nwo stringlengths 5 86 | sha stringlengths 40 40 | path stringlengths 4 189 | language stringclasses 1 value | identifier stringlengths 1 94 | parameters stringlengths 2 4.03k | argument_list stringclasses 1 value | return_statement stringlengths 0 11.5k | docstring stringlengths 1 33.2k | docstring_summary stringlengths 0 5.15k | docstring_tokens list | function stringlengths 34 151k | function_tokens list | url stringlengths 90 278 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/AWSPythonSDK/1.5.8/docutils/parsers/rst/states.py | python | RSTState.nested_list_parse | (self, block, input_offset, node, initial_state,
blank_finish,
blank_finish_state=None,
extra_settings={},
match_titles=False,
state_machine_class=None,
state_machine_kwargs=None) | return state_machine.abs_line_offset(), blank_finish | Create a new StateMachine rooted at `node` and run it over the input
`block`. Also keep track of optional intermediate blank lines and the
required final one. | Create a new StateMachine rooted at `node` and run it over the input
`block`. Also keep track of optional intermediate blank lines and the
required final one. | [
"Create",
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"."
] | def nested_list_parse(self, block, input_offset, node, initial_state,
blank_finish,
blank_finish_state=None,
extra_settings={},
match_titles=False,
state_machine_class=None,
state_machine_kwargs=None):
"""
Create a new StateMachine rooted at `node` and run it over the input
`block`. Also keep track of optional intermediate blank lines and the
required final one.
"""
if state_machine_class is None:
state_machine_class = self.nested_sm
if state_machine_kwargs is None:
state_machine_kwargs = self.nested_sm_kwargs.copy()
state_machine_kwargs['initial_state'] = initial_state
state_machine = state_machine_class(debug=self.debug,
**state_machine_kwargs)
if blank_finish_state is None:
blank_finish_state = initial_state
state_machine.states[blank_finish_state].blank_finish = blank_finish
for key, value in extra_settings.items():
setattr(state_machine.states[initial_state], key, value)
state_machine.run(block, input_offset, memo=self.memo,
node=node, match_titles=match_titles)
blank_finish = state_machine.states[blank_finish_state].blank_finish
state_machine.unlink()
return state_machine.abs_line_offset(), blank_finish | [
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ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | tools/gen_keyboard_overlay_data/gen_keyboard_overlay_data.py | python | FetchSpreadsheetFeeds | (client, key, sheets, cols) | return worksheets_data | Fetch feeds from the spreadsheet.
Args:
client: A spreadsheet client to be used for fetching data.
key: A key string of the spreadsheet to be fetched.
sheets: A list of the sheet names to read data from.
cols: A list of columns to read data from. | Fetch feeds from the spreadsheet. | [
"Fetch",
"feeds",
"from",
"the",
"spreadsheet",
"."
] | def FetchSpreadsheetFeeds(client, key, sheets, cols):
"""Fetch feeds from the spreadsheet.
Args:
client: A spreadsheet client to be used for fetching data.
key: A key string of the spreadsheet to be fetched.
sheets: A list of the sheet names to read data from.
cols: A list of columns to read data from.
"""
worksheets_feed = client.GetWorksheetsFeed(key)
print 'Fetching data from the worksheet: %s' % worksheets_feed.title.text
worksheets_data = {}
titles = []
for entry in worksheets_feed.entry:
worksheet_id = entry.id.text.split('/')[-1]
list_feed = client.GetListFeed(key, worksheet_id)
list_data = []
# Hack to deal with sheet names like 'sv (Copy of fl)'
title = list_feed.title.text.split('(')[0].strip()
titles.append(title)
if title not in sheets:
continue
print 'Reading data from the sheet: %s' % list_feed.title.text
for i, entry in enumerate(list_feed.entry):
line_data = {}
for k in entry.custom:
if (k not in cols) or (not entry.custom[k].text):
continue
line_data[k] = entry.custom[k].text
list_data.append(line_data)
worksheets_data[title] = list_data
PrintDiffs('Exist only on the spreadsheet: ', titles, sheets)
PrintDiffs('Specified but do not exist on the spreadsheet: ', sheets, titles)
return worksheets_data | [
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BlazingDB/blazingsql | a35643d4c983334757eee96d5b9005b8b9fbd21b | pyblazing/pyblazing/apiv2/context.py | python | BlazingContext.s3 | (self, prefix, **kwargs) | return self.fs.s3(self.dask_client, prefix, **kwargs) | Register an AWS S3 bucket.
