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@@ -342,3 +342,4 @@ parrot/lib/python3.10/site-packages/sympy/tensor/__pycache__/tensor.cpython-310.
 parrot/lib/python3.10/site-packages/sympy/solvers/tests/__pycache__/test_solvers.cpython-310.pyc filter=lfs diff=lfs merge=lfs -text
 parrot/lib/python3.10/site-packages/mpl_toolkits/mplot3d/__pycache__/axes3d.cpython-310.pyc filter=lfs diff=lfs merge=lfs -text
 parrot/lib/libcrypto.so.3 filter=lfs diff=lfs merge=lfs -text
+llava_next/lib/python3.10/site-packages/torch/bin/nvfuser_tests filter=lfs diff=lfs merge=lfs -text

llava_next/lib/python3.10/site-packages/scipy/stats/tests/data/_mvt.py

@@ -0,0 +1,171 @@
+import math
+import numpy as np
+from scipy import special
+from scipy.stats._qmc import primes_from_2_to
+
+
+def _primes(n):
+    # Defined to facilitate comparison between translation and source
+    # In Matlab, primes(10.5) -> first four primes, primes(11.5) -> first five
+    return primes_from_2_to(math.ceil(n))
+
+
+def _gaminv(a, b):
+    # Defined to facilitate comparison between translation and source
+    # Matlab's `gaminv` is like `special.gammaincinv` but args are reversed
+    return special.gammaincinv(b, a)
+
+
+def _qsimvtv(m, nu, sigma, a, b, rng):
+    """Estimates the multivariate t CDF using randomized QMC
+
+    Parameters
+    ----------
+    m : int
+        The number of points
+    nu : float
+        Degrees of freedom
+    sigma : ndarray
+        A 2D positive semidefinite covariance matrix
+    a : ndarray
+        Lower integration limits
+    b : ndarray
+        Upper integration limits.
+    rng : Generator
+        Pseudorandom number generator
+
+    Returns
+    -------
+    p : float
+        The estimated CDF.
+    e : float
+        An absolute error estimate.
+
+    """
+    # _qsimvtv is a Python translation of the Matlab function qsimvtv,
+    # semicolons and all.
+    #
+    #   This function uses an algorithm given in the paper
+    #      "Comparison of Methods for the Numerical Computation of
+    #       Multivariate t Probabilities", in
+    #      J. of Computational and Graphical Stat., 11(2002), pp. 950-971, by
+    #          Alan Genz and Frank Bretz
+    #
+    #   The primary references for the numerical integration are
+    #    "On a Number-Theoretical Integration Method"
+    #    H. Niederreiter, Aequationes Mathematicae, 8(1972), pp. 304-11.
+    #    and
+    #    "Randomization of Number Theoretic Methods for Multiple Integration"
+    #     R. Cranley & T.N.L. Patterson, SIAM J Numer Anal, 13(1976), pp. 904-14.
+    #
+    #   Alan Genz is the author of this function and following Matlab functions.
+    #          Alan Genz, WSU Math, PO Box 643113, Pullman, WA 99164-3113
+    #          Email : alangenz@wsu.edu
+    #
+    # Copyright (C) 2013, Alan Genz,  All rights reserved.
+    #
+    # Redistribution and use in source and binary forms, with or without
+    # modification, are permitted provided the following conditions are met:
+    #   1. Redistributions of source code must retain the above copyright
+    #      notice, this list of conditions and the following disclaimer.
+    #   2. Redistributions in binary form must reproduce the above copyright
+    #      notice, this list of conditions and the following disclaimer in
+    #      the documentation and/or other materials provided with the
+    #      distribution.
+    #   3. The contributor name(s) may not be used to endorse or promote
+    #      products derived from this software without specific prior
+    #      written permission.
+    # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    # "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    # LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
+    # FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
+    # COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
+    # INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
+    # BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS
+    # OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+    # ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR
+    # TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF USE
+    # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+    # Initialization
+    sn = max(1, math.sqrt(nu)); ch, az, bz = _chlrps(sigma, a/sn, b/sn)
+    n = len(sigma); N = 10; P = math.ceil(m/N); on = np.ones(P); p = 0; e = 0
+    ps = np.sqrt(_primes(5*n*math.log(n+4)/4)); q = ps[:, np.newaxis]  # Richtmyer gens.
+
+    # Randomization loop for ns samples
+    c = None; dc = None
+    for S in range(N):
+        vp = on.copy(); s = np.zeros((n, P))
+        for i in range(n):
+            x = np.abs(2*np.mod(q[i]*np.arange(1, P+1) + rng.random(), 1)-1)  # periodizing transform
+            if i == 0:
+                r = on
+                if nu > 0:
+                    r = np.sqrt(2*_gaminv(x, nu/2))
+            else:
+                y = _Phinv(c + x*dc)
+                s[i:] += ch[i:, i-1:i] * y
+            si = s[i, :]; c = on.copy(); ai = az[i]*r - si; d = on.copy(); bi = bz[i]*r - si
+            c[ai <= -9] = 0; tl = abs(ai) < 9; c[tl] = _Phi(ai[tl])
+            d[bi <= -9] = 0; tl = abs(bi) < 9; d[tl] = _Phi(bi[tl])
+            dc = d - c; vp = vp * dc
+        d = (np.mean(vp) - p)/(S + 1); p = p + d; e = (S - 1)*e/(S + 1) + d**2
+    e = math.sqrt(e)  # error estimate is 3 times std error with N samples.
+    return p, e
+
+
+#  Standard statistical normal distribution functions
+def _Phi(z):
+    return special.ndtr(z)
+
+
+def _Phinv(p):
+    return special.ndtri(p)
+
+
+def _chlrps(R, a, b):
+    """
+    Computes permuted and scaled lower Cholesky factor c for R which may be
+    singular, also permuting and scaling integration limit vectors a and b.
+    """
+    ep = 1e-10  # singularity tolerance
+    eps = np.finfo(R.dtype).eps
+
+    n = len(R); c = R.copy(); ap = a.copy(); bp = b.copy(); d = np.sqrt(np.maximum(np.diag(c), 0))
+    for i in range(n):
+        if d[i] > 0:
+            c[:, i] /= d[i]; c[i, :] /= d[i]
+            ap[i] /= d[i]; bp[i] /= d[i]
+    y = np.zeros((n, 1)); sqtp = math.sqrt(2*math.pi)
+
+    for k in range(n):
+        im = k; ckk = 0; dem = 1; s = 0
+        for i in range(k, n):
+            if c[i, i] > eps:
+                cii = math.sqrt(max(c[i, i], 0))
+                if i > 0: s = c[i, :k] @ y[:k]
+                ai = (ap[i]-s)/cii; bi = (bp[i]-s)/cii; de = _Phi(bi)-_Phi(ai)
+                if de <= dem:
+                    ckk = cii; dem = de; am = ai; bm = bi; im = i
+        if im > k:
+            ap[[im, k]] = ap[[k, im]]; bp[[im, k]] = bp[[k, im]]; c[im, im] = c[k, k]
+            t = c[im, :k].copy(); c[im, :k] = c[k, :k]; c[k, :k] = t
+            t = c[im+1:, im].copy(); c[im+1:, im] = c[im+1:, k]; c[im+1:, k] = t
+            t = c[k+1:im, k].copy(); c[k+1:im, k] = c[im, k+1:im].T; c[im, k+1:im] = t.T
+        if ckk > ep*(k+1):
+            c[k, k] = ckk; c[k, k+1:] = 0
+            for i in range(k+1, n):
+                c[i, k] = c[i, k]/ckk; c[i, k+1:i+1] = c[i, k+1:i+1] - c[i, k]*c[k+1:i+1, k].T
+            if abs(dem) > ep:
+                y[k] = (np.exp(-am**2/2) - np.exp(-bm**2/2)) / (sqtp*dem)
+            else:
+                y[k] = (am + bm) / 2
+                if am < -10:
+                    y[k] = bm
+                elif bm > 10:
+                    y[k] = am
+            c[k, :k+1] /= ckk; ap[k] /= ckk; bp[k] /= ckk
+        else:
+            c[k:, k] = 0; y[k] = (ap[k] + bp[k])/2
+        pass
+    return c, ap, bp

llava_next/lib/python3.10/site-packages/scipy/stats/tests/data/fisher_exact_results_from_r.py

