGoM/Po7PEpKDoNulCcF3FGbSnbHfxgjMI=", version = "v0.19.4", ) go_repository( name = "com_github_go_openapi_spec", build_file_proto_mode = "disable", importpath = "github.com/go-openapi/spec", sum = "h1:rMMMj8cV38KVXK7SFc+I2MWClbEfbK705+j+dyqun5g=", version = "v0.19.6", ) go_repository( name = "com_github_go_openapi_strfmt", build_file_proto_mode = "disable", importpath = "github.com/go-openapi/strfmt", sum = "h1:eRfyY5SkaNJCAwmmMcADjY31ow9+N7MCLW7oRkbsINA=", version = "v0.19.3", ) go_repository( name = "com_github_go_openapi_swag", build_file_proto_mode = "disable", importpath = "github.com/go-openapi/swag", sum = "h1:VRuXN2EnMSsZdauzdss6JBC29YotDqG59BZ+tdlIL1s=", version = "v0.19.7", ) go_repository( name = "com_github_go_openapi_validate", build_file_proto_mode = "disable", importpath = "github.com/go-openapi/validate", sum = "h1:QhCBKRYqZR+SKo4gl1lPhPahope8/RLt6EVgY8X80w0=", version = "v0.19.5", ) go_repository( name = "com_github_go_sql_driver_mysql", build_file_proto_mode = "disable", importpath = "github.com/go-sql-driver/mysql", sum = "h1:ozyZYNQW3x3HtqT1jira07DN2PArx2v7/mN66gGcHOs=", version = "v1.5.0", ) go_repository( name = "com_github_go_stack_stack", build_file_proto_mode = "disable", importpath = "github.com/go-stack/stack", sum = "h1:5SgMzNM5HxrEjV0ww2lTmX6E2Izsfxas4+YHWRs3Lsk=", version = "v1.8.0", ) go_repository( name = "com_github_go_task_slim_sprig", build_file_proto_mode = "disable", importpath = "github.com/go-task/slim-sprig", sum = "h1:p104kn46Q8WdvHunIJ9dAyjPVtrBPhSr3KT2yUst43I=", version = "v0.0.0-20210107165309-348f09dbbbc0", ) go_repository( name = "com_github_go_test_deep", build_file_proto_mode = "disable", importpath = "github.com/go-test/deep", sum = "h1:/VSMRlnY/JSyqxQUzQLKVMAskpY/NZKFA5j2P+0pP2M=", version = "v1.0.7", ) go_repository( name = "com_github_go_toolsmith_astcast", build_file_proto_mode = "disable", importpath = "github.com/go-toolsmith/astcast", sum = "h1:JojxlmI6STnFVG9yOImLeGREv8W2ocNUM+iOhR6jE7g=", version = "v1.0.0", ) go_repository( name = "com_github_go_toolsmith_astcopy", build_file_proto_mode = "disable", importpath = "github.com/go-toolsmith/astcopy", sum = "h1:OMgl1b1MEpjFQ1m5ztEO06rz5CUd3oBv9RF7+DyvdG8=", version = "v1.0.0", ) go_repository( name = "com_github_go_toolsmith_astequal", build_file_proto_mode = "disable", importpath = "github.com/go-toolsmith/astequal", sum = "h1:4zxD8j3JRFNyLN46lodQuqz3xdKSrur7U/sr0SDS/gQ=", version = "v1.0.0", ) go_repository( name = "com_github_go_toolsmith_astfmt", build_file_proto_mode = "disable", importpath = "github.com/go-toolsmith/astfmt", sum = "h1:A0vDDXt+vsvLEdbMFJAUBI/uTbRw1ffOPnxsILnFL6k=", version = "v1.0.0", ) go_repository( name = "com_github_go_toolsmith_astinfo", build_file_proto_mode = "disable", importpath = "github.com/go-toolsmith/astinfo", sum = "h1:wP6mXeB2V/d1P1K7bZ5vDUO3YqEzcvOREOxZPEu3gVI=", version = "v0.0.0-20180906194353-9809ff7efb21", ) go_repository( name = "com_github_go_toolsmith_astp", build_file_proto_mode = "disable", importpath = "github.com/go-toolsmith/astp", sum = "h1:alXE75TXgcmupDsMK1fRAy0YUzLzqPVvBKoyWV+KPXg=", version = "v1.0.0", ) go_repository( name = "com_github_go_toolsmith_pkgload", build_file_proto_mode = "disable", importpath = "github.com/go-toolsmith/pkgload", sum = "h1:4DFWWMXVfbcN5So1sBNW9+yeiMqLFGl1wFLTL5R0Tgg=", version = "v1.0.0", ) go_repository( name = "com_github_go_toolsmith_strparse", build_file_proto_mode = "disable", importpath = "github.com/go-toolsmith/strparse", sum = "h1:Vcw78DnpCAKlM20kSbAyO4mPfJn/lyYA4BJUDxe2Jb4=", version = "v1.0.0", ) go_repository( name = "com_github_go_toolsmith_typep", build_file_proto_mode = "disable", importpath = "github.com/go-toolsmith/typep", sum = "h1:8xdsa1+FSIH/RhEkgnD1j2CJOy5mNllW1Q9tRiYwvlk=", version = "v1.0.2", ) go_repository( name = "com_github_go_xmlfmt_xmlfmt", build_file_proto_mode = "disable", importpath = "github.com/go-xmlfmt/xmlfmt", sum = "h1:khEcpUM4yFcxg4/FHQWkvVRmgijNXRfzkIDHh23ggEo=", version = "v0.0.0-20191208150333-d5b6f63a941b", ) go_repository( name = "com_github_go_yaml_yaml", build_file_proto_mode = "disable", importpath = "github.com/go-yaml/yaml", sum = "h1:RYi2hDdss1u4YE7GwixGzWwVo47T8UQwnTLB6vQiq+o=", version = "v2.1.0+incompatible", ) go_repository( name = "com_github_gobuffalo_envy", build_file_proto_mode = "disable", importpath = "github.com/gobuffalo/envy", sum = "h1:OQl5ys5MBea7OGCdvPbBJWRgnhC/fGona6QKfvFeau8=", version = "v1.7.1", ) go_repository( name = "com_github_gobuffalo_flect", build_file_proto_mode = "disable", importpath = "github.com/gobuffalo/flect", sum = "h1:EWCvMGGxOjsgwlWaP+f4+Hh6yrrte7JeFL2S6b+0hdM=", version = "v0.2.0", ) go_repository( name = "com_github_gobwas_glob", build_file_proto_mode = "disable", importpath = "github.com/gobwas/glob", sum = "h1:A4xDbljILXROh+kObIiy5kIaPYD8e96x1tgBhUI5J+Y=", version = "v0.2.3", ) go_repository( name = "com_github_godbus_dbus", build_file_proto_mode = "disable", importpath = "github.com/godbus/dbus", sum = "h1:BWhy2j3IXJhjCbC68FptL43tDKIq8FladmaTs3Xs7Z8=", version = "v0.0.0-20190422162347-ade71ed3457e", ) go_repository( name = "com_github_gofrs_flock", build_file_proto_mode = "disable", importpath = "github.com/gofrs/flock", sum = "h1:DP+LD/t0njgoPBvT5MJLeliUIVQR03hiKR6vezdwHlc=", version = "v0.7.1", ) go_repository( name = "com_github_gogo_googleapis", build_file_proto_mode = "disable", importpath = "github.com/gogo/googleapis", sum = "h1:kFkMAZBNAn4j7K0GiZr8cRYzejq68VbheufiV3YuyFI=", version = "v1.1.0", ) go_repository( name = "com_github_gogo_protobuf", build_file_proto_mode = "disable", importpath = "github.com/gogo/protobuf", sum = "h1:Ov1cvc58UF3b5XjBnZv7+opcTcQFZebYjWzi34vdm4Q=", version = "v1.3.2", ) go_repository( name = "com_github_golang_gddo", build_file_proto_mode = "disable", importpath = "github.com/golang/gddo", sum = "h1:KRMr9A3qfbVM7iV/WcLY/rL5LICqwMHLhwRXKu99fXw=", version = "v0.0.0-20190419222130-af0f2af80721", ) go_repository( name = "com_github_golang_glog", build_file_proto_mode = "disable", importpath = "github.com/golang/glog", sum = "h1:VKtxabqXZkF25pY9ekfRL6a582T4P37/31XEstQ5p58=", version = "v0.0.0-20160126235308-23def4e6c14b", ) go_repository( name = "com_github_golang_groupcache", build_file_proto_mode = "disable", importpath = "github.com/golang/groupcache", sum = "h1:1r7pUrabqp18hOBcwBwiTsbnFeTZHV9eER/QT5JVZxY=", version = "v0.0.0-20200121045136-8c9f03a8e57e", ) go_repository( name = "com_github_golang_lint", build_file_proto_mode = "disable", importpath = "github.com/golang/lint", sum = "h1:2hRPrmiwPrp3fQX967rNJIhQPtiGXdlQWAxKbKw3VHA=", version = "v0.0.0-20180702182130-06c8688daad7", ) go_repository( name = "com_github_golang_mock", build_file_proto_mode = "disable", importpath = "github.com/golang/mock", sum = "h1:jlYHihg//f7RRwuPfptm04yp4s7O6Kw8EZiVYIGcH0g=", version = "v1.5.0", ) go_repository( name = "com_github_golang_protobuf", build_file_proto_mode = "disable", importpath = "github.com/golang/protobuf", sum = "h1:ROPKBNFfQgOUMifHyP+KYbvpjbdoFNs+aK7DXlji0Tw=", version = "v1.5.2", ) go_repository( name = "com_github_golang_snappy", build_file_proto_mode = "disable", importpath = "github.com/golang/snappy", sum = "h1:Qgr9rKW7uDUkrbSmQeiDsGa8SjGyCOGtuasMWwvp2P4=", version = "v0.0.1", ) go_repository( name = "com_github_golang_sql_civil", build_file_proto_mode = "disable", importpath = "github.com/golang-sql/civil", sum = "h1:lXe2qZdvpiX5WZkZR4hgp4KJVfY3nMkvmwbVkpv1rVY=", version = "v0.0.0-20190719163853-cb61b32ac6fe", ) go_repository( name = "com_github_golangci_check", build_file_proto_mode = "disable", importpath = "github.com/golangci/check", sum = "h1:23T5iq8rbUYlhpt5DB4XJkc6BU31uODLD1o1gKvZmD0=", version = "v0.0.0-20180506172741-cfe4005ccda2", ) go_repository( name = "com_github_golangci_dupl", build_file_proto_mode = "disable", importpath = "github.com/golangci/dupl", sum = "h1:w8hkcTqaFpzKqonE9uMCefW1WDie15eSP/4MssdenaM=", version = "v0.0.0-20180902072040-3e9179ac440a", ) go_repository( name = "com_github_golangci_errcheck", build_file_proto_mode = "disable", importpath = "github.com/golangci/errcheck", sum = "h1:YYWNAGTKWhKpcLLt7aSj/odlKrSrelQwlovBpDuf19w=", version = "v0.0.0-20181223084120-ef45e06d44b6", ) go_repository( name = "com_github_golangci_go_misc", build_file_proto_mode = "disable", importpath = "github.com/golangci/go-misc", sum = "h1:9kfjN3AdxcbsZBf8NjltjWihK2QfBBBZuv91cMFfDHw=", version = "v0.0.0-20180628070357-927a3d87b613", ) go_repository( name = "com_github_golangci_goconst", build_file_proto_mode = "disable", importpath = "github.com/golangci/goconst", sum = "h1:pe9JHs3cHHDQgOFXJJdYkK6fLz2PWyYtP4hthoCMvs8=", version = "v0.0.0-20180610141641-041c5f2b40f3", ) go_repository( name = "com_github_golangci_gocyclo", build_file_proto_mode = "disable", importpath = "github.com/golangci/gocyclo", sum = "h1:pXTK/gkVNs7Zyy7WKgLXmpQ5bHTrq5GDsp8R9Qs67g0=", version = "v0.0.0-20180528144436-0a533e8fa43d", ) go_repository( name = "com_github_golangci_gofmt", build_file_proto_mode = "disable", importpath = "github.com/golangci/gofmt", sum = "h1:iR3fYXUjHCR97qWS8ch1y9zPNsgXThGwjKPrYfqMPks=", version = "v0.0.0-20190930125516-244bba706f1a", ) go_repository( name = "com_github_golangci_golangci_lint", build_file_proto_mode = "disable", importpath = "github.com/golangci/golangci-lint", sum = "h1:VYLx63qb+XJsHdZ27PMS2w5JZacN0XG8ffUwe7yQomo=", version = "v1.27.0", ) go_repository( name = "com_github_golangci_ineffassign", build_file_proto_mode = "disable", importpath = "github.com/golangci/ineffassign", sum = "h1:gLLhTLMk2/SutryVJ6D4VZCU3CUqr8YloG7FPIBWFpI=", version = "v0.0.0-20190609212857-42439a7714cc", ) go_repository( name = "com_github_golangci_lint_1", build_file_proto_mode = "disable", importpath = "github.com/golangci/lint-1", sum = "h1:MfyDlzVjl1hoaPzPD4Gpb/QgoRfSBR0jdhwGyAWwMSA=", version = "v0.0.0-20191013205115-297bf364a8e0", ) go_repository( name = "com_github_golangci_maligned", build_file_proto_mode = "disable", importpath = "github.com/golangci/maligned", sum = "h1:kNY3/svz5T29MYHubXix4aDDuE3RWHkPvopM/EDv/MA=", version = "v0.0.0-20180506175553-b1d89398deca", ) go_repository( name = "com_github_golangci_misspell", build_file_proto_mode = "disable", importpath = "github.com/golangci/misspell", sum = "h1:pLzmVdl3VxTOncgzHcvLOKirdvcx/TydsClUQXTehjo=", version = "v0.3.5", ) go_repository( name = "com_github_golangci_prealloc", build_file_proto_mode = "disable", importpath = "github.com/golangci/prealloc", sum = "h1:leSNB7iYzLYSSx3J/s5sVf4Drkc68W2wm4Ixh/mr0us=", version = "v0.0.0-20180630174525-215b22d4de21", ) go_repository( name = "com_github_golangci_revgrep", build_file_proto_mode = "disable", importpath = "github.com/golangci/revgrep", sum = "h1:XQKc8IYQOeRwVs36tDrEmTgDgP88d5iEURwpmtiAlOM=", version = "v0.0.0-20180812185044-276a5c0a1039", ) go_repository( name = "com_github_golangci_unconvert", build_file_proto_mode = "disable", importpath = "github.com/golangci/unconvert", sum = "h1:zwtduBRr5SSWhqsYNgcuWO2kFlpdOZbP0+yRjmvPGys=", version = "v0.0.0-20180507085042-28b1c447d1f4", ) go_repository( name = "com_github_golangplus_bytes", build_file_proto_mode = "disable", importpath = "github.com/golangplus/bytes", sum = "h1:7xqw01UYS+KCI25bMrPxwNYkSns2Db1ziQPpVq99FpE=", version = "v0.0.0-20160111154220-45c989fe5450", ) go_repository( name = "com_github_golangplus_fmt", build_file_proto_mode = "disable", importpath = "github.com/golangplus/fmt", sum = "h1:f5gsjBiF9tRRVomCvrkGMMWI8W1f2OBFar2c5oakAP0=", version = "v0.0.0-20150411045040-2a5d6d7d2995", ) go_repository( name = "com_github_golangplus_testing", build_file_proto_mode = "disable", importpath = "github.com/golangplus/testing", sum = "h1:KhcknUwkWHKZPbFy2P7jH5LKJ3La+0ZeknkkmrSgqb0=", version = "v0.0.0-20180327235837-af21d9c3145e", ) go_repository( name = "com_github_gomodule_redigo", build_file_proto_mode = "disable", importpath = "github.com/gomodule/redigo", sum = "h1:nRAxCa+SVsyjSBrtZmG/cqb6VbTmuRzpg/PoTFlpumc=", version = "v1.8.5", ) go_repository( name = "com_github_google_btree", build_file_proto_mode = "disable", importpath = "github.com/google/btree", sum = "h1:0udJVsspx3VBr5FwtLhQQtuAsVc79tTq0ocGIPAU6qo=", version = "v1.0.0", ) go_repository( name = "com_github_google_go_cmp", build_file_proto_mode = "disable", importpath = "github.com/google/go-cmp", sum = "h1:Khx7svrCpmxxtHBq5j2mp/xVjsi8hQMfNLvJFAlrGgU=", version = "v0.5.5", ) go_repository( name = "com_github_google_go_containerregistry", build_file_proto_mode = "disable", importpath = "github.com/google/go-containerregistry", sum = "h1:AG8FSAfXglim2l5qSrqp5VK2Xl03PiBf25NiTGGamws=", version = "v0.1.1", ) go_repository( name = "com_github_google_go_github", build_file_proto_mode = "disable", importpath = "github.com/google/go-github", sum = "h1:N0LgJ1j65A7kfXrZnUDaYCs/Sf4rEjNlfyDHW9dolSY=", version = "v17.0.0+incompatible", ) go_repository( name = "com_github_google_go_github_v27", build_file_proto_mode = "disable", importpath = "github.com/google/go-github/v27", sum = "h1:oiOZuBmGHvrGM1X9uNUAUlLgp5r1UUO/M/KnbHnLRlQ=", version = "v27.0.6", ) go_repository( name = "com_github_google_go_github_v28", build_file_proto_mode = "disable", importpath = "github.com/google/go-github/v28", sum = "h1:kORf5ekX5qwXO2mGzXXOjMe/g6ap8ahVe0sBEulhSxo=", version = "v28.1.1", ) go_repository( name = "com_github_google_go_github_v29", build_file_proto_mode = "disable", importpath = "github.com/google/go-github/v29", sum = "h1:IktKCTwU//aFHnpA+2SLIi7Oo9uhAzgsdZNbcAqhgdc=", version = "v29.0.3", ) go_repository( name = "com_github_google_go_github_v32", build_file_proto_mode = "disable", importpath = "github.com/google/go-github/v32", sum = "h1:q74KVb22spUq0U5HqZ9VCYqQz8YRuOtL/39ZnfwO+NM=", version = "v32.0.0", ) go_repository( name = "com_github_google_go_licenses", build_file_proto_mode = "disable", importpath = "github.com/google/go-licenses", sum = "h1:eVR9gT5gBPTHXeyGAcA8OF/SKNUFFg+a0BJqfx4z5eE=", version = "v0.0.0-20200227160636-0fa8c766a591", ) go_repository( name = "com_github_google_go_querystring", build_file_proto_mode = "disable", importpath = "github.com/google/go-querystring", sum = "h1:AnCroh3fv4ZBgVIf1Iwtovgjaw/GiKJo8M8yD/fhyJ8=", version = "v1.1.0", ) go_repository( name = "com_github_google_go_replayers_grpcreplay", build_file_proto_mode = "disable", importpath = "github.com/google/go-replayers/grpcreplay", sum = "h1:eNb1y9rZFmY4ax45uEEECSa8fsxGRU+8Bil52ASAwic=", version = "v0.1.0", ) go_repository( name = "com_github_google_go_replayers_httpreplay", build_file_proto_mode = "disable", importpath = "github.com/google/go-replayers/httpreplay", sum = "h1:AX7FUb4BjrrzNvblr/OlgwrmFiep6soj5K2QSDW7BGk=", version = "v0.1.0", ) go_repository( name = "com_github_google_gofuzz", build_file_proto_mode = "disable_global", importpath = "github.com/google/gofuzz", sum = "h1:VcIYpAGBae3Z6BVncE0OnTE/ZjlDXqtYhOZky88neLM=", version = "v1.2.1-0.20210504230335-f78f29fc09ea", ) go_repository( name = "com_github_google_licenseclassifier", build_file_proto_mode = "disable", importpath = "github.com/google/licenseclassifier", sum = "h1:OggOMmdI0JLwg1FkOKH9S7fVHF0oEm8PX6S8kAdpOps=", version = "v0.0.0-20200402202327-879cb1424de0", ) go_repository( name = "com_github_google_mako", build_file_proto_mode = "disable", importpath = "github.com/google/mako", sum = "h1:/o5e44nTD/QEEiWPGSFT3bSqcq3Qg7q27N9bv4gKh5M=", version = "v0.0.0-20190821191249-122f8dcef9e3", ) go_repository( name = "com_github_google_martian", build_file_proto_mode = "disable", importpath = "github.com/google/martian", sum = "h1:xmapqc1AyLoB+ddYT6r04bD9lIjlOqGaREovi0SzFaE=", version = "v2.1.1-0.20190517191504-25dcb96d9e51+incompatible", ) go_repository( name = "com_github_google_martian_v3", build_file_proto_mode = "disable", importpath = "github.com/google/martian/v3", sum = "h1:pMen7vLs8nvgEYhywH3KDWJIJTeEr2ULsVWHWYHQyBs=", version = "v3.0.0", ) go_repository( name = "com_github_google_pprof", build_file_proto_mode = "disable", importpath = "github.com/google/pprof", sum = "h1:k+KkMRk8mGOu1xG38StS7dQ+Z6oW1i9n3dgrAVU9Q/E=", version = "v0.0.0-20200905233945-acf8798be1f7", ) go_repository( name = "com_github_google_renameio", build_file_proto_mode = "disable", importpath = "github.com/google/renameio", sum = "h1:GOZbcHa3HfsPKPlmyPyN2KEohoMXOhdMbHrvbpl2QaA=", version = "v0.1.0", ) go_repository( name = "com_github_google_rpmpack", build_file_proto_mode = "disable", importpath = "github.com/google/rpmpack", sum = "h1:BW6OvS3kpT5UEPbCZ+KyX/OB4Ks9/MNMhWjqPPkZxsE=", version = "v0.0.0-20191226140753-aa36bfddb3a0", ) go_repository( name = "com_github_google_subcommands", build_file_proto_mode = "disable", importpath = "github.com/google/subcommands", sum = "h1:/eqq+otEXm5vhfBrbREPCSVQbvofip6kIz+mX5TUH7k=", version = "v1.0.1", ) go_repository( name = "com_github_google_uuid", build_file_proto_mode = "disable", importpath = "github.com/google/uuid", sum = "h1:qJYtXnJRWmpe7m/3XlyhrsLrEURqHRM2kxzoxXqyUDs=", version = "v1.2.0", ) go_repository( name = "com_github_google_wire", build_file_proto_mode = "disable", importpath = "github.com/google/wire", sum = "h1:kXcsA/rIGzJImVqPdhfnr6q0xsS9gU0515q1EPpJ9fE=", version = "v0.4.0", ) go_repository( name = "com_github_googleapis_gax_go", build_file_proto_mode = "disable", importpath = "github.com/googleapis/gax-go", sum = "h1:silFMLAnr330+NRuag/VjIGF7TLp/LBrV2CJKFLWEww=", version = "v2.0.2+incompatible", ) go_repository( name = "com_github_googleapis_gax_go_v2", build_file_proto_mode = "disable", importpath = "github.com/googleapis/gax-go/v2", sum = "h1:sjZBwGj9Jlw33ImPtvFviGYvseOtDM7hkSKB7+Tv3SM=", version = "v2.0.5", ) go_repository( name = "com_github_googleapis_gnostic", build_file_generation = "on", build_file_proto_mode = "disable", importpath = "github.com/googleapis/gnostic", replace = "github.com/googleapis/gnostic", sum = "h1:DLJCy1n/vrD4HPjOvYcT8aYQXpPIzoRZONaYwyycI+I=", version = "v0.4.1", ) go_repository( name = "com_github_googlecloudplatform_cloud_builders_gcs_fetcher", build_file_proto_mode = "disable", importpath = "github.com/GoogleCloudPlatform/cloud-builders/gcs-fetcher", sum = "h1:Pjo3SOZigEnIGevhFqcbFndnqyCH8WimcREd3hRM9vU=", version = "v0.0.0-20191203181535-308b93ad1f39", ) go_repository( name = "com_github_googlecloudplatform_cloudsql_proxy", build_file_proto_mode = "disable", importpath = "github.com/GoogleCloudPlatform/cloudsql-proxy", sum = "h1:sTOp2Ajiew5XIH92YSdwhYc+bgpUX5j5TKK/Ac8Saw8=", version = "v0.0.0-20191009163259-e802c2cb94ae", ) go_repository( name = "com_github_googlecloudplatform_k8s_cloud_provider", build_file_proto_mode = "disable", importpath = "github.com/GoogleCloudPlatform/k8s-cloud-provider", sum = "h1:N7lSsF+R7wSulUADi36SInSQA3RvfO/XclHQfedr0qk=", version = "v0.0.0-20190822182118-27a4ced34534", ) go_repository( name = "com_github_googlecloudplatform_testgrid", build_file_proto_mode = "disable", importpath = "github.com/GoogleCloudPlatform/testgrid", sum = "h1:qs3/BQpz3j3qsgnfjV8aVBfPopkGxp/TnWjjiboUVf8=", version = "v0.0.68", ) go_repository( name = "com_github_gookit_color", build_file_proto_mode = "disable", importpath = "github.com/gookit/color", sum = "h1:xOYBan3Fwlrqj1M1UN2TlHOCRiek3bGzWf/vPnJ1roE=", version = "v1.2.4", ) go_repository( name = "com_github_gophercloud_gophercloud", build_file_proto_mode = "disable", importpath = "github.com/gophercloud/gophercloud", sum = "h1:P/nh25+rzXouhytV2pUHBb65fnds26Ghl8/391+sT5o=", version = "v0.1.0", ) go_repository( name = "com_github_gopherjs_gopherjs", build_file_proto_mode = "disable", importpath = "github.com/gopherjs/gopherjs", sum = "h1:EGx4pi6eqNxGaHF6qqu48+N2wcFQ5qg5FXgOdqsJ5d8=", version = "v0.0.0-20181017120253-0766667cb4d1", ) go_repository( name = "com_github_goreleaser_goreleaser", build_file_proto_mode = "disable", importpath = "github.com/goreleaser/goreleaser", sum = "h1:Z+7XPrfGK11s/Sp+a06sx2FzGuCjTBdxN2ubpGvQbjY=", version = "v0.136.0", ) go_repository( name = "com_github_goreleaser_nfpm", build_file_proto_mode = "disable", importpath = "github.com/goreleaser/nfpm", sum = "h1:BPwIomC+e+yuDX9poJowzV7JFVcYA0+LwGSkbAPs2Hw=", version = "v1.3.0", ) go_repository( name = "com_github_gorilla_context", build_file_proto_mode = "disable", importpath = "github.com/gorilla/context", sum = "h1:AWwleXJkX/nhcU9bZSnZoi3h/qGYqQAGhq6zZe/aQW8=", version = "v1.1.1", ) go_repository( name = "com_github_gorilla_csrf", build_file_proto_mode = "disable", importpath = "github.com/gorilla/csrf", sum = "h1:QqQ/OWwuFp4jMKgBFAzJVW3FMULdyUW7JoM4pEWuqKg=", version = "v1.6.2", ) go_repository( name = "com_github_gorilla_handlers", build_file_proto_mode = "disable", importpath = "github.com/gorilla/handlers", sum = "h1:0QniY0USkHQ1RGCLfKxeNHK9bkDHGRYGNDFBCS+YARg=", version = "v1.4.2", ) go_repository( name = "com_github_gorilla_mux", build_file_proto_mode = "disable", importpath = "github.com/gorilla/mux", sum = "h1:i40aqfkR1h2SlN9hojwV5ZA91wcXFOvkdNIeFDP5koI=", version = "v1.8.0", ) go_repository( name = "com_github_gorilla_securecookie", build_file_proto_mode = "disable", importpath = "github.com/gorilla/securecookie", sum = "h1:miw7JPhV+b/lAHSXz4qd/nN9jRiAFV5FwjeKyCS8BvQ=", version = "v1.1.1", ) go_repository( name = "com_github_gorilla_sessions", build_file_proto_mode = "disable", importpath = "github.com/gorilla/sessions", sum = "h1:S7P+1Hm5V/AT9cjEcUD5uDaQSX0OE577aCXgoaKpYbQ=", version = "v1.2.0", ) go_repository( name = "com_github_gorilla_websocket", build_file_proto_mode = "disable", importpath = "github.com/gorilla/websocket", sum = "h1:+/TMaTYc4QFitKJxsQ7Yye35DkWvkdLcvGKqM+x0Ufc=", version = "v1.4.2", ) go_repository( name = "com_github_gostaticanalysis_analysisutil", build_file_proto_mode = "disable", importpath = "github.com/gostaticanalysis/analysisutil", sum = "h1:iwp+5/UAyzQSFgQ4uR2sni99sJ8Eo9DEacKWM5pekIg=", version = "v0.0.3", ) go_repository( name = "com_github_gosuri_uitable", build_file_proto_mode = "disable", importpath = "github.com/gosuri/uitable", sum = "h1:IG2xLKRvErL3uhY6e1BylFzG+aJiwQviDDTfOKeKTpY=", version = "v0.0.4", ) go_repository( name = "com_github_gotestyourself_gotestyourself", build_file_proto_mode = "disable", importpath = "github.com/gotestyourself/gotestyourself", sum = "h1:AQwinXlbQR2HvPjQZOmDhRqsv5mZf+Jb1RnSLxcqZcI=", version = "v2.2.0+incompatible", ) go_repository( name = "com_github_gregjones_httpcache", build_file_proto_mode = "disable", importpath = "github.com/gregjones/httpcache", sum = "h1:f8eY6cV/x1x+HLjOp4r72s/31/V2aTUtg5oKRRPf8/Q=", version = "v0.0.0-20190212212710-3befbb6ad0cc", ) go_repository( name = "com_github_grpc_ecosystem_go_grpc_middleware", build_file_proto_mode = "disable", importpath = "github.com/grpc-ecosystem/go-grpc-middleware", sum = "h1:z53tR0945TRRQO/fLEVPI6SMv7ZflF0TEaTAoU7tOzg=", version = "v1.0.1-0.20190118093823-f849b5445de4", ) go_repository( name = "com_github_grpc_ecosystem_go_grpc_prometheus", build_file_proto_mode = "disable", importpath = "github.com/grpc-ecosystem/go-grpc-prometheus", sum = "h1:Ovs26xHkKqVztRpIrF/92BcuyuQ/YW4NSIpoGtfXNho=", version = "v1.2.0", ) go_repository( name = "com_github_grpc_ecosystem_grpc_gateway", build_file_proto_mode = "disable", importpath = "github.com/grpc-ecosystem/grpc-gateway", sum = "h1:D0EVSTwQoQOyfY35QNSuPJA4jpZRtkoGYWQMB7XNg5o=", version = "v1.12.2", ) go_repository( name = "com_github_h2non_gock", build_file_proto_mode = "disable", importpath = "github.com/h2non/gock", sum = "h1:17gCehSo8ZOgEsFKpQgqHiR7VLyjxdAG3lkhVvO9QZU=", version = "v1.0.9", ) go_repository( name = "com_github_hashicorp_consul_api", build_file_proto_mode = "disable", importpath = "github.com/hashicorp/consul/api", sum = "h1:BNQPM9ytxj6jbjjdRPioQ94T6YXriSopn0i8COv6SRA=", version = "v1.1.0", ) go_repository( name = "com_github_hashicorp_consul_sdk", build_file_proto_mode = "disable", importpath = "github.com/hashicorp/consul/sdk", sum = "h1:LnuDWGNsoajlhGyHJvuWW6FVqRl8JOTPqS6CPTsYjhY=", version = "v0.1.1", ) go_repository( name = "com_github_hashicorp_errwrap", build_file_proto_mode = "disable", importpath = "github.com/hashicorp/errwrap", sum = "h1:hLrqtEDnRye3+sgx6z4qVLNuviH3MR5aQ0ykNJa/UYA=", version = "v1.0.0", ) go_repository( name = "com_github_hashicorp_go_cleanhttp", build_file_proto_mode = "disable", importpath = "github.com/hashicorp/go-cleanhttp", sum = "h1:dH3aiDG9Jvb5r5+bYHsikaOUIpcM0xvgMXVoDkXMzJM=", version = "v0.5.1", ) go_repository( name = "com_github_hashicorp_go_hclog", build_file_proto_mode = "disable", importpath = "github.com/hashicorp/go-hclog", sum = "h1:CG6TE5H9/JXsFWJCfoIVpKFIkFe6ysEuHirp4DxCsHI=", version = "v0.9.2", ) go_repository( name = "com_github_hashicorp_go_immutable_radix", build_file_proto_mode = "disable", importpath = "github.com/hashicorp/go-immutable-radix", sum = "h1:AKDB1HM5PWEA7i4nhcpwOrO2byshxBjXVn/J/3+z5/0=", version = "v1.0.0", ) go_repository( name = "com_github_hashicorp_go_msgpack", build_file_proto_mode = "disable", importpath = "github.com/hashicorp/go-msgpack", sum = "h1:zKjpN5BK/P5lMYrLmBHdBULWbJ0XpYR+7NGzqkZzoD4=", version = "v0.5.3", ) go_repository( name = "com_github_hashicorp_go_multierror", build_file_proto_mode = "disable", importpath = "github.com/hashicorp/go-multierror", sum = "h1:B9UzwGQJehnUY1yNrnwREHc3fGbC2xefo8g4TbElacI=", version = "v1.1.0", ) go_repository( name = "com_github_hashicorp_go_net", build_file_proto_mode = "disable", importpath = "github.com/hashicorp/go.net", sum = "h1:sNCoNyDEvN1xa+X0baata4RdcpKwcMS6DH+xwfqPgjw=", version = "v0.0.1", ) go_repository( name = "com_github_hashicorp_go_plugin", build_file_proto_mode = "disable", importpath = "github.com/hashicorp/go-plugin", sum = "h1:4OtAfUGbnKC6yS48p0CtMX2oFYtzFZVv6rok3cRWgnE=", version = "v1.0.1", ) go_repository( name = "com_github_hashicorp_go_retryablehttp", build_file_proto_mode = "disable", importpath = "github.com/hashicorp/go-retryablehttp", sum = "h1:HJunrbHTDDbBb/ay4kxa1n+dLmttUlnP3V9oNE4hmsM=", version = "v0.6.6", ) go_repository( name = "com_github_hashicorp_go_rootcerts", build_file_proto_mode = "disable", importpath = "github.com/hashicorp/go-rootcerts", sum = "h1:DMo4fmknnz0E0evoNYnV48RjWndOsmd6OW+09R3cEP8=", version = "v1.0.1", ) go_repository( name = "com_github_hashicorp_go_sockaddr", build_file_proto_mode = "disable", importpath = "github.com/hashicorp/go-sockaddr", sum = "h1:ztczhD1jLxIRjVejw8gFomI1BQZOe2WoVOu0SyteCQc=", version = "v1.0.2", ) go_repository( name = "com_github_hashicorp_go_syslog", build_file_proto_mode = "disable", importpath = "github.com/hashicorp/go-syslog", sum = "h1:KaodqZuhUoZereWVIYmpUgZysurB1kBLX2j0MwMrUAE=", version = "v1.0.0", ) go_repository( name = "com_github_hashicorp_go_uuid", build_file_proto_mode = "disable", importpath = "github.com/hashicorp/go-uuid", sum = "h1:fv1ep09latC32wFoVwnqcnKJGnMSdBanPczbHAYm1BE=", version = "v1.0.1", ) go_repository( name = "com_github_hashicorp_go_version", build_file_proto_mode = "disable", importpath = "github.com/hashicorp/go-version", sum = "h1:3vNe/fWF5CBgRIguda1meWhsZHy3m8gCJ5wx+dIzX/E=", version = "v1.2.0", ) go_repository( name = "com_github_hashicorp_golang_lru", build_file_proto_mode = "disable", importpath = "github.com/hashicorp/golang-lru", sum = "h1:YDjusn29QI/Das2iO9M0BHnIbxPeyuCHsjMW+lJfyTc=", version = "v0.5.4", ) go_repository( name = "com_github_hashicorp_hcl", build_file_proto_mode = "disable", importpath = "github.com/hashicorp/hcl", sum = "h1:0Anlzjpi4vEasTeNFn2mLJgTSwt0+6sfsiTG8qcWGx4=", version = "v1.0.0", ) go_repository( name = "com_github_hashicorp_logutils", build_file_proto_mode = "disable", importpath = "github.com/hashicorp/logutils", sum = "h1:dLEQVugN8vlakKOUE3ihGLTZJRB4j+M2cdTm/ORI65Y=", version = "v1.0.0", ) go_repository( name = "com_github_hashicorp_mdns", build_file_proto_mode = "disable", importpath = "github.com/hashicorp/mdns", sum = "h1:WhIgCr5a7AaVH6jPUwjtRuuE7/RDufnUvzIr48smyxs=", version = "v1.0.0", ) go_repository( name = "com_github_hashicorp_memberlist", build_file_proto_mode = "disable", importpath = "github.com/hashicorp/memberlist", sum = "h1:EmmoJme1matNzb+hMpDuR/0sbJSUisxyqBGG676r31M=", version = "v0.1.3", ) go_repository( name = "com_github_hashicorp_serf", build_file_proto_mode = "disable", importpath = "github.com/hashicorp/serf", sum = "h1:YZ7UKsJv+hKjqGVUUbtE3HNj79Eln2oQ75tniF6iPt0=", version = "v0.8.2", ) go_repository( name = "com_github_hashicorp_vault_api", build_file_proto_mode = "disable", importpath = "github.com/hashicorp/vault/api", sum = "h1:j08Or/wryXT4AcHj1oCbMd7IijXcKzYUGw59LGu9onU=", version = "v1.0.4", ) go_repository( name = "com_github_hashicorp_vault_sdk", build_file_proto_mode = "disable", importpath = "github.com/hashicorp/vault/sdk", sum = "h1:mOEPeOhT7jl0J4AMl1E705+BcmeRs1VmKNb9F0sMLy8=", version = "v0.1.13", ) go_repository( name = "com_github_hashicorp_yamux", build_file_proto_mode = "disable", importpath = "github.com/hashicorp/yamux", sum = "h1:kJCB4vdITiW1eC1vq2e6IsrXKrZit1bv/TDYFGMp4BQ=", version = "v0.0.0-20181012175058-2f1d1f20f75d", ) go_repository( name = "com_github_howeyc_gopass", build_file_proto_mode = "disable", importpath = "github.com/howeyc/gopass", sum = "h1:kQWxfPIHVLbgLzphqk3QUflDy9QdksZR4ygR807bpy0=", version = "v0.0.0-20170109162249-bf9dde6d0d2c", ) go_repository( name = "com_github_hpcloud_tail", build_file_proto_mode = "disable", importpath = "github.com/hpcloud/tail", sum = "h1:nfCOvKYfkgYP8hkirhJocXT2+zOD8yUNjXaWfTlyFKI=", version = "v1.0.0", ) go_repository( name = "com_github_huandu_xstrings", build_file_proto_mode = "disable", importpath = "github.com/huandu/xstrings", sum = "h1:yPeWdRnmynF7p+lLYz0H2tthW9lqhMJrQV/U7yy4wX0=", version = "v1.2.0", ) go_repository( name = "com_github_ianlancetaylor_demangle", build_file_proto_mode = "disable", importpath = "github.com/ianlancetaylor/demangle", sum = "h1:UDMh68UUwekSh5iP2OMhRRZJiiBccgV7axzUG8vi56c=", version = "v0.0.0-20181102032728-5e5cf60278f6", ) go_repository( name = "com_github_imdario_mergo", build_file_proto_mode = "disable", importpath = "github.com/imdario/mergo", sum = "h1:b6R2BslTbIEToALKP7LxUvijTsNI9TAe80pLWN2g/HU=", version = "v0.3.12", ) go_repository( name = "com_github_inconshreveable_mousetrap", build_file_proto_mode = "disable", importpath = "github.com/inconshreveable/mousetrap", sum = "h1:Z8tu5sraLXCXIcARxBp/8cbvlwVa7Z1NHg9XEKhtSvM=", version = "v1.0.0", ) go_repository( name = "com_github_influxdata_influxdb", build_file_proto_mode = "disable", importpath = "github.com/influxdata/influxdb", sum = "h1:AciJ2ei/llFRundm7CtqwF6B2aOds1A7QG3sMW8QiaQ=", version = "v0.0.0-20161215172503-049f9b42e9a5", ) go_repository( name = "com_github_influxdata_tdigest", build_file_proto_mode = "disable", importpath = "github.com/influxdata/tdigest", sum = "h1:MHTrDWmQpHq/hkq+7cw9oYAt2PqUw52TZazRA0N7PGE=", version = "v0.0.0-20181121200506-bf2b5ad3c0a9", ) go_repository( name = "com_github_jarcoal_httpmock", build_file_proto_mode = "disable", importpath = "github.com/jarcoal/httpmock", sum = "h1:cHtVEcTxRSX4J0je7mWPfc9BpDpqzXSJ5HbymZmyHck=", version = "v1.0.5", ) go_repository( name = "com_github_jbenet_go_context", build_file_proto_mode = "disable", importpath = "github.com/jbenet/go-context", sum = "h1:BQSFePA1RWJOlocH6Fxy8MmwDt+yVQYULKfN0RoTN8A=", version = "v0.0.0-20150711004518-d14ea06fba99", ) go_repository( name = "com_github_jcmturner_gofork", build_file_proto_mode = "disable", importpath = "github.com/jcmturner/gofork", sum = "h1:J7uCkflzTEhUZ64xqKnkDxq3kzc96ajM1Gli5ktUem8=", version = "v1.0.0", ) go_repository( name = "com_github_jenkins_x_go_scm", build_file_proto_mode = "disable", importpath = "github.com/jenkins-x/go-scm", sum = "h1:D7d1sDWUU+xocCNLQVoYKpMjVKnQvsPva+hPzruchbM=", version = "v1.5.117", ) go_repository( name = "com_github_jessevdk_go_flags", build_file_proto_mode = "disable", importpath = "github.com/jessevdk/go-flags", sum = "h1:1jKYvbxEjfUl0fmqTCOfonvskHHXMjBySTLW4y9LFvc=", version = "v1.5.0", ) go_repository( name = "com_github_jetstack_cert_manager", build_file_proto_mode = "disable", importpath = "github.com/jetstack/cert-manager", sum = "h1:gEhBV9I83m+kpQShDhNO4+J8O2qfNDjvAEL27pThGmg=", version = "v1.1.0", ) go_repository( name = "com_github_jingyugao_rowserrcheck", build_file_proto_mode = "disable", importpath = "github.com/jingyugao/rowserrcheck", sum = "h1:GmsqmapfzSJkm28dhRoHz2tLRbJmqhU86IPgBtN3mmk=", version = "v0.0.0-20191204022205-72ab7603b68a", ) go_repository( name = "com_github_jinzhu_gorm", build_file_proto_mode = "disable", importpath = "github.com/jinzhu/gorm", sum = "h1:Drgk1clyWT9t9ERbzHza6Mj/8FY/CqMyVzOiHviMo6Q=", version = "v1.9.12", ) go_repository( name = "com_github_jinzhu_inflection", build_file_proto_mode = "disable", importpath = "github.com/jinzhu/inflection", sum = "h1:K317FqzuhWc8YvSVlFMCCUb36O/S9MCKRDI7QkRKD/E=", version = "v1.0.0", ) go_repository( name = "com_github_jinzhu_now", build_file_proto_mode = "disable", importpath = "github.com/jinzhu/now", sum = "h1:g39TucaRWyV3dwDO++eEc6qf8TVIQ/Da48WmqjZ3i7E=", version = "v1.1.1", ) go_repository( name = "com_github_jirfag_go_printf_func_name", build_file_proto_mode = "disable", importpath = "github.com/jirfag/go-printf-func-name", sum = "h1:KA9BjwUk7KlCh6S9EAGWBt1oExIUv9WyNCiRz5amv48=", version = "v0.0.0-20200119135958-7558a9eaa5af", ) go_repository( name = "com_github_jmespath_go_jmespath", build_file_proto_mode = "disable", importpath = "github.com/jmespath/go-jmespath", sum = "h1:BEgLn5cpjn8UN1mAw4NjwDrS35OdebyEtFe+9YPoQUg=", version = "v0.4.0", ) go_repository( name = "com_github_jmespath_go_jmespath_internal_testify", build_file_proto_mode = "disable", importpath = "github.com/jmespath/go-jmespath/internal/testify", sum = "h1:shLQSRRSCCPj3f2gpwzGwWFoC7ycTf1rcQZHOlsJ6N8=", version = "v1.5.1", ) go_repository( name = "com_github_jmoiron_sqlx", build_file_proto_mode = "disable", importpath = "github.com/jmoiron/sqlx", sum = "h1:lrdPtrORjGv1HbbEvKWDUAy97mPpFm4B8hp77tcCUJY=", version = "v1.2.1-0.20190826204134-d7d95172beb5", ) go_repository( name = "com_github_joefitzgerald_rainbow_reporter", build_file_proto_mode = "disable", importpath = "github.com/joefitzgerald/rainbow-reporter", sum = "h1:AuMG652zjdzI0YCCnXAqATtRBpGXMcAnrajcaTrSeuo=", version = "v0.1.0", ) go_repository( name = "com_github_joho_godotenv", build_file_proto_mode = "disable", importpath = "github.com/joho/godotenv", sum = "h1:Zjp+RcGpHhGlrMbJzXTrZZPrWj+1vfm90La1wgB6Bhc=", version = "v1.3.0", ) go_repository( name = "com_github_jonboulle_clockwork", build_file_proto_mode = "disable", importpath = "github.com/jonboulle/clockwork", sum = "h1:VKV+ZcuP6l3yW9doeqz6ziZGgcynBVQO+obU0+0hcPo=", version = "v0.1.0", ) go_repository( name = "com_github_joshdk_go_junit", build_file_proto_mode = "disable", importpath = "github.com/joshdk/go-junit", sum = "h1:Bp5LAZasx/ev9wUmIIC74+MsXgwD99VjV1JmDVbpJm8=", version = "v0.0.0-20190428045703-ad7e11aa49ff", ) go_repository( name = "com_github_jpillora_backoff", build_file_proto_mode = "disable", importpath = "github.com/jpillora/backoff", sum = "h1:uvFg412JmmHBHw7iwprIxkPMI+sGQ4kzOWsMeHnm2EA=", version = "v1.0.0", ) go_repository( name = "com_github_json_iterator_go", build_file_proto_mode = "disable", importpath = "github.com/json-iterator/go", sum = "h1:uVUAXhF2To8cbw/3xN3pxj6kk7TYKs98NIrTqPlMWAQ=", version = "v1.1.11", ) go_repository( name = "com_github_jstemmer_go_junit_report", build_file_proto_mode = "disable", importpath = "github.com/jstemmer/go-junit-report", sum = "h1:6QPYqodiu3GuPL+7mfx+NwDdp2eTkp9IfEUpgAwUN0o=", version = "v0.9.1", ) go_repository( name = "com_github_jtolds_gls", build_file_proto_mode = "disable", importpath = "github.com/jtolds/gls", sum = "h1:xdiiI2gbIgH/gLH7ADydsJ1uDOEzR8yvV7C0MuV77Wo=", version = "v4.20.0+incompatible", ) go_repository( name = "com_github_julienschmidt_httprouter", build_file_proto_mode = "disable", importpath = "github.com/julienschmidt/httprouter", sum = "h1:U0609e9tgbseu3rBINet9P48AI/D3oJs4dN7jwJOQ1U=", version = "v1.3.0", ) go_repository( name = "com_github_kballard_go_shellquote", build_file_proto_mode = "disable", importpath = "github.com/kballard/go-shellquote", sum = "h1:Z9n2FFNUXsshfwJMBgNA0RU6/i7WVaAegv3PtuIHPMs=", version = "v0.0.0-20180428030007-95032a82bc51", ) go_repository( name = "com_github_kelseyhightower_envconfig", build_file_proto_mode = "disable", importpath = "github.com/kelseyhightower/envconfig", sum = "h1:Im6hONhd3pLkfDFsbRgu68RDNkGF1r3dvMUtDTo2cv8=", version = "v1.4.0", ) go_repository( name = "com_github_kevinburke_ssh_config", build_file_proto_mode = "disable", importpath = "github.com/kevinburke/ssh_config", sum = "h1:DowS9hvgyYSX4TO5NpyC606/Z4SxnNYbT+WX27or6Ck=", version = "v0.0.0-20201106050909-4977a11b4351", ) go_repository( name = "com_github_kisielk_errcheck", build_file_proto_mode = "disable", importpath = "github.com/kisielk/errcheck", sum = "h1:e8esj/e4R+SAOwFwN+n3zr0nYeCyeweozKfO23MvHzY=", version = "v1.5.0", ) go_repository( name = "com_github_kisielk_gotool", build_file_proto_mode = "disable", importpath = "github.com/kisielk/gotool", sum = "h1:AV2c/EiW3KqPNT9ZKl07ehoAGi4C5/01Cfbblndcapg=", version = "v1.0.0", ) go_repository( name = "com_github_klauspost_compress", build_file_proto_mode = "disable", importpath = "github.com/klauspost/compress", sum = "h1:Znfn6hXZAHaLPNnlqUYRrBSReFHYybslgv4PTiyz6P0=", version = "v1.10.2", ) go_repository( name = "com_github_klauspost_cpuid", build_file_proto_mode = "disable", importpath = "github.com/klauspost/cpuid", sum = "h1:1xAgYebNnsb9LKCdLOvFWtAxGU/33mjJtyOVbmUa0Us=", version = "v1.2.2", ) go_repository( name = "com_github_klauspost_pgzip", build_file_proto_mode = "disable", importpath = "github.com/klauspost/pgzip", sum = "h1:oIPZROsWuPHpOdMVWLuJZXwgjhrW8r1yEX8UqMyeNHM=", version = "v1.2.1", ) go_repository( name = "com_github_knative_build", build_file_proto_mode = "disable", importpath = "github.com/knative/build", sum = "h1:o/VYWA3HKyZlNqdU2hDE5LHpanBe8gazgPKL97XJ6bo=", version = "v0.1.2", ) go_repository( name = "com_github_konsorten_go_windows_terminal_sequences", build_file_proto_mode = "disable", importpath = "github.com/konsorten/go-windows-terminal-sequences", sum = "h1:CE8S1cTafDpPvMhIxNJKvHsGVBgn1xWYf1NbHQhywc8=", version = "v1.0.3", ) go_repository( name = "com_github_kr_logfmt", build_file_proto_mode = "disable", importpath = "github.com/kr/logfmt", sum = "h1:T+h1c/A9Gawja4Y9mFVWj2vyii2bbUNDw3kt9VxK2EY=", version = "v0.0.0-20140226030751-b84e30acd515", ) go_repository( name = "com_github_kr_pretty", build_file_proto_mode = "disable", importpath = "github.com/kr/pretty", sum = "h1:Fmg33tUaq4/8ym9TJN1x7sLJnHVwhP33CNkpYV/7rwI=", version = "v0.2.1", ) go_repository( name = "com_github_kr_pty", build_file_proto_mode = "disable", importpath = "github.com/kr/pty", sum = "h1:AkaSdXYQOWeaO3neb8EM634ahkXXe3jYbVh/F9lq+GI=", version = "v1.1.8", ) go_repository( name = "com_github_kr_text", build_file_proto_mode = "disable", importpath = "github.com/kr/text", sum = "h1:5Nx0Ya0ZqY2ygV366QzturHI13Jq95ApcVaJBhpS+AY=", version = "v0.2.0", ) go_repository( name = "com_github_lib_pq", build_file_proto_mode = "disable", importpath = "github.com/lib/pq", sum = "h1:LXpIM/LZ5xGFhOpXAQUIMM1HdyqzVYM13zNdjCEEcA0=", version = "v1.2.0", ) go_repository( name = "com_github_liggitt_tabwriter", build_file_proto_mode = "disable", importpath = "github.com/liggitt/tabwriter", sum = "h1:9TO3cAIGXtEhnIaL+V+BEER86oLrvS+kWobKpbJuye0=", version = "v0.0.0-20181228230101-89fcab3d43de", ) go_repository( name = "com_github_lightstep_tracecontext_go", build_file_proto_mode = "disable", importpath = "github.com/lightstep/tracecontext.go", sum = "h1:+2b6iGRJe3hvV/yVXrd41yVEjxuFHxasJqDhkIjS4gk=", version = "v0.0.0-20181129014701-1757c391b1ac", ) go_repository( name = "com_github_lithammer_dedent", build_file_proto_mode = "disable", importpath = "github.com/lithammer/dedent", sum = "h1:VNzHMVCBNG1j0fh3OrsFRkVUwStdDArbgBWoPAffktY=", version = "v1.1.0", ) go_repository( name = "com_github_logrusorgru_aurora", build_file_proto_mode = "disable", importpath = "github.com/logrusorgru/aurora", sum = "h1:9MlwzLdW7QSDrhDjFlsEYmxpFyIoXmYRon3dt0io31k=", version = "v0.0.0-20181002194514-a7b3b318ed4e", ) go_repository( name = "com_github_lyft_protoc_gen_validate", build_file_proto_mode = "disable", importpath = "github.com/lyft/protoc-gen-validate", sum = "h1:KNt/RhmQTOLr7Aj8PsJ7mTronaFyx80mRTT9qF261dA=", version = "v0.0.13", ) go_repository( name = "com_github_magiconair_properties", build_file_proto_mode = "disable", importpath = "github.com/magiconair/properties", sum = "h1:ZC2Vc7/ZFkGmsVC9KvOjumD+G5lXy2RtTKyzRKO2BQ4=", version = "v1.8.1", ) go_repository( name = "com_github_mailru_easyjson", build_file_proto_mode = "disable", importpath = "github.com/mailru/easyjson", sum = "h1:jcoUdG1TzY/M/eM5BLFLP8DJeMximx5NQYSlLL9YeWc=", version = "v0.7.1-0.20191009090205-6c0755d89d1e", ) go_repository( name = "com_github_makenowjust_heredoc", build_file_proto_mode = "disable", importpath = "github.com/MakeNowJust/heredoc", sum = "h1:sjQovDkwrZp8u+gxLtPgKGjk5hCxuy2hrRejBTA9xFU=", version = "v0.0.0-20170808103936-bb23615498cd", ) go_repository( name = "com_github_maratori_testpackage", build_file_proto_mode = "disable", importpath = "github.com/maratori/testpackage", sum = "h1:QtJ5ZjqapShm0w5DosRjg0PRlSdAdlx+W6cCKoALdbQ=", version = "v1.0.1", ) go_repository( name = "com_github_markbates_inflect", build_file_proto_mode = "disable", importpath = "github.com/markbates/inflect", sum = "h1:5fh1gzTFhfae06u3hzHYO9xe3l3v3nW5Pwt3naLTP5g=", version = "v1.0.4", ) go_repository( name = "com_github_marstr_guid", build_file_proto_mode = "disable", importpath = "github.com/marstr/guid", sum = "h1:/M4H/1G4avsieL6BbUwCOBzulmoeKVP5ux/3mQNnbyI=", version = "v1.1.0", ) go_repository( name = "com_github_masterminds_goutils", build_file_proto_mode = "disable", importpath = "github.com/Masterminds/goutils", sum = "h1:zukEsf/1JZwCMgHiK3GZftabmxiCw4apj3a28RPBiVg=", version = "v1.1.0", ) go_repository( name = "com_github_masterminds_semver", build_file_proto_mode = "disable", importpath = "github.com/Masterminds/semver", sum = "h1:H65muMkzWKEuNDnfl9d70GUjFniHKHRbFPGBuZ3QEww=", version = "v1.5.0", ) go_repository( name = "com_github_masterminds_semver_v3", build_file_proto_mode = "disable", importpath = "github.com/Masterminds/semver/v3", sum = "h1:Y2lUDsFKVRSYGojLJ1yLxSXdMmMYTYls0rCvoqmMUQk=", version = "v3.1.0", ) go_repository( name = "com_github_masterminds_sprig_v3", build_file_proto_mode = "disable", importpath = "github.com/Masterminds/sprig/v3", sum = "h1:wz22D0CiSctrliXiI9ZO3HoNApweeRGftyDN+BQa3B8=", version = "v3.0.2", ) go_repository( name = "com_github_masterminds_vcs", build_file_proto_mode = "disable", importpath = "github.com/Masterminds/vcs", sum = "h1:NL3G1X7/7xduQtA2sJLpVpfHTNBALVNSjob6KEjPXNQ=", version = "v1.13.1", ) go_repository( name = "com_github_matoous_godox", build_file_proto_mode = "disable", importpath = "github.com/matoous/godox", sum = "h1:RHba4YImhrUVQDHUCe2BNSOz4tVy2yGyXhvYDvxGgeE=", version = "v0.0.0-20190911065817-5d6d842e92eb", ) go_repository( name = "com_github_mattbaird_jsonpatch", build_file_proto_mode = "disable", importpath = "github.com/mattbaird/jsonpatch", sum = "h1:+J2gw7Bw77w/fbK7wnNJJDKmw1IbWft2Ul5BzrG1Qm8=", version = "v0.0.0-20171005235357-81af80346b1a", ) go_repository( name = "com_github_mattn_go_colorable", build_file_proto_mode = "disable", importpath = "github.com/mattn/go-colorable", sum = "h1:6Su7aK7lXmJ/U79bYtBjLNaha4Fs1Rg9plHpcH+vvnE=", version = "v0.1.6", ) go_repository( name = "com_github_mattn_go_ieproxy", build_file_proto_mode = "disable", importpath = "github.com/mattn/go-ieproxy", sum = "h1:qiyop7gCflfhwCzGyeT0gro3sF9AIg9HU98JORTkqfI=", version = "v0.0.1", ) go_repository( name = "com_github_mattn_go_isatty", build_file_proto_mode = "disable", importpath = "github.com/mattn/go-isatty", sum = "h1:wuysRhFDzyxgEmMf5xjvJ2M9dZoWAXNNr5LSBS7uHXY=", version = "v0.0.12", ) go_repository( name = "com_github_mattn_go_runewidth", build_file_proto_mode = "disable", importpath = "github.com/mattn/go-runewidth", sum = "h1:3tS41NlGYSmhhe/8fhGRzc+z3AYCw1Fe1WAyLuujKs0=", version = "v0.0.8", ) go_repository( name = "com_github_mattn_go_shellwords", build_file_proto_mode = "disable", importpath = "github.com/mattn/go-shellwords", sum = "h1:Y7Xqm8piKOO3v10Thp7Z36h4FYFjt5xB//6XvOrs2Gw=", version = "v1.0.10", ) go_repository( name = "com_github_mattn_go_sqlite3", build_file_proto_mode = "disable", importpath = "github.com/mattn/go-sqlite3", sum = "h1:xQ15muvnzGBHpIpdrNi1DA5x0+TcBZzsIDwmw9uTHzw=", version = "v2.0.1+incompatible", ) go_repository( name = "com_github_mattn_go_zglob", build_file_proto_mode = "disable", importpath = "github.com/mattn/go-zglob", sum = "h1:0qT24o2wsZ8cOXQAERwBX6s+rPMs/bJTKxLVVtgfDXc=", version = "v0.0.2", ) go_repository( name = "com_github_mattn_goveralls", build_file_proto_mode = "disable", importpath = "github.com/mattn/goveralls", sum = "h1:7eJB6EqsPhRVxvwEXGnqdO2sJI0PTsrWoTMXEk9/OQc=", version = "v0.0.2", ) go_repository( name = "com_github_matttproud_golang_protobuf_extensions", build_file_proto_mode = "disable", importpath = "github.com/matttproud/golang_protobuf_extensions", sum = "h1:I0XW9+e1XWDxdcEniV4rQAIOPUGDq67JSCiRCgGCZLI=", version = "v1.0.2-0.20181231171920-c182affec369", ) go_repository( name = "com_github_maxbrunsfeld_counterfeiter_v6", build_file_proto_mode = "disable", importpath = "github.com/maxbrunsfeld/counterfeiter/v6", sum = "h1:g+4J5sZg6osfvEfkRZxJ1em0VT95/UOZgi/l7zi1/oE=", version = "v6.2.2", ) go_repository( name = "com_github_mgutz_ansi", build_file_proto_mode = "disable", importpath = "github.com/mgutz/ansi", sum = "h1:j7+1HpAFS1zy5+Q4qx1fWh90gTKwiN4QCGoY9TWyyO4=", version = "v0.0.0-20170206155736-9520e82c474b", ) go_repository( name = "com_github_mholt_archiver_v3", build_file_proto_mode = "disable", importpath = "github.com/mholt/archiver/v3", sum = "h1:vWjhY8SQp5yzM9P6OJ/eZEkmi3UAbRrxCq48MxjAzig=", version = "v3.3.0", ) go_repository( name = "com_github_microsoft_go_winio", build_file_proto_mode = "disable", importpath = "github.com/Microsoft/go-winio", sum = "h1:FtSW/jqD+l4ba5iPBj9CODVtgfYAD8w2wS923g/cFDk=", version = "v0.4.16", ) go_repository( name = "com_github_microsoft_hcsshim", build_file_proto_mode = "disable", importpath = "github.com/Microsoft/hcsshim", sum = "h1:ptnOoufxGSzauVTsdE+wMYnCWA301PdoN4xg5oRdZpg=", version = "v0.8.7", ) go_repository( name = "com_github_miekg_dns", build_file_proto_mode = "disable", importpath = "github.com/miekg/dns", sum = "h1:sJFOl9BgwbYAWOGEwr61FU28pqsBNdpRBnhGXtO06Oo=", version = "v1.1.31", ) go_repository( name = "com_github_mitchellh_cli", build_file_proto_mode = "disable", importpath = "github.com/mitchellh/cli", sum = "h1:iGBIsUe3+HZ/AD/Vd7DErOt5sU9fa8Uj7A2s1aggv1Y=", version = "v1.0.0", ) go_repository( name = "com_github_mitchellh_copystructure", build_file_proto_mode = "disable", importpath = "github.com/mitchellh/copystructure", sum = "h1:Laisrj+bAB6b/yJwB5Bt3ITZhGJdqmxquMKeZ+mmkFQ=", version = "v1.0.0", ) go_repository( name = "com_github_mitchellh_go_homedir", build_file_proto_mode = "disable", importpath = "github.com/mitchellh/go-homedir", sum = "h1:lukF9ziXFxDFPkA1vsr5zpc1XuPDn/wFntq5mG+4E0Y=", version = "v1.1.0", ) go_repository( name = "com_github_mitchellh_go_ps", build_file_proto_mode = "disable", importpath = "github.com/mitchellh/go-ps", sum = "h1:9+ke9YJ9KGWw5ANXK6ozjoK47uI3uNbXv4YVINBnGm8=", version = "v0.0.0-20190716172923-621e5597135b", ) go_repository( name = "com_github_mitchellh_go_testing_interface", build_file_proto_mode = "disable", importpath = "github.com/mitchellh/go-testing-interface", sum = "h1:fzU/JVNcaqHQEcVFAKeR41fkiLdIPrefOvVG1VZ96U0=", version = "v1.0.0", ) go_repository( name = "com_github_mitchellh_go_wordwrap", build_file_proto_mode = "disable", importpath = "github.com/mitchellh/go-wordwrap", sum = "h1:6GlHJ/LTGMrIJbwgdqdl2eEH8o+Exx/0m8ir9Gns0u4=", version = "v1.0.0", ) go_repository( name = "com_github_mitchellh_gox", build_file_proto_mode = "disable", importpath = "github.com/mitchellh/gox", sum = "h1:lfGJxY7ToLJQjHHwi0EX6uYBdK78egf954SQl13PQJc=", version = "v0.4.0", ) go_repository( name = "com_github_mitchellh_iochan", build_file_proto_mode = "disable", importpath = "github.com/mitchellh/iochan", sum = "h1:C+X3KsSTLFVBr/tK1eYN/vs4rJcvsiLU338UhYPJWeY=", version = "v1.0.0", ) go_repository( name = "com_github_mitchellh_ioprogress", build_file_proto_mode = "disable", importpath = "github.com/mitchellh/ioprogress", sum = "h1:Qa6dnn8DlasdXRnacluu8HzPts0S1I9zvvUPDbBnXFI=", version = "v0.0.0-20180201004757-6a23b12fa88e", ) go_repository( name = "com_github_mitchellh_mapstructure", build_file_proto_mode = "disable", importpath = "github.com/mitchellh/mapstructure", sum = "h1:cCBH2gTD2K0OtLlv/Y5H01VQCqmlDxz30kS5Y5bqfLA=", version = "v1.3.1", ) go_repository( name = "com_github_mitchellh_osext", build_file_proto_mode = "disable", importpath = "github.com/mitchellh/osext", sum = "h1:2+myh5ml7lgEU/51gbeLHfKGNfgEQQIWrlbdaOsidbQ=", version = "v0.0.0-20151018003038-5e2d6d41470f", ) go_repository( name = "com_github_mitchellh_reflectwalk", build_file_proto_mode = "disable", importpath = "github.com/mitchellh/reflectwalk", sum = "h1:9D+8oIskB4VJBN5SFlmc27fSlIBZaov1Wpk/IfikLNY=", version = "v1.0.0", ) go_repository( name = "com_github_moby_spdystream", build_file_proto_mode = "disable", importpath = "github.com/moby/spdystream", sum = "h1:cjW1zVyyoiM0T7b6UoySUFqzXMoqRckQtXwGPiBhOM8=", version = "v0.2.0", ) go_repository( name = "com_github_moby_term", build_file_proto_mode = "disable", importpath = "github.com/moby/term", sum = "h1:rzf0wL0CHVc8CEsgyygG0Mn9CNCCPZqOPaz8RiiHYQk=", version = "v0.0.0-20201216013528-df9cb8a40635", ) go_repository( name = "com_github_modern_go_concurrent", build_file_proto_mode = "disable", importpath = "github.com/modern-go/concurrent", sum = "h1:TRLaZ9cD/w8PVh93nsPXa1VrQ6jlwL5oN8l14QlcNfg=", version = "v0.0.0-20180306012644-bacd9c7ef1dd", ) go_repository( name = "com_github_modern_go_reflect2", build_file_proto_mode = "disable", importpath = "github.com/modern-go/reflect2", sum = "h1:9f412s+6RmYXLWZSEzVVgPGK7C2PphHj5RJrvfx9AWI=", version = "v1.0.1", ) go_repository( name = "com_github_mohae_deepcopy", build_file_proto_mode = "disable", importpath = "github.com/mohae/deepcopy", sum = "h1:RWengNIwukTxcDr9M+97sNutRR1RKhG96O6jWumTTnw=", version = "v0.0.0-20170929034955-c48cc78d4826", ) go_repository( name = "com_github_morikuni_aec", build_file_proto_mode = "disable", importpath = "github.com/morikuni/aec", sum = "h1:nP9CBfwrvYnBRgY6qfDQkygYDmYwOilePFkwzv4dU8A=", version = "v1.0.0", ) go_repository( name = "com_github_mozilla_tls_observatory", build_file_proto_mode = "disable", importpath = "github.com/mozilla/tls-observatory", sum = "h1:1xJ+Xi9lYWLaaP4yB67ah0+548CD3110mCPWhVVjFkI=", version = "v0.0.0-20200317151703-4fa42e1c2dee", ) go_repository( name = "com_github_munnerz_crd_schema_fuzz", build_file_proto_mode = "disable", importpath = "github.com/munnerz/crd-schema-fuzz", sum = "h1:8erI9yzEnOGw9K5O+a8zZdoo8N/OwrFi7c7SjBtkHAs=", version = "v1.0.0", ) go_repository( name = "com_github_munnerz_goautoneg", build_file_proto_mode = "disable", importpath = "github.com/munnerz/goautoneg", sum = "h1:C3w9PqII01/Oq1c1nUAm88MOHcQC9l5mIlSMApZMrHA=", version = "v0.0.0-20191010083416-a7dc8b61c822", ) go_repository( name = "com_github_mwitkow_go_conntrack", build_file_proto_mode = "disable", importpath = "github.com/mwitkow/go-conntrack", sum = "h1:KUppIJq7/+SVif2QVs3tOP0zanoHgBEVAwHxUSIzRqU=", version = "v0.0.0-20190716064945-2f068394615f", ) go_repository( name = "com_github_mxk_go_flowrate", build_file_proto_mode = "disable", importpath = "github.com/mxk/go-flowrate", sum = "h1:y5//uYreIhSUg3J1GEMiLbxo1LJaP8RfCpH6pymGZus=", version = "v0.0.0-20140419014527-cca7078d478f", ) go_repository( name = "com_github_nakabonne_nestif", build_file_proto_mode = "disable", importpath = "github.com/nakabonne/nestif", sum = "h1:+yOViDGhg8ygGrmII72nV9B/zGxY188TYpfolntsaPw=", version = "v0.3.0", ) go_repository( name = "com_github_natefinch_lumberjack", build_file_proto_mode = "disable", importpath = "github.com/natefinch/lumberjack", sum = "h1:4QJd3OLAMgj7ph+yZTuX13Ld4UpgHp07nNdFX7mqFfM=", version = "v2.0.0+incompatible", ) go_repository( name = "com_github_nats_io_gnatsd", build_file_proto_mode = "disable", importpath = "github.com/nats-io/gnatsd", sum = "h1:RconcfDeWpKCD6QIIwiVFcvForlXpWeJP7i5/lDLy44=", version = "v1.4.1", ) go_repository( name = "com_github_nats_io_go_nats", build_file_proto_mode = "disable", importpath = "github.com/nats-io/go-nats", sum = "h1:oQOfHcLr8hb43QG8yeVyY2jtarIaTjOv41CGdF3tTvQ=", version = "v1.7.0", ) go_repository( name = "com_github_nats_io_jwt", build_file_proto_mode = "disable", importpath = "github.com/nats-io/jwt", sum = "h1:+RB5hMpXUUA2dfxuhBTEkMOrYmM+gKIZYS1KjSostMI=", version = "v0.3.2", ) go_repository( name = "com_github_nats_io_nats_go", build_file_proto_mode = "disable", importpath = "github.com/nats-io/nats.go", sum = "h1:ik3HbLhZ0YABLto7iX80pZLPw/6dx3T+++MZJwLnMrQ=", version = "v1.9.1", ) go_repository( name = "com_github_nats_io_nats_server_v2", build_file_proto_mode = "disable", importpath = "github.com/nats-io/nats-server/v2", sum = "h1:i2Ly0B+1+rzNZHHWtD4ZwKi+OU5l+uQo1iDHZ2PmiIc=", version = "v2.1.2", ) go_repository( name = "com_github_nats_io_nkeys", build_file_proto_mode = "disable", importpath = "github.com/nats-io/nkeys", sum = "h1:6JrEfig+HzTH85yxzhSVbjHRJv9cn0p6n3IngIcM5/k=", version = "v0.1.3", ) go_repository( name = "com_github_nats_io_nuid", build_file_proto_mode = "disable", importpath = "github.com/nats-io/nuid", sum = "h1:5iA8DT8V7q8WK2EScv2padNa/rTESc1KdnPw4TC2paw=", version = "v1.0.1", ) go_repository( name = "com_github_nbio_st", build_file_proto_mode = "disable", importpath = "github.com/nbio/st", sum = "h1:W6apQkHrMkS0Muv8G/TipAy/FJl/rCYT0+EuS8+Z0z4=", version = "v0.0.0-20140626010706-e9e8d9816f32", ) go_repository( name = "com_github_nbutton23_zxcvbn_go", build_file_proto_mode = "disable", importpath = "github.com/nbutton23/zxcvbn-go", sum = "h1:AREM5mwr4u1ORQBMvzfzBgpsctsbQikCVpvC+tX285E=", version = "v0.0.0-20180912185939-ae427f1e4c1d", ) go_repository( name = "com_github_ncw_swift", build_file_proto_mode = "disable", importpath = "github.com/ncw/swift", sum = "h1:4DQRPj35Y41WogBxyhOXlrI37nzGlyEcsforeudyYPQ=", version = "v1.0.47", ) go_repository( name = "com_github_niemeyer_pretty", build_file_proto_mode = "disable", importpath = "github.com/niemeyer/pretty", sum = "h1:fD57ERR4JtEqsWbfPhv4DMiApHyliiK5xCTNVSPiaAs=", version = "v0.0.0-20200227124842-a10e7caefd8e", ) go_repository( name = "com_github_nwaples_rardecode", build_file_proto_mode = "disable", importpath = "github.com/nwaples/rardecode", sum = "h1:r7vGuS5akxOnR4JQSkko62RJ1ReCMXxQRPtxsiFMBOs=", version = "v1.0.0", ) go_repository( name = "com_github_nxadm_tail", build_file_proto_mode = "disable", importpath = "github.com/nxadm/tail", sum = "h1:nPr65rt6Y5JFSKQO7qToXr7pePgD6Gwiw05lkbyAQTE=", version = "v1.4.8", ) go_repository( name = "com_github_nytimes_gziphandler", build_file_proto_mode = "disable", importpath = "github.com/NYTimes/gziphandler", sum = "h1:ZUDjpQae29j0ryrS0u/B8HZfJBtBQHjqw2rQ2cqUQ3I=", version = "v1.1.1", ) go_repository( name = "com_github_octago_sflags", build_file_proto_mode = "disable", importpath = "github.com/octago/sflags", sum = "h1:XceYzkRXGAHa/lSFmKLcaxSrsh4MTuOMQdIGsUD0wlk=", version = "v0.2.0", ) go_repository( name = "com_github_oklog_run", build_file_proto_mode = "disable", importpath = "github.com/oklog/run", sum = "h1:Ru7dDtJNOyC66gQ5dQmaCa0qIsAUFY3sFpK1Xk8igrw=", version = "v1.0.0", ) go_repository( name = "com_github_oklog_ulid", build_file_proto_mode = "disable", importpath = "github.com/oklog/ulid", sum = "h1:EGfNDEx6MqHz8B3uNV6QAib1UR2Lm97sHi3ocA6ESJ4=", version = "v1.3.1", ) go_repository( name = "com_github_olekukonko_tablewriter", build_file_proto_mode = "disable", importpath = "github.com/olekukonko/tablewriter", sum = "h1:vHD/YYe1Wolo78koG299f7V/VAS08c6IpCLn+Ejf/w8=", version = "v0.0.4", ) go_repository( name = "com_github_oneofone_xxhash", build_file_proto_mode = "disable", importpath = "github.com/OneOfOne/xxhash", sum = "h1:KMrpdQIwFcEqXDklaen+P1axHaj9BSKzvpUUfnHldSE=", version = "v1.2.2", ) go_repository( name = "com_github_onsi_ginkgo", build_file_proto_mode = "disable", importpath = "github.com/onsi/ginkgo", sum = "h1:29JGrr5oVBm5ulCWet69zQkzWipVXIol6ygQUe/EzNc=", version = "v1.16.4", ) go_repository( name = "com_github_onsi_gomega", build_file_proto_mode = "disable", importpath = "github.com/onsi/gomega", sum = "h1:7lLHu94wT9Ij0o6EWWclhu0aOh32VxhkwEJvzuWPeak=", version = "v1.13.0", ) go_repository( name = "com_github_op_go_logging", build_file_proto_mode = "disable", importpath = "github.com/op/go-logging", sum = "h1:lDH9UUVJtmYCjyT0CI4q8xvlXPxeZ0gYCVvWbmPlp88=", version = "v0.0.0-20160315200505-970db520ece7", ) go_repository( name = "com_github_opencontainers_go_digest", build_file_proto_mode = "disable", importpath = "github.com/opencontainers/go-digest", sum = "h1:WzifXhOVOEOuFYOJAW6aQqW0TooG2iki3E3Ii+WN7gQ=", version = "v1.0.0-rc1", ) go_repository( name = "com_github_opencontainers_image_spec", build_file_proto_mode = "disable", importpath = "github.com/opencontainers/image-spec", sum = "h1:JMemWkRwHx4Zj+fVxWoMCFm/8sYGGrUVojFA6h/TRcI=", version = "v1.0.1", ) go_repository( name = "com_github_opencontainers_runc", build_file_proto_mode = "disable", importpath = "github.com/opencontainers/runc", sum = "h1:GlxAyO6x8rfZYN9Tt0Kti5a/cP41iuiO2yYT0IJGY8Y=", version = "v0.1.1", ) go_repository( name = "com_github_opencontainers_runtime_spec", build_file_proto_mode = "disable", importpath = "github.com/opencontainers/runtime-spec", sum = "h1:eNUVfm/RFLIi1G7flU5/ZRTHvd4kcVuzfRnL6OFlzCI=", version = "v0.1.2-0.20190507144316-5b71a03e2700", ) go_repository( name = "com_github_opencontainers_runtime_tools", build_file_proto_mode = "disable", importpath = "github.com/opencontainers/runtime-tools", sum = "h1:H7DMc6FAjgwZZi8BRqjrAAHWoqEr5e5L6pS4V0ezet4=", version = "v0.0.0-20181011054405-1d69bd0f9c39", ) go_repository( name = "com_github_openpeedeep_depguard", build_file_proto_mode = "disable", importpath = "github.com/OpenPeeDeeP/depguard", sum = "h1:VlW4R6jmBIv3/u1JNlawEvJMM4J+dPORPaZasQee8Us=", version = "v1.0.1", ) go_repository( name = "com_github_openzipkin_zipkin_go", build_file_proto_mode = "disable", importpath = "github.com/openzipkin/zipkin-go", sum = "h1:nY8Hti+WKaP0cRsSeQ026wU03QsM762XBeCXBb9NAWI=", version = "v0.2.2", ) go_repository( name = "com_github_otiai10_copy", build_file_proto_mode = "disable", importpath = "github.com/otiai10/copy", sum = "h1:DDNipYy6RkIkjMwy+AWzgKiNTyj2RUI9yEMeETEpVyc=", version = "v1.0.2", ) go_repository( name = "com_github_otiai10_curr", build_file_proto_mode = "disable", importpath = "github.com/otiai10/curr", sum = "h1:+OLn68pqasWca0z5ryit9KGfp3sUsW4Lqg32iRMJyzs=", version = "v0.0.0-20150429015615-9b4961190c95", ) go_repository( name = "com_github_otiai10_mint", build_file_proto_mode = "disable", importpath = "github.com/otiai10/mint", sum = "h1:Ady6MKVezQwHBkGzLFbrsywyp09Ah7rkmfjV3Bcr5uc=", version = "v1.3.0", ) go_repository( name = "com_github_pascaldekloe_goe", build_file_proto_mode = "disable", importpath = "github.com/pascaldekloe/goe", sum = "h1:cBOtyMzM9HTpWjXfbbunk26uA6nG3a8n06Wieeh0MwY=", version = "v0.1.0", ) go_repository( name = "com_github_pavel_v_chernykh_keystore_go", build_file_proto_mode = "disable", importpath = "github.com/pavel-v-chernykh/keystore-go", sum = "h1:Jd6xfriVlJ6hWPvYOE0Ni0QWcNTLRehfGPFxr3eSL80=", version = "v2.1.0+incompatible", ) go_repository( name = "com_github_pborman_uuid", build_file_proto_mode = "disable", importpath = "github.com/pborman/uuid", sum = "h1:J7Q5mO4ysT1dv8hyrUGHb9+ooztCXu1D8MY8DZYsu3g=", version = "v1.2.0", ) go_repository( name = "com_github_pelletier_go_buffruneio", build_file_proto_mode = "disable", importpath = "github.com/pelletier/go-buffruneio", sum = "h1:U4t4R6YkofJ5xHm3dJzuRpPZ0mr5MMCoAWooScCR7aA=", version = "v0.2.0", ) go_repository( name = "com_github_pelletier_go_toml", build_file_proto_mode = "disable", importpath = "github.com/pelletier/go-toml", sum = "h1:Keo9qb7iRJs2voHvunFtuuYFsbWeOBh8/P9v/kVMFtw=", version = "v1.8.0", ) go_repository( name = "com_github_peterbourgon_diskv", build_file_proto_mode = "disable", importpath = "github.com/peterbourgon/diskv", sum = "h1:UBdAOUP5p4RWqPBg048CAvpKN+vxiaj6gdUUzhl4XmI=", version = "v2.0.1+incompatible", ) go_repository( name = "com_github_phayes_checkstyle", build_file_proto_mode = "disable", importpath = "github.com/phayes/checkstyle", sum = "h1:CdDQnGF8Nq9ocOS/xlSptM1N3BbrA6/kmaep5ggwaIA=", version = "v0.0.0-20170904204023-bfd46e6a821d", ) go_repository( name = "com_github_phayes_freeport", build_file_proto_mode = "disable", importpath = "github.com/phayes/freeport", sum = "h1:JhzVVoYvbOACxoUmOs6V/G4D5nPVUW73rKvXxP4XUJc=", version = "v0.0.0-20180830031419-95f893ade6f2", ) go_repository( name = "com_github_pierrec_lz4", build_file_proto_mode = "disable", importpath = "github.com/pierrec/lz4", sum = "h1:6aCX4/YZ9v8q69hTyiR7dNLnTA3fgtKHVVW5BCd5Znw=", version = "v2.2.6+incompatible", ) go_repository( name = "com_github_pkg_errors", build_file_proto_mode = "disable", importpath = "github.com/pkg/errors", sum = "h1:FEBLx1zS214owpjy7qsBeixbURkuhQAwrK5UwLGTwt4=", version = "v0.9.1", ) go_repository( name = "com_github_pkg_profile", build_file_proto_mode = "disable", importpath = "github.com/pkg/profile", sum = "h1:F++O52m40owAmADcojzM+9gyjmMOY/T4oYJkgFDH8RE=", version = "v1.2.1", ) go_repository( name = "com_github_pmezard_go_difflib", build_file_proto_mode = "disable", importpath = "github.com/pmezard/go-difflib", sum = "h1:4DBwDE0NGyQoBHbLQYPwSUPoCMWR5BEzIk/f1lZbAQM=", version = "v1.0.0", ) go_repository( name = "com_github_posener_complete", build_file_proto_mode = "disable", importpath = "github.com/posener/complete", sum = "h1:ccV59UEOTzVDnDUEFdT95ZzHVZ+5+158q8+SJb2QV5w=", version = "v1.1.1", ) go_repository( name = "com_github_pquerna_cachecontrol", build_file_proto_mode = "disable", importpath = "github.com/pquerna/cachecontrol", sum = "h1:0XM1XL/OFFJjXsYXlG30spTkV/E9+gmd5GD1w2HE8xM=", version = "v0.0.0-20171018203845-0dec1b30a021", ) go_repository( name = "com_github_prometheus_client_golang", build_file_proto_mode = "disable", importpath = "github.com/prometheus/client_golang", sum = "h1:HNkLOAEQMIDv/K+04rukrLx6ch7msSRwf3/SASFAGtQ=", version = "v1.11.0", ) go_repository( name = "com_github_prometheus_client_model", build_file_proto_mode = "disable", importpath = "github.com/prometheus/client_model", sum = "h1:uq5h0d+GuxiXLJLNABMgp2qUWDPiLvgCzz2dUR+/W/M=", version = "v0.2.0", ) go_repository( name = "com_github_prometheus_common", build_file_proto_mode = "disable", importpath = "github.com/prometheus/common", sum = "h1:iMAkS2TDoNWnKM+Kopnx/8tnEStIfpYA0ur0xQzzhMQ=", version = "v0.26.0", ) go_repository( name = "com_github_prometheus_procfs", build_file_proto_mode = "disable", importpath = "github.com/prometheus/procfs", sum = "h1:mxy4L2jP6qMonqmq+aTtOx1ifVWUgG/TAmntgbh3xv4=", version = "v0.6.0", ) go_repository( name = "com_github_prometheus_tsdb", build_file_proto_mode = "disable", importpath = "github.com/prometheus/tsdb", sum = "h1:YZcsG11NqnK4czYLrWd9mpEuAJIHVQLwdrleYfszMAA=", version = "v0.7.1", ) go_repository( name = "com_github_puerkitobio_purell", build_file_proto_mode = "disable", importpath = "github.com/PuerkitoBio/purell", sum = "h1:WEQqlqaGbrPkxLJWfBwQmfEAE1Z7ONdDLqrN38tNFfI=", version = "v1.1.1", ) go_repository( name = "com_github_puerkitobio_urlesc", build_file_proto_mode = "disable", importpath = "github.com/PuerkitoBio/urlesc", sum = "h1:d+Bc7a5rLufV/sSk/8dngufqelfh6jnri85riMAaF/M=", version = "v0.0.0-20170810143723-de5bf2ad4578", ) go_repository( name = "com_github_quasilyte_go_consistent", build_file_proto_mode = "disable", importpath = "github.com/quasilyte/go-consistent", sum = "h1:JoUA0uz9U0FVFq5p4LjEq4C0VgQ0El320s3Ms0V4eww=", version = "v0.0.0-20190521200055-c6f3937de18c", ) go_repository( name = "com_github_quasilyte_go_ruleguard", build_file_proto_mode = "disable", importpath = "github.com/quasilyte/go-ruleguard", sum = "h1:DvnesvLtRPQOvaUbfXfh0tpMHg29by0H7F2U+QIkSu8=", version = "v0.1.2-0.20200318202121-b00d7a75d3d8", ) go_repository( name = "com_github_rcrowley_go_metrics", build_file_proto_mode = "disable", importpath = "github.com/rcrowley/go-metrics", sum = "h1:eUm8ma4+yPknhXtkYlWh3tMkE6gBjXZToDned9s2gbQ=", version = "v0.0.0-20190706150252-9beb055b7962", ) go_repository( name = "com_github_remyoudompheng_bigfft", build_file_proto_mode = "disable", importpath = "github.com/remyoudompheng/bigfft", sum = "h1:/NRJ5vAYoqz+7sG51ubIDHXeWO8DlTSrToPu6q11ziA=", version = "v0.0.0-20170806203942-52369c62f446", ) go_repository( name = "com_github_rogpeppe_fastuuid", build_file_proto_mode = "disable", importpath = "github.com/rogpeppe/fastuuid", sum = "h1:Ppwyp6VYCF1nvBTXL3trRso7mXMlRrw9ooo375wvi2s=", version = "v1.2.0", ) go_repository( name = "com_github_rogpeppe_go_internal", build_file_proto_mode = "disable", importpath = "github.com/rogpeppe/go-internal", sum = "h1:qLvObTrvO/XRCqmkKxUlOBc48bI3efyDuAZe25QiF0w=", version = "v1.5.2", ) go_repository( name = "com_github_rubiojr_go_vhd", build_file_proto_mode = "disable", importpath = "github.com/rubiojr/go-vhd", sum = "h1:ht7N4d/B7Ezf58nvMNVF3OlvDlz9pp+WHVcRNS0nink=", version = "v0.0.0-20160810183302-0bfd3b39853c", ) go_repository( name = "com_github_russross_blackfriday", build_file_proto_mode = "disable", importpath = "github.com/russross/blackfriday", sum = "h1:HyvC0ARfnZBqnXwABFeSZHpKvJHJJfPz81GNueLj0oo=", version = "v1.5.2", ) go_repository( name = "com_github_russross_blackfriday_v2", build_file_proto_mode = "disable", importpath = "github.com/russross/blackfriday/v2", sum = "h1:lPqVAte+HuHNfhJ/0LC98ESWRz8afy9tM/0RK8m9o+Q=", version = "v2.0.1", ) go_repository( name = "com_github_rwcarlsen_goexif", build_file_proto_mode = "disable", importpath = "github.com/rwcarlsen/goexif", sum = "h1:CmH9+J6ZSsIjUK3dcGsnCnO41eRBOnY12zwkn5qVwgc=", version = "v0.0.0-20190401172101-9e8deecbddbd", ) go_repository( name = "com_github_ryancurrah_gomodguard", build_file_proto_mode = "disable", importpath = "github.com/ryancurrah/gomodguard", sum = "h1:DWbye9KyMgytn8uYpuHkwf0RHqAYO6Ay/D0TbCpPtVU=", version = "v1.1.0", ) go_repository( name = "com_github_ryanuber_columnize", build_file_proto_mode = "disable", importpath = "github.com/ryanuber/columnize", sum = "h1:j1Wcmh8OrK4Q7GXY+V7SVSY8nUWQxHW5TkBe7YUl+2s=", version = "v2.1.0+incompatible", ) go_repository( name = "com_github_ryanuber_go_glob", build_file_proto_mode = "disable", importpath = "github.com/ryanuber/go-glob", sum = "h1:iQh3xXAumdQ+4Ufa5b25cRpC5TYKlno6hsv6Cb3pkBk=", version = "v1.0.0", ) go_repository( name = "com_github_sassoftware_go_rpmutils", build_file_proto_mode = "disable", importpath = "github.com/sassoftware/go-rpmutils", sum = "h1:+gCnWOZV8Z/8jehJ2CdqB47Z3S+SREmQcuXkRFLNsiI=", version = "v0.0.0-20190420191620-a8f1baeba37b", ) go_repository( name = "com_github_satori_go_uuid", build_file_proto_mode = "disable", importpath = "github.com/satori/go.uuid", sum = "h1:0uYX9dsZ2yD7q2RtLRtPSdGDWzjeM3TbMJP9utgA0ww=", version = "v1.2.0", ) go_repository( name = "com_github_sclevine_spec", build_file_proto_mode = "disable", importpath = "github.com/sclevine/spec", sum = "h1:1Jwdf9jSfDl9NVmt8ndHqbTZ7XCCPbh1jI3hkDBHVYA=", version = "v1.2.0", ) go_repository( name = "com_github_sean_seed", build_file_proto_mode = "disable", importpath = "github.com/sean-/seed", sum = "h1:nn5Wsu0esKSJiIVhscUtVbo7ada43DJhG55ua/hjS5I=", version = "v0.0.0-20170313163322-e2103e2c3529", ) go_repository( name = "com_github_securego_gosec", build_file_proto_mode = "disable", importpath = "github.com/securego/gosec", sum = "h1:rq2/kILQnPtq5oL4+IAjgVOjh5e2yj2aaCYi7squEvI=", version = "v0.0.0-20200401082031-e946c8c39989", ) go_repository( name = "com_github_securego_gosec_v2", build_file_proto_mode = "disable", importpath = "github.com/securego/gosec/v2", sum = "h1:y/9mCF2WPDbSDpL3QDWZD3HHGrSYw0QSHnCqTfs4JPE=", version = "v2.3.0", ) go_repository( name = "com_github_sergi_go_diff", build_file_proto_mode = "disable", importpath = "github.com/sergi/go-diff", sum = "h1:we8PVUC3FE2uYfodKH/nBHMSetSfHDR6scGdBi+erh0=", version = "v1.1.0", ) go_repository( name = "com_github_shirou_gopsutil", build_file_proto_mode = "disable", importpath = "github.com/shirou/gopsutil", sum = "h1:WokF3GuxBeL+n4Lk4Fa8v9mbdjlrl7bHuneF4N1bk2I=", version = "v0.0.0-20190901111213-e4ec7b275ada", ) go_repository( name = "com_github_shirou_w32", build_file_proto_mode = "disable", importpath = "github.com/shirou/w32", sum = "h1:udFKJ0aHUL60LboW/A+DfgoHVedieIzIXE8uylPue0U=", version = "v0.0.0-20160930032740-bb4de0191aa4", ) go_repository( name = "com_github_shopify_logrus_bugsnag", build_file_proto_mode = "disable", importpath = "github.com/Shopify/logrus-bugsnag", sum = "h1:UrqY+r/OJnIp5u0s1SbQ8dVfLCZJsnvazdBP5hS4iRs=", version = "v0.0.0-20171204204709-577dee27f20d", ) go_repository( name = "com_github_shopify_sarama", build_file_proto_mode = "disable", importpath = "github.com/Shopify/sarama", sum = "h1:XxJBCZEoWJtoWjf/xRbmGUpAmTZGnuuF0ON0EvxxBrs=", version = "v1.23.1", ) go_repository( name = "com_github_shopify_toxiproxy", build_file_proto_mode = "disable", importpath = "github.com/Shopify/toxiproxy", sum = "h1:TKdv8HiTLgE5wdJuEML90aBgNWsokNbMijUGhmcoBJc=", version = "v2.1.4+incompatible", ) go_repository( name = "com_github_shurcool_githubv4", build_file_proto_mode = "disable", importpath = "github.com/shurcooL/githubv4", sum = "h1:N5B+JgvM/DVYIxreItPJMM3yWrNO/GB2q4nESrtBisM=", version = "v0.0.0-20210725200734-83ba7b4c9228", ) go_repository( name = "com_github_shurcool_go", build_file_proto_mode = "disable", importpath = "github.com/shurcooL/go", sum = "h1:MZM7FHLqUHYI0Y/mQAt3d2aYa0SiNms/hFqC9qJYolM=", version = "v0.0.0-20180423040247-9e1955d9fb6e", ) go_repository( name = "com_github_shurcool_go_goon", build_file_proto_mode = "disable", importpath = "github.com/shurcooL/go-goon", sum = "h1:llrF3Fs4018ePo4+G/HV/uQUqEI1HMDjCeOf2V6puPc=", version = "v0.0.0-20170922171312-37c2f522c041", ) go_repository( name = "com_github_shurcool_graphql", build_file_proto_mode = "disable", importpath = "github.com/shurcooL/graphql", sum = "h1:tygelZueB1EtXkPI6mQ4o9DQ0+FKW41hTbunoXZCTqk=", version = "v0.0.0-20181231061246-d48a9a75455f", ) go_repository( name = "com_github_shurcool_sanitized_anchor_name", build_file_proto_mode = "disable", importpath = "github.com/shurcooL/sanitized_anchor_name", sum = "h1:PdmoCO6wvbs+7yrJyMORt4/BmY5IYyJwS/kOiWx8mHo=", version = "v1.0.0", ) go_repository( name = "com_github_sirupsen_logrus", build_file_proto_mode = "disable", importpath = "github.com/sirupsen/logrus", sum = "h1:dJKuHgqk1NNQlqoA6BTlM1Wf9DOH3NBjQyu0h9+AZZE=", version = "v1.8.1", ) go_repository( name = "com_github_smartystreets_assertions", build_file_proto_mode = "disable", importpath = "github.com/smartystreets/assertions", sum = "h1:42S6lae5dvLc7BrLu/0ugRtcFVjoJNMC/N3yZFZkDFs=", version = "v1.2.0", ) go_repository( name = "com_github_smartystreets_go_aws_auth", build_file_proto_mode = "disable", importpath = "github.com/smartystreets/go-aws-auth", sum = "h1:hp2CYQUINdZMHdvTdXtPOY2ainKl4IoMcpAXEf2xj3Q=", version = "v0.0.0-20180515143844-0c1422d1fdb9", ) go_repository( name = "com_github_smartystreets_goconvey", build_file_proto_mode = "disable", importpath = "github.com/smartystreets/goconvey", sum = "h1:fv0U8FUIMPNf1L9lnHLvLhgicrIVChEkdzIKYqbNC9s=", version = "v1.6.4", ) go_repository( name = "com_github_smartystreets_gunit", build_file_proto_mode = "disable", importpath = "github.com/smartystreets/gunit", sum = "h1:RyPDUFcJbvtXlhJPk7v+wnxZRY2EUokhEYl2EJOPToI=", version = "v1.0.0", ) go_repository( name = "com_github_soheilhy_cmux", build_file_proto_mode = "disable", importpath = "github.com/soheilhy/cmux", sum = "h1:0HKaf1o97UwFjHH9o5XsHUOF+tqmdA7KEzXLpiyaw0E=", version = "v0.1.4", ) go_repository( name = "com_github_sourcegraph_go_diff", build_file_proto_mode = "disable", importpath = "github.com/sourcegraph/go-diff", sum = "h1:lhIKJ2nXLZZ+AfbHpYxTn0pXpNTTui0DX7DO3xeb1Zs=", version = "v0.5.3", ) go_repository( name = "com_github_spaolacci_murmur3", build_file_proto_mode = "disable", importpath = "github.com/spaolacci/murmur3", sum = "h1:qLC7fQah7D6K1B0ujays3HV9gkFtllcxhzImRR7ArPQ=", version = "v0.0.0-20180118202830-f09979ecbc72", ) go_repository( name = "com_github_spf13_afero", build_file_proto_mode = "disable", importpath = "github.com/spf13/afero", sum = "h1:5jhuqJyZCZf2JRofRvN/nIFgIWNzPa3/Vz8mYylgbWc=", version = "v1.2.2", ) go_repository( name = "com_github_spf13_cast", build_file_proto_mode = "disable", importpath = "github.com/spf13/cast", sum = "h1:nFm6S0SMdyzrzcmThSipiEubIDy8WEXKNZ0UOgiRpng=", version = "v1.3.1", ) go_repository( name = "com_github_spf13_cobra", build_file_proto_mode = "disable", importpath = "github.com/spf13/cobra", sum = "h1:xghbfqPkxzxP3C/f3n5DdpAbdKLj4ZE4BWQI362l53M=", version = "v1.1.3", ) go_repository( name = "com_github_spf13_jwalterweatherman", build_file_proto_mode = "disable", importpath = "github.com/spf13/jwalterweatherman", sum = "h1:ue6voC5bR5F8YxI5S67j9i582FU4Qvo2bmqnqMYADFk=", version = "v1.1.0", ) go_repository( name = "com_github_spf13_pflag", build_file_proto_mode = "disable", importpath = "github.com/spf13/pflag", sum = "h1:iy+VFUOCP1a+8yFto/drg2CJ5u0yRoB7fZw3DKv/JXA=", version = "v1.0.5", ) go_repository( name = "com_github_spf13_viper", build_file_proto_mode = "disable", importpath = "github.com/spf13/viper", sum = "h1:pM5oEahlgWv/WnHXpgbKz7iLIxRf65tye2Ci+XFK5sk=", version = "v1.7.1", ) go_repository( name = "com_github_src_d_gcfg", build_file_proto_mode = "disable", importpath = "github.com/src-d/gcfg", sum = "h1:xXbNR5AlLSA315x2UO+fTSSAXCDf+Ar38/6oyGbDKQ4=", version = "v1.4.0", ) go_repository( name = "com_github_stackexchange_wmi", build_file_proto_mode = "disable", importpath = "github.com/StackExchange/wmi", sum = "h1:fLjPD/aNc3UIOA6tDi6QXUemppXK3P9BI7mr2hd6gx8=", version = "v0.0.0-20180116203802-5d049714c4a6", ) go_repository( name = "com_github_streadway_amqp", build_file_proto_mode = "disable", importpath = "github.com/streadway/amqp", sum = "h1:0ngsPmuP6XIjiFRNFYlvKwSr5zff2v+uPHaffZ6/M4k=", version = "v0.0.0-20190404075320-75d898a42a94", ) go_repository( name = "com_github_streadway_quantile", build_file_proto_mode = "disable", importpath = "github.com/streadway/quantile", sum = "h1:7z3LSn867ex6VSaahyKadf4WtSsJIgne6A1WLOAGM8A=", version = "v0.0.0-20150917103942-b0c588724d25", ) go_repository( name = "com_github_stretchr_objx", build_file_proto_mode = "disable", importpath = "github.com/stretchr/objx", sum = "h1:Hbg2NidpLE8veEBkEZTL3CvlkUIVzuU9jDplZO54c48=", version = "v0.2.0", ) go_repository( name = "com_github_stretchr_testify", build_file_proto_mode = "disable", importpath = "github.com/stretchr/testify", sum = "h1:nwc3DEeHmmLAfoZucVR881uASk0Mfjw8xYJ99tb5CcY=", version = "v1.7.0", ) go_repository( name = "com_github_subosito_gotenv", build_file_proto_mode = "disable", importpath = "github.com/subosito/gotenv", sum = "h1:Slr1R9HxAlEKefgq5jn9U+DnETlIUa6HfgEzj0g5d7s=", version = "v1.2.0", ) go_repository( name = "com_github_syndtr_gocapability", build_file_proto_mode = "disable", importpath = "github.com/syndtr/gocapability", sum = "h1:zLV6q4e8Jv9EHjNg/iHfzwDkCve6Ua5jCygptrtXHvI=", version = "v0.0.0-20170704070218-db04d3cc01c8", ) go_repository( name = "com_github_tdakkota_asciicheck", build_file_proto_mode = "disable", importpath = "github.com/tdakkota/asciicheck", sum = "h1:HxLVTlqcHhFAz3nWUcuvpH7WuOMv8LQoCWmruLfFH2U=", version = "v0.0.0-20200416200610-e657995f937b", ) go_repository( name = "com_github_tektoncd_pipeline", build_file_proto_mode = "disable", importpath = "github.com/tektoncd/pipeline", sum = "h1:hWdWj5bDjkSGYLlJS+u+Kh9ZktBJgs2JNUv/kP0LVOA=", version = "v0.13.1-0.20200625065359-44f22a067b75", ) go_repository( name = "com_github_tektoncd_plumbing", build_file_proto_mode = "disable", importpath = "github.com/tektoncd/plumbing", sum = "h1:crv70CBAJ2gZFSbf13aRVwdbjR2GYwTms/ZEok/SnFM=", version = "v0.0.0-20200430135134-e53521e1d887", ) go_repository( name = "com_github_tektoncd_plumbing_pipelinerun_logs", build_file_proto_mode = "disable", importpath = "github.com/tektoncd/plumbing/pipelinerun-logs", sum = "h1:9qeyrQsoPZbHOyOPt0OeB1TCYXfYb5swrxlFWzTIYYk=", version = "v0.0.0-20191206114338-712d544c2c21", ) go_repository( name = "com_github_tetafro_godot", build_file_proto_mode = "disable", importpath = "github.com/tetafro/godot", sum = "h1:Dib7un+rYJFUi8vN0Bk6EHheKy6fv6ZzFURHw75g6m8=", version = "v0.4.2", ) go_repository( name = "com_github_tidwall_pretty", build_file_proto_mode = "disable", importpath = "github.com/tidwall/pretty", sum = "h1:HsD+QiTn7sK6flMKIvNmpqz1qrpP3Ps6jOKIKMooyg4=", version = "v1.0.0", ) go_repository( name = "com_github_timakin_bodyclose", build_file_proto_mode = "disable", importpath = "github.com/timakin/bodyclose", sum = "h1:ig99OeTyDwQWhPe2iw9lwfQVF1KB3Q4fpP3X7/2VBG8=", version = "v0.0.0-20200424151742-cb6215831a94", ) go_repository( name = "com_github_tj_assert", build_file_proto_mode = "disable", importpath = "github.com/tj/assert", sum = "h1:Rw8kxzWo1mr6FSaYXjQELRe88y2KdfynXdnK72rdjtA=", version = "v0.0.0-20171129193455-018094318fb0", ) go_repository( name = "com_github_tj_go_elastic", build_file_proto_mode = "disable", importpath = "github.com/tj/go-elastic", sum = "h1:eGaGNxrtoZf/mBURsnNQKDR7u50Klgcf2eFDQEnc8Bc=", version = "v0.0.0-20171221160941-36157cbbebc2", ) go_repository( name = "com_github_tj_go_kinesis", build_file_proto_mode = "disable", importpath = "github.com/tj/go-kinesis", sum = "h1:m74UWYy+HBs+jMFR9mdZU6shPewugMyH5+GV6LNgW8w=", version = "v0.0.0-20171128231115-08b17f58cb1b", ) go_repository( name = "com_github_tj_go_spin", build_file_proto_mode = "disable", importpath = "github.com/tj/go-spin", sum = "h1:lhdWZsvImxvZ3q1C5OIB7d72DuOwP4O2NdBg9PyzNds=", version = "v1.1.0", ) go_repository( name = "com_github_tmc_grpc_websocket_proxy", build_file_proto_mode = "disable", importpath = "github.com/tmc/grpc-websocket-proxy", sum = "h1:LnC5Kc/wtumK+WB441p7ynQJzVuNRJiqddSIE3IlSEQ=", version = "v0.0.0-20190109142713-0ad062ec5ee5", ) go_repository( name = "com_github_tommy_muehle_go_mnd", build_file_proto_mode = "disable", importpath = "github.com/tommy-muehle/go-mnd", sum = "h1:RC4maTWLKKwb7p1cnoygsbKIgNlJqSYBeAFON3Ar8As=", version = "v1.3.1-0.20200224220436-e6f9a994e8fa", ) go_repository( name = "com_github_trivago_tgo", build_file_proto_mode = "disable", importpath = "github.com/trivago/tgo", sum = "h1:bxatjJIXNIpV18bucU4Uk/LaoxvxuOlp/oowRHyncLQ=", version = "v1.0.1", ) go_repository( name = "com_github_tsenart_vegeta", build_file_proto_mode = "disable", importpath = "github.com/tsenart/vegeta", sum = "h1:ErZrHhRveAoznVW80gbrxz+qxJNydpA2fcQxTPHkZbU=", version = "v12.7.1-0.20190725001342-b5f4fca92137+incompatible", ) go_repository( name = "com_github_ugorji_go", build_file_proto_mode = "disable", importpath = "github.com/ugorji/go", sum = "h1:j4s+tAvLfL3bZyefP2SEWmhBzmuIlH/eqNuPdFPgngw=", version = "v1.1.4", ) go_repository( name = "com_github_ugorji_go_codec", build_file_proto_mode = "disable", importpath = "github.com/ugorji/go/codec", sum = "h1:3SVOIvH7Ae1KRYyQWRjXWJEA9sS/c/pjvH++55Gr648=", version = "v0.0.0-20181204163529-d75b2dcb6bc8", ) go_repository( name = "com_github_ulikunitz_xz", build_file_proto_mode = "disable", importpath = "github.com/ulikunitz/xz", sum = "h1:YvTNdFzX6+W5m9msiYg/zpkSURPPtOlzbqYjrFn7Yt4=", version = "v0.5.7", ) go_repository( name = "com_github_ultraware_funlen", build_file_proto_mode = "disable", importpath = "github.com/ultraware/funlen", sum = "h1:Av96YVBwwNSe4MLR7iI/BIa3VyI7/djnto/pK3Uxbdo=", version = "v0.0.2", ) go_repository( name = "com_github_ultraware_whitespace", build_file_proto_mode = "disable", importpath = "github.com/ultraware/whitespace", sum = "h1:If7Va4cM03mpgrNH9k49/VOicWpGoG70XPBFFODYDsg=", version = "v0.0.4", ) go_repository( name = "com_github_urfave_cli", build_file_proto_mode = "disable", importpath = "github.com/urfave/cli", sum = "h1:u7tSpNPPswAFymm8IehJhy4uJMlUuU/GmqSkvJ1InXA=", version = "v1.22.4", ) go_repository( name = "com_github_urfave_cli_v2", build_file_proto_mode = "disable", importpath = "github.com/urfave/cli/v2", sum = "h1:Qt8FeAtxE/vfdrLmR3rxR6JRE0RoVmbXu8+6kZtYU4k=", version = "v2.1.1", ) go_repository( name = "com_github_uudashr_gocognit", build_file_proto_mode = "disable", importpath = "github.com/uudashr/gocognit", sum = "h1:MoG2fZ0b/Eo7NXoIwCVFLG5JED3qgQz5/NEE+rOsjPs=", version = "v1.0.1", ) go_repository( name = "com_github_valyala_bytebufferpool", build_file_proto_mode = "disable", importpath = "github.com/valyala/bytebufferpool", sum = "h1:GqA5TC/0021Y/b9FG4Oi9Mr3q7XYx6KllzawFIhcdPw=", version = "v1.0.0", ) go_repository( name = "com_github_valyala_fasthttp", build_file_proto_mode = "disable", importpath = "github.com/valyala/fasthttp", sum = "h1:dzZJf2IuMiclVjdw0kkT+f9u4YdrapbNyGAN47E/qnk=", version = "v1.2.0", ) go_repository( name = "com_github_valyala_quicktemplate", build_file_proto_mode = "disable", importpath = "github.com/valyala/quicktemplate", sum = "h1:BaO1nHTkspYzmAjPXj0QiDJxai96tlcZyKcI9dyEGvM=", version = "v1.2.0", ) go_repository( name = "com_github_valyala_tcplisten", build_file_proto_mode = "disable", importpath = "github.com/valyala/tcplisten", sum = "h1:0R4NLDRDZX6JcmhJgXi5E4b8Wg84ihbmUKp/GvSPEzc=", version = "v0.0.0-20161114210144-ceec8f93295a", ) go_repository( name = "com_github_vdemeester_k8s_pkg_credentialprovider", build_file_proto_mode = "disable", importpath = "github.com/vdemeester/k8s-pkg-credentialprovider", sum = "h1:czKEIG2Q3YRTgs6x/8xhjVMJD5byPo6cZuostkbTM74=", version = "v1.17.4", ) go_repository( name = "com_github_vektah_gqlparser", build_file_proto_mode = "disable", importpath = "github.com/vektah/gqlparser", sum = "h1:ZsyLGn7/7jDNI+y4SEhI4yAxRChlv15pUHMjijT+e68=", version = "v1.1.2", ) go_repository( name = "com_github_venafi_vcert_v4", build_file_proto_mode = "disable", importpath = "github.com/Venafi/vcert/v4", sum = "h1:37gfyjS9v5YvZcIABwNPo1fAC31lIZT7glVK1vfUxk4=", version = "v4.11.0", ) go_repository( name = "com_github_vmware_govmomi", build_file_proto_mode = "disable", importpath = "github.com/vmware/govmomi", sum = "h1:gpw/0Ku+6RgF3jsi7fnCLmlcikBHfKBCUcu1qgc16OU=", version = "v0.20.3", ) go_repository( name = "com_github_xanzy_go_gitlab", build_file_proto_mode = "disable", importpath = "github.com/xanzy/go-gitlab", sum = "h1:tBm+OXv1t+KBsqlXkSDFz+YUjRM0GFsjpOWYOod3Ebs=", version = "v0.32.0", ) go_repository( name = "com_github_xanzy_ssh_agent", build_file_proto_mode = "disable", importpath = "github.com/xanzy/ssh-agent", sum = "h1:wUMzuKtKilRgBAD1sUb8gOwwRr2FGoBVumcjoOACClI=", version = "v0.3.0", ) go_repository( name = "com_github_xdg_scram", build_file_proto_mode = "disable", importpath = "github.com/xdg/scram", sum = "h1:u40Z8hqBAAQyv+vATcGgV0YCnDjqSL7/q/JyPhhJSPk=", version = "v0.0.0-20180814205039-7eeb5667e42c", ) go_repository( name = "com_github_xdg_stringprep", build_file_proto_mode = "disable", importpath = "github.com/xdg/stringprep", sum = "h1:d9X0esnoa3dFsV0FG35rAT0RIhYFlPq7MiP+DW89La0=", version = "v1.0.0", ) go_repository( name = "com_github_xeipuuv_gojsonpointer", build_file_proto_mode = "disable", importpath = "github.com/xeipuuv/gojsonpointer", sum = "h1:J9EGpcZtP0E/raorCMxlFGSTBrsSlaDGf3jU/qvAE2c=", version = "v0.0.0-20180127040702-4e3ac2762d5f", ) go_repository( name = "com_github_xeipuuv_gojsonreference", build_file_proto_mode = "disable", importpath = "github.com/xeipuuv/gojsonreference", sum = "h1:EzJWgHovont7NscjpAxXsDA8S8BMYve8Y5+7cuRE7R0=", version = "v0.0.0-20180127040603-bd5ef7bd5415", ) go_repository( name = "com_github_xeipuuv_gojsonschema", build_file_proto_mode = "disable", importpath = "github.com/xeipuuv/gojsonschema", sum = "h1:ngVtJC9TY/lg0AA/1k48FYhBrhRoFlEmWzsehpNAaZg=", version = "v1.1.0", ) go_repository( name = "com_github_xi2_xz", build_file_proto_mode = "disable", importpath = "github.com/xi2/xz", sum = "h1:nIPpBwaJSVYIxUFsDv3M8ofmx9yWTog9BfvIu0q41lo=", version = "v0.0.0-20171230120015-48954b6210f8", ) go_repository( name = "com_github_xiang90_probing", build_file_proto_mode = "disable", importpath = "github.com/xiang90/probing", sum = "h1:eY9dn8+vbi4tKz5Qo6v2eYzo7kUS51QINcR5jNpbZS8=", version = "v0.0.0-20190116061207-43a291ad63a2", ) go_repository( name = "com_github_xlab_handysort", build_file_proto_mode = "disable", importpath = "github.com/xlab/handysort", sum = "h1:j2hhcujLRHAg872RWAV5yaUrEjHEObwDv3aImCaNLek=", version = "v0.0.0-20150421192137-fb3537ed64a1", ) go_repository( name = "com_github_xordataexchange_crypt", build_file_proto_mode = "disable", importpath = "github.com/xordataexchange/crypt", sum = "h1:ESFSdwYZvkeru3RtdrYueztKhOBCSAAzS4Gf+k0tEow=", version = "v0.0.3-0.20170626215501-b2862e3d0a77", ) go_repository( name = "com_github_yuin_goldmark", build_file_proto_mode = "disable", importpath = "github.com/yuin/goldmark", sum = "h1:ruQGxdhGHe7FWOJPT0mKs5+pD2Xs1Bm/kdGlHO04FmM=", version = "v1.2.1", ) go_repository( name = "com_github_yvasiyarov_go_metrics", build_file_proto_mode = "disable", importpath = "github.com/yvasiyarov/go-metrics", sum = "h1:+lm10QQTNSBd8DVTNGHx7o/IKu9HYDvLMffDhbyLccI=", version = "v0.0.0-20140926110328-57bccd1ccd43", ) go_repository( name = "com_github_yvasiyarov_gorelic", build_file_proto_mode = "disable", importpath = "github.com/yvasiyarov/gorelic", sum = "h1:hlE8//ciYMztlGpl/VA+Zm1AcTPHYkHJPbHqE6WJUXE=", version = "v0.0.0-20141212073537-a9bba5b9ab50", ) go_repository( name = "com_github_yvasiyarov_newrelic_platform_go", build_file_proto_mode = "disable", importpath = "github.com/yvasiyarov/newrelic_platform_go", sum = "h1:ERexzlUfuTvpE74urLSbIQW0Z/6hF9t8U4NsJLaioAY=", version = "v0.0.0-20140908184405-b21fdbd4370f", ) go_repository( name = "com_google_cloud_go", build_file_proto_mode = "disable", importpath = "cloud.google.com/go", sum = "h1:DZeAkuQGQqnm9Xv36SbMJEU8aFBz4wL04UpMWPWwjzg=", version = "v0.66.0", ) go_repository( name = "com_google_cloud_go_bigquery", build_file_proto_mode = "disable", importpath = "cloud.google.com/go/bigquery", sum = "h1:PQcPefKFdaIzjQFbiyOgAqyx8q5djaE7x9Sqe712DPA=", version = "v1.8.0", ) go_repository( name = "com_google_cloud_go_datastore", build_file_proto_mode = "disable", importpath = "cloud.google.com/go/datastore", sum = "h1:/May9ojXjRkPBNVrq+oWLqmWCkr4OU5uRY29bu0mRyQ=", version = "v1.1.0", ) go_repository( name = "com_google_cloud_go_firestore", build_file_proto_mode = "disable", importpath = "cloud.google.com/go/firestore", sum = "h1:9x7Bx0A9R5/M9jibeJeZWqjeVEIxYW9fZYqB9a70/bY=", version = "v1.1.0", ) go_repository( name = "com_google_cloud_go_logging", build_file_proto_mode = "disable", importpath = "cloud.google.com/go/logging", sum = "h1:kaunpnoEh9L4hu6JUsBa8Y20LBfKnCuDhKUgdZp7oK8=", version = "v1.0.0", ) go_repository( name = "com_google_cloud_go_pubsub", build_file_proto_mode = "disable", importpath = "cloud.google.com/go/pubsub", sum = "h1:76oR7VBOkL7ivoIrFKyW0k7YDCRelrlxktIzQiIUGgg=", version = "v1.4.0", ) go_repository( name = "com_google_cloud_go_storage", build_file_proto_mode = "disable", importpath = "cloud.google.com/go/storage", sum = "h1:4y3gHptW1EHVtcPAVE0eBBlFuGqEejTTG3KdIE0lUX4=", version = "v1.12.0", ) go_repository( name = "com_shuralyov_dmitri_gpu_mtl", build_file_proto_mode = "disable", importpath = "dmitri.shuralyov.com/gpu/mtl", sum = "h1:VpgP7xuJadIUuKccphEpTJnWhS2jkQyMt6Y7pJCD7fY=", version = "v0.0.0-20190408044501-666a987793e9", ) go_repository( name = "com_sourcegraph_sqs_pbtypes", build_file_proto_mode = "disable", importpath = "sourcegraph.com/sqs/pbtypes", sum = "h1:f7lAwqviDEGvON4kRv0o5V7FT/IQK+tbkF664XMbP3o=", version = "v1.0.0", ) go_repository( name = "com_sslmate_software_src_go_pkcs12", build_file_proto_mode = "disable", importpath = "software.sslmate.com/src/go-pkcs12", sum = "h1:AVd6O+azYjVQYW1l55IqkbL8/JxjrLtO6q4FCmV8N5c=", version = "v0.0.0-20200830195227-52f69702a001", ) go_repository( name = "dev_gocloud", build_file_proto_mode = "disable", importpath = "gocloud.dev", sum = "h1:EDRyaRAnMGSq/QBto486gWFxMLczAfIYUmusV7XLNBM=", version = "v0.19.0", ) go_repository( name = "dev_knative_caching", build_file_proto_mode = "disable", importpath = "knative.dev/caching", sum = "h1:mxrur6DsVK8uIjhIq7c1OMls4YjBcRlyvnh3Vx13a0M=", version = "v0.0.0-20200116200605-67bca2c83dfa", ) go_repository( name = "dev_knative_eventing_contrib", build_file_proto_mode = "disable", importpath = "knative.dev/eventing-contrib", sum = "h1:xncT+JrokPG+hPUFJwue8ubPpzmziV9GUIZqYt01JDo=", version = "v0.11.2", ) go_repository( name = "dev_knative_pkg", build_file_proto_mode = "disable", importpath = "knative.dev/pkg", sum = "h1:NDQS+236vhwCP9oiBBGvQ5WGzbD0Y8Pcv9dtE2stg+Q=", version = "v0.0.0-20200711004937-22502028e31a", ) go_repository( name = "dev_knative_test_infra", build_file_proto_mode = "disable", importpath = "knative.dev/test-infra", sum = "h1:wNlGK4f5Ykqh3KLC5RlyR9kvzvRgo/LwJQNsZWGVHnU=", version = "v0.0.0-20200707183444-aed09e56ddc7", ) go_repository( name = "in_gopkg_airbrake_gobrake_v2", build_file_proto_mode = "disable", importpath = "gopkg.in/airbrake/gobrake.v2", sum = "h1:7z2uVWwn7oVeeugY1DtlPAy5H+KYgB1KeKTnqjNatLo=", version = "v2.0.9", ) go_repository( name = "in_gopkg_alecthomas_kingpin_v2", build_file_proto_mode = "disable", importpath = "gopkg.in/alecthomas/kingpin.v2", sum = "h1:jMFz6MfLP0/4fUyZle81rXUoxOBFi19VUFKVDOQfozc=", version = "v2.2.6", ) go_repository( name = "in_gopkg_asn1_ber_v1", build_file_proto_mode = "disable", importpath = "gopkg.in/asn1-ber.v1", sum = "h1:TxyelI5cVkbREznMhfzycHdkp5cLA7DpE+GKjSslYhM=", version = "v1.0.0-20181015200546-f715ec2f112d", ) go_repository( name = "in_gopkg_check_v1", build_file_proto_mode = "disable", importpath = "gopkg.in/check.v1", sum = "h1:Hei/4ADfdWqJk1ZMxUNpqntNwaWcugrBjAiHlqqRiVk=", version = "v1.0.0-20201130134442-10cb98267c6c", ) go_repository( name = "in_gopkg_cheggaaa_pb_v1", build_file_proto_mode = "disable", importpath = "gopkg.in/cheggaaa/pb.v1", sum = "h1:Ev7yu1/f6+d+b3pi5vPdRPc6nNtP1umSfcWiEfRqv6I=", version = "v1.0.25", ) go_repository( name = "in_gopkg_errgo_v2", build_file_proto_mode = "disable", importpath = "gopkg.in/errgo.v2", sum = "h1:0vLT13EuvQ0hNvakwLuFZ/jYrLp5F3kcWHXdRggjCE8=", version = "v2.1.0", ) go_repository( name = "in_gopkg_fsnotify_v1", build_file_proto_mode = "disable", importpath = "gopkg.in/fsnotify.v1", sum = "h1:xOHLXZwVvI9hhs+cLKq5+I5onOuwQLhQwiu63xxlHs4=", version = "v1.4.7", ) go_repository( name = "in_gopkg_gcfg_v1", build_file_proto_mode = "disable", importpath = "gopkg.in/gcfg.v1", sum = "h1:0HIbH907iBTAntm+88IJV2qmJALDAh8sPekI9Vc1fm0=", version = "v1.2.0", ) go_repository( name = "in_gopkg_gemnasium_logrus_airbrake_hook_v2", build_file_proto_mode = "disable", importpath = "gopkg.in/gemnasium/logrus-airbrake-hook.v2", sum = "h1:OAj3g0cR6Dx/R07QgQe8wkA9RNjB2u4i700xBkIT4e0=", version = "v2.1.2", ) go_repository( name = "in_gopkg_inf_v0", build_file_proto_mode = "disable", importpath = "gopkg.in/inf.v0", sum = "h1:73M5CoZyi3ZLMOyDlQh031Cx6N9NDJ2Vvfl76EDAgDc=", version = "v0.9.1", ) go_repository( name = "in_gopkg_ini_v1", build_file_proto_mode = "disable", importpath = "gopkg.in/ini.v1", sum = "h1:DPMeDvGTM54DXbPkVIZsp19fp/I2K7zwA/itHYHKo8Y=", version = "v1.56.0", ) go_repository( name = "in_gopkg_jcmturner_aescts_v1", build_file_proto_mode = "disable", importpath = "gopkg.in/jcmturner/aescts.v1", sum = "h1:cVVZBK2b1zY26haWB4vbBiZrfFQnfbTVrE3xZq6hrEw=", version = "v1.0.1", ) go_repository( name = "in_gopkg_jcmturner_dnsutils_v1", build_file_proto_mode = "disable", importpath = "gopkg.in/jcmturner/dnsutils.v1", sum = "h1:cIuC1OLRGZrld+16ZJvvZxVJeKPsvd5eUIvxfoN5hSM=", version = "v1.0.1", ) go_repository( name = "in_gopkg_jcmturner_gokrb5_v7", build_file_proto_mode = "disable", importpath = "gopkg.in/jcmturner/gokrb5.v7", sum = "h1:0709Jtq/6QXEuWRfAm260XqlpcwL1vxtO1tUE2qK8Z4=", version = "v7.3.0", ) go_repository( name = "in_gopkg_jcmturner_rpc_v1", build_file_proto_mode = "disable", importpath = "gopkg.in/jcmturner/rpc.v1", sum = "h1:QHIUxTX1ISuAv9dD2wJ9HWQVuWDX/Zc0PfeC2tjc4rU=", version = "v1.1.0", ) go_repository( name = "in_gopkg_natefinch_lumberjack_v2", build_file_proto_mode = "disable", importpath = "gopkg.in/natefinch/lumberjack.v2", sum = "h1:1Lc07Kr7qY4U2YPouBjpCLxpiyxIVoxqXgkXLknAOE8=", version = "v2.0.0", ) go_repository( name = "in_gopkg_resty_v1", build_file_proto_mode = "disable", importpath = "gopkg.in/resty.v1", sum = "h1:CuXP0Pjfw9rOuY6EP+UvtNvt5DSqHpIxILZKT/quCZI=", version = "v1.12.0", ) go_repository( name = "in_gopkg_robfig_cron_v2", build_file_proto_mode = "disable", importpath = "gopkg.in/robfig/cron.v2", sum = "h1:E846t8CnR+lv5nE+VuiKTDG/v1U2stad0QzddfJC7kY=", version = "v2.0.0-20150107220207-be2e0b0deed5", ) go_repository( name = "in_gopkg_square_go_jose_v2", build_file_proto_mode = "disable", importpath = "gopkg.in/square/go-jose.v2", sum = "h1:SK5KegNXmKmqE342YYN2qPHEnUYeoMiXXl1poUlI+o4=", version = "v2.3.1", ) go_repository( name = "in_gopkg_src_d_go_billy_v4", build_file_proto_mode = "disable", importpath = "gopkg.in/src-d/go-billy.v4", sum = "h1:0SQA1pRztfTFx2miS8sA97XvooFeNOmvUenF4o0EcVg=", version = "v4.3.2", ) go_repository( name = "in_gopkg_src_d_go_git_fixtures_v3", build_file_proto_mode = "disable", importpath = "gopkg.in/src-d/go-git-fixtures.v3", sum = "h1:ivZFOIltbce2Mo8IjzUHAFoq/IylO9WHhNOAJK+LsJg=", version = "v3.5.0", ) go_repository( name = "in_gopkg_src_d_go_git_v4", build_file_proto_mode = "disable", importpath = "gopkg.in/src-d/go-git.v4", sum = "h1:SRtFyV8Kxc0UP7aCHcijOMQGPxHSmMOPrzulQWolkYE=", version = "v4.13.1", ) go_repository( name = "in_gopkg_tomb_v1", build_file_proto_mode = "disable", importpath = "gopkg.in/tomb.v1", sum = "h1:uRGJdciOHaEIrze2W8Q3AKkepLTh2hOroT7a+7czfdQ=", version = "v1.0.0-20141024135613-dd632973f1e7", ) go_repository( name = "in_gopkg_warnings_v0", build_file_proto_mode = "disable", importpath = "gopkg.in/warnings.v0", sum = "h1:wFXVbFY8DY5/xOe1ECiWdKCzZlxgshcYVNkBHstARME=", version = "v0.1.2", ) go_repository( name = "in_gopkg_yaml_v1", build_file_proto_mode = "disable", importpath = "gopkg.in/yaml.v1", sum = "h1:POO/ycCATvegFmVuPpQzZFJ+pGZeX22Ufu6fibxDVjU=", version = "v1.0.0-20140924161607-9f9df34309c0", ) go_repository( name = "in_gopkg_yaml_v2", build_file_proto_mode = "disable", importpath = "gopkg.in/yaml.v2", sum = "h1:D8xgwECY7CYvx+Y2n4sBz93Jn9JRvxdiyyo8CTfuKaY=", version = "v2.4.0", ) go_repository( name = "in_gopkg_yaml_v3", build_file_proto_mode = "disable", importpath = "gopkg.in/yaml.v3", sum = "h1:h8qDotaEPuJATrMmW04NCwg7v22aHH28wwpauUhK9Oo=", version = "v3.0.0-20210107192922-496545a6307b", ) go_repository( name = "io_etcd_go_bbolt", build_file_proto_mode = "disable", importpath = "go.etcd.io/bbolt", sum = "h1:XAzx9gjCb0Rxj7EoqcClPD1d5ZBxZJk0jbuoPHenBt0=", version = "v1.3.5", ) go_repository( name = "io_etcd_go_etcd", build_file_proto_mode = "disable", importpath = "go.etcd.io/etcd", sum = "h1:1JFLBqwIgdyHN1ZtgjTBwO+blA6gVOmZurpiMEsETKo=", version = "v0.5.0-alpha.5.0.20200910180754-dd1b699fc489", ) go_repository( name = "io_gitea_code_sdk_gitea", build_file_proto_mode = "disable", importpath = "code.gitea.io/sdk/gitea", sum = "h1:hvDCz4wtFvo7rf5Ebj8tGd4aJ4wLPKX3BKFX9Dk1Pgs=", version = "v0.12.0", ) go_repository( name = "io_k8s_api", build_file_proto_mode = "disable_global", importpath = "k8s.io/api", sum = "h1:94bbZ5NTjdINJEdzOkpS4vdPhkb1VFpTYC9zh43f75c=", version = "v0.21.1", ) go_repository( name = "io_k8s_apiextensions_apiserver", build_file_proto_mode = "disable", importpath = "k8s.io/apiextensions-apiserver", sum = "h1:AA+cnsb6w7SZ1vD32Z+zdgfXdXY8X9uGX5bN6EoPEIo=", version = "v0.21.1", ) go_repository( name = "io_k8s_apimachinery", build_file_proto_mode = "disable_global", importpath = "k8s.io/apimachinery", sum = "h1:Q6XuHGlj2xc+hlMCvqyYfbv3H7SRGn2c8NycxJquDVs=", version = "v0.21.1", ) go_repository( name = "io_k8s_apiserver", build_file_proto_mode = "disable", importpath = "k8s.io/apiserver", sum = "h1:wTRcid53IhxhbFt4KTrFSw8tAncfr01EP91lzfcygVg=", version = "v0.21.1", ) go_repository( name = "io_k8s_cli_runtime", build_file_proto_mode = "disable", importpath = "k8s.io/cli-runtime", sum = "h1:wLe+osHSqcItyS3MYQXVyGFa54fppORVA8Jn7DBGSWw=", version = "v0.19.0", ) go_repository( name = "io_k8s_client_go", build_file_proto_mode = "disable_global", importpath = "k8s.io/client-go", replace = "k8s.io/client-go", sum = "h1:bhblWYLZKUu+pm50plvQF8WpY6TXdRRtcS/K9WauOj4=", version = "v0.21.1", ) go_repository( name = "io_k8s_cloud_provider", build_file_proto_mode = "disable", importpath = "k8s.io/cloud-provider", sum = "h1:ELMIQwweSNu8gfVEnLDypxd9034S1sZJg6QcdWJOvMI=", version = "v0.17.4", ) go_repository( name = "io_k8s_code_generator", build_file_proto_mode = "disable", importpath = "k8s.io/code-generator", sum = "h1:jvcxHpVu5dm/LMXr3GOj/jroiP8+v2YnJE9i2OVRenk=", version = "v0.21.1", ) go_repository( name = "io_k8s_component_base", build_file_proto_mode = "disable", importpath = "k8s.io/component-base", sum = "h1:iLpj2btXbR326s/xNQWmPNGu0gaYSjzn7IN/5i28nQw=", version = "v0.21.1", ) go_repository( name = "io_k8s_csi_translation_lib", build_file_proto_mode = "disable", importpath = "k8s.io/csi-translation-lib", sum = "h1:bP9yGfCJDknP7tklCwizZtwgJNRePMVcEaFIfeA11ho=", version = "v0.17.4", ) go_repository( name = "io_k8s_gengo", build_file_proto_mode = "disable", importpath = "k8s.io/gengo", sum = "h1:Uusb3oh8XcdzDF/ndlI4ToKTYVlkCSJP39SRY2mfRAw=", version = "v0.0.0-20201214224949-b6c5ce23f027", ) go_repository( name = "io_k8s_klog", build_file_proto_mode = "disable", importpath = "k8s.io/klog", sum = "h1:Pt+yjF5aB1xDSVbau4VsWe+dQNzA0qv1LlXdC2dF6Q8=", version = "v1.0.0", ) go_repository( name = "io_k8s_klog_v2", build_file_proto_mode = "disable", importpath = "k8s.io/klog/v2", sum = "h1:Q3gmuM9hKEjefWFFYF0Mat+YyFJvsUyYuwyNNJ5C9Ts=", version = "v2.8.0", ) go_repository( name = "io_k8s_kube_aggregator", build_file_proto_mode = "disable", importpath = "k8s.io/kube-aggregator", sum = "h1:rL4fsftMaqkKjaibArYDaBeqN41CHaJzgRJjUB9IrIg=", version = "v0.19.0", ) go_repository( name = "io_k8s_kube_openapi", build_file_proto_mode = "disable", importpath = "k8s.io/kube-openapi", sum = "h1:vEx13qjvaZ4yfObSSXW7BrMc/KQBBT/Jyee8XtLf4x0=", version = "v0.0.0-20210305001622-591a79e4bda7", ) go_repository( name = "io_k8s_kubectl", build_file_proto_mode = "disable", importpath = "k8s.io/kubectl", sum = "h1:t9uxaZzGvqc2jY96mjnPSjFHtaKOxoUegeGZdaGT6aw=", version = "v0.19.0", ) go_repository( name = "io_k8s_kubernetes", build_file_proto_mode = "disable", importpath = "k8s.io/kubernetes", sum = "h1:wJx/r2HuPVaaBeCUk/P47GSK0eyrj3mI/kESRFBp6/A=", version = "v1.14.7", ) go_repository( name = "io_k8s_legacy_cloud_providers", build_file_proto_mode = "disable", importpath = "k8s.io/legacy-cloud-providers", sum = "h1:VvFqJGiYAr2gIdoNuqbeZLEdxIFeN4Yt6OLJS9l2oIE=", version = "v0.17.4", ) go_repository( name = "io_k8s_metrics", build_file_proto_mode = "disable", importpath = "k8s.io/metrics", sum = "h1:cKq0+Z7wg5qkK1n8dryNffKfU22DBX83JguGpR+TCk0=", version = "v0.19.0", ) go_repository( name = "io_k8s_sigs_apiserver_network_proxy_konnectivity_client", build_file_proto_mode = "disable", importpath = "sigs.k8s.io/apiserver-network-proxy/konnectivity-client", sum = "h1:4uqm9Mv+w2MmBYD+F4qf/v6tDFUdPOk29C095RbU5mY=", version = "v0.0.15", ) go_repository( name = "io_k8s_sigs_boskos", build_file_proto_mode = "disable", importpath = "sigs.k8s.io/boskos", sum = "h1:OCr84Jrq4HgrYxP9wrfSsGioR1VSpTZMh/RXMu5sm+8=", version = "v0.0.0-20210730172138-093b54882439", ) go_repository( name = "io_k8s_sigs_controller_runtime", build_file_proto_mode = "disable", importpath = "sigs.k8s.io/controller-runtime", sum = "h1:ZIZ/dtpboPSbZYY7uUz2OzrkaBTOThx2yekLtpGB+zY=", version = "v0.9.0", ) go_repository( name = "io_k8s_sigs_controller_tools", build_file_proto_mode = "disable", importpath = "sigs.k8s.io/controller-tools", sum = "h1:PXOHvyYAjWfO0UfQvaUo33HpXNCOilV3i/Vjc7iM1/A=", version = "v0.2.9-0.20200414181213-645d44dca7c0", ) go_repository( name = "io_k8s_sigs_kustomize", build_file_proto_mode = "disable", importpath = "sigs.k8s.io/kustomize", sum = "h1:JUufWFNlI44MdtnjUqVnvh29rR37PQFzPbLXqhyOyX0=", version = "v2.0.3+incompatible", ) go_repository( name = "io_k8s_sigs_structured_merge_diff", build_file_proto_mode = "disable", importpath = "sigs.k8s.io/structured-merge-diff", sum = "h1:LOs1LZWMsz1xs77Phr/pkB4LFaavH7IVq/3+WTN9XTA=", version = "v1.0.1", ) go_repository( name = "io_k8s_sigs_structured_merge_diff_v2", build_file_proto_mode = "disable", importpath = "sigs.k8s.io/structured-merge-diff/v2", sum = "h1:I0h4buiCqDtPztO3NOiyoNMtqSIfld49D4Wj3UBXYZA=", version = "v2.0.1", ) go_repository( name = "io_k8s_sigs_structured_merge_diff_v3", build_file_proto_mode = "disable", importpath = "sigs.k8s.io/structured-merge-diff/v3", sum = "h1:dOmIZBMfhcHS09XZkMyUgkq5trg3/jRyJYFZUiaOp8E=", version = "v3.0.0", ) go_repository( name = "io_k8s_sigs_structured_merge_diff_v4", build_file_proto_mode = "disable", importpath = "sigs.k8s.io/structured-merge-diff/v4", sum = "h1:C4r9BgJ98vrKnnVCjwCSXcWjWe0NKcUQkmzDXZXGwH8=", version = "v4.1.0", ) go_repository( name = "io_k8s_sigs_testing_frameworks", build_file_proto_mode = "disable", importpath = "sigs.k8s.io/testing_frameworks", sum = "h1:vK0+tvjF0BZ/RYFeZ1E6BYBwHJJXhjuZ3TdsEKH+UQM=", version = "v0.1.2", ) go_repository( name = "io_k8s_sigs_yaml", build_file_proto_mode = "disable", importpath = "sigs.k8s.io/yaml", sum = "h1:kr/MCeFWJWTwyaHoR9c8EjH9OumOmoF9YGiZd7lFm/Q=", version = "v1.2.0", ) go_repository( name = "io_k8s_test_infra", build_file_proto_mode = "disable", importpath = "k8s.io/test-infra", sum = "h1:g5GLdRIOMs9vnEM/ZWG67f1Stn8cW1dER+MNK9P7Xn8=", version = "v0.0.0-20210903101950-5c7809e9c5e9", ) go_repository( name = "io_k8s_utils", build_file_proto_mode = "disable", importpath = "k8s.io/utils", sum = "h1:MSqsVQ3pZvPGTqCjptfimO2WjG7A9un2zcpiHkA6M/s=", version = "v0.0.0-20210527160623-6fdb442a123b", ) go_repository( name = "io_opencensus_go", build_file_proto_mode = "disable", importpath = "go.opencensus.io", sum = "h1:LYy1Hy3MJdrCdMwwzxA/dRok4ejH+RwNGbuoD9fCjto=", version = "v0.22.4", ) go_repository( name = "io_opencensus_go_contrib_exporter_aws", build_file_proto_mode = "disable", importpath = "contrib.go.opencensus.io/exporter/aws", sum = "h1:YsbWYxDZkC7x2OxlsDEYvvEXZ3cBI3qBgUK5BqkZvRw=", version = "v0.0.0-20181029163544-2befc13012d0", ) go_repository( name = "io_opencensus_go_contrib_exporter_ocagent", build_file_proto_mode = "disable", importpath = "contrib.go.opencensus.io/exporter/ocagent", sum = "h1:Z1n6UAyr0QwM284yUuh5Zd8JlvxUGAhFZcgMJkMPrGM=", version = "v0.6.0", ) go_repository( name = "io_opencensus_go_contrib_exporter_prometheus", build_file_proto_mode = "disable", importpath = "contrib.go.opencensus.io/exporter/prometheus", sum = "h1:SByaIoWwNgMdPSgl5sMqM2KDE5H/ukPWBRo314xiDvg=", version = "v0.1.0", ) go_repository( name = "io_opencensus_go_contrib_exporter_stackdriver", build_file_proto_mode = "disable", importpath = "contrib.go.opencensus.io/exporter/stackdriver", sum = "h1:RX9W6FelAqTVnBi/bRXJLXr9n18v4QkQwZYIdnNS51I=", version = "v0.13.1", ) go_repository( name = "io_opencensus_go_contrib_exporter_zipkin", build_file_proto_mode = "disable", importpath = "contrib.go.opencensus.io/exporter/zipkin", sum = "h1:PR+1zWqY8ceXs1qDQQIlgXe+sdiwCf0n32bH4+Epk8g=", version = "v0.1.1", ) go_repository( name = "io_opencensus_go_contrib_integrations_ocsql", build_file_proto_mode = "disable", importpath = "contrib.go.opencensus.io/integrations/ocsql", sum = "h1:kfg5Yyy1nYUrqzyfW5XX+dzMASky8IJXhtHe0KTYNS4=", version = "v0.1.4", ) go_repository( name = "io_opencensus_go_contrib_resource", build_file_proto_mode = "disable", importpath = "contrib.go.opencensus.io/resource", sum = "h1:4r2CANuYhKGmYWP02+5E94rLRcS/YeD+KlxSrOsMxk0=", version = "v0.1.1", ) go_repository( name = "io_rsc_binaryregexp", build_file_proto_mode = "disable", importpath = "rsc.io/binaryregexp", sum = "h1:HfqmD5MEmC0zvwBuF187nq9mdnXjXsSivRiXN7SmRkE=", version = "v0.2.0", ) go_repository( name = "io_rsc_letsencrypt", build_file_proto_mode = "disable", importpath = "rsc.io/letsencrypt", sum = "h1:H7xDfhkaFFSYEJlKeq38RwX2jYcnTeHuDQyT+mMNMwM=", version = "v0.0.3", ) go_repository( name = "io_rsc_quote_v3", build_file_proto_mode = "disable", importpath = "rsc.io/quote/v3", sum = "h1:9JKUTTIUgS6kzR9mK1YuGKv6Nl+DijDNIc0ghT58FaY=", version = "v3.1.0", ) go_repository( name = "io_rsc_sampler", build_file_proto_mode = "disable", importpath = "rsc.io/sampler", sum = "h1:7uVkIFmeBqHfdjD+gZwtXXI+RODJ2Wc4O7MPEh/QiW4=", version = "v1.3.0", ) go_repository( name = "ml_vbom_util", build_file_proto_mode = "disable", importpath = "vbom.ml/util", sum = "h1:O69FD9pJA4WUZlEwYatBEEkRWKQ5cKodWpdKTrCS/iQ=", version = "v0.0.0-20180919145318-efcd4e0f9787", ) go_repository( name = "org_apache_git_thrift_git", build_file_proto_mode = "disable", importpath = "git.apache.org/thrift.git", sum = "h1:CMxsZlAmxKs+VAZMlDDL0wXciMblJcutQbEe3A9CYUM=", version = "v0.12.0", ) go_repository( name = "org_bazil_fuse", build_file_proto_mode = "disable", importpath = "bazil.org/fuse", sum = "h1:FNCRpXiquG1aoyqcIWVFmpTSKVcx2bQD38uZZeGtdlw=", version = "v0.0.0-20180421153158-65cc252bf669", ) go_repository( name = "org_go4", build_file_proto_mode = "disable", importpath = "go4.org", sum = "h1:iqAGo78tVOJXELHQFRjR6TMwItrvXH4hrGJ32I/NFF8=", version = "v0.0.0-20201209231011-d4a079459e60", ) go_repository( name = "org_golang_google_api", build_file_proto_mode = "disable", importpath = "google.golang.org/api", sum = "h1:Le77IccnTqEa8ryp9wIpX5W3zYm7Gf9LhOp9PHcwFts=", version = "v0.32.0", ) go_repository( name = "org_golang_google_appengine", build_file_proto_mode = "disable", importpath = "google.golang.org/appengine", sum = "h1:FZR1q0exgwxzPzp/aF+VccGrSfxfPpkBqjIIEq3ru6c=", version = "v1.6.7", ) go_repository( name = "org_golang_google_cloud", build_file_proto_mode = "disable", importpath = "google.golang.org/cloud", sum = "h1:Cpp2P6TPjujNoC5M2KHY6g7wfyLYfIWRZaSdIKfDasA=", version = "v0.0.0-20151119220103-975617b05ea8", ) go_repository( name = "org_golang_google_genproto", build_file_proto_mode = "disable", importpath = "google.golang.org/genproto", sum = "h1:pOwg4OoaRYScjmR4LlLgdtnyoHYTSAVhhqe5uPdpII8=", version = "v0.0.0-20201110150050-8816d57aaa9a", ) go_repository( name = "org_golang_google_grpc", build_file_proto_mode = "disable", importpath = "google.golang.org/grpc", sum = "h1:zWTV+LMdc3kaiJMSTOFz2UgSBgx8RNQoTGiZu3fR9S0=", version = "v1.32.0", ) go_repository( name = "org_golang_google_protobuf", build_file_proto_mode = "disable", importpath = "google.golang.org/protobuf", sum = "h1:bxAC2xTBsZGibn2RTntX0oH50xLsqy1OxA9tTL3p/lk=", version = "v1.26.0", ) go_repository( name = "org_golang_x_crypto", build_file_proto_mode = "disable", importpath = "golang.org/x/crypto", sum = "h1:It14KIkyBFYkHkwZ7k45minvA9aorojkyjGk9KJ5B/w=", version = "v0.0.0-20210322153248-0c34fe9e7dc2", ) go_repository( name = "org_golang_x_exp", build_file_proto_mode = "disable", importpath = "golang.org/x/exp", sum = "h1:QE6XYQK6naiK1EPAe1g/ILLxN5RBoH5xkJk3CqlMI/Y=", version = "v0.0.0-20200224162631-6cc2880d07d6", ) go_repository( name = "org_golang_x_image", build_file_proto_mode = "disable", importpath = "golang.org/x/image", sum = "h1:+qEpEAPhDZ1o0x3tHzZTQDArnOixOzGD9HUJfcg0mb4=", version = "v0.0.0-20190802002840-cff245a6509b", ) go_repository( name = "org_golang_x_lint", build_file_proto_mode = "disable", importpath = "golang.org/x/lint", replace = "golang.org/x/lint", sum = "h1:QzoH/1pFpZguR8NrRHLcO6jKqfv2zpuSqZLgdm7ZmjI=", version = "v0.0.0-20190409202823-959b441ac422", ) go_repository( name = "org_golang_x_mobile", build_file_proto_mode = "disable", importpath = "golang.org/x/mobile", sum = "h1:b373EGXtj0o+ssqkOkdVphTCZ/fVg2LwhctJn2QQbqA=", version = "v0.0.0-20190806162312-597adff16ade", ) go_repository( name = "org_golang_x_mod", build_file_proto_mode = "disable", importpath = "golang.org/x/mod", sum = "h1:8pl+sMODzuvGJkmj2W4kZihvVb5mKm8pB/X44PIQHv8=", version = "v0.4.0", ) go_repository( name = "org_golang_x_net", build_file_proto_mode = "disable", importpath = "golang.org/x/net", sum = "h1:DzZ89McO9/gWPsQXS/FVKAlG02ZjaQ6AlZRBimEYOd0=", version = "v0.0.0-20210428140749-89ef3d95e781", ) go_repository( name = "org_golang_x_oauth2", build_file_proto_mode = "disable", importpath = "golang.org/x/oauth2", sum = "h1:ld7aEMNHoBnnDAX15v1T6z31v8HwR2A9FYOuAhWqkwc=", version = "v0.0.0-20200902213428-5d25da1a8d43", ) go_repository( name = "org_golang_x_sync", build_file_proto_mode = "disable", importpath = "golang.org/x/sync", sum = "h1:DcqTD9SDLc+1P/r1EmRBwnVsrOwW+kk2vWf9n+1sGhs=", version = "v0.0.0-20201207232520-09787c993a3a", ) go_repository( name = "org_golang_x_sys", build_file_proto_mode = "disable", importpath = "golang.org/x/sys", sum = "h1:JWgyZ1qgdTaF3N3oxC+MdTV7qvEEgHo3otj+HB5CM7Q=", version = "v0.0.0-20210603081109-ebe580a85c40", ) go_repository( name = "org_golang_x_term", build_file_proto_mode = "disable", importpath = "golang.org/x/term", sum = "h1:SZxvLBoTP5yHO3Frd4z4vrF+DBX9vMVanchswa69toE=", version = "v0.0.0-20210220032956-6a3ed077a48d", ) go_repository( name = "org_golang_x_text", build_file_proto_mode = "disable", importpath = "golang.org/x/text", sum = "h1:aRYxNxv6iGQlyVaZmk6ZgYEDa+Jg18DxebPSrd6bg1M=", version = "v0.3.6", ) go_repository( name = "org_golang_x_time", build_file_proto_mode = "disable", importpath = "golang.org/x/time", sum = "h1:O8mE0/t419eoIwhTFpKVkHiTs/Igowgfkj25AcZrtiE=", version = "v0.0.0-20210220033141-f8bda1e9f3ba", ) go_repository( name = "org_golang_x_tools", build_file_proto_mode = "disable", importpath = "golang.org/x/tools", sum = "h1:po9/4sTYwZU9lPhi1tOrb4hCv3qrhiQ77LZfGa2OjwY=", version = "v0.1.0", ) go_repository( name = "org_golang_x_xerrors", build_file_proto_mode = "disable", importpath = "golang.org/x/xerrors", sum = "h1:go1bK/D/BFZV2I8cIQd1NKEZ+0owSTG1fDTci4IqFcE=", version = "v0.0.0-20200804184101-5ec99f83aff1", ) go_repository( name = "org_gonum_v1_gonum", build_file_proto_mode = "disable", importpath = "gonum.org/v1/gonum", sum = "h1:OB/uP/Puiu5vS5QMRPrXCDWUPb+kt8f1KW8oQzFejQw=", version = "v0.0.0-20190331200053-3d26580ed485", ) go_repository( name = "org_gonum_v1_netlib", build_file_proto_mode = "disable", importpath = "gonum.org/v1/netlib", sum = "h1:jRyg0XfpwWlhEV8mDfdNGBeSJM2fuyh9Yjrnd8kF2Ts=", version = "v0.0.0-20190331212654-76723241ea4e", ) go_repository( name = "org_modernc_cc", build_file_proto_mode = "disable", importpath = "modernc.org/cc", sum = "h1:nPibNuDEx6tvYrUAtvDTTw98rx5juGsa5zuDnKwEEQQ=", version = "v1.0.0", ) go_repository( name = "org_modernc_golex", build_file_proto_mode = "disable", importpath = "modernc.org/golex", sum = "h1:wWpDlbK8ejRfSyi0frMyhilD3JBvtcx2AdGDnU+JtsE=", version = "v1.0.0", ) go_repository( name = "org_modernc_mathutil", build_file_proto_mode = "disable", importpath = "modernc.org/mathutil", sum = "h1:93vKjrJopTPrtTNpZ8XIovER7iCIH1QU7wNbOQXC60I=", version = "v1.0.0", ) go_repository( name = "org_modernc_strutil", build_file_proto_mode = "disable", importpath = "modernc.org/strutil", sum = "h1:XVFtQwFVwc02Wk+0L/Z/zDDXO81r5Lhe6iMKmGX3KhE=", version = "v1.0.0", ) go_repository( name = "org_modernc_xc", build_file_proto_mode = "disable", importpath = "modernc.org/xc", sum = "h1:7ccXrupWZIS3twbUGrtKmHS2DXY6xegFua+6O3xgAFU=", version = "v1.0.0", ) go_repository( name = "org_mongodb_go_mongo_driver", build_file_proto_mode = "disable", importpath = "go.mongodb.org/mongo-driver", sum = "h1:jxcFYjlkl8xaERsgLo+RNquI0epW6zuy/ZRQs6jnrFA=", version = "v1.1.2", ) go_repository( name = "org_uber_go_atomic", build_file_proto_mode = "disable", importpath = "go.uber.org/atomic", sum = "h1:ADUqmZGgLDDfbSL9ZmPxKTybcoEYHgpYfELNoN+7hsw=", version = "v1.7.0", ) go_repository( name = "org_uber_go_goleak", build_file_proto_mode = "disable", importpath = "go.uber.org/goleak", sum = "h1:z+mqJhf6ss6BSfSM671tgKyZBFPTTJM+HLxnhPC3wu0=", version = "v1.1.10", ) go_repository( name = "org_uber_go_multierr", build_file_proto_mode = "disable", importpath = "go.uber.org/multierr", sum = "h1:y6IPFStTAIT5Ytl7/XYmHvzXQ7S3g/IeZW9hyZ5thw4=", version = "v1.6.0", ) go_repository( name = "org_uber_go_tools", build_file_proto_mode = "disable", importpath = "go.uber.org/tools", sum = "h1:0mgffUl7nfd+FpvXMVz4IDEaUSmT1ysygQC7qYo7sG4=", version = "v0.0.0-20190618225709-2cfd321de3ee", ) go_repository( name = "org_uber_go_zap", build_file_proto_mode = "disable", importpath = "go.uber.org/zap", sum = "h1:MTjgFu6ZLKvY6Pvaqk97GlxNBuMpV4Hy/3P6tRGlI2U=", version = "v1.17.0", ) go_repository( name = "sh_helm_helm_v3", build_file_proto_mode = "disable", importpath = "helm.sh/helm/v3", sum = "h1:aykwPMVyQyncZ8iLNVMXgJ1l3c6W0+LSOPmqp8JdCjs=", version = "v3.1.1", ) go_repository( name = "tools_gotest", build_file_proto_mode = "disable", importpath = "gotest.tools", sum = "h1:VsBPFP1AI068pPrMxtb/S8Zkgf9xEmTLJjfM+P5UIEo=", version = "v2.2.0+incompatible", ) go_repository( name = "tools_gotest_v3", build_file_proto_mode = "disable", importpath = "gotest.tools/v3", sum = "h1:4AuOwCGf4lLR9u3YOe2awrHygurzhO/HeQ6laiA6Sx0=", version = "v3.0.3", ) go_repository( name = "xyz_gomodules_jsonpatch_v2", build_file_proto_mode = "disable", importpath = "gomodules.xyz/jsonpatch/v2", sum = "h1:4pT439QV83L+G9FkcCriY6EkpcK6r6bK+A5FBUMI7qY=", version = "v2.2.0", )