returns a boolean meaning True when Registered Successfully
Parameters
----------
name : string that represents the name with which you will refer to
your S3 bucket.
bucket_name : string name of your S3 bucket.
access_key_id : string of your AWS IAM access key. not required for
public buckets.
secret_key : string of your AWS IAM secret key. not required for
public buckets.
encryption_type (optional) : None (default), 'AES_256', or 'AWS_KMS'.
session_token (optional) : string of your AWS IAM session token.
root (optional) : string path of your bucket that will be used as a
shortcut path.
kms_key_amazon_resource (optional) : string value, required for KMS
encryption only.
Examples
--------
Register and create table from a public S3 bucket:
>>> bc.s3('blazingsql-colab', bucket_name='blazingsql-colab')
>>> bc.create_table('taxi',
>>> 's3://blazingsql-colab/yellow_taxi/1_0_0.parquet')
<pyblazing.apiv2.context.BlazingTable at 0x7f6d4e640c90>
Register and create table from a private S3 bucket:
>>> bc.s3('other-data', bucket_name='kws-parquet-data',
>>> access_key_id='AKIASPFMPQMQD2OG54IQ',
>>> secret_key='bmt+TLTosdkIelsdw9VQjMe0nBnvAA5nPt0kaSx/Y',
>>> encryption_type=S3EncryptionType.AWS_KMS,
>>> kms_key_amazon_resource_name=
>>> 'arn:aws:kms:region:acct-id:key/key-id')
>>> bc.create_table('taxi',
's3://other-data/yellow_taxi/1_0_0.parquet')
<pyblazing.apiv2.context.BlazingTable at 0x7f12327c0310>
Docs: https://docs.blazingdb.com/docs/s3 | Register an AWS S3 bucket.
returns a boolean meaning True when Registered Successfully | [
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"S3",
"bucket",
".",
"returns",
"a",
"boolean",
"meaning",
"True",
"when",
"Registered",
"Successfully"
] | def s3(self, prefix, **kwargs):
"""
Register an AWS S3 bucket.
returns a boolean meaning True when Registered Successfully
Parameters
----------
name : string that represents the name with which you will refer to
your S3 bucket.
bucket_name : string name of your S3 bucket.
access_key_id : string of your AWS IAM access key. not required for
public buckets.
secret_key : string of your AWS IAM secret key. not required for
public buckets.
encryption_type (optional) : None (default), 'AES_256', or 'AWS_KMS'.
session_token (optional) : string of your AWS IAM session token.
root (optional) : string path of your bucket that will be used as a
shortcut path.
kms_key_amazon_resource (optional) : string value, required for KMS
encryption only.
Examples
--------
Register and create table from a public S3 bucket:
>>> bc.s3('blazingsql-colab', bucket_name='blazingsql-colab')
>>> bc.create_table('taxi',
>>> 's3://blazingsql-colab/yellow_taxi/1_0_0.parquet')
<pyblazing.apiv2.context.BlazingTable at 0x7f6d4e640c90>
Register and create table from a private S3 bucket:
>>> bc.s3('other-data', bucket_name='kws-parquet-data',
>>> access_key_id='AKIASPFMPQMQD2OG54IQ',
>>> secret_key='bmt+TLTosdkIelsdw9VQjMe0nBnvAA5nPt0kaSx/Y',
>>> encryption_type=S3EncryptionType.AWS_KMS,
>>> kms_key_amazon_resource_name=
>>> 'arn:aws:kms:region:acct-id:key/key-id')
>>> bc.create_table('taxi',
's3://other-data/yellow_taxi/1_0_0.parquet')
<pyblazing.apiv2.context.BlazingTable at 0x7f12327c0310>
Docs: https://docs.blazingdb.com/docs/s3
"""
kwargs_validation(kwargs, "s3")
return self.fs.s3(self.dask_client, prefix, **kwargs) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/media.py | python | MediaCtrl.LoadURI | (*args, **kwargs) | return _media.MediaCtrl_LoadURI(*args, **kwargs) | LoadURI(self, String fileName) -> bool | LoadURI(self, String fileName) -> bool | [
"LoadURI",
"(",
"self",
"String",
"fileName",
")",
"-",
">",
"bool"
] | def LoadURI(*args, **kwargs):
"""LoadURI(self, String fileName) -> bool"""
return _media.MediaCtrl_LoadURI(*args, **kwargs) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/AWSPythonSDK/1.5.8/botocore/paginate.py | python | TokenEncoder._encode_bytes | (self, data, path) | return base64.b64encode(data).decode('utf-8'), [path] | Base64 encode a byte string. | Base64 encode a byte string. | [
"Base64",
"encode",
"a",
"byte",
"string",
"."