@@ -0,0 +1,607 @@
+# DO NOT EDIT THIS FILE!
+# This file was generated by the R script
+#     generate_fisher_exact_results_from_r.R
+# The script was run with R version 3.6.2 (2019-12-12) at 2020-11-09 06:16:09
+
+
+from collections import namedtuple
+import numpy as np
+
+
+Inf = np.inf
+
+Parameters = namedtuple('Parameters',
+                        ['table', 'confidence_level', 'alternative'])
+RResults = namedtuple('RResults',
+                      ['pvalue', 'conditional_odds_ratio',
+                       'conditional_odds_ratio_ci'])
+data = [
+    (Parameters(table=[[100, 2], [1000, 5]],
+                confidence_level=0.95,
+                alternative='two.sided'),
+     RResults(pvalue=0.1300759363430016,
+              conditional_odds_ratio=0.25055839934223,
+              conditional_odds_ratio_ci=(0.04035202926536294,
+                                         2.662846672960251))),
+    (Parameters(table=[[2, 7], [8, 2]],
+                confidence_level=0.95,
+                alternative='two.sided'),
+     RResults(pvalue=0.02301413756522116,
+              conditional_odds_ratio=0.0858623513573622,
+              conditional_odds_ratio_ci=(0.004668988338943325,
+                                         0.895792956493601))),
+    (Parameters(table=[[5, 1], [10, 10]],
+                confidence_level=0.95,
+                alternative='two.sided'),
+     RResults(pvalue=0.1973244147157191,
+              conditional_odds_ratio=4.725646047336587,
+              conditional_odds_ratio_ci=(0.4153910882532168,
+                                         259.2593661129417))),
+    (Parameters(table=[[5, 15], [20, 20]],
+                confidence_level=0.95,
+                alternative='two.sided'),
+     RResults(pvalue=0.09580440012477633,
+              conditional_odds_ratio=0.3394396617440851,
+              conditional_odds_ratio_ci=(0.08056337526385809,
+                                         1.22704788545557))),
+    (Parameters(table=[[5, 16], [16, 25]],
+                confidence_level=0.95,
+                alternative='two.sided'),
+     RResults(pvalue=0.2697004098849359,
+              conditional_odds_ratio=0.4937791394540491,
+              conditional_odds_ratio_ci=(0.1176691231650079,
+                                         1.787463657995973))),
+    (Parameters(table=[[10, 5], [10, 1]],
+                confidence_level=0.95,
+                alternative='two.sided'),
+     RResults(pvalue=0.1973244147157192,
+              conditional_odds_ratio=0.2116112781158479,
+              conditional_odds_ratio_ci=(0.003857141267422399,
+                                         2.407369893767229))),
+    (Parameters(table=[[10, 5], [10, 0]],
+                confidence_level=0.95,
+                alternative='two.sided'),
+     RResults(pvalue=0.06126482213438735,
+              conditional_odds_ratio=0,
+              conditional_odds_ratio_ci=(0,
+                                         1.451643573543705))),
+    (Parameters(table=[[5, 0], [1, 4]],
+                confidence_level=0.95,
+                alternative='two.sided'),
+     RResults(pvalue=0.04761904761904762,
+              conditional_odds_ratio=Inf,
+              conditional_odds_ratio_ci=(1.024822256141754,
+                                         Inf))),
+    (Parameters(table=[[0, 5], [1, 4]],
+                confidence_level=0.95,
+                alternative='two.sided'),
+     RResults(pvalue=1,
+              conditional_odds_ratio=0,
+              conditional_odds_ratio_ci=(0,
+                                         39.00054996869288))),
+    (Parameters(table=[[5, 1], [0, 4]],
+                confidence_level=0.95,
+                alternative='two.sided'),
+     RResults(pvalue=0.04761904761904761,
+              conditional_odds_ratio=Inf,
+              conditional_odds_ratio_ci=(1.024822256141754,
+                                         Inf))),
+    (Parameters(table=[[0, 1], [3, 2]],
+                confidence_level=0.95,
+                alternative='two.sided'),
+     RResults(pvalue=1,
+              conditional_odds_ratio=0,
+              conditional_odds_ratio_ci=(0,
+                                         39.00054996869287))),
+    (Parameters(table=[[200, 7], [8, 300]],
+                confidence_level=0.95,
+                alternative='two.sided'),
+     RResults(pvalue=2.005657880389071e-122,
+              conditional_odds_ratio=977.7866978606228,
+              conditional_odds_ratio_ci=(349.2595113327733,
+                                         3630.382605689872))),
+    (Parameters(table=[[28, 21], [6, 1957]],
+                confidence_level=0.95,
+                alternative='two.sided'),
+     RResults(pvalue=5.728437460831947e-44,
+              conditional_odds_ratio=425.2403028434684,
+              conditional_odds_ratio_ci=(152.4166024390096,
+                                         1425.700792178893))),
+    (Parameters(table=[[190, 800], [200, 900]],
+                confidence_level=0.95,
+                alternative='two.sided'),
+     RResults(pvalue=0.574111858126088,
+              conditional_odds_ratio=1.068697577856801,
+              conditional_odds_ratio_ci=(0.8520462587912048,
+                                         1.340148950273938))),
+    (Parameters(table=[[100, 2], [1000, 5]],
+                confidence_level=0.99,
+                alternative='two.sided'),
+     RResults(pvalue=0.1300759363430016,
+              conditional_odds_ratio=0.25055839934223,
+              conditional_odds_ratio_ci=(0.02502345007115455,
+                                         6.304424772117853))),
+    (Parameters(table=[[2, 7], [8, 2]],
+                confidence_level=0.99,
+                alternative='two.sided'),
+     RResults(pvalue=0.02301413756522116,
+              conditional_odds_ratio=0.0858623513573622,
+              conditional_odds_ratio_ci=(0.001923034001462487,
+                                         1.53670836950172))),
+    (Parameters(table=[[5, 1], [10, 10]],
+                confidence_level=0.99,
+                alternative='two.sided'),
+     RResults(pvalue=0.1973244147157191,
+              conditional_odds_ratio=4.725646047336587,
+              conditional_odds_ratio_ci=(0.2397970951413721,
+                                         1291.342011095509))),
+    (Parameters(table=[[5, 15], [20, 20]],
+                confidence_level=0.99,
+                alternative='two.sided'),
+     RResults(pvalue=0.09580440012477633,
+              conditional_odds_ratio=0.3394396617440851,
+              conditional_odds_ratio_ci=(0.05127576113762925,
+                                         1.717176678806983))),
+    (Parameters(table=[[5, 16], [16, 25]],
+                confidence_level=0.99,
+                alternative='two.sided'),
+     RResults(pvalue=0.2697004098849359,
+              conditional_odds_ratio=0.4937791394540491,
+              conditional_odds_ratio_ci=(0.07498546954483619,
+                                         2.506969905199901))),
+    (Parameters(table=[[10, 5], [10, 1]],
+                confidence_level=0.99,
+                alternative='two.sided'),
+     RResults(pvalue=0.1973244147157192,
+              conditional_odds_ratio=0.2116112781158479,
+              conditional_odds_ratio_ci=(0.0007743881879531337,
+                                         4.170192301163831))),
+    (Parameters(table=[[10, 5], [10, 0]],
+                confidence_level=0.99,
+                alternative='two.sided'),
+     RResults(pvalue=0.06126482213438735,
+              conditional_odds_ratio=0,
+              conditional_odds_ratio_ci=(0,
+                                         2.642491011905582))),
+    (Parameters(table=[[5, 0], [1, 4]],
+                confidence_level=0.99,
+                alternative='two.sided'),
+     RResults(pvalue=0.04761904761904762,
+              conditional_odds_ratio=Inf,
+              conditional_odds_ratio_ci=(0.496935393325443,
+                                         Inf))),
+    (Parameters(table=[[0, 5], [1, 4]],
+                confidence_level=0.99,
+                alternative='two.sided'),
+     RResults(pvalue=1,
+              conditional_odds_ratio=0,
+              conditional_odds_ratio_ci=(0,
+                                         198.019801980198))),
+    (Parameters(table=[[5, 1], [0, 4]],
+                confidence_level=0.99,
+                alternative='two.sided'),
+     RResults(pvalue=0.04761904761904761,
+              conditional_odds_ratio=Inf,
+              conditional_odds_ratio_ci=(0.496935393325443,
+                                         Inf))),
+    (Parameters(table=[[0, 1], [3, 2]],
+                confidence_level=0.99,
+                alternative='two.sided'),
+     RResults(pvalue=1,
+              conditional_odds_ratio=0,
+              conditional_odds_ratio_ci=(0,
+                                         198.019801980198))),
+    (Parameters(table=[[200, 7], [8, 300]],
+                confidence_level=0.99,
+                alternative='two.sided'),
+     RResults(pvalue=2.005657880389071e-122,
+              conditional_odds_ratio=977.7866978606228,
+              conditional_odds_ratio_ci=(270.0334165523604,
+                                         5461.333333326708))),
+    (Parameters(table=[[28, 21], [6, 1957]],
+                confidence_level=0.99,
+                alternative='two.sided'),
+     RResults(pvalue=5.728437460831947e-44,
+              conditional_odds_ratio=425.2403028434684,
+              conditional_odds_ratio_ci=(116.7944750275836,
+                                         1931.995993191814))),
+    (Parameters(table=[[190, 800], [200, 900]],
+                confidence_level=0.99,
+                alternative='two.sided'),
+     RResults(pvalue=0.574111858126088,
+              conditional_odds_ratio=1.068697577856801,
+              conditional_odds_ratio_ci=(0.7949398282935892,
+                                         1.436229679394333))),
+    (Parameters(table=[[100, 2], [1000, 5]],
+                confidence_level=0.95,
+                alternative='less'),
+     RResults(pvalue=0.1300759363430016,
+              conditional_odds_ratio=0.25055839934223,
+              conditional_odds_ratio_ci=(0,
+                                         1.797867027270803))),
+    (Parameters(table=[[2, 7], [8, 2]],
+                confidence_level=0.95,
+                alternative='less'),
+     RResults(pvalue=0.0185217259520665,
+              conditional_odds_ratio=0.0858623513573622,
+              conditional_odds_ratio_ci=(0,
+                                         0.6785254803404526))),
+    (Parameters(table=[[5, 1], [10, 10]],
+                confidence_level=0.95,
+                alternative='less'),
+     RResults(pvalue=0.9782608695652173,
+              conditional_odds_ratio=4.725646047336587,
+              conditional_odds_ratio_ci=(0,
+                                         127.8497388102893))),
+    (Parameters(table=[[5, 15], [20, 20]],
+                confidence_level=0.95,
+                alternative='less'),
+     RResults(pvalue=0.05625775074399956,
+              conditional_odds_ratio=0.3394396617440851,
+              conditional_odds_ratio_ci=(0,
+                                         1.032332939718425))),
+    (Parameters(table=[[5, 16], [16, 25]],
+                confidence_level=0.95,
+                alternative='less'),
+     RResults(pvalue=0.1808979350599346,
+              conditional_odds_ratio=0.4937791394540491,
+              conditional_odds_ratio_ci=(0,
+                                         1.502407513296985))),
+    (Parameters(table=[[10, 5], [10, 1]],
+                confidence_level=0.95,
+                alternative='less'),
+     RResults(pvalue=0.1652173913043479,
+              conditional_odds_ratio=0.2116112781158479,
+              conditional_odds_ratio_ci=(0,
+                                         1.820421051562392))),
+    (Parameters(table=[[10, 5], [10, 0]],
+                confidence_level=0.95,
+                alternative='less'),
+     RResults(pvalue=0.0565217391304348,
+              conditional_odds_ratio=0,
+              conditional_odds_ratio_ci=(0,
+                                         1.06224603077045))),
+    (Parameters(table=[[5, 0], [1, 4]],
+                confidence_level=0.95,
+                alternative='less'),
+     RResults(pvalue=1,
+              conditional_odds_ratio=Inf,
+              conditional_odds_ratio_ci=(0,
+                                         Inf))),
+    (Parameters(table=[[0, 5], [1, 4]],
+                confidence_level=0.95,
+                alternative='less'),
+     RResults(pvalue=0.5,
+              conditional_odds_ratio=0,
+              conditional_odds_ratio_ci=(0,
+                                         19.00192394479939))),
+    (Parameters(table=[[5, 1], [0, 4]],
+                confidence_level=0.95,
+                alternative='less'),
+     RResults(pvalue=1,
+              conditional_odds_ratio=Inf,
+              conditional_odds_ratio_ci=(0,
+                                         Inf))),
+    (Parameters(table=[[0, 1], [3, 2]],
+                confidence_level=0.95,
+                alternative='less'),
+     RResults(pvalue=0.4999999999999999,
+              conditional_odds_ratio=0,
+              conditional_odds_ratio_ci=(0,
+                                         19.00192394479939))),
+    (Parameters(table=[[200, 7], [8, 300]],
+                confidence_level=0.95,
+                alternative='less'),
+     RResults(pvalue=1,
+              conditional_odds_ratio=977.7866978606228,
+              conditional_odds_ratio_ci=(0,
+                                         3045.460216525746))),
+    (Parameters(table=[[28, 21], [6, 1957]],
+                confidence_level=0.95,
+                alternative='less'),
+     RResults(pvalue=1,