38.474667

81

0.641536

load("@bazel_gazelle//:deps.bzl", "go_repository") def go_deps(): go_repository( name = "ag_pack_amqp", build_file_proto_mode = "disable", importpath = "pack.ag/amqp", sum = "h1:cuNDWLUTbKRtEZwhB0WQBXf9pGbm87pUBXQhvcFxBWg=", version = "v0.11.2", ) go_repository( name = "cc_mvdan_interfacer", build_file_proto_mode = "disable", importpath = "mvdan.cc/interfacer", sum = "h1:WX1yoOaKQfddO/mLzdV4wptyWgoH/6hwLs7QHTixo0I=", version = "v0.0.0-20180901003855-c20040233aed", ) go_repository( name = "cc_mvdan_lint", build_file_proto_mode = "disable", importpath = "mvdan.cc/lint", sum = "h1:DxJ5nJdkhDlLok9K6qO+5290kphDJbHOQO1DFFFTeBo=", version = "v0.0.0-20170908181259-adc824a0674b", ) go_repository( name = "cc_mvdan_unparam", build_file_proto_mode = "disable", importpath = "mvdan.cc/unparam", sum = "h1:kAREL6MPwpsk1/PQPFD3Eg7WAQR5mPTWZJaBiG5LDbY=", version = "v0.0.0-20200501210554-b37ab49443f7", ) go_repository( name = "cc_mvdan_xurls_v2", build_file_proto_mode = "disable", importpath = "mvdan.cc/xurls/v2", sum = "h1:r1zSOSNS/kqtpmATyMMMvaZ4/djsesbYz5kr0+qMRWc=", version = "v2.0.0", ) go_repository( name = "co_honnef_go_tools", build_file_proto_mode = "disable", importpath = "honnef.co/go/tools", sum = "h1:UoveltGrhghAA7ePc+e+QYDHXrBps2PqFZiHkGR/xK8=", version = "v0.0.1-2020.1.4", ) go_repository( name = "com_github_agnivade_levenshtein", build_file_proto_mode = "disable", importpath = "github.com/agnivade/levenshtein", sum = "h1:3oJU7J3FGFmyhn8KHjmVaZCN5hxTr7GxgRue+sxIXdQ=", version = "v1.0.1", ) go_repository( name = "com_github_alcortesm_tgz", build_file_proto_mode = "disable", importpath = "github.com/alcortesm/tgz", sum = "h1:uSoVVbwJiQipAclBbw+8quDsfcvFjOpI5iCf4p/cqCs=", version = "v0.0.0-20161220082320-9c5fe88206d7", ) go_repository( name = "com_github_alecthomas_kingpin", build_file_proto_mode = "disable", importpath = "github.com/alecthomas/kingpin", sum = "h1:5svnBTFgJjZvGKyYBtMB0+m5wvrbUHiqye8wRJMlnYI=", version = "v2.2.6+incompatible", ) go_repository( name = "com_github_alecthomas_template", build_file_proto_mode = "disable", importpath = "github.com/alecthomas/template", sum = "h1:JYp7IbQjafoB+tBA3gMyHYHrpOtNuDiK/uB5uXxq5wM=", version = "v0.0.0-20190718012654-fb15b899a751", ) go_repository( name = "com_github_alecthomas_units", build_file_proto_mode = "disable", importpath = "github.com/alecthomas/units", sum = "h1:UQZhZ2O0vMHr2cI+DC1Mbh0TJxzA3RcLoMsFw+aXw7E=", version = "v0.0.0-20190924025748-f65c72e2690d", ) go_repository( name = "com_github_andreyvit_diff", build_file_proto_mode = "disable", importpath = "github.com/andreyvit/diff", sum = "h1:bvNMNQO63//z+xNgfBlViaCIJKLlCJ6/fmUseuG0wVQ=", version = "v0.0.0-20170406064948-c7f18ee00883", ) go_repository( name = "com_github_andybalholm_brotli", build_file_proto_mode = "disable", importpath = "github.com/andybalholm/brotli", sum = "h1:bZ28Hqta7TFAK3Q08CMvv8y3/8ATaEqv2nGoc6yff6c=", version = "v0.0.0-20190621154722-5f990b63d2d6", ) go_repository( name = "com_github_andygrunwald_go_gerrit", build_file_proto_mode = "disable", importpath = "github.com/andygrunwald/go-gerrit", sum = "h1:7gokoTWteZhP1t2f0OzrFFXlyL8o0+b0r4ZaRV9PXOs=", version = "v0.0.0-20210709065208-9d38b0be0268", ) go_repository( name = "com_github_andygrunwald_go_jira", build_file_proto_mode = "disable", importpath = "github.com/andygrunwald/go-jira", sum = "h1:vvIImGgX32bHfoiyUwkNo+/YrPnRczNarvhLOncP6dE=", version = "v1.13.0", ) go_repository( name = "com_github_anmitsu_go_shlex", build_file_proto_mode = "disable", importpath = "github.com/anmitsu/go-shlex", sum = "h1:kFOfPq6dUM1hTo4JG6LR5AXSUEsOjtdm0kw0FtQtMJA=", version = "v0.0.0-20161002113705-648efa622239", ) go_repository( name = "com_github_antihax_optional", build_file_proto_mode = "disable", importpath = "github.com/antihax/optional", sum = "h1:uZuxRZCz65cG1o6K/xUqImNcYKtmk9ylqaH0itMSvzA=", version = "v0.0.0-20180407024304-ca021399b1a6", ) go_repository( name = "com_github_apache_thrift", build_file_proto_mode = "disable", importpath = "github.com/apache/thrift", sum = "h1:pODnxUFNcjP9UTLZGTdeh+j16A8lJbRvD3rOtrk/7bs=", version = "v0.12.0", ) go_repository( name = "com_github_apex_log", build_file_proto_mode = "disable", importpath = "github.com/apex/log", sum = "h1:1fyfbPvUwD10nMoh3hY6MXzvZShJQn9/ck7ATgAt5pA=", version = "v1.3.0", ) go_repository( name = "com_github_apex_logs", build_file_proto_mode = "disable", importpath = "github.com/apex/logs", sum = "h1:KmEBVwfDUOTFcBO8cfkJYwdQ5487UZSN+GteOGPmiro=", version = "v0.0.4", ) go_repository( name = "com_github_aphistic_golf", build_file_proto_mode = "disable", importpath = "github.com/aphistic/golf", sum = "h1:2KLQMJ8msqoPHIPDufkxVcoTtcmE5+1sL9950m4R9Pk=", version = "v0.0.0-20180712155816-02c07f170c5a", ) go_repository( name = "com_github_aphistic_sweet", build_file_proto_mode = "disable", importpath = "github.com/aphistic/sweet", sum = "h1:I4z+fAUqvKfvZV/CHi5dV0QuwbmIvYYFDjG0Ss5QpAs=", version = "v0.2.0", ) go_repository( name = "com_github_armon_circbuf", build_file_proto_mode = "disable", importpath = "github.com/armon/circbuf", sum = "h1:QEF07wC0T1rKkctt1RINW/+RMTVmiwxETico2l3gxJA=", version = "v0.0.0-20150827004946-bbbad097214e", ) go_repository( name = "com_github_armon_consul_api", build_file_proto_mode = "disable", importpath = "github.com/armon/consul-api", sum = "h1:G1bPvciwNyF7IUmKXNt9Ak3m6u9DE1rF+RmtIkBpVdA=", version = "v0.0.0-20180202201655-eb2c6b5be1b6", ) go_repository( name = "com_github_armon_go_metrics", build_file_proto_mode = "disable", importpath = "github.com/armon/go-metrics", sum = "h1:8GUt8eRujhVEGZFFEjBj46YV4rDjvGrNxb0KMWYkL2I=", version = "v0.0.0-20180917152333-f0300d1749da", ) go_repository( name = "com_github_armon_go_radix", build_file_proto_mode = "disable", importpath = "github.com/armon/go-radix", sum = "h1:BUAU3CGlLvorLI26FmByPp2eC2qla6E1Tw+scpcg/to=", version = "v0.0.0-20180808171621-7fddfc383310", ) go_repository( name = "com_github_armon_go_socks5", build_file_proto_mode = "disable", importpath = "github.com/armon/go-socks5", sum = "h1:0CwZNZbxp69SHPdPJAN/hZIm0C4OItdklCFmMRWYpio=", version = "v0.0.0-20160902184237-e75332964ef5", ) go_repository( name = "com_github_asaskevich_govalidator", build_file_proto_mode = "disable", importpath = "github.com/asaskevich/govalidator", sum = "h1:zV3ejI06GQ59hwDQAvmK1qxOQGB3WuVTRoY0okPTAv0=", version = "v0.0.0-20200108200545-475eaeb16496", ) go_repository( name = "com_github_aws_aws_k8s_tester", build_file_proto_mode = "disable", importpath = "github.com/aws/aws-k8s-tester", sum = "h1:Zr5NWiRK5fhmRIlhrsTwrY8yB488FyN6iulci2D7VaI=", version = "v1.0.0", ) go_repository( name = "com_github_aws_aws_sdk_go", build_file_proto_mode = "disable", importpath = "github.com/aws/aws-sdk-go", sum = "h1:cyZp8TvUbH9rrShdrwULtCj4pB5szddrw9aKHUsw1Ic=", version = "v1.37.22", ) go_repository( name = "com_github_aybabtme_rgbterm", build_file_proto_mode = "disable", importpath = "github.com/aybabtme/rgbterm", sum = "h1:WWB576BN5zNSZc/M9d/10pqEx5VHNhaQ/yOVAkmj5Yo=", version = "v0.0.0-20170906152045-cc83f3b3ce59", ) go_repository( name = "com_github_azure_azure_amqp_common_go_v2", build_file_proto_mode = "disable", importpath = "github.com/Azure/azure-amqp-common-go/v2", sum = "h1:+QbFgmWCnPzdaRMfsI0Yb6GrRdBj5jVL8N3EXuEUcBQ=", version = "v2.1.0", ) go_repository( name = "com_github_azure_azure_pipeline_go", build_file_proto_mode = "disable", importpath = "github.com/Azure/azure-pipeline-go", sum = "h1:6oiIS9yaG6XCCzhgAgKFfIWyo4LLCiDhZot6ltoThhY=", version = "v0.2.2", ) go_repository( name = "com_github_azure_azure_sdk_for_go", build_file_proto_mode = "disable", importpath = "github.com/Azure/azure-sdk-for-go", sum = "h1:m4oQOm3HXtQh2Ipata+pLSS1kGUD/7ikkvNq81XM/7s=", version = "v46.3.0+incompatible", ) go_repository( name = "com_github_azure_azure_service_bus_go", build_file_proto_mode = "disable", importpath = "github.com/Azure/azure-service-bus-go", sum = "h1:G1qBLQvHCFDv9pcpgwgFkspzvnGknJRR0PYJ9ytY/JA=", version = "v0.9.1", ) go_repository( name = "com_github_azure_azure_storage_blob_go", build_file_proto_mode = "disable", importpath = "github.com/Azure/azure-storage-blob-go", sum = "h1:53qhf0Oxa0nOjgbDeeYPUeyiNmafAFEY95rZLK0Tj6o=", version = "v0.8.0", ) go_repository( name = "com_github_azure_go_ansiterm", build_file_proto_mode = "disable", importpath = "github.com/Azure/go-ansiterm", sum = "h1:w+iIsaOQNcT7OZ575w+acHgRric5iCyQh+xv+KJ4HB8=", version = "v0.0.0-20170929234023-d6e3b3328b78", ) go_repository( name = "com_github_azure_go_autorest", build_file_proto_mode = "disable", importpath = "github.com/Azure/go-autorest", replace = "github.com/Azure/go-autorest", sum = "h1:V5VMDjClD3GiElqLWO7mz2MxNAK/vTfRHdAubSIPRgs=", version = "v14.2.0+incompatible", ) go_repository( name = "com_github_azure_go_autorest_autorest", build_file_proto_mode = "disable", importpath = "github.com/Azure/go-autorest/autorest", sum = "h1:gI8ytXbxMfI+IVbI9mP2JGCTXIuhHLgRlvQ9X4PsnHE=", version = "v0.11.12", ) go_repository( name = "com_github_azure_go_autorest_autorest_adal", build_file_proto_mode = "disable", importpath = "github.com/Azure/go-autorest/autorest/adal", sum = "h1:Y3bBUV4rTuxenJJs41HU3qmqsb+auo+a3Lz+PlJPpL0=", version = "v0.9.5", ) go_repository( name = "com_github_azure_go_autorest_autorest_azure_auth", build_file_proto_mode = "disable", importpath = "github.com/Azure/go-autorest/autorest/azure/auth", sum = "h1:iM6UAvjR97ZIeR93qTcwpKNMpV+/FTWjwEbuPD495Tk=", version = "v0.4.2", ) go_repository( name = "com_github_azure_go_autorest_autorest_azure_cli", build_file_proto_mode = "disable", importpath = "github.com/Azure/go-autorest/autorest/azure/cli", sum = "h1:LXl088ZQlP0SBppGFsRZonW6hSvwgL5gRByMbvUbx8U=", version = "v0.3.1", ) go_repository( name = "com_github_azure_go_autorest_autorest_date", build_file_proto_mode = "disable", importpath = "github.com/Azure/go-autorest/autorest/date", sum = "h1:7gUk1U5M/CQbp9WoqinNzJar+8KY+LPI6wiWrP/myHw=", version = "v0.3.0", ) go_repository( name = "com_github_azure_go_autorest_autorest_mocks", build_file_proto_mode = "disable", importpath = "github.com/Azure/go-autorest/autorest/mocks", sum = "h1:K0laFcLE6VLTOwNgSxaGbUcLPuGXlNkbVvq4cW4nIHk=", version = "v0.4.1", ) go_repository( name = "com_github_azure_go_autorest_autorest_to", build_file_proto_mode = "disable", importpath = "github.com/Azure/go-autorest/autorest/to", sum = "h1:oXVqrxakqqV1UZdSazDOPOLvOIz+XA683u8EctwboHk=", version = "v0.4.0", ) go_repository( name = "com_github_azure_go_autorest_autorest_validation", build_file_proto_mode = "disable", importpath = "github.com/Azure/go-autorest/autorest/validation", sum = "h1:3I9AAI63HfcLtphd9g39ruUwRI+Ca+z/f36KHPFRUss=", version = "v0.3.0", ) go_repository( name = "com_github_azure_go_autorest_logger", build_file_proto_mode = "disable", importpath = "github.com/Azure/go-autorest/logger", sum = "h1:e4RVHVZKC5p6UANLJHkM4OfR1UKZPj8Wt8Pcx+3oqrE=", version = "v0.2.0", ) go_repository( name = "com_github_azure_go_autorest_tracing", build_file_proto_mode = "disable", importpath = "github.com/Azure/go-autorest/tracing", sum = "h1:TYi4+3m5t6K48TGI9AUdb+IzbnSxvnvUMfuitfgcfuo=", version = "v0.6.0", ) go_repository( name = "com_github_beorn7_perks", build_file_proto_mode = "disable", importpath = "github.com/beorn7/perks", sum = "h1:VlbKKnNfV8bJzeqoa4cOKqO6bYr3WgKZxO8Z16+hsOM=", version = "v1.0.1", ) go_repository( name = "com_github_bgentry_speakeasy", build_file_proto_mode = "disable", importpath = "github.com/bgentry/speakeasy", sum = "h1:ByYyxL9InA1OWqxJqqp2A5pYHUrCiAL6K3J+LKSsQkY=", version = "v0.1.0", ) go_repository( name = "com_github_bitly_go_simplejson", build_file_proto_mode = "disable", importpath = "github.com/bitly/go-simplejson", sum = "h1:6IH+V8/tVMab511d5bn4M7EwGXZf9Hj6i2xSwkNEM+Y=", version = "v0.5.0", ) go_repository( name = "com_github_bketelsen_crypt", build_file_proto_mode = "disable", importpath = "github.com/bketelsen/crypt", sum = "h1:+0HFd5KSZ/mm3JmhmrDukiId5iR6w4+BdFtfSy4yWIc=", version = "v0.0.3-0.20200106085610-5cbc8cc4026c", ) go_repository( name = "com_github_blakesmith_ar", build_file_proto_mode = "disable", importpath = "github.com/blakesmith/ar", sum = "h1:m935MPodAbYS46DG4pJSv7WO+VECIWUQ7OJYSoTrMh4=", version = "v0.0.0-20190502131153-809d4375e1fb", ) go_repository( name = "com_github_blang_semver", build_file_proto_mode = "disable", importpath = "github.com/blang/semver", sum = "h1:cQNTCjp13qL8KC3Nbxr/y2Bqb63oX6wdnnjpJbkM4JQ=", version = "v3.5.1+incompatible", ) go_repository( name = "com_github_bmizerany_assert", build_file_proto_mode = "disable", importpath = "github.com/bmizerany/assert", sum = "h1:DDGfHa7BWjL4YnC6+E63dPcxHo2sUxDIu8g3QgEJdRY=", version = "v0.0.0-20160611221934-b7ed37b82869", ) go_repository( name = "com_github_bmizerany_perks", build_file_proto_mode = "disable", importpath = "github.com/bmizerany/perks", sum = "h1:AP/Y7sqYicnjGDfD5VcY4CIfh1hRXBUavxrvELjTiOE=", version = "v0.0.0-20141205001514-d9a9656a3a4b", ) go_repository( name = "com_github_bombsimon_wsl_v2", build_file_proto_mode = "disable", importpath = "github.com/bombsimon/wsl/v2", sum = "h1:/DdSteYCq4lPX+LqDg7mdoxm14UxzZPoDT0taYc3DTU=", version = "v2.2.0", ) go_repository( name = "com_github_bombsimon_wsl_v3", build_file_proto_mode = "disable", importpath = "github.com/bombsimon/wsl/v3", sum = "h1:E5SRssoBgtVFPcYWUOFJEcgaySgdtTNYzsSKDOY7ss8=", version = "v3.1.0", ) go_repository( name = "com_github_bshuster_repo_logrus_logstash_hook", build_file_proto_mode = "disable", importpath = "github.com/bshuster-repo/logrus-logstash-hook", sum = "h1:pgAtgj+A31JBVtEHu2uHuEx0n+2ukqUJnS2vVe5pQNA=", version = "v0.4.1", ) go_repository( name = "com_github_bugsnag_bugsnag_go", build_file_proto_mode = "disable", importpath = "github.com/bugsnag/bugsnag-go", sum = "h1:rFt+Y/IK1aEZkEHchZRSq9OQbsSzIT/OrI8YFFmRIng=", version = "v0.0.0-20141110184014-b1d153021fcd", ) go_repository( name = "com_github_bugsnag_osext", build_file_proto_mode = "disable", importpath = "github.com/bugsnag/osext", sum = "h1:otBG+dV+YK+Soembjv71DPz3uX/V/6MMlSyD9JBQ6kQ=", version = "v0.0.0-20130617224835-0dd3f918b21b", ) go_repository( name = "com_github_bugsnag_panicwrap", build_file_proto_mode = "disable", importpath = "github.com/bugsnag/panicwrap", sum = "h1:nvj0OLI3YqYXer/kZD8Ri1aaunCxIEsOst1BVJswV0o=", version = "v0.0.0-20151223152923-e2c28503fcd0", ) go_repository( name = "com_github_burntsushi_toml", build_file_proto_mode = "disable", importpath = "github.com/BurntSushi/toml", sum = "h1:WXkYYl6Yr3qBf1K79EBnL4mak0OimBfB0XUf9Vl28OQ=", version = "v0.3.1", ) go_repository( name = "com_github_burntsushi_xgb", build_file_proto_mode = "disable", importpath = "github.com/BurntSushi/xgb", sum = "h1:1BDTz0u9nC3//pOCMdNH+CiXJVYJh5UQNCOBG7jbELc=", version = "v0.0.0-20160522181843-27f122750802", ) go_repository( name = "com_github_bwmarrin_snowflake", build_file_proto_mode = "disable", importpath = "github.com/bwmarrin/snowflake", sum = "h1:dRbqXFjM10uA3wdrVZ8Kh19uhciRMOroUYJ7qAqDLhY=", version = "v0.0.0", ) go_repository( name = "com_github_caarlos0_ctrlc", build_file_proto_mode = "disable", importpath = "github.com/caarlos0/ctrlc", sum = "h1:2DtF8GSIcajgffDFJzyG15vO+1PuBWOMUdFut7NnXhw=", version = "v1.0.0", ) go_repository( name = "com_github_campoy_unique", build_file_proto_mode = "disable", importpath = "github.com/campoy/unique", sum = "h1:V9a67dfYqPLAvzk5hMQOXYJlZ4SLIXgyKIE+ZiHzgGQ=", version = "v0.0.0-20180121183637-88950e537e7e", ) go_repository( name = "com_github_cavaliercoder_go_cpio", build_file_proto_mode = "disable", importpath = "github.com/cavaliercoder/go-cpio", sum = "h1:hHg27A0RSSp2Om9lubZpiMgVbvn39bsUmW9U5h0twqc=", version = "v0.0.0-20180626203310-925f9528c45e", ) go_repository( name = "com_github_census_instrumentation_opencensus_proto", build_file_proto_mode = "disable", importpath = "github.com/census-instrumentation/opencensus-proto", sum = "h1:glEXhBS5PSLLv4IXzLA5yPRVX4bilULVyxxbrfOtDAk=", version = "v0.2.1", ) go_repository( name = "com_github_cespare_xxhash", build_file_proto_mode = "disable", importpath = "github.com/cespare/xxhash", sum = "h1:a6HrQnmkObjyL+Gs60czilIUGqrzKutQD6XZog3p+ko=", version = "v1.1.0", ) go_repository( name = "com_github_cespare_xxhash_v2", build_file_proto_mode = "disable", importpath = "github.com/cespare/xxhash/v2", sum = "h1:6MnRN8NT7+YBpUIWxHtefFZOKTAPgGjpQSxqLNn0+qY=", version = "v2.1.1", ) go_repository( name = "com_github_chai2010_gettext_go", build_file_proto_mode = "disable", importpath = "github.com/chai2010/gettext-go", sum = "h1:7aWHqerlJ41y6FOsEUvknqgXnGmJyJSbjhAWq5pO4F8=", version = "v0.0.0-20160711120539-c6fed771bfd5", ) go_repository( name = "com_github_chzyer_logex", build_file_proto_mode = "disable", importpath = "github.com/chzyer/logex", sum = "h1:Swpa1K6QvQznwJRcfTfQJmTE72DqScAa40E+fbHEXEE=", version = "v1.1.10", ) go_repository( name = "com_github_chzyer_readline", build_file_proto_mode = "disable", importpath = "github.com/chzyer/readline", sum = "h1:fY5BOSpyZCqRo5OhCuC+XN+r/bBCmeuuJtjz+bCNIf8=", version = "v0.0.0-20180603132655-2972be24d48e", ) go_repository( name = "com_github_chzyer_test", build_file_proto_mode = "disable", importpath = "github.com/chzyer/test", sum = "h1:q763qf9huN11kDQavWsoZXJNW3xEE4JJyHa5Q25/sd8=", version = "v0.0.0-20180213035817-a1ea475d72b1", ) go_repository( name = "com_github_cihub_seelog", build_file_proto_mode = "disable", importpath = "github.com/cihub/seelog", sum = "h1:kHaBemcxl8o/pQ5VM1c8PVE1PubbNx3mjUr09OqWGCs=", version = "v0.0.0-20170130134532-f561c5e57575", ) go_repository( name = "com_github_clarketm_json", build_file_proto_mode = "disable", importpath = "github.com/clarketm/json", sum = "h1:0JketcMdLC16WGnRGJiNmTXuQznDEQaiknxSPRBxg+k=", version = "v1.13.4", ) go_repository( name = "com_github_client9_misspell", build_file_proto_mode = "disable", importpath = "github.com/client9/misspell", sum = "h1:ta993UF76GwbvJcIo3Y68y/M3WxlpEHPWIGDkJYwzJI=", version = "v0.3.4", ) go_repository( name = "com_github_cloudevents_sdk_go", build_file_proto_mode = "disable", importpath = "github.com/cloudevents/sdk-go", sum = "h1:gS5I0s2qPmdc4GBPlUmzZU7RH30BaiOdcRJ1RkXnPrc=", version = "v1.0.0", ) go_repository( name = "com_github_cloudevents_sdk_go_v2", build_file_proto_mode = "disable", importpath = "github.com/cloudevents/sdk-go/v2", sum = "h1:AUdGJwaSUnA+VvepKqgjy6XDkPcf0hf/3L7icEs1ibs=", version = "v2.0.0", ) go_repository( name = "com_github_cloudflare_cloudflare_go", build_file_proto_mode = "disable", importpath = "github.com/cloudflare/cloudflare-go", sum = "h1:bhMGoNhAg21DuqJjU9jQepRRft6vYfo6pejT3NN4V6A=", version = "v0.13.2", ) go_repository( name = "com_github_cncf_udpa_go", build_file_proto_mode = "disable", importpath = "github.com/cncf/udpa/go", sum = "h1:WBZRG4aNOuI15bLRrCgN8fCq8E5Xuty6jGbmSNEvSsU=", version = "v0.0.0-20191209042840-269d4d468f6f", ) go_repository( name = "com_github_cockroachdb_datadriven", build_file_proto_mode = "disable", importpath = "github.com/cockroachdb/datadriven", sum = "h1:OaNxuTZr7kxeODyLWsRMC+OD03aFUH+mW6r2d+MWa5Y=", version = "v0.0.0-20190809214429-80d97fb3cbaa", ) go_repository( name = "com_github_containerd_cgroups", build_file_proto_mode = "disable", importpath = "github.com/containerd/cgroups", sum = "h1:tSNMc+rJDfmYntojat8lljbt1mgKNpTxUZJsSzJ9Y1s=", version = "v0.0.0-20190919134610-bf292b21730f", ) go_repository( name = "com_github_containerd_console", build_file_proto_mode = "disable", importpath = "github.com/containerd/console", sum = "h1:uict5mhHFTzKLUCufdSLym7z/J0CbBJT59lYbP9wtbg=", version = "v0.0.0-20180822173158-c12b1e7919c1", ) go_repository( name = "com_github_containerd_containerd", build_file_proto_mode = "disable", importpath = "github.com/containerd/containerd", sum = "h1:LoIzb5y9x5l8VKAlyrbusNPXqBY0+kviRloxFUMFwKc=", version = "v1.3.3", ) go_repository( name = "com_github_containerd_continuity", build_file_proto_mode = "disable", importpath = "github.com/containerd/continuity", sum = "h1:kIFnQBO7rQ0XkMe6xEwbybYHBEaWmh/f++laI6Emt7M=", version = "v0.0.0-20200107194136-26c1120b8d41", ) go_repository( name = "com_github_containerd_fifo", build_file_proto_mode = "disable", importpath = "github.com/containerd/fifo", sum = "h1:PUD50EuOMkXVcpBIA/R95d56duJR9VxhwncsFbNnxW4=", version = "v0.0.0-20190226154929-a9fb20d87448", ) go_repository( name = "com_github_containerd_go_runc", build_file_proto_mode = "disable", importpath = "github.com/containerd/go-runc", sum = "h1:esQOJREg8nw8aXj6uCN5dfW5cKUBiEJ/+nni1Q/D/sw=", version = "v0.0.0-20180907222934-5a6d9f37cfa3", ) go_repository( name = "com_github_containerd_ttrpc", build_file_proto_mode = "disable", importpath = "github.com/containerd/ttrpc", sum = "h1:dlfGmNcE3jDAecLqwKPMNX6nk2qh1c1Vg1/YTzpOOF4=", version = "v0.0.0-20190828154514-0e0f228740de", ) go_repository( name = "com_github_containerd_typeurl", build_file_proto_mode = "disable", importpath = "github.com/containerd/typeurl", sum = "h1:JNn81o/xG+8NEo3bC/vx9pbi/g2WI8mtP2/nXzu297Y=", version = "v0.0.0-20180627222232-a93fcdb778cd", ) go_repository( name = "com_github_coreos_bbolt", build_file_proto_mode = "disable", importpath = "github.com/coreos/bbolt", sum = "h1:n6AiVyVRKQFNb6mJlwESEvvLoDyiTzXX7ORAUlkeBdY=", version = "v1.3.3", ) go_repository( name = "com_github_coreos_etcd", build_file_proto_mode = "disable", importpath = "github.com/coreos/etcd", sum = "h1:f/Z3EoDSx1yjaIjLQGo1diYUlQYSBrrAQ5vP8NjwXwo=", version = "v3.3.17+incompatible", ) go_repository( name = "com_github_coreos_go_etcd", build_file_proto_mode = "disable", importpath = "github.com/coreos/go-etcd", sum = "h1:bXhRBIXoTm9BYHS3gE0TtQuyNZyeEMux2sDi4oo5YOo=", version = "v2.0.0+incompatible", ) go_repository( name = "com_github_coreos_go_oidc", build_file_proto_mode = "disable", importpath = "github.com/coreos/go-oidc", sum = "h1:sdJrfw8akMnCuUlaZU3tE/uYXFgfqom8DBE9so9EBsM=", version = "v2.1.0+incompatible", ) go_repository( name = "com_github_coreos_go_semver", build_file_proto_mode = "disable", importpath = "github.com/coreos/go-semver", sum = "h1:wkHLiw0WNATZnSG7epLsujiMCgPAc9xhjJ4tgnAxmfM=", version = "v0.3.0", ) go_repository( name = "com_github_coreos_go_systemd", build_file_proto_mode = "disable", importpath = "github.com/coreos/go-systemd", sum = "h1:Wf6HqHfScWJN9/ZjdUKyjop4mf3Qdd+1TvvltAvM3m8=", version = "v0.0.0-20190321100706-95778dfbb74e", ) go_repository( name = "com_github_coreos_pkg", build_file_proto_mode = "disable", importpath = "github.com/coreos/pkg", sum = "h1:lBNOc5arjvs8E5mO2tbpBpLoyyu8B6e44T7hJy6potg=", version = "v0.0.0-20180928190104-399ea9e2e55f", ) go_repository( name = "com_github_cpu_goacmedns", build_file_proto_mode = "disable", importpath = "github.com/cpu/goacmedns", sum = "h1:QOeMpIEsIdm1LSASSswjaTf8CXmzcrgy5OeCfHjppA4=", version = "v0.0.3", ) go_repository( name = "com_github_cpuguy83_go_md2man", build_file_proto_mode = "disable", importpath = "github.com/cpuguy83/go-md2man", sum = "h1:BSKMNlYxDvnunlTymqtgONjNnaRV1sTpcovwwjF22jk=", version = "v1.0.10", ) go_repository( name = "com_github_cpuguy83_go_md2man_v2", build_file_proto_mode = "disable", importpath = "github.com/cpuguy83/go-md2man/v2", sum = "h1:EoUDS0afbrsXAZ9YQ9jdu/mZ2sXgT1/2yyNng4PGlyM=", version = "v2.0.0", ) go_repository( name = "com_github_creack_pty", build_file_proto_mode = "disable", importpath = "github.com/creack/pty", sum = "h1:07n33Z8lZxZ2qwegKbObQohDhXDQxiMMz1NOUGYlesw=", version = "v1.1.11", ) go_repository( name = "com_github_cyphar_filepath_securejoin", build_file_proto_mode = "disable", importpath = "github.com/cyphar/filepath-securejoin", sum = "h1:jCwT2GTP+PY5nBz3c/YL5PAIbusElVrPujOBSCj8xRg=", version = "v0.2.2", ) go_repository( name = "com_github_datadog_zstd", build_file_proto_mode = "disable", importpath = "github.com/DataDog/zstd", sum = "h1:3oxKN3wbHibqx897utPC2LTQU4J+IHWWJO+glkAkpFM=", version = "v1.4.1", ) go_repository( name = "com_github_davecgh_go_spew", build_file_proto_mode = "disable", importpath = "github.com/davecgh/go-spew", sum = "h1:vj9j/u1bqnvCEfJOwUhtlOARqs3+rkHYY13jYWTU97c=", version = "v1.1.1", ) go_repository( name = "com_github_daviddengcn_go_colortext", build_file_proto_mode = "disable", importpath = "github.com/daviddengcn/go-colortext", sum = "h1:uVsMphB1eRx7xB1njzL3fuMdWRN8HtVzoUOItHMwv5c=", version = "v0.0.0-20160507010035-511bcaf42ccd", ) go_repository( name = "com_github_deislabs_oras", build_file_proto_mode = "disable", importpath = "github.com/deislabs/oras", sum = "h1:If674KraJVpujYR00rzdi0QAmW4BxzMJPVAZJKuhQ0c=", version = "v0.8.1", ) go_repository( name = "com_github_denisenkom_go_mssqldb", build_file_proto_mode = "disable", importpath = "github.com/denisenkom/go-mssqldb", sum = "h1:83Wprp6ROGeiHFAP8WJdI2RoxALQYgdllERc3N5N2DM=", version = "v0.0.0-20191124224453-732737034ffd", ) go_repository( name = "com_github_denverdino_aliyungo", build_file_proto_mode = "disable", importpath = "github.com/denverdino/aliyungo", sum = "h1:p6poVbjHDkKa+wtC8frBMwQtT3BmqGYBjzMwJ63tuR4=", version = "v0.0.0-20190125010748-a747050bb1ba", ) go_repository( name = "com_github_devigned_tab", build_file_proto_mode = "disable", importpath = "github.com/devigned/tab", sum = "h1:3mD6Kb1mUOYeLpJvTVSDwSg5ZsfSxfvxGRTxRsJsITA=", version = "v0.1.1", ) go_repository( name = "com_github_dgrijalva_jwt_go", build_file_proto_mode = "disable", importpath = "github.com/dgrijalva/jwt-go", sum = "h1:7qlOGliEKZXTDg6OTjfoBKDXWrumCAMpl/TFQ4/5kLM=", version = "v3.2.0+incompatible", ) go_repository( name = "com_github_dgrijalva_jwt_go_v4", build_file_proto_mode = "disable", importpath = "github.com/dgrijalva/jwt-go/v4", sum = "h1:CaO/zOnF8VvUfEbhRatPcwKVWamvbYd8tQGRWacE9kU=", version = "v4.0.0-preview1", ) go_repository( name = "com_github_dgryski_go_gk", build_file_proto_mode = "disable", importpath = "github.com/dgryski/go-gk", sum = "h1:XOPLOMn/zT4jIgxfxSsoXPxkrzz0FaCHwp33x5POJ+Q=", version = "v0.0.0-20200319235926-a69029f61654", ) go_repository( name = "com_github_dgryski_go_sip13", build_file_proto_mode = "disable", importpath = "github.com/dgryski/go-sip13", sum = "h1:RMLoZVzv4GliuWafOuPuQDKSm1SJph7uCRnnS61JAn4=", version = "v0.0.0-20181026042036-e10d5fee7954", ) go_repository( name = "com_github_digitalocean_godo", build_file_proto_mode = "disable", importpath = "github.com/digitalocean/godo", sum = "h1:IMElzMUpO1dVR8qjSg53+5vDkOLzMbhJt4yTAq7NGCQ=", version = "v1.44.0", ) go_repository( name = "com_github_dimchansky_utfbom", build_file_proto_mode = "disable", importpath = "github.com/dimchansky/utfbom", sum = "h1:FcM3g+nofKgUteL8dm/UpdRXNC9KmADgTpLKsu0TRo4=", version = "v1.1.0", ) go_repository( name = "com_github_djarvur_go_err113", build_file_proto_mode = "disable", importpath = "github.com/Djarvur/go-err113", sum = "h1:uCRZZOdMQ0TZPHYTdYpoC0bLYJKPEHPUJ8MeAa51lNU=", version = "v0.1.0", ) go_repository( name = "com_github_djherbis_atime", build_file_proto_mode = "disable", importpath = "github.com/djherbis/atime", sum = "h1:ySLvBAM0EvOGaX7TI4dAM5lWj+RdJUCKtGSEHN8SGBg=", version = "v1.0.0", ) go_repository( name = "com_github_dnaeon_go_vcr", build_file_proto_mode = "disable", importpath = "github.com/dnaeon/go-vcr", sum = "h1:r8L/HqC0Hje5AXMu1ooW8oyQyOFv4GxqpL0nRP7SLLY=", version = "v1.0.1", ) go_repository( name = "com_github_docker_cli", build_file_proto_mode = "disable", importpath = "github.com/docker/cli", sum = "h1:AbI1uj9w4yt6TvfKHfRu7G55KuQe7NCvWPQRKDoXggE=", version = "v0.0.0-20200210162036-a4bedce16568", ) go_repository( name = "com_github_docker_distribution", build_file_proto_mode = "disable", importpath = "github.com/docker/distribution", sum = "h1:a5mlkVzth6W5A4fOsS3D2EO5BUmsJpcB+cRlLU7cSug=", version = "v2.7.1+incompatible", ) go_repository( name = "com_github_docker_docker", build_file_proto_mode = "disable", importpath = "github.com/docker/docker", sum = "h1:IkZjBSIc8hBjLpqeAbeE5mca5mNgeatLHBy3GO78BWo=", version = "v1.13.1", ) go_repository( name = "com_github_docker_docker_credential_helpers", build_file_proto_mode = "disable", importpath = "github.com/docker/docker-credential-helpers", sum = "h1:zI2p9+1NQYdnG6sMU26EX4aVGlqbInSQxQXLvzJ4RPQ=", version = "v0.6.3", ) go_repository( name = "com_github_docker_go_connections", build_file_proto_mode = "disable", importpath = "github.com/docker/go-connections", sum = "h1:El9xVISelRB7BuFusrZozjnkIM5YnzCViNKohAFqRJQ=", version = "v0.4.0", ) go_repository( name = "com_github_docker_go_metrics", build_file_proto_mode = "disable", importpath = "github.com/docker/go-metrics", sum = "h1:yWHOI+vFjEsAakUTSrtqc/SAHrhSkmn48pqjidZX3QA=", version = "v0.0.0-20180209012529-399ea8c73916", ) go_repository( name = "com_github_docker_go_units", build_file_proto_mode = "disable", importpath = "github.com/docker/go-units", sum = "h1:3uh0PgVws3nIA0Q+MwDC8yjEPf9zjRfZZWXZYDct3Tw=", version = "v0.4.0", ) go_repository( name = "com_github_docker_libtrust", build_file_proto_mode = "disable", importpath = "github.com/docker/libtrust", sum = "h1:ZClxb8laGDf5arXfYcAtECDFgAgHklGI8CxgjHnXKJ4=", version = "v0.0.0-20150114040149-fa567046d9b1", ) go_repository( name = "com_github_docker_spdystream", build_file_proto_mode = "disable", importpath = "github.com/docker/spdystream", sum = "h1:cenwrSVm+Z7QLSV/BsnenAOcDXdX4cMv4wP0B/5QbPg=", version = "v0.0.0-20160310174837-449fdfce4d96", ) go_repository( name = "com_github_docopt_docopt_go", build_file_proto_mode = "disable", importpath = "github.com/docopt/docopt-go", sum = "h1:bWDMxwH3px2JBh6AyO7hdCn/PkvCZXii8TGj7sbtEbQ=", version = "v0.0.0-20180111231733-ee0de3bc6815", ) go_repository( name = "com_github_dsnet_compress", build_file_proto_mode = "disable", importpath = "github.com/dsnet/compress", sum = "h1:PlZu0n3Tuv04TzpfPbrnI0HW/YwodEXDS+oPKahKF0Q=", version = "v0.0.1", ) go_repository( name = "com_github_dsnet_golib", build_file_proto_mode = "disable", importpath = "github.com/dsnet/golib", sum = "h1:tFh1tRc4CA31yP6qDcu+Trax5wW5GuMxvkIba07qVLY=", version = "v0.0.0-20171103203638-1ea166775780", ) go_repository( name = "com_github_dustin_go_humanize", build_file_proto_mode = "disable", importpath = "github.com/dustin/go-humanize", sum = "h1:VSnTsYCnlFHaM2/igO1h6X3HA71jcobQuxemgkq4zYo=", version = "v1.0.0", ) go_repository( name = "com_github_eapache_go_resiliency", build_file_proto_mode = "disable", importpath = "github.com/eapache/go-resiliency", sum = "h1:v7g92e/KSN71Rq7vSThKaWIq68fL4YHvWyiUKorFR1Q=", version = "v1.2.0", ) go_repository( name = "com_github_eapache_go_xerial_snappy", build_file_proto_mode = "disable", importpath = "github.com/eapache/go-xerial-snappy", sum = "h1:YEetp8/yCZMuEPMUDHG0CW/brkkEp8mzqk2+ODEitlw=", version = "v0.0.0-20180814174437-776d5712da21", ) go_repository( name = "com_github_eapache_queue", build_file_proto_mode = "disable", importpath = "github.com/eapache/queue", sum = "h1:YOEu7KNc61ntiQlcEeUIoDTJ2o8mQznoNvUhiigpIqc=", version = "v1.1.0", ) go_repository( name = "com_github_elazarl_goproxy", build_file_proto_mode = "disable", importpath = "github.com/elazarl/goproxy", sum = "h1:yUdfgN0XgIJw7foRItutHYUIhlcKzcSf5vDpdhQAKTc=", version = "v0.0.0-20180725130230-947c36da3153", ) go_repository( name = "com_github_emicklei_go_restful", build_file_proto_mode = "disable", importpath = "github.com/emicklei/go-restful", sum = "h1:spTtZBk5DYEvbxMVutUuTyh1Ao2r4iyvLdACqsl/Ljk=", version = "v2.9.5+incompatible", ) go_repository( name = "com_github_emirpasic_gods", build_file_proto_mode = "disable", importpath = "github.com/emirpasic/gods", sum = "h1:QAUIPSaCu4G+POclxeqb3F+WPpdKqFGlw36+yOzGlrg=", version = "v1.12.0", ) go_repository( name = "com_github_envoyproxy_go_control_plane", build_file_proto_mode = "disable", importpath = "github.com/envoyproxy/go-control-plane", sum = "h1:rEvIZUSZ3fx39WIi3JkQqQBitGwpELBIYWeBVh6wn+E=", version = "v0.9.4", ) go_repository( name = "com_github_envoyproxy_protoc_gen_validate", build_file_proto_mode = "disable", importpath = "github.com/envoyproxy/protoc-gen-validate", sum = "h1:EQciDnbrYxy13PgWoY8AqoxGiPrpgBZ1R8UNe3ddc+A=", version = "v0.1.0", ) go_repository( name = "com_github_erikstmartin_go_testdb", build_file_proto_mode = "disable", importpath = "github.com/erikstmartin/go-testdb", sum = "h1:Yzb9+7DPaBjB8zlTR87/ElzFsnQfuHnVUVqpZZIcV5Y=", version = "v0.0.0-20160219214506-8d10e4a1bae5", ) go_repository( name = "com_github_evanphx_json_patch", build_file_proto_mode = "disable", importpath = "github.com/evanphx/json-patch", sum = "h1:glyUF9yIYtMHzn8xaKw5rMhdWcwsYV8dZHIq5567/xs=", version = "v4.11.0+incompatible", ) go_repository( name = "com_github_exponent_io_jsonpath", build_file_proto_mode = "disable", importpath = "github.com/exponent-io/jsonpath", sum = "h1:105gxyaGwCFad8crR9dcMQWvV9Hvulu6hwUh4tWPJnM=", version = "v0.0.0-20151013193312-d6023ce2651d", ) go_repository( name = "com_github_fatih_camelcase", build_file_proto_mode = "disable", importpath = "github.com/fatih/camelcase", sum = "h1:hxNvNX/xYBp0ovncs8WyWZrOrpBNub/JfaMvbURyft8=", version = "v1.0.0", ) go_repository( name = "com_github_fatih_color", build_file_proto_mode = "disable", importpath = "github.com/fatih/color", sum = "h1:8xPHl4/q1VyqGIPif1F+1V3Y3lSmrq01EabUW3CoW5s=", version = "v1.9.0", ) go_repository( name = "com_github_fatih_structs", build_file_proto_mode = "disable", importpath = "github.com/fatih/structs", sum = "h1:Q7juDM0QtcnhCpeyLGQKyg4TOIghuNXrkL32pHAUMxo=", version = "v1.1.0", ) go_repository( name = "com_github_felixge_fgprof", build_file_proto_mode = "disable", importpath = "github.com/felixge/fgprof", sum = "h1:E6FUJ2Mlv043ipLOCFqo8+cHo9MhQ203E2cdEK/isEs=", version = "v0.9.1", ) go_repository( name = "com_github_flynn_go_shlex", build_file_proto_mode = "disable", importpath = "github.com/flynn/go-shlex", sum = "h1:BHsljHzVlRcyQhjrss6TZTdY2VfCqZPbv5k3iBFa2ZQ=", version = "v0.0.0-20150515145356-3f9db97f8568", ) go_repository( name = "com_github_form3tech_oss_jwt_go", build_file_proto_mode = "disable", importpath = "github.com/form3tech-oss/jwt-go", sum = "h1:TcekIExNqud5crz4xD2pavyTgWiPvpYe4Xau31I0PRk=", version = "v3.2.2+incompatible", ) go_repository( name = "com_github_fortytw2_leaktest", build_file_proto_mode = "disable", importpath = "github.com/fortytw2/leaktest", sum = "h1:u8491cBMTQ8ft8aeV+adlcytMZylmA5nnwwkRZjI8vw=", version = "v1.3.0", ) go_repository( name = "com_github_frankban_quicktest", build_file_proto_mode = "disable", importpath = "github.com/frankban/quicktest", sum = "h1:PvpJR0Uq8SdX+zagCMsarBMlhz6ysGTf1+pRmCsRXqY=", version = "v1.8.1", ) go_repository( name = "com_github_fsnotify_fsnotify", build_file_proto_mode = "disable", importpath = "github.com/fsnotify/fsnotify", sum = "h1:hsms1Qyu0jgnwNXIxa+/V/PDsU6CfLf6CNO8H7IWoS4=", version = "v1.4.9", ) go_repository( name = "com_github_fsouza_fake_gcs_server", build_file_proto_mode = "disable", importpath = "github.com/fsouza/fake-gcs-server", sum = "h1:3bRRh/rQnB2XbrMolHAj9oX/PFiWVQFVVfPR5y2pxb8=", version = "v1.19.4", ) go_repository( name = "com_github_fvbommel_sortorder", build_file_proto_mode = "disable", importpath = "github.com/fvbommel/sortorder", sum = "h1:dSnXLt4mJYH25uDDGa3biZNQsozaUWDSWeKJ0qqFfzE=", version = "v1.0.1", ) go_repository( name = "com_github_garyburd_redigo", build_file_proto_mode = "disable", importpath = "github.com/garyburd/redigo", sum = "h1:LofdAjjjqCSXMwLGgOgnE+rdPuvX9DxCqaHwKy7i/ko=", version = "v0.0.0-20150301180006-535138d7bcd7", ) go_repository( name = "com_github_ghodss_yaml", build_file_proto_mode = "disable", importpath = "github.com/ghodss/yaml", sum = "h1:wQHKEahhL6wmXdzwWG11gIVCkOv05bNOh+Rxn0yngAk=", version = "v1.0.0", ) go_repository( name = "com_github_gliderlabs_ssh", build_file_proto_mode = "disable", importpath = "github.com/gliderlabs/ssh", sum = "h1:6zsha5zo/TWhRhwqCD3+EarCAgZ2yN28ipRnGPnwkI0=", version = "v0.2.2", ) go_repository( name = "com_github_globalsign_mgo", build_file_proto_mode = "disable", importpath = "github.com/globalsign/mgo", sum = "h1:DujepqpGd1hyOd7aW59XpK7Qymp8iy83xq74fLr21is=", version = "v0.0.0-20181015135952-eeefdecb41b8", ) go_repository( name = "com_github_go_bindata_go_bindata_v3", build_file_proto_mode = "disable", importpath = "github.com/go-bindata/go-bindata/v3", sum = "h1:F0nVttLC3ws0ojc7p60veTurcOm//D4QBODNM7EGrCI=", version = "v3.1.3", ) go_repository( name = "com_github_go_critic_go_critic", build_file_proto_mode = "disable", importpath = "github.com/go-critic/go-critic", sum = "h1:sGEEdiuvLV0OC7/yC6MnK3K6LCPBplspK45B0XVdFAc=", version = "v0.4.3", ) go_repository( name = "com_github_go_git_gcfg", build_file_proto_mode = "disable", importpath = "github.com/go-git/gcfg", sum = "h1:Q5ViNfGF8zFgyJWPqYwA7qGFoMTEiBmdlkcfRmpIMa4=", version = "v1.5.0", ) go_repository( name = "com_github_go_git_go_billy_v5", build_file_proto_mode = "disable", importpath = "github.com/go-git/go-billy/v5", sum = "h1:4pl5BV4o7ZG/lterP4S6WzJ6xr49Ba5ET9ygheTYahk=", version = "v5.1.0", ) go_repository( name = "com_github_go_git_go_git_fixtures_v4", build_file_proto_mode = "disable", importpath = "github.com/go-git/go-git-fixtures/v4", sum = "h1:PbKy9zOy4aAKrJ5pibIRpVO2BXnK1Tlcg+caKI7Ox5M=", version = "v4.0.2-0.20200613231340-f56387b50c12", ) go_repository( name = "com_github_go_git_go_git_v5", build_file_proto_mode = "disable", importpath = "github.com/go-git/go-git/v5", sum = "h1:8WKMtJR2j8RntEXR/uvTKagfEt4GYlwQ7mntE4+0GWc=", version = "v5.3.0", ) go_repository( name = "com_github_go_gl_glfw", build_file_proto_mode = "disable", importpath = "github.com/go-gl/glfw", sum = "h1:QbL/5oDUmRBzO9/Z7Seo6zf912W/a6Sr4Eu0G/3Jho0=", version = "v0.0.0-20190409004039-e6da0acd62b1", ) go_repository( name = "com_github_go_gl_glfw_v3_3_glfw", build_file_proto_mode = "disable", importpath = "github.com/go-gl/glfw/v3.3/glfw", sum = "h1:WtGNWLvXpe6ZudgnXrq0barxBImvnnJoMEhXAzcbM0I=", version = "v0.0.0-20200222043503-6f7a984d4dc4", ) go_repository( name = "com_github_go_ini_ini", build_file_proto_mode = "disable", importpath = "github.com/go-ini/ini", sum = "h1:0wVcG9udk2C3TGgmdIGKK9ScOZHZB5nbG+gwji9fhhc=", version = "v1.55.0", ) go_repository( name = "com_github_go_kit_kit", build_file_proto_mode = "disable", importpath = "github.com/go-kit/kit", sum = "h1:wDJmvq38kDhkVxi50ni9ykkdUr1PKgqKOoi01fa0Mdk=", version = "v0.9.0", ) go_repository( name = "com_github_go_kit_log", build_file_proto_mode = "disable", importpath = "github.com/go-kit/log", sum = "h1:DGJh0Sm43HbOeYDNnVZFl8BvcYVvjD5bqYJvp0REbwQ=", version = "v0.1.0", ) go_repository( name = "com_github_go_ldap_ldap", build_file_proto_mode = "disable", importpath = "github.com/go-ldap/ldap", sum = "h1:kD5HQcAzlQ7yrhfn+h+MSABeAy/jAJhvIJ/QDllP44g=", version = "v3.0.2+incompatible", ) go_repository( name = "com_github_go_lintpack_lintpack", build_file_proto_mode = "disable", importpath = "github.com/go-lintpack/lintpack", sum = "h1:DI5mA3+eKdWeJ40nU4d6Wc26qmdG8RCi/btYq0TuRN0=", version = "v0.5.2", ) go_repository( name = "com_github_go_logfmt_logfmt", build_file_proto_mode = "disable", importpath = "github.com/go-logfmt/logfmt", sum = "h1:TrB8swr/68K7m9CcGut2g3UOihhbcbiMAYiuTXdEih4=", version = "v0.5.0", ) go_repository( name = "com_github_go_logr_logr", build_file_proto_mode = "disable", importpath = "github.com/go-logr/logr", sum = "h1:K7/B1jt6fIBQVd4Owv2MqGQClcgf0R266+7C/QjRcLc=", version = "v0.4.0", ) go_repository( name = "com_github_go_logr_zapr", build_file_proto_mode = "disable", importpath = "github.com/go-logr/zapr", sum = "h1:uc1uML3hRYL9/ZZPdgHS/n8Nzo+eaYL/Efxkkamf7OM=", version = "v0.4.0", ) go_repository( name = "com_github_go_ole_go_ole", build_file_proto_mode = "disable", importpath = "github.com/go-ole/go-ole", sum = "h1:2lOsA72HgjxAuMlKpFiCbHTvu44PIVkZ5hqm3RSdI/E=", version = "v1.2.1", ) go_repository( name = "com_github_go_openapi_analysis", build_file_proto_mode = "disable", importpath = "github.com/go-openapi/analysis", sum = "h1:8b2ZgKfKIUTVQpTb77MoRDIMEIwvDVw40o3aOXdfYzI=", version = "v0.19.5", ) go_repository( name = "com_github_go_openapi_errors", build_file_proto_mode = "disable", importpath = "github.com/go-openapi/errors", sum = "h1:a2kIyV3w+OS3S97zxUndRVD46+FhGOUBDFY7nmu4CsY=", version = "v0.19.2", ) go_repository( name = "com_github_go_openapi_jsonpointer", build_file_proto_mode = "disable", importpath = "github.com/go-openapi/jsonpointer", sum = "h1:gihV7YNZK1iK6Tgwwsxo2rJbD1GTbdm72325Bq8FI3w=", version = "v0.19.3", ) go_repository( name = "com_github_go_openapi_jsonreference", build_file_proto_mode = "disable", importpath = "github.com/go-openapi/jsonreference", sum = "h1:5cxNfTy0UVC3X8JL5ymxzyoUZmo8iZb+jeTWn7tUa8o=", version = "v0.19.3", ) go_repository( name = "com_github_go_openapi_loads", build_file_proto_mode = "disable", importpath = "github.com/go-openapi/loads", sum = "h1:5I4CCSqoWzT+82bBkNIvmLc0UOsoKKQ4Fz+3VxOB7SY=", version = "v0.19.4", ) go_repository( name = "com_github_go_openapi_runtime", build_file_proto_mode = "disable", importpath = "github.com/go-openapi/runtime", sum = "h1:csnOgcgAiuGoM/Po7PEpKDoNulCcF3FGbSnbHfxgjMI=", version = "v0.19.4", ) go_repository( name = "com_github_go_openapi_spec", build_file_proto_mode = "disable", importpath = "github.com/go-openapi/spec", sum = "h1:rMMMj8cV38KVXK7SFc+I2MWClbEfbK705+j+dyqun5g=", version = "v0.19.6", ) go_repository( name = "com_github_go_openapi_strfmt", build_file_proto_mode = "disable", importpath = "github.com/go-openapi/strfmt", sum = "h1:eRfyY5SkaNJCAwmmMcADjY31ow9+N7MCLW7oRkbsINA=", version = "v0.19.3", ) go_repository( name = "com_github_go_openapi_swag", build_file_proto_mode = "disable", importpath = "github.com/go-openapi/swag", sum = "h1:VRuXN2EnMSsZdauzdss6JBC29YotDqG59BZ+tdlIL1s=", version = "v0.19.7", ) go_repository( name = "com_github_go_openapi_validate", build_file_proto_mode = "disable", importpath = "github.com/go-openapi/validate", sum = "h1:QhCBKRYqZR+SKo4gl1lPhPahope8/RLt6EVgY8X80w0=", version = "v0.19.5", ) go_repository( name = "com_github_go_sql_driver_mysql", build_file_proto_mode = "disable", importpath = "github.com/go-sql-driver/mysql", sum = "h1:ozyZYNQW3x3HtqT1jira07DN2PArx2v7/mN66gGcHOs=", version = "v1.5.0", ) go_repository( name = "com_github_go_stack_stack", build_file_proto_mode = "disable", importpath = "github.com/go-stack/stack", sum = "h1:5SgMzNM5HxrEjV0ww2lTmX6E2Izsfxas4+YHWRs3Lsk=", version = "v1.8.0", ) go_repository( name = "com_github_go_task_slim_sprig", build_file_proto_mode = "disable", importpath = "github.com/go-task/slim-sprig", sum = "h1:p104kn46Q8WdvHunIJ9dAyjPVtrBPhSr3KT2yUst43I=", version = "v0.0.0-20210107165309-348f09dbbbc0", ) go_repository( name = "com_github_go_test_deep", build_file_proto_mode = "disable", importpath = "github.com/go-test/deep", sum = "h1:/VSMRlnY/JSyqxQUzQLKVMAskpY/NZKFA5j2P+0pP2M=", version = "v1.0.7", ) go_repository( name = "com_github_go_toolsmith_astcast", build_file_proto_mode = "disable", importpath = "github.com/go-toolsmith/astcast", sum = "h1:JojxlmI6STnFVG9yOImLeGREv8W2ocNUM+iOhR6jE7g=", version = "v1.0.0", ) go_repository( name = "com_github_go_toolsmith_astcopy", build_file_proto_mode = "disable", importpath = "github.com/go-toolsmith/astcopy", sum = "h1:OMgl1b1MEpjFQ1m5ztEO06rz5CUd3oBv9RF7+DyvdG8=", version = "v1.0.0", ) go_repository( name = "com_github_go_toolsmith_astequal", build_file_proto_mode = "disable", importpath = "github.com/go-toolsmith/astequal", sum = "h1:4zxD8j3JRFNyLN46lodQuqz3xdKSrur7U/sr0SDS/gQ=", version = "v1.0.0", ) go_repository( name = "com_github_go_toolsmith_astfmt", build_file_proto_mode = "disable", importpath = "github.com/go-toolsmith/astfmt", sum = "h1:A0vDDXt+vsvLEdbMFJAUBI/uTbRw1ffOPnxsILnFL6k=", version = "v1.0.0", ) go_repository( name = "com_github_go_toolsmith_astinfo", build_file_proto_mode = "disable", importpath = "github.com/go-toolsmith/astinfo", sum = "h1:wP6mXeB2V/d1P1K7bZ5vDUO3YqEzcvOREOxZPEu3gVI=", version = "v0.0.0-20180906194353-9809ff7efb21", ) go_repository( name = "com_github_go_toolsmith_astp", build_file_proto_mode = "disable", importpath = "github.com/go-toolsmith/astp", sum = "h1:alXE75TXgcmupDsMK1fRAy0YUzLzqPVvBKoyWV+KPXg=", version = "v1.0.0", ) go_repository( name = "com_github_go_toolsmith_pkgload", build_file_proto_mode = "disable", importpath = "github.com/go-toolsmith/pkgload", sum = "h1:4DFWWMXVfbcN5So1sBNW9+yeiMqLFGl1wFLTL5R0Tgg=", version = "v1.0.0", ) go_repository( name = "com_github_go_toolsmith_strparse", build_file_proto_mode = "disable", importpath = "github.com/go-toolsmith/strparse", sum = "h1:Vcw78DnpCAKlM20kSbAyO4mPfJn/lyYA4BJUDxe2Jb4=", version = "v1.0.0", ) go_repository( name = "com_github_go_toolsmith_typep", build_file_proto_mode = "disable", importpath = "github.com/go-toolsmith/typep", sum = "h1:8xdsa1+FSIH/RhEkgnD1j2CJOy5mNllW1Q9tRiYwvlk=", version = "v1.0.2", ) go_repository( name = "com_github_go_xmlfmt_xmlfmt", build_file_proto_mode = "disable", importpath = "github.com/go-xmlfmt/xmlfmt", sum = "h1:khEcpUM4yFcxg4/FHQWkvVRmgijNXRfzkIDHh23ggEo=", version = "v0.0.0-20191208150333-d5b6f63a941b", ) go_repository( name = "com_github_go_yaml_yaml", build_file_proto_mode = "disable", importpath = "github.com/go-yaml/yaml", sum = "h1:RYi2hDdss1u4YE7GwixGzWwVo47T8UQwnTLB6vQiq+o=", version = "v2.1.0+incompatible", ) go_repository( name = "com_github_gobuffalo_envy", build_file_proto_mode = "disable", importpath = "github.com/gobuffalo/envy", sum = "h1:OQl5ys5MBea7OGCdvPbBJWRgnhC/fGona6QKfvFeau8=", version = "v1.7.1", ) go_repository( name = "com_github_gobuffalo_flect", build_file_proto_mode = "disable", importpath = "github.com/gobuffalo/flect", sum = "h1:EWCvMGGxOjsgwlWaP+f4+Hh6yrrte7JeFL2S6b+0hdM=", version = "v0.2.0", ) go_repository( name = "com_github_gobwas_glob", build_file_proto_mode = "disable", importpath = "github.com/gobwas/glob", sum = "h1:A4xDbljILXROh+kObIiy5kIaPYD8e96x1tgBhUI5J+Y=", version = "v0.2.3", ) go_repository( name = "com_github_godbus_dbus", build_file_proto_mode = "disable", importpath = "github.com/godbus/dbus", sum = "h1:BWhy2j3IXJhjCbC68FptL43tDKIq8FladmaTs3Xs7Z8=", version = "v0.0.0-20190422162347-ade71ed3457e", ) go_repository( name = "com_github_gofrs_flock", build_file_proto_mode = "disable", importpath = "github.com/gofrs/flock", sum = "h1:DP+LD/t0njgoPBvT5MJLeliUIVQR03hiKR6vezdwHlc=", version = "v0.7.1", ) go_repository( name = "com_github_gogo_googleapis", build_file_proto_mode = "disable", importpath = "github.com/gogo/googleapis", sum = "h1:kFkMAZBNAn4j7K0GiZr8cRYzejq68VbheufiV3YuyFI=", version = "v1.1.0", ) go_repository( name = "com_github_gogo_protobuf", build_file_proto_mode = "disable", importpath = "github.com/gogo/protobuf", sum = "h1:Ov1cvc58UF3b5XjBnZv7+opcTcQFZebYjWzi34vdm4Q=", version = "v1.3.2", ) go_repository( name = "com_github_golang_gddo", build_file_proto_mode = "disable", importpath = "github.com/golang/gddo", sum = "h1:KRMr9A3qfbVM7iV/WcLY/rL5LICqwMHLhwRXKu99fXw=", version = "v0.0.0-20190419222130-af0f2af80721", ) go_repository( name = "com_github_golang_glog", build_file_proto_mode = "disable", importpath = "github.com/golang/glog", sum = "h1:VKtxabqXZkF25pY9ekfRL6a582T4P37/31XEstQ5p58=", version = "v0.0.0-20160126235308-23def4e6c14b", ) go_repository( name = "com_github_golang_groupcache", build_file_proto_mode = "disable", importpath = "github.com/golang/groupcache", sum = "h1:1r7pUrabqp18hOBcwBwiTsbnFeTZHV9eER/QT5JVZxY=", version = "v0.0.0-20200121045136-8c9f03a8e57e", ) go_repository( name = "com_github_golang_lint", build_file_proto_mode = "disable", importpath = "github.com/golang/lint", sum = "h1:2hRPrmiwPrp3fQX967rNJIhQPtiGXdlQWAxKbKw3VHA=", version = "v0.0.0-20180702182130-06c8688daad7", ) go_repository( name = "com_github_golang_mock", build_file_proto_mode = "disable", importpath = "github.com/golang/mock", sum = "h1:jlYHihg//f7RRwuPfptm04yp4s7O6Kw8EZiVYIGcH0g=", version = "v1.5.0", ) go_repository( name = "com_github_golang_protobuf", build_file_proto_mode = "disable", importpath = "github.com/golang/protobuf", sum = "h1:ROPKBNFfQgOUMifHyP+KYbvpjbdoFNs+aK7DXlji0Tw=", version = "v1.5.2", ) go_repository( name = "com_github_golang_snappy", build_file_proto_mode = "disable", importpath = "github.com/golang/snappy", sum = "h1:Qgr9rKW7uDUkrbSmQeiDsGa8SjGyCOGtuasMWwvp2P4=", version = "v0.0.1", ) go_repository( name = "com_github_golang_sql_civil", build_file_proto_mode = "disable", importpath = "github.com/golang-sql/civil", sum = "h1:lXe2qZdvpiX5WZkZR4hgp4KJVfY3nMkvmwbVkpv1rVY=", version = "v0.0.0-20190719163853-cb61b32ac6fe", ) go_repository( name = "com_github_golangci_check", build_file_proto_mode = "disable", importpath = "github.com/golangci/check", sum = "h1:23T5iq8rbUYlhpt5DB4XJkc6BU31uODLD1o1gKvZmD0=", version = "v0.0.0-20180506172741-cfe4005ccda2", ) go_repository( name = "com_github_golangci_dupl", build_file_proto_mode = "disable", importpath = "github.com/golangci/dupl", sum = "h1:w8hkcTqaFpzKqonE9uMCefW1WDie15eSP/4MssdenaM=", version = "v0.0.0-20180902072040-3e9179ac440a", ) go_repository( name = "com_github_golangci_errcheck", build_file_proto_mode = "disable", importpath = "github.com/golangci/errcheck", sum = "h1:YYWNAGTKWhKpcLLt7aSj/odlKrSrelQwlovBpDuf19w=", version = "v0.0.0-20181223084120-ef45e06d44b6", ) go_repository( name = "com_github_golangci_go_misc", build_file_proto_mode = "disable", importpath = "github.com/golangci/go-misc", sum = "h1:9kfjN3AdxcbsZBf8NjltjWihK2QfBBBZuv91cMFfDHw=", version = "v0.0.0-20180628070357-927a3d87b613", ) go_repository( name = "com_github_golangci_goconst", build_file_proto_mode = "disable", importpath = "github.com/golangci/goconst", sum = "h1:pe9JHs3cHHDQgOFXJJdYkK6fLz2PWyYtP4hthoCMvs8=", version = "v0.0.0-20180610141641-041c5f2b40f3", ) go_repository( name = "com_github_golangci_gocyclo", build_file_proto_mode = "disable", importpath = "github.com/golangci/gocyclo", sum = "h1:pXTK/gkVNs7Zyy7WKgLXmpQ5bHTrq5GDsp8R9Qs67g0=", version = "v0.0.0-20180528144436-0a533e8fa43d", ) go_repository( name = "com_github_golangci_gofmt", build_file_proto_mode = "disable", importpath = "github.com/golangci/gofmt", sum = "h1:iR3fYXUjHCR97qWS8ch1y9zPNsgXThGwjKPrYfqMPks=", version = "v0.0.0-20190930125516-244bba706f1a", ) go_repository( name = "com_github_golangci_golangci_lint", build_file_proto_mode = "disable", importpath = "github.com/golangci/golangci-lint", sum = "h1:VYLx63qb+XJsHdZ27PMS2w5JZacN0XG8ffUwe7yQomo=", version = "v1.27.0", ) go_repository( name = "com_github_golangci_ineffassign", build_file_proto_mode = "disable", importpath = "github.com/golangci/ineffassign", sum = "h1:gLLhTLMk2/SutryVJ6D4VZCU3CUqr8YloG7FPIBWFpI=", version = "v0.0.0-20190609212857-42439a7714cc", ) go_repository( name = "com_github_golangci_lint_1", build_file_proto_mode = "disable", importpath = "github.com/golangci/lint-1", sum = "h1:MfyDlzVjl1hoaPzPD4Gpb/QgoRfSBR0jdhwGyAWwMSA=", version = "v0.0.0-20191013205115-297bf364a8e0", ) go_repository( name = "com_github_golangci_maligned", build_file_proto_mode = "disable", importpath = "github.com/golangci/maligned", sum = "h1:kNY3/svz5T29MYHubXix4aDDuE3RWHkPvopM/EDv/MA=", version = "v0.0.0-20180506175553-b1d89398deca", ) go_repository( name = "com_github_golangci_misspell", build_file_proto_mode = "disable", importpath = "github.com/golangci/misspell", sum = "h1:pLzmVdl3VxTOncgzHcvLOKirdvcx/TydsClUQXTehjo=", version = "v0.3.5", ) go_repository( name = "com_github_golangci_prealloc", build_file_proto_mode = "disable", importpath = "github.com/golangci/prealloc", sum = "h1:leSNB7iYzLYSSx3J/s5sVf4Drkc68W2wm4Ixh/mr0us=", version = "v0.0.0-20180630174525-215b22d4de21", ) go_repository( name = "com_github_golangci_revgrep", build_file_proto_mode = "disable", importpath = "github.com/golangci/revgrep", sum = "h1:XQKc8IYQOeRwVs36tDrEmTgDgP88d5iEURwpmtiAlOM=", version = "v0.0.0-20180812185044-276a5c0a1039", ) go_repository( name = "com_github_golangci_unconvert", build_file_proto_mode = "disable", importpath = "github.com/golangci/unconvert", sum = "h1:zwtduBRr5SSWhqsYNgcuWO2kFlpdOZbP0+yRjmvPGys=", version = "v0.0.0-20180507085042-28b1c447d1f4", ) go_repository( name = "com_github_golangplus_bytes", build_file_proto_mode = "disable", importpath = "github.com/golangplus/bytes", sum = "h1:7xqw01UYS+KCI25bMrPxwNYkSns2Db1ziQPpVq99FpE=", version = "v0.0.0-20160111154220-45c989fe5450", ) go_repository( name = "com_github_golangplus_fmt", build_file_proto_mode = "disable", importpath = "github.com/golangplus/fmt", sum = "h1:f5gsjBiF9tRRVomCvrkGMMWI8W1f2OBFar2c5oakAP0=", version = "v0.0.0-20150411045040-2a5d6d7d2995", ) go_repository( name = "com_github_golangplus_testing", build_file_proto_mode = "disable", importpath = "github.com/golangplus/testing", sum = "h1:KhcknUwkWHKZPbFy2P7jH5LKJ3La+0ZeknkkmrSgqb0=", version = "v0.0.0-20180327235837-af21d9c3145e", ) go_repository( name = "com_github_gomodule_redigo", build_file_proto_mode = "disable", importpath = "github.com/gomodule/redigo", sum = "h1:nRAxCa+SVsyjSBrtZmG/cqb6VbTmuRzpg/PoTFlpumc=", version = "v1.8.5", ) go_repository( name = "com_github_google_btree", build_file_proto_mode = "disable", importpath = "github.com/google/btree", sum = "h1:0udJVsspx3VBr5FwtLhQQtuAsVc79tTq0ocGIPAU6qo=", version = "v1.0.0", ) go_repository( name = "com_github_google_go_cmp", build_file_proto_mode = "disable", importpath = "github.com/google/go-cmp", sum = "h1:Khx7svrCpmxxtHBq5j2mp/xVjsi8hQMfNLvJFAlrGgU=", version = "v0.5.5", ) go_repository( name = "com_github_google_go_containerregistry", build_file_proto_mode = "disable", importpath = "github.com/google/go-containerregistry", sum = "h1:AG8FSAfXglim2l5qSrqp5VK2Xl03PiBf25NiTGGamws=", version = "v0.1.1", ) go_repository( name = "com_github_google_go_github", build_file_proto_mode = "disable", importpath = "github.com/google/go-github", sum = "h1:N0LgJ1j65A7kfXrZnUDaYCs/Sf4rEjNlfyDHW9dolSY=", version = "v17.0.0+incompatible", ) go_repository( name = "com_github_google_go_github_v27", build_file_proto_mode = "disable", importpath = "github.com/google/go-github/v27", sum = "h1:oiOZuBmGHvrGM1X9uNUAUlLgp5r1UUO/M/KnbHnLRlQ=", version = "v27.0.6", ) go_repository( name = "com_github_google_go_github_v28", build_file_proto_mode = "disable", importpath = "github.com/google/go-github/v28", sum = "h1:kORf5ekX5qwXO2mGzXXOjMe/g6ap8ahVe0sBEulhSxo=", version = "v28.1.1", ) go_repository( name = "com_github_google_go_github_v29", build_file_proto_mode = "disable", importpath = "github.com/google/go-github/v29", sum = "h1:IktKCTwU//aFHnpA+2SLIi7Oo9uhAzgsdZNbcAqhgdc=", version = "v29.0.3", ) go_repository( name = "com_github_google_go_github_v32", build_file_proto_mode = "disable", importpath = "github.com/google/go-github/v32", sum = "h1:q74KVb22spUq0U5HqZ9VCYqQz8YRuOtL/39ZnfwO+NM=", version = "v32.0.0", ) go_repository( name = "com_github_google_go_licenses", build_file_proto_mode = "disable", importpath = "github.com/google/go-licenses", sum = "h1:eVR9gT5gBPTHXeyGAcA8OF/SKNUFFg+a0BJqfx4z5eE=", version = "v0.0.0-20200227160636-0fa8c766a591", ) go_repository( name = "com_github_google_go_querystring", build_file_proto_mode = "disable", importpath = "github.com/google/go-querystring", sum = "h1:AnCroh3fv4ZBgVIf1Iwtovgjaw/GiKJo8M8yD/fhyJ8=", version = "v1.1.0", ) go_repository( name = "com_github_google_go_replayers_grpcreplay", build_file_proto_mode = "disable", importpath = "github.com/google/go-replayers/grpcreplay", sum = "h1:eNb1y9rZFmY4ax45uEEECSa8fsxGRU+8Bil52ASAwic=", version = "v0.1.0", ) go_repository( name = "com_github_google_go_replayers_httpreplay", build_file_proto_mode = "disable", importpath = "github.com/google/go-replayers/httpreplay", sum = "h1:AX7FUb4BjrrzNvblr/OlgwrmFiep6soj5K2QSDW7BGk=", version = "v0.1.0", ) go_repository( name = "com_github_google_gofuzz", build_file_proto_mode = "disable_global", importpath = "github.com/google/gofuzz", sum = "h1:VcIYpAGBae3Z6BVncE0OnTE/ZjlDXqtYhOZky88neLM=", version = "v1.2.1-0.20210504230335-f78f29fc09ea", ) go_repository( name = "com_github_google_licenseclassifier", build_file_proto_mode = "disable", importpath = "github.com/google/licenseclassifier", sum = "h1:OggOMmdI0JLwg1FkOKH9S7fVHF0oEm8PX6S8kAdpOps=", version = "v0.0.0-20200402202327-879cb1424de0", ) go_repository( name = "com_github_google_mako", build_file_proto_mode = "disable", importpath = "github.com/google/mako", sum = "h1:/o5e44nTD/QEEiWPGSFT3bSqcq3Qg7q27N9bv4gKh5M=", version = "v0.0.0-20190821191249-122f8dcef9e3", ) go_repository( name = "com_github_google_martian", build_file_proto_mode = "disable", importpath = "github.com/google/martian", sum = "h1:xmapqc1AyLoB+ddYT6r04bD9lIjlOqGaREovi0SzFaE=", version = "v2.1.1-0.20190517191504-25dcb96d9e51+incompatible", ) go_repository( name = "com_github_google_martian_v3", build_file_proto_mode = "disable", importpath = "github.com/google/martian/v3", sum = "h1:pMen7vLs8nvgEYhywH3KDWJIJTeEr2ULsVWHWYHQyBs=", version = "v3.0.0", ) go_repository( name = "com_github_google_pprof", build_file_proto_mode = "disable", importpath = "github.com/google/pprof", sum = "h1:k+KkMRk8mGOu1xG38StS7dQ+Z6oW1i9n3dgrAVU9Q/E=", version = "v0.0.0-20200905233945-acf8798be1f7", ) go_repository( name = "com_github_google_renameio", build_file_proto_mode = "disable", importpath = "github.com/google/renameio", sum = "h1:GOZbcHa3HfsPKPlmyPyN2KEohoMXOhdMbHrvbpl2QaA=", version = "v0.1.0", ) go_repository( name = "com_github_google_rpmpack", build_file_proto_mode = "disable", importpath = "github.com/google/rpmpack", sum = "h1:BW6OvS3kpT5UEPbCZ+KyX/OB4Ks9/MNMhWjqPPkZxsE=", version = "v0.0.0-20191226140753-aa36bfddb3a0", ) go_repository( name = "com_github_google_subcommands", build_file_proto_mode = "disable", importpath = "github.com/google/subcommands", sum = "h1:/eqq+otEXm5vhfBrbREPCSVQbvofip6kIz+mX5TUH7k=", version = "v1.0.1", ) go_repository( name = "com_github_google_uuid", build_file_proto_mode = "disable", importpath = "github.com/google/uuid", sum = "h1:qJYtXnJRWmpe7m/3XlyhrsLrEURqHRM2kxzoxXqyUDs=", version = "v1.2.0", ) go_repository( name = "com_github_google_wire", build_file_proto_mode = "disable", importpath = "github.com/google/wire", sum = "h1:kXcsA/rIGzJImVqPdhfnr6q0xsS9gU0515q1EPpJ9fE=", version = "v0.4.0", ) go_repository( name = "com_github_googleapis_gax_go", build_file_proto_mode = "disable", importpath = "github.com/googleapis/gax-go", sum = "h1:silFMLAnr330+NRuag/VjIGF7TLp/LBrV2CJKFLWEww=", version = "v2.0.2+incompatible", ) go_repository( name = "com_github_googleapis_gax_go_v2", build_file_proto_mode = "disable", importpath = "github.com/googleapis/gax-go/v2", sum = "h1:sjZBwGj9Jlw33ImPtvFviGYvseOtDM7hkSKB7+Tv3SM=", version = "v2.0.5", ) go_repository( name = "com_github_googleapis_gnostic", build_file_generation = "on", build_file_proto_mode = "disable", importpath = "github.com/googleapis/gnostic", replace = "github.com/googleapis/gnostic", sum = "h1:DLJCy1n/vrD4HPjOvYcT8aYQXpPIzoRZONaYwyycI+I=", version = "v0.4.1", ) go_repository( name = "com_github_googlecloudplatform_cloud_builders_gcs_fetcher", build_file_proto_mode = "disable", importpath = "github.com/GoogleCloudPlatform/cloud-builders/gcs-fetcher", sum = "h1:Pjo3SOZigEnIGevhFqcbFndnqyCH8WimcREd3hRM9vU=", version = "v0.0.0-20191203181535-308b93ad1f39", ) go_repository( name = "com_github_googlecloudplatform_cloudsql_proxy", build_file_proto_mode = "disable", importpath = "github.com/GoogleCloudPlatform/cloudsql-proxy", sum = "h1:sTOp2Ajiew5XIH92YSdwhYc+bgpUX5j5TKK/Ac8Saw8=", version = "v0.0.0-20191009163259-e802c2cb94ae", ) go_repository( name = "com_github_googlecloudplatform_k8s_cloud_provider", build_file_proto_mode = "disable", importpath = "github.com/GoogleCloudPlatform/k8s-cloud-provider", sum = "h1:N7lSsF+R7wSulUADi36SInSQA3RvfO/XclHQfedr0qk=", version = "v0.0.0-20190822182118-27a4ced34534", ) go_repository( name = "com_github_googlecloudplatform_testgrid", build_file_proto_mode = "disable", importpath = "github.com/GoogleCloudPlatform/testgrid", sum = "h1:qs3/BQpz3j3qsgnfjV8aVBfPopkGxp/TnWjjiboUVf8=", version = "v0.0.68", ) go_repository( name = "com_github_gookit_color", build_file_proto_mode = "disable", importpath = "github.com/gookit/color", sum = "h1:xOYBan3Fwlrqj1M1UN2TlHOCRiek3bGzWf/vPnJ1roE=", version = "v1.2.4", ) go_repository( name = "com_github_gophercloud_gophercloud", build_file_proto_mode = "disable", importpath = "github.com/gophercloud/gophercloud", sum = "h1:P/nh25+rzXouhytV2pUHBb65fnds26Ghl8/391+sT5o=", version = "v0.1.0", ) go_repository( name = "com_github_gopherjs_gopherjs", build_file_proto_mode = "disable", importpath = "github.com/gopherjs/gopherjs", sum = "h1:EGx4pi6eqNxGaHF6qqu48+N2wcFQ5qg5FXgOdqsJ5d8=", version = "v0.0.0-20181017120253-0766667cb4d1", ) go_repository( name = "com_github_goreleaser_goreleaser", build_file_proto_mode = "disable", importpath = "github.com/goreleaser/goreleaser", sum = "h1:Z+7XPrfGK11s/Sp+a06sx2FzGuCjTBdxN2ubpGvQbjY=", version = "v0.136.0", ) go_repository( name = "com_github_goreleaser_nfpm", build_file_proto_mode = "disable", importpath = "github.com/goreleaser/nfpm", sum = "h1:BPwIomC+e+yuDX9poJowzV7JFVcYA0+LwGSkbAPs2Hw=", version = "v1.3.0", ) go_repository( name = "com_github_gorilla_context", build_file_proto_mode = "disable", importpath = "github.com/gorilla/context", sum = "h1:AWwleXJkX/nhcU9bZSnZoi3h/qGYqQAGhq6zZe/aQW8=", version = "v1.1.1", ) go_repository( name = "com_github_gorilla_csrf", build_file_proto_mode = "disable", importpath = "github.com/gorilla/csrf", sum = "h1:QqQ/OWwuFp4jMKgBFAzJVW3FMULdyUW7JoM4pEWuqKg=", version = "v1.6.2", ) go_repository( name = "com_github_gorilla_handlers", build_file_proto_mode = "disable", importpath = "github.com/gorilla/handlers", sum = "h1:0QniY0USkHQ1RGCLfKxeNHK9bkDHGRYGNDFBCS+YARg=", version = "v1.4.2", ) go_repository( name = "com_github_gorilla_mux", build_file_proto_mode = "disable", importpath = "github.com/gorilla/mux", sum = "h1:i40aqfkR1h2SlN9hojwV5ZA91wcXFOvkdNIeFDP5koI=", version = "v1.8.0", ) go_repository( name = "com_github_gorilla_securecookie", build_file_proto_mode = "disable", importpath = "github.com/gorilla/securecookie", sum = "h1:miw7JPhV+b/lAHSXz4qd/nN9jRiAFV5FwjeKyCS8BvQ=", version = "v1.1.1", ) go_repository( name = "com_github_gorilla_sessions", build_file_proto_mode = "disable", importpath = "github.com/gorilla/sessions", sum = "h1:S7P+1Hm5V/AT9cjEcUD5uDaQSX0OE577aCXgoaKpYbQ=", version = "v1.2.0", ) go_repository( name = "com_github_gorilla_websocket", build_file_proto_mode = "disable", importpath = "github.com/gorilla/websocket", sum = "h1:+/TMaTYc4QFitKJxsQ7Yye35DkWvkdLcvGKqM+x0Ufc=", version = "v1.4.2", ) go_repository( name = "com_github_gostaticanalysis_analysisutil", build_file_proto_mode = "disable", importpath = "github.com/gostaticanalysis/analysisutil", sum = "h1:iwp+5/UAyzQSFgQ4uR2sni99sJ8Eo9DEacKWM5pekIg=", version = "v0.0.3", ) go_repository( name = "com_github_gosuri_uitable", build_file_proto_mode = "disable", importpath = "github.com/gosuri/uitable", sum = "h1:IG2xLKRvErL3uhY6e1BylFzG+aJiwQviDDTfOKeKTpY=", version = "v0.0.4", ) go_repository( name = "com_github_gotestyourself_gotestyourself", build_file_proto_mode = "disable", importpath = "github.com/gotestyourself/gotestyourself", sum = "h1:AQwinXlbQR2HvPjQZOmDhRqsv5mZf+Jb1RnSLxcqZcI=", version = "v2.2.0+incompatible", ) go_repository( name = "com_github_gregjones_httpcache", build_file_proto_mode = "disable", importpath = "github.com/gregjones/httpcache", sum = "h1:f8eY6cV/x1x+HLjOp4r72s/31/V2aTUtg5oKRRPf8/Q=", version = "v0.0.0-20190212212710-3befbb6ad0cc", ) go_repository( name = "com_github_grpc_ecosystem_go_grpc_middleware", build_file_proto_mode = "disable", importpath = "github.com/grpc-ecosystem/go-grpc-middleware", sum = "h1:z53tR0945TRRQO/fLEVPI6SMv7ZflF0TEaTAoU7tOzg=", version = "v1.0.1-0.20190118093823-f849b5445de4", ) go_repository( name = "com_github_grpc_ecosystem_go_grpc_prometheus", build_file_proto_mode = "disable", importpath = "github.com/grpc-ecosystem/go-grpc-prometheus", sum = "h1:Ovs26xHkKqVztRpIrF/92BcuyuQ/YW4NSIpoGtfXNho=", version = "v1.2.0", ) go_repository( name = "com_github_grpc_ecosystem_grpc_gateway", build_file_proto_mode = "disable", importpath = "github.com/grpc-ecosystem/grpc-gateway", sum = "h1:D0EVSTwQoQOyfY35QNSuPJA4jpZRtkoGYWQMB7XNg5o=", version = "v1.12.2", ) go_repository( name = "com_github_h2non_gock", build_file_proto_mode = "disable", importpath = "github.com/h2non/gock", sum = "h1:17gCehSo8ZOgEsFKpQgqHiR7VLyjxdAG3lkhVvO9QZU=", version = "v1.0.9", ) go_repository( name = "com_github_hashicorp_consul_api", build_file_proto_mode = "disable", importpath = "github.com/hashicorp/consul/api", sum = "h1:BNQPM9ytxj6jbjjdRPioQ94T6YXriSopn0i8COv6SRA=", version = "v1.1.0", ) go_repository( name = "com_github_hashicorp_consul_sdk", build_file_proto_mode = "disable", importpath = "github.com/hashicorp/consul/sdk", sum = "h1:LnuDWGNsoajlhGyHJvuWW6FVqRl8JOTPqS6CPTsYjhY=", version = "v0.1.1", ) go_repository( name = "com_github_hashicorp_errwrap", build_file_proto_mode = "disable", importpath = "github.com/hashicorp/errwrap", sum = "h1:hLrqtEDnRye3+sgx6z4qVLNuviH3MR5aQ0ykNJa/UYA=", version = "v1.0.0", ) go_repository( name = "com_github_hashicorp_go_cleanhttp", build_file_proto_mode = "disable", importpath = "github.com/hashicorp/go-cleanhttp", sum = "h1:dH3aiDG9Jvb5r5+bYHsikaOUIpcM0xvgMXVoDkXMzJM=", version = "v0.5.1", ) go_repository( name = "com_github_hashicorp_go_hclog", build_file_proto_mode = "disable", importpath = "github.com/hashicorp/go-hclog", sum = "h1:CG6TE5H9/JXsFWJCfoIVpKFIkFe6ysEuHirp4DxCsHI=", version = "v0.9.2", ) go_repository( name = "com_github_hashicorp_go_immutable_radix", build_file_proto_mode = "disable", importpath = "github.com/hashicorp/go-immutable-radix", sum = "h1:AKDB1HM5PWEA7i4nhcpwOrO2byshxBjXVn/J/3+z5/0=", version = "v1.0.0", ) go_repository( name = "com_github_hashicorp_go_msgpack", build_file_proto_mode = "disable", importpath = "github.com/hashicorp/go-msgpack", sum = "h1:zKjpN5BK/P5lMYrLmBHdBULWbJ0XpYR+7NGzqkZzoD4=", version = "v0.5.3", ) go_repository( name = "com_github_hashicorp_go_multierror", build_file_proto_mode = "disable", importpath = "github.com/hashicorp/go-multierror", sum = "h1:B9UzwGQJehnUY1yNrnwREHc3fGbC2xefo8g4TbElacI=", version = "v1.1.0", ) go_repository( name = "com_github_hashicorp_go_net", build_file_proto_mode = "disable", importpath = "github.com/hashicorp/go.net", sum = "h1:sNCoNyDEvN1xa+X0baata4RdcpKwcMS6DH+xwfqPgjw=", version = "v0.0.1", ) go_repository( name = "com_github_hashicorp_go_plugin", build_file_proto_mode = "disable", importpath = "github.com/hashicorp/go-plugin", sum = "h1:4OtAfUGbnKC6yS48p0CtMX2oFYtzFZVv6rok3cRWgnE=", version = "v1.0.1", ) go_repository( name = "com_github_hashicorp_go_retryablehttp", build_file_proto_mode = "disable", importpath = "github.com/hashicorp/go-retryablehttp", sum = "h1:HJunrbHTDDbBb/ay4kxa1n+dLmttUlnP3V9oNE4hmsM=", version = "v0.6.6", ) go_repository( name = "com_github_hashicorp_go_rootcerts", build_file_proto_mode = "disable", importpath = "github.com/hashicorp/go-rootcerts", sum = "h1:DMo4fmknnz0E0evoNYnV48RjWndOsmd6OW+09R3cEP8=", version = "v1.0.1", ) go_repository( name = "com_github_hashicorp_go_sockaddr", build_file_proto_mode = "disable", importpath = "github.com/hashicorp/go-sockaddr", sum = "h1:ztczhD1jLxIRjVejw8gFomI1BQZOe2WoVOu0SyteCQc=", version = "v1.0.2", ) go_repository( name = "com_github_hashicorp_go_syslog", build_file_proto_mode = "disable", importpath = "github.com/hashicorp/go-syslog", sum = "h1:KaodqZuhUoZereWVIYmpUgZysurB1kBLX2j0MwMrUAE=", version = "v1.0.0", ) go_repository( name = "com_github_hashicorp_go_uuid", build_file_proto_mode = "disable", importpath = "github.com/hashicorp/go-uuid", sum = "h1:fv1ep09latC32wFoVwnqcnKJGnMSdBanPczbHAYm1BE=", version = "v1.0.1", ) go_repository( name = "com_github_hashicorp_go_version", build_file_proto_mode = "disable", importpath = "github.com/hashicorp/go-version", sum = "h1:3vNe/fWF5CBgRIguda1meWhsZHy3m8gCJ5wx+dIzX/E=", version = "v1.2.0", ) go_repository( name = "com_github_hashicorp_golang_lru", build_file_proto_mode = "disable", importpath = "github.com/hashicorp/golang-lru", sum = "h1:YDjusn29QI/Das2iO9M0BHnIbxPeyuCHsjMW+lJfyTc=", version = "v0.5.4", ) go_repository( name = "com_github_hashicorp_hcl", build_file_proto_mode = "disable", importpath = "github.com/hashicorp/hcl", sum = "h1:0Anlzjpi4vEasTeNFn2mLJgTSwt0+6sfsiTG8qcWGx4=", version = "v1.0.0", ) go_repository( name = "com_github_hashicorp_logutils", build_file_proto_mode = "disable", importpath = "github.com/hashicorp/logutils", sum = "h1:dLEQVugN8vlakKOUE3ihGLTZJRB4j+M2cdTm/ORI65Y=", version = "v1.0.0", ) go_repository( name = "com_github_hashicorp_mdns", build_file_proto_mode = "disable", importpath = "github.com/hashicorp/mdns", sum = "h1:WhIgCr5a7AaVH6jPUwjtRuuE7/RDufnUvzIr48smyxs=", version = "v1.0.0", ) go_repository( name = "com_github_hashicorp_memberlist", build_file_proto_mode = "disable", importpath = "github.com/hashicorp/memberlist", sum = "h1:EmmoJme1matNzb+hMpDuR/0sbJSUisxyqBGG676r31M=", version = "v0.1.3", ) go_repository( name = "com_github_hashicorp_serf", build_file_proto_mode = "disable", importpath = "github.com/hashicorp/serf", sum = "h1:YZ7UKsJv+hKjqGVUUbtE3HNj79Eln2oQ75tniF6iPt0=", version = "v0.8.2", ) go_repository( name = "com_github_hashicorp_vault_api", build_file_proto_mode = "disable", importpath = "github.com/hashicorp/vault/api", sum = "h1:j08Or/wryXT4AcHj1oCbMd7IijXcKzYUGw59LGu9onU=", version = "v1.0.4", ) go_repository( name = "com_github_hashicorp_vault_sdk", build_file_proto_mode = "disable", importpath = "github.com/hashicorp/vault/sdk", sum = "h1:mOEPeOhT7jl0J4AMl1E705+BcmeRs1VmKNb9F0sMLy8=", version = "v0.1.13", ) go_repository( name = "com_github_hashicorp_yamux", build_file_proto_mode = "disable", importpath = "github.com/hashicorp/yamux", sum = "h1:kJCB4vdITiW1eC1vq2e6IsrXKrZit1bv/TDYFGMp4BQ=", version = "v0.0.0-20181012175058-2f1d1f20f75d", ) go_repository( name = "com_github_howeyc_gopass", build_file_proto_mode = "disable", importpath = "github.com/howeyc/gopass", sum = "h1:kQWxfPIHVLbgLzphqk3QUflDy9QdksZR4ygR807bpy0=", version = "v0.0.0-20170109162249-bf9dde6d0d2c", ) go_repository( name = "com_github_hpcloud_tail", build_file_proto_mode = "disable", importpath = "github.com/hpcloud/tail", sum = "h1:nfCOvKYfkgYP8hkirhJocXT2+zOD8yUNjXaWfTlyFKI=", version = "v1.0.0", ) go_repository( name = "com_github_huandu_xstrings", build_file_proto_mode = "disable", importpath = "github.com/huandu/xstrings", sum = "h1:yPeWdRnmynF7p+lLYz0H2tthW9lqhMJrQV/U7yy4wX0=", version = "v1.2.0", ) go_repository( name = "com_github_ianlancetaylor_demangle", build_file_proto_mode = "disable", importpath = "github.com/ianlancetaylor/demangle", sum = "h1:UDMh68UUwekSh5iP2OMhRRZJiiBccgV7axzUG8vi56c=", version = "v0.0.0-20181102032728-5e5cf60278f6", ) go_repository( name = "com_github_imdario_mergo", build_file_proto_mode = "disable", importpath = "github.com/imdario/mergo", sum = "h1:b6R2BslTbIEToALKP7LxUvijTsNI9TAe80pLWN2g/HU=", version = "v0.3.12", ) go_repository( name = "com_github_inconshreveable_mousetrap", build_file_proto_mode = "disable", importpath = "github.com/inconshreveable/mousetrap", sum = "h1:Z8tu5sraLXCXIcARxBp/8cbvlwVa7Z1NHg9XEKhtSvM=", version = "v1.0.0", ) go_repository( name = "com_github_influxdata_influxdb", build_file_proto_mode = "disable", importpath = "github.com/influxdata/influxdb", sum = "h1:AciJ2ei/llFRundm7CtqwF6B2aOds1A7QG3sMW8QiaQ=", version = "v0.0.0-20161215172503-049f9b42e9a5", ) go_repository( name = "com_github_influxdata_tdigest", build_file_proto_mode = "disable", importpath = "github.com/influxdata/tdigest", sum = "h1:MHTrDWmQpHq/hkq+7cw9oYAt2PqUw52TZazRA0N7PGE=", version = "v0.0.0-20181121200506-bf2b5ad3c0a9", ) go_repository( name = "com_github_jarcoal_httpmock", build_file_proto_mode = "disable", importpath = "github.com/jarcoal/httpmock", sum = "h1:cHtVEcTxRSX4J0je7mWPfc9BpDpqzXSJ5HbymZmyHck=", version = "v1.0.5", ) go_repository( name = "com_github_jbenet_go_context", build_file_proto_mode = "disable", importpath = "github.com/jbenet/go-context", sum = "h1:BQSFePA1RWJOlocH6Fxy8MmwDt+yVQYULKfN0RoTN8A=", version = "v0.0.0-20150711004518-d14ea06fba99", ) go_repository( name = "com_github_jcmturner_gofork", build_file_proto_mode = "disable", importpath = "github.com/jcmturner/gofork", sum = "h1:J7uCkflzTEhUZ64xqKnkDxq3kzc96ajM1Gli5ktUem8=", version = "v1.0.0", ) go_repository( name = "com_github_jenkins_x_go_scm", build_file_proto_mode = "disable", importpath = "github.com/jenkins-x/go-scm", sum = "h1:D7d1sDWUU+xocCNLQVoYKpMjVKnQvsPva+hPzruchbM=", version = "v1.5.117", ) go_repository( name = "com_github_jessevdk_go_flags", build_file_proto_mode = "disable", importpath = "github.com/jessevdk/go-flags", sum = "h1:1jKYvbxEjfUl0fmqTCOfonvskHHXMjBySTLW4y9LFvc=", version = "v1.5.0", ) go_repository( name = "com_github_jetstack_cert_manager", build_file_proto_mode = "disable", importpath = "github.com/jetstack/cert-manager", sum = "h1:gEhBV9I83m+kpQShDhNO4+J8O2qfNDjvAEL27pThGmg=", version = "v1.1.0", ) go_repository( name = "com_github_jingyugao_rowserrcheck", build_file_proto_mode = "disable", importpath = "github.com/jingyugao/rowserrcheck", sum = "h1:GmsqmapfzSJkm28dhRoHz2tLRbJmqhU86IPgBtN3mmk=", version = "v0.0.0-20191204022205-72ab7603b68a", ) go_repository( name = "com_github_jinzhu_gorm", build_file_proto_mode = "disable", importpath = "github.com/jinzhu/gorm", sum = "h1:Drgk1clyWT9t9ERbzHza6Mj/8FY/CqMyVzOiHviMo6Q=", version = "v1.9.12", ) go_repository( name = "com_github_jinzhu_inflection", build_file_proto_mode = "disable", importpath = "github.com/jinzhu/inflection", sum = "h1:K317FqzuhWc8YvSVlFMCCUb36O/S9MCKRDI7QkRKD/E=", version = "v1.0.0", ) go_repository( name = "com_github_jinzhu_now", build_file_proto_mode = "disable", importpath = "github.com/jinzhu/now", sum = "h1:g39TucaRWyV3dwDO++eEc6qf8TVIQ/Da48WmqjZ3i7E=", version = "v1.1.1", ) go_repository( name = "com_github_jirfag_go_printf_func_name", build_file_proto_mode = "disable", importpath = "github.com/jirfag/go-printf-func-name", sum = "h1:KA9BjwUk7KlCh6S9EAGWBt1oExIUv9WyNCiRz5amv48=", version = "v0.0.0-20200119135958-7558a9eaa5af", ) go_repository( name = "com_github_jmespath_go_jmespath", build_file_proto_mode = "disable", importpath = "github.com/jmespath/go-jmespath", sum = "h1:BEgLn5cpjn8UN1mAw4NjwDrS35OdebyEtFe+9YPoQUg=", version = "v0.4.0", ) go_repository( name = "com_github_jmespath_go_jmespath_internal_testify", build_file_proto_mode = "disable", importpath = "github.com/jmespath/go-jmespath/internal/testify", sum = "h1:shLQSRRSCCPj3f2gpwzGwWFoC7ycTf1rcQZHOlsJ6N8=", version = "v1.5.1", ) go_repository( name = "com_github_jmoiron_sqlx", build_file_proto_mode = "disable", importpath = "github.com/jmoiron/sqlx", sum = "h1:lrdPtrORjGv1HbbEvKWDUAy97mPpFm4B8hp77tcCUJY=", version = "v1.2.1-0.20190826204134-d7d95172beb5", ) go_repository( name = "com_github_joefitzgerald_rainbow_reporter", build_file_proto_mode = "disable", importpath = "github.com/joefitzgerald/rainbow-reporter", sum = "h1:AuMG652zjdzI0YCCnXAqATtRBpGXMcAnrajcaTrSeuo=", version = "v0.1.0", ) go_repository( name = "com_github_joho_godotenv", build_file_proto_mode = "disable", importpath = "github.com/joho/godotenv", sum = "h1:Zjp+RcGpHhGlrMbJzXTrZZPrWj+1vfm90La1wgB6Bhc=", version = "v1.3.0", ) go_repository( name = "com_github_jonboulle_clockwork", build_file_proto_mode = "disable", importpath = "github.com/jonboulle/clockwork", sum = "h1:VKV+ZcuP6l3yW9doeqz6ziZGgcynBVQO+obU0+0hcPo=", version = "v0.1.0", ) go_repository( name = "com_github_joshdk_go_junit", build_file_proto_mode = "disable", importpath = "github.com/joshdk/go-junit", sum = "h1:Bp5LAZasx/ev9wUmIIC74+MsXgwD99VjV1JmDVbpJm8=", version = "v0.0.0-20190428045703-ad7e11aa49ff", ) go_repository( name = "com_github_jpillora_backoff", build_file_proto_mode = "disable", importpath = "github.com/jpillora/backoff", sum = "h1:uvFg412JmmHBHw7iwprIxkPMI+sGQ4kzOWsMeHnm2EA=", version = "v1.0.0", ) go_repository( name = "com_github_json_iterator_go", build_file_proto_mode = "disable", importpath = "github.com/json-iterator/go", sum = "h1:uVUAXhF2To8cbw/3xN3pxj6kk7TYKs98NIrTqPlMWAQ=", version = "v1.1.11", ) go_repository( name = "com_github_jstemmer_go_junit_report", build_file_proto_mode = "disable", importpath = "github.com/jstemmer/go-junit-report", sum = "h1:6QPYqodiu3GuPL+7mfx+NwDdp2eTkp9IfEUpgAwUN0o=", version = "v0.9.1", ) go_repository( name = "com_github_jtolds_gls", build_file_proto_mode = "disable", importpath = "github.com/jtolds/gls", sum = "h1:xdiiI2gbIgH/gLH7ADydsJ1uDOEzR8yvV7C0MuV77Wo=", version = "v4.20.0+incompatible", ) go_repository( name = "com_github_julienschmidt_httprouter", build_file_proto_mode = "disable", importpath = "github.com/julienschmidt/httprouter", sum = "h1:U0609e9tgbseu3rBINet9P48AI/D3oJs4dN7jwJOQ1U=", version = "v1.3.0", ) go_repository( name = "com_github_kballard_go_shellquote", build_file_proto_mode = "disable", importpath = "github.com/kballard/go-shellquote", sum = "h1:Z9n2FFNUXsshfwJMBgNA0RU6/i7WVaAegv3PtuIHPMs=", version = "v0.0.0-20180428030007-95032a82bc51", ) go_repository( name = "com_github_kelseyhightower_envconfig", build_file_proto_mode = "disable", importpath = "github.com/kelseyhightower/envconfig", sum = "h1:Im6hONhd3pLkfDFsbRgu68RDNkGF1r3dvMUtDTo2cv8=", version = "v1.4.0", ) go_repository( name = "com_github_kevinburke_ssh_config", build_file_proto_mode = "disable", importpath = "github.com/kevinburke/ssh_config", sum = "h1:DowS9hvgyYSX4TO5NpyC606/Z4SxnNYbT+WX27or6Ck=", version = "v0.0.0-20201106050909-4977a11b4351", ) go_repository( name = "com_github_kisielk_errcheck", build_file_proto_mode = "disable", importpath = "github.com/kisielk/errcheck", sum = "h1:e8esj/e4R+SAOwFwN+n3zr0nYeCyeweozKfO23MvHzY=", version = "v1.5.0", ) go_repository( name = "com_github_kisielk_gotool", build_file_proto_mode = "disable", importpath = "github.com/kisielk/gotool", sum = "h1:AV2c/EiW3KqPNT9ZKl07ehoAGi4C5/01Cfbblndcapg=", version = "v1.0.0", ) go_repository( name = "com_github_klauspost_compress", build_file_proto_mode = "disable", importpath = "github.com/klauspost/compress", sum = "h1:Znfn6hXZAHaLPNnlqUYRrBSReFHYybslgv4PTiyz6P0=", version = "v1.10.2", ) go_repository( name = "com_github_klauspost_cpuid", build_file_proto_mode = "disable", importpath = "github.com/klauspost/cpuid", sum = "h1:1xAgYebNnsb9LKCdLOvFWtAxGU/33mjJtyOVbmUa0Us=", version = "v1.2.2", ) go_repository( name = "com_github_klauspost_pgzip", build_file_proto_mode = "disable", importpath = "github.com/klauspost/pgzip", sum = "h1:oIPZROsWuPHpOdMVWLuJZXwgjhrW8r1yEX8UqMyeNHM=", version = "v1.2.1", ) go_repository( name = "com_github_knative_build", build_file_proto_mode = "disable", importpath = "github.com/knative/build", sum = "h1:o/VYWA3HKyZlNqdU2hDE5LHpanBe8gazgPKL97XJ6bo=", version = "v0.1.2", ) go_repository( name = "com_github_konsorten_go_windows_terminal_sequences", build_file_proto_mode = "disable", importpath = "github.com/konsorten/go-windows-terminal-sequences", sum = "h1:CE8S1cTafDpPvMhIxNJKvHsGVBgn1xWYf1NbHQhywc8=", version = "v1.0.3", ) go_repository( name = "com_github_kr_logfmt", build_file_proto_mode = "disable", importpath = "github.com/kr/logfmt", sum = "h1:T+h1c/A9Gawja4Y9mFVWj2vyii2bbUNDw3kt9VxK2EY=", version = "v0.0.0-20140226030751-b84e30acd515", ) go_repository( name = "com_github_kr_pretty", build_file_proto_mode = "disable", importpath = "github.com/kr/pretty", sum = "h1:Fmg33tUaq4/8ym9TJN1x7sLJnHVwhP33CNkpYV/7rwI=", version = "v0.2.1", ) go_repository( name = "com_github_kr_pty", build_file_proto_mode = "disable", importpath = "github.com/kr/pty", sum = "h1:AkaSdXYQOWeaO3neb8EM634ahkXXe3jYbVh/F9lq+GI=", version = "v1.1.8", ) go_repository( name = "com_github_kr_text", build_file_proto_mode = "disable", importpath = "github.com/kr/text", sum = "h1:5Nx0Ya0ZqY2ygV366QzturHI13Jq95ApcVaJBhpS+AY=", version = "v0.2.0", ) go_repository( name = "com_github_lib_pq", build_file_proto_mode = "disable", importpath = "github.com/lib/pq", sum = "h1:LXpIM/LZ5xGFhOpXAQUIMM1HdyqzVYM13zNdjCEEcA0=", version = "v1.2.0", ) go_repository( name = "com_github_liggitt_tabwriter", build_file_proto_mode = "disable", importpath = "github.com/liggitt/tabwriter", sum = "h1:9TO3cAIGXtEhnIaL+V+BEER86oLrvS+kWobKpbJuye0=", version = "v0.0.0-20181228230101-89fcab3d43de", ) go_repository( name = "com_github_lightstep_tracecontext_go", build_file_proto_mode = "disable", importpath = "github.com/lightstep/tracecontext.go", sum = "h1:+2b6iGRJe3hvV/yVXrd41yVEjxuFHxasJqDhkIjS4gk=", version = "v0.0.0-20181129014701-1757c391b1ac", ) go_repository( name = "com_github_lithammer_dedent", build_file_proto_mode = "disable", importpath = "github.com/lithammer/dedent", sum = "h1:VNzHMVCBNG1j0fh3OrsFRkVUwStdDArbgBWoPAffktY=", version = "v1.1.0", ) go_repository( name = "com_github_logrusorgru_aurora", build_file_proto_mode = "disable", importpath = "github.com/logrusorgru/aurora", sum = "h1:9MlwzLdW7QSDrhDjFlsEYmxpFyIoXmYRon3dt0io31k=", version = "v0.0.0-20181002194514-a7b3b318ed4e", ) go_repository( name = "com_github_lyft_protoc_gen_validate", build_file_proto_mode = "disable", importpath = "github.com/lyft/protoc-gen-validate", sum = "h1:KNt/RhmQTOLr7Aj8PsJ7mTronaFyx80mRTT9qF261dA=", version = "v0.0.13", ) go_repository( name = "com_github_magiconair_properties", build_file_proto_mode = "disable", importpath = "github.com/magiconair/properties", sum = "h1:ZC2Vc7/ZFkGmsVC9KvOjumD+G5lXy2RtTKyzRKO2BQ4=", version = "v1.8.1", ) go_repository( name = "com_github_mailru_easyjson", build_file_proto_mode = "disable", importpath = "github.com/mailru/easyjson", sum = "h1:jcoUdG1TzY/M/eM5BLFLP8DJeMximx5NQYSlLL9YeWc=", version = "v0.7.1-0.20191009090205-6c0755d89d1e", ) go_repository( name = "com_github_makenowjust_heredoc", build_file_proto_mode = "disable", importpath = "github.com/MakeNowJust/heredoc", sum = "h1:sjQovDkwrZp8u+gxLtPgKGjk5hCxuy2hrRejBTA9xFU=", version = "v0.0.0-20170808103936-bb23615498cd", ) go_repository( name = "com_github_maratori_testpackage", build_file_proto_mode = "disable", importpath = "github.com/maratori/testpackage", sum = "h1:QtJ5ZjqapShm0w5DosRjg0PRlSdAdlx+W6cCKoALdbQ=", version = "v1.0.1", ) go_repository( name = "com_github_markbates_inflect", build_file_proto_mode = "disable", importpath = "github.com/markbates/inflect", sum = "h1:5fh1gzTFhfae06u3hzHYO9xe3l3v3nW5Pwt3naLTP5g=", version = "v1.0.4", ) go_repository( name = "com_github_marstr_guid", build_file_proto_mode = "disable", importpath = "github.com/marstr/guid", sum = "h1:/M4H/1G4avsieL6BbUwCOBzulmoeKVP5ux/3mQNnbyI=", version = "v1.1.0", ) go_repository( name = "com_github_masterminds_goutils", build_file_proto_mode = "disable", importpath = "github.com/Masterminds/goutils", sum = "h1:zukEsf/1JZwCMgHiK3GZftabmxiCw4apj3a28RPBiVg=", version = "v1.1.0", ) go_repository( name = "com_github_masterminds_semver", build_file_proto_mode = "disable", importpath = "github.com/Masterminds/semver", sum = "h1:H65muMkzWKEuNDnfl9d70GUjFniHKHRbFPGBuZ3QEww=", version = "v1.5.0", ) go_repository( name = "com_github_masterminds_semver_v3", build_file_proto_mode = "disable", importpath = "github.com/Masterminds/semver/v3", sum = "h1:Y2lUDsFKVRSYGojLJ1yLxSXdMmMYTYls0rCvoqmMUQk=", version = "v3.1.0", ) go_repository( name = "com_github_masterminds_sprig_v3", build_file_proto_mode = "disable", importpath = "github.com/Masterminds/sprig/v3", sum = "h1:wz22D0CiSctrliXiI9ZO3HoNApweeRGftyDN+BQa3B8=", version = "v3.0.2", ) go_repository( name = "com_github_masterminds_vcs", build_file_proto_mode = "disable", importpath = "github.com/Masterminds/vcs", sum = "h1:NL3G1X7/7xduQtA2sJLpVpfHTNBALVNSjob6KEjPXNQ=", version = "v1.13.1", ) go_repository( name = "com_github_matoous_godox", build_file_proto_mode = "disable", importpath = "github.com/matoous/godox", sum = "h1:RHba4YImhrUVQDHUCe2BNSOz4tVy2yGyXhvYDvxGgeE=", version = "v0.0.0-20190911065817-5d6d842e92eb", ) go_repository( name = "com_github_mattbaird_jsonpatch", build_file_proto_mode = "disable", importpath = "github.com/mattbaird/jsonpatch", sum = "h1:+J2gw7Bw77w/fbK7wnNJJDKmw1IbWft2Ul5BzrG1Qm8=", version = "v0.0.0-20171005235357-81af80346b1a", ) go_repository( name = "com_github_mattn_go_colorable", build_file_proto_mode = "disable", importpath = "github.com/mattn/go-colorable", sum = "h1:6Su7aK7lXmJ/U79bYtBjLNaha4Fs1Rg9plHpcH+vvnE=", version = "v0.1.6", ) go_repository( name = "com_github_mattn_go_ieproxy", build_file_proto_mode = "disable", importpath = "github.com/mattn/go-ieproxy", sum = "h1:qiyop7gCflfhwCzGyeT0gro3sF9AIg9HU98JORTkqfI=", version = "v0.0.1", ) go_repository( name = "com_github_mattn_go_isatty", build_file_proto_mode = "disable", importpath = "github.com/mattn/go-isatty", sum = "h1:wuysRhFDzyxgEmMf5xjvJ2M9dZoWAXNNr5LSBS7uHXY=", version = "v0.0.12", ) go_repository( name = "com_github_mattn_go_runewidth", build_file_proto_mode = "disable", importpath = "github.com/mattn/go-runewidth", sum = "h1:3tS41NlGYSmhhe/8fhGRzc+z3AYCw1Fe1WAyLuujKs0=", version = "v0.0.8", ) go_repository( name = "com_github_mattn_go_shellwords", build_file_proto_mode = "disable", importpath = "github.com/mattn/go-shellwords", sum = "h1:Y7Xqm8piKOO3v10Thp7Z36h4FYFjt5xB//6XvOrs2Gw=", version = "v1.0.10", ) go_repository( name = "com_github_mattn_go_sqlite3", build_file_proto_mode = "disable", importpath = "github.com/mattn/go-sqlite3", sum = "h1:xQ15muvnzGBHpIpdrNi1DA5x0+TcBZzsIDwmw9uTHzw=", version = "v2.0.1+incompatible", ) go_repository( name = "com_github_mattn_go_zglob", build_file_proto_mode = "disable", importpath = "github.com/mattn/go-zglob", sum = "h1:0qT24o2wsZ8cOXQAERwBX6s+rPMs/bJTKxLVVtgfDXc=", version = "v0.0.2", ) go_repository( name = "com_github_mattn_goveralls", build_file_proto_mode = "disable", importpath = "github.com/mattn/goveralls", sum = "h1:7eJB6EqsPhRVxvwEXGnqdO2sJI0PTsrWoTMXEk9/OQc=", version = "v0.0.2", ) go_repository( name = "com_github_matttproud_golang_protobuf_extensions", build_file_proto_mode = "disable", importpath = "github.com/matttproud/golang_protobuf_extensions", sum = "h1:I0XW9+e1XWDxdcEniV4rQAIOPUGDq67JSCiRCgGCZLI=", version = "v1.0.2-0.20181231171920-c182affec369", ) go_repository( name = "com_github_maxbrunsfeld_counterfeiter_v6", build_file_proto_mode = "disable", importpath = "github.com/maxbrunsfeld/counterfeiter/v6", sum = "h1:g+4J5sZg6osfvEfkRZxJ1em0VT95/UOZgi/l7zi1/oE=", version = "v6.2.2", ) go_repository( name = "com_github_mgutz_ansi", build_file_proto_mode = "disable", importpath = "github.com/mgutz/ansi", sum = "h1:j7+1HpAFS1zy5+Q4qx1fWh90gTKwiN4QCGoY9TWyyO4=", version = "v0.0.0-20170206155736-9520e82c474b", ) go_repository( name = "com_github_mholt_archiver_v3", build_file_proto_mode = "disable", importpath = "github.com/mholt/archiver/v3", sum = "h1:vWjhY8SQp5yzM9P6OJ/eZEkmi3UAbRrxCq48MxjAzig=", version = "v3.3.0", ) go_repository( name = "com_github_microsoft_go_winio", build_file_proto_mode = "disable", importpath = "github.com/Microsoft/go-winio", sum = "h1:FtSW/jqD+l4ba5iPBj9CODVtgfYAD8w2wS923g/cFDk=", version = "v0.4.16", ) go_repository( name = "com_github_microsoft_hcsshim", build_file_proto_mode = "disable", importpath = "github.com/Microsoft/hcsshim", sum = "h1:ptnOoufxGSzauVTsdE+wMYnCWA301PdoN4xg5oRdZpg=", version = "v0.8.7", ) go_repository( name = "com_github_miekg_dns", build_file_proto_mode = "disable", importpath = "github.com/miekg/dns", sum = "h1:sJFOl9BgwbYAWOGEwr61FU28pqsBNdpRBnhGXtO06Oo=", version = "v1.1.31", ) go_repository( name = "com_github_mitchellh_cli", build_file_proto_mode = "disable", importpath = "github.com/mitchellh/cli", sum = "h1:iGBIsUe3+HZ/AD/Vd7DErOt5sU9fa8Uj7A2s1aggv1Y=", version = "v1.0.0", ) go_repository( name = "com_github_mitchellh_copystructure", build_file_proto_mode = "disable", importpath = "github.com/mitchellh/copystructure", sum = "h1:Laisrj+bAB6b/yJwB5Bt3ITZhGJdqmxquMKeZ+mmkFQ=", version = "v1.0.0", ) go_repository( name = "com_github_mitchellh_go_homedir", build_file_proto_mode = "disable", importpath = "github.com/mitchellh/go-homedir", sum = "h1:lukF9ziXFxDFPkA1vsr5zpc1XuPDn/wFntq5mG+4E0Y=", version = "v1.1.0", ) go_repository( name = "com_github_mitchellh_go_ps", build_file_proto_mode = "disable", importpath = "github.com/mitchellh/go-ps", sum = "h1:9+ke9YJ9KGWw5ANXK6ozjoK47uI3uNbXv4YVINBnGm8=", version = "v0.0.0-20190716172923-621e5597135b", ) go_repository( name = "com_github_mitchellh_go_testing_interface", build_file_proto_mode = "disable", importpath = "github.com/mitchellh/go-testing-interface", sum = "h1:fzU/JVNcaqHQEcVFAKeR41fkiLdIPrefOvVG1VZ96U0=", version = "v1.0.0", ) go_repository( name = "com_github_mitchellh_go_wordwrap", build_file_proto_mode = "disable", importpath = "github.com/mitchellh/go-wordwrap", sum = "h1:6GlHJ/LTGMrIJbwgdqdl2eEH8o+Exx/0m8ir9Gns0u4=", version = "v1.0.0", ) go_repository( name = "com_github_mitchellh_gox", build_file_proto_mode = "disable", importpath = "github.com/mitchellh/gox", sum = "h1:lfGJxY7ToLJQjHHwi0EX6uYBdK78egf954SQl13PQJc=", version = "v0.4.0", ) go_repository( name = "com_github_mitchellh_iochan", build_file_proto_mode = "disable", importpath = "github.com/mitchellh/iochan", sum = "h1:C+X3KsSTLFVBr/tK1eYN/vs4rJcvsiLU338UhYPJWeY=", version = "v1.0.0", ) go_repository( name = "com_github_mitchellh_ioprogress", build_file_proto_mode = "disable", importpath = "github.com/mitchellh/ioprogress", sum = "h1:Qa6dnn8DlasdXRnacluu8HzPts0S1I9zvvUPDbBnXFI=", version = "v0.0.0-20180201004757-6a23b12fa88e", ) go_repository( name = "com_github_mitchellh_mapstructure", build_file_proto_mode = "disable", importpath = "github.com/mitchellh/mapstructure", sum = "h1:cCBH2gTD2K0OtLlv/Y5H01VQCqmlDxz30kS5Y5bqfLA=", version = "v1.3.1", ) go_repository( name = "com_github_mitchellh_osext", build_file_proto_mode = "disable", importpath = "github.com/mitchellh/osext", sum = "h1:2+myh5ml7lgEU/51gbeLHfKGNfgEQQIWrlbdaOsidbQ=", version = "v0.0.0-20151018003038-5e2d6d41470f", ) go_repository( name = "com_github_mitchellh_reflectwalk", build_file_proto_mode = "disable", importpath = "github.com/mitchellh/reflectwalk", sum = "h1:9D+8oIskB4VJBN5SFlmc27fSlIBZaov1Wpk/IfikLNY=", version = "v1.0.0", ) go_repository( name = "com_github_moby_spdystream", build_file_proto_mode = "disable", importpath = "github.com/moby/spdystream", sum = "h1:cjW1zVyyoiM0T7b6UoySUFqzXMoqRckQtXwGPiBhOM8=", version = "v0.2.0", ) go_repository( name = "com_github_moby_term", build_file_proto_mode = "disable", importpath = "github.com/moby/term", sum = "h1:rzf0wL0CHVc8CEsgyygG0Mn9CNCCPZqOPaz8RiiHYQk=", version = "v0.0.0-20201216013528-df9cb8a40635", ) go_repository( name = "com_github_modern_go_concurrent", build_file_proto_mode = "disable", importpath = "github.com/modern-go/concurrent", sum = "h1:TRLaZ9cD/w8PVh93nsPXa1VrQ6jlwL5oN8l14QlcNfg=", version = "v0.0.0-20180306012644-bacd9c7ef1dd", ) go_repository( name = "com_github_modern_go_reflect2", build_file_proto_mode = "disable", importpath = "github.com/modern-go/reflect2", sum = "h1:9f412s+6RmYXLWZSEzVVgPGK7C2PphHj5RJrvfx9AWI=", version = "v1.0.1", ) go_repository( name = "com_github_mohae_deepcopy", build_file_proto_mode = "disable", importpath = "github.com/mohae/deepcopy", sum = "h1:RWengNIwukTxcDr9M+97sNutRR1RKhG96O6jWumTTnw=", version = "v0.0.0-20170929034955-c48cc78d4826", ) go_repository( name = "com_github_morikuni_aec", build_file_proto_mode = "disable", importpath = "github.com/morikuni/aec", sum = "h1:nP9CBfwrvYnBRgY6qfDQkygYDmYwOilePFkwzv4dU8A=", version = "v1.0.0", ) go_repository( name = "com_github_mozilla_tls_observatory", build_file_proto_mode = "disable", importpath = "github.com/mozilla/tls-observatory", sum = "h1:1xJ+Xi9lYWLaaP4yB67ah0+548CD3110mCPWhVVjFkI=", version = "v0.0.0-20200317151703-4fa42e1c2dee", ) go_repository( name = "com_github_munnerz_crd_schema_fuzz", build_file_proto_mode = "disable", importpath = "github.com/munnerz/crd-schema-fuzz", sum = "h1:8erI9yzEnOGw9K5O+a8zZdoo8N/OwrFi7c7SjBtkHAs=", version = "v1.0.0", ) go_repository( name = "com_github_munnerz_goautoneg", build_file_proto_mode = "disable", importpath = "github.com/munnerz/goautoneg", sum = "h1:C3w9PqII01/Oq1c1nUAm88MOHcQC9l5mIlSMApZMrHA=", version = "v0.0.0-20191010083416-a7dc8b61c822", ) go_repository( name = "com_github_mwitkow_go_conntrack", build_file_proto_mode = "disable", importpath = "github.com/mwitkow/go-conntrack", sum = "h1:KUppIJq7/+SVif2QVs3tOP0zanoHgBEVAwHxUSIzRqU=", version = "v0.0.0-20190716064945-2f068394615f", ) go_repository( name = "com_github_mxk_go_flowrate", build_file_proto_mode = "disable", importpath = "github.com/mxk/go-flowrate", sum = "h1:y5//uYreIhSUg3J1GEMiLbxo1LJaP8RfCpH6pymGZus=", version = "v0.0.0-20140419014527-cca7078d478f", ) go_repository( name = "com_github_nakabonne_nestif", build_file_proto_mode = "disable", importpath = "github.com/nakabonne/nestif", sum = "h1:+yOViDGhg8ygGrmII72nV9B/zGxY188TYpfolntsaPw=", version = "v0.3.0", ) go_repository( name = "com_github_natefinch_lumberjack", build_file_proto_mode = "disable", importpath = "github.com/natefinch/lumberjack", sum = "h1:4QJd3OLAMgj7ph+yZTuX13Ld4UpgHp07nNdFX7mqFfM=", version = "v2.0.0+incompatible", ) go_repository( name = "com_github_nats_io_gnatsd", build_file_proto_mode = "disable", importpath = "github.com/nats-io/gnatsd", sum = "h1:RconcfDeWpKCD6QIIwiVFcvForlXpWeJP7i5/lDLy44=", version = "v1.4.1", ) go_repository( name = "com_github_nats_io_go_nats", build_file_proto_mode = "disable", importpath = "github.com/nats-io/go-nats", sum = "h1:oQOfHcLr8hb43QG8yeVyY2jtarIaTjOv41CGdF3tTvQ=", version = "v1.7.0", ) go_repository( name = "com_github_nats_io_jwt", build_file_proto_mode = "disable", importpath = "github.com/nats-io/jwt", sum = "h1:+RB5hMpXUUA2dfxuhBTEkMOrYmM+gKIZYS1KjSostMI=", version = "v0.3.2", ) go_repository( name = "com_github_nats_io_nats_go", build_file_proto_mode = "disable", importpath = "github.com/nats-io/nats.go", sum = "h1:ik3HbLhZ0YABLto7iX80pZLPw/6dx3T+++MZJwLnMrQ=", version = "v1.9.1", ) go_repository( name = "com_github_nats_io_nats_server_v2", build_file_proto_mode = "disable", importpath = "github.com/nats-io/nats-server/v2", sum = "h1:i2Ly0B+1+rzNZHHWtD4ZwKi+OU5l+uQo1iDHZ2PmiIc=", version = "v2.1.2", ) go_repository( name = "com_github_nats_io_nkeys", build_file_proto_mode = "disable", importpath = "github.com/nats-io/nkeys", sum = "h1:6JrEfig+HzTH85yxzhSVbjHRJv9cn0p6n3IngIcM5/k=", version = "v0.1.3", ) go_repository( name = "com_github_nats_io_nuid", build_file_proto_mode = "disable", importpath = "github.com/nats-io/nuid", sum = "h1:5iA8DT8V7q8WK2EScv2padNa/rTESc1KdnPw4TC2paw=", version = "v1.0.1", ) go_repository( name = "com_github_nbio_st", build_file_proto_mode = "disable", importpath = "github.com/nbio/st", sum = "h1:W6apQkHrMkS0Muv8G/TipAy/FJl/rCYT0+EuS8+Z0z4=", version = "v0.0.0-20140626010706-e9e8d9816f32", ) go_repository( name = "com_github_nbutton23_zxcvbn_go", build_file_proto_mode = "disable", importpath = "github.com/nbutton23/zxcvbn-go", sum = "h1:AREM5mwr4u1ORQBMvzfzBgpsctsbQikCVpvC+tX285E=", version = "v0.0.0-20180912185939-ae427f1e4c1d", ) go_repository( name = "com_github_ncw_swift", build_file_proto_mode = "disable", importpath = "github.com/ncw/swift", sum = "h1:4DQRPj35Y41WogBxyhOXlrI37nzGlyEcsforeudyYPQ=", version = "v1.0.47", ) go_repository( name = "com_github_niemeyer_pretty", build_file_proto_mode = "disable", importpath = "github.com/niemeyer/pretty", sum = "h1:fD57ERR4JtEqsWbfPhv4DMiApHyliiK5xCTNVSPiaAs=", version = "v0.0.0-20200227124842-a10e7caefd8e", ) go_repository( name = "com_github_nwaples_rardecode", build_file_proto_mode = "disable", importpath = "github.com/nwaples/rardecode", sum = "h1:r7vGuS5akxOnR4JQSkko62RJ1ReCMXxQRPtxsiFMBOs=", version = "v1.0.0", ) go_repository( name = "com_github_nxadm_tail", build_file_proto_mode = "disable", importpath = "github.com/nxadm/tail", sum = "h1:nPr65rt6Y5JFSKQO7qToXr7pePgD6Gwiw05lkbyAQTE=", version = "v1.4.8", ) go_repository( name = "com_github_nytimes_gziphandler", build_file_proto_mode = "disable", importpath = "github.com/NYTimes/gziphandler", sum = "h1:ZUDjpQae29j0ryrS0u/B8HZfJBtBQHjqw2rQ2cqUQ3I=", version = "v1.1.1", ) go_repository( name = "com_github_octago_sflags", build_file_proto_mode = "disable", importpath = "github.com/octago/sflags", sum = "h1:XceYzkRXGAHa/lSFmKLcaxSrsh4MTuOMQdIGsUD0wlk=", version = "v0.2.0", ) go_repository( name = "com_github_oklog_run", build_file_proto_mode = "disable", importpath = "github.com/oklog/run", sum = "h1:Ru7dDtJNOyC66gQ5dQmaCa0qIsAUFY3sFpK1Xk8igrw=", version = "v1.0.0", ) go_repository( name = "com_github_oklog_ulid", build_file_proto_mode = "disable", importpath = "github.com/oklog/ulid", sum = "h1:EGfNDEx6MqHz8B3uNV6QAib1UR2Lm97sHi3ocA6ESJ4=", version = "v1.3.1", ) go_repository( name = "com_github_olekukonko_tablewriter", build_file_proto_mode = "disable", importpath = "github.com/olekukonko/tablewriter", sum = "h1:vHD/YYe1Wolo78koG299f7V/VAS08c6IpCLn+Ejf/w8=", version = "v0.0.4", ) go_repository( name = "com_github_oneofone_xxhash", build_file_proto_mode = "disable", importpath = "github.com/OneOfOne/xxhash", sum = "h1:KMrpdQIwFcEqXDklaen+P1axHaj9BSKzvpUUfnHldSE=", version = "v1.2.2", ) go_repository( name = "com_github_onsi_ginkgo", build_file_proto_mode = "disable", importpath = "github.com/onsi/ginkgo", sum = "h1:29JGrr5oVBm5ulCWet69zQkzWipVXIol6ygQUe/EzNc=", version = "v1.16.4", ) go_repository( name = "com_github_onsi_gomega", build_file_proto_mode = "disable", importpath = "github.com/onsi/gomega", sum = "h1:7lLHu94wT9Ij0o6EWWclhu0aOh32VxhkwEJvzuWPeak=", version = "v1.13.0", ) go_repository( name = "com_github_op_go_logging", build_file_proto_mode = "disable", importpath = "github.com/op/go-logging", sum = "h1:lDH9UUVJtmYCjyT0CI4q8xvlXPxeZ0gYCVvWbmPlp88=", version = "v0.0.0-20160315200505-970db520ece7", ) go_repository( name = "com_github_opencontainers_go_digest", build_file_proto_mode = "disable", importpath = "github.com/opencontainers/go-digest", sum = "h1:WzifXhOVOEOuFYOJAW6aQqW0TooG2iki3E3Ii+WN7gQ=", version = "v1.0.0-rc1", ) go_repository( name = "com_github_opencontainers_image_spec", build_file_proto_mode = "disable", importpath = "github.com/opencontainers/image-spec", sum = "h1:JMemWkRwHx4Zj+fVxWoMCFm/8sYGGrUVojFA6h/TRcI=", version = "v1.0.1", ) go_repository( name = "com_github_opencontainers_runc", build_file_proto_mode = "disable", importpath = "github.com/opencontainers/runc", sum = "h1:GlxAyO6x8rfZYN9Tt0Kti5a/cP41iuiO2yYT0IJGY8Y=", version = "v0.1.1", ) go_repository( name = "com_github_opencontainers_runtime_spec", build_file_proto_mode = "disable", importpath = "github.com/opencontainers/runtime-spec", sum = "h1:eNUVfm/RFLIi1G7flU5/ZRTHvd4kcVuzfRnL6OFlzCI=", version = "v0.1.2-0.20190507144316-5b71a03e2700", ) go_repository( name = "com_github_opencontainers_runtime_tools", build_file_proto_mode = "disable", importpath = "github.com/opencontainers/runtime-tools", sum = "h1:H7DMc6FAjgwZZi8BRqjrAAHWoqEr5e5L6pS4V0ezet4=", version = "v0.0.0-20181011054405-1d69bd0f9c39", ) go_repository( name = "com_github_openpeedeep_depguard", build_file_proto_mode = "disable", importpath = "github.com/OpenPeeDeeP/depguard", sum = "h1:VlW4R6jmBIv3/u1JNlawEvJMM4J+dPORPaZasQee8Us=", version = "v1.0.1", ) go_repository( name = "com_github_openzipkin_zipkin_go", build_file_proto_mode = "disable", importpath = "github.com/openzipkin/zipkin-go", sum = "h1:nY8Hti+WKaP0cRsSeQ026wU03QsM762XBeCXBb9NAWI=", version = "v0.2.2", ) go_repository( name = "com_github_otiai10_copy", build_file_proto_mode = "disable", importpath = "github.com/otiai10/copy", sum = "h1:DDNipYy6RkIkjMwy+AWzgKiNTyj2RUI9yEMeETEpVyc=", version = "v1.0.2", ) go_repository( name = "com_github_otiai10_curr", build_file_proto_mode = "disable", importpath = "github.com/otiai10/curr", sum = "h1:+OLn68pqasWca0z5ryit9KGfp3sUsW4Lqg32iRMJyzs=", version = "v0.0.0-20150429015615-9b4961190c95", ) go_repository( name = "com_github_otiai10_mint", build_file_proto_mode = "disable", importpath = "github.com/otiai10/mint", sum = "h1:Ady6MKVezQwHBkGzLFbrsywyp09Ah7rkmfjV3Bcr5uc=", version = "v1.3.0", ) go_repository( name = "com_github_pascaldekloe_goe", build_file_proto_mode = "disable", importpath = "github.com/pascaldekloe/goe", sum = "h1:cBOtyMzM9HTpWjXfbbunk26uA6nG3a8n06Wieeh0MwY=", version = "v0.1.0", ) go_repository( name = "com_github_pavel_v_chernykh_keystore_go", build_file_proto_mode = "disable", importpath = "github.com/pavel-v-chernykh/keystore-go", sum = "h1:Jd6xfriVlJ6hWPvYOE0Ni0QWcNTLRehfGPFxr3eSL80=", version = "v2.1.0+incompatible", ) go_repository( name = "com_github_pborman_uuid", build_file_proto_mode = "disable", importpath = "github.com/pborman/uuid", sum = "h1:J7Q5mO4ysT1dv8hyrUGHb9+ooztCXu1D8MY8DZYsu3g=", version = "v1.2.0", ) go_repository( name = "com_github_pelletier_go_buffruneio", build_file_proto_mode = "disable", importpath = "github.com/pelletier/go-buffruneio", sum = "h1:U4t4R6YkofJ5xHm3dJzuRpPZ0mr5MMCoAWooScCR7aA=", version = "v0.2.0", ) go_repository( name = "com_github_pelletier_go_toml", build_file_proto_mode = "disable", importpath = "github.com/pelletier/go-toml", sum = "h1:Keo9qb7iRJs2voHvunFtuuYFsbWeOBh8/P9v/kVMFtw=", version = "v1.8.0", ) go_repository( name = "com_github_peterbourgon_diskv", build_file_proto_mode = "disable", importpath = "github.com/peterbourgon/diskv", sum = "h1:UBdAOUP5p4RWqPBg048CAvpKN+vxiaj6gdUUzhl4XmI=", version = "v2.0.1+incompatible", ) go_repository( name = "com_github_phayes_checkstyle", build_file_proto_mode = "disable", importpath = "github.com/phayes/checkstyle", sum = "h1:CdDQnGF8Nq9ocOS/xlSptM1N3BbrA6/kmaep5ggwaIA=", version = "v0.0.0-20170904204023-bfd46e6a821d", ) go_repository( name = "com_github_phayes_freeport", build_file_proto_mode = "disable", importpath = "github.com/phayes/freeport", sum = "h1:JhzVVoYvbOACxoUmOs6V/G4D5nPVUW73rKvXxP4XUJc=", version = "v0.0.0-20180830031419-95f893ade6f2", ) go_repository( name = "com_github_pierrec_lz4", build_file_proto_mode = "disable", importpath = "github.com/pierrec/lz4", sum = "h1:6aCX4/YZ9v8q69hTyiR7dNLnTA3fgtKHVVW5BCd5Znw=", version = "v2.2.6+incompatible", ) go_repository( name = "com_github_pkg_errors", build_file_proto_mode = "disable", importpath = "github.com/pkg/errors", sum = "h1:FEBLx1zS214owpjy7qsBeixbURkuhQAwrK5UwLGTwt4=", version = "v0.9.1", ) go_repository( name = "com_github_pkg_profile", build_file_proto_mode = "disable", importpath = "github.com/pkg/profile", sum = "h1:F++O52m40owAmADcojzM+9gyjmMOY/T4oYJkgFDH8RE=", version = "v1.2.1", ) go_repository( name = "com_github_pmezard_go_difflib", build_file_proto_mode = "disable", importpath = "github.com/pmezard/go-difflib", sum = "h1:4DBwDE0NGyQoBHbLQYPwSUPoCMWR5BEzIk/f1lZbAQM=", version = "v1.0.0", ) go_repository( name = "com_github_posener_complete", build_file_proto_mode = "disable", importpath = "github.com/posener/complete", sum = "h1:ccV59UEOTzVDnDUEFdT95ZzHVZ+5+158q8+SJb2QV5w=", version = "v1.1.1", ) go_repository( name = "com_github_pquerna_cachecontrol", build_file_proto_mode = "disable", importpath = "github.com/pquerna/cachecontrol", sum = "h1:0XM1XL/OFFJjXsYXlG30spTkV/E9+gmd5GD1w2HE8xM=", version = "v0.0.0-20171018203845-0dec1b30a021", ) go_repository( name = "com_github_prometheus_client_golang", build_file_proto_mode = "disable", importpath = "github.com/prometheus/client_golang", sum = "h1:HNkLOAEQMIDv/K+04rukrLx6ch7msSRwf3/SASFAGtQ=", version = "v1.11.0", ) go_repository( name = "com_github_prometheus_client_model", build_file_proto_mode = "disable", importpath = "github.com/prometheus/client_model", sum = "h1:uq5h0d+GuxiXLJLNABMgp2qUWDPiLvgCzz2dUR+/W/M=", version = "v0.2.0", ) go_repository( name = "com_github_prometheus_common", build_file_proto_mode = "disable", importpath = "github.com/prometheus/common", sum = "h1:iMAkS2TDoNWnKM+Kopnx/8tnEStIfpYA0ur0xQzzhMQ=", version = "v0.26.0", ) go_repository( name = "com_github_prometheus_procfs", build_file_proto_mode = "disable", importpath = "github.com/prometheus/procfs", sum = "h1:mxy4L2jP6qMonqmq+aTtOx1ifVWUgG/TAmntgbh3xv4=", version = "v0.6.0", ) go_repository( name = "com_github_prometheus_tsdb", build_file_proto_mode = "disable", importpath = "github.com/prometheus/tsdb", sum = "h1:YZcsG11NqnK4czYLrWd9mpEuAJIHVQLwdrleYfszMAA=", version = "v0.7.1", ) go_repository( name = "com_github_puerkitobio_purell", build_file_proto_mode = "disable", importpath = "github.com/PuerkitoBio/purell", sum = "h1:WEQqlqaGbrPkxLJWfBwQmfEAE1Z7ONdDLqrN38tNFfI=", version = "v1.1.1", ) go_repository( name = "com_github_puerkitobio_urlesc", build_file_proto_mode = "disable", importpath = "github.com/PuerkitoBio/urlesc", sum = "h1:d+Bc7a5rLufV/sSk/8dngufqelfh6jnri85riMAaF/M=", version = "v0.0.0-20170810143723-de5bf2ad4578", ) go_repository( name = "com_github_quasilyte_go_consistent", build_file_proto_mode = "disable", importpath = "github.com/quasilyte/go-consistent", sum = "h1:JoUA0uz9U0FVFq5p4LjEq4C0VgQ0El320s3Ms0V4eww=", version = "v0.0.0-20190521200055-c6f3937de18c", ) go_repository( name = "com_github_quasilyte_go_ruleguard", build_file_proto_mode = "disable", importpath = "github.com/quasilyte/go-ruleguard", sum = "h1:DvnesvLtRPQOvaUbfXfh0tpMHg29by0H7F2U+QIkSu8=", version = "v0.1.2-0.20200318202121-b00d7a75d3d8", ) go_repository( name = "com_github_rcrowley_go_metrics", build_file_proto_mode = "disable", importpath = "github.com/rcrowley/go-metrics", sum = "h1:eUm8ma4+yPknhXtkYlWh3tMkE6gBjXZToDned9s2gbQ=", version = "v0.0.0-20190706150252-9beb055b7962", ) go_repository( name = "com_github_remyoudompheng_bigfft", build_file_proto_mode = "disable", importpath = "github.com/remyoudompheng/bigfft", sum = "h1:/NRJ5vAYoqz+7sG51ubIDHXeWO8DlTSrToPu6q11ziA=", version = "v0.0.0-20170806203942-52369c62f446", ) go_repository( name = "com_github_rogpeppe_fastuuid", build_file_proto_mode = "disable", importpath = "github.com/rogpeppe/fastuuid", sum = "h1:Ppwyp6VYCF1nvBTXL3trRso7mXMlRrw9ooo375wvi2s=", version = "v1.2.0", ) go_repository( name = "com_github_rogpeppe_go_internal", build_file_proto_mode = "disable", importpath = "github.com/rogpeppe/go-internal", sum = "h1:qLvObTrvO/XRCqmkKxUlOBc48bI3efyDuAZe25QiF0w=", version = "v1.5.2", ) go_repository( name = "com_github_rubiojr_go_vhd", build_file_proto_mode = "disable", importpath = "github.com/rubiojr/go-vhd", sum = "h1:ht7N4d/B7Ezf58nvMNVF3OlvDlz9pp+WHVcRNS0nink=", version = "v0.0.0-20160810183302-0bfd3b39853c", ) go_repository( name = "com_github_russross_blackfriday", build_file_proto_mode = "disable", importpath = "github.com/russross/blackfriday", sum = "h1:HyvC0ARfnZBqnXwABFeSZHpKvJHJJfPz81GNueLj0oo=", version = "v1.5.2", ) go_repository( name = "com_github_russross_blackfriday_v2", build_file_proto_mode = "disable", importpath = "github.com/russross/blackfriday/v2", sum = "h1:lPqVAte+HuHNfhJ/0LC98ESWRz8afy9tM/0RK8m9o+Q=", version = "v2.0.1", ) go_repository( name = "com_github_rwcarlsen_goexif", build_file_proto_mode = "disable", importpath = "github.com/rwcarlsen/goexif", sum = "h1:CmH9+J6ZSsIjUK3dcGsnCnO41eRBOnY12zwkn5qVwgc=", version = "v0.0.0-20190401172101-9e8deecbddbd", ) go_repository( name = "com_github_ryancurrah_gomodguard", build_file_proto_mode = "disable", importpath = "github.com/ryancurrah/gomodguard", sum = "h1:DWbye9KyMgytn8uYpuHkwf0RHqAYO6Ay/D0TbCpPtVU=", version = "v1.1.0", ) go_repository( name = "com_github_ryanuber_columnize", build_file_proto_mode = "disable", importpath = "github.com/ryanuber/columnize", sum = "h1:j1Wcmh8OrK4Q7GXY+V7SVSY8nUWQxHW5TkBe7YUl+2s=", version = "v2.1.0+incompatible", ) go_repository( name = "com_github_ryanuber_go_glob", build_file_proto_mode = "disable", importpath = "github.com/ryanuber/go-glob", sum = "h1:iQh3xXAumdQ+4Ufa5b25cRpC5TYKlno6hsv6Cb3pkBk=", version = "v1.0.0", ) go_repository( name = "com_github_sassoftware_go_rpmutils", build_file_proto_mode = "disable", importpath = "github.com/sassoftware/go-rpmutils", sum = "h1:+gCnWOZV8Z/8jehJ2CdqB47Z3S+SREmQcuXkRFLNsiI=", version = "v0.0.0-20190420191620-a8f1baeba37b", ) go_repository( name = "com_github_satori_go_uuid", build_file_proto_mode = "disable", importpath = "github.com/satori/go.uuid", sum = "h1:0uYX9dsZ2yD7q2RtLRtPSdGDWzjeM3TbMJP9utgA0ww=", version = "v1.2.0", ) go_repository( name = "com_github_sclevine_spec", build_file_proto_mode = "disable", importpath = "github.com/sclevine/spec", sum = "h1:1Jwdf9jSfDl9NVmt8ndHqbTZ7XCCPbh1jI3hkDBHVYA=", version = "v1.2.0", ) go_repository( name = "com_github_sean_seed", build_file_proto_mode = "disable", importpath = "github.com/sean-/seed", sum = "h1:nn5Wsu0esKSJiIVhscUtVbo7ada43DJhG55ua/hjS5I=", version = "v0.0.0-20170313163322-e2103e2c3529", ) go_repository( name = "com_github_securego_gosec", build_file_proto_mode = "disable", importpath = "github.com/securego/gosec", sum = "h1:rq2/kILQnPtq5oL4+IAjgVOjh5e2yj2aaCYi7squEvI=", version = "v0.0.0-20200401082031-e946c8c39989", ) go_repository( name = "com_github_securego_gosec_v2", build_file_proto_mode = "disable", importpath = "github.com/securego/gosec/v2", sum = "h1:y/9mCF2WPDbSDpL3QDWZD3HHGrSYw0QSHnCqTfs4JPE=", version = "v2.3.0", ) go_repository( name = "com_github_sergi_go_diff", build_file_proto_mode = "disable", importpath = "github.com/sergi/go-diff", sum = "h1:we8PVUC3FE2uYfodKH/nBHMSetSfHDR6scGdBi+erh0=", version = "v1.1.0", ) go_repository( name = "com_github_shirou_gopsutil", build_file_proto_mode = "disable", importpath = "github.com/shirou/gopsutil", sum = "h1:WokF3GuxBeL+n4Lk4Fa8v9mbdjlrl7bHuneF4N1bk2I=", version = "v0.0.0-20190901111213-e4ec7b275ada", ) go_repository( name = "com_github_shirou_w32", build_file_proto_mode = "disable", importpath = "github.com/shirou/w32", sum = "h1:udFKJ0aHUL60LboW/A+DfgoHVedieIzIXE8uylPue0U=", version = "v0.0.0-20160930032740-bb4de0191aa4", ) go_repository( name = "com_github_shopify_logrus_bugsnag", build_file_proto_mode = "disable", importpath = "github.com/Shopify/logrus-bugsnag", sum = "h1:UrqY+r/OJnIp5u0s1SbQ8dVfLCZJsnvazdBP5hS4iRs=", version = "v0.0.0-20171204204709-577dee27f20d", ) go_repository( name = "com_github_shopify_sarama", build_file_proto_mode = "disable", importpath = "github.com/Shopify/sarama", sum = "h1:XxJBCZEoWJtoWjf/xRbmGUpAmTZGnuuF0ON0EvxxBrs=", version = "v1.23.1", ) go_repository( name = "com_github_shopify_toxiproxy", build_file_proto_mode = "disable", importpath = "github.com/Shopify/toxiproxy", sum = "h1:TKdv8HiTLgE5wdJuEML90aBgNWsokNbMijUGhmcoBJc=", version = "v2.1.4+incompatible", ) go_repository( name = "com_github_shurcool_githubv4", build_file_proto_mode = "disable", importpath = "github.com/shurcooL/githubv4", sum = "h1:N5B+JgvM/DVYIxreItPJMM3yWrNO/GB2q4nESrtBisM=", version = "v0.0.0-20210725200734-83ba7b4c9228", ) go_repository( name = "com_github_shurcool_go", build_file_proto_mode = "disable", importpath = "github.com/shurcooL/go", sum = "h1:MZM7FHLqUHYI0Y/mQAt3d2aYa0SiNms/hFqC9qJYolM=", version = "v0.0.0-20180423040247-9e1955d9fb6e", ) go_repository( name = "com_github_shurcool_go_goon", build_file_proto_mode = "disable", importpath = "github.com/shurcooL/go-goon", sum = "h1:llrF3Fs4018ePo4+G/HV/uQUqEI1HMDjCeOf2V6puPc=", version = "v0.0.0-20170922171312-37c2f522c041", ) go_repository( name = "com_github_shurcool_graphql", build_file_proto_mode = "disable", importpath = "github.com/shurcooL/graphql", sum = "h1:tygelZueB1EtXkPI6mQ4o9DQ0+FKW41hTbunoXZCTqk=", version = "v0.0.0-20181231061246-d48a9a75455f", ) go_repository( name = "com_github_shurcool_sanitized_anchor_name", build_file_proto_mode = "disable", importpath = "github.com/shurcooL/sanitized_anchor_name", sum = "h1:PdmoCO6wvbs+7yrJyMORt4/BmY5IYyJwS/kOiWx8mHo=", version = "v1.0.0", ) go_repository( name = "com_github_sirupsen_logrus", build_file_proto_mode = "disable", importpath = "github.com/sirupsen/logrus", sum = "h1:dJKuHgqk1NNQlqoA6BTlM1Wf9DOH3NBjQyu0h9+AZZE=", version = "v1.8.1", ) go_repository( name = "com_github_smartystreets_assertions", build_file_proto_mode = "disable", importpath = "github.com/smartystreets/assertions", sum = "h1:42S6lae5dvLc7BrLu/0ugRtcFVjoJNMC/N3yZFZkDFs=", version = "v1.2.0", ) go_repository( name = "com_github_smartystreets_go_aws_auth", build_file_proto_mode = "disable", importpath = "github.com/smartystreets/go-aws-auth", sum = "h1:hp2CYQUINdZMHdvTdXtPOY2ainKl4IoMcpAXEf2xj3Q=", version = "v0.0.0-20180515143844-0c1422d1fdb9", ) go_repository( name = "com_github_smartystreets_goconvey", build_file_proto_mode = "disable", importpath = "github.com/smartystreets/goconvey", sum = "h1:fv0U8FUIMPNf1L9lnHLvLhgicrIVChEkdzIKYqbNC9s=", version = "v1.6.4", ) go_repository( name = "com_github_smartystreets_gunit", build_file_proto_mode = "disable", importpath = "github.com/smartystreets/gunit", sum = "h1:RyPDUFcJbvtXlhJPk7v+wnxZRY2EUokhEYl2EJOPToI=", version = "v1.0.0", ) go_repository( name = "com_github_soheilhy_cmux", build_file_proto_mode = "disable", importpath = "github.com/soheilhy/cmux", sum = "h1:0HKaf1o97UwFjHH9o5XsHUOF+tqmdA7KEzXLpiyaw0E=", version = "v0.1.4", ) go_repository( name = "com_github_sourcegraph_go_diff", build_file_proto_mode = "disable", importpath = "github.com/sourcegraph/go-diff", sum = "h1:lhIKJ2nXLZZ+AfbHpYxTn0pXpNTTui0DX7DO3xeb1Zs=", version = "v0.5.3", ) go_repository( name = "com_github_spaolacci_murmur3", build_file_proto_mode = "disable", importpath = "github.com/spaolacci/murmur3", sum = "h1:qLC7fQah7D6K1B0ujays3HV9gkFtllcxhzImRR7ArPQ=", version = "v0.0.0-20180118202830-f09979ecbc72", ) go_repository( name = "com_github_spf13_afero", build_file_proto_mode = "disable", importpath = "github.com/spf13/afero", sum = "h1:5jhuqJyZCZf2JRofRvN/nIFgIWNzPa3/Vz8mYylgbWc=", version = "v1.2.2", ) go_repository( name = "com_github_spf13_cast", build_file_proto_mode = "disable", importpath = "github.com/spf13/cast", sum = "h1:nFm6S0SMdyzrzcmThSipiEubIDy8WEXKNZ0UOgiRpng=", version = "v1.3.1", ) go_repository( name = "com_github_spf13_cobra", build_file_proto_mode = "disable", importpath = "github.com/spf13/cobra", sum = "h1:xghbfqPkxzxP3C/f3n5DdpAbdKLj4ZE4BWQI362l53M=", version = "v1.1.3", ) go_repository( name = "com_github_spf13_jwalterweatherman", build_file_proto_mode = "disable", importpath = "github.com/spf13/jwalterweatherman", sum = "h1:ue6voC5bR5F8YxI5S67j9i582FU4Qvo2bmqnqMYADFk=", version = "v1.1.0", ) go_repository( name = "com_github_spf13_pflag", build_file_proto_mode = "disable", importpath = "github.com/spf13/pflag", sum = "h1:iy+VFUOCP1a+8yFto/drg2CJ5u0yRoB7fZw3DKv/JXA=", version = "v1.0.5", ) go_repository( name = "com_github_spf13_viper", build_file_proto_mode = "disable", importpath = "github.com/spf13/viper", sum = "h1:pM5oEahlgWv/WnHXpgbKz7iLIxRf65tye2Ci+XFK5sk=", version = "v1.7.1", ) go_repository( name = "com_github_src_d_gcfg", build_file_proto_mode = "disable", importpath = "github.com/src-d/gcfg", sum = "h1:xXbNR5AlLSA315x2UO+fTSSAXCDf+Ar38/6oyGbDKQ4=", version = "v1.4.0", ) go_repository( name = "com_github_stackexchange_wmi", build_file_proto_mode = "disable", importpath = "github.com/StackExchange/wmi", sum = "h1:fLjPD/aNc3UIOA6tDi6QXUemppXK3P9BI7mr2hd6gx8=", version = "v0.0.0-20180116203802-5d049714c4a6", ) go_repository( name = "com_github_streadway_amqp", build_file_proto_mode = "disable", importpath = "github.com/streadway/amqp", sum = "h1:0ngsPmuP6XIjiFRNFYlvKwSr5zff2v+uPHaffZ6/M4k=", version = "v0.0.0-20190404075320-75d898a42a94", ) go_repository( name = "com_github_streadway_quantile", build_file_proto_mode = "disable", importpath = "github.com/streadway/quantile", sum = "h1:7z3LSn867ex6VSaahyKadf4WtSsJIgne6A1WLOAGM8A=", version = "v0.0.0-20150917103942-b0c588724d25", ) go_repository( name = "com_github_stretchr_objx", build_file_proto_mode = "disable", importpath = "github.com/stretchr/objx", sum = "h1:Hbg2NidpLE8veEBkEZTL3CvlkUIVzuU9jDplZO54c48=", version = "v0.2.0", ) go_repository( name = "com_github_stretchr_testify", build_file_proto_mode = "disable", importpath = "github.com/stretchr/testify", sum = "h1:nwc3DEeHmmLAfoZucVR881uASk0Mfjw8xYJ99tb5CcY=", version = "v1.7.0", ) go_repository( name = "com_github_subosito_gotenv", build_file_proto_mode = "disable", importpath = "github.com/subosito/gotenv", sum = "h1:Slr1R9HxAlEKefgq5jn9U+DnETlIUa6HfgEzj0g5d7s=", version = "v1.2.0", ) go_repository( name = "com_github_syndtr_gocapability", build_file_proto_mode = "disable", importpath = "github.com/syndtr/gocapability", sum = "h1:zLV6q4e8Jv9EHjNg/iHfzwDkCve6Ua5jCygptrtXHvI=", version = "v0.0.0-20170704070218-db04d3cc01c8", ) go_repository( name = "com_github_tdakkota_asciicheck", build_file_proto_mode = "disable", importpath = "github.com/tdakkota/asciicheck", sum = "h1:HxLVTlqcHhFAz3nWUcuvpH7WuOMv8LQoCWmruLfFH2U=", version = "v0.0.0-20200416200610-e657995f937b", ) go_repository( name = "com_github_tektoncd_pipeline", build_file_proto_mode = "disable", importpath = "github.com/tektoncd/pipeline", sum = "h1:hWdWj5bDjkSGYLlJS+u+Kh9ZktBJgs2JNUv/kP0LVOA=", version = "v0.13.1-0.20200625065359-44f22a067b75", ) go_repository( name = "com_github_tektoncd_plumbing", build_file_proto_mode = "disable", importpath = "github.com/tektoncd/plumbing", sum = "h1:crv70CBAJ2gZFSbf13aRVwdbjR2GYwTms/ZEok/SnFM=", version = "v0.0.0-20200430135134-e53521e1d887", ) go_repository( name = "com_github_tektoncd_plumbing_pipelinerun_logs", build_file_proto_mode = "disable", importpath = "github.com/tektoncd/plumbing/pipelinerun-logs", sum = "h1:9qeyrQsoPZbHOyOPt0OeB1TCYXfYb5swrxlFWzTIYYk=", version = "v0.0.0-20191206114338-712d544c2c21", ) go_repository( name = "com_github_tetafro_godot", build_file_proto_mode = "disable", importpath = "github.com/tetafro/godot", sum = "h1:Dib7un+rYJFUi8vN0Bk6EHheKy6fv6ZzFURHw75g6m8=", version = "v0.4.2", ) go_repository( name = "com_github_tidwall_pretty", build_file_proto_mode = "disable", importpath = "github.com/tidwall/pretty", sum = "h1:HsD+QiTn7sK6flMKIvNmpqz1qrpP3Ps6jOKIKMooyg4=", version = "v1.0.0", ) go_repository( name = "com_github_timakin_bodyclose", build_file_proto_mode = "disable", importpath = "github.com/timakin/bodyclose", sum = "h1:ig99OeTyDwQWhPe2iw9lwfQVF1KB3Q4fpP3X7/2VBG8=", version = "v0.0.0-20200424151742-cb6215831a94", ) go_repository( name = "com_github_tj_assert", build_file_proto_mode = "disable", importpath = "github.com/tj/assert", sum = "h1:Rw8kxzWo1mr6FSaYXjQELRe88y2KdfynXdnK72rdjtA=", version = "v0.0.0-20171129193455-018094318fb0", ) go_repository( name = "com_github_tj_go_elastic", build_file_proto_mode = "disable", importpath = "github.com/tj/go-elastic", sum = "h1:eGaGNxrtoZf/mBURsnNQKDR7u50Klgcf2eFDQEnc8Bc=", version = "v0.0.0-20171221160941-36157cbbebc2", ) go_repository( name = "com_github_tj_go_kinesis", build_file_proto_mode = "disable", importpath = "github.com/tj/go-kinesis", sum = "h1:m74UWYy+HBs+jMFR9mdZU6shPewugMyH5+GV6LNgW8w=", version = "v0.0.0-20171128231115-08b17f58cb1b", ) go_repository( name = "com_github_tj_go_spin", build_file_proto_mode = "disable", importpath = "github.com/tj/go-spin", sum = "h1:lhdWZsvImxvZ3q1C5OIB7d72DuOwP4O2NdBg9PyzNds=", version = "v1.1.0", ) go_repository( name = "com_github_tmc_grpc_websocket_proxy", build_file_proto_mode = "disable", importpath = "github.com/tmc/grpc-websocket-proxy", sum = "h1:LnC5Kc/wtumK+WB441p7ynQJzVuNRJiqddSIE3IlSEQ=", version = "v0.0.0-20190109142713-0ad062ec5ee5", ) go_repository( name = "com_github_tommy_muehle_go_mnd", build_file_proto_mode = "disable", importpath = "github.com/tommy-muehle/go-mnd", sum = "h1:RC4maTWLKKwb7p1cnoygsbKIgNlJqSYBeAFON3Ar8As=", version = "v1.3.1-0.20200224220436-e6f9a994e8fa", ) go_repository( name = "com_github_trivago_tgo", build_file_proto_mode = "disable", importpath = "github.com/trivago/tgo", sum = "h1:bxatjJIXNIpV18bucU4Uk/LaoxvxuOlp/oowRHyncLQ=", version = "v1.0.1", ) go_repository( name = "com_github_tsenart_vegeta", build_file_proto_mode = "disable", importpath = "github.com/tsenart/vegeta", sum = "h1:ErZrHhRveAoznVW80gbrxz+qxJNydpA2fcQxTPHkZbU=", version = "v12.7.1-0.20190725001342-b5f4fca92137+incompatible", ) go_repository( name = "com_github_ugorji_go", build_file_proto_mode = "disable", importpath = "github.com/ugorji/go", sum = "h1:j4s+tAvLfL3bZyefP2SEWmhBzmuIlH/eqNuPdFPgngw=", version = "v1.1.4", ) go_repository( name = "com_github_ugorji_go_codec", build_file_proto_mode = "disable", importpath = "github.com/ugorji/go/codec", sum = "h1:3SVOIvH7Ae1KRYyQWRjXWJEA9sS/c/pjvH++55Gr648=", version = "v0.0.0-20181204163529-d75b2dcb6bc8", ) go_repository( name = "com_github_ulikunitz_xz", build_file_proto_mode = "disable", importpath = "github.com/ulikunitz/xz", sum = "h1:YvTNdFzX6+W5m9msiYg/zpkSURPPtOlzbqYjrFn7Yt4=", version = "v0.5.7", ) go_repository( name = "com_github_ultraware_funlen", build_file_proto_mode = "disable", importpath = "github.com/ultraware/funlen", sum = "h1:Av96YVBwwNSe4MLR7iI/BIa3VyI7/djnto/pK3Uxbdo=", version = "v0.0.2", ) go_repository( name = "com_github_ultraware_whitespace", build_file_proto_mode = "disable", importpath = "github.com/ultraware/whitespace", sum = "h1:If7Va4cM03mpgrNH9k49/VOicWpGoG70XPBFFODYDsg=", version = "v0.0.4", ) go_repository( name = "com_github_urfave_cli", build_file_proto_mode = "disable", importpath = "github.com/urfave/cli", sum = "h1:u7tSpNPPswAFymm8IehJhy4uJMlUuU/GmqSkvJ1InXA=", version = "v1.22.4", ) go_repository( name = "com_github_urfave_cli_v2", build_file_proto_mode = "disable", importpath = "github.com/urfave/cli/v2", sum = "h1:Qt8FeAtxE/vfdrLmR3rxR6JRE0RoVmbXu8+6kZtYU4k=", version = "v2.1.1", ) go_repository( name = "com_github_uudashr_gocognit", build_file_proto_mode = "disable", importpath = "github.com/uudashr/gocognit", sum = "h1:MoG2fZ0b/Eo7NXoIwCVFLG5JED3qgQz5/NEE+rOsjPs=", version = "v1.0.1", ) go_repository( name = "com_github_valyala_bytebufferpool", build_file_proto_mode = "disable", importpath = "github.com/valyala/bytebufferpool", sum = "h1:GqA5TC/0021Y/b9FG4Oi9Mr3q7XYx6KllzawFIhcdPw=", version = "v1.0.0", ) go_repository( name = "com_github_valyala_fasthttp", build_file_proto_mode = "disable", importpath = "github.com/valyala/fasthttp", sum = "h1:dzZJf2IuMiclVjdw0kkT+f9u4YdrapbNyGAN47E/qnk=", version = "v1.2.0", ) go_repository( name = "com_github_valyala_quicktemplate", build_file_proto_mode = "disable", importpath = "github.com/valyala/quicktemplate", sum = "h1:BaO1nHTkspYzmAjPXj0QiDJxai96tlcZyKcI9dyEGvM=", version = "v1.2.0", ) go_repository( name = "com_github_valyala_tcplisten", build_file_proto_mode = "disable", importpath = "github.com/valyala/tcplisten", sum = "h1:0R4NLDRDZX6JcmhJgXi5E4b8Wg84ihbmUKp/GvSPEzc=", version = "v0.0.0-20161114210144-ceec8f93295a", ) go_repository( name = "com_github_vdemeester_k8s_pkg_credentialprovider", build_file_proto_mode = "disable", importpath = "github.com/vdemeester/k8s-pkg-credentialprovider", sum = "h1:czKEIG2Q3YRTgs6x/8xhjVMJD5byPo6cZuostkbTM74=", version = "v1.17.4", ) go_repository( name = "com_github_vektah_gqlparser", build_file_proto_mode = "disable", importpath = "github.com/vektah/gqlparser", sum = "h1:ZsyLGn7/7jDNI+y4SEhI4yAxRChlv15pUHMjijT+e68=", version = "v1.1.2", ) go_repository( name = "com_github_venafi_vcert_v4", build_file_proto_mode = "disable", importpath = "github.com/Venafi/vcert/v4", sum = "h1:37gfyjS9v5YvZcIABwNPo1fAC31lIZT7glVK1vfUxk4=", version = "v4.11.0", ) go_repository( name = "com_github_vmware_govmomi", build_file_proto_mode = "disable", importpath = "github.com/vmware/govmomi", sum = "h1:gpw/0Ku+6RgF3jsi7fnCLmlcikBHfKBCUcu1qgc16OU=", version = "v0.20.3", ) go_repository( name = "com_github_xanzy_go_gitlab", build_file_proto_mode = "disable", importpath = "github.com/xanzy/go-gitlab", sum = "h1:tBm+OXv1t+KBsqlXkSDFz+YUjRM0GFsjpOWYOod3Ebs=", version = "v0.32.0", ) go_repository( name = "com_github_xanzy_ssh_agent", build_file_proto_mode = "disable", importpath = "github.com/xanzy/ssh-agent", sum = "h1:wUMzuKtKilRgBAD1sUb8gOwwRr2FGoBVumcjoOACClI=", version = "v0.3.0", ) go_repository( name = "com_github_xdg_scram", build_file_proto_mode = "disable", importpath = "github.com/xdg/scram", sum = "h1:u40Z8hqBAAQyv+vATcGgV0YCnDjqSL7/q/JyPhhJSPk=", version = "v0.0.0-20180814205039-7eeb5667e42c", ) go_repository( name = "com_github_xdg_stringprep", build_file_proto_mode = "disable", importpath = "github.com/xdg/stringprep", sum = "h1:d9X0esnoa3dFsV0FG35rAT0RIhYFlPq7MiP+DW89La0=", version = "v1.0.0", ) go_repository( name = "com_github_xeipuuv_gojsonpointer", build_file_proto_mode = "disable", importpath = "github.com/xeipuuv/gojsonpointer", sum = "h1:J9EGpcZtP0E/raorCMxlFGSTBrsSlaDGf3jU/qvAE2c=", version = "v0.0.0-20180127040702-4e3ac2762d5f", ) go_repository( name = "com_github_xeipuuv_gojsonreference", build_file_proto_mode = "disable", importpath = "github.com/xeipuuv/gojsonreference", sum = "h1:EzJWgHovont7NscjpAxXsDA8S8BMYve8Y5+7cuRE7R0=", version = "v0.0.0-20180127040603-bd5ef7bd5415", ) go_repository( name = "com_github_xeipuuv_gojsonschema", build_file_proto_mode = "disable", importpath = "github.com/xeipuuv/gojsonschema", sum = "h1:ngVtJC9TY/lg0AA/1k48FYhBrhRoFlEmWzsehpNAaZg=", version = "v1.1.0", ) go_repository( name = "com_github_xi2_xz", build_file_proto_mode = "disable", importpath = "github.com/xi2/xz", sum = "h1:nIPpBwaJSVYIxUFsDv3M8ofmx9yWTog9BfvIu0q41lo=", version = "v0.0.0-20171230120015-48954b6210f8", ) go_repository( name = "com_github_xiang90_probing", build_file_proto_mode = "disable", importpath = "github.com/xiang90/probing", sum = "h1:eY9dn8+vbi4tKz5Qo6v2eYzo7kUS51QINcR5jNpbZS8=", version = "v0.0.0-20190116061207-43a291ad63a2", ) go_repository( name = "com_github_xlab_handysort", build_file_proto_mode = "disable", importpath = "github.com/xlab/handysort", sum = "h1:j2hhcujLRHAg872RWAV5yaUrEjHEObwDv3aImCaNLek=", version = "v0.0.0-20150421192137-fb3537ed64a1", ) go_repository( name = "com_github_xordataexchange_crypt", build_file_proto_mode = "disable", importpath = "github.com/xordataexchange/crypt", sum = "h1:ESFSdwYZvkeru3RtdrYueztKhOBCSAAzS4Gf+k0tEow=", version = "v0.0.3-0.20170626215501-b2862e3d0a77", ) go_repository( name = "com_github_yuin_goldmark", build_file_proto_mode = "disable", importpath = "github.com/yuin/goldmark", sum = "h1:ruQGxdhGHe7FWOJPT0mKs5+pD2Xs1Bm/kdGlHO04FmM=", version = "v1.2.1", ) go_repository( name = "com_github_yvasiyarov_go_metrics", build_file_proto_mode = "disable", importpath = "github.com/yvasiyarov/go-metrics", sum = "h1:+lm10QQTNSBd8DVTNGHx7o/IKu9HYDvLMffDhbyLccI=", version = "v0.0.0-20140926110328-57bccd1ccd43", ) go_repository( name = "com_github_yvasiyarov_gorelic", build_file_proto_mode = "disable", importpath = "github.com/yvasiyarov/gorelic", sum = "h1:hlE8//ciYMztlGpl/VA+Zm1AcTPHYkHJPbHqE6WJUXE=", version = "v0.0.0-20141212073537-a9bba5b9ab50", ) go_repository( name = "com_github_yvasiyarov_newrelic_platform_go", build_file_proto_mode = "disable", importpath = "github.com/yvasiyarov/newrelic_platform_go", sum = "h1:ERexzlUfuTvpE74urLSbIQW0Z/6hF9t8U4NsJLaioAY=", version = "v0.0.0-20140908184405-b21fdbd4370f", ) go_repository( name = "com_google_cloud_go", build_file_proto_mode = "disable", importpath = "cloud.google.com/go", sum = "h1:DZeAkuQGQqnm9Xv36SbMJEU8aFBz4wL04UpMWPWwjzg=", version = "v0.66.0", ) go_repository( name = "com_google_cloud_go_bigquery", build_file_proto_mode = "disable", importpath = "cloud.google.com/go/bigquery", sum = "h1:PQcPefKFdaIzjQFbiyOgAqyx8q5djaE7x9Sqe712DPA=", version = "v1.8.0", ) go_repository( name = "com_google_cloud_go_datastore", build_file_proto_mode = "disable", importpath = "cloud.google.com/go/datastore", sum = "h1:/May9ojXjRkPBNVrq+oWLqmWCkr4OU5uRY29bu0mRyQ=", version = "v1.1.0", ) go_repository( name = "com_google_cloud_go_firestore", build_file_proto_mode = "disable", importpath = "cloud.google.com/go/firestore", sum = "h1:9x7Bx0A9R5/M9jibeJeZWqjeVEIxYW9fZYqB9a70/bY=", version = "v1.1.0", ) go_repository( name = "com_google_cloud_go_logging", build_file_proto_mode = "disable", importpath = "cloud.google.com/go/logging", sum = "h1:kaunpnoEh9L4hu6JUsBa8Y20LBfKnCuDhKUgdZp7oK8=", version = "v1.0.0", ) go_repository( name = "com_google_cloud_go_pubsub", build_file_proto_mode = "disable", importpath = "cloud.google.com/go/pubsub", sum = "h1:76oR7VBOkL7ivoIrFKyW0k7YDCRelrlxktIzQiIUGgg=", version = "v1.4.0", ) go_repository( name = "com_google_cloud_go_storage", build_file_proto_mode = "disable", importpath = "cloud.google.com/go/storage", sum = "h1:4y3gHptW1EHVtcPAVE0eBBlFuGqEejTTG3KdIE0lUX4=", version = "v1.12.0", ) go_repository( name = "com_shuralyov_dmitri_gpu_mtl", build_file_proto_mode = "disable", importpath = "dmitri.shuralyov.com/gpu/mtl", sum = "h1:VpgP7xuJadIUuKccphEpTJnWhS2jkQyMt6Y7pJCD7fY=", version = "v0.0.0-20190408044501-666a987793e9", ) go_repository( name = "com_sourcegraph_sqs_pbtypes", build_file_proto_mode = "disable", importpath = "sourcegraph.com/sqs/pbtypes", sum = "h1:f7lAwqviDEGvON4kRv0o5V7FT/IQK+tbkF664XMbP3o=", version = "v1.0.0", ) go_repository( name = "com_sslmate_software_src_go_pkcs12", build_file_proto_mode = "disable", importpath = "software.sslmate.com/src/go-pkcs12", sum = "h1:AVd6O+azYjVQYW1l55IqkbL8/JxjrLtO6q4FCmV8N5c=", version = "v0.0.0-20200830195227-52f69702a001", ) go_repository( name = "dev_gocloud", build_file_proto_mode = "disable", importpath = "gocloud.dev", sum = "h1:EDRyaRAnMGSq/QBto486gWFxMLczAfIYUmusV7XLNBM=", version = "v0.19.0", ) go_repository( name = "dev_knative_caching", build_file_proto_mode = "disable", importpath = "knative.dev/caching", sum = "h1:mxrur6DsVK8uIjhIq7c1OMls4YjBcRlyvnh3Vx13a0M=", version = "v0.0.0-20200116200605-67bca2c83dfa", ) go_repository( name = "dev_knative_eventing_contrib", build_file_proto_mode = "disable", importpath = "knative.dev/eventing-contrib", sum = "h1:xncT+JrokPG+hPUFJwue8ubPpzmziV9GUIZqYt01JDo=", version = "v0.11.2", ) go_repository( name = "dev_knative_pkg", build_file_proto_mode = "disable", importpath = "knative.dev/pkg", sum = "h1:NDQS+236vhwCP9oiBBGvQ5WGzbD0Y8Pcv9dtE2stg+Q=", version = "v0.0.0-20200711004937-22502028e31a", ) go_repository( name = "dev_knative_test_infra", build_file_proto_mode = "disable", importpath = "knative.dev/test-infra", sum = "h1:wNlGK4f5Ykqh3KLC5RlyR9kvzvRgo/LwJQNsZWGVHnU=", version = "v0.0.0-20200707183444-aed09e56ddc7", ) go_repository( name = "in_gopkg_airbrake_gobrake_v2", build_file_proto_mode = "disable", importpath = "gopkg.in/airbrake/gobrake.v2", sum = "h1:7z2uVWwn7oVeeugY1DtlPAy5H+KYgB1KeKTnqjNatLo=", version = "v2.0.9", ) go_repository( name = "in_gopkg_alecthomas_kingpin_v2", build_file_proto_mode = "disable", importpath = "gopkg.in/alecthomas/kingpin.v2", sum = "h1:jMFz6MfLP0/4fUyZle81rXUoxOBFi19VUFKVDOQfozc=", version = "v2.2.6", ) go_repository( name = "in_gopkg_asn1_ber_v1", build_file_proto_mode = "disable", importpath = "gopkg.in/asn1-ber.v1", sum = "h1:TxyelI5cVkbREznMhfzycHdkp5cLA7DpE+GKjSslYhM=", version = "v1.0.0-20181015200546-f715ec2f112d", ) go_repository( name = "in_gopkg_check_v1", build_file_proto_mode = "disable", importpath = "gopkg.in/check.v1", sum = "h1:Hei/4ADfdWqJk1ZMxUNpqntNwaWcugrBjAiHlqqRiVk=", version = "v1.0.0-20201130134442-10cb98267c6c", ) go_repository( name = "in_gopkg_cheggaaa_pb_v1", build_file_proto_mode = "disable", importpath = "gopkg.in/cheggaaa/pb.v1", sum = "h1:Ev7yu1/f6+d+b3pi5vPdRPc6nNtP1umSfcWiEfRqv6I=", version = "v1.0.25", ) go_repository( name = "in_gopkg_errgo_v2", build_file_proto_mode = "disable", importpath = "gopkg.in/errgo.v2", sum = "h1:0vLT13EuvQ0hNvakwLuFZ/jYrLp5F3kcWHXdRggjCE8=", version = "v2.1.0", ) go_repository( name = "in_gopkg_fsnotify_v1", build_file_proto_mode = "disable", importpath = "gopkg.in/fsnotify.v1", sum = "h1:xOHLXZwVvI9hhs+cLKq5+I5onOuwQLhQwiu63xxlHs4=", version = "v1.4.7", ) go_repository( name = "in_gopkg_gcfg_v1", build_file_proto_mode = "disable", importpath = "gopkg.in/gcfg.v1", sum = "h1:0HIbH907iBTAntm+88IJV2qmJALDAh8sPekI9Vc1fm0=", version = "v1.2.0", ) go_repository( name = "in_gopkg_gemnasium_logrus_airbrake_hook_v2", build_file_proto_mode = "disable", importpath = "gopkg.in/gemnasium/logrus-airbrake-hook.v2", sum = "h1:OAj3g0cR6Dx/R07QgQe8wkA9RNjB2u4i700xBkIT4e0=", version = "v2.1.2", ) go_repository( name = "in_gopkg_inf_v0", build_file_proto_mode = "disable", importpath = "gopkg.in/inf.v0", sum = "h1:73M5CoZyi3ZLMOyDlQh031Cx6N9NDJ2Vvfl76EDAgDc=", version = "v0.9.1", ) go_repository( name = "in_gopkg_ini_v1", build_file_proto_mode = "disable", importpath = "gopkg.in/ini.v1", sum = "h1:DPMeDvGTM54DXbPkVIZsp19fp/I2K7zwA/itHYHKo8Y=", version = "v1.56.0", ) go_repository( name = "in_gopkg_jcmturner_aescts_v1", build_file_proto_mode = "disable", importpath = "gopkg.in/jcmturner/aescts.v1", sum = "h1:cVVZBK2b1zY26haWB4vbBiZrfFQnfbTVrE3xZq6hrEw=", version = "v1.0.1", ) go_repository( name = "in_gopkg_jcmturner_dnsutils_v1", build_file_proto_mode = "disable", importpath = "gopkg.in/jcmturner/dnsutils.v1", sum = "h1:cIuC1OLRGZrld+16ZJvvZxVJeKPsvd5eUIvxfoN5hSM=", version = "v1.0.1", ) go_repository( name = "in_gopkg_jcmturner_gokrb5_v7", build_file_proto_mode = "disable", importpath = "gopkg.in/jcmturner/gokrb5.v7", sum = "h1:0709Jtq/6QXEuWRfAm260XqlpcwL1vxtO1tUE2qK8Z4=", version = "v7.3.0", ) go_repository( name = "in_gopkg_jcmturner_rpc_v1", build_file_proto_mode = "disable", importpath = "gopkg.in/jcmturner/rpc.v1", sum = "h1:QHIUxTX1ISuAv9dD2wJ9HWQVuWDX/Zc0PfeC2tjc4rU=", version = "v1.1.0", ) go_repository( name = "in_gopkg_natefinch_lumberjack_v2", build_file_proto_mode = "disable", importpath = "gopkg.in/natefinch/lumberjack.v2", sum = "h1:1Lc07Kr7qY4U2YPouBjpCLxpiyxIVoxqXgkXLknAOE8=", version = "v2.0.0", ) go_repository( name = "in_gopkg_resty_v1", build_file_proto_mode = "disable", importpath = "gopkg.in/resty.v1", sum = "h1:CuXP0Pjfw9rOuY6EP+UvtNvt5DSqHpIxILZKT/quCZI=", version = "v1.12.0", ) go_repository( name = "in_gopkg_robfig_cron_v2", build_file_proto_mode = "disable", importpath = "gopkg.in/robfig/cron.v2", sum = "h1:E846t8CnR+lv5nE+VuiKTDG/v1U2stad0QzddfJC7kY=", version = "v2.0.0-20150107220207-be2e0b0deed5", ) go_repository( name = "in_gopkg_square_go_jose_v2", build_file_proto_mode = "disable", importpath = "gopkg.in/square/go-jose.v2", sum = "h1:SK5KegNXmKmqE342YYN2qPHEnUYeoMiXXl1poUlI+o4=", version = "v2.3.1", ) go_repository( name = "in_gopkg_src_d_go_billy_v4", build_file_proto_mode = "disable", importpath = "gopkg.in/src-d/go-billy.v4", sum = "h1:0SQA1pRztfTFx2miS8sA97XvooFeNOmvUenF4o0EcVg=", version = "v4.3.2", ) go_repository( name = "in_gopkg_src_d_go_git_fixtures_v3", build_file_proto_mode = "disable", importpath = "gopkg.in/src-d/go-git-fixtures.v3", sum = "h1:ivZFOIltbce2Mo8IjzUHAFoq/IylO9WHhNOAJK+LsJg=", version = "v3.5.0", ) go_repository( name = "in_gopkg_src_d_go_git_v4", build_file_proto_mode = "disable", importpath = "gopkg.in/src-d/go-git.v4", sum = "h1:SRtFyV8Kxc0UP7aCHcijOMQGPxHSmMOPrzulQWolkYE=", version = "v4.13.1", ) go_repository( name = "in_gopkg_tomb_v1", build_file_proto_mode = "disable", importpath = "gopkg.in/tomb.v1", sum = "h1:uRGJdciOHaEIrze2W8Q3AKkepLTh2hOroT7a+7czfdQ=", version = "v1.0.0-20141024135613-dd632973f1e7", ) go_repository( name = "in_gopkg_warnings_v0", build_file_proto_mode = "disable", importpath = "gopkg.in/warnings.v0", sum = "h1:wFXVbFY8DY5/xOe1ECiWdKCzZlxgshcYVNkBHstARME=", version = "v0.1.2", ) go_repository( name = "in_gopkg_yaml_v1", build_file_proto_mode = "disable", importpath = "gopkg.in/yaml.v1", sum = "h1:POO/ycCATvegFmVuPpQzZFJ+pGZeX22Ufu6fibxDVjU=", version = "v1.0.0-20140924161607-9f9df34309c0", ) go_repository( name = "in_gopkg_yaml_v2", build_file_proto_mode = "disable", importpath = "gopkg.in/yaml.v2", sum = "h1:D8xgwECY7CYvx+Y2n4sBz93Jn9JRvxdiyyo8CTfuKaY=", version = "v2.4.0", ) go_repository( name = "in_gopkg_yaml_v3", build_file_proto_mode = "disable", importpath = "gopkg.in/yaml.v3", sum = "h1:h8qDotaEPuJATrMmW04NCwg7v22aHH28wwpauUhK9Oo=", version = "v3.0.0-20210107192922-496545a6307b", ) go_repository( name = "io_etcd_go_bbolt", build_file_proto_mode = "disable", importpath = "go.etcd.io/bbolt", sum = "h1:XAzx9gjCb0Rxj7EoqcClPD1d5ZBxZJk0jbuoPHenBt0=", version = "v1.3.5", ) go_repository( name = "io_etcd_go_etcd", build_file_proto_mode = "disable", importpath = "go.etcd.io/etcd", sum = "h1:1JFLBqwIgdyHN1ZtgjTBwO+blA6gVOmZurpiMEsETKo=", version = "v0.5.0-alpha.5.0.20200910180754-dd1b699fc489", ) go_repository( name = "io_gitea_code_sdk_gitea", build_file_proto_mode = "disable", importpath = "code.gitea.io/sdk/gitea", sum = "h1:hvDCz4wtFvo7rf5Ebj8tGd4aJ4wLPKX3BKFX9Dk1Pgs=", version = "v0.12.0", ) go_repository( name = "io_k8s_api", build_file_proto_mode = "disable_global", importpath = "k8s.io/api", sum = "h1:94bbZ5NTjdINJEdzOkpS4vdPhkb1VFpTYC9zh43f75c=", version = "v0.21.1", ) go_repository( name = "io_k8s_apiextensions_apiserver", build_file_proto_mode = "disable", importpath = "k8s.io/apiextensions-apiserver", sum = "h1:AA+cnsb6w7SZ1vD32Z+zdgfXdXY8X9uGX5bN6EoPEIo=", version = "v0.21.1", ) go_repository( name = "io_k8s_apimachinery", build_file_proto_mode = "disable_global", importpath = "k8s.io/apimachinery", sum = "h1:Q6XuHGlj2xc+hlMCvqyYfbv3H7SRGn2c8NycxJquDVs=", version = "v0.21.1", ) go_repository( name = "io_k8s_apiserver", build_file_proto_mode = "disable", importpath = "k8s.io/apiserver", sum = "h1:wTRcid53IhxhbFt4KTrFSw8tAncfr01EP91lzfcygVg=", version = "v0.21.1", ) go_repository( name = "io_k8s_cli_runtime", build_file_proto_mode = "disable", importpath = "k8s.io/cli-runtime", sum = "h1:wLe+osHSqcItyS3MYQXVyGFa54fppORVA8Jn7DBGSWw=", version = "v0.19.0", ) go_repository( name = "io_k8s_client_go", build_file_proto_mode = "disable_global", importpath = "k8s.io/client-go", replace = "k8s.io/client-go", sum = "h1:bhblWYLZKUu+pm50plvQF8WpY6TXdRRtcS/K9WauOj4=", version = "v0.21.1", ) go_repository( name = "io_k8s_cloud_provider", build_file_proto_mode = "disable", importpath = "k8s.io/cloud-provider", sum = "h1:ELMIQwweSNu8gfVEnLDypxd9034S1sZJg6QcdWJOvMI=", version = "v0.17.4", ) go_repository( name = "io_k8s_code_generator", build_file_proto_mode = "disable", importpath = "k8s.io/code-generator", sum = "h1:jvcxHpVu5dm/LMXr3GOj/jroiP8+v2YnJE9i2OVRenk=", version = "v0.21.1", ) go_repository( name = "io_k8s_component_base", build_file_proto_mode = "disable", importpath = "k8s.io/component-base", sum = "h1:iLpj2btXbR326s/xNQWmPNGu0gaYSjzn7IN/5i28nQw=", version = "v0.21.1", ) go_repository( name = "io_k8s_csi_translation_lib", build_file_proto_mode = "disable", importpath = "k8s.io/csi-translation-lib", sum = "h1:bP9yGfCJDknP7tklCwizZtwgJNRePMVcEaFIfeA11ho=", version = "v0.17.4", ) go_repository( name = "io_k8s_gengo", build_file_proto_mode = "disable", importpath = "k8s.io/gengo", sum = "h1:Uusb3oh8XcdzDF/ndlI4ToKTYVlkCSJP39SRY2mfRAw=", version = "v0.0.0-20201214224949-b6c5ce23f027", ) go_repository( name = "io_k8s_klog", build_file_proto_mode = "disable", importpath = "k8s.io/klog", sum = "h1:Pt+yjF5aB1xDSVbau4VsWe+dQNzA0qv1LlXdC2dF6Q8=", version = "v1.0.0", ) go_repository( name = "io_k8s_klog_v2", build_file_proto_mode = "disable", importpath = "k8s.io/klog/v2", sum = "h1:Q3gmuM9hKEjefWFFYF0Mat+YyFJvsUyYuwyNNJ5C9Ts=", version = "v2.8.0", ) go_repository( name = "io_k8s_kube_aggregator", build_file_proto_mode = "disable", importpath = "k8s.io/kube-aggregator", sum = "h1:rL4fsftMaqkKjaibArYDaBeqN41CHaJzgRJjUB9IrIg=", version = "v0.19.0", ) go_repository( name = "io_k8s_kube_openapi", build_file_proto_mode = "disable", importpath = "k8s.io/kube-openapi", sum = "h1:vEx13qjvaZ4yfObSSXW7BrMc/KQBBT/Jyee8XtLf4x0=", version = "v0.0.0-20210305001622-591a79e4bda7", ) go_repository( name = "io_k8s_kubectl", build_file_proto_mode = "disable", importpath = "k8s.io/kubectl", sum = "h1:t9uxaZzGvqc2jY96mjnPSjFHtaKOxoUegeGZdaGT6aw=", version = "v0.19.0", ) go_repository( name = "io_k8s_kubernetes", build_file_proto_mode = "disable", importpath = "k8s.io/kubernetes", sum = "h1:wJx/r2HuPVaaBeCUk/P47GSK0eyrj3mI/kESRFBp6/A=", version = "v1.14.7", ) go_repository( name = "io_k8s_legacy_cloud_providers", build_file_proto_mode = "disable", importpath = "k8s.io/legacy-cloud-providers", sum = "h1:VvFqJGiYAr2gIdoNuqbeZLEdxIFeN4Yt6OLJS9l2oIE=", version = "v0.17.4", ) go_repository( name = "io_k8s_metrics", build_file_proto_mode = "disable", importpath = "k8s.io/metrics", sum = "h1:cKq0+Z7wg5qkK1n8dryNffKfU22DBX83JguGpR+TCk0=", version = "v0.19.0", ) go_repository( name = "io_k8s_sigs_apiserver_network_proxy_konnectivity_client", build_file_proto_mode = "disable", importpath = "sigs.k8s.io/apiserver-network-proxy/konnectivity-client", sum = "h1:4uqm9Mv+w2MmBYD+F4qf/v6tDFUdPOk29C095RbU5mY=", version = "v0.0.15", ) go_repository( name = "io_k8s_sigs_boskos", build_file_proto_mode = "disable", importpath = "sigs.k8s.io/boskos", sum = "h1:OCr84Jrq4HgrYxP9wrfSsGioR1VSpTZMh/RXMu5sm+8=", version = "v0.0.0-20210730172138-093b54882439", ) go_repository( name = "io_k8s_sigs_controller_runtime", build_file_proto_mode = "disable", importpath = "sigs.k8s.io/controller-runtime", sum = "h1:ZIZ/dtpboPSbZYY7uUz2OzrkaBTOThx2yekLtpGB+zY=", version = "v0.9.0", ) go_repository( name = "io_k8s_sigs_controller_tools", build_file_proto_mode = "disable", importpath = "sigs.k8s.io/controller-tools", sum = "h1:PXOHvyYAjWfO0UfQvaUo33HpXNCOilV3i/Vjc7iM1/A=", version = "v0.2.9-0.20200414181213-645d44dca7c0", ) go_repository( name = "io_k8s_sigs_kustomize", build_file_proto_mode = "disable", importpath = "sigs.k8s.io/kustomize", sum = "h1:JUufWFNlI44MdtnjUqVnvh29rR37PQFzPbLXqhyOyX0=", version = "v2.0.3+incompatible", ) go_repository( name = "io_k8s_sigs_structured_merge_diff", build_file_proto_mode = "disable", importpath = "sigs.k8s.io/structured-merge-diff", sum = "h1:LOs1LZWMsz1xs77Phr/pkB4LFaavH7IVq/3+WTN9XTA=", version = "v1.0.1", ) go_repository( name = "io_k8s_sigs_structured_merge_diff_v2", build_file_proto_mode = "disable", importpath = "sigs.k8s.io/structured-merge-diff/v2", sum = "h1:I0h4buiCqDtPztO3NOiyoNMtqSIfld49D4Wj3UBXYZA=", version = "v2.0.1", ) go_repository( name = "io_k8s_sigs_structured_merge_diff_v3", build_file_proto_mode = "disable", importpath = "sigs.k8s.io/structured-merge-diff/v3", sum = "h1:dOmIZBMfhcHS09XZkMyUgkq5trg3/jRyJYFZUiaOp8E=", version = "v3.0.0", ) go_repository( name = "io_k8s_sigs_structured_merge_diff_v4", build_file_proto_mode = "disable", importpath = "sigs.k8s.io/structured-merge-diff/v4", sum = "h1:C4r9BgJ98vrKnnVCjwCSXcWjWe0NKcUQkmzDXZXGwH8=", version = "v4.1.0", ) go_repository( name = "io_k8s_sigs_testing_frameworks", build_file_proto_mode = "disable", importpath = "sigs.k8s.io/testing_frameworks", sum = "h1:vK0+tvjF0BZ/RYFeZ1E6BYBwHJJXhjuZ3TdsEKH+UQM=", version = "v0.1.2", ) go_repository( name = "io_k8s_sigs_yaml", build_file_proto_mode = "disable", importpath = "sigs.k8s.io/yaml", sum = "h1:kr/MCeFWJWTwyaHoR9c8EjH9OumOmoF9YGiZd7lFm/Q=", version = "v1.2.0", ) go_repository( name = "io_k8s_test_infra", build_file_proto_mode = "disable", importpath = "k8s.io/test-infra", sum = "h1:g5GLdRIOMs9vnEM/ZWG67f1Stn8cW1dER+MNK9P7Xn8=", version = "v0.0.0-20210903101950-5c7809e9c5e9", ) go_repository( name = "io_k8s_utils", build_file_proto_mode = "disable", importpath = "k8s.io/utils", sum = "h1:MSqsVQ3pZvPGTqCjptfimO2WjG7A9un2zcpiHkA6M/s=", version = "v0.0.0-20210527160623-6fdb442a123b", ) go_repository( name = "io_opencensus_go", build_file_proto_mode = "disable", importpath = "go.opencensus.io", sum = "h1:LYy1Hy3MJdrCdMwwzxA/dRok4ejH+RwNGbuoD9fCjto=", version = "v0.22.4", ) go_repository( name = "io_opencensus_go_contrib_exporter_aws", build_file_proto_mode = "disable", importpath = "contrib.go.opencensus.io/exporter/aws", sum = "h1:YsbWYxDZkC7x2OxlsDEYvvEXZ3cBI3qBgUK5BqkZvRw=", version = "v0.0.0-20181029163544-2befc13012d0", ) go_repository( name = "io_opencensus_go_contrib_exporter_ocagent", build_file_proto_mode = "disable", importpath = "contrib.go.opencensus.io/exporter/ocagent", sum = "h1:Z1n6UAyr0QwM284yUuh5Zd8JlvxUGAhFZcgMJkMPrGM=", version = "v0.6.0", ) go_repository( name = "io_opencensus_go_contrib_exporter_prometheus", build_file_proto_mode = "disable", importpath = "contrib.go.opencensus.io/exporter/prometheus", sum = "h1:SByaIoWwNgMdPSgl5sMqM2KDE5H/ukPWBRo314xiDvg=", version = "v0.1.0", ) go_repository( name = "io_opencensus_go_contrib_exporter_stackdriver", build_file_proto_mode = "disable", importpath = "contrib.go.opencensus.io/exporter/stackdriver", sum = "h1:RX9W6FelAqTVnBi/bRXJLXr9n18v4QkQwZYIdnNS51I=", version = "v0.13.1", ) go_repository( name = "io_opencensus_go_contrib_exporter_zipkin", build_file_proto_mode = "disable", importpath = "contrib.go.opencensus.io/exporter/zipkin", sum = "h1:PR+1zWqY8ceXs1qDQQIlgXe+sdiwCf0n32bH4+Epk8g=", version = "v0.1.1", ) go_repository( name = "io_opencensus_go_contrib_integrations_ocsql", build_file_proto_mode = "disable", importpath = "contrib.go.opencensus.io/integrations/ocsql", sum = "h1:kfg5Yyy1nYUrqzyfW5XX+dzMASky8IJXhtHe0KTYNS4=", version = "v0.1.4", ) go_repository( name = "io_opencensus_go_contrib_resource", build_file_proto_mode = "disable", importpath = "contrib.go.opencensus.io/resource", sum = "h1:4r2CANuYhKGmYWP02+5E94rLRcS/YeD+KlxSrOsMxk0=", version = "v0.1.1", ) go_repository( name = "io_rsc_binaryregexp", build_file_proto_mode = "disable", importpath = "rsc.io/binaryregexp", sum = "h1:HfqmD5MEmC0zvwBuF187nq9mdnXjXsSivRiXN7SmRkE=", version = "v0.2.0", ) go_repository( name = "io_rsc_letsencrypt", build_file_proto_mode = "disable", importpath = "rsc.io/letsencrypt", sum = "h1:H7xDfhkaFFSYEJlKeq38RwX2jYcnTeHuDQyT+mMNMwM=", version = "v0.0.3", ) go_repository( name = "io_rsc_quote_v3", build_file_proto_mode = "disable", importpath = "rsc.io/quote/v3", sum = "h1:9JKUTTIUgS6kzR9mK1YuGKv6Nl+DijDNIc0ghT58FaY=", version = "v3.1.0", ) go_repository( name = "io_rsc_sampler", build_file_proto_mode = "disable", importpath = "rsc.io/sampler", sum = "h1:7uVkIFmeBqHfdjD+gZwtXXI+RODJ2Wc4O7MPEh/QiW4=", version = "v1.3.0", ) go_repository( name = "ml_vbom_util", build_file_proto_mode = "disable", importpath = "vbom.ml/util", sum = "h1:O69FD9pJA4WUZlEwYatBEEkRWKQ5cKodWpdKTrCS/iQ=", version = "v0.0.0-20180919145318-efcd4e0f9787", ) go_repository( name = "org_apache_git_thrift_git", build_file_proto_mode = "disable", importpath = "git.apache.org/thrift.git", sum = "h1:CMxsZlAmxKs+VAZMlDDL0wXciMblJcutQbEe3A9CYUM=", version = "v0.12.0", ) go_repository( name = "org_bazil_fuse", build_file_proto_mode = "disable", importpath = "bazil.org/fuse", sum = "h1:FNCRpXiquG1aoyqcIWVFmpTSKVcx2bQD38uZZeGtdlw=", version = "v0.0.0-20180421153158-65cc252bf669", ) go_repository( name = "org_go4", build_file_proto_mode = "disable", importpath = "go4.org", sum = "h1:iqAGo78tVOJXELHQFRjR6TMwItrvXH4hrGJ32I/NFF8=", version = "v0.0.0-20201209231011-d4a079459e60", ) go_repository( name = "org_golang_google_api", build_file_proto_mode = "disable", importpath = "google.golang.org/api", sum = "h1:Le77IccnTqEa8ryp9wIpX5W3zYm7Gf9LhOp9PHcwFts=", version = "v0.32.0", ) go_repository( name = "org_golang_google_appengine", build_file_proto_mode = "disable", importpath = "google.golang.org/appengine", sum = "h1:FZR1q0exgwxzPzp/aF+VccGrSfxfPpkBqjIIEq3ru6c=", version = "v1.6.7", ) go_repository( name = "org_golang_google_cloud", build_file_proto_mode = "disable", importpath = "google.golang.org/cloud", sum = "h1:Cpp2P6TPjujNoC5M2KHY6g7wfyLYfIWRZaSdIKfDasA=", version = "v0.0.0-20151119220103-975617b05ea8", ) go_repository( name = "org_golang_google_genproto", build_file_proto_mode = "disable", importpath = "google.golang.org/genproto", sum = "h1:pOwg4OoaRYScjmR4LlLgdtnyoHYTSAVhhqe5uPdpII8=", version = "v0.0.0-20201110150050-8816d57aaa9a", ) go_repository( name = "org_golang_google_grpc", build_file_proto_mode = "disable", importpath = "google.golang.org/grpc", sum = "h1:zWTV+LMdc3kaiJMSTOFz2UgSBgx8RNQoTGiZu3fR9S0=", version = "v1.32.0", ) go_repository( name = "org_golang_google_protobuf", build_file_proto_mode = "disable", importpath = "google.golang.org/protobuf", sum = "h1:bxAC2xTBsZGibn2RTntX0oH50xLsqy1OxA9tTL3p/lk=", version = "v1.26.0", ) go_repository( name = "org_golang_x_crypto", build_file_proto_mode = "disable", importpath = "golang.org/x/crypto", sum = "h1:It14KIkyBFYkHkwZ7k45minvA9aorojkyjGk9KJ5B/w=", version = "v0.0.0-20210322153248-0c34fe9e7dc2", ) go_repository( name = "org_golang_x_exp", build_file_proto_mode = "disable", importpath = "golang.org/x/exp", sum = "h1:QE6XYQK6naiK1EPAe1g/ILLxN5RBoH5xkJk3CqlMI/Y=", version = "v0.0.0-20200224162631-6cc2880d07d6", ) go_repository( name = "org_golang_x_image", build_file_proto_mode = "disable", importpath = "golang.org/x/image", sum = "h1:+qEpEAPhDZ1o0x3tHzZTQDArnOixOzGD9HUJfcg0mb4=", version = "v0.0.0-20190802002840-cff245a6509b", ) go_repository( name = "org_golang_x_lint", build_file_proto_mode = "disable", importpath = "golang.org/x/lint", replace = "golang.org/x/lint", sum = "h1:QzoH/1pFpZguR8NrRHLcO6jKqfv2zpuSqZLgdm7ZmjI=", version = "v0.0.0-20190409202823-959b441ac422", ) go_repository( name = "org_golang_x_mobile", build_file_proto_mode = "disable", importpath = "golang.org/x/mobile", sum = "h1:b373EGXtj0o+ssqkOkdVphTCZ/fVg2LwhctJn2QQbqA=", version = "v0.0.0-20190806162312-597adff16ade", ) go_repository( name = "org_golang_x_mod", build_file_proto_mode = "disable", importpath = "golang.org/x/mod", sum = "h1:8pl+sMODzuvGJkmj2W4kZihvVb5mKm8pB/X44PIQHv8=", version = "v0.4.0", ) go_repository( name = "org_golang_x_net", build_file_proto_mode = "disable", importpath = "golang.org/x/net", sum = "h1:DzZ89McO9/gWPsQXS/FVKAlG02ZjaQ6AlZRBimEYOd0=", version = "v0.0.0-20210428140749-89ef3d95e781", ) go_repository( name = "org_golang_x_oauth2", build_file_proto_mode = "disable", importpath = "golang.org/x/oauth2", sum = "h1:ld7aEMNHoBnnDAX15v1T6z31v8HwR2A9FYOuAhWqkwc=", version = "v0.0.0-20200902213428-5d25da1a8d43", ) go_repository( name = "org_golang_x_sync", build_file_proto_mode = "disable", importpath = "golang.org/x/sync", sum = "h1:DcqTD9SDLc+1P/r1EmRBwnVsrOwW+kk2vWf9n+1sGhs=", version = "v0.0.0-20201207232520-09787c993a3a", ) go_repository( name = "org_golang_x_sys", build_file_proto_mode = "disable", importpath = "golang.org/x/sys", sum = "h1:JWgyZ1qgdTaF3N3oxC+MdTV7qvEEgHo3otj+HB5CM7Q=", version = "v0.0.0-20210603081109-ebe580a85c40", ) go_repository( name = "org_golang_x_term", build_file_proto_mode = "disable", importpath = "golang.org/x/term", sum = "h1:SZxvLBoTP5yHO3Frd4z4vrF+DBX9vMVanchswa69toE=", version = "v0.0.0-20210220032956-6a3ed077a48d", ) go_repository( name = "org_golang_x_text", build_file_proto_mode = "disable", importpath = "golang.org/x/text", sum = "h1:aRYxNxv6iGQlyVaZmk6ZgYEDa+Jg18DxebPSrd6bg1M=", version = "v0.3.6", ) go_repository( name = "org_golang_x_time", build_file_proto_mode = "disable", importpath = "golang.org/x/time", sum = "h1:O8mE0/t419eoIwhTFpKVkHiTs/Igowgfkj25AcZrtiE=", version = "v0.0.0-20210220033141-f8bda1e9f3ba", ) go_repository( name = "org_golang_x_tools", build_file_proto_mode = "disable", importpath = "golang.org/x/tools", sum = "h1:po9/4sTYwZU9lPhi1tOrb4hCv3qrhiQ77LZfGa2OjwY=", version = "v0.1.0", ) go_repository( name = "org_golang_x_xerrors", build_file_proto_mode = "disable", importpath = "golang.org/x/xerrors", sum = "h1:go1bK/D/BFZV2I8cIQd1NKEZ+0owSTG1fDTci4IqFcE=", version = "v0.0.0-20200804184101-5ec99f83aff1", ) go_repository( name = "org_gonum_v1_gonum", build_file_proto_mode = "disable", importpath = "gonum.org/v1/gonum", sum = "h1:OB/uP/Puiu5vS5QMRPrXCDWUPb+kt8f1KW8oQzFejQw=", version = "v0.0.0-20190331200053-3d26580ed485", ) go_repository( name = "org_gonum_v1_netlib", build_file_proto_mode = "disable", importpath = "gonum.org/v1/netlib", sum = "h1:jRyg0XfpwWlhEV8mDfdNGBeSJM2fuyh9Yjrnd8kF2Ts=", version = "v0.0.0-20190331212654-76723241ea4e", ) go_repository( name = "org_modernc_cc", build_file_proto_mode = "disable", importpath = "modernc.org/cc", sum = "h1:nPibNuDEx6tvYrUAtvDTTw98rx5juGsa5zuDnKwEEQQ=", version = "v1.0.0", ) go_repository( name = "org_modernc_golex", build_file_proto_mode = "disable", importpath = "modernc.org/golex", sum = "h1:wWpDlbK8ejRfSyi0frMyhilD3JBvtcx2AdGDnU+JtsE=", version = "v1.0.0", ) go_repository( name = "org_modernc_mathutil", build_file_proto_mode = "disable", importpath = "modernc.org/mathutil", sum = "h1:93vKjrJopTPrtTNpZ8XIovER7iCIH1QU7wNbOQXC60I=", version = "v1.0.0", ) go_repository( name = "org_modernc_strutil", build_file_proto_mode = "disable", importpath = "modernc.org/strutil", sum = "h1:XVFtQwFVwc02Wk+0L/Z/zDDXO81r5Lhe6iMKmGX3KhE=", version = "v1.0.0", ) go_repository( name = "org_modernc_xc", build_file_proto_mode = "disable", importpath = "modernc.org/xc", sum = "h1:7ccXrupWZIS3twbUGrtKmHS2DXY6xegFua+6O3xgAFU=", version = "v1.0.0", ) go_repository( name = "org_mongodb_go_mongo_driver", build_file_proto_mode = "disable", importpath = "go.mongodb.org/mongo-driver", sum = "h1:jxcFYjlkl8xaERsgLo+RNquI0epW6zuy/ZRQs6jnrFA=", version = "v1.1.2", ) go_repository( name = "org_uber_go_atomic", build_file_proto_mode = "disable", importpath = "go.uber.org/atomic", sum = "h1:ADUqmZGgLDDfbSL9ZmPxKTybcoEYHgpYfELNoN+7hsw=", version = "v1.7.0", ) go_repository( name = "org_uber_go_goleak", build_file_proto_mode = "disable", importpath = "go.uber.org/goleak", sum = "h1:z+mqJhf6ss6BSfSM671tgKyZBFPTTJM+HLxnhPC3wu0=", version = "v1.1.10", ) go_repository( name = "org_uber_go_multierr", build_file_proto_mode = "disable", importpath = "go.uber.org/multierr", sum = "h1:y6IPFStTAIT5Ytl7/XYmHvzXQ7S3g/IeZW9hyZ5thw4=", version = "v1.6.0", ) go_repository( name = "org_uber_go_tools", build_file_proto_mode = "disable", importpath = "go.uber.org/tools", sum = "h1:0mgffUl7nfd+FpvXMVz4IDEaUSmT1ysygQC7qYo7sG4=", version = "v0.0.0-20190618225709-2cfd321de3ee", ) go_repository( name = "org_uber_go_zap", build_file_proto_mode = "disable", importpath = "go.uber.org/zap", sum = "h1:MTjgFu6ZLKvY6Pvaqk97GlxNBuMpV4Hy/3P6tRGlI2U=", version = "v1.17.0", ) go_repository( name = "sh_helm_helm_v3", build_file_proto_mode = "disable", importpath = "helm.sh/helm/v3", sum = "h1:aykwPMVyQyncZ8iLNVMXgJ1l3c6W0+LSOPmqp8JdCjs=", version = "v3.1.1", ) go_repository( name = "tools_gotest", build_file_proto_mode = "disable", importpath = "gotest.tools", sum = "h1:VsBPFP1AI068pPrMxtb/S8Zkgf9xEmTLJjfM+P5UIEo=", version = "v2.2.0+incompatible", ) go_repository( name = "tools_gotest_v3", build_file_proto_mode = "disable", importpath = "gotest.tools/v3", sum = "h1:4AuOwCGf4lLR9u3YOe2awrHygurzhO/HeQ6laiA6Sx0=", version = "v3.0.3", ) go_repository( name = "xyz_gomodules_jsonpatch_v2", build_file_proto_mode = "disable", importpath = "gomodules.xyz/jsonpatch/v2", sum = "h1:4pT439QV83L+G9FkcCriY6EkpcK6r6bK+A5FBUMI7qY=", version = "v2.2.0", )