] | def _encode_bytes(self, data, path):
"""Base64 encode a byte string."""
return base64.b64encode(data).decode('utf-8'), [path] | [
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klzgrad/naiveproxy | ed2c513637c77b18721fe428d7ed395b4d284c83 | src/build/android/gyp/util/md5_check.py | python | _Metadata.GetTag | (self, path, subpath=None) | return ret and ret['tag'] | Returns the tag for the given path / subpath. | Returns the tag for the given path / subpath. | [
"Returns",
"the",
"tag",
"for",
"the",
"given",
"path",
"/",
"subpath",
"."
] | def GetTag(self, path, subpath=None):
"""Returns the tag for the given path / subpath."""
ret = self._GetEntry(path, subpath)
return ret and ret['tag'] | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | scripts/Inelastic/Direct/RunDescriptor.py | python | build_run_file_name | (run_num,inst,file_path='',fext='') | return fname | Build the full name of a runfile from all possible components | Build the full name of a runfile from all possible components | [
"Build",
"the",
"full",
"name",
"of",
"a",
"runfile",
"from",
"all",
"possible",
"components"
] | def build_run_file_name(run_num,inst,file_path='',fext=''):
"""Build the full name of a runfile from all possible components"""
if fext is None:
fext = ''
if isinstance(run_num, str):
run_num_str = run_num
else:
#pylint: disable=protected-access
fac = RunDescriptor._holder.facility
zero_padding = fac.instrument(inst).zeroPadding(run_num)
run_num_str = str(run_num).zfill(zero_padding)
fname = '{0}{1}{2}'.format(inst,run_num_str,fext)
if file_path is not None:
if os.path.exists(file_path):
fname = os.path.join(file_path,fname)
return fname | [
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/python/ops/rnn.py | python | static_bidirectional_rnn | (cell_fw,
cell_bw,
inputs,
initial_state_fw=None,
initial_state_bw=None,
dtype=None,
sequence_length=None,
scope=None) | return (outputs, output_state_fw, output_state_bw) | Creates a bidirectional recurrent neural network.
Similar to the unidirectional case above (rnn) but takes input and builds
independent forward and backward RNNs with the final forward and backward
outputs depth-concatenated, such that the output will have the format
[time][batch][cell_fw.output_size + cell_bw.output_size]. The input_size of
forward and backward cell must match. The initial state for both directions
is zero by default (but can be set optionally) and no intermediate states are
ever returned -- the network is fully unrolled for the given (passed in)
length(s) of the sequence(s) or completely unrolled if length(s) is not given.
Args:
cell_fw: An instance of RNNCell, to be used for forward direction.
cell_bw: An instance of RNNCell, to be used for backward direction.
inputs: A length T list of inputs, each a tensor of shape
[batch_size, input_size], or a nested tuple of such elements.
initial_state_fw: (optional) An initial state for the forward RNN.
This must be a tensor of appropriate type and shape
`[batch_size, cell_fw.state_size]`.
If `cell_fw.state_size` is a tuple, this should be a tuple of
tensors having shapes `[batch_size, s] for s in cell_fw.state_size`.
initial_state_bw: (optional) Same as for `initial_state_fw`, but using
the corresponding properties of `cell_bw`.
dtype: (optional) The data type for the initial state. Required if
either of the initial states are not provided.
sequence_length: (optional) An int32/int64 vector, size `[batch_size]`,
containing the actual lengths for each of the sequences.
scope: VariableScope for the created subgraph; defaults to
"bidirectional_rnn"
Returns:
A tuple (outputs, output_state_fw, output_state_bw) where:
outputs is a length `T` list of outputs (one for each input), which
are depth-concatenated forward and backward outputs.
output_state_fw is the final state of the forward rnn.
output_state_bw is the final state of the backward rnn.