+              conditional_odds_ratio=425.2403028434684,
+              conditional_odds_ratio_ci=(0,
+                                         1186.440170942579))),
+    (Parameters(table=[[190, 800], [200, 900]],
+                confidence_level=0.95,
+                alternative='less'),
+     RResults(pvalue=0.7416227010368963,
+              conditional_odds_ratio=1.068697577856801,
+              conditional_odds_ratio_ci=(0,
+                                         1.293551891610822))),
+    (Parameters(table=[[100, 2], [1000, 5]],
+                confidence_level=0.99,
+                alternative='less'),
+     RResults(pvalue=0.1300759363430016,
+              conditional_odds_ratio=0.25055839934223,
+              conditional_odds_ratio_ci=(0,
+                                         4.375946050832565))),
+    (Parameters(table=[[2, 7], [8, 2]],
+                confidence_level=0.99,
+                alternative='less'),
+     RResults(pvalue=0.0185217259520665,
+              conditional_odds_ratio=0.0858623513573622,
+              conditional_odds_ratio_ci=(0,
+                                         1.235282118191202))),
+    (Parameters(table=[[5, 1], [10, 10]],
+                confidence_level=0.99,
+                alternative='less'),
+     RResults(pvalue=0.9782608695652173,
+              conditional_odds_ratio=4.725646047336587,
+              conditional_odds_ratio_ci=(0,
+                                         657.2063583945989))),
+    (Parameters(table=[[5, 15], [20, 20]],
+                confidence_level=0.99,
+                alternative='less'),
+     RResults(pvalue=0.05625775074399956,
+              conditional_odds_ratio=0.3394396617440851,
+              conditional_odds_ratio_ci=(0,
+                                         1.498867660683128))),
+    (Parameters(table=[[5, 16], [16, 25]],
+                confidence_level=0.99,
+                alternative='less'),
+     RResults(pvalue=0.1808979350599346,
+              conditional_odds_ratio=0.4937791394540491,
+              conditional_odds_ratio_ci=(0,
+                                         2.186159386716762))),
+    (Parameters(table=[[10, 5], [10, 1]],
+                confidence_level=0.99,
+                alternative='less'),
+     RResults(pvalue=0.1652173913043479,
+              conditional_odds_ratio=0.2116112781158479,
+              conditional_odds_ratio_ci=(0,
+                                         3.335351451901569))),
+    (Parameters(table=[[10, 5], [10, 0]],
+                confidence_level=0.99,
+                alternative='less'),
+     RResults(pvalue=0.0565217391304348,
+              conditional_odds_ratio=0,
+              conditional_odds_ratio_ci=(0,
+                                         2.075407697450433))),
+    (Parameters(table=[[5, 0], [1, 4]],
+                confidence_level=0.99,
+                alternative='less'),
+     RResults(pvalue=1,
+              conditional_odds_ratio=Inf,
+              conditional_odds_ratio_ci=(0,
+                                         Inf))),
+    (Parameters(table=[[0, 5], [1, 4]],
+                confidence_level=0.99,
+                alternative='less'),
+     RResults(pvalue=0.5,
+              conditional_odds_ratio=0,
+              conditional_odds_ratio_ci=(0,
+                                         99.00009507969122))),
+    (Parameters(table=[[5, 1], [0, 4]],
+                confidence_level=0.99,
+                alternative='less'),
+     RResults(pvalue=1,
+              conditional_odds_ratio=Inf,
+              conditional_odds_ratio_ci=(0,
+                                         Inf))),
+    (Parameters(table=[[0, 1], [3, 2]],
+                confidence_level=0.99,
+                alternative='less'),
+     RResults(pvalue=0.4999999999999999,
+              conditional_odds_ratio=0,
+              conditional_odds_ratio_ci=(0,
+                                         99.00009507969123))),
+    (Parameters(table=[[200, 7], [8, 300]],
+                confidence_level=0.99,
+                alternative='less'),
+     RResults(pvalue=1,
+              conditional_odds_ratio=977.7866978606228,
+              conditional_odds_ratio_ci=(0,
+                                         4503.078257659934))),
+    (Parameters(table=[[28, 21], [6, 1957]],
+                confidence_level=0.99,
+                alternative='less'),
+     RResults(pvalue=1,
+              conditional_odds_ratio=425.2403028434684,
+              conditional_odds_ratio_ci=(0,
+                                         1811.766127544222))),
+    (Parameters(table=[[190, 800], [200, 900]],
+                confidence_level=0.99,
+                alternative='less'),
+     RResults(pvalue=0.7416227010368963,
+              conditional_odds_ratio=1.068697577856801,
+              conditional_odds_ratio_ci=(0,
+                                         1.396522811516685))),
+    (Parameters(table=[[100, 2], [1000, 5]],
+                confidence_level=0.95,
+                alternative='greater'),
+     RResults(pvalue=0.979790445314723,
+              conditional_odds_ratio=0.25055839934223,
+              conditional_odds_ratio_ci=(0.05119649909830196,
+                                         Inf))),
+    (Parameters(table=[[2, 7], [8, 2]],
+                confidence_level=0.95,
+                alternative='greater'),
+     RResults(pvalue=0.9990149169715733,
+              conditional_odds_ratio=0.0858623513573622,
+              conditional_odds_ratio_ci=(0.007163749169069961,
+                                         Inf))),
+    (Parameters(table=[[5, 1], [10, 10]],
+                confidence_level=0.95,
+                alternative='greater'),
+     RResults(pvalue=0.1652173913043478,
+              conditional_odds_ratio=4.725646047336587,
+              conditional_odds_ratio_ci=(0.5493234651081089,
+                                         Inf))),
+    (Parameters(table=[[5, 15], [20, 20]],
+                confidence_level=0.95,
+                alternative='greater'),
+     RResults(pvalue=0.9849086665340765,
+              conditional_odds_ratio=0.3394396617440851,
+              conditional_odds_ratio_ci=(0.1003538933958604,
+                                         Inf))),
+    (Parameters(table=[[5, 16], [16, 25]],
+                confidence_level=0.95,
+                alternative='greater'),
+     RResults(pvalue=0.9330176609214881,
+              conditional_odds_ratio=0.4937791394540491,
+              conditional_odds_ratio_ci=(0.146507416280863,
+                                         Inf))),
+    (Parameters(table=[[10, 5], [10, 1]],
+                confidence_level=0.95,
+                alternative='greater'),
+     RResults(pvalue=0.9782608695652174,
+              conditional_odds_ratio=0.2116112781158479,
+              conditional_odds_ratio_ci=(0.007821681994077808,
+                                         Inf))),
+    (Parameters(table=[[10, 5], [10, 0]],
+                confidence_level=0.95,
+                alternative='greater'),
+     RResults(pvalue=1,
+              conditional_odds_ratio=0,
+              conditional_odds_ratio_ci=(0,
+                                         Inf))),
+    (Parameters(table=[[5, 0], [1, 4]],
+                confidence_level=0.95,
+                alternative='greater'),
+     RResults(pvalue=0.02380952380952382,
+              conditional_odds_ratio=Inf,
+              conditional_odds_ratio_ci=(1.487678929918272,
+                                         Inf))),
+    (Parameters(table=[[0, 5], [1, 4]],
+                confidence_level=0.95,
+                alternative='greater'),
+     RResults(pvalue=1,
+              conditional_odds_ratio=0,
+              conditional_odds_ratio_ci=(0,
+                                         Inf))),
+    (Parameters(table=[[5, 1], [0, 4]],
+                confidence_level=0.95,
+                alternative='greater'),
+     RResults(pvalue=0.0238095238095238,
+              conditional_odds_ratio=Inf,
+              conditional_odds_ratio_ci=(1.487678929918272,
+                                         Inf))),
+    (Parameters(table=[[0, 1], [3, 2]],
+                confidence_level=0.95,
+                alternative='greater'),
+     RResults(pvalue=1,
+              conditional_odds_ratio=0,
+              conditional_odds_ratio_ci=(0,
+                                         Inf))),
+    (Parameters(table=[[200, 7], [8, 300]],
+                confidence_level=0.95,
+                alternative='greater'),
+     RResults(pvalue=2.005657880388915e-122,
+              conditional_odds_ratio=977.7866978606228,
+              conditional_odds_ratio_ci=(397.784359748113,
+                                         Inf))),
+    (Parameters(table=[[28, 21], [6, 1957]],
+                confidence_level=0.95,
+                alternative='greater'),
+     RResults(pvalue=5.728437460831983e-44,
+              conditional_odds_ratio=425.2403028434684,
+              conditional_odds_ratio_ci=(174.7148056880929,
+                                         Inf))),
+    (Parameters(table=[[190, 800], [200, 900]],
+                confidence_level=0.95,
+                alternative='greater'),
+     RResults(pvalue=0.2959825901308897,
+              conditional_odds_ratio=1.068697577856801,
+              conditional_odds_ratio_ci=(0.8828406663967776,
+                                         Inf))),
+    (Parameters(table=[[100, 2], [1000, 5]],
+                confidence_level=0.99,
+                alternative='greater'),
+     RResults(pvalue=0.979790445314723,
+              conditional_odds_ratio=0.25055839934223,
+              conditional_odds_ratio_ci=(0.03045407081240429,
+                                         Inf))),
+    (Parameters(table=[[2, 7], [8, 2]],
+                confidence_level=0.99,
+                alternative='greater'),
+     RResults(pvalue=0.9990149169715733,
+              conditional_odds_ratio=0.0858623513573622,
+              conditional_odds_ratio_ci=(0.002768053063547901,
+                                         Inf))),
+    (Parameters(table=[[5, 1], [10, 10]],
+                confidence_level=0.99,
+                alternative='greater'),
+     RResults(pvalue=0.1652173913043478,
+              conditional_odds_ratio=4.725646047336587,
+              conditional_odds_ratio_ci=(0.2998184792279909,
+                                         Inf))),
+    (Parameters(table=[[5, 15], [20, 20]],
+                confidence_level=0.99,
+                alternative='greater'),
+     RResults(pvalue=0.9849086665340765,
+              conditional_odds_ratio=0.3394396617440851,
+              conditional_odds_ratio_ci=(0.06180414342643172,
+                                         Inf))),
+    (Parameters(table=[[5, 16], [16, 25]],
+                confidence_level=0.99,
+                alternative='greater'),
+     RResults(pvalue=0.9330176609214881,
+              conditional_odds_ratio=0.4937791394540491,
+              conditional_odds_ratio_ci=(0.09037094010066403,
+                                         Inf))),
+    (Parameters(table=[[10, 5], [10, 1]],
+                confidence_level=0.99,
+                alternative='greater'),
+     RResults(pvalue=0.9782608695652174,
+              conditional_odds_ratio=0.2116112781158479,
+              conditional_odds_ratio_ci=(0.001521592095430679,
+                                         Inf))),
+    (Parameters(table=[[10, 5], [10, 0]],
+                confidence_level=0.99,
+                alternative='greater'),
+     RResults(pvalue=1,
+              conditional_odds_ratio=0,
+              conditional_odds_ratio_ci=(0,
+                                         Inf))),
+    (Parameters(table=[[5, 0], [1, 4]],
+                confidence_level=0.99,
+                alternative='greater'),
+     RResults(pvalue=0.02380952380952382,
+              conditional_odds_ratio=Inf,
+              conditional_odds_ratio_ci=(0.6661157890359722,
+                                         Inf))),
+    (Parameters(table=[[0, 5], [1, 4]],
+                confidence_level=0.99,
+                alternative='greater'),
+     RResults(pvalue=1,
+              conditional_odds_ratio=0,
+              conditional_odds_ratio_ci=(0,
+                                         Inf))),
+    (Parameters(table=[[5, 1], [0, 4]],
+                confidence_level=0.99,
+                alternative='greater'),
+     RResults(pvalue=0.0238095238095238,
+              conditional_odds_ratio=Inf,
+              conditional_odds_ratio_ci=(0.6661157890359725,
+                                         Inf))),
+    (Parameters(table=[[0, 1], [3, 2]],
+                confidence_level=0.99,
+                alternative='greater'),
+     RResults(pvalue=1,
+              conditional_odds_ratio=0,
+              conditional_odds_ratio_ci=(0,
+                                         Inf))),
+    (Parameters(table=[[200, 7], [8, 300]],
+                confidence_level=0.99,
+                alternative='greater'),
+     RResults(pvalue=2.005657880388915e-122,
+              conditional_odds_ratio=977.7866978606228,
+              conditional_odds_ratio_ci=(297.9619252357688,
+                                         Inf))),
+    (Parameters(table=[[28, 21], [6, 1957]],
+                confidence_level=0.99,
+                alternative='greater'),
+     RResults(pvalue=5.728437460831983e-44,
+              conditional_odds_ratio=425.2403028434684,
+              conditional_odds_ratio_ci=(130.3213490295859,
+                                         Inf))),
+    (Parameters(table=[[190, 800], [200, 900]],
+                confidence_level=0.99,
+                alternative='greater'),
+     RResults(pvalue=0.2959825901308897,
+              conditional_odds_ratio=1.068697577856801,
+              conditional_odds_ratio_ci=(0.8176272148267533,
+                                         Inf))),
+]