true

f731a62bb6c4d06bd43c8c0f54f539302a734f73

539

py

Python

regexlib/python_re2_test_file/regexlib_7018.py

yetingli/ReDoS-Benchmarks

f5b5094d835649e957bf3fec6b8bd4f6efdb35fc

[ "MIT" ]

1

2022-01-24T14:43:23.000Z

regexlib/python_re2_test_file/regexlib_7018.py

yetingli/ReDoS-Benchmarks

f5b5094d835649e957bf3fec6b8bd4f6efdb35fc

[ "MIT" ]

null

regexlib/python_re2_test_file/regexlib_7018.py

yetingli/ReDoS-Benchmarks

f5b5094d835649e957bf3fec6b8bd4f6efdb35fc

[ "MIT" ]

null

# 7018 # ^([a-zA-Z](?:(?:(?:\w[\.\_]?)*)\w)+)([a-zA-Z0-9])$ # EXPONENT # nums:5 # EXPONENT AttackString:"a"+"_"*32+"!1 __EOA(iii)" import re2 as re from time import perf_counter regex = """^([a-zA-Z](?:(?:(?:\w[\.\_]?)*)\w)+)([a-zA-Z0-9])$""" REGEX = re.compile(regex) for i in range(0, 150000): ATTACK = "a" + "_" * i * 1 + "!1 __EOA(iii)" LEN = len(ATTACK) BEGIN = perf_counter() m = REGEX.search(ATTACK) # m = REGEX.match(ATTACK) DURATION = perf_counter() - BEGIN print(f"{i *1}: took {DURATION} seconds!")

28.368421

64

0.541744

import re2 as re from time import perf_counter regex = """^([a-zA-Z](?:(?:(?:\w[\.\_]?)*)\w)+)([a-zA-Z0-9])$""" REGEX = re.compile(regex) for i in range(0, 150000): ATTACK = "a" + "_" * i * 1 + "!1 __EOA(iii)" LEN = len(ATTACK) BEGIN = perf_counter() m = REGEX.search(ATTACK) DURATION = perf_counter() - BEGIN print(f"{i *1}: took {DURATION} seconds!")

true

f731a7aa0c3b474639722781f113b0d34999a1c2

1,011

py

Python

nova/api/openstack/compute/legacy_v2/contrib/used_limits_for_admin.py

ebalduf/nova-backports

6bf97ec73467de522d34ab7a17ca0e0874baa7f9

[ "Apache-2.0" ]

7

2015-09-22T11:27:16.000Z

2015-11-02T12:33:46.000Z

nova/api/openstack/compute/legacy_v2/contrib/used_limits_for_admin.py

ebalduf/nova-backports

6bf97ec73467de522d34ab7a17ca0e0874baa7f9

[ "Apache-2.0" ]

9

2015-05-20T11:20:17.000Z

2017-07-27T08:21:33.000Z

nova/api/openstack/compute/legacy_v2/contrib/used_limits_for_admin.py

ebalduf/nova-backports

6bf97ec73467de522d34ab7a17ca0e0874baa7f9

[ "Apache-2.0" ]

13

2015-05-05T09:34:04.000Z

2017-11-08T02:03:46.000Z

# Copyright 2013 OpenStack Foundation # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. from nova.api.openstack import extensions class Used_limits_for_admin(extensions.ExtensionDescriptor): """Provide data to admin on limited resources used by other tenants.""" name = "UsedLimitsForAdmin" alias = "os-used-limits-for-admin" namespace = ("http://docs.openstack.org/compute/ext/used_limits_for_admin" "/api/v1.1") updated = "2013-05-02T00:00:00Z"

38.884615

78

0.721068

from nova.api.openstack import extensions class Used_limits_for_admin(extensions.ExtensionDescriptor): name = "UsedLimitsForAdmin" alias = "os-used-limits-for-admin" namespace = ("http://docs.openstack.org/compute/ext/used_limits_for_admin" "/api/v1.1") updated = "2013-05-02T00:00:00Z"

true

f731a834130281feea54b8acb4fe90404145a770

1,064

py

Python

checkov/terraform/checks/resource/aws/PasswordPolicyExpiration.py

gustavotabares/checkov

79250dba0fc5227ef3027b3c1300739f063b0d05

[ "Apache-2.0" ]

null

checkov/terraform/checks/resource/aws/PasswordPolicyExpiration.py

gustavotabares/checkov

79250dba0fc5227ef3027b3c1300739f063b0d05

[ "Apache-2.0" ]

null

checkov/terraform/checks/resource/aws/PasswordPolicyExpiration.py

gustavotabares/checkov

79250dba0fc5227ef3027b3c1300739f063b0d05

[ "Apache-2.0" ]

null

from checkov.common.models.enums import CheckResult, CheckCategories from checkov.terraform.checks.resource.base_resource_check import BaseResourceCheck class PasswordPolicyExpiration(BaseResourceCheck): def __init__(self): name = "Ensure IAM password policy expires passwords within 90 days or less" id = "CKV_AWS_9" supported_resources = ['aws_iam_account_password_policy'] categories = [CheckCategories.IAM] super().__init__(name=name, id=id, categories=categories, supported_resources=supported_resources) def scan_resource_conf(self, conf): """ validates iam password policy https://www.terraform.io/docs/providers/aws/r/iam_account_password_policy.html :param conf: aws_iam_account_password_policy configuration :return: <CheckResult> """ key = 'max_password_age' if key in conf.keys(): if conf[key][0] >= 90: return CheckResult.PASSED return CheckResult.FAILED check = PasswordPolicyExpiration()

38

106

0.698308

from checkov.common.models.enums import CheckResult, CheckCategories from checkov.terraform.checks.resource.base_resource_check import BaseResourceCheck class PasswordPolicyExpiration(BaseResourceCheck): def __init__(self): name = "Ensure IAM password policy expires passwords within 90 days or less" id = "CKV_AWS_9" supported_resources = ['aws_iam_account_password_policy'] categories = [CheckCategories.IAM] super().__init__(name=name, id=id, categories=categories, supported_resources=supported_resources) def scan_resource_conf(self, conf): key = 'max_password_age' if key in conf.keys(): if conf[key][0] >= 90: return CheckResult.PASSED return CheckResult.FAILED check = PasswordPolicyExpiration()

true

f731aa3598934a92decae817cbccea150452d087

1,898

py

Python

python_modules/dagster/dagster_tests/api_tests/test_api_snapshot_execution_plan.py

JPeer264/dagster-fork

32cc87a36134be7c442fa85d6867eb1d3301aea0

[ "Apache-2.0" ]

1

2020-09-19T16:35:59.000Z

python_modules/dagster/dagster_tests/api_tests/test_api_snapshot_execution_plan.py

JPeer264/dagster-fork

32cc87a36134be7c442fa85d6867eb1d3301aea0

[ "Apache-2.0" ]

null

python_modules/dagster/dagster_tests/api_tests/test_api_snapshot_execution_plan.py

JPeer264/dagster-fork

32cc87a36134be7c442fa85d6867eb1d3301aea0

[ "Apache-2.0" ]

null

from dagster.api.snapshot_execution_plan import sync_get_external_execution_plan from dagster.core.snap.execution_plan_snapshot import ExecutionPlanSnapshot from .utils import get_foo_pipeline_handle def test_execution_plan_snapshot_api(): pipeline_handle = get_foo_pipeline_handle() execution_plan_snapshot = sync_get_external_execution_plan( pipeline_handle.get_origin(), environment_dict={}, mode="default", snapshot_id="12345", ) assert isinstance(execution_plan_snapshot, ExecutionPlanSnapshot) assert execution_plan_snapshot.step_keys_to_execute == [ 'do_something.compute', 'do_input.compute', ] assert len(execution_plan_snapshot.steps) == 2 def test_execution_plan_with_step_keys_to_execute_snapshot_api(): pipeline_handle = get_foo_pipeline_handle() execution_plan_snapshot = sync_get_external_execution_plan( pipeline_handle.get_origin(), environment_dict={}, mode="default", snapshot_id="12345", step_keys_to_execute=['do_something.compute'], ) assert isinstance(execution_plan_snapshot, ExecutionPlanSnapshot) assert execution_plan_snapshot.step_keys_to_execute == [ 'do_something.compute', ] assert len(execution_plan_snapshot.steps) == 2 def test_execution_plan_with_subset_snapshot_api(): pipeline_handle = get_foo_pipeline_handle() execution_plan_snapshot = sync_get_external_execution_plan( pipeline_handle.get_origin(), environment_dict={'solids': {'do_input': {'inputs': {'x': {'value': "test"}}}}}, mode="default", snapshot_id="12345", solid_selection=["do_input"], ) assert isinstance(execution_plan_snapshot, ExecutionPlanSnapshot) assert execution_plan_snapshot.step_keys_to_execute == [ 'do_input.compute', ] assert len(execution_plan_snapshot.steps) == 1

33.892857

95

0.74236

from dagster.api.snapshot_execution_plan import sync_get_external_execution_plan from dagster.core.snap.execution_plan_snapshot import ExecutionPlanSnapshot from .utils import get_foo_pipeline_handle def test_execution_plan_snapshot_api(): pipeline_handle = get_foo_pipeline_handle() execution_plan_snapshot = sync_get_external_execution_plan( pipeline_handle.get_origin(), environment_dict={}, mode="default", snapshot_id="12345", ) assert isinstance(execution_plan_snapshot, ExecutionPlanSnapshot) assert execution_plan_snapshot.step_keys_to_execute == [ 'do_something.compute', 'do_input.compute', ] assert len(execution_plan_snapshot.steps) == 2 def test_execution_plan_with_step_keys_to_execute_snapshot_api(): pipeline_handle = get_foo_pipeline_handle() execution_plan_snapshot = sync_get_external_execution_plan( pipeline_handle.get_origin(), environment_dict={}, mode="default", snapshot_id="12345", step_keys_to_execute=['do_something.compute'], ) assert isinstance(execution_plan_snapshot, ExecutionPlanSnapshot) assert execution_plan_snapshot.step_keys_to_execute == [ 'do_something.compute', ] assert len(execution_plan_snapshot.steps) == 2 def test_execution_plan_with_subset_snapshot_api(): pipeline_handle = get_foo_pipeline_handle() execution_plan_snapshot = sync_get_external_execution_plan( pipeline_handle.get_origin(), environment_dict={'solids': {'do_input': {'inputs': {'x': {'value': "test"}}}}}, mode="default", snapshot_id="12345", solid_selection=["do_input"], ) assert isinstance(execution_plan_snapshot, ExecutionPlanSnapshot) assert execution_plan_snapshot.step_keys_to_execute == [ 'do_input.compute', ] assert len(execution_plan_snapshot.steps) == 1

true

f731aa9055ffb5d097e1f239a5baee16564f7fec

1,564

py

Python

improved-ver1/6.Real_Estate_Own_Data_Prediction_improved1.py

ARAN1218/RealEstateRentPrediction_AI

da537f3204fa1bc80a499a03b2fd015926ccc755

[ "MIT" ]

1

2021-06-26T04:44:14.000Z

improved-ver1/6.Real_Estate_Own_Data_Prediction_improved1.py

ARAN1218/RealEstateRentPrediction_AI

da537f3204fa1bc80a499a03b2fd015926ccc755

[ "MIT" ]

1

2021-08-06T15:58:27.000Z

2021-11-27T14:59:00.000Z

improved-ver1/6.Real_Estate_Own_Data_Prediction_improved1.py

ARAN1218/RealEstateRentPrediction_AI

da537f3204fa1bc80a499a03b2fd015926ccc755

[ "MIT" ]

null

#必要なライブラリをインポート import numpy as np import pandas as pd import xgboost as xgb from sklearn.preprocessing import LabelEncoder #numpyのリスト表示制限を解除しておく np.set_printoptions(threshold=np.inf) #既に学習データとREDP関数を用いてAIが作成されているものとする #model = RERL(df_l) #住所と路線と間取りはラベルエンコーディングの都合により学習データ/テストデータにあったものしか使えない為、予め確保しておいた使える要素を表示させる #上記の3つの要素はこの中から選んでもらう #このプログラムは別のセルで起動させると見やすい print(adressC,'\n','\n',stationC,'\n','\n',layoutC) #(学習した範囲内の)任意のデータを入力して賃料を予測できる関数 #Real_Estate_Own_Data_Prediction def REODP(address,station,access,mc_fees,k_fees,s_fees,area,layout,age): #入力したデータを辞書d_tryに格納する d_try = { '住所':address, '路線':station, '交通':access, '管理共益費':mc_fees, '礼金':k_fees, '敷金':s_fees, '専有面積':area, '間取り':layout, '築年数':age } #辞書d_tryをデータフレームdf_tryに変換する df_try = pd.DataFrame(d_try,index=['own']) #入力情報の確認用 display(df_try) #ラベルエンコーディングを行い、文字列を数値化する df_try.住所 = LE1.transform(df_try.住所) df_try.路線 = LE2.transform(df_try.路線) df_try.間取り = LE3.transform(df_try.間取り) #データ型をfloat64で統一する df_try = df_try.astype('float64') #予測結果(少数第二位まで)を表示する df_try = xgb.DMatrix(df_try) return print('予想賃料:',round(float(model.predict(df_try)),2),'万円') #REODP(住所, 路線, 交通, 管理共益費, 礼金, 敷金, 専有面積, 間取り, 築年数) #データ型に気をつける #住所と路線と間取りはラベルエンコーディングの都合により学習データ/テストデータにあったものしか使えない為、上で表示させた要素から選ぶこと REODP(address='' ,station='' ,access=0 ,mc_fees=0 ,k_fees=0 ,s_fees=0 ,area=0 ,layout='' ,age=0 )

22.342857

74

0.671995

import numpy as np import pandas as pd import xgboost as xgb from sklearn.preprocessing import LabelEncoder np.set_printoptions(threshold=np.inf) print(adressC,'\n','\n',stationC,'\n','\n',layoutC) def REODP(address,station,access,mc_fees,k_fees,s_fees,area,layout,age): d_try = { '住所':address, '路線':station, '交通':access, '管理共益費':mc_fees, '礼金':k_fees, '敷金':s_fees, '専有面積':area, '間取り':layout, '築年数':age } df_try = pd.DataFrame(d_try,index=['own']) display(df_try) df_try.住所 = LE1.transform(df_try.住所) df_try.路線 = LE2.transform(df_try.路線) df_try.間取り = LE3.transform(df_try.間取り) df_try = df_try.astype('float64') df_try = xgb.DMatrix(df_try) return print('予想賃料:',round(float(model.predict(df_try)),2),'万円') REODP(address='' ,station='' ,access=0 ,mc_fees=0 ,k_fees=0 ,s_fees=0 ,area=0 ,layout='' ,age=0 )

true

f731aaf8a13f76df61485cd65cdaf166bf910a5e

7,617

py

Python

modules/filter_bed.py

hillerlab/TOGA

c4bce48c760d51739a8a63132316f49de77e0c71

[ "MIT" ]

32

2020-07-20T03:18:28.000Z

2022-03-28T09:04:48.000Z

modules/filter_bed.py

hillerlab/TOGA

c4bce48c760d51739a8a63132316f49de77e0c71

[ "MIT" ]

28

2020-07-20T14:03:55.000Z

2022-02-28T08:08:39.000Z

modules/filter_bed.py

hillerlab/TOGA

c4bce48c760d51739a8a63132316f49de77e0c71

[ "MIT" ]

5

2020-07-23T13:03:32.000Z

2021-09-16T15:39:23.000Z

#!/usr/bin/env python3 """Filter bed-12 file. Remove: - incomplete annotations - genes without CDS """ import argparse import sys import re from collections import Counter try: from modules.common import die from modules.common import eprint except ImportError: from common import die from commom import eprint __author__ = "Bogdan Kirilenko, 2020." __version__ = "1.0" __email__ = "bogdan.kirilenko@senckenberg.de" __credits__ = ["Michael Hiller", "Virag Sharma", "David Jebb"] ALLOWED_CHARSET = "a-zA-Z0-9._-" ALLOWED_CHARSET_RE = rf"[^{ALLOWED_CHARSET}]" def parse_args(): """Read args, check.""" app = argparse.ArgumentParser() app.add_argument("input", help="Bed-12 formatted annotation track.") app.add_argument( "output", default="stdout", help="Output destination, stdout as default" ) app.add_argument( "--out_of_frame", action="store_true", dest="out_of_frame", help="Do not skip out-of-frame genes.", ) # print help if there are no args if len(sys.argv) < 2: app.print_help() sys.exit(0) args = app.parse_args() return args def prepare_bed_file(bed_file, output, ouf=False, save_rejected=None, only_chrom=None): """Filter the bed file given and save the updated version.""" new_lines = [] # keep updated lines rejected = [] # keep IDs of skipped transcripts + the reason why names = Counter() # we need to make sure that all names are unique allowed_re = re.compile(ALLOWED_CHARSET_RE).search broken_names = [] f = open(bed_file, "r") for num, line in enumerate(f, 1): # parse bed file according to specification line_data = line.rstrip().split("\t") if len(line_data) != 12: f.close() # this is for sure an error # it is possible only if something except a bed12 was provided die( "Error! Bed 12 file is required! Got a file with {len(line_data)} fields instead" ) chrom = line_data[0] if only_chrom and chrom != only_chrom: # TOGA allows to perform the analysis on a specific chromosome only # is so, we can skip all transcripts that located on other chromosomes continue chromStart = int(line_data[1]) chromEnd = int(line_data[2]) name = line_data[3] # gene_name usually corr_name = not bool(allowed_re(name)) if corr_name is False: broken_names.append(name) # TODO: check weird characters in the transcript name # bed_score = int(line_data[4]) # never used # strand = line_data[5] # otherwise: # strand = True if line_data[5] == '+' else False thickStart = int(line_data[6]) thickEnd = int(line_data[7]) # itemRgb = line_data[8] # never used blockCount = int(line_data[9]) blockSizes = [int(x) for x in line_data[10].split(",") if x != ""] blockStarts = [int(x) for x in line_data[11].split(",") if x != ""] blockEnds = [blockStarts[i] + blockSizes[i] for i in range(blockCount)] blockAbsStarts = [blockStarts[i] + chromStart for i in range(blockCount)] blockAbsEnds = [blockEnds[i] + chromStart for i in range(blockCount)] blockNewStarts, blockNewEnds = [], [] names[name] += 1 if thickStart > thickEnd: f.close() # according to bed12 specification this should never happen sys.stderr.write(f"Problem occurred at line {num}, gene {name}\n") die("Error! Bed file is corrupted, thickEnd MUST be >= thickStart") elif thickStart == thickEnd: # this means that this is a non-coding transcript # TOGA cannot process them: we can skip it rejected.append((name, "No CDS")) continue if thickStart < chromStart or thickEnd > chromEnd: # a very strange (but still possible) case f.close() # for sure an error with input data sys.stderr.write(f"Problem occurred at line {num}, gene {name}\n") die("Error! Bed file is corrupted, thickRange is outside chromRange!") # now select CDS only # we keep UTRs in the filtered file # however, we need CDS to check whether it's correct (% 3 == 0) for block_num in range(blockCount): blockStart = blockAbsStarts[block_num] blockEnd = blockAbsEnds[block_num] # skip the block if it is entirely UTR if blockEnd <= thickStart: continue elif blockStart >= thickEnd: continue # if we are here: this is not an entirely UTR exon # it might intersect the CDS border or to be in the CDS entirely # remove UTRs: block start must be >= CDS_start (thickStart) # block end must be <= CDS_end (thickEnd) blockNewStart = blockStart if blockStart >= thickStart else thickStart blockNewEnd = blockEnd if blockEnd <= thickEnd else thickEnd blockNewStarts.append(blockNewStart - thickStart) blockNewEnds.append(blockNewEnd - thickStart) if len(blockNewStarts) == 0: # even it thickStart != thickEnd this transcript still can be non-coding # but if there are no blocks in the CDS -> we can catch this rejected.append((name, "No CDS")) continue block_new_count = len(blockNewStarts) blockNewSizes = [ blockNewEnds[i] - blockNewStarts[i] for i in range(block_new_count) ] if sum(blockNewSizes) % 3 != 0 and not ouf: # this is an out-of-frame (or incomplete transcript) # ideally CDS length should be divisible by 3 # not ouf means that we like to keep such transcripts for some reason rejected.append((name, "Out-of-frame gene")) continue # we keep this transcript: add in to the list new_line = "\t".join([str(x) for x in line_data]) new_lines.append(new_line) f.close() # if not allowed characters in transcript names: list them if len(broken_names) > 0: eprint("Error! Some transcript names contain not allowed characters") for t in broken_names: eprint(t) die(f"Allowed characters are: {ALLOWED_CHARSET}") # if there are non-unique transcript IDs: die # I kill it there, not earlier to show them altogether if any(v > 1 for v in names.values()): eprint("Error! There are non-uniq transcript IDs:") duplicates = [k for k, v in names.items() if v > 1] for d in duplicates: eprint(d) die("Abort") if len(new_lines) == 0: # no transcripts pass the filter: probably an input data mistake sys.exit( f"Error! No reference annotation tracks left after filtering procedure! Abort" ) # write transcripts that passed the filter to the output file f = open(output, "w") if output != "stdout" else sys.stdout f.write("\n".join(new_lines) + "\n") f.close() if output != "stdout" else None if save_rejected: # save transcripts that didn't pass the filter + reason why f = open(save_rejected, "w") for elem in rejected: f.write(f"{elem[0]}\t{elem[1]}\n") f.close() def main(): """Entry point.""" args = parse_args() prepare_bed_file(args.input, args.output, args.out_of_frame) sys.exit(0) if __name__ == "__main__": main()