Raises:
TypeError: If `cell_fw` or `cell_bw` is not an instance of `RNNCell`.
ValueError: If inputs is None or an empty list. | Creates a bidirectional recurrent neural network. | [
"Creates",
"a",
"bidirectional",
"recurrent",
"neural",
"network",
"."
] | def static_bidirectional_rnn(cell_fw,
cell_bw,
inputs,
initial_state_fw=None,
initial_state_bw=None,
dtype=None,
sequence_length=None,
scope=None):
"""Creates a bidirectional recurrent neural network.
Similar to the unidirectional case above (rnn) but takes input and builds
independent forward and backward RNNs with the final forward and backward
outputs depth-concatenated, such that the output will have the format
[time][batch][cell_fw.output_size + cell_bw.output_size]. The input_size of
forward and backward cell must match. The initial state for both directions
is zero by default (but can be set optionally) and no intermediate states are
ever returned -- the network is fully unrolled for the given (passed in)
length(s) of the sequence(s) or completely unrolled if length(s) is not given.
Args:
cell_fw: An instance of RNNCell, to be used for forward direction.
cell_bw: An instance of RNNCell, to be used for backward direction.
inputs: A length T list of inputs, each a tensor of shape
[batch_size, input_size], or a nested tuple of such elements.
initial_state_fw: (optional) An initial state for the forward RNN.
This must be a tensor of appropriate type and shape
`[batch_size, cell_fw.state_size]`.
If `cell_fw.state_size` is a tuple, this should be a tuple of
tensors having shapes `[batch_size, s] for s in cell_fw.state_size`.
initial_state_bw: (optional) Same as for `initial_state_fw`, but using
the corresponding properties of `cell_bw`.
dtype: (optional) The data type for the initial state. Required if
either of the initial states are not provided.
sequence_length: (optional) An int32/int64 vector, size `[batch_size]`,
containing the actual lengths for each of the sequences.
scope: VariableScope for the created subgraph; defaults to
"bidirectional_rnn"
Returns:
A tuple (outputs, output_state_fw, output_state_bw) where:
outputs is a length `T` list of outputs (one for each input), which
are depth-concatenated forward and backward outputs.
output_state_fw is the final state of the forward rnn.
output_state_bw is the final state of the backward rnn.
Raises:
TypeError: If `cell_fw` or `cell_bw` is not an instance of `RNNCell`.
ValueError: If inputs is None or an empty list.
"""
if not _like_rnncell(cell_fw):
raise TypeError("cell_fw must be an instance of RNNCell")
if not _like_rnncell(cell_bw):
raise TypeError("cell_bw must be an instance of RNNCell")
if not nest.is_sequence(inputs):
raise TypeError("inputs must be a sequence")
if not inputs:
raise ValueError("inputs must not be empty")
with vs.variable_scope(scope or "bidirectional_rnn"):
# Forward direction
with vs.variable_scope("fw") as fw_scope:
output_fw, output_state_fw = static_rnn(
cell_fw,
inputs,
initial_state_fw,
dtype,
sequence_length,
scope=fw_scope)
# Backward direction
with vs.variable_scope("bw") as bw_scope:
reversed_inputs = _reverse_seq(inputs, sequence_length)
tmp, output_state_bw = static_rnn(
cell_bw,
reversed_inputs,
initial_state_bw,
dtype,
sequence_length,
scope=bw_scope)
output_bw = _reverse_seq(tmp, sequence_length)
# Concat each of the forward/backward outputs
flat_output_fw = nest.flatten(output_fw)
flat_output_bw = nest.flatten(output_bw)
flat_outputs = tuple(
array_ops.concat([fw, bw], 1)
for fw, bw in zip(flat_output_fw, flat_output_bw))
outputs = nest.pack_sequence_as(
structure=output_fw, flat_sequence=flat_outputs)
return (outputs, output_state_fw, output_state_bw) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scikit-learn/py3/sklearn/feature_extraction/image.py | python | _compute_n_patches | (i_h, i_w, p_h, p_w, max_patches=None) | Compute the number of patches that will be extracted in an image.
Read more in the :ref:`User Guide <image_feature_extraction>`.