llava_next/lib/python3.10/site-packages/scipy/stats/tests/data/nist_anova/AtmWtAg.dat

@@ -0,0 +1,108 @@
+NIST/ITL StRD 
+Dataset Name:   AtmWtAg   (AtmWtAg.dat)
+
+
+File Format:    ASCII
+                Certified Values   (lines 41 to 47)
+                Data               (lines 61 to 108) 
+
+
+Procedure:      Analysis of Variance
+
+
+Reference:      Powell, L.J., Murphy, T.J. and Gramlich, J.W. (1982).
+                "The Absolute Isotopic Abundance & Atomic Weight
+                of a Reference Sample of Silver".
+                NBS Journal of Research, 87, pp. 9-19.
+
+
+Data:           1 Factor
+                2 Treatments
+                24 Replicates/Cell
+                48 Observations
+                7 Constant Leading Digits
+                Average Level of Difficulty
+                Observed Data
+
+
+Model:          3 Parameters (mu, tau_1, tau_2)
+                y_{ij} = mu + tau_i + epsilon_{ij}
+
+
+
+
+
+
+Certified Values:
+
+Source of                  Sums of               Mean               
+Variation          df      Squares              Squares             F Statistic
+
+
+Between Instrument  1 3.63834187500000E-09 3.63834187500000E-09 1.59467335677930E+01
+Within Instrument  46 1.04951729166667E-08 2.28155932971014E-10
+
+                   Certified R-Squared 2.57426544538321E-01
+
+                   Certified Residual
+                   Standard Deviation  1.51048314446410E-05
+
+
+
+
+
+
+
+
+
+
+
+Data:  Instrument           AgWt
+           1            107.8681568
+           1            107.8681465
+           1            107.8681572
+           1            107.8681785
+           1            107.8681446
+           1            107.8681903
+           1            107.8681526
+           1            107.8681494
+           1            107.8681616
+           1            107.8681587
+           1            107.8681519
+           1            107.8681486
+           1            107.8681419
+           1            107.8681569
+           1            107.8681508
+           1            107.8681672
+           1            107.8681385
+           1            107.8681518
+           1            107.8681662
+           1            107.8681424
+           1            107.8681360
+           1            107.8681333
+           1            107.8681610
+           1            107.8681477
+           2            107.8681079
+           2            107.8681344
+           2            107.8681513
+           2            107.8681197
+           2            107.8681604
+           2            107.8681385
+           2            107.8681642
+           2            107.8681365
+           2            107.8681151
+           2            107.8681082
+           2            107.8681517
+           2            107.8681448
+           2            107.8681198
+           2            107.8681482
+           2            107.8681334
+           2            107.8681609
+           2            107.8681101
+           2            107.8681512
+           2            107.8681469
+           2            107.8681360
+           2            107.8681254
+           2            107.8681261
+           2            107.8681450
+           2            107.8681368