37.895522

97

0.616516

import argparse import sys import re from collections import Counter try: from modules.common import die from modules.common import eprint except ImportError: from common import die from commom import eprint __author__ = "Bogdan Kirilenko, 2020." __version__ = "1.0" __email__ = "bogdan.kirilenko@senckenberg.de" __credits__ = ["Michael Hiller", "Virag Sharma", "David Jebb"] ALLOWED_CHARSET = "a-zA-Z0-9._-" ALLOWED_CHARSET_RE = rf"[^{ALLOWED_CHARSET}]" def parse_args(): app = argparse.ArgumentParser() app.add_argument("input", help="Bed-12 formatted annotation track.") app.add_argument( "output", default="stdout", help="Output destination, stdout as default" ) app.add_argument( "--out_of_frame", action="store_true", dest="out_of_frame", help="Do not skip out-of-frame genes.", ) if len(sys.argv) < 2: app.print_help() sys.exit(0) args = app.parse_args() return args def prepare_bed_file(bed_file, output, ouf=False, save_rejected=None, only_chrom=None): new_lines = [] rejected = [] names = Counter() allowed_re = re.compile(ALLOWED_CHARSET_RE).search broken_names = [] f = open(bed_file, "r") for num, line in enumerate(f, 1): line_data = line.rstrip().split("\t") if len(line_data) != 12: f.close() die( "Error! Bed 12 file is required! Got a file with {len(line_data)} fields instead" ) chrom = line_data[0] if only_chrom and chrom != only_chrom: continue chromStart = int(line_data[1]) chromEnd = int(line_data[2]) name = line_data[3] corr_name = not bool(allowed_re(name)) if corr_name is False: broken_names.append(name) thickStart = int(line_data[6]) thickEnd = int(line_data[7]) ckCount = int(line_data[9]) blockSizes = [int(x) for x in line_data[10].split(",") if x != ""] blockStarts = [int(x) for x in line_data[11].split(",") if x != ""] blockEnds = [blockStarts[i] + blockSizes[i] for i in range(blockCount)] blockAbsStarts = [blockStarts[i] + chromStart for i in range(blockCount)] blockAbsEnds = [blockEnds[i] + chromStart for i in range(blockCount)] blockNewStarts, blockNewEnds = [], [] names[name] += 1 if thickStart > thickEnd: f.close() sys.stderr.write(f"Problem occurred at line {num}, gene {name}\n") die("Error! Bed file is corrupted, thickEnd MUST be >= thickStart") elif thickStart == thickEnd: rejected.append((name, "No CDS")) continue if thickStart < chromStart or thickEnd > chromEnd: f.close() sys.stderr.write(f"Problem occurred at line {num}, gene {name}\n") die("Error! Bed file is corrupted, thickRange is outside chromRange!") for block_num in range(blockCount): blockStart = blockAbsStarts[block_num] blockEnd = blockAbsEnds[block_num] # skip the block if it is entirely UTR if blockEnd <= thickStart: continue elif blockStart >= thickEnd: continue # if we are here: this is not an entirely UTR exon # it might intersect the CDS border or to be in the CDS entirely # remove UTRs: block start must be >= CDS_start (thickStart) # block end must be <= CDS_end (thickEnd) blockNewStart = blockStart if blockStart >= thickStart else thickStart blockNewEnd = blockEnd if blockEnd <= thickEnd else thickEnd blockNewStarts.append(blockNewStart - thickStart) blockNewEnds.append(blockNewEnd - thickStart) if len(blockNewStarts) == 0: # even it thickStart != thickEnd this transcript still can be non-coding # but if there are no blocks in the CDS -> we can catch this rejected.append((name, "No CDS")) continue block_new_count = len(blockNewStarts) blockNewSizes = [ blockNewEnds[i] - blockNewStarts[i] for i in range(block_new_count) ] if sum(blockNewSizes) % 3 != 0 and not ouf: # this is an out-of-frame (or incomplete transcript) # ideally CDS length should be divisible by 3 # not ouf means that we like to keep such transcripts for some reason rejected.append((name, "Out-of-frame gene")) continue # we keep this transcript: add in to the list new_line = "\t".join([str(x) for x in line_data]) new_lines.append(new_line) f.close() # if not allowed characters in transcript names: list them if len(broken_names) > 0: eprint("Error! Some transcript names contain not allowed characters") for t in broken_names: eprint(t) die(f"Allowed characters are: {ALLOWED_CHARSET}") # if there are non-unique transcript IDs: die # I kill it there, not earlier to show them altogether if any(v > 1 for v in names.values()): eprint("Error! There are non-uniq transcript IDs:") duplicates = [k for k, v in names.items() if v > 1] for d in duplicates: eprint(d) die("Abort") if len(new_lines) == 0: # no transcripts pass the filter: probably an input data mistake sys.exit( f"Error! No reference annotation tracks left after filtering procedure! Abort" ) # write transcripts that passed the filter to the output file f = open(output, "w") if output != "stdout" else sys.stdout f.write("\n".join(new_lines) + "\n") f.close() if output != "stdout" else None if save_rejected: # save transcripts that didn't pass the filter + reason why f = open(save_rejected, "w") for elem in rejected: f.write(f"{elem[0]}\t{elem[1]}\n") f.close() def main(): args = parse_args() prepare_bed_file(args.input, args.output, args.out_of_frame) sys.exit(0) if __name__ == "__main__": main()

true

f731abd403e3a3f072d4bde30a655735bb7f1420

8,680

py

Python

sdk/network/azure-mgmt-network/azure/mgmt/network/v2017_09_01/operations/_default_security_rules_operations.py

LianwMS/azure-sdk-for-python

612d7bca9de86ee1bd1fa59291d7bf897ba9213f

[ "MIT" ]

2

2019-05-17T21:24:53.000Z

2020-02-12T11:13:42.000Z

sdk/network/azure-mgmt-network/azure/mgmt/network/v2017_09_01/operations/_default_security_rules_operations.py

LianwMS/azure-sdk-for-python

612d7bca9de86ee1bd1fa59291d7bf897ba9213f

[ "MIT" ]

15

2019-07-12T18:18:04.000Z

2019-07-25T20:55:51.000Z

sdk/network/azure-mgmt-network/azure/mgmt/network/v2017_09_01/operations/_default_security_rules_operations.py

LianwMS/azure-sdk-for-python

612d7bca9de86ee1bd1fa59291d7bf897ba9213f

[ "MIT" ]

2

2020-05-21T22:51:22.000Z

2020-05-26T20:53:01.000Z

# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is regenerated. # -------------------------------------------------------------------------- from typing import TYPE_CHECKING import warnings from azure.core.exceptions import HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error from azure.core.paging import ItemPaged from azure.core.pipeline import PipelineResponse from azure.core.pipeline.transport import HttpRequest, HttpResponse from azure.mgmt.core.exceptions import ARMErrorFormat from .. import models if TYPE_CHECKING: # pylint: disable=unused-import,ungrouped-imports from typing import Any, Callable, Dict, Generic, Iterable, Optional, TypeVar T = TypeVar('T') ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]] class DefaultSecurityRulesOperations(object): """DefaultSecurityRulesOperations operations. You should not instantiate this class directly. Instead, you should create a Client instance that instantiates it for you and attaches it as an attribute. :ivar models: Alias to model classes used in this operation group. :type models: ~azure.mgmt.network.v2017_09_01.models :param client: Client for service requests. :param config: Configuration of service client. :param serializer: An object model serializer. :param deserializer: An object model deserializer. """ models = models def __init__(self, client, config, serializer, deserializer): self._client = client self._serialize = serializer self._deserialize = deserializer self._config = config def list( self, resource_group_name, # type: str network_security_group_name, # type: str **kwargs # type: Any ): # type: (...) -> Iterable["models.SecurityRuleListResult"] """Gets all default security rules in a network security group. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param network_security_group_name: The name of the network security group. :type network_security_group_name: str :keyword callable cls: A custom type or function that will be passed the direct response :return: An iterator like instance of either SecurityRuleListResult or the result of cls(response) :rtype: ~azure.core.paging.ItemPaged[~azure.mgmt.network.v2017_09_01.models.SecurityRuleListResult] :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["models.SecurityRuleListResult"] error_map = {404: ResourceNotFoundError, 409: ResourceExistsError} error_map.update(kwargs.pop('error_map', {})) api_version = "2017-09-01" def prepare_request(next_link=None): if not next_link: # Construct URL url = self.list.metadata['url'] # type: ignore path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkSecurityGroupName': self._serialize.url("network_security_group_name", network_security_group_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') else: url = next_link query_parameters = {} # type: Dict[str, Any] # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Accept'] = 'application/json' # Construct and send request request = self._client.get(url, query_parameters, header_parameters) return request def extract_data(pipeline_response): deserialized = self._deserialize('SecurityRuleListResult', pipeline_response) list_of_elem = deserialized.value if cls: list_of_elem = cls(list_of_elem) return deserialized.next_link or None, iter(list_of_elem) def get_next(next_link=None): request = prepare_request(next_link) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) return pipeline_response return ItemPaged( get_next, extract_data ) list.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkSecurityGroups/{networkSecurityGroupName}/defaultSecurityRules'} # type: ignore def get( self, resource_group_name, # type: str network_security_group_name, # type: str default_security_rule_name, # type: str **kwargs # type: Any ): # type: (...) -> "models.SecurityRule" """Get the specified default network security rule. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param network_security_group_name: The name of the network security group. :type network_security_group_name: str :param default_security_rule_name: The name of the default security rule. :type default_security_rule_name: str :keyword callable cls: A custom type or function that will be passed the direct response :return: SecurityRule, or the result of cls(response) :rtype: ~azure.mgmt.network.v2017_09_01.models.SecurityRule :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["models.SecurityRule"] error_map = {404: ResourceNotFoundError, 409: ResourceExistsError} error_map.update(kwargs.pop('error_map', {})) api_version = "2017-09-01" # Construct URL url = self.get.metadata['url'] # type: ignore path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkSecurityGroupName': self._serialize.url("network_security_group_name", network_security_group_name, 'str'), 'defaultSecurityRuleName': self._serialize.url("default_security_rule_name", default_security_rule_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Accept'] = 'application/json' # Construct and send request request = self._client.get(url, query_parameters, header_parameters) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) deserialized = self._deserialize('SecurityRule', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkSecurityGroups/{networkSecurityGroupName}/defaultSecurityRules/{defaultSecurityRuleName}'} # type: ignore

48.222222

236

0.673272

from typing import TYPE_CHECKING import warnings from azure.core.exceptions import HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error from azure.core.paging import ItemPaged from azure.core.pipeline import PipelineResponse from azure.core.pipeline.transport import HttpRequest, HttpResponse from azure.mgmt.core.exceptions import ARMErrorFormat from .. import models if TYPE_CHECKING: from typing import Any, Callable, Dict, Generic, Iterable, Optional, TypeVar T = TypeVar('T') ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]] class DefaultSecurityRulesOperations(object): models = models def __init__(self, client, config, serializer, deserializer): self._client = client self._serialize = serializer self._deserialize = deserializer self._config = config def list( self, resource_group_name, network_security_group_name, **kwargs ): cls = kwargs.pop('cls', None) error_map = {404: ResourceNotFoundError, 409: ResourceExistsError} error_map.update(kwargs.pop('error_map', {})) api_version = "2017-09-01" def prepare_request(next_link=None): if not next_link: url = self.list.metadata['url'] path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkSecurityGroupName': self._serialize.url("network_security_group_name", network_security_group_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) query_parameters = {} query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') else: url = next_link query_parameters = {} header_parameters = {} header_parameters['Accept'] = 'application/json' request = self._client.get(url, query_parameters, header_parameters) return request def extract_data(pipeline_response): deserialized = self._deserialize('SecurityRuleListResult', pipeline_response) list_of_elem = deserialized.value if cls: list_of_elem = cls(list_of_elem) return deserialized.next_link or None, iter(list_of_elem) def get_next(next_link=None): request = prepare_request(next_link) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) return pipeline_response return ItemPaged( get_next, extract_data ) list.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkSecurityGroups/{networkSecurityGroupName}/defaultSecurityRules'} def get( self, resource_group_name, network_security_group_name, default_security_rule_name, **kwargs ): cls = kwargs.pop('cls', None) error_map = {404: ResourceNotFoundError, 409: ResourceExistsError} error_map.update(kwargs.pop('error_map', {})) api_version = "2017-09-01" url = self.get.metadata['url'] path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'networkSecurityGroupName': self._serialize.url("network_security_group_name", network_security_group_name, 'str'), 'defaultSecurityRuleName': self._serialize.url("default_security_rule_name", default_security_rule_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) query_parameters = {} query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') header_parameters = {} header_parameters['Accept'] = 'application/json' request = self._client.get(url, query_parameters, header_parameters) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) deserialized = self._deserialize('SecurityRule', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/networkSecurityGroups/{networkSecurityGroupName}/defaultSecurityRules/{defaultSecurityRuleName}'}

true

f731ac74f48315280995265c0525ff9726f39e0d

1,156

py

Python

02_ukb/src/01_data_preparation/02_qdec_table/01_creat_qdec_table.py

Lifebrain/p025_education_brain

507cca3514b8ddbf65df7a047dba5bae1295badf

[ "MIT" ]

null

02_ukb/src/01_data_preparation/02_qdec_table/01_creat_qdec_table.py

Lifebrain/p025_education_brain

507cca3514b8ddbf65df7a047dba5bae1295badf

[ "MIT" ]

null

02_ukb/src/01_data_preparation/02_qdec_table/01_creat_qdec_table.py

Lifebrain/p025_education_brain

507cca3514b8ddbf65df7a047dba5bae1295badf

[ "MIT" ]

null

#!/usr/bin/env python3 # Purpose: Create qdec table import pandas as pd import numpy as np import glob import os.path as op import os data_csv = op.join(os.environ['TABULAR_DATA_DIR'],'data.csv') output_file = op.join(os.environ['QDEC_DATA_DIR'],'qdec.table.dat') fs_dir = "/cluster/projects/p23/data/open_datasets/ukb_long/bids/derivatives/freesurfer.7.1.0/recon" df = pd.read_csv(data_csv) def extract_fs_long_dir(id,timepoint): """ Extract fs long dir based on id and timepoint """ search = glob.glob(op.join(fs_dir,"*"+str(id)+"*"+str(timepoint+1)+"*"+".long.*")) try: return op.basename(search[0]) except: return np.nan df['fs_long_dir'] = df[['eid','mr_timepoint']].apply(lambda x: extract_fs_long_dir(x.eid,x.mr_timepoint), axis=1) df = df.dropna() df['fsid'] = df['fs_long_dir'].apply(lambda x: x.split(".long.")[0]) df['fsid_base'] = df['fs_long_dir'].apply(lambda x: x.split(".long.")[1]) df['edu_coded'] = df['education'].apply(lambda x: 1 if x==1 else 0) df['sex'] = df['sex'].apply(lambda x: int(x)) df[['fsid','fsid_base','int','bl_age','sex','edu_coded']].to_csv(output_file, sep=" ", index=False)

31.243243

113

0.675606

import pandas as pd import numpy as np import glob import os.path as op import os data_csv = op.join(os.environ['TABULAR_DATA_DIR'],'data.csv') output_file = op.join(os.environ['QDEC_DATA_DIR'],'qdec.table.dat') fs_dir = "/cluster/projects/p23/data/open_datasets/ukb_long/bids/derivatives/freesurfer.7.1.0/recon" df = pd.read_csv(data_csv) def extract_fs_long_dir(id,timepoint): search = glob.glob(op.join(fs_dir,"*"+str(id)+"*"+str(timepoint+1)+"*"+".long.*")) try: return op.basename(search[0]) except: return np.nan df['fs_long_dir'] = df[['eid','mr_timepoint']].apply(lambda x: extract_fs_long_dir(x.eid,x.mr_timepoint), axis=1) df = df.dropna() df['fsid'] = df['fs_long_dir'].apply(lambda x: x.split(".long.")[0]) df['fsid_base'] = df['fs_long_dir'].apply(lambda x: x.split(".long.")[1]) df['edu_coded'] = df['education'].apply(lambda x: 1 if x==1 else 0) df['sex'] = df['sex'].apply(lambda x: int(x)) df[['fsid','fsid_base','int','bl_age','sex','edu_coded']].to_csv(output_file, sep=" ", index=False)

true

f731ad39fbfd6798ad047b1f2307e945dfddf5eb

14,114

py

Python

utime/callbacks/callbacks.py

amiyapatanaik/U-Time

a9ed4892da77d165a71dbfef1d069d782c909757

[ "MIT" ]

1

2022-03-15T12:31:30.000Z

utime/callbacks/callbacks.py

amiyapatanaik/U-Time

a9ed4892da77d165a71dbfef1d069d782c909757

[ "MIT" ]

null

utime/callbacks/callbacks.py

amiyapatanaik/U-Time

a9ed4892da77d165a71dbfef1d069d782c909757

[ "MIT" ]

null

import numpy as np import pandas as pd from carbontracker.tracker import CarbonTracker from tensorflow.keras.callbacks import Callback from utime.utils import get_memory_usage from mpunet.utils import highlighted from mpunet.logging import ScreenLogger from collections import defaultdict from datetime import timedelta class Validation(Callback): """ Validation computation callback. Samples a number of validation batches from a deepsleep ValidationMultiSequence object and computes for all tasks: - Batch-wise validation loss - Batch-wise metrics as specified in model.metrics_tensors - Epoch-wise pr-class and average precision - Epoch-wise pr-class and average recall - Epoch-wise pr-class and average dice coefficients ... and adds all results to the log dict Note: The purpose of this callback over the default tf.keras evaluation mechanism is to calculate certain metrics over the entire epoch of data as opposed to averaged batch-wise computations. """ def __init__(self, val_sequence, max_val_studies_per_dataset=20, logger=None, verbose=True): """ Args: val_sequence: A deepsleep ValidationMultiSequence object logger: An instance of a MultiPlanar Logger that prints to screen and/or file verbose: Print progress to screen - OBS does not use Logger """ super().__init__() self.logger = logger or ScreenLogger() self.sequences = val_sequence.sequences self.verbose = verbose self.max_studies = max_val_studies_per_dataset self.n_classes = val_sequence.n_classes self.IDs = val_sequence.IDs self.print_round = 3 self.log_round = 4 def _compute_counts(self, pred, true, ignore_class=None): # Argmax and CM elements pred = pred.argmax(-1).ravel() true = true.ravel() if ignore_class: mask = np.where(true != ignore_class) true = true[mask] pred = pred[mask] # Compute relevant CM elements # We select the number following the largest class integer when # y != pred, then bincount and remove the added dummy class tps = np.bincount(np.where(true == pred, true, self.n_classes), minlength=self.n_classes+1)[:-1].astype(np.uint64) rel = np.bincount(true, minlength=self.n_classes).astype(np.uint64) sel = np.bincount(pred, minlength=self.n_classes).astype(np.uint64) return tps, rel, sel def predict(self): # Get tensors to run and their names metrics = self.model.loss_functions + self.model.metrics metrics_names = self.model.metrics_names self.model.reset_metrics() assert len(metrics_names) == len(metrics) # Prepare arrays for CM summary stats true_pos, relevant, selected, metrics_results = {}, {}, {}, {} for id_, sequence in zip(self.IDs, self.sequences): # Add count arrays to the result dictionaries true_pos[id_] = np.zeros(shape=(self.n_classes,), dtype=np.uint64) relevant[id_] = np.zeros(shape=(self.n_classes,), dtype=np.uint64) selected[id_] = np.zeros(shape=(self.n_classes,), dtype=np.uint64) # Get validation sleep study loader n_val = min(len(sequence.dataset_queue), self.max_studies) study_iterator = sequence.dataset_queue.get_study_iterator(n_val) # Predict and evaluate on all studies per_study_metrics = defaultdict(list) for i, sleep_study_context in enumerate(study_iterator): if self.verbose: s = " {}Validation subject: {}/{}".format(f"[{id_}] " if id_ else "", i+1, n_val) print(s, end="\r", flush=True) with sleep_study_context as ss: x, y = sequence.get_single_study_full_seq(ss.identifier, reshape=True) pred = self.model.predict_on_batch(x) # Compute counts tps, rel, sel = self._compute_counts(pred=pred.numpy(), true=y, ignore_class=5) true_pos[id_] += tps relevant[id_] += rel selected[id_] += sel # Run all metrics for metric, name in zip(metrics, metrics_names): per_study_metrics[name].append(metric(y, pred).numpy()) # Compute mean metrics for the dataset metrics_results[id_] = {} for metric, name in zip(metrics, metrics_names): metrics_results[id_][name] = np.mean(per_study_metrics[name]) self.model.reset_metrics() self.logger("") self.logger("") return true_pos, relevant, selected, metrics_results @staticmethod def _compute_dice(tp, rel, sel): # Get data masks (to avoid div. by zero warnings) # We set precision, recall, dice to 0 in for those particular cls. sel_mask = sel > 0 rel_mask = rel > 0 # prepare arrays precisions = np.zeros(shape=tp.shape, dtype=np.float32) recalls = np.zeros_like(precisions) dices = np.zeros_like(precisions) # Compute precisions, recalls precisions[sel_mask] = tp[sel_mask] / sel[sel_mask] recalls[rel_mask] = tp[rel_mask] / rel[rel_mask] # Compute dice intrs = (2 * precisions * recalls) union = (precisions + recalls) dice_mask = union > 0 dices[dice_mask] = intrs[dice_mask] / union[dice_mask] return precisions, recalls, dices def _print_val_results(self, precisions, recalls, dices, metrics, epoch, name, classes): # Log the results # We add them to a pd dataframe just for the pretty print output index = ["cls %i" % i for i in classes] metric_keys, metric_vals = map(list, list(zip(*metrics.items()))) col_order = metric_keys + ["precision", "recall", "dice"] nan_arr = np.empty(shape=len(precisions)) nan_arr[:] = np.nan value_dict = {"precision": precisions, "recall": recalls, "dice": dices} value_dict.update({key: nan_arr for key in metrics}) val_results = pd.DataFrame(value_dict, index=index).loc[:, col_order] # ensure order # Transpose the results to have metrics in rows val_results = val_results.T # Add mean and set in first row means = metric_vals + [precisions.mean(), recalls.mean(), dices.mean()] val_results["mean"] = means cols = list(val_results.columns) cols.insert(0, cols.pop(cols.index('mean'))) val_results = val_results.loc[:, cols] # Print the df to screen self.logger(highlighted(("[%s] Validation Results for " "Epoch %i" % (name, epoch)).lstrip(" "))) print_string = val_results.round(self.print_round).to_string() self.logger(print_string.replace("NaN", "---") + "\n") def on_epoch_end(self, epoch, logs={}): self.logger("\n") # Predict and get CM TPs, relevant, selected, metrics = self.predict() for id_ in self.IDs: tp, rel, sel = TPs[id_], relevant[id_], selected[id_] precisions, recalls, dices = self._compute_dice(tp=tp, sel=sel, rel=rel) classes = np.arange(len(dices)) # Add to log n = (id_ + "_") if len(self.IDs) > 1 else "" logs[f"{n}val_dice"] = dices.mean().round(self.log_round) logs[f"{n}val_precision"] = precisions.mean().round(self.log_round) logs[f"{n}val_recall"] = recalls.mean().round(self.log_round) for m_name, value in metrics[id_].items(): logs[f"{n}val_{m_name}"] = value.round(self.log_round) if self.verbose: self._print_val_results(precisions=precisions, recalls=recalls, dices=dices, metrics=metrics[id_], epoch=epoch, name=id_, classes=classes) if len(self.IDs) > 1: # Print cross-dataset mean values if self.verbose: self.logger(highlighted(f"[ALL DATASETS] Means Across Classes" f" for Epoch {epoch}")) fetch = ("val_dice", "val_precision", "val_recall") m_fetch = tuple(["val_" + s for s in self.model.metrics_names]) to_print = {} for f in fetch + m_fetch: scores = [logs["%s_%s" % (name, f)] for name in self.IDs] res = np.mean(scores) logs[f] = res.round(self.log_round) # Add to log file to_print[f.split("_")[-1]] = list(scores) + [res] if self.verbose: df = pd.DataFrame(to_print) df.index = self.IDs + ["mean"] self.logger(df.round(self.print_round)) self.logger("") class MemoryConsumption(Callback): def __init__(self, max_gib=None, round_=2, logger=None, set_limit=False): self.max_gib = max_gib self.logger = logger self.round_ = round_ if set_limit: import resource _, hard = resource.getrlimit(resource.RLIMIT_AS) resource.setrlimit(resource.RLIMIT_AS, (self._gib_to_bytes(max_gib), hard)) self.logger("Setting memory limit to {} GiB".format(max_gib)) @staticmethod def _gib_to_bytes(gib): return gib * (1024 ** 3) @staticmethod def _bytes_to_gib(bytes): return bytes / (1024 ** 3) def on_epoch_end(self, epoch, logs={}): mem_bytes = get_memory_usage() mem_gib = round(self._bytes_to_gib(mem_bytes), self.round_) logs['memory_usage_gib'] = mem_gib if self.max_gib and mem_gib >= self.max_gib: self.logger.warn("Stopping training from callback 'MemoryConsumption'! " "Total memory consumption of {} GiB exceeds limitation" " (self.max_gib = {}) ".format(mem_gib, self.max_gib)) self.model.stop_training = True class MaxTrainingTime(Callback): def __init__(self, max_minutes, log_name='train_time_total', logger=None): """ TODO Args: """ super().__init__() self.max_minutes = int(max_minutes) self.log_name = log_name self.logger = logger or ScreenLogger() def on_epoch_end(self, epochs, logs={}): """ TODO Args: epochs: logs: Returns: """ train_time_str = logs.get(self.log_name, None) if not train_time_str: self.logger.warn("Did not find log entry '{}' (needed in callback " "'MaxTrainingTime')".format(self.log_name)) return train_time_m = timedelta( days=int(train_time_str[:2]), hours=int(train_time_str[4:6]), minutes=int(train_time_str[8:10]), seconds=int(train_time_str[12:14]) ).total_seconds() / 60 if train_time_m >= self.max_minutes: # Stop training self.warn("Stopping training from callback 'MaxTrainingTime'! " "Total training length of {} minutes exceeded (now {}) " "".format(self.max_minutes, train_time_m)) self.model.stop_training = True class CarbonUsageTracking(Callback): """ tf.keras Callback for the Carbontracker package. See https://github.com/lfwa/carbontracker. """ def __init__(self, epochs, add_to_logs=True, monitor_epochs=-1, epochs_before_pred=-1, devices_by_pid=True, **additional_tracker_kwargs): """ Accepts parameters as per CarbonTracker.__init__ Sets other default values for key parameters. Args: add_to_logs: bool, Add total_energy_kwh and total_co2_g to the keras logs after each epoch For other arguments, please refer to CarbonTracker.__init__ """ super().__init__() self.tracker = None self.add_to_logs = bool(add_to_logs) self.parameters = {"epochs": epochs, "monitor_epochs": monitor_epochs, "epochs_before_pred": epochs_before_pred, "devices_by_pid": devices_by_pid} self.parameters.update(additional_tracker_kwargs) def on_train_end(self, logs={}): """ Ensure actual consumption is reported """ self.tracker.stop() def on_epoch_begin(self, epoch, logs={}): """ Start tracking this epoch """ if self.tracker is None: # At this point all CPUs should be discoverable self.tracker = CarbonTracker(**self.parameters) self.tracker.epoch_start() def on_epoch_end(self, epoch, logs={}): """ End tracking this epoch """ self.tracker.epoch_end() if self.add_to_logs: energy_kwh = self.tracker.tracker.total_energy_per_epoch().sum() co2eq_g = self.tracker._co2eq(energy_kwh) logs["total_energy_kwh"] = round(energy_kwh, 6) logs["total_co2_g"] = round(co2eq_g, 6)

41.511765

102

0.568372

import numpy as np import pandas as pd from carbontracker.tracker import CarbonTracker from tensorflow.keras.callbacks import Callback from utime.utils import get_memory_usage from mpunet.utils import highlighted from mpunet.logging import ScreenLogger from collections import defaultdict from datetime import timedelta class Validation(Callback): def __init__(self, val_sequence, max_val_studies_per_dataset=20, logger=None, verbose=True): super().__init__() self.logger = logger or ScreenLogger() self.sequences = val_sequence.sequences self.verbose = verbose self.max_studies = max_val_studies_per_dataset self.n_classes = val_sequence.n_classes self.IDs = val_sequence.IDs self.print_round = 3 self.log_round = 4 def _compute_counts(self, pred, true, ignore_class=None): pred = pred.argmax(-1).ravel() true = true.ravel() if ignore_class: mask = np.where(true != ignore_class) true = true[mask] pred = pred[mask] tps = np.bincount(np.where(true == pred, true, self.n_classes), minlength=self.n_classes+1)[:-1].astype(np.uint64) rel = np.bincount(true, minlength=self.n_classes).astype(np.uint64) sel = np.bincount(pred, minlength=self.n_classes).astype(np.uint64) return tps, rel, sel def predict(self): metrics = self.model.loss_functions + self.model.metrics metrics_names = self.model.metrics_names self.model.reset_metrics() assert len(metrics_names) == len(metrics) true_pos, relevant, selected, metrics_results = {}, {}, {}, {} for id_, sequence in zip(self.IDs, self.sequences): true_pos[id_] = np.zeros(shape=(self.n_classes,), dtype=np.uint64) relevant[id_] = np.zeros(shape=(self.n_classes,), dtype=np.uint64) selected[id_] = np.zeros(shape=(self.n_classes,), dtype=np.uint64) n_val = min(len(sequence.dataset_queue), self.max_studies) study_iterator = sequence.dataset_queue.get_study_iterator(n_val) per_study_metrics = defaultdict(list) for i, sleep_study_context in enumerate(study_iterator): if self.verbose: s = " {}Validation subject: {}/{}".format(f"[{id_}] " if id_ else "", i+1, n_val) print(s, end="\r", flush=True) with sleep_study_context as ss: x, y = sequence.get_single_study_full_seq(ss.identifier, reshape=True) pred = self.model.predict_on_batch(x) tps, rel, sel = self._compute_counts(pred=pred.numpy(), true=y, ignore_class=5) true_pos[id_] += tps relevant[id_] += rel selected[id_] += sel for metric, name in zip(metrics, metrics_names): per_study_metrics[name].append(metric(y, pred).numpy()) metrics_results[id_] = {} for metric, name in zip(metrics, metrics_names): metrics_results[id_][name] = np.mean(per_study_metrics[name]) self.model.reset_metrics() self.logger("") self.logger("") return true_pos, relevant, selected, metrics_results @staticmethod def _compute_dice(tp, rel, sel): sel_mask = sel > 0 rel_mask = rel > 0 precisions = np.zeros(shape=tp.shape, dtype=np.float32) recalls = np.zeros_like(precisions) dices = np.zeros_like(precisions) precisions[sel_mask] = tp[sel_mask] / sel[sel_mask] recalls[rel_mask] = tp[rel_mask] / rel[rel_mask] intrs = (2 * precisions * recalls) union = (precisions + recalls) dice_mask = union > 0 dices[dice_mask] = intrs[dice_mask] / union[dice_mask] return precisions, recalls, dices def _print_val_results(self, precisions, recalls, dices, metrics, epoch, name, classes): index = ["cls %i" % i for i in classes] metric_keys, metric_vals = map(list, list(zip(*metrics.items()))) col_order = metric_keys + ["precision", "recall", "dice"] nan_arr = np.empty(shape=len(precisions)) nan_arr[:] = np.nan value_dict = {"precision": precisions, "recall": recalls, "dice": dices} value_dict.update({key: nan_arr for key in metrics}) val_results = pd.DataFrame(value_dict, index=index).loc[:, col_order] val_results = val_results.T means = metric_vals + [precisions.mean(), recalls.mean(), dices.mean()] val_results["mean"] = means cols = list(val_results.columns) cols.insert(0, cols.pop(cols.index('mean'))) val_results = val_results.loc[:, cols] self.logger(highlighted(("[%s] Validation Results for " "Epoch %i" % (name, epoch)).lstrip(" "))) print_string = val_results.round(self.print_round).to_string() self.logger(print_string.replace("NaN", "---") + "\n") def on_epoch_end(self, epoch, logs={}): self.logger("\n") TPs, relevant, selected, metrics = self.predict() for id_ in self.IDs: tp, rel, sel = TPs[id_], relevant[id_], selected[id_] precisions, recalls, dices = self._compute_dice(tp=tp, sel=sel, rel=rel) classes = np.arange(len(dices)) n = (id_ + "_") if len(self.IDs) > 1 else "" logs[f"{n}val_dice"] = dices.mean().round(self.log_round) logs[f"{n}val_precision"] = precisions.mean().round(self.log_round) logs[f"{n}val_recall"] = recalls.mean().round(self.log_round) for m_name, value in metrics[id_].items(): logs[f"{n}val_{m_name}"] = value.round(self.log_round) if self.verbose: self._print_val_results(precisions=precisions, recalls=recalls, dices=dices, metrics=metrics[id_], epoch=epoch, name=id_, classes=classes) if len(self.IDs) > 1: if self.verbose: self.logger(highlighted(f"[ALL DATASETS] Means Across Classes" f" for Epoch {epoch}")) fetch = ("val_dice", "val_precision", "val_recall") m_fetch = tuple(["val_" + s for s in self.model.metrics_names]) to_print = {} for f in fetch + m_fetch: scores = [logs["%s_%s" % (name, f)] for name in self.IDs] res = np.mean(scores) logs[f] = res.round(self.log_round) to_print[f.split("_")[-1]] = list(scores) + [res] if self.verbose: df = pd.DataFrame(to_print) df.index = self.IDs + ["mean"] self.logger(df.round(self.print_round)) self.logger("") class MemoryConsumption(Callback): def __init__(self, max_gib=None, round_=2, logger=None, set_limit=False): self.max_gib = max_gib self.logger = logger self.round_ = round_ if set_limit: import resource _, hard = resource.getrlimit(resource.RLIMIT_AS) resource.setrlimit(resource.RLIMIT_AS, (self._gib_to_bytes(max_gib), hard)) self.logger("Setting memory limit to {} GiB".format(max_gib)) @staticmethod def _gib_to_bytes(gib): return gib * (1024 ** 3) @staticmethod def _bytes_to_gib(bytes): return bytes / (1024 ** 3) def on_epoch_end(self, epoch, logs={}): mem_bytes = get_memory_usage() mem_gib = round(self._bytes_to_gib(mem_bytes), self.round_) logs['memory_usage_gib'] = mem_gib if self.max_gib and mem_gib >= self.max_gib: self.logger.warn("Stopping training from callback 'MemoryConsumption'! " "Total memory consumption of {} GiB exceeds limitation" " (self.max_gib = {}) ".format(mem_gib, self.max_gib)) self.model.stop_training = True class MaxTrainingTime(Callback): def __init__(self, max_minutes, log_name='train_time_total', logger=None): super().__init__() self.max_minutes = int(max_minutes) self.log_name = log_name self.logger = logger or ScreenLogger() def on_epoch_end(self, epochs, logs={}): train_time_str = logs.get(self.log_name, None) if not train_time_str: self.logger.warn("Did not find log entry '{}' (needed in callback " "'MaxTrainingTime')".format(self.log_name)) return train_time_m = timedelta( days=int(train_time_str[:2]), hours=int(train_time_str[4:6]), minutes=int(train_time_str[8:10]), seconds=int(train_time_str[12:14]) ).total_seconds() / 60 if train_time_m >= self.max_minutes: self.warn("Stopping training from callback 'MaxTrainingTime'! " "Total training length of {} minutes exceeded (now {}) " "".format(self.max_minutes, train_time_m)) self.model.stop_training = True class CarbonUsageTracking(Callback): def __init__(self, epochs, add_to_logs=True, monitor_epochs=-1, epochs_before_pred=-1, devices_by_pid=True, **additional_tracker_kwargs): super().__init__() self.tracker = None self.add_to_logs = bool(add_to_logs) self.parameters = {"epochs": epochs, "monitor_epochs": monitor_epochs, "epochs_before_pred": epochs_before_pred, "devices_by_pid": devices_by_pid} self.parameters.update(additional_tracker_kwargs) def on_train_end(self, logs={}): self.tracker.stop() def on_epoch_begin(self, epoch, logs={}): if self.tracker is None: self.tracker = CarbonTracker(**self.parameters) self.tracker.epoch_start() def on_epoch_end(self, epoch, logs={}): self.tracker.epoch_end() if self.add_to_logs: energy_kwh = self.tracker.tracker.total_energy_per_epoch().sum() co2eq_g = self.tracker._co2eq(energy_kwh) logs["total_energy_kwh"] = round(energy_kwh, 6) logs["total_co2_g"] = round(co2eq_g, 6)

true

f731ad77a89b21efe9c339549c926df276f7e654

3,152

py

Python

stackalytics/processor/config.py

duongmn89/stackalytics

7b2412cddf5f48f42f92bc3400754a6c21742f0c

[ "Apache-2.0" ]

null

stackalytics/processor/config.py

duongmn89/stackalytics

7b2412cddf5f48f42f92bc3400754a6c21742f0c

[ "Apache-2.0" ]

null

stackalytics/processor/config.py

duongmn89/stackalytics

7b2412cddf5f48f42f92bc3400754a6c21742f0c

[ "Apache-2.0" ]

null

# Copyright (c) 2013 Mirantis Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or # implied. # See the License for the specific language governing permissions and # limitations under the License. import copy from oslo_config import cfg CONNECTION_OPTS = [ cfg.StrOpt('runtime-storage-uri', default='memcached://127.0.0.1:11211', help='Storage URI'), ] PROCESSOR_OPTS = [ cfg.StrOpt('default-data-uri', default='https://raw.githubusercontent.com/stackalytics/' 'default_data/master/default_data.json', help='URI for default data. A local file can be used with the ' 'prefix "file://". For example, ' 'default_data_uri = file:///path/to/default_data.json'), cfg.StrOpt('sources-root', default='/var/local/stackalytics', help='The folder that holds all project sources to analyze'), cfg.IntOpt('days_to_update_members', default=30, help='Number of days to update members'), cfg.StrOpt('corrections-uri', default=('https://git.openstack.org/cgit/' 'openstack/stackalytics/plain/etc/corrections.json'), help='The address of file with corrections data'), cfg.StrOpt('review-uri', default='gerrit://review.openstack.org', help='URI of review system'), cfg.StrOpt('git-base-uri', default='git://git.openstack.org', help='git base location'), cfg.StrOpt('ssh-key-filename', default='/home/user/.ssh/id_rsa', help='SSH key for gerrit review system access'), cfg.StrOpt('ssh-username', default='user', help='SSH username for gerrit review system access'), cfg.StrOpt('github-login', default=None, help='Login for github access'), cfg.StrOpt('github-password', default=None, help='Password for github access'), cfg.StrOpt('translation-team-uri', default='https://git.openstack.org/cgit/openstack/i18n/' 'plain/tools/zanata/translation_team.yaml', help='URI of translation team data'), cfg.StrOpt("fetching-user-source", default='launchpad', choices=['launchpad', '<None>'], help="Source for fetching user profiles"), cfg.IntOpt('members-look-ahead', default=250, help='How many member profiles to look ahead after the last'), cfg.IntOpt('read-timeout', default=120, help='Number of seconds to wait for remote response'), cfg.IntOpt('gerrit-retry', default=10, help='How many times to retry after Gerrit errors'), ] def list_opts(): yield (None, copy.deepcopy(CONNECTION_OPTS + PROCESSOR_OPTS))

44.394366

78

0.63769

import copy from oslo_config import cfg CONNECTION_OPTS = [ cfg.StrOpt('runtime-storage-uri', default='memcached://127.0.0.1:11211', help='Storage URI'), ] PROCESSOR_OPTS = [ cfg.StrOpt('default-data-uri', default='https://raw.githubusercontent.com/stackalytics/' 'default_data/master/default_data.json', help='URI for default data. A local file can be used with the ' 'prefix "file://". For example, ' 'default_data_uri = file:///path/to/default_data.json'), cfg.StrOpt('sources-root', default='/var/local/stackalytics', help='The folder that holds all project sources to analyze'), cfg.IntOpt('days_to_update_members', default=30, help='Number of days to update members'), cfg.StrOpt('corrections-uri', default=('https://git.openstack.org/cgit/' 'openstack/stackalytics/plain/etc/corrections.json'), help='The address of file with corrections data'), cfg.StrOpt('review-uri', default='gerrit://review.openstack.org', help='URI of review system'), cfg.StrOpt('git-base-uri', default='git://git.openstack.org', help='git base location'), cfg.StrOpt('ssh-key-filename', default='/home/user/.ssh/id_rsa', help='SSH key for gerrit review system access'), cfg.StrOpt('ssh-username', default='user', help='SSH username for gerrit review system access'), cfg.StrOpt('github-login', default=None, help='Login for github access'), cfg.StrOpt('github-password', default=None, help='Password for github access'), cfg.StrOpt('translation-team-uri', default='https://git.openstack.org/cgit/openstack/i18n/' 'plain/tools/zanata/translation_team.yaml', help='URI of translation team data'), cfg.StrOpt("fetching-user-source", default='launchpad', choices=['launchpad', '<None>'], help="Source for fetching user profiles"), cfg.IntOpt('members-look-ahead', default=250, help='How many member profiles to look ahead after the last'), cfg.IntOpt('read-timeout', default=120, help='Number of seconds to wait for remote response'), cfg.IntOpt('gerrit-retry', default=10, help='How many times to retry after Gerrit errors'), ] def list_opts(): yield (None, copy.deepcopy(CONNECTION_OPTS + PROCESSOR_OPTS))

true

f731ad9e1174078405c48bb42fee69eb69fa5af1

1,103

py

Python

piqa/model/tokenizers.py

AndrzejGretkowski/masters-piqa

5924cb5261bbcc2bb9970622c12c29f49bdea6e7

[ "MIT" ]

null

piqa/model/tokenizers.py

AndrzejGretkowski/masters-piqa

5924cb5261bbcc2bb9970622c12c29f49bdea6e7

[ "MIT" ]

null

piqa/model/tokenizers.py

AndrzejGretkowski/masters-piqa

5924cb5261bbcc2bb9970622c12c29f49bdea6e7

[ "MIT" ]

null

from transformers import AlbertTokenizerFast, RobertaTokenizerFast, DistilBertTokenizerFast from piqa.model.tokenizers_base import BaseTokenizerPIQA class PIQATokenizer(object): tokenizer_mapping = dict() @classmethod def register(cls, *args): def decorator(fn): for arg in args: cls.tokenizer_mapping[arg] = fn return fn return decorator @classmethod def get(cls, tokenizer): return cls.tokenizer_mapping.get(tokenizer) @PIQATokenizer.register('roberta-base', 'roberta-large') class RobertaPIQATokenizer(BaseTokenizerPIQA): @property def get_tokenizer(self): return RobertaTokenizerFast @PIQATokenizer.register('albert-base-v2', 'albert-large-v2', 'albert-xlarge-v2', 'albert-xxlarge-v2') class AlbertPIQATokenizer(BaseTokenizerPIQA): @property def get_tokenizer(self): return AlbertTokenizerFast @PIQATokenizer.register('distilbert-base-uncased') class DistilPIQATokenizer(BaseTokenizerPIQA): @property def get_tokenizer(self): return DistilBertTokenizerFast

27.575

101

0.724388

from transformers import AlbertTokenizerFast, RobertaTokenizerFast, DistilBertTokenizerFast from piqa.model.tokenizers_base import BaseTokenizerPIQA class PIQATokenizer(object): tokenizer_mapping = dict() @classmethod def register(cls, *args): def decorator(fn): for arg in args: cls.tokenizer_mapping[arg] = fn return fn return decorator @classmethod def get(cls, tokenizer): return cls.tokenizer_mapping.get(tokenizer) @PIQATokenizer.register('roberta-base', 'roberta-large') class RobertaPIQATokenizer(BaseTokenizerPIQA): @property def get_tokenizer(self): return RobertaTokenizerFast @PIQATokenizer.register('albert-base-v2', 'albert-large-v2', 'albert-xlarge-v2', 'albert-xxlarge-v2') class AlbertPIQATokenizer(BaseTokenizerPIQA): @property def get_tokenizer(self): return AlbertTokenizerFast @PIQATokenizer.register('distilbert-base-uncased') class DistilPIQATokenizer(BaseTokenizerPIQA): @property def get_tokenizer(self): return DistilBertTokenizerFast

true

f731ae5909a825418b54595ca18abd70d6832fb9

1,895

py

Python

home/urls.py

Paphra/recruitment-cms

2894d5285a6da4ff47c114377968c8fda95c24b6

[ "MIT" ]

2

2021-02-07T12:08:47.000Z

2021-02-22T07:12:53.000Z

home/urls.py

Paphra/recruitment-cms

2894d5285a6da4ff47c114377968c8fda95c24b6

[ "MIT" ]

null

home/urls.py

Paphra/recruitment-cms

2894d5285a6da4ff47c114377968c8fda95c24b6

[ "MIT" ]

null

from django.urls import path from . import views from files import views as files from finances import views as finances from others import views as others from operations import views as operations urlpatterns = [ path('clients/<int:client_id>/pay-reg-fee', views.pay_reg_fee, name='pay-reg-fee'), path('clients/<int:client_id>/register', views.register, name='register'), path('clients', views.clients, name='clients'), path('jobs', views.jobs, name='jobs'), path('branches', views.branches, name='branches'), # operations path('clearances', operations.clearances, name='clearances'), path('contracts', operations.contracts, name='contracts'), path('interpols', operations.interpols, name='interpols'), path('interviews', operations.interviews, name='interviews'), path('trainings', operations.trainings, name='trainings'), path('medicals', operations.medicals, name='medicals'), path('passports', operations.passports, name='passports'), path('tickets', operations.tickets, name='tickets'), path('vettings', operations.vettings, name='vettings'), path('vetting-album', operations.vetting_album, name='vetting-album'), path('visas', operations.visas, name='visas'), path('travel-plans', operations.travel_plans, name='travel-plans'), path('other-ops', operations.other_ops, name='other-ops'), # finances path('payments', finances.payments, name='payments'), path('expenses', finances.expenses, name='expenses'), path('fees', finances.fees, name='fees'), # files path('clearance-files', files.clearance_files, name='clearance-files'), path('client-files', files.client_files, name='client-files'), # others path('tasks', others.tasks, name='tasks'), path('recruitments', others.recruitments, name='recruitments'), path('', views.index, name="index"), ]

39.479167

87

0.692876

from django.urls import path from . import views from files import views as files from finances import views as finances from others import views as others from operations import views as operations urlpatterns = [ path('clients/<int:client_id>/pay-reg-fee', views.pay_reg_fee, name='pay-reg-fee'), path('clients/<int:client_id>/register', views.register, name='register'), path('clients', views.clients, name='clients'), path('jobs', views.jobs, name='jobs'), path('branches', views.branches, name='branches'), path('clearances', operations.clearances, name='clearances'), path('contracts', operations.contracts, name='contracts'), path('interpols', operations.interpols, name='interpols'), path('interviews', operations.interviews, name='interviews'), path('trainings', operations.trainings, name='trainings'), path('medicals', operations.medicals, name='medicals'), path('passports', operations.passports, name='passports'), path('tickets', operations.tickets, name='tickets'), path('vettings', operations.vettings, name='vettings'), path('vetting-album', operations.vetting_album, name='vetting-album'), path('visas', operations.visas, name='visas'), path('travel-plans', operations.travel_plans, name='travel-plans'), path('other-ops', operations.other_ops, name='other-ops'), path('payments', finances.payments, name='payments'), path('expenses', finances.expenses, name='expenses'), path('fees', finances.fees, name='fees'), path('clearance-files', files.clearance_files, name='clearance-files'), path('client-files', files.client_files, name='client-files'), path('tasks', others.tasks, name='tasks'), path('recruitments', others.recruitments, name='recruitments'), path('', views.index, name="index"), ]

true

f731afae0e47cb38488bcaa0764e4ae4aadbc6ac

6,801

py

Python

kerberos.py

bbhunter/kerberoast

e983ba1d5290e94c71e04fc15dc2cee482873f18

[ "Apache-2.0" ]

1,011

2015-03-17T21:52:07.000Z

2022-03-31T09:25:41.000Z

kerberos.py

bbhunter/kerberoast

e983ba1d5290e94c71e04fc15dc2cee482873f18

[ "Apache-2.0" ]

16

2015-07-08T09:12:27.000Z

2022-03-05T19:04:26.000Z

kerberos.py

bbhunter/kerberoast

e983ba1d5290e94c71e04fc15dc2cee482873f18

[ "Apache-2.0" ]

274

2015-04-28T21:24:23.000Z

2022-03-29T18:37:27.000Z

#!/usr/bin/env python3 -tt import hashlib import hmac from pyasn1.type import univ, char, useful, tag from pyasn1.codec.ber import encoder, decoder import datetime import base64 import sys #REF: http://tools.ietf.org/id/draft-brezak-win2k-krb-rc4-hmac-03.txt #T = 1 for TS-ENC-TS in the AS-Request #T = 8 for the AS-Reply #T = 7 for the Authenticator in the TGS-Request #T = 8 for the TGS-Reply #T = 2 for the Server Ticket in the AP-Request #T = 11 for the Authenticator in the AP-Request #T = 12 for the Server returned AP-Reply #T = 15 in the generation of checksum for the MIC token #T = 0 in the generation of sequence number for the MIC token #T = 13 in the generation of checksum for the WRAP token #T = 0 in the generation of sequence number for the WRAP token #T = 0 in the generation of encrypted data for the WRAPPED token def ntlmhash(s): hash = hashlib.new('md4', s.encode('utf-16le')).digest() return hash #return binascii.hexlify(hash) def rc4crypt(key, data): x = 0 box = list(range(256)) for i in range(256): x = (x + box[i] + (key[i % len(key)])) % 256 box[i], box[x] = box[x], box[i] x = 0 y = 0 out = b'' for char in data: x = (x + 1) % 256 y = (y + box[x]) % 256 box[x], box[y] = box[y], box[x] out += bytes([char ^ box[(box[x] + box[y]) % 256]]) return out #print decoder.decode(enc) #define KERB_ETYPE_RC4_HMAC 23 KERB_ETYPE_RC4_HMAC = 23 #define KERB_ETYPE_RC4_HMAC_EXP 24 def decrypt(key, messagetype, edata): #DECRYPT (K, fRC4_EXP, T, edata, edata_len, data, data_len) #{ # if (fRC4_EXP){ # *((DWORD *)(L40+10)) = T; # HMAC (K, L40, 14, K1); # }else{ # HMAC (K, &T, 4, K1); # } #K1 = hmac.new(key, chr(messagetype) + "\x00\x00\x00", hashlib.md5).digest() # \x0b = 11 K1 = hmac.new(key, bytes([messagetype]) + b"\x00\x00\x00", hashlib.md5).digest() # \x0b = 11 # memcpy (K2, K1, 16); K2 = K1 # if (fRC4_EXP) memset (K1+7, 0xAB, 9); # HMAC (K1, edata, 16, K3); // checksum is at edata K3 = hmac.new(K1, edata[:16], hashlib.md5).digest() # RC4(K3, edata + 16, edata_len - 16, edata + 16); ddata = rc4crypt(K3, edata[16:]) # data_len = edata_len - 16 - 8; # memcpy (data, edata + 16 + 8, data_len); # # // verify generated and received checksums # HMAC (K2, edata + 16, edata_len - 16, checksum); checksum = hmac.new(K2, ddata, hashlib.md5).digest() # if (memcmp(edata, checksum, 16) != 0) # printf("CHECKSUM ERROR !!!!!!\n"); #} if checksum == edata[:16]: #print "Decrypt Checksum: %s" % str(checksum).encode('hex') # == edata[:16]) #print "Checksum Calc: %s" % str(checksum).encode('hex') #print "Checksum Pkct: %s" % str(edata[:16]).encode('hex') #print messagetype #print data #print "Nonce: %s" % ddata[:8].encode('hex') #return ddata[8:] # first 8 bytes are nonce, the rest is data #return { # 'data': ddata[8:], # 'nonce': ddata[:8] #} return ddata[8:], ddata[:8] else: #print "CHECKSUM ERROR!" return None, None def encrypt(key, messagetype, data, nonce): # if (fRC4_EXP){ # *((DWORD *)(L40+10)) = T; # HMAC (K, L40, 10 + 4, K1); # }else{ # HMAC (K, &T, 4, K1); # } K1 = hmac.new(key, chr(messagetype) + "\x00\x00\x00", hashlib.md5).digest() # \x0b = 11 # memcpy (K2, K1, 16); K2 = K1 # if (fRC4_EXP) memset (K1+7, 0xAB, 9); # add_8_random_bytes(data, data_len, conf_plus_data); ddata = nonce + data # HMAC (K2, conf_plus_data, 8 + data_len, checksum); checksum = hmac.new(K2, ddata, hashlib.md5).digest() # HMAC (K1, checksum, 16, K3); K3 = hmac.new(K1, checksum, hashlib.md5).digest() #print "K3: %s" % K3.encode('hex') # RC4(K3, conf_plus_data, 8 + data_len, edata + 16); # print "EN DDATA: %s" % ddata[:32].encode('hex') edata = rc4crypt(K3, ddata) # memcpy (edata, checksum, 16); # edata_len = 16 + 8 + data_len; return checksum + edata def zerosigs(data): d = list(map(ord, data)) for i in range(5, 21): # zero out the 16 char sig, KDC d[len(d) - i] = 0 for i in range(29, 45): # zero out the 16 char sig, Server d[len(d) - i] = 0 retval = "".join(map(chr, d)) #print retval.encode('hex') return retval def chksum(K, T, data): data = zerosigs(data) # K = the Key #T = the message type, encoded as a little-endian four-byte integer #Ksign = HMAC(K, "signaturekey") //includes zero octet at end SIGNATUREKEY = 'signaturekey\x00' Ksign = hmac.new(K, SIGNATUREKEY, hashlib.md5).digest() #tmp = MD5(concat(T, data)) tmp = hashlib.md5(T + data).digest() #CHKSUM = HMAC(Ksign, tmp) chksum = hmac.new(Ksign, tmp, hashlib.md5).digest() return chksum def getservsig(encchunk): return str(encchunk[-44:-28]) def getprivsig(encchunk): return str(encchunk[-20:-4]) def printdecode(kerbpayload, ktype=2): d = decoder.decode(kerbpayload) if ktype == 32: #print "Protocol Version (pvno): " + str(d[0][0]) print("Message Type: " + str(d[0][1])) print("Realm: " + str(d[0][2])) print("Principal: " + str(d[0][3][1][0])) print("Ticket Version (tkt-vno): " + str(d[0][4][0])) print("Ticket Realm: " + str(d[0][4][1])) #print "Name-Type (Service & Instance): " + str(d[0][4][2][0]) print("Server, Name: " + str(d[0][4][2][1][0])) print("Server, Name: " + str(d[0][4][2][1][1])) #print "Data: " + str(d[0][4][3][2]).encode('hex') #print "Encryption Type: : " + str(d[0][5][0]) #print "Data: " + str(d[0]) #print "Server Realm: " + str(d[0][4][2][4]) elif ktype == 2: print("a") def extract_ticket_from_kirbi(filename): with open(filename, 'rb') as fd: data = fd.read() return extract_ticket(data) def extract_ticket(data): if data[0] == 0x76: # ram dump #enctickets.append(((decoder.decode(data)[0][2][0][3][2]).asOctets(), i, f)) return (decoder.decode(data)[0][2][0][3][2]).asOctets() elif data[:2] == b'6d': # honestly, i completely forgot. I think this is from a pcap -Tim #enctickets.append(((decoder.decode(ticket.decode('hex'))[0][4][3][2]).asOctets(), i, f)) return (decoder.decode(ticket.decode('hex'))[0][4][3][2]).asOctets()

34.522843

97

0.551242

import hashlib import hmac from pyasn1.type import univ, char, useful, tag from pyasn1.codec.ber import encoder, decoder import datetime import base64 import sys def ntlmhash(s): hash = hashlib.new('md4', s.encode('utf-16le')).digest() return hash def rc4crypt(key, data): x = 0 box = list(range(256)) for i in range(256): x = (x + box[i] + (key[i % len(key)])) % 256 box[i], box[x] = box[x], box[i] x = 0 y = 0 out = b'' for char in data: x = (x + 1) % 256 y = (y + box[x]) % 256 box[x], box[y] = box[y], box[x] out += bytes([char ^ box[(box[x] + box[y]) % 256]]) return out KERB_ETYPE_RC4_HMAC = 23 def decrypt(key, messagetype, edata): mac.new(key, bytes([messagetype]) + b"\x00\x00\x00", hashlib.md5).digest() K2 = K1 K3 = hmac.new(K1, edata[:16], hashlib.md5).digest() ddata = rc4crypt(K3, edata[16:]) checksum = hmac.new(K2, ddata, hashlib.md5).digest() if checksum == edata[:16]: return ddata[8:], ddata[:8] else: return None, None def encrypt(key, messagetype, data, nonce): K1 = hmac.new(key, chr(messagetype) + "\x00\x00\x00", hashlib.md5).digest() K2 = K1 ddata = nonce + data checksum = hmac.new(K2, ddata, hashlib.md5).digest() K3 = hmac.new(K1, checksum, hashlib.md5).digest() edata = rc4crypt(K3, ddata) return checksum + edata def zerosigs(data): d = list(map(ord, data)) for i in range(5, 21): d[len(d) - i] = 0 for i in range(29, 45): d[len(d) - i] = 0 retval = "".join(map(chr, d)) return retval def chksum(K, T, data): data = zerosigs(data) SIGNATUREKEY = 'signaturekey\x00' Ksign = hmac.new(K, SIGNATUREKEY, hashlib.md5).digest() tmp = hashlib.md5(T + data).digest() chksum = hmac.new(Ksign, tmp, hashlib.md5).digest() return chksum def getservsig(encchunk): return str(encchunk[-44:-28]) def getprivsig(encchunk): return str(encchunk[-20:-4]) def printdecode(kerbpayload, ktype=2): d = decoder.decode(kerbpayload) if ktype == 32: print("Message Type: " + str(d[0][1])) print("Realm: " + str(d[0][2])) print("Principal: " + str(d[0][3][1][0])) print("Ticket Version (tkt-vno): " + str(d[0][4][0])) print("Ticket Realm: " + str(d[0][4][1])) print("Server, Name: " + str(d[0][4][2][1][0])) print("Server, Name: " + str(d[0][4][2][1][1])) elif ktype == 2: print("a") def extract_ticket_from_kirbi(filename): with open(filename, 'rb') as fd: data = fd.read() return extract_ticket(data) def extract_ticket(data): if data[0] == 0x76: return (decoder.decode(data)[0][2][0][3][2]).asOctets() elif data[:2] == b'6d': return (decoder.decode(ticket.decode('hex'))[0][4][3][2]).asOctets()

true

f731b06c3942398040ef6910dde20647365a1923

992

py

Python

IPAddressPool/IPAddressPool/ip/IpAcquire.py

ruxuezhuimeng/spider

badf61a66e99cf1936b5c404ad85f1b00838943e

[ "MIT" ]

1

2020-06-17T05:56:19.000Z

IPAddressPool/IPAddressPool/ip/IpAcquire.py

ruxuezhuimeng/spider

badf61a66e99cf1936b5c404ad85f1b00838943e

[ "MIT" ]

null

IPAddressPool/IPAddressPool/ip/IpAcquire.py

ruxuezhuimeng/spider

badf61a66e99cf1936b5c404ad85f1b00838943e

[ "MIT" ]

null

from config.SourceUrl import getUrl from ip.Ip2Db import insert import threading import requests from Log import log header = { 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/67.0.3396.99 Safari/537.36' } def acquireIp(): aUrl = getUrl() log.info('获取ip地址:{}'.format(aUrl)) try: reponse = requests.get(aUrl, headers=header, timeout=5) if reponse.status_code == 200: parseHtml(reponse.text) except: # traceback.print_exc() log.error('请求ip异常:{}'.format(aUrl)) def parseHtml(html): html = html.replace('\'', '').replace('b', '').replace('<r/>', '').replace('\r', '') ips = html.split("\n") for ip in ips: ip = ip.strip() if 'false' in ip: log.war('您的套餐今日已到达上限') return elif '' == ip: return else: if '.' in ip: threading.Thread(target=insert, args=(ip,)).start()

26.810811

134

0.566532

from config.SourceUrl import getUrl from ip.Ip2Db import insert import threading import requests from Log import log header = { 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/67.0.3396.99 Safari/537.36' } def acquireIp(): aUrl = getUrl() log.info('获取ip地址:{}'.format(aUrl)) try: reponse = requests.get(aUrl, headers=header, timeout=5) if reponse.status_code == 200: parseHtml(reponse.text) except: log.error('请求ip异常:{}'.format(aUrl)) def parseHtml(html): html = html.replace('\'', '').replace('b', '').replace('<r/>', '').replace('\r', '') ips = html.split("\n") for ip in ips: ip = ip.strip() if 'false' in ip: log.war('您的套餐今日已到达上限') return elif '' == ip: return else: if '.' in ip: threading.Thread(target=insert, args=(ip,)).start()

true

f731b08e6a086de9df0318dd73e3fdb7854f1ed6

1,413

py

Python

models/diabetes/train.py

aditya9126/pipelines-azureml

0c747f12e02ee3d3976746663bd1da0ab5935887

[ "CC-BY-4.0", "MIT" ]

null

models/diabetes/train.py

aditya9126/pipelines-azureml

0c747f12e02ee3d3976746663bd1da0ab5935887

[ "CC-BY-4.0", "MIT" ]

null

models/diabetes/train.py

aditya9126/pipelines-azureml

0c747f12e02ee3d3976746663bd1da0ab5935887

[ "CC-BY-4.0", "MIT" ]

null

# new update import pickle import os import numpy as np from sklearn.datasets import load_diabetes from sklearn.linear_model import Ridge from sklearn.metrics import mean_squared_error from sklearn.model_selection import train_test_split from azureml.core.run import Run from utils import mylib os.makedirs('./outputs', exist_ok=True) X, y = load_diabetes(return_X_y=True) run = Run.get_context() X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0) data = {"train": {"X": X_train, "y": y_train}, "test": {"X": X_test, "y": y_test}} # list of numbers from 0.0 to 1.0 with a 0.05 interval alphas = mylib.get_alphas() for alpha in alphas: # Use Ridge algorithm to create a regression model reg = Ridge(alpha=alpha) reg.fit(data["train"]["X"], data["train"]["y"]) preds = reg.predict(data["test"]["X"]) mse = mean_squared_error(preds, data["test"]["y"]) run.log('alpha', alpha) run.log('mse', mse) # Save model in the outputs folder so it automatically get uploaded when running on AML Compute model_file_name = 'ridge_{0:.2f}.pkl'.format(alpha) with open(os.path.join('./outputs/', model_file_name), 'wb') as file: pickle.dump(reg, file) print('alpha is {0:.2f}, and mse is {1:0.2f}'.format(alpha, mse))

31.4

99

0.643312

import pickle import os import numpy as np from sklearn.datasets import load_diabetes from sklearn.linear_model import Ridge from sklearn.metrics import mean_squared_error from sklearn.model_selection import train_test_split from azureml.core.run import Run from utils import mylib os.makedirs('./outputs', exist_ok=True) X, y = load_diabetes(return_X_y=True) run = Run.get_context() X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0) data = {"train": {"X": X_train, "y": y_train}, "test": {"X": X_test, "y": y_test}} alphas = mylib.get_alphas() for alpha in alphas: reg = Ridge(alpha=alpha) reg.fit(data["train"]["X"], data["train"]["y"]) preds = reg.predict(data["test"]["X"]) mse = mean_squared_error(preds, data["test"]["y"]) run.log('alpha', alpha) run.log('mse', mse) model_file_name = 'ridge_{0:.2f}.pkl'.format(alpha) with open(os.path.join('./outputs/', model_file_name), 'wb') as file: pickle.dump(reg, file) print('alpha is {0:.2f}, and mse is {1:0.2f}'.format(alpha, mse))

true

f731b0a7f4cf43d68c7253dd502f524c7bafe510

1,522

py

Python

src/ewaluacja2021/management/commands/raport_3n_to_xlsx.py

iplweb/bpp

f027415cc3faf1ca79082bf7bacd4be35b1a6fdf

[ "BSD-3-Clause" ]

null

src/ewaluacja2021/management/commands/raport_3n_to_xlsx.py

iplweb/bpp

f027415cc3faf1ca79082bf7bacd4be35b1a6fdf

[ "BSD-3-Clause" ]

41

2019-11-07T00:07:02.000Z

2022-02-27T22:09:39.000Z

src/ewaluacja2021/management/commands/raport_3n_to_xlsx.py

iplweb/bpp

f027415cc3faf1ca79082bf7bacd4be35b1a6fdf

[ "BSD-3-Clause" ]

null

# -*- encoding: utf-8 -*- import os from argparse import FileType from django.core.management import BaseCommand from ewaluacja2021.reports import load_data, rekordy from ewaluacja2021.util import autor2fn from ewaluacja2021.xlsy import AutorskiXLSX, CalosciowyXLSX from bpp.models import Autor from bpp.util import pbar class Command(BaseCommand): def add_arguments(self, parser): parser.add_argument("wejscie", type=FileType("r")) parser.add_argument("--katalog-wyjsciowy", type=str, default=None) def handle(self, wejscie, katalog_wyjsciowy, liczba_n=None, *args, **options): dane = load_data(wejscie) if katalog_wyjsciowy is None: katalog_wyjsciowy = wejscie.name.replace(".json", "_output") if not os.path.exists(katalog_wyjsciowy): os.mkdir(katalog_wyjsciowy) rekordy_danych = rekordy(dane) CalosciowyXLSX( "AAA_rekordy", rekordy=rekordy_danych, dane=dane, katalog_wyjsciowy=katalog_wyjsciowy, ).zrob() for autor in pbar( Autor.objects.filter(pk__in=(x.autor_id for x in rekordy(dane))), label="Dane autorow...", ): rekordy_autora = rekordy_danych.filter(autor_id=autor.id) AutorskiXLSX( autor=autor, title=autor2fn(autor), rekordy=rekordy_autora, dane=dane, katalog_wyjsciowy=katalog_wyjsciowy, ).zrob()

30.44

82

0.635348

import os from argparse import FileType from django.core.management import BaseCommand from ewaluacja2021.reports import load_data, rekordy from ewaluacja2021.util import autor2fn from ewaluacja2021.xlsy import AutorskiXLSX, CalosciowyXLSX from bpp.models import Autor from bpp.util import pbar class Command(BaseCommand): def add_arguments(self, parser): parser.add_argument("wejscie", type=FileType("r")) parser.add_argument("--katalog-wyjsciowy", type=str, default=None) def handle(self, wejscie, katalog_wyjsciowy, liczba_n=None, *args, **options): dane = load_data(wejscie) if katalog_wyjsciowy is None: katalog_wyjsciowy = wejscie.name.replace(".json", "_output") if not os.path.exists(katalog_wyjsciowy): os.mkdir(katalog_wyjsciowy) rekordy_danych = rekordy(dane) CalosciowyXLSX( "AAA_rekordy", rekordy=rekordy_danych, dane=dane, katalog_wyjsciowy=katalog_wyjsciowy, ).zrob() for autor in pbar( Autor.objects.filter(pk__in=(x.autor_id for x in rekordy(dane))), label="Dane autorow...", ): rekordy_autora = rekordy_danych.filter(autor_id=autor.id) AutorskiXLSX( autor=autor, title=autor2fn(autor), rekordy=rekordy_autora, dane=dane, katalog_wyjsciowy=katalog_wyjsciowy, ).zrob()

true

f731b0e7750a7bea8517170165c3c68c3dc22cf8

844

py

Python

setup.py

jbarbadillo/pycoinmon

035e64652c533a569c6b236f54e12aff35ad82b1

[ "MIT" ]

null

setup.py

jbarbadillo/pycoinmon

035e64652c533a569c6b236f54e12aff35ad82b1

[ "MIT" ]

null

setup.py

jbarbadillo/pycoinmon

035e64652c533a569c6b236f54e12aff35ad82b1

[ "MIT" ]

null

from setuptools import setup from pycoinmon.metadata import Metadata metadata = Metadata() setup( name = 'pycoinmon', packages = ['pycoinmon'], version = metadata.get_version(), license = 'MIT', description = 'Python Port Based on COINMON', url = 'https://github.com/RDCH106/pycoinmon', keywords = ['bitcoin', 'criptocurrency', 'crypto', 'ticker', 'python', 'cli', 'price-tracker', 'command-line'], classifiers = ['Programming Language :: Python', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3.2', 'Programming Language :: Python :: 3.3', 'Programming Language :: Python :: 3.4', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6'], )

40.190476

115

0.575829

from setuptools import setup from pycoinmon.metadata import Metadata metadata = Metadata() setup( name = 'pycoinmon', packages = ['pycoinmon'], version = metadata.get_version(), license = 'MIT', description = 'Python Port Based on COINMON', url = 'https://github.com/RDCH106/pycoinmon', keywords = ['bitcoin', 'criptocurrency', 'crypto', 'ticker', 'python', 'cli', 'price-tracker', 'command-line'], classifiers = ['Programming Language :: Python', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3.2', 'Programming Language :: Python :: 3.3', 'Programming Language :: Python :: 3.4', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6'], )

true

f731b187b5cb80cd697134e200a8839565bad37f

453

py

Python

card.py

yehudareisler/risky-game

ea919bd07a2acf75dfd184b5c59ad80d41f47428

[ "MIT" ]

3

2021-01-21T02:06:12.000Z

2022-03-14T10:26:43.000Z

card.py

yehudareisler/risky-game

ea919bd07a2acf75dfd184b5c59ad80d41f47428

[ "MIT" ]

null

card.py

yehudareisler/risky-game

ea919bd07a2acf75dfd184b5c59ad80d41f47428

[ "MIT" ]

1

2021-08-29T07:47:12.000Z

from enum import Enum class Card: card_type = None territory_name = '' def __init__(self, territory_name, card_type): self.territory_name = territory_name self.card_type = card_type def __str__(self): return f'Card of {self.territory_name} with {self.card_type} type' class CardType(Enum): WILDCARD = 0 INFANTRY = 1 CAVALRY = 2 ARTILLERY = 3 def __str__(self): return self.name

18.875

74

0.646799

from enum import Enum class Card: card_type = None territory_name = '' def __init__(self, territory_name, card_type): self.territory_name = territory_name self.card_type = card_type def __str__(self): return f'Card of {self.territory_name} with {self.card_type} type' class CardType(Enum): WILDCARD = 0 INFANTRY = 1 CAVALRY = 2 ARTILLERY = 3 def __str__(self): return self.name

true

f731b1ea9b5a643b7fe01d0c7b531d8169710a1a

401

py

Python

mmtbx/command_line/ntc_validation.py

dperl-sol/cctbx_project

b9e390221a2bc4fd00b9122e97c3b79c632c6664

[ "BSD-3-Clause-LBNL" ]

155

2016-11-23T12:52:16.000Z

2022-03-31T15:35:44.000Z

mmtbx/command_line/ntc_validation.py

dperl-sol/cctbx_project

b9e390221a2bc4fd00b9122e97c3b79c632c6664

[ "BSD-3-Clause-LBNL" ]

590

2016-12-10T11:31:18.000Z

2022-03-30T23:10:09.000Z

mmtbx/command_line/ntc_validation.py

dperl-sol/cctbx_project

b9e390221a2bc4fd00b9122e97c3b79c632c6664

[ "BSD-3-Clause-LBNL" ]

115

2016-11-15T08:17:28.000Z

2022-02-09T15:30:14.000Z

# -*- coding: utf-8 -*- from __future__ import absolute_import, division, print_function # LIBTBX_SET_DISPATCHER_NAME cctbx.development.ntc_validation from iotbx.cli_parser import run_program from mmtbx.programs.ntc_validation import Program # ============================================================================= if (__name__ == '__main__'): results = run_program(program_class=Program)

33.416667

79

0.645885

from __future__ import absolute_import, division, print_function from iotbx.cli_parser import run_program from mmtbx.programs.ntc_validation import Program if (__name__ == '__main__'): results = run_program(program_class=Program)

true

f731b41b93060edbb8a60d852567a8c02dfe18c9

19,457

py

Python

hive/indexer/cached_post.py

RailCoin/hivemind

cd78b952b55911b63a59c762d1d4530a25d7d8f5

[ "MIT" ]

null

hive/indexer/cached_post.py

RailCoin/hivemind

cd78b952b55911b63a59c762d1d4530a25d7d8f5

[ "MIT" ]

null

hive/indexer/cached_post.py

RailCoin/hivemind

cd78b952b55911b63a59c762d1d4530a25d7d8f5

[ "MIT" ]