Parameters
----------
i_h : int
The image height
i_w : int
The image with
p_h : int
The height of a patch
p_w : int
The width of a patch
max_patches : integer or float, optional default is None
The maximum number of patches to extract. If max_patches is a float
between 0 and 1, it is taken to be a proportion of the total number
of patches. | Compute the number of patches that will be extracted in an image. | [
"Compute",
"the",
"number",
"of",
"patches",
"that",
"will",
"be",
"extracted",
"in",
"an",
"image",
"."
] | def _compute_n_patches(i_h, i_w, p_h, p_w, max_patches=None):
"""Compute the number of patches that will be extracted in an image.
Read more in the :ref:`User Guide <image_feature_extraction>`.
Parameters
----------
i_h : int
The image height
i_w : int
The image with
p_h : int
The height of a patch
p_w : int
The width of a patch
max_patches : integer or float, optional default is None
The maximum number of patches to extract. If max_patches is a float
between 0 and 1, it is taken to be a proportion of the total number
of patches.
"""
n_h = i_h - p_h + 1
n_w = i_w - p_w + 1
all_patches = n_h * n_w
if max_patches:
if (isinstance(max_patches, (numbers.Integral))
and max_patches < all_patches):
return max_patches
elif (isinstance(max_patches, (numbers.Integral))
and max_patches >= all_patches):
return all_patches
elif (isinstance(max_patches, (numbers.Real))
and 0 < max_patches < 1):
return int(max_patches * all_patches)
else:
raise ValueError("Invalid value for max_patches: %r" % max_patches)
else:
return all_patches | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/python/mantidqtinterfaces/mantidqtinterfaces/reduction_gui/widgets/diffraction/diffraction_adv_setup.py | python | AdvancedSetupWidget._cache_dir_browse_3 | (self) | r"""Event handling for browsing the cache directory | r"""Event handling for browsing the cache directory | [
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if dir_path:
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/decimal.py | python | Decimal.__pow__ | (self, other, modulo=None, context=None) | return ans | Return self ** other [ % modulo].
With two arguments, compute self**other.
With three arguments, compute (self**other) % modulo. For the
three argument form, the following restrictions on the
arguments hold:
- all three arguments must be integral
- other must be nonnegative
- either self or other (or both) must be nonzero
- modulo must be nonzero and must have at most p digits,
where p is the context precision.
If any of these restrictions is violated the InvalidOperation
flag is raised.
The result of pow(self, other, modulo) is identical to the
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modulo with unbounded precision, but is computed more
efficiently. It is always exact. | Return self ** other [ % modulo]. | [
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"""Return self ** other [ % modulo].
With two arguments, compute self**other.
With three arguments, compute (self**other) % modulo. For the
three argument form, the following restrictions on the
arguments hold:
- all three arguments must be integral
- other must be nonnegative
- either self or other (or both) must be nonzero
- modulo must be nonzero and must have at most p digits,
where p is the context precision.
If any of these restrictions is violated the InvalidOperation
flag is raised.
The result of pow(self, other, modulo) is identical to the
result that would be obtained by computing (self**other) %
modulo with unbounded precision, but is computed more
efficiently. It is always exact.