llava_next/lib/python3.10/site-packages/scipy/stats/tests/data/nist_anova/SiRstv.dat

@@ -0,0 +1,85 @@
+NIST/ITL StRD 
+Dataset Name:   SiRstv     (SiRstv.dat)
+
+
+File Format:    ASCII
+                Certified Values   (lines 41 to 47)
+                Data               (lines 61 to 85) 
+
+
+Procedure:      Analysis of Variance
+
+
+Reference:      Ehrstein, James and Croarkin, M. Carroll.
+                Unpublished NIST dataset.
+
+
+Data:           1 Factor
+                5 Treatments
+                5  Replicates/Cell
+                25 Observations
+                3 Constant Leading Digits
+                Lower Level of Difficulty
+                Observed Data
+
+
+Model:          6 Parameters (mu,tau_1, ... , tau_5)
+                y_{ij} = mu + tau_i + epsilon_{ij}
+
+
+
+
+
+
+
+
+Certified Values:
+
+Source of                  Sums of               Mean               
+Variation          df      Squares              Squares             F Statistic
+
+Between Instrument  4 5.11462616000000E-02 1.27865654000000E-02 1.18046237440255E+00
+Within Instrument  20 2.16636560000000E-01 1.08318280000000E-02
+
+                   Certified R-Squared 1.90999039051129E-01
+
+                   Certified Residual
+                   Standard Deviation  1.04076068334656E-01
+
+
+
+
+
+
+
+
+
+
+
+
+Data:  Instrument   Resistance
+           1         196.3052
+           1         196.1240
+           1         196.1890
+           1         196.2569
+           1         196.3403
+           2         196.3042
+           2         196.3825
+           2         196.1669
+           2         196.3257
+           2         196.0422
+           3         196.1303
+           3         196.2005
+           3         196.2889
+           3         196.0343
+           3         196.1811
+           4         196.2795
+           4         196.1748
+           4         196.1494
+           4         196.1485
+           4         195.9885
+           5         196.2119
+           5         196.1051
+           5         196.1850
+           5         196.0052
+           5         196.2090

llava_next/lib/python3.10/site-packages/scipy/stats/tests/data/nist_anova/SmLs01.dat