1

2020-03-24T12:26:08.000Z

"""Manages cached post data.""" import math import collections import logging import ujson as json from toolz import partition_all from hive.db.adapter import Db from hive.utils.post import post_basic, post_legacy, post_payout, post_stats from hive.utils.timer import Timer from hive.indexer.accounts import Accounts log = logging.getLogger(__name__) DB = Db.instance() # levels of post dirtiness, in order of decreasing priority LEVELS = ['insert', 'payout', 'update', 'upvote', 'recount'] def _keyify(items): return dict(map(lambda x: ("val_%d" % x[0], x[1]), enumerate(items))) class CachedPost: """Maintain update queue and writing to `hive_posts_cache`.""" # cursor signifying upper bound of cached post span _last_id = -1 # cached id map _ids = {} # urls which are missing from id map _noids = set() # dirty posts; {key: dirty_level} _queue = collections.OrderedDict() # new promoted values, pending write _pending_promoted = {} @classmethod def update_promoted_amount(cls, post_id, amount): """Set a new pending amount for a post for its next update.""" cls._pending_promoted[post_id] = amount @classmethod def _dirty(cls, level, author, permlink, pid=None): """Mark a post as dirty.""" assert level in LEVELS, "invalid level {}".format(level) mode = LEVELS.index(level) url = author + '/' + permlink # add to appropriate queue. if url not in cls._queue: cls._queue[url] = mode # upgrade priority if needed elif cls._queue[url] > mode: cls._queue[url] = mode # add to id map, or register missing if pid and url in cls._ids: assert pid == cls._ids[url], "pid map conflict #78" elif pid: cls._ids[url] = pid else: cls._noids.add(url) @classmethod def _get_id(cls, url): """Given a post url, get its id.""" if url in cls._ids: return cls._ids[url] raise Exception("requested id for %s not in map" % url) @classmethod def recount(cls, author, permlink, pid=None): """Force a child re-count.""" cls._dirty('recount', author, permlink, pid) @classmethod def vote(cls, author, permlink, pid=None): """Handle a post dirtied by a `vote` op.""" cls._dirty('upvote', author, permlink, pid) Accounts.dirty(set([author])) # rep changed @classmethod def insert(cls, author, permlink, pid): """Handle a post created by a `comment` op.""" cls._dirty('insert', author, permlink, pid) @classmethod def update(cls, author, permlink, pid): """Handle a post updated by a `comment` op.""" cls._dirty('update', author, permlink, pid) @classmethod def delete(cls, post_id, author, permlink): """Handle a post deleted by a `delete_comment` op. With steemd, posts can be 'deleted' or unallocated in certain conditions. It requires foregoing convenient assumptions, e.g.: - author/permlink is unique and always references the same post - you can always get_content on any author/permlink you see in an op """ DB.query("DELETE FROM hive_posts_cache WHERE post_id = :id", id=post_id) # if it was queued for a write, remove it url = author+'/'+permlink if url in cls._queue: del cls._queue[url] if url in cls._ids: del cls._ids[url] @classmethod def undelete(cls, post_id, author, permlink): """Handle a post 'undeleted' by a `comment` op. 'Undeletion' occurs when hive detects that a previously deleted author/permlink combination has been reused on a new post. Hive does not delete hive_posts entries because they are currently irreplaceable in case of a fork. Instead, we reuse the slot. It's important to immediately insert a placeholder in the cache table since hive only scans forward. This row's properties push it to the front of update-immediately queue. Alternate ways of handling undeletes: - delete row from hive_posts so that it can be re-indexed (re-id'd) - comes at a risk of losing expensive entry on fork (and no undo) - create undo table for hive_posts, hive_follows, etc, & link to block - rely on steemd's post.id instead of database autoincrement - requires way to query steemd post objects by id to be useful - batch get_content_by_ids in steemd would be /huge/ speedup - create a consistent cache queue table or dirty flag col """ # do not force-write unless cache spans this id. if post_id > cls.last_id(): cls.insert(author, permlink, post_id) return # force-create dummy row to ensure cache is aware. only needed when # cache already spans this id, in case in-mem buffer is lost. default # value for payout_at ensures that it will get picked up for update. DB.query(cls._insert({ 'post_id': post_id, 'author': author, 'permlink': permlink})) cls.update(author, permlink, post_id) @classmethod def flush(cls, steem, trx=False, spread=1, full_total=None): """Process all posts which have been marked as dirty.""" cls._load_noids() # load missing ids assert spread == 1, "not fully tested, use with caution" counts = {} tuples = [] for level in LEVELS: tups = cls._get_tuples_for_level(level, spread) counts[level] = len(tups) tuples.extend(tups) if trx or len(tuples) > 250: changed = filter(lambda t: t[1], counts.items()) summary = list(map(lambda group: "%d %ss" % group[::-1], changed)) summary = ', '.join(summary) if summary else 'none' log.info("[PREP] posts cache process: %s", summary) cls._update_batch(steem, tuples, trx, full_total=full_total) for url, _, _ in tuples: del cls._queue[url] if url in cls._ids: del cls._ids[url] return counts @classmethod def _get_tuples_for_level(cls, level, fraction=1): """Query tuples to be updated. Given a specific flush level (insert, payout, update, upvote), returns a list of tuples to be passed to _update_batch, in the form of: `[(url, id, level)*]` """ mode = LEVELS.index(level) urls = [url for url, i in cls._queue.items() if i == mode] if fraction > 1 and level != 'insert': # inserts must be full flush urls = urls[0:math.ceil(len(urls) / fraction)] return [(url, cls._get_id(url), level) for url in urls] @classmethod def _load_noids(cls): """Load ids for posts we don't know the ids of. When posts are marked dirty, specifying the id is optional because a successive call might be able to provide it "for free". Before flushing changes this method should be called to fill in any gaps. """ from hive.indexer.posts import Posts noids = cls._noids - set(cls._ids.keys()) tuples = [(Posts.get_id(*url.split('/')), url) for url in noids] for pid, url in tuples: assert pid, "WARNING: missing id for %s" % url cls._ids[url] = pid cls._noids = set() return len(tuples) @classmethod def _select_paidout_tuples(cls, date): """Query hive_posts_cache for payout sweep. Select all posts which should have been paid out before `date` yet do not have the `is_paidout` flag set. We perform this sweep to ensure that we always have accurate final payout state. Since payout values vary even between votes, we'd have stale data if we didn't sweep, and only waited for incoming votes before an update. """ from hive.indexer.posts import Posts sql = """SELECT post_id FROM hive_posts_cache WHERE is_paidout = '0' AND payout_at <= :date""" ids = DB.query_col(sql, date=date) if not ids: return [] sql = """SELECT id, author, permlink FROM hive_posts WHERE id IN :ids""" results = DB.query_all(sql, ids=tuple(ids)) return Posts.save_ids_from_tuples(results) @classmethod def dirty_paidouts(cls, date): """Mark dirty all paidout posts not yet updated in db.""" paidout = cls._select_paidout_tuples(date) authors = set() for (pid, author, permlink) in paidout: authors.add(author) cls._dirty('payout', author, permlink, pid) Accounts.dirty(authors) # force-update accounts on payout if len(paidout) > 200: log.info("[PREP] Found %d payouts for %d authors since %s", len(paidout), len(authors), date) return len(paidout) @classmethod def _select_missing_tuples(cls, last_cached_id, limit=1000000): """Fetch posts inserted into main posts table but not cache.""" from hive.indexer.posts import Posts sql = """SELECT id, author, permlink, promoted FROM hive_posts WHERE is_deleted = '0' AND id > :id ORDER BY id LIMIT :limit""" results = DB.query_all(sql, id=last_cached_id, limit=limit) return Posts.save_ids_from_tuples(results) @classmethod def dirty_missing(cls, limit=250000): """Mark dirty all hive_posts records not yet written to cache.""" from hive.indexer.posts import Posts # cached posts inserted sequentially, so compare MAX(id)'s last_cached_id = cls.last_id() last_post_id = Posts.last_id() gap = last_post_id - last_cached_id if gap: missing = cls._select_missing_tuples(last_cached_id, limit) for pid, author, permlink, promoted in missing: if promoted > 0: # ensure we don't miss promote amount cls.update_promoted_amount(pid, promoted) cls._dirty('insert', author, permlink, pid) return gap @classmethod def recover_missing_posts(cls, steem): """Startup routine that cycles through missing posts. This is used for (1) initial sync, and (2) recovering missing cache records upon launch if hive fast-sync was interrupted. """ gap = cls.dirty_missing() log.info("[INIT] %d missing post cache entries", gap) while cls.flush(steem, trx=True, full_total=gap)['insert']: gap = cls.dirty_missing() @classmethod def _update_batch(cls, steem, tuples, trx=True, full_total=None): """Fetch, process, and write a batch of posts. Given a set of posts, fetch from steemd and write them to the db. The `tuples` arg is the form of `[(url, id, level)*]` representing posts which are to be fetched from steemd and updated in cache. Regarding _bump_last_id: there's a rare edge case when the last hive_post entry has been deleted "in the future" (ie, we haven't seen the delete op yet). So even when the post is not found (i.e. `not post['author']`), it's important to advance _last_id, because this cursor is used to deduce any missing cache entries. """ timer = Timer(total=len(tuples), entity='post', laps=['rps', 'wps'], full_total=full_total) tuples = sorted(tuples, key=lambda x: x[1]) # enforce ASC id's for tups in partition_all(1000, tuples): timer.batch_start() buffer = [] post_args = [tup[0].split('/') for tup in tups] posts = steem.get_content_batch(post_args) post_ids = [tup[1] for tup in tups] post_levels = [tup[2] for tup in tups] for pid, post, level in zip(post_ids, posts, post_levels): if post['author']: buffer.extend(cls._sql(pid, post, level=level)) else: # When a post has been deleted (or otherwise DNE), # steemd simply returns a blank post object w/ all # fields blank. While it's best to not try to cache # already-deleted posts, it can happen during missed # post sweep and while using `trail_blocks` > 0. pass cls._bump_last_id(pid) timer.batch_lap() DB.batch_queries(buffer, trx) timer.batch_finish(len(posts)) if len(tuples) >= 1000: log.info(timer.batch_status()) @classmethod def last_id(cls): """Retrieve the latest post_id that was cached.""" if cls._last_id == -1: # after initial query, we maintain last_id w/ _bump_last_id() sql = "SELECT COALESCE(MAX(post_id), 0) FROM hive_posts_cache" cls._last_id = DB.query_one(sql) return cls._last_id @classmethod def _bump_last_id(cls, next_id): """Update our last_id based on a recent insert.""" last_id = cls.last_id() if next_id <= last_id: return if next_id - last_id > 2: cls._ensure_safe_gap(last_id, next_id) if next_id - last_id > 4: # gap of 2 is common due to deletions. report on larger gaps. log.warning("skipping post ids %d -> %d", last_id, next_id) cls._last_id = next_id @classmethod def _ensure_safe_gap(cls, last_id, next_id): """Paranoid check of important operating assumption.""" sql = """ SELECT COUNT(*) FROM hive_posts WHERE id BETWEEN :x1 AND :x2 AND is_deleted = '0' """ missing_posts = DB.query_one(sql, x1=(last_id + 1), x2=(next_id - 1)) if not missing_posts: return raise Exception("found large cache gap: %d --> %d (%d)" % (last_id, next_id, missing_posts)) @classmethod def _sql(cls, pid, post, level=None): """Given a post and "update level", generate SQL edit statement. Valid levels are: - `insert`: post does not yet exist in cache - `update`: post was modified - `payout`: post was paidout - `upvote`: post payout/votes changed """ #pylint: disable=bad-whitespace assert post['author'], "post {} is blank".format(pid) # last-minute sanity check to ensure `pid` is correct #78 pid2 = cls._get_id(post['author']+'/'+post['permlink']) assert pid == pid2, "hpc id %d maps to %d" % (pid, pid2) # inserts always sequential. if pid > last_id, this operation # *must* be an insert; so `level` must not be any form of update. if pid > cls.last_id() and level != 'insert': raise Exception("WARNING: new pid, but level=%s. #%d vs %d, %s" % (level, pid, cls.last_id(), repr(post))) # start building the queries tag_sqls = [] values = [('post_id', pid)] # immutable; write only once (*edge case: undeleted posts) if level == 'insert': values.extend([ ('author', post['author']), ('permlink', post['permlink']), ('category', post['category']), ('depth', post['depth'])]) # always write, unless simple vote update if level in ['insert', 'payout', 'update']: basic = post_basic(post) values.extend([ ('created_at', post['created']), # immutable* ('updated_at', post['last_update']), ('title', post['title']), ('payout_at', basic['payout_at']), # immutable* ('preview', basic['preview']), ('body', basic['body']), ('img_url', basic['image']), ('is_nsfw', basic['is_nsfw']), ('is_declined', basic['is_payout_declined']), ('is_full_power', basic['is_full_power']), ('is_paidout', basic['is_paidout']), ('json', json.dumps(basic['json_metadata'])), ('raw_json', json.dumps(post_legacy(post))), ]) # update tags if action is insert/update and is root post if level in ['insert', 'update'] and not post['depth']: diff = level != 'insert' # do not attempt tag diff on insert tag_sqls.extend(cls._tag_sqls(pid, basic['tags'], diff=diff)) # if there's a pending promoted value to write, pull it out if pid in cls._pending_promoted: bal = cls._pending_promoted.pop(pid) values.append(('promoted', bal)) # update unconditionally payout = post_payout(post) stats = post_stats(post) values.extend([ ('payout', "%f" % payout['payout']), ('rshares', "%d" % payout['rshares']), ('votes', "%s" % payout['csvotes']), ('sc_trend', "%f" % payout['sc_trend']), ('sc_hot', "%f" % payout['sc_hot']), ('flag_weight', "%f" % stats['flag_weight']), ('total_votes', "%d" % stats['total_votes']), ('up_votes', "%d" % stats['up_votes']), ('is_hidden', "%d" % stats['hide']), ('is_grayed', "%d" % stats['gray']), ('author_rep', "%f" % stats['author_rep']), ('children', "%d" % min(post['children'], 32767)), ]) # if recounting, update the parent next pass. if level == 'recount' and post['depth']: cls.recount(post['parent_author'], post['parent_permlink']) # build the post insert/update SQL, add tag SQLs if level == 'insert': sql = cls._insert(values) else: sql = cls._update(values) return [sql] + tag_sqls @classmethod def _tag_sqls(cls, pid, tags, diff=True): """Generate SQL "deltas" for a post_id's associated tags.""" next_tags = set(tags) curr_tags = set() if diff: sql = "SELECT tag FROM hive_post_tags WHERE post_id = :id" curr_tags = set(DB.query_col(sql, id=pid)) to_rem = (curr_tags - next_tags) if to_rem: sql = "DELETE FROM hive_post_tags WHERE post_id = :id AND tag IN :tags" yield (sql, dict(id=pid, tags=tuple(to_rem))) to_add = (next_tags - curr_tags) if to_add: params = _keyify(to_add) vals = ["(:id, :%s)" % key for key in params.keys()] sql = "INSERT INTO hive_post_tags (post_id, tag) VALUES %s" sql += " ON CONFLICT DO NOTHING" # (conflicts due to collation) yield (sql % ','.join(vals), {'id': pid, **params}) @classmethod def _insert(cls, values): return DB.build_insert('hive_posts_cache', values, pk='post_id') @classmethod def _update(cls, values): return DB.build_update('hive_posts_cache', values, pk='post_id')

38.991984

83

0.582412

import math import collections import logging import ujson as json from toolz import partition_all from hive.db.adapter import Db from hive.utils.post import post_basic, post_legacy, post_payout, post_stats from hive.utils.timer import Timer from hive.indexer.accounts import Accounts log = logging.getLogger(__name__) DB = Db.instance() LEVELS = ['insert', 'payout', 'update', 'upvote', 'recount'] def _keyify(items): return dict(map(lambda x: ("val_%d" % x[0], x[1]), enumerate(items))) class CachedPost: _last_id = -1 _ids = {} _noids = set() _queue = collections.OrderedDict() _pending_promoted = {} @classmethod def update_promoted_amount(cls, post_id, amount): cls._pending_promoted[post_id] = amount @classmethod def _dirty(cls, level, author, permlink, pid=None): assert level in LEVELS, "invalid level {}".format(level) mode = LEVELS.index(level) url = author + '/' + permlink if url not in cls._queue: cls._queue[url] = mode elif cls._queue[url] > mode: cls._queue[url] = mode if pid and url in cls._ids: assert pid == cls._ids[url], "pid map conflict #78" elif pid: cls._ids[url] = pid else: cls._noids.add(url) @classmethod def _get_id(cls, url): if url in cls._ids: return cls._ids[url] raise Exception("requested id for %s not in map" % url) @classmethod def recount(cls, author, permlink, pid=None): cls._dirty('recount', author, permlink, pid) @classmethod def vote(cls, author, permlink, pid=None): cls._dirty('upvote', author, permlink, pid) Accounts.dirty(set([author])) @classmethod def insert(cls, author, permlink, pid): cls._dirty('insert', author, permlink, pid) @classmethod def update(cls, author, permlink, pid): cls._dirty('update', author, permlink, pid) @classmethod def delete(cls, post_id, author, permlink): DB.query("DELETE FROM hive_posts_cache WHERE post_id = :id", id=post_id) url = author+'/'+permlink if url in cls._queue: del cls._queue[url] if url in cls._ids: del cls._ids[url] @classmethod def undelete(cls, post_id, author, permlink): if post_id > cls.last_id(): cls.insert(author, permlink, post_id) return DB.query(cls._insert({ 'post_id': post_id, 'author': author, 'permlink': permlink})) cls.update(author, permlink, post_id) @classmethod def flush(cls, steem, trx=False, spread=1, full_total=None): cls._load_noids() assert spread == 1, "not fully tested, use with caution" counts = {} tuples = [] for level in LEVELS: tups = cls._get_tuples_for_level(level, spread) counts[level] = len(tups) tuples.extend(tups) if trx or len(tuples) > 250: changed = filter(lambda t: t[1], counts.items()) summary = list(map(lambda group: "%d %ss" % group[::-1], changed)) summary = ', '.join(summary) if summary else 'none' log.info("[PREP] posts cache process: %s", summary) cls._update_batch(steem, tuples, trx, full_total=full_total) for url, _, _ in tuples: del cls._queue[url] if url in cls._ids: del cls._ids[url] return counts @classmethod def _get_tuples_for_level(cls, level, fraction=1): mode = LEVELS.index(level) urls = [url for url, i in cls._queue.items() if i == mode] if fraction > 1 and level != 'insert': urls = urls[0:math.ceil(len(urls) / fraction)] return [(url, cls._get_id(url), level) for url in urls] @classmethod def _load_noids(cls): from hive.indexer.posts import Posts noids = cls._noids - set(cls._ids.keys()) tuples = [(Posts.get_id(*url.split('/')), url) for url in noids] for pid, url in tuples: assert pid, "WARNING: missing id for %s" % url cls._ids[url] = pid cls._noids = set() return len(tuples) @classmethod def _select_paidout_tuples(cls, date): from hive.indexer.posts import Posts sql = """SELECT post_id FROM hive_posts_cache WHERE is_paidout = '0' AND payout_at <= :date""" ids = DB.query_col(sql, date=date) if not ids: return [] sql = """SELECT id, author, permlink FROM hive_posts WHERE id IN :ids""" results = DB.query_all(sql, ids=tuple(ids)) return Posts.save_ids_from_tuples(results) @classmethod def dirty_paidouts(cls, date): paidout = cls._select_paidout_tuples(date) authors = set() for (pid, author, permlink) in paidout: authors.add(author) cls._dirty('payout', author, permlink, pid) Accounts.dirty(authors) if len(paidout) > 200: log.info("[PREP] Found %d payouts for %d authors since %s", len(paidout), len(authors), date) return len(paidout) @classmethod def _select_missing_tuples(cls, last_cached_id, limit=1000000): from hive.indexer.posts import Posts sql = """SELECT id, author, permlink, promoted FROM hive_posts WHERE is_deleted = '0' AND id > :id ORDER BY id LIMIT :limit""" results = DB.query_all(sql, id=last_cached_id, limit=limit) return Posts.save_ids_from_tuples(results) @classmethod def dirty_missing(cls, limit=250000): from hive.indexer.posts import Posts last_cached_id = cls.last_id() last_post_id = Posts.last_id() gap = last_post_id - last_cached_id if gap: missing = cls._select_missing_tuples(last_cached_id, limit) for pid, author, permlink, promoted in missing: if promoted > 0: # ensure we don't miss promote amount cls.update_promoted_amount(pid, promoted) cls._dirty('insert', author, permlink, pid) return gap @classmethod def recover_missing_posts(cls, steem): gap = cls.dirty_missing() log.info("[INIT] %d missing post cache entries", gap) while cls.flush(steem, trx=True, full_total=gap)['insert']: gap = cls.dirty_missing() @classmethod def _update_batch(cls, steem, tuples, trx=True, full_total=None): timer = Timer(total=len(tuples), entity='post', laps=['rps', 'wps'], full_total=full_total) tuples = sorted(tuples, key=lambda x: x[1]) for tups in partition_all(1000, tuples): timer.batch_start() buffer = [] post_args = [tup[0].split('/') for tup in tups] posts = steem.get_content_batch(post_args) post_ids = [tup[1] for tup in tups] post_levels = [tup[2] for tup in tups] for pid, post, level in zip(post_ids, posts, post_levels): if post['author']: buffer.extend(cls._sql(pid, post, level=level)) else: # When a post has been deleted (or otherwise DNE), # steemd simply returns a blank post object w/ all # fields blank. While it's best to not try to cache pass cls._bump_last_id(pid) timer.batch_lap() DB.batch_queries(buffer, trx) timer.batch_finish(len(posts)) if len(tuples) >= 1000: log.info(timer.batch_status()) @classmethod def last_id(cls): if cls._last_id == -1: sql = "SELECT COALESCE(MAX(post_id), 0) FROM hive_posts_cache" cls._last_id = DB.query_one(sql) return cls._last_id @classmethod def _bump_last_id(cls, next_id): last_id = cls.last_id() if next_id <= last_id: return if next_id - last_id > 2: cls._ensure_safe_gap(last_id, next_id) if next_id - last_id > 4: log.warning("skipping post ids %d -> %d", last_id, next_id) cls._last_id = next_id @classmethod def _ensure_safe_gap(cls, last_id, next_id): sql = """ SELECT COUNT(*) FROM hive_posts WHERE id BETWEEN :x1 AND :x2 AND is_deleted = '0' """ missing_posts = DB.query_one(sql, x1=(last_id + 1), x2=(next_id - 1)) if not missing_posts: return raise Exception("found large cache gap: %d --> %d (%d)" % (last_id, next_id, missing_posts)) @classmethod def _sql(cls, pid, post, level=None): assert post['author'], "post {} is blank".format(pid) pid2 = cls._get_id(post['author']+'/'+post['permlink']) assert pid == pid2, "hpc id %d maps to %d" % (pid, pid2) if pid > cls.last_id() and level != 'insert': raise Exception("WARNING: new pid, but level=%s. #%d vs %d, %s" % (level, pid, cls.last_id(), repr(post))) tag_sqls = [] values = [('post_id', pid)] if level == 'insert': values.extend([ ('author', post['author']), ('permlink', post['permlink']), ('category', post['category']), ('depth', post['depth'])]) if level in ['insert', 'payout', 'update']: basic = post_basic(post) values.extend([ ('created_at', post['created']), ('updated_at', post['last_update']), ('title', post['title']), ('payout_at', basic['payout_at']), ('preview', basic['preview']), ('body', basic['body']), ('img_url', basic['image']), ('is_nsfw', basic['is_nsfw']), ('is_declined', basic['is_payout_declined']), ('is_full_power', basic['is_full_power']), ('is_paidout', basic['is_paidout']), ('json', json.dumps(basic['json_metadata'])), ('raw_json', json.dumps(post_legacy(post))), ]) if level in ['insert', 'update'] and not post['depth']: diff = level != 'insert' tag_sqls.extend(cls._tag_sqls(pid, basic['tags'], diff=diff)) if pid in cls._pending_promoted: bal = cls._pending_promoted.pop(pid) values.append(('promoted', bal)) # update unconditionally payout = post_payout(post) stats = post_stats(post) values.extend([ ('payout', "%f" % payout['payout']), ('rshares', "%d" % payout['rshares']), ('votes', "%s" % payout['csvotes']), ('sc_trend', "%f" % payout['sc_trend']), ('sc_hot', "%f" % payout['sc_hot']), ('flag_weight', "%f" % stats['flag_weight']), ('total_votes', "%d" % stats['total_votes']), ('up_votes', "%d" % stats['up_votes']), ('is_hidden', "%d" % stats['hide']), ('is_grayed', "%d" % stats['gray']), ('author_rep', "%f" % stats['author_rep']), ('children', "%d" % min(post['children'], 32767)), ]) # if recounting, update the parent next pass. if level == 'recount' and post['depth']: cls.recount(post['parent_author'], post['parent_permlink']) # build the post insert/update SQL, add tag SQLs if level == 'insert': sql = cls._insert(values) else: sql = cls._update(values) return [sql] + tag_sqls @classmethod def _tag_sqls(cls, pid, tags, diff=True): next_tags = set(tags) curr_tags = set() if diff: sql = "SELECT tag FROM hive_post_tags WHERE post_id = :id" curr_tags = set(DB.query_col(sql, id=pid)) to_rem = (curr_tags - next_tags) if to_rem: sql = "DELETE FROM hive_post_tags WHERE post_id = :id AND tag IN :tags" yield (sql, dict(id=pid, tags=tuple(to_rem))) to_add = (next_tags - curr_tags) if to_add: params = _keyify(to_add) vals = ["(:id, :%s)" % key for key in params.keys()] sql = "INSERT INTO hive_post_tags (post_id, tag) VALUES %s" sql += " ON CONFLICT DO NOTHING" # (conflicts due to collation) yield (sql % ','.join(vals), {'id': pid, **params}) @classmethod def _insert(cls, values): return DB.build_insert('hive_posts_cache', values, pk='post_id') @classmethod def _update(cls, values): return DB.build_update('hive_posts_cache', values, pk='post_id')

true

f731b44538e1b9c630d1af353fa35d3677a745d9

1,210

py

Python

BotClean_Large.py

Aditya148/Hackerrank-Artificial-Intelligence

5abbe561115b75cb28397661b7b0d7d53486ffee

[ "MIT" ]

null

BotClean_Large.py

Aditya148/Hackerrank-Artificial-Intelligence

5abbe561115b75cb28397661b7b0d7d53486ffee

[ "MIT" ]

null

BotClean_Large.py

Aditya148/Hackerrank-Artificial-Intelligence

5abbe561115b75cb28397661b7b0d7d53486ffee

[ "MIT" ]

null

import math def update_position(posr, posc, dirties): nearest_dirt = [] for i in range(len(dirties)): # Euclidean distance result = math.sqrt(((dirties[i][0] - posr) ** 2) + ((dirties[i][1] - posc) ** 2)) nearest_dirt.append(result) return [x for (y,x) in sorted(zip(nearest_dirt,dirties))] # Set the bot in your new position def next_move(posr, posc, x, y, board): dirties = [] for i in range(x): for j in range(y): if board[i][j] == 'd': dirties.append([i, j]) next_dirt = update_position(posr, posc, dirties) if next_dirt[0][1] < posc: print('LEFT') elif next_dirt[0][1] > posc: print('RIGHT') elif next_dirt[0][0] < posr: print('UP') elif next_dirt[0][0] > posr: print('DOWN') else: print('CLEAN') if __name__ == "__main__": pos = [int(i) for i in input().strip().split()] dim = [int(i) for i in input().strip().split()] board = [[j for j in input().strip()] for i in range(dim[0])] next_move(pos[0], pos[1], dim[0], dim[1], board) ''' Sample Input 0 0 5 5 b---d -d--d --dd- --d-- ----d Sample Output RIGHT '''

22.407407

89

0.539669

import math def update_position(posr, posc, dirties): nearest_dirt = [] for i in range(len(dirties)): result = math.sqrt(((dirties[i][0] - posr) ** 2) + ((dirties[i][1] - posc) ** 2)) nearest_dirt.append(result) return [x for (y,x) in sorted(zip(nearest_dirt,dirties))] def next_move(posr, posc, x, y, board): dirties = [] for i in range(x): for j in range(y): if board[i][j] == 'd': dirties.append([i, j]) next_dirt = update_position(posr, posc, dirties) if next_dirt[0][1] < posc: print('LEFT') elif next_dirt[0][1] > posc: print('RIGHT') elif next_dirt[0][0] < posr: print('UP') elif next_dirt[0][0] > posr: print('DOWN') else: print('CLEAN') if __name__ == "__main__": pos = [int(i) for i in input().strip().split()] dim = [int(i) for i in input().strip().split()] board = [[j for j in input().strip()] for i in range(dim[0])] next_move(pos[0], pos[1], dim[0], dim[1], board)

true

f731b4a38a9bc8969c194157663764cf285ad1bc

4,079

py

Python

AC_Network.py

pasindubawantha/just-copy

919b1723c87cadc5946f891da53f4abc7d50ff6e

[ "Apache-2.0" ]

1

2020-07-30T19:29:06.000Z

AC_Network.py

pasindubawantha/just-copy

919b1723c87cadc5946f891da53f4abc7d50ff6e

[ "Apache-2.0" ]

null

AC_Network.py

pasindubawantha/just-copy

919b1723c87cadc5946f891da53f4abc7d50ff6e

[ "Apache-2.0" ]

null

import tensorflow as tf import tensorflow.contrib.slim as slim #import tensorflow.nn as slim import numpy as np from helpers import * class AC_Network(): def __init__(self,s_size,a_size,scope,trainer,s_shape): with tf.variable_scope(scope): #Input and visual encoding layers self.inputs = tf.placeholder(shape=[None,s_size],dtype=tf.float32) self.imageIn = tf.reshape(self.inputs,shape=[-1,s_shape[0],s_shape[1],s_shape[2]]) self.conv1 = slim.conv2d(activation_fn=tf.nn.elu, inputs=self.imageIn,num_outputs=16, kernel_size=[8,8],stride=[4,4],padding='VALID') self.conv2 = slim.conv2d(activation_fn=tf.nn.elu, inputs=self.conv1,num_outputs=32, kernel_size=[4,4],stride=[2,2],padding='VALID') hidden = slim.fully_connected(slim.flatten(self.conv2),256,activation_fn=tf.nn.elu) #Recurrent network for temporal dependencies lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(256,state_is_tuple=True) c_init = np.zeros((1, lstm_cell.state_size.c), np.float32) h_init = np.zeros((1, lstm_cell.state_size.h), np.float32) self.state_init = [c_init, h_init] c_in = tf.placeholder(tf.float32, [1, lstm_cell.state_size.c]) h_in = tf.placeholder(tf.float32, [1, lstm_cell.state_size.h]) self.state_in = (c_in, h_in) rnn_in = tf.expand_dims(hidden, [0]) step_size = tf.shape(self.imageIn)[:1] state_in = tf.nn.rnn_cell.LSTMStateTuple(c_in, h_in) lstm_outputs, lstm_state = tf.nn.dynamic_rnn( lstm_cell, rnn_in, initial_state=state_in, sequence_length=step_size, time_major=False) lstm_c, lstm_h = lstm_state self.state_out = (lstm_c[:1, :], lstm_h[:1, :]) rnn_out = tf.reshape(lstm_outputs, [-1, 256]) #Output layers for policy and value estimations self.policy = slim.fully_connected(rnn_out,a_size, activation_fn=tf.nn.softmax, weights_initializer=normalized_columns_initializer(0.01), biases_initializer=None) self.value = slim.fully_connected(rnn_out,1, activation_fn=None, weights_initializer=normalized_columns_initializer(1.0), biases_initializer=None) #Only the worker network need ops for loss functions and gradient updating. if scope != 'global': self.actions = tf.placeholder(shape=[None],dtype=tf.int32) self.actions_onehot = tf.one_hot(self.actions,a_size,dtype=tf.float32) self.target_v = tf.placeholder(shape=[None],dtype=tf.float32) self.advantages = tf.placeholder(shape=[None],dtype=tf.float32) self.responsible_outputs = tf.reduce_sum(self.policy * self.actions_onehot, [1]) #Loss functions self.value_loss = 0.5 * tf.reduce_sum(tf.square(self.target_v - tf.reshape(self.value,[-1]))) self.entropy = - tf.reduce_sum(self.policy * tf.log(self.policy)) self.policy_loss = -tf.reduce_sum(tf.log(self.responsible_outputs)*self.advantages) self.loss = 0.5 * self.value_loss + self.policy_loss - self.entropy * 0.01 #Get gradients from local network using local losses local_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope) self.gradients = tf.gradients(self.loss,local_vars) self.var_norms = tf.global_norm(local_vars) self.grads,self.grad_norms = tf.clip_by_global_norm(self.gradients,40.0) #Apply local gradients to global network self.global_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, 'global') self.apply_grads = trainer.apply_gradients(zip(self.grads,self.global_vars))

53.671053

109

0.617308

import tensorflow as tf import tensorflow.contrib.slim as slim import numpy as np from helpers import * class AC_Network(): def __init__(self,s_size,a_size,scope,trainer,s_shape): with tf.variable_scope(scope): self.inputs = tf.placeholder(shape=[None,s_size],dtype=tf.float32) self.imageIn = tf.reshape(self.inputs,shape=[-1,s_shape[0],s_shape[1],s_shape[2]]) self.conv1 = slim.conv2d(activation_fn=tf.nn.elu, inputs=self.imageIn,num_outputs=16, kernel_size=[8,8],stride=[4,4],padding='VALID') self.conv2 = slim.conv2d(activation_fn=tf.nn.elu, inputs=self.conv1,num_outputs=32, kernel_size=[4,4],stride=[2,2],padding='VALID') hidden = slim.fully_connected(slim.flatten(self.conv2),256,activation_fn=tf.nn.elu) lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(256,state_is_tuple=True) c_init = np.zeros((1, lstm_cell.state_size.c), np.float32) h_init = np.zeros((1, lstm_cell.state_size.h), np.float32) self.state_init = [c_init, h_init] c_in = tf.placeholder(tf.float32, [1, lstm_cell.state_size.c]) h_in = tf.placeholder(tf.float32, [1, lstm_cell.state_size.h]) self.state_in = (c_in, h_in) rnn_in = tf.expand_dims(hidden, [0]) step_size = tf.shape(self.imageIn)[:1] state_in = tf.nn.rnn_cell.LSTMStateTuple(c_in, h_in) lstm_outputs, lstm_state = tf.nn.dynamic_rnn( lstm_cell, rnn_in, initial_state=state_in, sequence_length=step_size, time_major=False) lstm_c, lstm_h = lstm_state self.state_out = (lstm_c[:1, :], lstm_h[:1, :]) rnn_out = tf.reshape(lstm_outputs, [-1, 256]) self.policy = slim.fully_connected(rnn_out,a_size, activation_fn=tf.nn.softmax, weights_initializer=normalized_columns_initializer(0.01), biases_initializer=None) self.value = slim.fully_connected(rnn_out,1, activation_fn=None, weights_initializer=normalized_columns_initializer(1.0), biases_initializer=None) if scope != 'global': self.actions = tf.placeholder(shape=[None],dtype=tf.int32) self.actions_onehot = tf.one_hot(self.actions,a_size,dtype=tf.float32) self.target_v = tf.placeholder(shape=[None],dtype=tf.float32) self.advantages = tf.placeholder(shape=[None],dtype=tf.float32) self.responsible_outputs = tf.reduce_sum(self.policy * self.actions_onehot, [1]) self.value_loss = 0.5 * tf.reduce_sum(tf.square(self.target_v - tf.reshape(self.value,[-1]))) self.entropy = - tf.reduce_sum(self.policy * tf.log(self.policy)) self.policy_loss = -tf.reduce_sum(tf.log(self.responsible_outputs)*self.advantages) self.loss = 0.5 * self.value_loss + self.policy_loss - self.entropy * 0.01 local_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope) self.gradients = tf.gradients(self.loss,local_vars) self.var_norms = tf.global_norm(local_vars) self.grads,self.grad_norms = tf.clip_by_global_norm(self.gradients,40.0) self.global_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, 'global') self.apply_grads = trainer.apply_gradients(zip(self.grads,self.global_vars))

true

f731b554fa6750e8f70b9ce4eeb1ca9577514c53

7,291

py

Python

tests/pipeline/test_node_run.py

andmikey/kedro

9b4e4135720609d44ffdf5248246fe805f0b5469

[ "Apache-2.0" ]

1

2021-11-19T05:36:47.000Z

tests/pipeline/test_node_run.py

andmikey/kedro

9b4e4135720609d44ffdf5248246fe805f0b5469

[ "Apache-2.0" ]

null

tests/pipeline/test_node_run.py

andmikey/kedro

9b4e4135720609d44ffdf5248246fe805f0b5469

[ "Apache-2.0" ]

1

2021-11-19T05:36:49.000Z

# Copyright 2018-2019 QuantumBlack Visual Analytics Limited # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, # EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES # OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, AND # NONINFRINGEMENT. IN NO EVENT WILL THE LICENSOR OR OTHER CONTRIBUTORS # BE LIABLE FOR ANY CLAIM, DAMAGES, OR OTHER LIABILITY, WHETHER IN AN # ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF, OR IN # CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # # The QuantumBlack Visual Analytics Limited ("QuantumBlack") name and logo # (either separately or in combination, "QuantumBlack Trademarks") are # trademarks of QuantumBlack. The License does not grant you any right or # license to the QuantumBlack Trademarks. You may not use the QuantumBlack # Trademarks or any confusingly similar mark as a trademark for your product, # or use the QuantumBlack Trademarks in any other manner that might cause # confusion in the marketplace, including but not limited to in advertising, # on websites, or on software. # # See the License for the specific language governing permissions and # limitations under the License. # pylint: disable=unused-argument import pytest from kedro.io import LambdaDataSet from kedro.pipeline import node @pytest.fixture def mocked_dataset(mocker): load = mocker.Mock(return_value=42) save = mocker.Mock() return LambdaDataSet(load, save) def one_in_one_out(arg): return arg def one_in_dict_out(arg): return dict(ret=arg) def two_in_first_out(arg1, arg2): return arg1 @pytest.fixture def valid_nodes_with_inputs(): return [ (node(one_in_one_out, "ds1", "dsOut"), dict(ds1=42)), (node(one_in_dict_out, dict(arg="ds1"), dict(ret="dsOut")), dict(ds1=42)), (node(two_in_first_out, ["ds1", "ds2"], "dsOut"), dict(ds1=42, ds2=58)), ] def test_valid_nodes(valid_nodes_with_inputs): """Check if node.run works as expected.""" for node_, input_ in valid_nodes_with_inputs: output = node_.run(input_) assert output["dsOut"] == 42 def test_run_got_dataframe(mocked_dataset): """Check an exception when non-dictionary (class object) is passed.""" pattern = r"Node.run expects a dictionary or None, " pattern += r"but got <class \'kedro.io.lambda_data_set.LambdaDataSet\'> instead" with pytest.raises(ValueError, match=pattern): node(one_in_one_out, dict(arg="ds1"), "A").run(mocked_dataset) class TestNodeRunInvalidInput: def test_unresolved(self): """Pass no input when one is expected.""" with pytest.raises(ValueError, match=r"expected one input"): node(one_in_one_out, "unresolved", "ds1").run(None) def test_no_inputs_node_error(self, mocked_dataset): """Pass one input when none is expected.""" with pytest.raises(ValueError, match=r"expected no inputs"): node(lambda: 1, None, "A").run(dict(unexpected=mocked_dataset)) def test_one_input_error(self, mocked_dataset): """Pass a different input.""" pattern = r"expected one input named 'ds1', but got the " pattern += r"following 1 input$s$ instead: \['arg'\]" with pytest.raises(ValueError, match=pattern): node(one_in_dict_out, "ds1", dict(ret="B", ans="C")).run( dict(arg=mocked_dataset) ) def test_run_diff_size_lists(self, mocked_dataset): """Pass only one dict input when two (list) are expected.""" pattern = r"expected 2 input$s$ \['ds1', 'ds2'\], but " pattern += r"got the following 1 input$s$ instead." with pytest.raises(ValueError, match=pattern): node(two_in_first_out, ["ds1", "ds2"], "A").run(dict(ds1=mocked_dataset)) def test_run_diff_size_list_dict(self, mocked_dataset): """Pass two dict inputs when one (list) are expected.""" pattern = r"expected 1 input$s$ \['ds1'\], but got the " pattern += r"following 2 input$s$ instead: \['ds1', 'ds2'\]\." with pytest.raises(ValueError, match=pattern): node(one_in_one_out, ["ds1"], "A").run(dict(ds1=mocked_dataset, ds2=2)) def test_run_list_dict_unavailable(self, mocked_dataset): """Pass one dict which is different from expected.""" pattern = r"expected 1 input$s$ \['ds1'\], but got the " pattern += r"following 1 input$s$ instead: \['ds2'\]\." with pytest.raises(ValueError, match=pattern): node(one_in_one_out, ["ds1"], "A").run(dict(ds2=mocked_dataset)) def test_run_dict_unavailable(self, mocked_dataset): """Pass one dict which is different from expected.""" pattern = r"expected 1 input$s$ \['ds1'\], but got the " pattern += r"following 1 input$s$ instead: \['ds2'\]\." with pytest.raises(ValueError, match=pattern): node(one_in_one_out, dict(arg="ds1"), "A").run(dict(ds2=mocked_dataset)) def test_run_dict_diff_size(self, mocked_dataset): """Pass two dict inputs when one is expected.""" pattern = r"expected 1 input$s$ \['ds1'\], but got the " pattern += r"following 2 input$s$ instead: \['ds1', 'ds2'\]\." with pytest.raises(ValueError, match=pattern): node(one_in_one_out, dict(arg="ds1"), "A").run( dict(ds1=mocked_dataset, ds2=2) ) class TestNodeRunInvalidOutput: def test_miss_matching_output_types(self, mocked_dataset): pattern = r"The node output is a dictionary, whereas the function " pattern += r"output is not\." with pytest.raises(ValueError, match=pattern): node(one_in_one_out, "ds1", dict(a="ds")).run(dict(ds1=mocked_dataset)) def test_miss_matching_output_keys(self, mocked_dataset): pattern = r"The node's output keys {'ret'} do not match " pattern += r"with the returned output's keys" with pytest.raises(ValueError, match=pattern): node(one_in_dict_out, "ds1", dict(ret="B", ans="C")).run( dict(ds1=mocked_dataset) ) def test_node_not_list_output(self, mocked_dataset): pattern = r"The node definition contains a list of outputs " pattern += r"\['B', 'C'\], whereas the node function returned " pattern += r"a `LambdaDataSet`" with pytest.raises(ValueError, match=pattern): node(one_in_one_out, "ds1", ["B", "C"]).run(dict(ds1=mocked_dataset)) def test_node_wrong_num_of_outputs(self, mocker, mocked_dataset): def one_in_two_out(arg): load = mocker.Mock(return_value=42) save = mocker.Mock() return [LambdaDataSet(load, save), LambdaDataSet(load, save)] pattern = r"The node function returned 2 output$s$, whereas " pattern += r"the node definition contains 3 output$s$\." with pytest.raises(ValueError, match=pattern): node(one_in_two_out, "ds1", ["A", "B", "C"]).run(dict(ds1=mocked_dataset))

42.888235

86

0.665478

import pytest from kedro.io import LambdaDataSet from kedro.pipeline import node @pytest.fixture def mocked_dataset(mocker): load = mocker.Mock(return_value=42) save = mocker.Mock() return LambdaDataSet(load, save) def one_in_one_out(arg): return arg def one_in_dict_out(arg): return dict(ret=arg) def two_in_first_out(arg1, arg2): return arg1 @pytest.fixture def valid_nodes_with_inputs(): return [ (node(one_in_one_out, "ds1", "dsOut"), dict(ds1=42)), (node(one_in_dict_out, dict(arg="ds1"), dict(ret="dsOut")), dict(ds1=42)), (node(two_in_first_out, ["ds1", "ds2"], "dsOut"), dict(ds1=42, ds2=58)), ] def test_valid_nodes(valid_nodes_with_inputs): for node_, input_ in valid_nodes_with_inputs: output = node_.run(input_) assert output["dsOut"] == 42 def test_run_got_dataframe(mocked_dataset): pattern = r"Node.run expects a dictionary or None, " pattern += r"but got <class \'kedro.io.lambda_data_set.LambdaDataSet\'> instead" with pytest.raises(ValueError, match=pattern): node(one_in_one_out, dict(arg="ds1"), "A").run(mocked_dataset) class TestNodeRunInvalidInput: def test_unresolved(self): with pytest.raises(ValueError, match=r"expected one input"): node(one_in_one_out, "unresolved", "ds1").run(None) def test_no_inputs_node_error(self, mocked_dataset): with pytest.raises(ValueError, match=r"expected no inputs"): node(lambda: 1, None, "A").run(dict(unexpected=mocked_dataset)) def test_one_input_error(self, mocked_dataset): pattern = r"expected one input named 'ds1', but got the " pattern += r"following 1 input$s$ instead: \['arg'\]" with pytest.raises(ValueError, match=pattern): node(one_in_dict_out, "ds1", dict(ret="B", ans="C")).run( dict(arg=mocked_dataset) ) def test_run_diff_size_lists(self, mocked_dataset): pattern = r"expected 2 input$s$ \['ds1', 'ds2'\], but " pattern += r"got the following 1 input$s$ instead." with pytest.raises(ValueError, match=pattern): node(two_in_first_out, ["ds1", "ds2"], "A").run(dict(ds1=mocked_dataset)) def test_run_diff_size_list_dict(self, mocked_dataset): pattern = r"expected 1 input$s$ \['ds1'\], but got the " pattern += r"following 2 input$s$ instead: \['ds1', 'ds2'\]\." with pytest.raises(ValueError, match=pattern): node(one_in_one_out, ["ds1"], "A").run(dict(ds1=mocked_dataset, ds2=2)) def test_run_list_dict_unavailable(self, mocked_dataset): pattern = r"expected 1 input$s$ \['ds1'\], but got the " pattern += r"following 1 input$s$ instead: \['ds2'\]\." with pytest.raises(ValueError, match=pattern): node(one_in_one_out, ["ds1"], "A").run(dict(ds2=mocked_dataset)) def test_run_dict_unavailable(self, mocked_dataset): pattern = r"expected 1 input$s$ \['ds1'\], but got the " pattern += r"following 1 input$s$ instead: \['ds2'\]\." with pytest.raises(ValueError, match=pattern): node(one_in_one_out, dict(arg="ds1"), "A").run(dict(ds2=mocked_dataset)) def test_run_dict_diff_size(self, mocked_dataset): pattern = r"expected 1 input$s$ \['ds1'\], but got the " pattern += r"following 2 input$s$ instead: \['ds1', 'ds2'\]\." with pytest.raises(ValueError, match=pattern): node(one_in_one_out, dict(arg="ds1"), "A").run( dict(ds1=mocked_dataset, ds2=2) ) class TestNodeRunInvalidOutput: def test_miss_matching_output_types(self, mocked_dataset): pattern = r"The node output is a dictionary, whereas the function " pattern += r"output is not\." with pytest.raises(ValueError, match=pattern): node(one_in_one_out, "ds1", dict(a="ds")).run(dict(ds1=mocked_dataset)) def test_miss_matching_output_keys(self, mocked_dataset): pattern = r"The node's output keys {'ret'} do not match " pattern += r"with the returned output's keys" with pytest.raises(ValueError, match=pattern): node(one_in_dict_out, "ds1", dict(ret="B", ans="C")).run( dict(ds1=mocked_dataset) ) def test_node_not_list_output(self, mocked_dataset): pattern = r"The node definition contains a list of outputs " pattern += r"\['B', 'C'\], whereas the node function returned " pattern += r"a `LambdaDataSet`" with pytest.raises(ValueError, match=pattern): node(one_in_one_out, "ds1", ["B", "C"]).run(dict(ds1=mocked_dataset)) def test_node_wrong_num_of_outputs(self, mocker, mocked_dataset): def one_in_two_out(arg): load = mocker.Mock(return_value=42) save = mocker.Mock() return [LambdaDataSet(load, save), LambdaDataSet(load, save)] pattern = r"The node function returned 2 output$s$, whereas " pattern += r"the node definition contains 3 output$s$\." with pytest.raises(ValueError, match=pattern): node(one_in_two_out, "ds1", ["A", "B", "C"]).run(dict(ds1=mocked_dataset))

true

f731b5fbc7cc5db0a0a57a78bcbce234f1e07f3c

2,103

py

Python

beta_rec/recommenders/userKNN.py

mengzaiqiao/TVBR

cdac86a753c41f8f3c55a025be8d88dd305325f5

[ "MIT" ]

126

2020-03-19T02:30:23.000Z

2022-03-15T11:10:46.000Z

beta_rec/recommenders/userKNN.py

mengzaiqiao/TVBR

cdac86a753c41f8f3c55a025be8d88dd305325f5

[ "MIT" ]

174

2020-03-15T17:28:10.000Z

2022-03-15T22:38:51.000Z

beta_rec/recommenders/userKNN.py

mengzaiqiao/TVBR

cdac86a753c41f8f3c55a025be8d88dd305325f5

[ "MIT" ]

38

2020-03-19T00:38:47.000Z

2022-02-24T11:03:12.000Z

import os import time from munch import munchify from ray import tune from ..core.recommender import Recommender from ..models.userKNN import UserKNNEngine from ..utils.monitor import Monitor def tune_train(config): """Train the model with a hyper-parameter tuner (ray). Args: config (dict): All the parameters for the model. """ data = config["data"] train_engine = UserKNN(munchify(config)) result = train_engine.train(data) while train_engine.eval_engine.n_worker > 0: time.sleep(20) tune.report( valid_metric=result["valid_metric"], model_save_dir=result["model_save_dir"], ) class UserKNN(Recommender): """The User-based K Nearest Neighbour Model.""" def __init__(self, config): """Initialize the config of this recommender. Args: config: """ super(UserKNN, self).__init__(config, name="UserKNN") def init_engine(self, data): """Initialize the required parameters for the model. Args: data: the Dataset object. """ self.config["model"]["n_users"] = data.n_users self.config["model"]["n_items"] = data.n_items self.engine = UserKNNEngine(self.config) def train(self, data): """Training the model. Args: data: the Dataset object. Returns: dict: {} """ self.gpu_id, self.config["device_str"] = self.get_device() # Train the model. self.config["model"]["n_users"] = data.n_users self.config["model"]["n_items"] = data.n_items self.monitor = Monitor( log_dir=self.config["system"]["run_dir"], delay=1, gpu_id=self.gpu_id ) self.init_engine(data) print(type(data.train)) print(data.train.head()) self.engine.model.prepare_model(data) self.model_save_dir = os.path.join( self.config["system"]["model_save_dir"], self.config["model"]["save_name"] ) self.config["run_time"] = self.monitor.stop() return "data loaded"

27.311688

86

0.611032

import os import time from munch import munchify from ray import tune from ..core.recommender import Recommender from ..models.userKNN import UserKNNEngine from ..utils.monitor import Monitor def tune_train(config): data = config["data"] train_engine = UserKNN(munchify(config)) result = train_engine.train(data) while train_engine.eval_engine.n_worker > 0: time.sleep(20) tune.report( valid_metric=result["valid_metric"], model_save_dir=result["model_save_dir"], ) class UserKNN(Recommender): def __init__(self, config): super(UserKNN, self).__init__(config, name="UserKNN") def init_engine(self, data): self.config["model"]["n_users"] = data.n_users self.config["model"]["n_items"] = data.n_items self.engine = UserKNNEngine(self.config) def train(self, data): self.gpu_id, self.config["device_str"] = self.get_device() self.config["model"]["n_users"] = data.n_users self.config["model"]["n_items"] = data.n_items self.monitor = Monitor( log_dir=self.config["system"]["run_dir"], delay=1, gpu_id=self.gpu_id ) self.init_engine(data) print(type(data.train)) print(data.train.head()) self.engine.model.prepare_model(data) self.model_save_dir = os.path.join( self.config["system"]["model_save_dir"], self.config["model"]["save_name"] ) self.config["run_time"] = self.monitor.stop() return "data loaded"

true

f731b60bb39975a9d09169527228d3f67c163104

2,647

py

Python

sdk/python/tests/integration/feature_repos/universal/data_sources/redshift.py

yrbahn/feast

b5648f0cb6109594fcc8881237ddd583960d244f

[ "Apache-2.0" ]

null

sdk/python/tests/integration/feature_repos/universal/data_sources/redshift.py

yrbahn/feast

b5648f0cb6109594fcc8881237ddd583960d244f

[ "Apache-2.0" ]

null

sdk/python/tests/integration/feature_repos/universal/data_sources/redshift.py

yrbahn/feast

b5648f0cb6109594fcc8881237ddd583960d244f

[ "Apache-2.0" ]

null

from typing import Dict import pandas as pd from feast import RedshiftSource from feast.data_source import DataSource from feast.infra.offline_stores.redshift import RedshiftOfflineStoreConfig from feast.infra.utils import aws_utils from feast.repo_config import FeastConfigBaseModel from tests.integration.feature_repos.universal.data_source_creator import ( DataSourceCreator, ) class RedshiftDataSourceCreator(DataSourceCreator): tables = [] def __init__(self, project_name: str): super().__init__() self.project_name = project_name self.client = aws_utils.get_redshift_data_client("us-west-2") self.s3 = aws_utils.get_s3_resource("us-west-2") self.offline_store_config = RedshiftOfflineStoreConfig( cluster_id="feast-integration-tests", region="us-west-2", user="admin", database="feast", s3_staging_location="s3://feast-integration-tests/redshift/tests/ingestion", iam_role="arn:aws:iam::402087665549:role/redshift_s3_access_role", ) def create_data_sources( self, destination: str, df: pd.DataFrame, event_timestamp_column="ts", created_timestamp_column="created_ts", field_mapping: Dict[str, str] = None, ) -> DataSource: aws_utils.upload_df_to_redshift( self.client, self.offline_store_config.cluster_id, self.offline_store_config.database, self.offline_store_config.user, self.s3, f"{self.offline_store_config.s3_staging_location}/copy/{destination}.parquet", self.offline_store_config.iam_role, destination, df, ) self.tables.append(destination) return RedshiftSource( table=destination, event_timestamp_column=event_timestamp_column, created_timestamp_column=created_timestamp_column, date_partition_column="", field_mapping=field_mapping or {"ts_1": "ts"}, ) def create_offline_store_config(self) -> FeastConfigBaseModel: return self.offline_store_config def get_prefixed_table_name(self, name: str, suffix: str) -> str: return f"{name}_{suffix}" def teardown(self): for table in self.tables: aws_utils.execute_redshift_statement( self.client, self.offline_store_config.cluster_id, self.offline_store_config.database, self.offline_store_config.user, f"DROP TABLE IF EXISTS {table}", )

33.0875

90

0.656215

from typing import Dict import pandas as pd from feast import RedshiftSource from feast.data_source import DataSource from feast.infra.offline_stores.redshift import RedshiftOfflineStoreConfig from feast.infra.utils import aws_utils from feast.repo_config import FeastConfigBaseModel from tests.integration.feature_repos.universal.data_source_creator import ( DataSourceCreator, ) class RedshiftDataSourceCreator(DataSourceCreator): tables = [] def __init__(self, project_name: str): super().__init__() self.project_name = project_name self.client = aws_utils.get_redshift_data_client("us-west-2") self.s3 = aws_utils.get_s3_resource("us-west-2") self.offline_store_config = RedshiftOfflineStoreConfig( cluster_id="feast-integration-tests", region="us-west-2", user="admin", database="feast", s3_staging_location="s3://feast-integration-tests/redshift/tests/ingestion", iam_role="arn:aws:iam::402087665549:role/redshift_s3_access_role", ) def create_data_sources( self, destination: str, df: pd.DataFrame, event_timestamp_column="ts", created_timestamp_column="created_ts", field_mapping: Dict[str, str] = None, ) -> DataSource: aws_utils.upload_df_to_redshift( self.client, self.offline_store_config.cluster_id, self.offline_store_config.database, self.offline_store_config.user, self.s3, f"{self.offline_store_config.s3_staging_location}/copy/{destination}.parquet", self.offline_store_config.iam_role, destination, df, ) self.tables.append(destination) return RedshiftSource( table=destination, event_timestamp_column=event_timestamp_column, created_timestamp_column=created_timestamp_column, date_partition_column="", field_mapping=field_mapping or {"ts_1": "ts"}, ) def create_offline_store_config(self) -> FeastConfigBaseModel: return self.offline_store_config def get_prefixed_table_name(self, name: str, suffix: str) -> str: return f"{name}_{suffix}" def teardown(self): for table in self.tables: aws_utils.execute_redshift_statement( self.client, self.offline_store_config.cluster_id, self.offline_store_config.database, self.offline_store_config.user, f"DROP TABLE IF EXISTS {table}", )

true

f731b61e890831d1cd4fbf37958278bb584384cb

2,988

py

Python

setup.py

NeilBotelho/pip

d01bfcfaa13a4f06fa0ce61fa18cf06012f2e78f

[ "MIT" ]

null

setup.py

NeilBotelho/pip

d01bfcfaa13a4f06fa0ce61fa18cf06012f2e78f

[ "MIT" ]

1

2021-10-04T12:25:25.000Z

2021-10-05T07:30:54.000Z

setup.py

NeilBotelho/pip

d01bfcfaa13a4f06fa0ce61fa18cf06012f2e78f

[ "MIT" ]

1

2020-06-01T19:13:16.000Z

# The following comment should be removed at some point in the future. # mypy: disallow-untyped-defs=False import codecs import os import sys from setuptools import find_packages, setup def read(rel_path): here = os.path.abspath(os.path.dirname(__file__)) # intentionally *not* adding an encoding option to open, See: # https://github.com/pypa/virtualenv/issues/201#issuecomment-3145690 with codecs.open(os.path.join(here, rel_path), 'r') as fp: return fp.read() def get_version(rel_path): for line in read(rel_path).splitlines(): if line.startswith('__version__'): # __version__ = "0.9" delim = '"' if '"' in line else "'" return line.split(delim)[1] else: raise RuntimeError("Unable to find version string.") long_description = read('README.rst') setup( name="pip", version=get_version("src/pip/__init__.py"), description="The PyPA recommended tool for installing Python packages.", long_description=long_description, license='MIT', classifiers=[ "Development Status :: 5 - Production/Stable", "Intended Audience :: Developers", "License :: OSI Approved :: MIT License", "Topic :: Software Development :: Build Tools", "Programming Language :: Python", "Programming Language :: Python :: 2", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Programming Language :: Python :: 3.8", "Programming Language :: Python :: Implementation :: CPython", "Programming Language :: Python :: Implementation :: PyPy", ], url='https://pip.pypa.io/', keywords='distutils easy_install egg setuptools wheel virtualenv', project_urls={ "Documentation": "https://pip.pypa.io", "Source": "https://github.com/pypa/pip", "Changelog": "https://pip.pypa.io/en/stable/news/", }, author='The pip developers', author_email='distutils-sig@python.org', package_dir={"": "src"}, packages=find_packages( where="src", exclude=["contrib", "docs", "tests*", "tasks"], ), package_data={ "pip._vendor": ["vendor.txt"], "pip._vendor.certifi": ["*.pem"], "pip._vendor.requests": ["*.pem"], "pip._vendor.distlib._backport": ["sysconfig.cfg"], "pip._vendor.distlib": ["t32.exe", "t64.exe", "w32.exe", "w64.exe"], }, entry_points={ "console_scripts": [ "pip=pip._internal.cli.main:main", "pip{}=pip._internal.cli.main:main".format(sys.version_info[0]), "pip{}.{}=pip._internal.cli.main:main".format( *sys.version_info[:2] ), ], }, zip_safe=False, python_requires='>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*', )

33.2

76

0.598728

import codecs import os import sys from setuptools import find_packages, setup def read(rel_path): here = os.path.abspath(os.path.dirname(__file__)) (os.path.join(here, rel_path), 'r') as fp: return fp.read() def get_version(rel_path): for line in read(rel_path).splitlines(): if line.startswith('__version__'): delim = '"' if '"' in line else "'" return line.split(delim)[1] else: raise RuntimeError("Unable to find version string.") long_description = read('README.rst') setup( name="pip", version=get_version("src/pip/__init__.py"), description="The PyPA recommended tool for installing Python packages.", long_description=long_description, license='MIT', classifiers=[ "Development Status :: 5 - Production/Stable", "Intended Audience :: Developers", "License :: OSI Approved :: MIT License", "Topic :: Software Development :: Build Tools", "Programming Language :: Python", "Programming Language :: Python :: 2", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Programming Language :: Python :: 3.8", "Programming Language :: Python :: Implementation :: CPython", "Programming Language :: Python :: Implementation :: PyPy", ], url='https://pip.pypa.io/', keywords='distutils easy_install egg setuptools wheel virtualenv', project_urls={ "Documentation": "https://pip.pypa.io", "Source": "https://github.com/pypa/pip", "Changelog": "https://pip.pypa.io/en/stable/news/", }, author='The pip developers', author_email='distutils-sig@python.org', package_dir={"": "src"}, packages=find_packages( where="src", exclude=["contrib", "docs", "tests*", "tasks"], ), package_data={ "pip._vendor": ["vendor.txt"], "pip._vendor.certifi": ["*.pem"], "pip._vendor.requests": ["*.pem"], "pip._vendor.distlib._backport": ["sysconfig.cfg"], "pip._vendor.distlib": ["t32.exe", "t64.exe", "w32.exe", "w64.exe"], }, entry_points={ "console_scripts": [ "pip=pip._internal.cli.main:main", "pip{}=pip._internal.cli.main:main".format(sys.version_info[0]), "pip{}.{}=pip._internal.cli.main:main".format( *sys.version_info[:2] ), ], }, zip_safe=False, python_requires='>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*', )

true

f731b631d25327869866e36afed8e0d2ed3b20d4

184

py

Python

domain/message.py

wazatoki/IotLoggerClientSample

e69f0f5f4caa3714041f4d5e8a16cde03bbbd912

[ "MIT" ]

null

domain/message.py

wazatoki/IotLoggerClientSample

e69f0f5f4caa3714041f4d5e8a16cde03bbbd912

[ "MIT" ]

2

2021-04-06T18:18:36.000Z

2021-06-02T02:26:19.000Z

domain/message.py

wazatoki/IotLoggerClientSample

e69f0f5f4caa3714041f4d5e8a16cde03bbbd912

[ "MIT" ]

null

class message_data: device_id = "" message = "" def get_Data(self): return { "deviceID": self.device_id, "message": self.message }

18.4

39

0.505435

class message_data: device_id = "" message = "" def get_Data(self): return { "deviceID": self.device_id, "message": self.message }

true

f731b70415e7f66bf51834ee97e333db6e5dc6c6

52,857

py

Python

Lib/test/test_array.py

chexca/cpython

cfc6ce4d40f2f01314b7e283fb972a7bb3ed3faa

[ "CNRI-Python-GPL-Compatible" ]

1,318

2019-07-11T10:34:39.000Z

2022-03-29T15:05:19.000Z

Lib/test/test_array.py

chexca/cpython

cfc6ce4d40f2f01314b7e283fb972a7bb3ed3faa

[ "CNRI-Python-GPL-Compatible" ]

387

2020-12-15T14:54:04.000Z

2022-03-31T07:00:21.000Z

Lib/test/test_array.py

chexca/cpython

cfc6ce4d40f2f01314b7e283fb972a7bb3ed3faa

[ "CNRI-Python-GPL-Compatible" ]