"""
if modulo is not None:
return self._power_modulo(other, modulo, context)
other = _convert_other(other)
if other is NotImplemented:
return other
if context is None:
context = getcontext()
# either argument is a NaN => result is NaN
ans = self._check_nans(other, context)
if ans:
return ans
# 0**0 = NaN (!), x**0 = 1 for nonzero x (including +/-Infinity)
if not other:
if not self:
return context._raise_error(InvalidOperation, '0 ** 0')
else:
return _One
# result has sign 1 iff self._sign is 1 and other is an odd integer
result_sign = 0
if self._sign == 1:
if other._isinteger():
if not other._iseven():
result_sign = 1
else:
# -ve**noninteger = NaN
# (-0)**noninteger = 0**noninteger
if self:
return context._raise_error(InvalidOperation,
'x ** y with x negative and y not an integer')
# negate self, without doing any unwanted rounding
self = self.copy_negate()
# 0**(+ve or Inf)= 0; 0**(-ve or -Inf) = Infinity
if not self:
if other._sign == 0:
return _dec_from_triple(result_sign, '0', 0)
else:
return _SignedInfinity[result_sign]
# Inf**(+ve or Inf) = Inf; Inf**(-ve or -Inf) = 0
if self._isinfinity():
if other._sign == 0:
return _SignedInfinity[result_sign]
else:
return _dec_from_triple(result_sign, '0', 0)
# 1**other = 1, but the choice of exponent and the flags
# depend on the exponent of self, and on whether other is a
# positive integer, a negative integer, or neither
if self == _One:
if other._isinteger():
# exp = max(self._exp*max(int(other), 0),
# 1-context.prec) but evaluating int(other) directly
# is dangerous until we know other is small (other
# could be 1e999999999)
if other._sign == 1:
multiplier = 0
elif other > context.prec:
multiplier = context.prec
else:
multiplier = int(other)
exp = self._exp * multiplier
if exp < 1-context.prec:
exp = 1-context.prec
context._raise_error(Rounded)
else:
context._raise_error(Inexact)
context._raise_error(Rounded)
exp = 1-context.prec
return _dec_from_triple(result_sign, '1'+'0'*-exp, exp)
# compute adjusted exponent of self
self_adj = self.adjusted()
# self ** infinity is infinity if self > 1, 0 if self < 1
# self ** -infinity is infinity if self < 1, 0 if self > 1
if other._isinfinity():
if (other._sign == 0) == (self_adj < 0):
return _dec_from_triple(result_sign, '0', 0)
else:
return _SignedInfinity[result_sign]
# from here on, the result always goes through the call
# to _fix at the end of this function.
ans = None
exact = False
# crude test to catch cases of extreme overflow/underflow. If
# log10(self)*other >= 10**bound and bound >= len(str(Emax))
# then 10**bound >= 10**len(str(Emax)) >= Emax+1 and hence
# self**other >= 10**(Emax+1), so overflow occurs. The test
# for underflow is similar.
bound = self._log10_exp_bound() + other.adjusted()
if (self_adj >= 0) == (other._sign == 0):
# self > 1 and other +ve, or self < 1 and other -ve
# possibility of overflow
if bound >= len(str(context.Emax)):
ans = _dec_from_triple(result_sign, '1', context.Emax+1)
else:
# self > 1 and other -ve, or self < 1 and other +ve
# possibility of underflow to 0
Etiny = context.Etiny()
if bound >= len(str(-Etiny)):
ans = _dec_from_triple(result_sign, '1', Etiny-1)
# try for an exact result with precision +1
if ans is None:
ans = self._power_exact(other, context.prec + 1)
if ans is not None:
if result_sign == 1:
ans = _dec_from_triple(1, ans._int, ans._exp)
exact = True
# usual case: inexact result, x**y computed directly as exp(y*log(x))
if ans is None:
p = context.prec
x = _WorkRep(self)
xc, xe = x.int, x.exp
y = _WorkRep(other)
yc, ye = y.int, y.exp
if y.sign == 1:
yc = -yc
# compute correctly rounded result: start with precision +3,
# then increase precision until result is unambiguously roundable
extra = 3
while True:
coeff, exp = _dpower(xc, xe, yc, ye, p+extra)
if coeff % (5*10**(len(str(coeff))-p-1)):
break
extra += 3
ans = _dec_from_triple(result_sign, str(coeff), exp)
# unlike exp, ln and log10, the power function respects the
# rounding mode; no need to switch to ROUND_HALF_EVEN here
# There's a difficulty here when 'other' is not an integer and
# the result is exact. In this case, the specification
# requires that the Inexact flag be raised (in spite of
# exactness), but since the result is exact _fix won't do this
# for us. (Correspondingly, the Underflow signal should also
# be raised for subnormal results.) We can't directly raise
# these signals either before or after calling _fix, since
# that would violate the precedence for signals. So we wrap
# the ._fix call in a temporary context, and reraise
# afterwards.
if exact and not other._isinteger():