@@ -0,0 +1,249 @@
+NIST/ITL StRD 
+Dataset Name:   SmLs01   (SmLs01.dat)
+
+
+File Format:    ASCII
+                Certified Values   (lines 41 to 47)
+                Data               (lines 61 to 249) 
+
+
+Procedure:      Analysis of Variance
+
+
+Reference:      Simon, Stephen D. and Lesage, James P. (1989).
+                "Assessing the Accuracy of ANOVA Calculations in
+                Statistical Software".
+                Computational Statistics & Data Analysis, 8, pp. 325-332.
+
+
+Data:           1 Factor
+                9 Treatments
+                21 Replicates/Cell
+                189 Observations
+                1 Constant Leading Digit
+                Lower Level of Difficulty
+                Generated Data
+
+
+Model:          10 Parameters (mu,tau_1, ... , tau_9)
+                y_{ij} = mu + tau_i + epsilon_{ij}
+
+
+
+
+
+
+Certified Values:
+
+Source of                  Sums of               Mean               
+Variation          df      Squares              Squares            F Statistic
+
+Between Treatment   8 1.68000000000000E+00 2.10000000000000E-01 2.10000000000000E+01
+Within Treatment  180 1.80000000000000E+00 1.00000000000000E-02
+
+                  Certified R-Squared 4.82758620689655E-01
+
+                  Certified Residual
+                  Standard Deviation  1.00000000000000E-01
+
+
+
+
+
+
+
+
+
+
+
+
+Data:  Treatment   Response
+           1         1.4
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           2         1.3
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           3         1.5
+           3         1.4
+           3         1.6
+           3         1.4
+           3         1.6
+           3         1.4
+           3         1.6
+           3         1.4
+           3         1.6
+           3         1.4
+           3         1.6
+           3         1.4
+           3         1.6
+           3         1.4
+           3         1.6
+           3         1.4
+           3         1.6
+           3         1.4
+           3         1.6
+           3         1.4
+           3         1.6
+           4         1.3
+           4         1.2
+           4         1.4
+           4         1.2
+           4         1.4
+           4         1.2
+           4         1.4
+           4         1.2
+           4         1.4
+           4         1.2
+           4         1.4
+           4         1.2
+           4         1.4
+           4         1.2
+           4         1.4
+           4         1.2
+           4         1.4
+           4         1.2
+           4         1.4
+           4         1.2
+           4         1.4
+           5         1.5
+           5         1.4
+           5         1.6
+           5         1.4
+           5         1.6
+           5         1.4
+           5         1.6
+           5         1.4
+           5         1.6
+           5         1.4
+           5         1.6
+           5         1.4
+           5         1.6
+           5         1.4
+           5         1.6
+           5         1.4
+           5         1.6
+           5         1.4
+           5         1.6
+           5         1.4
+           5         1.6
+           6         1.3
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           7         1.5
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           8         1.3
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           9         1.5
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6

llava_next/lib/python3.10/site-packages/scipy/stats/tests/data/nist_anova/SmLs04.dat

@@ -0,0 +1,249 @@
+NIST/ITL StRD 
+Dataset Name:   SmLs04   (SmLs04.dat)
+
+
+File Format:    ASCII
+                Certified Values   (lines 41 to 47)
+                Data               (lines 61 to 249) 
+
+
+Procedure:      Analysis of Variance
+
+
+Reference:      Simon, Stephen D. and Lesage, James P. (1989).
+                "Assessing the Accuracy of ANOVA Calculations in
+                Statistical Software".
+                Computational Statistics & Data Analysis, 8, pp. 325-332.
+
+
+Data:           1 Factor
+                9 Treatments
+                21 Replicates/Cell
+                189 Observations
+                7 Constant Leading Digits
+                Average Level of Difficulty
+                Generated Data
+
+
+Model:          10 Parameters (mu,tau_1, ... , tau_9)
+                y_{ij} = mu + tau_i + epsilon_{ij}
+
+
+
+
+
+
+Certified Values:
+
+Source of                  Sums of               Mean               
+Variation          df      Squares              Squares             F Statistic
+
+Between Treatment   8 1.68000000000000E+00 2.10000000000000E-01 2.10000000000000E+01
+Within Treatment  180 1.80000000000000E+00 1.00000000000000E-02
+
+                  Certified R-Squared 4.82758620689655E-01
+
+                  Certified Residual
+                  Standard Deviation  1.00000000000000E-01
+
+
+
+
+
+
+
+
+
+
+
+
+Data:  Treatment   Response
+           1       1000000.4
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           2       1000000.3
+           2       1000000.2
+           2       1000000.4
+           2       1000000.2
+           2       1000000.4
+           2       1000000.2
+           2       1000000.4
+           2       1000000.2
+           2       1000000.4
+           2       1000000.2
+           2       1000000.4
+           2       1000000.2
+           2       1000000.4
+           2       1000000.2
+           2       1000000.4
+           2       1000000.2
+           2       1000000.4
+           2       1000000.2
+           2       1000000.4
+           2       1000000.2
+           2       1000000.4
+           3       1000000.5
+           3       1000000.4
+           3       1000000.6
+           3       1000000.4
+           3       1000000.6
+           3       1000000.4
+           3       1000000.6
+           3       1000000.4
+           3       1000000.6
+           3       1000000.4
+           3       1000000.6
+           3       1000000.4
+           3       1000000.6
+           3       1000000.4
+           3       1000000.6
+           3       1000000.4
+           3       1000000.6
+           3       1000000.4
+           3       1000000.6
+           3       1000000.4
+           3       1000000.6
+           4       1000000.3
+           4       1000000.2
+           4       1000000.4
+           4       1000000.2
+           4       1000000.4
+           4       1000000.2
+           4       1000000.4
+           4       1000000.2
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+           5       1000000.6
+           5       1000000.4
+           5       1000000.6
+           5       1000000.4
+           5       1000000.6
+           5       1000000.4
+           5       1000000.6
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+           5       1000000.4
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+           5       1000000.4
+           5       1000000.6
+           5       1000000.4
+           5       1000000.6
+           5       1000000.4
+           5       1000000.6
+           5       1000000.4
+           5       1000000.6
+           6       1000000.3
+           6       1000000.2
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+           6       1000000.2
+           6       1000000.4
+           6       1000000.2
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+           6       1000000.2
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+           6       1000000.2
+           6       1000000.4
+           6       1000000.2
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+           6       1000000.2
+           6       1000000.4
+           6       1000000.2
+           6       1000000.4
+           6       1000000.2
+           6       1000000.4
+           7       1000000.5
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+           7       1000000.4
+           7       1000000.6
+           7       1000000.4
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+           7       1000000.6
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+           8       1000000.3
+           8       1000000.2
+           8       1000000.4
+           8       1000000.2
+           8       1000000.4
+           8       1000000.2
+           8       1000000.4
+           8       1000000.2
+           8       1000000.4
+           8       1000000.2
+           8       1000000.4
+           8       1000000.2
+           8       1000000.4
+           8       1000000.2
+           8       1000000.4
+           8       1000000.2
+           8       1000000.4
+           8       1000000.2
+           8       1000000.4
+           8       1000000.2
+           8       1000000.4
+           9       1000000.5
+           9       1000000.4
+           9       1000000.6
+           9       1000000.4
+           9       1000000.6
+           9       1000000.4
+           9       1000000.6
+           9       1000000.4
+           9       1000000.6
+           9       1000000.4
+           9       1000000.6
+           9       1000000.4
+           9       1000000.6
+           9       1000000.4
+           9       1000000.6
+           9       1000000.4
+           9       1000000.6
+           9       1000000.4
+           9       1000000.6
+           9       1000000.4
+           9       1000000.6

llava_next/lib/python3.10/site-packages/scipy/stats/tests/data/nist_anova/SmLs05.dat