66

2019-11-11T15:33:12.000Z

2022-03-01T07:55:55.000Z

"""Test the arraymodule. Roger E. Masse """ import unittest from test import support from test.support import _2G import weakref import pickle import operator import struct import sys import array from array import _array_reconstructor as array_reconstructor sizeof_wchar = array.array('u').itemsize class ArraySubclass(array.array): pass class ArraySubclassWithKwargs(array.array): def __init__(self, typecode, newarg=None): array.array.__init__(self) typecodes = 'ubBhHiIlLfdqQ' class MiscTest(unittest.TestCase): def test_bad_constructor(self): self.assertRaises(TypeError, array.array) self.assertRaises(TypeError, array.array, spam=42) self.assertRaises(TypeError, array.array, 'xx') self.assertRaises(ValueError, array.array, 'x') def test_empty(self): # Exercise code for handling zero-length arrays a = array.array('B') a[:] = a self.assertEqual(len(a), 0) self.assertEqual(len(a + a), 0) self.assertEqual(len(a * 3), 0) a += a self.assertEqual(len(a), 0) # Machine format codes. # # Search for "enum machine_format_code" in Modules/arraymodule.c to get the # authoritative values. UNKNOWN_FORMAT = -1 UNSIGNED_INT8 = 0 SIGNED_INT8 = 1 UNSIGNED_INT16_LE = 2 UNSIGNED_INT16_BE = 3 SIGNED_INT16_LE = 4 SIGNED_INT16_BE = 5 UNSIGNED_INT32_LE = 6 UNSIGNED_INT32_BE = 7 SIGNED_INT32_LE = 8 SIGNED_INT32_BE = 9 UNSIGNED_INT64_LE = 10 UNSIGNED_INT64_BE = 11 SIGNED_INT64_LE = 12 SIGNED_INT64_BE = 13 IEEE_754_FLOAT_LE = 14 IEEE_754_FLOAT_BE = 15 IEEE_754_DOUBLE_LE = 16 IEEE_754_DOUBLE_BE = 17 UTF16_LE = 18 UTF16_BE = 19 UTF32_LE = 20 UTF32_BE = 21 class ArrayReconstructorTest(unittest.TestCase): def test_error(self): self.assertRaises(TypeError, array_reconstructor, "", "b", 0, b"") self.assertRaises(TypeError, array_reconstructor, str, "b", 0, b"") self.assertRaises(TypeError, array_reconstructor, array.array, "b", '', b"") self.assertRaises(TypeError, array_reconstructor, array.array, "b", 0, "") self.assertRaises(ValueError, array_reconstructor, array.array, "?", 0, b"") self.assertRaises(ValueError, array_reconstructor, array.array, "b", UNKNOWN_FORMAT, b"") self.assertRaises(ValueError, array_reconstructor, array.array, "b", 22, b"") self.assertRaises(ValueError, array_reconstructor, array.array, "d", 16, b"a") def test_numbers(self): testcases = ( (['B', 'H', 'I', 'L'], UNSIGNED_INT8, '=BBBB', [0x80, 0x7f, 0, 0xff]), (['b', 'h', 'i', 'l'], SIGNED_INT8, '=bbb', [-0x80, 0x7f, 0]), (['H', 'I', 'L'], UNSIGNED_INT16_LE, '<HHHH', [0x8000, 0x7fff, 0, 0xffff]), (['H', 'I', 'L'], UNSIGNED_INT16_BE, '>HHHH', [0x8000, 0x7fff, 0, 0xffff]), (['h', 'i', 'l'], SIGNED_INT16_LE, '<hhh', [-0x8000, 0x7fff, 0]), (['h', 'i', 'l'], SIGNED_INT16_BE, '>hhh', [-0x8000, 0x7fff, 0]), (['I', 'L'], UNSIGNED_INT32_LE, '<IIII', [1<<31, (1<<31)-1, 0, (1<<32)-1]), (['I', 'L'], UNSIGNED_INT32_BE, '>IIII', [1<<31, (1<<31)-1, 0, (1<<32)-1]), (['i', 'l'], SIGNED_INT32_LE, '<iii', [-1<<31, (1<<31)-1, 0]), (['i', 'l'], SIGNED_INT32_BE, '>iii', [-1<<31, (1<<31)-1, 0]), (['L'], UNSIGNED_INT64_LE, '<QQQQ', [1<<31, (1<<31)-1, 0, (1<<32)-1]), (['L'], UNSIGNED_INT64_BE, '>QQQQ', [1<<31, (1<<31)-1, 0, (1<<32)-1]), (['l'], SIGNED_INT64_LE, '<qqq', [-1<<31, (1<<31)-1, 0]), (['l'], SIGNED_INT64_BE, '>qqq', [-1<<31, (1<<31)-1, 0]), # The following tests for INT64 will raise an OverflowError # when run on a 32-bit machine. The tests are simply skipped # in that case. (['L'], UNSIGNED_INT64_LE, '<QQQQ', [1<<63, (1<<63)-1, 0, (1<<64)-1]), (['L'], UNSIGNED_INT64_BE, '>QQQQ', [1<<63, (1<<63)-1, 0, (1<<64)-1]), (['l'], SIGNED_INT64_LE, '<qqq', [-1<<63, (1<<63)-1, 0]), (['l'], SIGNED_INT64_BE, '>qqq', [-1<<63, (1<<63)-1, 0]), (['f'], IEEE_754_FLOAT_LE, '<ffff', [16711938.0, float('inf'), float('-inf'), -0.0]), (['f'], IEEE_754_FLOAT_BE, '>ffff', [16711938.0, float('inf'), float('-inf'), -0.0]), (['d'], IEEE_754_DOUBLE_LE, '<dddd', [9006104071832581.0, float('inf'), float('-inf'), -0.0]), (['d'], IEEE_754_DOUBLE_BE, '>dddd', [9006104071832581.0, float('inf'), float('-inf'), -0.0]) ) for testcase in testcases: valid_typecodes, mformat_code, struct_fmt, values = testcase arraystr = struct.pack(struct_fmt, *values) for typecode in valid_typecodes: try: a = array.array(typecode, values) except OverflowError: continue # Skip this test case. b = array_reconstructor( array.array, typecode, mformat_code, arraystr) self.assertEqual(a, b, msg="{0!r} != {1!r}; testcase={2!r}".format(a, b, testcase)) def test_unicode(self): teststr = "Bonne Journ\xe9e \U0002030a\U00020347" testcases = ( (UTF16_LE, "UTF-16-LE"), (UTF16_BE, "UTF-16-BE"), (UTF32_LE, "UTF-32-LE"), (UTF32_BE, "UTF-32-BE") ) for testcase in testcases: mformat_code, encoding = testcase a = array.array('u', teststr) b = array_reconstructor( array.array, 'u', mformat_code, teststr.encode(encoding)) self.assertEqual(a, b, msg="{0!r} != {1!r}; testcase={2!r}".format(a, b, testcase)) class BaseTest: # Required class attributes (provided by subclasses # typecode: the typecode to test # example: an initializer usable in the constructor for this type # smallerexample: the same length as example, but smaller # biggerexample: the same length as example, but bigger # outside: An entry that is not in example # minitemsize: the minimum guaranteed itemsize def assertEntryEqual(self, entry1, entry2): self.assertEqual(entry1, entry2) def badtypecode(self): # Return a typecode that is different from our own return typecodes[(typecodes.index(self.typecode)+1) % len(typecodes)] def test_constructor(self): a = array.array(self.typecode) self.assertEqual(a.typecode, self.typecode) self.assertGreaterEqual(a.itemsize, self.minitemsize) self.assertRaises(TypeError, array.array, self.typecode, None) def test_len(self): a = array.array(self.typecode) a.append(self.example[0]) self.assertEqual(len(a), 1) a = array.array(self.typecode, self.example) self.assertEqual(len(a), len(self.example)) def test_buffer_info(self): a = array.array(self.typecode, self.example) self.assertRaises(TypeError, a.buffer_info, 42) bi = a.buffer_info() self.assertIsInstance(bi, tuple) self.assertEqual(len(bi), 2) self.assertIsInstance(bi[0], int) self.assertIsInstance(bi[1], int) self.assertEqual(bi[1], len(a)) def test_byteswap(self): if self.typecode == 'u': example = '\U00100100' else: example = self.example a = array.array(self.typecode, example) self.assertRaises(TypeError, a.byteswap, 42) if a.itemsize in (1, 2, 4, 8): b = array.array(self.typecode, example) b.byteswap() if a.itemsize==1: self.assertEqual(a, b) else: self.assertNotEqual(a, b) b.byteswap() self.assertEqual(a, b) def test_copy(self): import copy a = array.array(self.typecode, self.example) b = copy.copy(a) self.assertNotEqual(id(a), id(b)) self.assertEqual(a, b) def test_deepcopy(self): import copy a = array.array(self.typecode, self.example) b = copy.deepcopy(a) self.assertNotEqual(id(a), id(b)) self.assertEqual(a, b) def test_reduce_ex(self): a = array.array(self.typecode, self.example) for protocol in range(3): self.assertIs(a.__reduce_ex__(protocol)[0], array.array) for protocol in range(3, pickle.HIGHEST_PROTOCOL + 1): self.assertIs(a.__reduce_ex__(protocol)[0], array_reconstructor) def test_pickle(self): for protocol in range(pickle.HIGHEST_PROTOCOL + 1): a = array.array(self.typecode, self.example) b = pickle.loads(pickle.dumps(a, protocol)) self.assertNotEqual(id(a), id(b)) self.assertEqual(a, b) a = ArraySubclass(self.typecode, self.example) a.x = 10 b = pickle.loads(pickle.dumps(a, protocol)) self.assertNotEqual(id(a), id(b)) self.assertEqual(a, b) self.assertEqual(a.x, b.x) self.assertEqual(type(a), type(b)) def test_pickle_for_empty_array(self): for protocol in range(pickle.HIGHEST_PROTOCOL + 1): a = array.array(self.typecode) b = pickle.loads(pickle.dumps(a, protocol)) self.assertNotEqual(id(a), id(b)) self.assertEqual(a, b) a = ArraySubclass(self.typecode) a.x = 10 b = pickle.loads(pickle.dumps(a, protocol)) self.assertNotEqual(id(a), id(b)) self.assertEqual(a, b) self.assertEqual(a.x, b.x) self.assertEqual(type(a), type(b)) def test_iterator_pickle(self): orig = array.array(self.typecode, self.example) data = list(orig) data2 = data[::-1] for proto in range(pickle.HIGHEST_PROTOCOL + 1): # initial iterator itorig = iter(orig) d = pickle.dumps((itorig, orig), proto) it, a = pickle.loads(d) a.fromlist(data2) self.assertEqual(type(it), type(itorig)) self.assertEqual(list(it), data + data2) # running iterator next(itorig) d = pickle.dumps((itorig, orig), proto) it, a = pickle.loads(d) a.fromlist(data2) self.assertEqual(type(it), type(itorig)) self.assertEqual(list(it), data[1:] + data2) # empty iterator for i in range(1, len(data)): next(itorig) d = pickle.dumps((itorig, orig), proto) it, a = pickle.loads(d) a.fromlist(data2) self.assertEqual(type(it), type(itorig)) self.assertEqual(list(it), data2) # exhausted iterator self.assertRaises(StopIteration, next, itorig) d = pickle.dumps((itorig, orig), proto) it, a = pickle.loads(d) a.fromlist(data2) self.assertEqual(list(it), []) def test_exhausted_iterator(self): a = array.array(self.typecode, self.example) self.assertEqual(list(a), list(self.example)) exhit = iter(a) empit = iter(a) for x in exhit: # exhaust the iterator next(empit) # not exhausted a.append(self.outside) self.assertEqual(list(exhit), []) self.assertEqual(list(empit), [self.outside]) self.assertEqual(list(a), list(self.example) + [self.outside]) def test_insert(self): a = array.array(self.typecode, self.example) a.insert(0, self.example[0]) self.assertEqual(len(a), 1+len(self.example)) self.assertEqual(a[0], a[1]) self.assertRaises(TypeError, a.insert) self.assertRaises(TypeError, a.insert, None) self.assertRaises(TypeError, a.insert, 0, None) a = array.array(self.typecode, self.example) a.insert(-1, self.example[0]) self.assertEqual( a, array.array( self.typecode, self.example[:-1] + self.example[:1] + self.example[-1:] ) ) a = array.array(self.typecode, self.example) a.insert(-1000, self.example[0]) self.assertEqual( a, array.array(self.typecode, self.example[:1] + self.example) ) a = array.array(self.typecode, self.example) a.insert(1000, self.example[0]) self.assertEqual( a, array.array(self.typecode, self.example + self.example[:1]) ) def test_tofromfile(self): a = array.array(self.typecode, 2*self.example) self.assertRaises(TypeError, a.tofile) support.unlink(support.TESTFN) f = open(support.TESTFN, 'wb') try: a.tofile(f) f.close() b = array.array(self.typecode) f = open(support.TESTFN, 'rb') self.assertRaises(TypeError, b.fromfile) b.fromfile(f, len(self.example)) self.assertEqual(b, array.array(self.typecode, self.example)) self.assertNotEqual(a, b) self.assertRaises(EOFError, b.fromfile, f, len(self.example)+1) self.assertEqual(a, b) f.close() finally: if not f.closed: f.close() support.unlink(support.TESTFN) def test_fromfile_ioerror(self): # Issue #5395: Check if fromfile raises a proper OSError # instead of EOFError. a = array.array(self.typecode) f = open(support.TESTFN, 'wb') try: self.assertRaises(OSError, a.fromfile, f, len(self.example)) finally: f.close() support.unlink(support.TESTFN) def test_filewrite(self): a = array.array(self.typecode, 2*self.example) f = open(support.TESTFN, 'wb') try: f.write(a) f.close() b = array.array(self.typecode) f = open(support.TESTFN, 'rb') b.fromfile(f, len(self.example)) self.assertEqual(b, array.array(self.typecode, self.example)) self.assertNotEqual(a, b) b.fromfile(f, len(self.example)) self.assertEqual(a, b) f.close() finally: if not f.closed: f.close() support.unlink(support.TESTFN) def test_tofromlist(self): a = array.array(self.typecode, 2*self.example) b = array.array(self.typecode) self.assertRaises(TypeError, a.tolist, 42) self.assertRaises(TypeError, b.fromlist) self.assertRaises(TypeError, b.fromlist, 42) self.assertRaises(TypeError, b.fromlist, [None]) b.fromlist(a.tolist()) self.assertEqual(a, b) def test_tofrombytes(self): a = array.array(self.typecode, 2*self.example) b = array.array(self.typecode) self.assertRaises(TypeError, a.tobytes, 42) self.assertRaises(TypeError, b.frombytes) self.assertRaises(TypeError, b.frombytes, 42) b.frombytes(a.tobytes()) c = array.array(self.typecode, bytearray(a.tobytes())) self.assertEqual(a, b) self.assertEqual(a, c) if a.itemsize>1: self.assertRaises(ValueError, b.frombytes, b"x") def test_fromarray(self): a = array.array(self.typecode, self.example) b = array.array(self.typecode, a) self.assertEqual(a, b) def test_repr(self): a = array.array(self.typecode, 2*self.example) self.assertEqual(a, eval(repr(a), {"array": array.array})) a = array.array(self.typecode) self.assertEqual(repr(a), "array('%s')" % self.typecode) def test_str(self): a = array.array(self.typecode, 2*self.example) str(a) def test_cmp(self): a = array.array(self.typecode, self.example) self.assertIs(a == 42, False) self.assertIs(a != 42, True) self.assertIs(a == a, True) self.assertIs(a != a, False) self.assertIs(a < a, False) self.assertIs(a <= a, True) self.assertIs(a > a, False) self.assertIs(a >= a, True) al = array.array(self.typecode, self.smallerexample) ab = array.array(self.typecode, self.biggerexample) self.assertIs(a == 2*a, False) self.assertIs(a != 2*a, True) self.assertIs(a < 2*a, True) self.assertIs(a <= 2*a, True) self.assertIs(a > 2*a, False) self.assertIs(a >= 2*a, False) self.assertIs(a == al, False) self.assertIs(a != al, True) self.assertIs(a < al, False) self.assertIs(a <= al, False) self.assertIs(a > al, True) self.assertIs(a >= al, True) self.assertIs(a == ab, False) self.assertIs(a != ab, True) self.assertIs(a < ab, True) self.assertIs(a <= ab, True) self.assertIs(a > ab, False) self.assertIs(a >= ab, False) def test_add(self): a = array.array(self.typecode, self.example) \ + array.array(self.typecode, self.example[::-1]) self.assertEqual( a, array.array(self.typecode, self.example + self.example[::-1]) ) b = array.array(self.badtypecode()) self.assertRaises(TypeError, a.__add__, b) self.assertRaises(TypeError, a.__add__, "bad") def test_iadd(self): a = array.array(self.typecode, self.example[::-1]) b = a a += array.array(self.typecode, 2*self.example) self.assertIs(a, b) self.assertEqual( a, array.array(self.typecode, self.example[::-1]+2*self.example) ) a = array.array(self.typecode, self.example) a += a self.assertEqual( a, array.array(self.typecode, self.example + self.example) ) b = array.array(self.badtypecode()) self.assertRaises(TypeError, a.__add__, b) self.assertRaises(TypeError, a.__iadd__, "bad") def test_mul(self): a = 5*array.array(self.typecode, self.example) self.assertEqual( a, array.array(self.typecode, 5*self.example) ) a = array.array(self.typecode, self.example)*5 self.assertEqual( a, array.array(self.typecode, self.example*5) ) a = 0*array.array(self.typecode, self.example) self.assertEqual( a, array.array(self.typecode) ) a = (-1)*array.array(self.typecode, self.example) self.assertEqual( a, array.array(self.typecode) ) a = 5 * array.array(self.typecode, self.example[:1]) self.assertEqual( a, array.array(self.typecode, [a[0]] * 5) ) self.assertRaises(TypeError, a.__mul__, "bad") def test_imul(self): a = array.array(self.typecode, self.example) b = a a *= 5 self.assertIs(a, b) self.assertEqual( a, array.array(self.typecode, 5*self.example) ) a *= 0 self.assertIs(a, b) self.assertEqual(a, array.array(self.typecode)) a *= 1000 self.assertIs(a, b) self.assertEqual(a, array.array(self.typecode)) a *= -1 self.assertIs(a, b) self.assertEqual(a, array.array(self.typecode)) a = array.array(self.typecode, self.example) a *= -1 self.assertEqual(a, array.array(self.typecode)) self.assertRaises(TypeError, a.__imul__, "bad") def test_getitem(self): a = array.array(self.typecode, self.example) self.assertEntryEqual(a[0], self.example[0]) self.assertEntryEqual(a[0], self.example[0]) self.assertEntryEqual(a[-1], self.example[-1]) self.assertEntryEqual(a[-1], self.example[-1]) self.assertEntryEqual(a[len(self.example)-1], self.example[-1]) self.assertEntryEqual(a[-len(self.example)], self.example[0]) self.assertRaises(TypeError, a.__getitem__) self.assertRaises(IndexError, a.__getitem__, len(self.example)) self.assertRaises(IndexError, a.__getitem__, -len(self.example)-1) def test_setitem(self): a = array.array(self.typecode, self.example) a[0] = a[-1] self.assertEntryEqual(a[0], a[-1]) a = array.array(self.typecode, self.example) a[0] = a[-1] self.assertEntryEqual(a[0], a[-1]) a = array.array(self.typecode, self.example) a[-1] = a[0] self.assertEntryEqual(a[0], a[-1]) a = array.array(self.typecode, self.example) a[-1] = a[0] self.assertEntryEqual(a[0], a[-1]) a = array.array(self.typecode, self.example) a[len(self.example)-1] = a[0] self.assertEntryEqual(a[0], a[-1]) a = array.array(self.typecode, self.example) a[-len(self.example)] = a[-1] self.assertEntryEqual(a[0], a[-1]) self.assertRaises(TypeError, a.__setitem__) self.assertRaises(TypeError, a.__setitem__, None) self.assertRaises(TypeError, a.__setitem__, 0, None) self.assertRaises( IndexError, a.__setitem__, len(self.example), self.example[0] ) self.assertRaises( IndexError, a.__setitem__, -len(self.example)-1, self.example[0] ) def test_delitem(self): a = array.array(self.typecode, self.example) del a[0] self.assertEqual( a, array.array(self.typecode, self.example[1:]) ) a = array.array(self.typecode, self.example) del a[-1] self.assertEqual( a, array.array(self.typecode, self.example[:-1]) ) a = array.array(self.typecode, self.example) del a[len(self.example)-1] self.assertEqual( a, array.array(self.typecode, self.example[:-1]) ) a = array.array(self.typecode, self.example) del a[-len(self.example)] self.assertEqual( a, array.array(self.typecode, self.example[1:]) ) self.assertRaises(TypeError, a.__delitem__) self.assertRaises(TypeError, a.__delitem__, None) self.assertRaises(IndexError, a.__delitem__, len(self.example)) self.assertRaises(IndexError, a.__delitem__, -len(self.example)-1) def test_getslice(self): a = array.array(self.typecode, self.example) self.assertEqual(a[:], a) self.assertEqual( a[1:], array.array(self.typecode, self.example[1:]) ) self.assertEqual( a[:1], array.array(self.typecode, self.example[:1]) ) self.assertEqual( a[:-1], array.array(self.typecode, self.example[:-1]) ) self.assertEqual( a[-1:], array.array(self.typecode, self.example[-1:]) ) self.assertEqual( a[-1:-1], array.array(self.typecode) ) self.assertEqual( a[2:1], array.array(self.typecode) ) self.assertEqual( a[1000:], array.array(self.typecode) ) self.assertEqual(a[-1000:], a) self.assertEqual(a[:1000], a) self.assertEqual( a[:-1000], array.array(self.typecode) ) self.assertEqual(a[-1000:1000], a) self.assertEqual( a[2000:1000], array.array(self.typecode) ) def test_extended_getslice(self): # Test extended slicing by comparing with list slicing # (Assumes list conversion works correctly, too) a = array.array(self.typecode, self.example) indices = (0, None, 1, 3, 19, 100, sys.maxsize, -1, -2, -31, -100) for start in indices: for stop in indices: # Everything except the initial 0 (invalid step) for step in indices[1:]: self.assertEqual(list(a[start:stop:step]), list(a)[start:stop:step]) def test_setslice(self): a = array.array(self.typecode, self.example) a[:1] = a self.assertEqual( a, array.array(self.typecode, self.example + self.example[1:]) ) a = array.array(self.typecode, self.example) a[:-1] = a self.assertEqual( a, array.array(self.typecode, self.example + self.example[-1:]) ) a = array.array(self.typecode, self.example) a[-1:] = a self.assertEqual( a, array.array(self.typecode, self.example[:-1] + self.example) ) a = array.array(self.typecode, self.example) a[1:] = a self.assertEqual( a, array.array(self.typecode, self.example[:1] + self.example) ) a = array.array(self.typecode, self.example) a[1:-1] = a self.assertEqual( a, array.array( self.typecode, self.example[:1] + self.example + self.example[-1:] ) ) a = array.array(self.typecode, self.example) a[1000:] = a self.assertEqual( a, array.array(self.typecode, 2*self.example) ) a = array.array(self.typecode, self.example) a[-1000:] = a self.assertEqual( a, array.array(self.typecode, self.example) ) a = array.array(self.typecode, self.example) a[:1000] = a self.assertEqual( a, array.array(self.typecode, self.example) ) a = array.array(self.typecode, self.example) a[:-1000] = a self.assertEqual( a, array.array(self.typecode, 2*self.example) ) a = array.array(self.typecode, self.example) a[1:0] = a self.assertEqual( a, array.array(self.typecode, self.example[:1] + self.example + self.example[1:]) ) a = array.array(self.typecode, self.example) a[2000:1000] = a self.assertEqual( a, array.array(self.typecode, 2*self.example) ) a = array.array(self.typecode, self.example) self.assertRaises(TypeError, a.__setitem__, slice(0, 0), None) self.assertRaises(TypeError, a.__setitem__, slice(0, 1), None) b = array.array(self.badtypecode()) self.assertRaises(TypeError, a.__setitem__, slice(0, 0), b) self.assertRaises(TypeError, a.__setitem__, slice(0, 1), b) def test_extended_set_del_slice(self): indices = (0, None, 1, 3, 19, 100, sys.maxsize, -1, -2, -31, -100) for start in indices: for stop in indices: # Everything except the initial 0 (invalid step) for step in indices[1:]: a = array.array(self.typecode, self.example) L = list(a) # Make sure we have a slice of exactly the right length, # but with (hopefully) different data. data = L[start:stop:step] data.reverse() L[start:stop:step] = data a[start:stop:step] = array.array(self.typecode, data) self.assertEqual(a, array.array(self.typecode, L)) del L[start:stop:step] del a[start:stop:step] self.assertEqual(a, array.array(self.typecode, L)) def test_index(self): example = 2*self.example a = array.array(self.typecode, example) self.assertRaises(TypeError, a.index) for x in example: self.assertEqual(a.index(x), example.index(x)) self.assertRaises(ValueError, a.index, None) self.assertRaises(ValueError, a.index, self.outside) def test_count(self): example = 2*self.example a = array.array(self.typecode, example) self.assertRaises(TypeError, a.count) for x in example: self.assertEqual(a.count(x), example.count(x)) self.assertEqual(a.count(self.outside), 0) self.assertEqual(a.count(None), 0) def test_remove(self): for x in self.example: example = 2*self.example a = array.array(self.typecode, example) pos = example.index(x) example2 = example[:pos] + example[pos+1:] a.remove(x) self.assertEqual(a, array.array(self.typecode, example2)) a = array.array(self.typecode, self.example) self.assertRaises(ValueError, a.remove, self.outside) self.assertRaises(ValueError, a.remove, None) def test_pop(self): a = array.array(self.typecode) self.assertRaises(IndexError, a.pop) a = array.array(self.typecode, 2*self.example) self.assertRaises(TypeError, a.pop, 42, 42) self.assertRaises(TypeError, a.pop, None) self.assertRaises(IndexError, a.pop, len(a)) self.assertRaises(IndexError, a.pop, -len(a)-1) self.assertEntryEqual(a.pop(0), self.example[0]) self.assertEqual( a, array.array(self.typecode, self.example[1:]+self.example) ) self.assertEntryEqual(a.pop(1), self.example[2]) self.assertEqual( a, array.array(self.typecode, self.example[1:2]+self.example[3:]+self.example) ) self.assertEntryEqual(a.pop(0), self.example[1]) self.assertEntryEqual(a.pop(), self.example[-1]) self.assertEqual( a, array.array(self.typecode, self.example[3:]+self.example[:-1]) ) def test_reverse(self): a = array.array(self.typecode, self.example) self.assertRaises(TypeError, a.reverse, 42) a.reverse() self.assertEqual( a, array.array(self.typecode, self.example[::-1]) ) def test_extend(self): a = array.array(self.typecode, self.example) self.assertRaises(TypeError, a.extend) a.extend(array.array(self.typecode, self.example[::-1])) self.assertEqual( a, array.array(self.typecode, self.example+self.example[::-1]) ) a = array.array(self.typecode, self.example) a.extend(a) self.assertEqual( a, array.array(self.typecode, self.example+self.example) ) b = array.array(self.badtypecode()) self.assertRaises(TypeError, a.extend, b) a = array.array(self.typecode, self.example) a.extend(self.example[::-1]) self.assertEqual( a, array.array(self.typecode, self.example+self.example[::-1]) ) def test_constructor_with_iterable_argument(self): a = array.array(self.typecode, iter(self.example)) b = array.array(self.typecode, self.example) self.assertEqual(a, b) # non-iterable argument self.assertRaises(TypeError, array.array, self.typecode, 10) # pass through errors raised in __iter__ class A: def __iter__(self): raise UnicodeError self.assertRaises(UnicodeError, array.array, self.typecode, A()) # pass through errors raised in next() def B(): raise UnicodeError yield None self.assertRaises(UnicodeError, array.array, self.typecode, B()) def test_coveritertraverse(self): try: import gc except ImportError: self.skipTest('gc module not available') a = array.array(self.typecode) l = [iter(a)] l.append(l) gc.collect() def test_buffer(self): a = array.array(self.typecode, self.example) m = memoryview(a) expected = m.tobytes() self.assertEqual(a.tobytes(), expected) self.assertEqual(a.tobytes()[0], expected[0]) # Resizing is forbidden when there are buffer exports. # For issue 4509, we also check after each error that # the array was not modified. self.assertRaises(BufferError, a.append, a[0]) self.assertEqual(m.tobytes(), expected) self.assertRaises(BufferError, a.extend, a[0:1]) self.assertEqual(m.tobytes(), expected) self.assertRaises(BufferError, a.remove, a[0]) self.assertEqual(m.tobytes(), expected) self.assertRaises(BufferError, a.pop, 0) self.assertEqual(m.tobytes(), expected) self.assertRaises(BufferError, a.fromlist, a.tolist()) self.assertEqual(m.tobytes(), expected) self.assertRaises(BufferError, a.frombytes, a.tobytes()) self.assertEqual(m.tobytes(), expected) if self.typecode == 'u': self.assertRaises(BufferError, a.fromunicode, a.tounicode()) self.assertEqual(m.tobytes(), expected) self.assertRaises(BufferError, operator.imul, a, 2) self.assertEqual(m.tobytes(), expected) self.assertRaises(BufferError, operator.imul, a, 0) self.assertEqual(m.tobytes(), expected) self.assertRaises(BufferError, operator.setitem, a, slice(0, 0), a) self.assertEqual(m.tobytes(), expected) self.assertRaises(BufferError, operator.delitem, a, 0) self.assertEqual(m.tobytes(), expected) self.assertRaises(BufferError, operator.delitem, a, slice(0, 1)) self.assertEqual(m.tobytes(), expected) def test_weakref(self): s = array.array(self.typecode, self.example) p = weakref.proxy(s) self.assertEqual(p.tobytes(), s.tobytes()) s = None self.assertRaises(ReferenceError, len, p) @unittest.skipUnless(hasattr(sys, 'getrefcount'), 'test needs sys.getrefcount()') def test_bug_782369(self): for i in range(10): b = array.array('B', range(64)) rc = sys.getrefcount(10) for i in range(10): b = array.array('B', range(64)) self.assertEqual(rc, sys.getrefcount(10)) def test_subclass_with_kwargs(self): # SF bug #1486663 -- this used to erroneously raise a TypeError ArraySubclassWithKwargs('b', newarg=1) def test_create_from_bytes(self): # XXX This test probably needs to be moved in a subclass or # generalized to use self.typecode. a = array.array('H', b"1234") self.assertEqual(len(a) * a.itemsize, 4) @support.cpython_only def test_sizeof_with_buffer(self): a = array.array(self.typecode, self.example) basesize = support.calcvobjsize('Pn2Pi') buffer_size = a.buffer_info()[1] * a.itemsize support.check_sizeof(self, a, basesize + buffer_size) @support.cpython_only def test_sizeof_without_buffer(self): a = array.array(self.typecode) basesize = support.calcvobjsize('Pn2Pi') support.check_sizeof(self, a, basesize) def test_initialize_with_unicode(self): if self.typecode != 'u': with self.assertRaises(TypeError) as cm: a = array.array(self.typecode, 'foo') self.assertIn("cannot use a str", str(cm.exception)) with self.assertRaises(TypeError) as cm: a = array.array(self.typecode, array.array('u', 'foo')) self.assertIn("cannot use a unicode array", str(cm.exception)) else: a = array.array(self.typecode, "foo") a = array.array(self.typecode, array.array('u', 'foo')) @support.cpython_only def test_obsolete_write_lock(self): from _testcapi import getbuffer_with_null_view a = array.array('B', b"") self.assertRaises(BufferError, getbuffer_with_null_view, a) def test_free_after_iterating(self): support.check_free_after_iterating(self, iter, array.array, (self.typecode,)) support.check_free_after_iterating(self, reversed, array.array, (self.typecode,)) class StringTest(BaseTest): def test_setitem(self): super().test_setitem() a = array.array(self.typecode, self.example) self.assertRaises(TypeError, a.__setitem__, 0, self.example[:2]) class UnicodeTest(StringTest, unittest.TestCase): typecode = 'u' example = '\x01\u263a\x00\ufeff' smallerexample = '\x01\u263a\x00\ufefe' biggerexample = '\x01\u263a\x01\ufeff' outside = str('\x33') minitemsize = 2 def test_unicode(self): self.assertRaises(TypeError, array.array, 'b', 'foo') a = array.array('u', '\xa0\xc2\u1234') a.fromunicode(' ') a.fromunicode('') a.fromunicode('') a.fromunicode('\x11abc\xff\u1234') s = a.tounicode() self.assertEqual(s, '\xa0\xc2\u1234 \x11abc\xff\u1234') self.assertEqual(a.itemsize, sizeof_wchar) s = '\x00="\'a\\b\x80\xff\u0000\u0001\u1234' a = array.array('u', s) self.assertEqual( repr(a), "array('u', '\\x00=\"\\'a\\\\b\\x80\xff\\x00\\x01\u1234')") self.assertRaises(TypeError, a.fromunicode) def test_issue17223(self): # this used to crash if sizeof_wchar == 4: # U+FFFFFFFF is an invalid code point in Unicode 6.0 invalid_str = b'\xff\xff\xff\xff' else: # PyUnicode_FromUnicode() cannot fail with 16-bit wchar_t self.skipTest("specific to 32-bit wchar_t") a = array.array('u', invalid_str) self.assertRaises(ValueError, a.tounicode) self.assertRaises(ValueError, str, a) class NumberTest(BaseTest): def test_extslice(self): a = array.array(self.typecode, range(5)) self.assertEqual(a[::], a) self.assertEqual(a[::2], array.array(self.typecode, [0,2,4])) self.assertEqual(a[1::2], array.array(self.typecode, [1,3])) self.assertEqual(a[::-1], array.array(self.typecode, [4,3,2,1,0])) self.assertEqual(a[::-2], array.array(self.typecode, [4,2,0])) self.assertEqual(a[3::-2], array.array(self.typecode, [3,1])) self.assertEqual(a[-100:100:], a) self.assertEqual(a[100:-100:-1], a[::-1]) self.assertEqual(a[-100:100:2], array.array(self.typecode, [0,2,4])) self.assertEqual(a[1000:2000:2], array.array(self.typecode, [])) self.assertEqual(a[-1000:-2000:-2], array.array(self.typecode, [])) def test_delslice(self): a = array.array(self.typecode, range(5)) del a[::2] self.assertEqual(a, array.array(self.typecode, [1,3])) a = array.array(self.typecode, range(5)) del a[1::2] self.assertEqual(a, array.array(self.typecode, [0,2,4])) a = array.array(self.typecode, range(5)) del a[1::-2] self.assertEqual(a, array.array(self.typecode, [0,2,3,4])) a = array.array(self.typecode, range(10)) del a[::1000] self.assertEqual(a, array.array(self.typecode, [1,2,3,4,5,6,7,8,9])) # test issue7788 a = array.array(self.typecode, range(10)) del a[9::1<<333] def test_assignment(self): a = array.array(self.typecode, range(10)) a[::2] = array.array(self.typecode, [42]*5) self.assertEqual(a, array.array(self.typecode, [42, 1, 42, 3, 42, 5, 42, 7, 42, 9])) a = array.array(self.typecode, range(10)) a[::-4] = array.array(self.typecode, [10]*3) self.assertEqual(a, array.array(self.typecode, [0, 10, 2, 3, 4, 10, 6, 7, 8 ,10])) a = array.array(self.typecode, range(4)) a[::-1] = a self.assertEqual(a, array.array(self.typecode, [3, 2, 1, 0])) a = array.array(self.typecode, range(10)) b = a[:] c = a[:] ins = array.array(self.typecode, range(2)) a[2:3] = ins b[slice(2,3)] = ins c[2:3:] = ins def test_iterationcontains(self): a = array.array(self.typecode, range(10)) self.assertEqual(list(a), list(range(10))) b = array.array(self.typecode, [20]) self.assertEqual(a[-1] in a, True) self.assertEqual(b[0] not in a, True) def check_overflow(self, lower, upper): # method to be used by subclasses # should not overflow assigning lower limit a = array.array(self.typecode, [lower]) a[0] = lower # should overflow assigning less than lower limit self.assertRaises(OverflowError, array.array, self.typecode, [lower-1]) self.assertRaises(OverflowError, a.__setitem__, 0, lower-1) # should not overflow assigning upper limit a = array.array(self.typecode, [upper]) a[0] = upper # should overflow assigning more than upper limit self.assertRaises(OverflowError, array.array, self.typecode, [upper+1]) self.assertRaises(OverflowError, a.__setitem__, 0, upper+1) def test_subclassing(self): typecode = self.typecode class ExaggeratingArray(array.array): __slots__ = ['offset'] def __new__(cls, typecode, data, offset): return array.array.__new__(cls, typecode, data) def __init__(self, typecode, data, offset): self.offset = offset def __getitem__(self, i): return array.array.__getitem__(self, i) + self.offset a = ExaggeratingArray(self.typecode, [3, 6, 7, 11], 4) self.assertEntryEqual(a[0], 7) self.assertRaises(AttributeError, setattr, a, "color", "blue") def test_frombytearray(self): a = array.array('b', range(10)) b = array.array(self.typecode, a) self.assertEqual(a, b) class IntegerNumberTest(NumberTest): def test_type_error(self): a = array.array(self.typecode) a.append(42) with self.assertRaises(TypeError): a.append(42.0) with self.assertRaises(TypeError): a[0] = 42.0 class Intable: def __init__(self, num): self._num = num def __index__(self): return self._num def __int__(self): return self._num def __sub__(self, other): return Intable(int(self) - int(other)) def __add__(self, other): return Intable(int(self) + int(other)) class SignedNumberTest(IntegerNumberTest): example = [-1, 0, 1, 42, 0x7f] smallerexample = [-1, 0, 1, 42, 0x7e] biggerexample = [-1, 0, 1, 43, 0x7f] outside = 23 def test_overflow(self): a = array.array(self.typecode) lower = -1 * int(pow(2, a.itemsize * 8 - 1)) upper = int(pow(2, a.itemsize * 8 - 1)) - 1 self.check_overflow(lower, upper) self.check_overflow(Intable(lower), Intable(upper)) class UnsignedNumberTest(IntegerNumberTest): example = [0, 1, 17, 23, 42, 0xff] smallerexample = [0, 1, 17, 23, 42, 0xfe] biggerexample = [0, 1, 17, 23, 43, 0xff] outside = 0xaa def test_overflow(self): a = array.array(self.typecode) lower = 0 upper = int(pow(2, a.itemsize * 8)) - 1 self.check_overflow(lower, upper) self.check_overflow(Intable(lower), Intable(upper)) def test_bytes_extend(self): s = bytes(self.example) a = array.array(self.typecode, self.example) a.extend(s) self.assertEqual( a, array.array(self.typecode, self.example+self.example) ) a = array.array(self.typecode, self.example) a.extend(bytearray(reversed(s))) self.assertEqual( a, array.array(self.typecode, self.example+self.example[::-1]) ) class ByteTest(SignedNumberTest, unittest.TestCase): typecode = 'b' minitemsize = 1 class UnsignedByteTest(UnsignedNumberTest, unittest.TestCase): typecode = 'B' minitemsize = 1 class ShortTest(SignedNumberTest, unittest.TestCase): typecode = 'h' minitemsize = 2 class UnsignedShortTest(UnsignedNumberTest, unittest.TestCase): typecode = 'H' minitemsize = 2 class IntTest(SignedNumberTest, unittest.TestCase): typecode = 'i' minitemsize = 2 class UnsignedIntTest(UnsignedNumberTest, unittest.TestCase): typecode = 'I' minitemsize = 2 class LongTest(SignedNumberTest, unittest.TestCase): typecode = 'l' minitemsize = 4 class UnsignedLongTest(UnsignedNumberTest, unittest.TestCase): typecode = 'L' minitemsize = 4 class LongLongTest(SignedNumberTest, unittest.TestCase): typecode = 'q' minitemsize = 8 class UnsignedLongLongTest(UnsignedNumberTest, unittest.TestCase): typecode = 'Q' minitemsize = 8 class FPTest(NumberTest): example = [-42.0, 0, 42, 1e5, -1e10] smallerexample = [-42.0, 0, 42, 1e5, -2e10] biggerexample = [-42.0, 0, 42, 1e5, 1e10] outside = 23 def assertEntryEqual(self, entry1, entry2): self.assertAlmostEqual(entry1, entry2) def test_nan(self): a = array.array(self.typecode, [float('nan')]) b = array.array(self.typecode, [float('nan')]) self.assertIs(a != b, True) self.assertIs(a == b, False) self.assertIs(a > b, False) self.assertIs(a >= b, False) self.assertIs(a < b, False) self.assertIs(a <= b, False) def test_byteswap(self): a = array.array(self.typecode, self.example) self.assertRaises(TypeError, a.byteswap, 42) if a.itemsize in (1, 2, 4, 8): b = array.array(self.typecode, self.example) b.byteswap() if a.itemsize==1: self.assertEqual(a, b) else: # On alphas treating the byte swapped bit patters as # floats/doubles results in floating point exceptions # => compare the 8bit string values instead self.assertNotEqual(a.tobytes(), b.tobytes()) b.byteswap() self.assertEqual(a, b) class FloatTest(FPTest, unittest.TestCase): typecode = 'f' minitemsize = 4 class DoubleTest(FPTest, unittest.TestCase): typecode = 'd' minitemsize = 8 def test_alloc_overflow(self): from sys import maxsize a = array.array('d', [-1]*65536) try: a *= maxsize//65536 + 1 except MemoryError: pass else: self.fail("Array of size > maxsize created - MemoryError expected") b = array.array('d', [ 2.71828183, 3.14159265, -1]) try: b * (maxsize//3 + 1) except MemoryError: pass else: self.fail("Array of size > maxsize created - MemoryError expected") class LargeArrayTest(unittest.TestCase): typecode = 'b' def example(self, size): # We assess a base memuse of <=2.125 for constructing this array base = array.array(self.typecode, [0, 1, 2, 3, 4, 5, 6, 7]) * (size // 8) base += array.array(self.typecode, [99]*(size % 8) + [8, 9, 10, 11]) return base @support.bigmemtest(_2G, memuse=2.125) def test_example_data(self, size): example = self.example(size) self.assertEqual(len(example), size+4) @support.bigmemtest(_2G, memuse=2.125) def test_access(self, size): example = self.example(size) self.assertEqual(example[0], 0) self.assertEqual(example[-(size+4)], 0) self.assertEqual(example[size], 8) self.assertEqual(example[-4], 8) self.assertEqual(example[size+3], 11) self.assertEqual(example[-1], 11) @support.bigmemtest(_2G, memuse=2.125+1) def test_slice(self, size): example = self.example(size) self.assertEqual(list(example[:4]), [0, 1, 2, 3]) self.assertEqual(list(example[-4:]), [8, 9, 10, 11]) part = example[1:-1] self.assertEqual(len(part), size+2) self.assertEqual(part[0], 1) self.assertEqual(part[-1], 10) del part part = example[::2] self.assertEqual(len(part), (size+5)//2) self.assertEqual(list(part[:4]), [0, 2, 4, 6]) if size % 2: self.assertEqual(list(part[-2:]), [9, 11]) else: self.assertEqual(list(part[-2:]), [8, 10]) @support.bigmemtest(_2G, memuse=2.125) def test_count(self, size): example = self.example(size) self.assertEqual(example.count(0), size//8) self.assertEqual(example.count(11), 1) @support.bigmemtest(_2G, memuse=2.125) def test_append(self, size): example = self.example(size) example.append(12) self.assertEqual(example[-1], 12) @support.bigmemtest(_2G, memuse=2.125) def test_extend(self, size): example = self.example(size) example.extend(iter([12, 13, 14, 15])) self.assertEqual(len(example), size+8) self.assertEqual(list(example[-8:]), [8, 9, 10, 11, 12, 13, 14, 15]) @support.bigmemtest(_2G, memuse=2.125) def test_frombytes(self, size): example = self.example(size) example.frombytes(b'abcd') self.assertEqual(len(example), size+8) self.assertEqual(list(example[-8:]), [8, 9, 10, 11] + list(b'abcd')) @support.bigmemtest(_2G, memuse=2.125) def test_fromlist(self, size): example = self.example(size) example.fromlist([12, 13, 14, 15]) self.assertEqual(len(example), size+8) self.assertEqual(list(example[-8:]), [8, 9, 10, 11, 12, 13, 14, 15]) @support.bigmemtest(_2G, memuse=2.125) def test_index(self, size): example = self.example(size) self.assertEqual(example.index(0), 0) self.assertEqual(example.index(1), 1) self.assertEqual(example.index(7), 7) self.assertEqual(example.index(11), size+3) @support.bigmemtest(_2G, memuse=2.125) def test_insert(self, size): example = self.example(size) example.insert(0, 12) example.insert(10, 13) example.insert(size+1, 14) self.assertEqual(len(example), size+7) self.assertEqual(example[0], 12) self.assertEqual(example[10], 13) self.assertEqual(example[size+1], 14) @support.bigmemtest(_2G, memuse=2.125) def test_pop(self, size): example = self.example(size) self.assertEqual(example.pop(0), 0) self.assertEqual(example[0], 1) self.assertEqual(example.pop(size+1), 10) self.assertEqual(example[size+1], 11) self.assertEqual(example.pop(1), 2) self.assertEqual(example[1], 3) self.assertEqual(len(example), size+1) self.assertEqual(example.pop(), 11) self.assertEqual(len(example), size) @support.bigmemtest(_2G, memuse=2.125) def test_remove(self, size): example = self.example(size) example.remove(0) self.assertEqual(len(example), size+3) self.assertEqual(example[0], 1) example.remove(10) self.assertEqual(len(example), size+2) self.assertEqual(example[size], 9) self.assertEqual(example[size+1], 11) @support.bigmemtest(_2G, memuse=2.125) def test_reverse(self, size): example = self.example(size) example.reverse() self.assertEqual(len(example), size+4) self.assertEqual(example[0], 11) self.assertEqual(example[3], 8) self.assertEqual(example[-1], 0) example.reverse() self.assertEqual(len(example), size+4) self.assertEqual(list(example[:4]), [0, 1, 2, 3]) self.assertEqual(list(example[-4:]), [8, 9, 10, 11]) # list takes about 9 bytes per element @support.bigmemtest(_2G, memuse=2.125+9) def test_tolist(self, size): example = self.example(size) ls = example.tolist() self.assertEqual(len(ls), len(example)) self.assertEqual(ls[:8], list(example[:8])) self.assertEqual(ls[-8:], list(example[-8:])) if __name__ == "__main__": unittest.main()

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import unittest from test import support from test.support import _2G import weakref import pickle import operator import struct import sys import array from array import _array_reconstructor as array_reconstructor sizeof_wchar = array.array('u').itemsize class ArraySubclass(array.array): pass class ArraySubclassWithKwargs(array.array): def __init__(self, typecode, newarg=None): array.array.__init__(self) typecodes = 'ubBhHiIlLfdqQ' class MiscTest(unittest.TestCase): def test_bad_constructor(self): self.assertRaises(TypeError, array.array) self.assertRaises(TypeError, array.array, spam=42) self.assertRaises(TypeError, array.array, 'xx') self.assertRaises(ValueError, array.array, 'x') def test_empty(self): a = array.array('B') a[:] = a self.assertEqual(len(a), 0) self.assertEqual(len(a + a), 0) self.assertEqual(len(a * 3), 0) a += a self.assertEqual(len(a), 0) UNKNOWN_FORMAT = -1 UNSIGNED_INT8 = 0 SIGNED_INT8 = 1 UNSIGNED_INT16_LE = 2 UNSIGNED_INT16_BE = 3 SIGNED_INT16_LE = 4 SIGNED_INT16_BE = 5 UNSIGNED_INT32_LE = 6 UNSIGNED_INT32_BE = 7 SIGNED_INT32_LE = 8 SIGNED_INT32_BE = 9 UNSIGNED_INT64_LE = 10 UNSIGNED_INT64_BE = 11 SIGNED_INT64_LE = 12 SIGNED_INT64_BE = 13 IEEE_754_FLOAT_LE = 14 IEEE_754_FLOAT_BE = 15 IEEE_754_DOUBLE_LE = 16 IEEE_754_DOUBLE_BE = 17 UTF16_LE = 18 UTF16_BE = 19 UTF32_LE = 20 UTF32_BE = 21 class ArrayReconstructorTest(unittest.TestCase): def test_error(self): self.assertRaises(TypeError, array_reconstructor, "", "b", 0, b"") self.assertRaises(TypeError, array_reconstructor, str, "b", 0, b"") self.assertRaises(TypeError, array_reconstructor, array.array, "b", '', b"") self.assertRaises(TypeError, array_reconstructor, array.array, "b", 0, "") self.assertRaises(ValueError, array_reconstructor, array.array, "?", 0, b"") self.assertRaises(ValueError, array_reconstructor, array.array, "b", UNKNOWN_FORMAT, b"") self.assertRaises(ValueError, array_reconstructor, array.array, "b", 22, b"") self.assertRaises(ValueError, array_reconstructor, array.array, "d", 16, b"a") def test_numbers(self): testcases = ( (['B', 'H', 'I', 'L'], UNSIGNED_INT8, '=BBBB', [0x80, 0x7f, 0, 0xff]), (['b', 'h', 'i', 'l'], SIGNED_INT8, '=bbb', [-0x80, 0x7f, 0]), (['H', 'I', 'L'], UNSIGNED_INT16_LE, '<HHHH', [0x8000, 0x7fff, 0, 0xffff]), (['H', 'I', 'L'], UNSIGNED_INT16_BE, '>HHHH', [0x8000, 0x7fff, 0, 0xffff]), (['h', 'i', 'l'], SIGNED_INT16_LE, '<hhh', [-0x8000, 0x7fff, 0]), (['h', 'i', 'l'], SIGNED_INT16_BE, '>hhh', [-0x8000, 0x7fff, 0]), (['I', 'L'], UNSIGNED_INT32_LE, '<IIII', [1<<31, (1<<31)-1, 0, (1<<32)-1]), (['I', 'L'], UNSIGNED_INT32_BE, '>IIII', [1<<31, (1<<31)-1, 0, (1<<32)-1]), (['i', 'l'], SIGNED_INT32_LE, '<iii', [-1<<31, (1<<31)-1, 0]), (['i', 'l'], SIGNED_INT32_BE, '>iii', [-1<<31, (1<<31)-1, 0]), (['L'], UNSIGNED_INT64_LE, '<QQQQ', [1<<31, (1<<31)-1, 0, (1<<32)-1]), (['L'], UNSIGNED_INT64_BE, '>QQQQ', [1<<31, (1<<31)-1, 0, (1<<32)-1]), (['l'], SIGNED_INT64_LE, '<qqq', [-1<<31, (1<<31)-1, 0]), (['l'], SIGNED_INT64_BE, '>qqq', [-1<<31, (1<<31)-1, 0]), (['L'], UNSIGNED_INT64_LE, '<QQQQ', [1<<63, (1<<63)-1, 0, (1<<64)-1]), (['L'], UNSIGNED_INT64_BE, '>QQQQ', [1<<63, (1<<63)-1, 0, (1<<64)-1]), (['l'], SIGNED_INT64_LE, '<qqq', [-1<<63, (1<<63)-1, 0]), (['l'], SIGNED_INT64_BE, '>qqq', [-1<<63, (1<<63)-1, 0]), (['f'], IEEE_754_FLOAT_LE, '<ffff', [16711938.0, float('inf'), float('-inf'), -0.0]), (['f'], IEEE_754_FLOAT_BE, '>ffff', [16711938.0, float('inf'), float('-inf'), -0.0]), (['d'], IEEE_754_DOUBLE_LE, '<dddd', [9006104071832581.0, float('inf'), float('-inf'), -0.0]), (['d'], IEEE_754_DOUBLE_BE, '>dddd', [9006104071832581.0, float('inf'), float('-inf'), -0.0]) ) for testcase in testcases: valid_typecodes, mformat_code, struct_fmt, values = testcase arraystr = struct.pack(struct_fmt, *values) for typecode in valid_typecodes: try: a = array.array(typecode, values) except OverflowError: continue b = array_reconstructor( array.array, typecode, mformat_code, arraystr) self.assertEqual(a, b, msg="{0!r} != {1!r}; testcase={2!r}".format(a, b, testcase)) def test_unicode(self): teststr = "Bonne Journ\xe9e \U0002030a\U00020347" testcases = ( (UTF16_LE, "UTF-16-LE"), (UTF16_BE, "UTF-16-BE"), (UTF32_LE, "UTF-32-LE"), (UTF32_BE, "UTF-32-BE") ) for testcase in testcases: mformat_code, encoding = testcase a = array.array('u', teststr) b = array_reconstructor( array.array, 'u', mformat_code, teststr.encode(encoding)) self.assertEqual(a, b, msg="{0!r} != {1!r}; testcase={2!r}".format(a, b, testcase)) class BaseTest: def assertEntryEqual(self, entry1, entry2): self.assertEqual(entry1, entry2) def badtypecode(self): return typecodes[(typecodes.index(self.typecode)+1) % len(typecodes)] def test_constructor(self): a = array.array(self.typecode) self.assertEqual(a.typecode, self.typecode) self.assertGreaterEqual(a.itemsize, self.minitemsize) self.assertRaises(TypeError, array.array, self.typecode, None) def test_len(self): a = array.array(self.typecode) a.append(self.example[0]) self.assertEqual(len(a), 1) a = array.array(self.typecode, self.example) self.assertEqual(len(a), len(self.example)) def test_buffer_info(self): a = array.array(self.typecode, self.example) self.assertRaises(TypeError, a.buffer_info, 42) bi = a.buffer_info() self.assertIsInstance(bi, tuple) self.assertEqual(len(bi), 2) self.assertIsInstance(bi[0], int) self.assertIsInstance(bi[1], int) self.assertEqual(bi[1], len(a)) def test_byteswap(self): if self.typecode == 'u': example = '\U00100100' else: example = self.example a = array.array(self.typecode, example) self.assertRaises(TypeError, a.byteswap, 42) if a.itemsize in (1, 2, 4, 8): b = array.array(self.typecode, example) b.byteswap() if a.itemsize==1: self.assertEqual(a, b) else: self.assertNotEqual(a, b) b.byteswap() self.assertEqual(a, b) def test_copy(self): import copy a = array.array(self.typecode, self.example) b = copy.copy(a) self.assertNotEqual(id(a), id(b)) self.assertEqual(a, b) def test_deepcopy(self): import copy a = array.array(self.typecode, self.example) b = copy.deepcopy(a) self.assertNotEqual(id(a), id(b)) self.assertEqual(a, b) def test_reduce_ex(self): a = array.array(self.typecode, self.example) for protocol in range(3): self.assertIs(a.__reduce_ex__(protocol)[0], array.array) for protocol in range(3, pickle.HIGHEST_PROTOCOL + 1): self.assertIs(a.__reduce_ex__(protocol)[0], array_reconstructor) def test_pickle(self): for protocol in range(pickle.HIGHEST_PROTOCOL + 1): a = array.array(self.typecode, self.example) b = pickle.loads(pickle.dumps(a, protocol)) self.assertNotEqual(id(a), id(b)) self.assertEqual(a, b) a = ArraySubclass(self.typecode, self.example) a.x = 10 b = pickle.loads(pickle.dumps(a, protocol)) self.assertNotEqual(id(a), id(b)) self.assertEqual(a, b) self.assertEqual(a.x, b.x) self.assertEqual(type(a), type(b)) def test_pickle_for_empty_array(self): for protocol in range(pickle.HIGHEST_PROTOCOL + 1): a = array.array(self.typecode) b = pickle.loads(pickle.dumps(a, protocol)) self.assertNotEqual(id(a), id(b)) self.assertEqual(a, b) a = ArraySubclass(self.typecode) a.x = 10 b = pickle.loads(pickle.dumps(a, protocol)) self.assertNotEqual(id(a), id(b)) self.assertEqual(a, b) self.assertEqual(a.x, b.x) self.assertEqual(type(a), type(b)) def test_iterator_pickle(self): orig = array.array(self.typecode, self.example) data = list(orig) data2 = data[::-1] for proto in range(pickle.HIGHEST_PROTOCOL + 1): itorig = iter(orig) d = pickle.dumps((itorig, orig), proto) it, a = pickle.loads(d) a.fromlist(data2) self.assertEqual(type(it), type(itorig)) self.assertEqual(list(it), data + data2) next(itorig) d = pickle.dumps((itorig, orig), proto) it, a = pickle.loads(d) a.fromlist(data2) self.assertEqual(type(it), type(itorig)) self.assertEqual(list(it), data[1:] + data2) for i in range(1, len(data)): next(itorig) d = pickle.dumps((itorig, orig), proto) it, a = pickle.loads(d) a.fromlist(data2) self.assertEqual(type(it), type(itorig)) self.assertEqual(list(it), data2) self.assertRaises(StopIteration, next, itorig) d = pickle.dumps((itorig, orig), proto) it, a = pickle.loads(d) a.fromlist(data2) self.assertEqual(list(it), []) def test_exhausted_iterator(self): a = array.array(self.typecode, self.example) self.assertEqual(list(a), list(self.example)) exhit = iter(a) empit = iter(a) for x in exhit: next(empit) a.append(self.outside) self.assertEqual(list(exhit), []) self.assertEqual(list(empit), [self.outside]) self.assertEqual(list(a), list(self.example) + [self.outside]) def test_insert(self): a = array.array(self.typecode, self.example) a.insert(0, self.example[0]) self.assertEqual(len(a), 1+len(self.example)) self.assertEqual(a[0], a[1]) self.assertRaises(TypeError, a.insert) self.assertRaises(TypeError, a.insert, None) self.assertRaises(TypeError, a.insert, 0, None) a = array.array(self.typecode, self.example) a.insert(-1, self.example[0]) self.assertEqual( a, array.array( self.typecode, self.example[:-1] + self.example[:1] + self.example[-1:] ) ) a = array.array(self.typecode, self.example) a.insert(-1000, self.example[0]) self.assertEqual( a, array.array(self.typecode, self.example[:1] + self.example) ) a = array.array(self.typecode, self.example) a.insert(1000, self.example[0]) self.assertEqual( a, array.array(self.typecode, self.example + self.example[:1]) ) def test_tofromfile(self): a = array.array(self.typecode, 2*self.example) self.assertRaises(TypeError, a.tofile) support.unlink(support.TESTFN) f = open(support.TESTFN, 'wb') try: a.tofile(f) f.close() b = array.array(self.typecode) f = open(support.TESTFN, 'rb') self.assertRaises(TypeError, b.fromfile) b.fromfile(f, len(self.example)) self.assertEqual(b, array.array(self.typecode, self.example)) self.assertNotEqual(a, b) self.assertRaises(EOFError, b.fromfile, f, len(self.example)+1) self.assertEqual(a, b) f.close() finally: if not f.closed: f.close() support.unlink(support.TESTFN) def test_fromfile_ioerror(self): f = open(support.TESTFN, 'wb') try: self.assertRaises(OSError, a.fromfile, f, len(self.example)) finally: f.close() support.unlink(support.TESTFN) def test_filewrite(self): a = array.array(self.typecode, 2*self.example) f = open(support.TESTFN, 'wb') try: f.write(a) f.close() b = array.array(self.typecode) f = open(support.TESTFN, 'rb') b.fromfile(f, len(self.example)) self.assertEqual(b, array.array(self.typecode, self.example)) self.assertNotEqual(a, b) b.fromfile(f, len(self.example)) self.assertEqual(a, b) f.close() finally: if not f.closed: f.close() support.unlink(support.TESTFN) def test_tofromlist(self): a = array.array(self.typecode, 2*self.example) b = array.array(self.typecode) self.assertRaises(TypeError, a.tolist, 42) self.assertRaises(TypeError, b.fromlist) self.assertRaises(TypeError, b.fromlist, 42) self.assertRaises(TypeError, b.fromlist, [None]) b.fromlist(a.tolist()) self.assertEqual(a, b) def test_tofrombytes(self): a = array.array(self.typecode, 2*self.example) b = array.array(self.typecode) self.assertRaises(TypeError, a.tobytes, 42) self.assertRaises(TypeError, b.frombytes) self.assertRaises(TypeError, b.frombytes, 42) b.frombytes(a.tobytes()) c = array.array(self.typecode, bytearray(a.tobytes())) self.assertEqual(a, b) self.assertEqual(a, c) if a.itemsize>1: self.assertRaises(ValueError, b.frombytes, b"x") def test_fromarray(self): a = array.array(self.typecode, self.example) b = array.array(self.typecode, a) self.assertEqual(a, b) def test_repr(self): a = array.array(self.typecode, 2*self.example) self.assertEqual(a, eval(repr(a), {"array": array.array})) a = array.array(self.typecode) self.assertEqual(repr(a), "array('%s')" % self.typecode) def test_str(self): a = array.array(self.typecode, 2*self.example) str(a) def test_cmp(self): a = array.array(self.typecode, self.example) self.assertIs(a == 42, False) self.assertIs(a != 42, True) self.assertIs(a == a, True) self.assertIs(a != a, False) self.assertIs(a < a, False) self.assertIs(a <= a, True) self.assertIs(a > a, False) self.assertIs(a >= a, True) al = array.array(self.typecode, self.smallerexample) ab = array.array(self.typecode, self.biggerexample) self.assertIs(a == 2*a, False) self.assertIs(a != 2*a, True) self.assertIs(a < 2*a, True) self.assertIs(a <= 2*a, True) self.assertIs(a > 2*a, False) self.assertIs(a >= 2*a, False) self.assertIs(a == al, False) self.assertIs(a != al, True) self.assertIs(a < al, False) self.assertIs(a <= al, False) self.assertIs(a > al, True) self.assertIs(a >= al, True) self.assertIs(a == ab, False) self.assertIs(a != ab, True) self.assertIs(a < ab, True) self.assertIs(a <= ab, True) self.assertIs(a > ab, False) self.assertIs(a >= ab, False) def test_add(self): a = array.array(self.typecode, self.example) \ + array.array(self.typecode, self.example[::-1]) self.assertEqual( a, array.array(self.typecode, self.example + self.example[::-1]) ) b = array.array(self.badtypecode()) self.assertRaises(TypeError, a.__add__, b) self.assertRaises(TypeError, a.__add__, "bad") def test_iadd(self): a = array.array(self.typecode, self.example[::-1]) b = a a += array.array(self.typecode, 2*self.example) self.assertIs(a, b) self.assertEqual( a, array.array(self.typecode, self.example[::-1]+2*self.example) ) a = array.array(self.typecode, self.example) a += a self.assertEqual( a, array.array(self.typecode, self.example + self.example) ) b = array.array(self.badtypecode()) self.assertRaises(TypeError, a.__add__, b) self.assertRaises(TypeError, a.__iadd__, "bad") def test_mul(self): a = 5*array.array(self.typecode, self.example) self.assertEqual( a, array.array(self.typecode, 5*self.example) ) a = array.array(self.typecode, self.example)*5 self.assertEqual( a, array.array(self.typecode, self.example*5) ) a = 0*array.array(self.typecode, self.example) self.assertEqual( a, array.array(self.typecode) ) a = (-1)*array.array(self.typecode, self.example) self.assertEqual( a, array.array(self.typecode) ) a = 5 * array.array(self.typecode, self.example[:1]) self.assertEqual( a, array.array(self.typecode, [a[0]] * 5) ) self.assertRaises(TypeError, a.__mul__, "bad") def test_imul(self): a = array.array(self.typecode, self.example) b = a a *= 5 self.assertIs(a, b) self.assertEqual( a, array.array(self.typecode, 5*self.example) ) a *= 0 self.assertIs(a, b) self.assertEqual(a, array.array(self.typecode)) a *= 1000 self.assertIs(a, b) self.assertEqual(a, array.array(self.typecode)) a *= -1 self.assertIs(a, b) self.assertEqual(a, array.array(self.typecode)) a = array.array(self.typecode, self.example) a *= -1 self.assertEqual(a, array.array(self.typecode)) self.assertRaises(TypeError, a.__imul__, "bad") def test_getitem(self): a = array.array(self.typecode, self.example) self.assertEntryEqual(a[0], self.example[0]) self.assertEntryEqual(a[0], self.example[0]) self.assertEntryEqual(a[-1], self.example[-1]) self.assertEntryEqual(a[-1], self.example[-1]) self.assertEntryEqual(a[len(self.example)-1], self.example[-1]) self.assertEntryEqual(a[-len(self.example)], self.example[0]) self.assertRaises(TypeError, a.__getitem__) self.assertRaises(IndexError, a.__getitem__, len(self.example)) self.assertRaises(IndexError, a.__getitem__, -len(self.example)-1) def test_setitem(self): a = array.array(self.typecode, self.example) a[0] = a[-1] self.assertEntryEqual(a[0], a[-1]) a = array.array(self.typecode, self.example) a[0] = a[-1] self.assertEntryEqual(a[0], a[-1]) a = array.array(self.typecode, self.example) a[-1] = a[0] self.assertEntryEqual(a[0], a[-1]) a = array.array(self.typecode, self.example) a[-1] = a[0] self.assertEntryEqual(a[0], a[-1]) a = array.array(self.typecode, self.example) a[len(self.example)-1] = a[0] self.assertEntryEqual(a[0], a[-1]) a = array.array(self.typecode, self.example) a[-len(self.example)] = a[-1] self.assertEntryEqual(a[0], a[-1]) self.assertRaises(TypeError, a.__setitem__) self.assertRaises(TypeError, a.__setitem__, None) self.assertRaises(TypeError, a.__setitem__, 0, None) self.assertRaises( IndexError, a.__setitem__, len(self.example), self.example[0] ) self.assertRaises( IndexError, a.__setitem__, -len(self.example)-1, self.example[0] ) def test_delitem(self): a = array.array(self.typecode, self.example) del a[0] self.assertEqual( a, array.array(self.typecode, self.example[1:]) ) a = array.array(self.typecode, self.example) del a[-1] self.assertEqual( a, array.array(self.typecode, self.example[:-1]) ) a = array.array(self.typecode, self.example) del a[len(self.example)-1] self.assertEqual( a, array.array(self.typecode, self.example[:-1]) ) a = array.array(self.typecode, self.example) del a[-len(self.example)] self.assertEqual( a, array.array(self.typecode, self.example[1:]) ) self.assertRaises(TypeError, a.__delitem__) self.assertRaises(TypeError, a.__delitem__, None) self.assertRaises(IndexError, a.__delitem__, len(self.example)) self.assertRaises(IndexError, a.__delitem__, -len(self.example)-1) def test_getslice(self): a = array.array(self.typecode, self.example) self.assertEqual(a[:], a) self.assertEqual( a[1:], array.array(self.typecode, self.example[1:]) ) self.assertEqual( a[:1], array.array(self.typecode, self.example[:1]) ) self.assertEqual( a[:-1], array.array(self.typecode, self.example[:-1]) ) self.assertEqual( a[-1:], array.array(self.typecode, self.example[-1:]) ) self.assertEqual( a[-1:-1], array.array(self.typecode) ) self.assertEqual( a[2:1], array.array(self.typecode) ) self.assertEqual( a[1000:], array.array(self.typecode) ) self.assertEqual(a[-1000:], a) self.assertEqual(a[:1000], a) self.assertEqual( a[:-1000], array.array(self.typecode) ) self.assertEqual(a[-1000:1000], a) self.assertEqual( a[2000:1000], array.array(self.typecode) ) def test_extended_getslice(self): a = array.array(self.typecode, self.example) indices = (0, None, 1, 3, 19, 100, sys.maxsize, -1, -2, -31, -100) for start in indices: for stop in indices: for step in indices[1:]: self.assertEqual(list(a[start:stop:step]), list(a)[start:stop:step]) def test_setslice(self): a = array.array(self.typecode, self.example) a[:1] = a self.assertEqual( a, array.array(self.typecode, self.example + self.example[1:]) ) a = array.array(self.typecode, self.example) a[:-1] = a self.assertEqual( a, array.array(self.typecode, self.example + self.example[-1:]) ) a = array.array(self.typecode, self.example) a[-1:] = a self.assertEqual( a, array.array(self.typecode, self.example[:-1] + self.example) ) a = array.array(self.typecode, self.example) a[1:] = a self.assertEqual( a, array.array(self.typecode, self.example[:1] + self.example) ) a = array.array(self.typecode, self.example) a[1:-1] = a self.assertEqual( a, array.array( self.typecode, self.example[:1] + self.example + self.example[-1:] ) ) a = array.array(self.typecode, self.example) a[1000:] = a self.assertEqual( a, array.array(self.typecode, 2*self.example) ) a = array.array(self.typecode, self.example) a[-1000:] = a self.assertEqual( a, array.array(self.typecode, self.example) ) a = array.array(self.typecode, self.example) a[:1000] = a self.assertEqual( a, array.array(self.typecode, self.example) ) a = array.array(self.typecode, self.example) a[:-1000] = a self.assertEqual( a, array.array(self.typecode, 2*self.example) ) a = array.array(self.typecode, self.example) a[1:0] = a self.assertEqual( a, array.array(self.typecode, self.example[:1] + self.example + self.example[1:]) ) a = array.array(self.typecode, self.example) a[2000:1000] = a self.assertEqual( a, array.array(self.typecode, 2*self.example) ) a = array.array(self.typecode, self.example) self.assertRaises(TypeError, a.__setitem__, slice(0, 0), None) self.assertRaises(TypeError, a.__setitem__, slice(0, 1), None) b = array.array(self.badtypecode()) self.assertRaises(TypeError, a.__setitem__, slice(0, 0), b) self.assertRaises(TypeError, a.__setitem__, slice(0, 1), b) def test_extended_set_del_slice(self): indices = (0, None, 1, 3, 19, 100, sys.maxsize, -1, -2, -31, -100) for start in indices: for stop in indices: for step in indices[1:]: a = array.array(self.typecode, self.example) L = list(a) data = L[start:stop:step] data.reverse() L[start:stop:step] = data a[start:stop:step] = array.array(self.typecode, data) self.assertEqual(a, array.array(self.typecode, L)) del L[start:stop:step] del a[start:stop:step] self.assertEqual(a, array.array(self.typecode, L)) def test_index(self): example = 2*self.example a = array.array(self.typecode, example) self.assertRaises(TypeError, a.index) for x in example: self.assertEqual(a.index(x), example.index(x)) self.assertRaises(ValueError, a.index, None) self.assertRaises(ValueError, a.index, self.outside) def test_count(self): example = 2*self.example a = array.array(self.typecode, example) self.assertRaises(TypeError, a.count) for x in example: self.assertEqual(a.count(x), example.count(x)) self.assertEqual(a.count(self.outside), 0) self.assertEqual(a.count(None), 0) def test_remove(self): for x in self.example: example = 2*self.example a = array.array(self.typecode, example) pos = example.index(x) example2 = example[:pos] + example[pos+1:] a.remove(x) self.assertEqual(a, array.array(self.typecode, example2)) a = array.array(self.typecode, self.example) self.assertRaises(ValueError, a.remove, self.outside) self.assertRaises(ValueError, a.remove, None) def test_pop(self): a = array.array(self.typecode) self.assertRaises(IndexError, a.pop) a = array.array(self.typecode, 2*self.example) self.assertRaises(TypeError, a.pop, 42, 42) self.assertRaises(TypeError, a.pop, None) self.assertRaises(IndexError, a.pop, len(a)) self.assertRaises(IndexError, a.pop, -len(a)-1) self.assertEntryEqual(a.pop(0), self.example[0]) self.assertEqual( a, array.array(self.typecode, self.example[1:]+self.example) ) self.assertEntryEqual(a.pop(1), self.example[2]) self.assertEqual( a, array.array(self.typecode, self.example[1:2]+self.example[3:]+self.example) ) self.assertEntryEqual(a.pop(0), self.example[1]) self.assertEntryEqual(a.pop(), self.example[-1]) self.assertEqual( a, array.array(self.typecode, self.example[3:]+self.example[:-1]) ) def test_reverse(self): a = array.array(self.typecode, self.example) self.assertRaises(TypeError, a.reverse, 42) a.reverse() self.assertEqual( a, array.array(self.typecode, self.example[::-1]) ) def test_extend(self): a = array.array(self.typecode, self.example) self.assertRaises(TypeError, a.extend) a.extend(array.array(self.typecode, self.example[::-1])) self.assertEqual( a, array.array(self.typecode, self.example+self.example[::-1]) ) a = array.array(self.typecode, self.example) a.extend(a) self.assertEqual( a, array.array(self.typecode, self.example+self.example) ) b = array.array(self.badtypecode()) self.assertRaises(TypeError, a.extend, b) a = array.array(self.typecode, self.example) a.extend(self.example[::-1]) self.assertEqual( a, array.array(self.typecode, self.example+self.example[::-1]) ) def test_constructor_with_iterable_argument(self): a = array.array(self.typecode, iter(self.example)) b = array.array(self.typecode, self.example) self.assertEqual(a, b) self.assertRaises(TypeError, array.array, self.typecode, 10) class A: def __iter__(self): raise UnicodeError self.assertRaises(UnicodeError, array.array, self.typecode, A()) def B(): raise UnicodeError yield None self.assertRaises(UnicodeError, array.array, self.typecode, B()) def test_coveritertraverse(self): try: import gc except ImportError: self.skipTest('gc module not available') a = array.array(self.typecode) l = [iter(a)] l.append(l) gc.collect() def test_buffer(self): a = array.array(self.typecode, self.example) m = memoryview(a) expected = m.tobytes() self.assertEqual(a.tobytes(), expected) self.assertEqual(a.tobytes()[0], expected[0]) self.assertRaises(BufferError, a.append, a[0]) self.assertEqual(m.tobytes(), expected) self.assertRaises(BufferError, a.extend, a[0:1]) self.assertEqual(m.tobytes(), expected) self.assertRaises(BufferError, a.remove, a[0]) self.assertEqual(m.tobytes(), expected) self.assertRaises(BufferError, a.pop, 0) self.assertEqual(m.tobytes(), expected) self.assertRaises(BufferError, a.fromlist, a.tolist()) self.assertEqual(m.tobytes(), expected) self.assertRaises(BufferError, a.frombytes, a.tobytes()) self.assertEqual(m.tobytes(), expected) if self.typecode == 'u': self.assertRaises(BufferError, a.fromunicode, a.tounicode()) self.assertEqual(m.tobytes(), expected) self.assertRaises(BufferError, operator.imul, a, 2) self.assertEqual(m.tobytes(), expected) self.assertRaises(BufferError, operator.imul, a, 0) self.assertEqual(m.tobytes(), expected) self.assertRaises(BufferError, operator.setitem, a, slice(0, 0), a) self.assertEqual(m.tobytes(), expected) self.assertRaises(BufferError, operator.delitem, a, 0) self.assertEqual(m.tobytes(), expected) self.assertRaises(BufferError, operator.delitem, a, slice(0, 1)) self.assertEqual(m.tobytes(), expected) def test_weakref(self): s = array.array(self.typecode, self.example) p = weakref.proxy(s) self.assertEqual(p.tobytes(), s.tobytes()) s = None self.assertRaises(ReferenceError, len, p) @unittest.skipUnless(hasattr(sys, 'getrefcount'), 'test needs sys.getrefcount()') def test_bug_782369(self): for i in range(10): b = array.array('B', range(64)) rc = sys.getrefcount(10) for i in range(10): b = array.array('B', range(64)) self.assertEqual(rc, sys.getrefcount(10)) def test_subclass_with_kwargs(self): ef test_create_from_bytes(self): a = array.array('H', b"1234") self.assertEqual(len(a) * a.itemsize, 4) @support.cpython_only def test_sizeof_with_buffer(self): a = array.array(self.typecode, self.example) basesize = support.calcvobjsize('Pn2Pi') buffer_size = a.buffer_info()[1] * a.itemsize support.check_sizeof(self, a, basesize + buffer_size) @support.cpython_only def test_sizeof_without_buffer(self): a = array.array(self.typecode) basesize = support.calcvobjsize('Pn2Pi') support.check_sizeof(self, a, basesize) def test_initialize_with_unicode(self): if self.typecode != 'u': with self.assertRaises(TypeError) as cm: a = array.array(self.typecode, 'foo') self.assertIn("cannot use a str", str(cm.exception)) with self.assertRaises(TypeError) as cm: a = array.array(self.typecode, array.array('u', 'foo')) self.assertIn("cannot use a unicode array", str(cm.exception)) else: a = array.array(self.typecode, "foo") a = array.array(self.typecode, array.array('u', 'foo')) @support.cpython_only def test_obsolete_write_lock(self): from _testcapi import getbuffer_with_null_view a = array.array('B', b"") self.assertRaises(BufferError, getbuffer_with_null_view, a) def test_free_after_iterating(self): support.check_free_after_iterating(self, iter, array.array, (self.typecode,)) support.check_free_after_iterating(self, reversed, array.array, (self.typecode,)) class StringTest(BaseTest): def test_setitem(self): super().test_setitem() a = array.array(self.typecode, self.example) self.assertRaises(TypeError, a.__setitem__, 0, self.example[:2]) class UnicodeTest(StringTest, unittest.TestCase): typecode = 'u' example = '\x01\u263a\x00\ufeff' smallerexample = '\x01\u263a\x00\ufefe' biggerexample = '\x01\u263a\x01\ufeff' outside = str('\x33') minitemsize = 2 def test_unicode(self): self.assertRaises(TypeError, array.array, 'b', 'foo') a = array.array('u', '\xa0\xc2\u1234') a.fromunicode(' ') a.fromunicode('') a.fromunicode('') a.fromunicode('\x11abc\xff\u1234') s = a.tounicode() self.assertEqual(s, '\xa0\xc2\u1234 \x11abc\xff\u1234') self.assertEqual(a.itemsize, sizeof_wchar) s = '\x00="\'a\\b\x80\xff\u0000\u0001\u1234' a = array.array('u', s) self.assertEqual( repr(a), "array('u', '\\x00=\"\\'a\\\\b\\x80\xff\\x00\\x01\u1234')") self.assertRaises(TypeError, a.fromunicode) def test_issue17223(self): if sizeof_wchar == 4: invalid_str = b'\xff\xff\xff\xff' else: self.skipTest("specific to 32-bit wchar_t") a = array.array('u', invalid_str) self.assertRaises(ValueError, a.tounicode) self.assertRaises(ValueError, str, a) class NumberTest(BaseTest): def test_extslice(self): a = array.array(self.typecode, range(5)) self.assertEqual(a[::], a) self.assertEqual(a[::2], array.array(self.typecode, [0,2,4])) self.assertEqual(a[1::2], array.array(self.typecode, [1,3])) self.assertEqual(a[::-1], array.array(self.typecode, [4,3,2,1,0])) self.assertEqual(a[::-2], array.array(self.typecode, [4,2,0])) self.assertEqual(a[3::-2], array.array(self.typecode, [3,1])) self.assertEqual(a[-100:100:], a) self.assertEqual(a[100:-100:-1], a[::-1]) self.assertEqual(a[-100:100:2], array.array(self.typecode, [0,2,4])) self.assertEqual(a[1000:2000:2], array.array(self.typecode, [])) self.assertEqual(a[-1000:-2000:-2], array.array(self.typecode, [])) def test_delslice(self): a = array.array(self.typecode, range(5)) del a[::2] self.assertEqual(a, array.array(self.typecode, [1,3])) a = array.array(self.typecode, range(5)) del a[1::2] self.assertEqual(a, array.array(self.typecode, [0,2,4])) a = array.array(self.typecode, range(5)) del a[1::-2] self.assertEqual(a, array.array(self.typecode, [0,2,3,4])) a = array.array(self.typecode, range(10)) del a[::1000] self.assertEqual(a, array.array(self.typecode, [1,2,3,4,5,6,7,8,9])) a = array.array(self.typecode, range(10)) del a[9::1<<333] def test_assignment(self): a = array.array(self.typecode, range(10)) a[::2] = array.array(self.typecode, [42]*5) self.assertEqual(a, array.array(self.typecode, [42, 1, 42, 3, 42, 5, 42, 7, 42, 9])) a = array.array(self.typecode, range(10)) a[::-4] = array.array(self.typecode, [10]*3) self.assertEqual(a, array.array(self.typecode, [0, 10, 2, 3, 4, 10, 6, 7, 8 ,10])) a = array.array(self.typecode, range(4)) a[::-1] = a self.assertEqual(a, array.array(self.typecode, [3, 2, 1, 0])) a = array.array(self.typecode, range(10)) b = a[:] c = a[:] ins = array.array(self.typecode, range(2)) a[2:3] = ins b[slice(2,3)] = ins c[2:3:] = ins def test_iterationcontains(self): a = array.array(self.typecode, range(10)) self.assertEqual(list(a), list(range(10))) b = array.array(self.typecode, [20]) self.assertEqual(a[-1] in a, True) self.assertEqual(b[0] not in a, True) def check_overflow(self, lower, upper): a = array.array(self.typecode, [lower]) a[0] = lower self.assertRaises(OverflowError, array.array, self.typecode, [lower-1]) self.assertRaises(OverflowError, a.__setitem__, 0, lower-1) a = array.array(self.typecode, [upper]) a[0] = upper self.assertRaises(OverflowError, array.array, self.typecode, [upper+1]) self.assertRaises(OverflowError, a.__setitem__, 0, upper+1) def test_subclassing(self): typecode = self.typecode class ExaggeratingArray(array.array): __slots__ = ['offset'] def __new__(cls, typecode, data, offset): return array.array.__new__(cls, typecode, data) def __init__(self, typecode, data, offset): self.offset = offset def __getitem__(self, i): return array.array.__getitem__(self, i) + self.offset a = ExaggeratingArray(self.typecode, [3, 6, 7, 11], 4) self.assertEntryEqual(a[0], 7) self.assertRaises(AttributeError, setattr, a, "color", "blue") def test_frombytearray(self): a = array.array('b', range(10)) b = array.array(self.typecode, a) self.assertEqual(a, b) class IntegerNumberTest(NumberTest): def test_type_error(self): a = array.array(self.typecode) a.append(42) with self.assertRaises(TypeError): a.append(42.0) with self.assertRaises(TypeError): a[0] = 42.0 class Intable: def __init__(self, num): self._num = num def __index__(self): return self._num def __int__(self): return self._num def __sub__(self, other): return Intable(int(self) - int(other)) def __add__(self, other): return Intable(int(self) + int(other)) class SignedNumberTest(IntegerNumberTest): example = [-1, 0, 1, 42, 0x7f] smallerexample = [-1, 0, 1, 42, 0x7e] biggerexample = [-1, 0, 1, 43, 0x7f] outside = 23 def test_overflow(self): a = array.array(self.typecode) lower = -1 * int(pow(2, a.itemsize * 8 - 1)) upper = int(pow(2, a.itemsize * 8 - 1)) - 1 self.check_overflow(lower, upper) self.check_overflow(Intable(lower), Intable(upper)) class UnsignedNumberTest(IntegerNumberTest): example = [0, 1, 17, 23, 42, 0xff] smallerexample = [0, 1, 17, 23, 42, 0xfe] biggerexample = [0, 1, 17, 23, 43, 0xff] outside = 0xaa def test_overflow(self): a = array.array(self.typecode) lower = 0 upper = int(pow(2, a.itemsize * 8)) - 1 self.check_overflow(lower, upper) self.check_overflow(Intable(lower), Intable(upper)) def test_bytes_extend(self): s = bytes(self.example) a = array.array(self.typecode, self.example) a.extend(s) self.assertEqual( a, array.array(self.typecode, self.example+self.example) ) a = array.array(self.typecode, self.example) a.extend(bytearray(reversed(s))) self.assertEqual( a, array.array(self.typecode, self.example+self.example[::-1]) ) class ByteTest(SignedNumberTest, unittest.TestCase): typecode = 'b' minitemsize = 1 class UnsignedByteTest(UnsignedNumberTest, unittest.TestCase): typecode = 'B' minitemsize = 1 class ShortTest(SignedNumberTest, unittest.TestCase): typecode = 'h' minitemsize = 2 class UnsignedShortTest(UnsignedNumberTest, unittest.TestCase): typecode = 'H' minitemsize = 2 class IntTest(SignedNumberTest, unittest.TestCase): typecode = 'i' minitemsize = 2 class UnsignedIntTest(UnsignedNumberTest, unittest.TestCase): typecode = 'I' minitemsize = 2 class LongTest(SignedNumberTest, unittest.TestCase): typecode = 'l' minitemsize = 4 class UnsignedLongTest(UnsignedNumberTest, unittest.TestCase): typecode = 'L' minitemsize = 4 class LongLongTest(SignedNumberTest, unittest.TestCase): typecode = 'q' minitemsize = 8 class UnsignedLongLongTest(UnsignedNumberTest, unittest.TestCase): typecode = 'Q' minitemsize = 8 class FPTest(NumberTest): example = [-42.0, 0, 42, 1e5, -1e10] smallerexample = [-42.0, 0, 42, 1e5, -2e10] biggerexample = [-42.0, 0, 42, 1e5, 1e10] outside = 23 def assertEntryEqual(self, entry1, entry2): self.assertAlmostEqual(entry1, entry2) def test_nan(self): a = array.array(self.typecode, [float('nan')]) b = array.array(self.typecode, [float('nan')]) self.assertIs(a != b, True) self.assertIs(a == b, False) self.assertIs(a > b, False) self.assertIs(a >= b, False) self.assertIs(a < b, False) self.assertIs(a <= b, False) def test_byteswap(self): a = array.array(self.typecode, self.example) self.assertRaises(TypeError, a.byteswap, 42) if a.itemsize in (1, 2, 4, 8): b = array.array(self.typecode, self.example) b.byteswap() if a.itemsize==1: self.assertEqual(a, b) else: self.assertNotEqual(a.tobytes(), b.tobytes()) b.byteswap() self.assertEqual(a, b) class FloatTest(FPTest, unittest.TestCase): typecode = 'f' minitemsize = 4 class DoubleTest(FPTest, unittest.TestCase): typecode = 'd' minitemsize = 8 def test_alloc_overflow(self): from sys import maxsize a = array.array('d', [-1]*65536) try: a *= maxsize//65536 + 1 except MemoryError: pass else: self.fail("Array of size > maxsize created - MemoryError expected") b = array.array('d', [ 2.71828183, 3.14159265, -1]) try: b * (maxsize//3 + 1) except MemoryError: pass else: self.fail("Array of size > maxsize created - MemoryError expected") class LargeArrayTest(unittest.TestCase): typecode = 'b' def example(self, size): base = array.array(self.typecode, [0, 1, 2, 3, 4, 5, 6, 7]) * (size // 8) base += array.array(self.typecode, [99]*(size % 8) + [8, 9, 10, 11]) return base @support.bigmemtest(_2G, memuse=2.125) def test_example_data(self, size): example = self.example(size) self.assertEqual(len(example), size+4) @support.bigmemtest(_2G, memuse=2.125) def test_access(self, size): example = self.example(size) self.assertEqual(example[0], 0) self.assertEqual(example[-(size+4)], 0) self.assertEqual(example[size], 8) self.assertEqual(example[-4], 8) self.assertEqual(example[size+3], 11) self.assertEqual(example[-1], 11) @support.bigmemtest(_2G, memuse=2.125+1) def test_slice(self, size): example = self.example(size) self.assertEqual(list(example[:4]), [0, 1, 2, 3]) self.assertEqual(list(example[-4:]), [8, 9, 10, 11]) part = example[1:-1] self.assertEqual(len(part), size+2) self.assertEqual(part[0], 1) self.assertEqual(part[-1], 10) del part part = example[::2] self.assertEqual(len(part), (size+5)//2) self.assertEqual(list(part[:4]), [0, 2, 4, 6]) if size % 2: self.assertEqual(list(part[-2:]), [9, 11]) else: self.assertEqual(list(part[-2:]), [8, 10]) @support.bigmemtest(_2G, memuse=2.125) def test_count(self, size): example = self.example(size) self.assertEqual(example.count(0), size//8) self.assertEqual(example.count(11), 1) @support.bigmemtest(_2G, memuse=2.125) def test_append(self, size): example = self.example(size) example.append(12) self.assertEqual(example[-1], 12) @support.bigmemtest(_2G, memuse=2.125) def test_extend(self, size): example = self.example(size) example.extend(iter([12, 13, 14, 15])) self.assertEqual(len(example), size+8) self.assertEqual(list(example[-8:]), [8, 9, 10, 11, 12, 13, 14, 15]) @support.bigmemtest(_2G, memuse=2.125) def test_frombytes(self, size): example = self.example(size) example.frombytes(b'abcd') self.assertEqual(len(example), size+8) self.assertEqual(list(example[-8:]), [8, 9, 10, 11] + list(b'abcd')) @support.bigmemtest(_2G, memuse=2.125) def test_fromlist(self, size): example = self.example(size) example.fromlist([12, 13, 14, 15]) self.assertEqual(len(example), size+8) self.assertEqual(list(example[-8:]), [8, 9, 10, 11, 12, 13, 14, 15]) @support.bigmemtest(_2G, memuse=2.125) def test_index(self, size): example = self.example(size) self.assertEqual(example.index(0), 0) self.assertEqual(example.index(1), 1) self.assertEqual(example.index(7), 7) self.assertEqual(example.index(11), size+3) @support.bigmemtest(_2G, memuse=2.125) def test_insert(self, size): example = self.example(size) example.insert(0, 12) example.insert(10, 13) example.insert(size+1, 14) self.assertEqual(len(example), size+7) self.assertEqual(example[0], 12) self.assertEqual(example[10], 13) self.assertEqual(example[size+1], 14) @support.bigmemtest(_2G, memuse=2.125) def test_pop(self, size): example = self.example(size) self.assertEqual(example.pop(0), 0) self.assertEqual(example[0], 1) self.assertEqual(example.pop(size+1), 10) self.assertEqual(example[size+1], 11) self.assertEqual(example.pop(1), 2) self.assertEqual(example[1], 3) self.assertEqual(len(example), size+1) self.assertEqual(example.pop(), 11) self.assertEqual(len(example), size) @support.bigmemtest(_2G, memuse=2.125) def test_remove(self, size): example = self.example(size) example.remove(0) self.assertEqual(len(example), size+3) self.assertEqual(example[0], 1) example.remove(10) self.assertEqual(len(example), size+2) self.assertEqual(example[size], 9) self.assertEqual(example[size+1], 11) @support.bigmemtest(_2G, memuse=2.125) def test_reverse(self, size): example = self.example(size) example.reverse() self.assertEqual(len(example), size+4) self.assertEqual(example[0], 11) self.assertEqual(example[3], 8) self.assertEqual(example[-1], 0) example.reverse() self.assertEqual(len(example), size+4) self.assertEqual(list(example[:4]), [0, 1, 2, 3]) self.assertEqual(list(example[-4:]), [8, 9, 10, 11]) @support.bigmemtest(_2G, memuse=2.125+9) def test_tolist(self, size): example = self.example(size) ls = example.tolist() self.assertEqual(len(ls), len(example)) self.assertEqual(ls[:8], list(example[:8])) self.assertEqual(ls[-8:], list(example[-8:])) if __name__ == "__main__": unittest.main()

true

f731b7992515f911392bdff02585fa6debdfa83b

1,504

py

Python

apps/contrib/models/mixins.py

vicobits/django-wise

3fdc01eabdff459b31e016f9f6d1cafc19c5a292

[ "MIT" ]

5

2020-04-11T20:11:48.000Z

2021-03-16T23:58:01.000Z

apps/contrib/models/mixins.py

victoraguilarc/django-wise

3fdc01eabdff459b31e016f9f6d1cafc19c5a292

[ "MIT" ]

5

2020-04-11T20:17:56.000Z

2021-06-16T19:18:29.000Z

apps/contrib/models/mixins.py

victoraguilarc/django-wise

3fdc01eabdff459b31e016f9f6d1cafc19c5a292

[ "MIT" ]

null

# -*- coding: utf-8 -*- import uuid from django.db import models class TimeStampedModelMixin(models.Model): """Timestamp extra field. An abstract base class model that provides self updating 'created' and 'modified' fields https://docs.djangoproject.com/en/2.1/ref/models/fields/#django.db.models.DateField.auto_now_add """ created_at = models.DateTimeField(auto_now_add=True) updated_at = models.DateTimeField(auto_now=True) class Meta: abstract = True ordering = ['-created_at', '-updated_at'] class UUIDModelMixin(models.Model): """An abstract base class model that provides an uuid field.""" uuid = models.UUIDField(default=uuid.uuid4, editable=False, unique=True) class Meta: abstract = True class SlugModelMixin(models.Model): """An abstract base class model that provides a slug field.""" slug = models.SlugField(max_length=255, unique=True) class Meta: abstract = True class UUIDPrimaryKeyModelMixin(models.Model): """An abstract base class model that provides an uuid field that is the primary key.""" uuid = models.UUIDField( verbose_name='UUID', primary_key=True, default=uuid.uuid4, editable=False, ) class Meta: abstract = True class UUIDWithTimestampMixin(UUIDPrimaryKeyModelMixin, TimeStampedModelMixin): """An abstract base class model that provides an uuid and timestamp fields.""" class Meta: abstract = True

25.491525

100

0.691489

import uuid from django.db import models class TimeStampedModelMixin(models.Model): created_at = models.DateTimeField(auto_now_add=True) updated_at = models.DateTimeField(auto_now=True) class Meta: abstract = True ordering = ['-created_at', '-updated_at'] class UUIDModelMixin(models.Model): uuid = models.UUIDField(default=uuid.uuid4, editable=False, unique=True) class Meta: abstract = True class SlugModelMixin(models.Model): slug = models.SlugField(max_length=255, unique=True) class Meta: abstract = True class UUIDPrimaryKeyModelMixin(models.Model): uuid = models.UUIDField( verbose_name='UUID', primary_key=True, default=uuid.uuid4, editable=False, ) class Meta: abstract = True class UUIDWithTimestampMixin(UUIDPrimaryKeyModelMixin, TimeStampedModelMixin): class Meta: abstract = True

true