# pad with zeros up to length context.prec+1 if necessary; this
# ensures that the Rounded signal will be raised.
if len(ans._int) <= context.prec:
expdiff = context.prec + 1 - len(ans._int)
ans = _dec_from_triple(ans._sign, ans._int+'0'*expdiff,
ans._exp-expdiff)
# create a copy of the current context, with cleared flags/traps
newcontext = context.copy()
newcontext.clear_flags()
for exception in _signals:
newcontext.traps[exception] = 0
# round in the new context
ans = ans._fix(newcontext)
# raise Inexact, and if necessary, Underflow
newcontext._raise_error(Inexact)
if newcontext.flags[Subnormal]:
newcontext._raise_error(Underflow)
# propagate signals to the original context; _fix could
# have raised any of Overflow, Underflow, Subnormal,
# Inexact, Rounded, Clamped. Overflow needs the correct
# arguments. Note that the order of the exceptions is
# important here.
if newcontext.flags[Overflow]:
context._raise_error(Overflow, 'above Emax', ans._sign)
for exception in Underflow, Subnormal, Inexact, Rounded, Clamped:
if newcontext.flags[exception]:
context._raise_error(exception)
else:
ans = ans._fix(context)
return ans | [
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BestSonny/SSTD | 174d452189f6bf9cf4b6957719392008bd974069 | python/caffe/io.py | python | Transformer.set_channel_swap | (self, in_, order) | Set the input channel order for e.g. RGB to BGR conversion
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N.B. this assumes the channels are the first dimension AFTER transpose.
Parameters
----------
in_ : which input to assign this channel order
order : the order to take the channels.
(2,1,0) maps RGB to BGR for example. | Set the input channel order for e.g. RGB to BGR conversion
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"""
Set the input channel order for e.g. RGB to BGR conversion
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N.B. this assumes the channels are the first dimension AFTER transpose.
Parameters
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in_ : which input to assign this channel order
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self.__check_input(in_)
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numba/roc/api.py | python | deregister | (*args) | Deregister data from the HSA system | Deregister data from the HSA system | [
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"""Deregister data from the HSA system
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/ops/init_ops_v2.py | python | lecun_uniform | (seed=None) | return VarianceScaling(
scale=1., mode="fan_in", distribution="uniform", seed=seed) | LeCun uniform initializer.
It draws samples from a uniform distribution within [-limit, limit]
where `limit` is `sqrt(3 / fan_in)`
where `fan_in` is the number of input units in the weight tensor.
Arguments:
seed: A Python integer. Used to seed the random generator.
Returns:
An initializer.
References:
- Self-Normalizing Neural Networks,
[Klambauer et al., 2017](https://papers.nips.cc/paper/6698-self-normalizing-neural-networks) # pylint: disable=line-too-long
([pdf](https://papers.nips.cc/paper/6698-self-normalizing-neural-networks.pdf))
- Efficient Backprop,
[Lecun et al., 1998](http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf) | LeCun uniform initializer. | [
"LeCun",
"uniform",
"initializer",
"."
] | def lecun_uniform(seed=None):
"""LeCun uniform initializer.
It draws samples from a uniform distribution within [-limit, limit]
where `limit` is `sqrt(3 / fan_in)`
where `fan_in` is the number of input units in the weight tensor.
Arguments:
seed: A Python integer. Used to seed the random generator.
Returns:
An initializer.
References:
- Self-Normalizing Neural Networks,
[Klambauer et al., 2017](https://papers.nips.cc/paper/6698-self-normalizing-neural-networks) # pylint: disable=line-too-long
([pdf](https://papers.nips.cc/paper/6698-self-normalizing-neural-networks.pdf))
- Efficient Backprop,
[Lecun et al., 1998](http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf)
"""
return VarianceScaling(
scale=1., mode="fan_in", distribution="uniform", seed=seed) | [
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xbmc/xbmc | 091211a754589fc40a2a1f239b0ce9f4ee138268 | addons/metadata.themoviedb.org.python/python/lib/tmdbscraper/tmdbapi.py | python | get_movie | (mid, language=None, append_to_response=None) | return api_utils.load_info(theurl, params=_set_params(append_to_response, language)) | Get movie details
:param mid: TMDb movie ID
:param language: the language filter for TMDb (optional)
:append_to_response: the additional data to get from TMDb (optional)
:return: the movie or error | Get movie details | [
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"movie",
"details"
] | def get_movie(mid, language=None, append_to_response=None):
# type: (Text) -> List[InfoType]
"""
Get movie details
:param mid: TMDb movie ID
:param language: the language filter for TMDb (optional)
:append_to_response: the additional data to get from TMDb (optional)
:return: the movie or error
"""
xbmc.log('using movie id of %s to get movie details' % mid, xbmc.LOGDEBUG)
theurl = MOVIE_URL.format(mid)
return api_utils.load_info(theurl, params=_set_params(append_to_response, language)) | [
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tensorflow/minigo | 6d89c202cdceaf449aefc3149ab2110d44f1a6a4 | minigo_model.py | python | read_model | (path) | return metadata, model_bytes | Reads a serialized model & metadata in Minigo format.