@@ -0,0 +1,1869 @@
+NIST/ITL StRD 
+Dataset Name:   SmLs05   (SmLs05.dat)
+
+
+File Format:    ASCII
+                Certified Values   (lines 41 to 47)
+                Data               (lines 61 to 1869) 
+
+
+Procedure:      Analysis of Variance
+
+
+Reference:      Simon, Stephen D. and Lesage, James P. (1989).
+                "Assessing the Accuracy of ANOVA Calculations in
+                Statistical Software".
+                Computational Statistics & Data Analysis, 8, pp. 325-332.
+
+
+Data:           1 Factor
+                9 Treatments
+                201 Replicates/Cell
+                1809 Observations
+                7 Constant Leading Digits
+                Average Level of Difficulty
+                Generated Data
+
+
+Model:          10 Parameters (mu,tau_1, ... , tau_9)
+                y_{ij} = mu + tau_i + epsilon_{ij}
+
+
+
+
+
+
+Certified Values:
+
+Source of                  Sums of               Mean               
+Variation          df      Squares              Squares            F Statistic
+
+Between Treatment    8 1.60800000000000E+01 2.01000000000000E+00 2.01000000000000E+02
+Within Treatment  1800 1.80000000000000E+01 1.00000000000000E-02
+
+                  Certified R-Squared 4.71830985915493E-01
+
+                  Certified Residual
+                  Standard Deviation  1.00000000000000E-01
+
+
+
+
+
+
+
+
+
+
+
+
+Data:  Treatment   Response
+           1       1000000.4
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
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llava_next/lib/python3.10/site-packages/scipy/stats/tests/data/nist_anova/SmLs07.dat

@@ -0,0 +1,249 @@
+NIST/ITL StRD 
+Dataset Name:   SmLs07   (SmLs07.dat)
+
+
+File Format:    ASCII
+                Certified Values   (lines 41 to 47)
+                Data               (lines 61 to 249) 
+
+
+Procedure:      Analysis of Variance
+
+
+Reference:      Simon, Stephen D. and Lesage, James P. (1989).
+                "Assessing the Accuracy of ANOVA Calculations in
+                Statistical Software".
+                Computational Statistics & Data Analysis, 8, pp. 325-332.
+
+
+Data:           1 Factor
+                9 Treatments
+                21 Replicates/Cell
+                189 Observations
+                13 Constant Leading Digits
+                Higher Level of Difficulty
+                Generated Data
+
+
+Model:          10 Parameters (mu,tau_1, ... , tau_9)
+                y_{ij} = mu + tau_i + epsilon_{ij}
+
+
+
+
+
+
+Certified Values:
+
+Source of                  Sums of               Mean               
+Variation          df      Squares              Squares            F Statistic
+
+Between Treatment   8 1.68000000000000E+00 2.10000000000000E-01 2.10000000000000E+01
+Within Treatment  180 1.80000000000000E+00 1.00000000000000E-02
+
+                  Certified R-Squared 4.82758620689655E-01
+
+                  Certified Residual
+                  Standard Deviation  1.00000000000000E-01
+
+
+
+
+
+
+
+
+
+
+
+
+Data:  Treatment   Response
+           1    1000000000000.4
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           2    1000000000000.3
+           2    1000000000000.2
+           2    1000000000000.4
+           2    1000000000000.2
+           2    1000000000000.4
+           2    1000000000000.2
+           2    1000000000000.4
+           2    1000000000000.2
+           2    1000000000000.4
+           2    1000000000000.2
+           2    1000000000000.4
+           2    1000000000000.2
+           2    1000000000000.4
+           2    1000000000000.2
+           2    1000000000000.4
+           2    1000000000000.2
+           2    1000000000000.4
+           2    1000000000000.2
+           2    1000000000000.4
+           2    1000000000000.2
+           2    1000000000000.4
+           3    1000000000000.5
+           3    1000000000000.4
+           3    1000000000000.6
+           3    1000000000000.4
+           3    1000000000000.6
+           3    1000000000000.4
+           3    1000000000000.6
+           3    1000000000000.4
+           3    1000000000000.6
+           3    1000000000000.4
+           3    1000000000000.6
+           3    1000000000000.4
+           3    1000000000000.6
+           3    1000000000000.4
+           3    1000000000000.6
+           3    1000000000000.4
+           3    1000000000000.6
+           3    1000000000000.4
+           3    1000000000000.6
+           3    1000000000000.4
+           3    1000000000000.6
+           4    1000000000000.3
+           4    1000000000000.2
+           4    1000000000000.4
+           4    1000000000000.2
+           4    1000000000000.4
+           4    1000000000000.2
+           4    1000000000000.4
+           4    1000000000000.2
+           4    1000000000000.4
+           4    1000000000000.2
+           4    1000000000000.4
+           4    1000000000000.2
+           4    1000000000000.4
+           4    1000000000000.2
+           4    1000000000000.4
+           4    1000000000000.2
+           4    1000000000000.4
+           4    1000000000000.2
+           4    1000000000000.4
+           4    1000000000000.2
+           4    1000000000000.4
+           5    1000000000000.5
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+           5    1000000000000.6
+           5    1000000000000.4
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+           5    1000000000000.4
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+           5    1000000000000.4
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+           5    1000000000000.4
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+           5    1000000000000.6
+           5    1000000000000.4
+           5    1000000000000.6
+           5    1000000000000.4
+           5    1000000000000.6
+           5    1000000000000.4
+           5    1000000000000.6
+           6    1000000000000.3
+           6    1000000000000.2
+           6    1000000000000.4
+           6    1000000000000.2
+           6    1000000000000.4
+           6    1000000000000.2
+           6    1000000000000.4
+           6    1000000000000.2
+           6    1000000000000.4
+           6    1000000000000.2
+           6    1000000000000.4
+           6    1000000000000.2
+           6    1000000000000.4
+           6    1000000000000.2
+           6    1000000000000.4
+           6    1000000000000.2
+           6    1000000000000.4
+           6    1000000000000.2
+           6    1000000000000.4
+           6    1000000000000.2
+           6    1000000000000.4
+           7    1000000000000.5
+           7    1000000000000.4
+           7    1000000000000.6
+           7    1000000000000.4
+           7    1000000000000.6
+           7    1000000000000.4
+           7    1000000000000.6
+           7    1000000000000.4
+           7    1000000000000.6
+           7    1000000000000.4
+           7    1000000000000.6
+           7    1000000000000.4
+           7    1000000000000.6
+           7    1000000000000.4
+           7    1000000000000.6
+           7    1000000000000.4
+           7    1000000000000.6
+           7    1000000000000.4
+           7    1000000000000.6
+           7    1000000000000.4
+           7    1000000000000.6
+           8    1000000000000.3
+           8    1000000000000.2
+           8    1000000000000.4
+           8    1000000000000.2
+           8    1000000000000.4
+           8    1000000000000.2
+           8    1000000000000.4
+           8    1000000000000.2
+           8    1000000000000.4
+           8    1000000000000.2
+           8    1000000000000.4
+           8    1000000000000.2
+           8    1000000000000.4
+           8    1000000000000.2
+           8    1000000000000.4
+           8    1000000000000.2
+           8    1000000000000.4
+           8    1000000000000.2
+           8    1000000000000.4
+           8    1000000000000.2
+           8    1000000000000.4
+           9    1000000000000.5
+           9    1000000000000.4
+           9    1000000000000.6
+           9    1000000000000.4
+           9    1000000000000.6
+           9    1000000000000.4
+           9    1000000000000.6
+           9    1000000000000.4
+           9    1000000000000.6
+           9    1000000000000.4
+           9    1000000000000.6
+           9    1000000000000.4
+           9    1000000000000.6
+           9    1000000000000.4
+           9    1000000000000.6
+           9    1000000000000.4
+           9    1000000000000.6
+           9    1000000000000.4
+           9    1000000000000.6
+           9    1000000000000.4
+           9    1000000000000.6

llava_next/lib/python3.10/site-packages/scipy/stats/tests/data/nist_linregress/Norris.dat

@@ -0,0 +1,97 @@
+NIST/ITL StRD
+Dataset Name:  Norris (Norris.dat)
+
+File Format:   ASCII
+               Certified Values  (lines 31 to 46)
+               Data              (lines 61 to 96)
+
+Procedure:     Linear Least Squares Regression
+
+Reference:     Norris, J., NIST.  
+               Calibration of Ozone Monitors.
+
+Data:          1 Response Variable (y)
+               1 Predictor Variable (x)
+               36 Observations
+               Lower Level of Difficulty
+               Observed Data
+
+Model:         Linear Class
+               2 Parameters (B0,B1)
+
+               y = B0 + B1*x + e
+
+
+
+               Certified Regression Statistics
+
+                                          Standard Deviation
+     Parameter          Estimate             of Estimate
+
+        B0        -0.262323073774029     0.232818234301152
+        B1         1.00211681802045      0.429796848199937E-03
+
+     Residual
+     Standard Deviation   0.884796396144373
+
+     R-Squared            0.999993745883712
+
+
+               Certified Analysis of Variance Table
+
+Source of Degrees of    Sums of             Mean  
+Variation  Freedom      Squares            Squares           F Statistic
+              
+Regression    1     4255954.13232369   4255954.13232369   5436385.54079785
+Residual     34     26.6173985294224   0.782864662630069
+
+                 
+                                          
+                                          
+                                                           
+
+                            
+                                   
+                                                       
+
+
+
+
+Data:       y          x
+           0.1        0.2
+         338.8      337.4
+         118.1      118.2
+         888.0      884.6
+           9.2       10.1
+         228.1      226.5
+         668.5      666.3
+         998.5      996.3
+         449.1      448.6
+         778.9      777.0
+         559.2      558.2
+           0.3        0.4
+           0.1        0.6
+         778.1      775.5
+         668.8      666.9
+         339.3      338.0
+         448.9      447.5
+          10.8       11.6
+         557.7      556.0
+         228.3      228.1
+         998.0      995.8
+         888.8      887.6
+         119.6      120.2
+           0.3        0.3
+           0.6        0.3
+         557.6      556.8
+         339.3      339.1
+         888.0      887.2
+         998.5      999.0
+         778.9      779.0
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llava_next/lib/python3.10/site-packages/scipy/stats/tests/data/studentized_range_mpmath_ref.json

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parrot/lib/python3.10/site-packages/hjson-3.1.0.dist-info/INSTALLER

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+pip

parrot/lib/python3.10/site-packages/hjson-3.1.0.dist-info/LICENSE.txt

@@ -0,0 +1,81 @@
+hjson (based on simplejson) is dual-licensed software. It is available under the terms
+of the MIT license, or the Academic Free License version 2.1. The full
+text of each license agreement is included below. This code is also
+licensed to the Python Software Foundation (PSF) under a Contributor
+Agreement.
+
+MIT License
+===========
+
+Copyright (c) 2006 Bob Ippolito
+Copyright (c) 2015 Christian Zangl
+
+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.
+
+Academic Free License v. 2.1
+============================
+
+Copyright (c) 2006 Bob Ippolito.  All rights reserved.
+Copyright (c) 2015 Christian Zangl
+
+This Academic Free License (the "License") applies to any original work of authorship (the "Original Work") whose owner (the "Licensor") has placed the following notice immediately following the copyright notice for the Original Work:
+
+Licensed under the Academic Free License version 2.1
+
+1) Grant of Copyright License. Licensor hereby grants You a world-wide, royalty-free, non-exclusive, perpetual, sublicenseable license to do the following:
+
+a) to reproduce the Original Work in copies;
+
+b) to prepare derivative works ("Derivative Works") based upon the Original Work;
+
+c) to distribute copies of the Original Work and Derivative Works to the public;
+
+d) to perform the Original Work publicly; and
+
+e) to display the Original Work publicly.
+
+2) Grant of Patent License. Licensor hereby grants You a world-wide, royalty-free, non-exclusive, perpetual, sublicenseable license, under patent claims owned or controlled by the Licensor that are embodied in the Original Work as furnished by the Licensor, to make, use, sell and offer for sale the Original Work and Derivative Works.
+
+3) Grant of Source Code License. The term "Source Code" means the preferred form of the Original Work for making modifications to it and all available documentation describing how to modify the Original Work. Licensor hereby agrees to provide a machine-readable copy of the Source Code of the Original Work along with each copy of the Original Work that Licensor distributes. Licensor reserves the right to satisfy this obligation by placing a machine-readable copy of the Source Code in an information repository reasonably calculated to permit inexpensive and convenient access by You for as long as Licensor continues to distribute the Original Work, and by publishing the address of that information repository in a notice immediately following the copyright notice that applies to the Original Work.
+
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parrot/lib/python3.10/site-packages/hjson-3.1.0.dist-info/METADATA

@@ -0,0 +1,125 @@
+Metadata-Version: 2.1
+Name: hjson
+Version: 3.1.0
+Summary: Hjson, a user interface for JSON.
+Home-page: http://github.com/hjson/hjson-py
+Author: Christian Zangl
+Author-email: laktak@cdak.net
+License: MIT License
+Keywords: json comments configuration
+Platform: any
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: MIT License
+Classifier: License :: OSI Approved :: Academic Free License (AFL)
+Classifier: Programming Language :: Python
+Classifier: Programming Language :: Python :: 2
+Classifier: Programming Language :: Python :: 2.6
+Classifier: Programming Language :: Python :: 2.7
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.3
+Classifier: Programming Language :: Python :: 3.4
+Classifier: Programming Language :: Python :: 3.5
+Classifier: Programming Language :: Python :: Implementation :: CPython
+Classifier: Programming Language :: Python :: Implementation :: PyPy
+Classifier: Topic :: Software Development :: Libraries :: Python Modules
+License-File: LICENSE.txt
+
+hjson-py
+========
+
+`Hjson`_, a user interface for JSON
+
+Hjson works with Python 2.5+ and Python 3.3+ (based on `simplejson`_)
+
+Installation
+============
+
+-  ``pip install hjson``
+
+-  or download from https://pypi.python.org/pypi/hjson
+
+Commandline
+-----------
+
+::
+
+    Usage:
+      hjson [options]
+      hjson [options] <input>
+      hjson (-h | --help)
+      hjson (-V | --version)
+
+    Options:
+      -h --help     Show this screen.
+      -j            Output as formatted JSON.
+      -c            Output as JSON.
+      -V --version  Show version.
+
+E.g. ``echo '{"json":"obj"}' | hjson``
+
+
+Usage
+=====
+
+.. code-block:: python
+
+    import hjson
+
+Decoding Hjson
+--------------
+
+.. code-block:: python
+
+    text = """{
+      foo: a
+      bar: 1
+    }"""
+
+    hjson.loads(text)
+
+Result:
+
+.. code-block:: python
+
+    OrderedDict([('foo', 'a'), ('bar', 1)])
+
+Encoding Python object hierarchies
+----------------------------------
+
+.. code-block:: python
+
+    hjson.dumps({'foo': 'text', 'bar': (1, 2)})
+
+Result:
+
+::
+
+    {
+      foo: text
+      bar:
+      [
+        1
+        2
+      ]
+    }
+
+Encoding as JSON
+----------------
+
+Note that this is probably not as performant as the simplejson version.
+
+.. code-block:: python
+
+    hjson.dumpsJSON(['foo', {'bar': ('baz', None, 1.0, 2)}])
+
+Result: ``'["foo", {"bar": ["baz", null, 1.0, 2]}]'``
+
+API
+===
+
+`hjson-py`_
+
+.. _Hjson: https://hjson.github.io
+.. _simplejson: https://github.com/simplejson/simplejson
+.. _hjson-py: http://hjson.github.io/hjson-py/

parrot/lib/python3.10/site-packages/hjson-3.1.0.dist-info/WHEEL

@@ -0,0 +1,5 @@
+Wheel-Version: 1.0
+Generator: bdist_wheel (0.37.1)
+Root-Is-Purelib: true
+Tag: py3-none-any
+

parrot/lib/python3.10/site-packages/hjson-3.1.0.dist-info/top_level.txt

@@ -0,0 +1 @@
+hjson