Args:
path: the model path.
Returns:
A (metadata, model_bytes) pair of the model's metadata as a dictionary
and the serialized model as bytes. | Reads a serialized model & metadata in Minigo format. | [
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"""Reads a serialized model & metadata in Minigo format.
Args:
path: the model path.
Returns:
A (metadata, model_bytes) pair of the model's metadata as a dictionary
and the serialized model as bytes.
"""
with tf.io.gfile.GFile(path, 'rb') as f:
magic = f.read(MAGIC_SIZE).decode('utf-8')
if magic != MAGIC:
raise RuntimeError(
'expected magic string %s, got %s' % (MAGIC, magic))
version, file_size, metadata_size = struct.unpack(
'<QQQ', f.read(HEADER_SIZE))
if version != 1:
raise RuntimeError('expected version == 1, got %d' % version)
metadata_bytes = f.read(metadata_size).decode('utf-8')
if len(metadata_bytes) != metadata_size:
raise RuntimeError('expected %dB of metadata, read only %dB' % (
metadata_size, len(metadata_bytes)))
metadata = json.loads(metadata_bytes)
model_bytes = f.read()
model_size = len(model_bytes)
bytes_read = MAGIC_SIZE + HEADER_SIZE + model_size + metadata_size
if bytes_read != file_size:
raise RuntimeError('expected %dB, read only %dB' %
(file_size, bytes_read))
return metadata, model_bytes | [
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/lib-tk/tkSimpleDialog.py | python | askfloat | (title, prompt, **kw) | return d.result | get a float from the user
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title -- the dialog title
prompt -- the label text
**kw -- see SimpleDialog class
Return value is a float | get a float from the user | [
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title -- the dialog title
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Return value is a float
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d = _QueryFloat(title, prompt, **kw)
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cyberbotics/webots | af7fa7d68dcf7b4550f1f2e132092b41e83698fc | resources/web/server/simulation_server.py | python | Client.on_exit | (self) | Callback issued when Webots quits. | Callback issued when Webots quits. | [
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"""Callback issued when Webots quits."""
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self.webots_process.wait()
self.webots_process = None
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openbabel/openbabel | f3ed2a9a5166dbd3b9ce386e636a176074a6c34c | scripts/python/openbabel/pybel.py | python | Molecule.calcfp | (self, fptype="FP2") | return Fingerprint(fp) | Calculate a molecular fingerprint.
Optional parameters:
fptype -- the fingerprint type (default is "FP2"). See the
fps variable for a list of of available fingerprint
types. | Calculate a molecular fingerprint. | [
"Calculate",
"a",
"molecular",
"fingerprint",
"."
] | def calcfp(self, fptype="FP2"):
"""Calculate a molecular fingerprint.
Optional parameters:
fptype -- the fingerprint type (default is "FP2"). See the
fps variable for a list of of available fingerprint
types.
"""
if sys.platform[:3] == "cli":
fp = ob.VectorUInt()
else:
fp = ob.vectorUnsignedInt()
fptype = fptype.lower()
try:
fingerprinter = _fingerprinters[fptype]
except KeyError:
raise ValueError(
"%s is not a recognised Open Babel Fingerprint type" % fptype)
fingerprinter.GetFingerprint(self.OBMol, fp)
return Fingerprint(fp) | [
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"... | https://github.com/openbabel/openbabel/blob/f3ed2a9a5166dbd3b9ce386e636a176074a6c34c/scripts/python/openbabel/pybel.py#L472-L491 | |
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/setuptools/py2/setuptools/_vendor/pyparsing.py | python | ParserElement.parseFile | ( self, file_or_filename, parseAll=False ) | Execute the parse expression on the given file or filename.
If a filename is specified (instead of a file object),
the entire file is opened, read, and closed before parsing. | Execute the parse expression on the given file or filename.
If a filename is specified (instead of a file object),
the entire file is opened, read, and closed before parsing. | [
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"""
Execute the parse expression on the given file or filename.
If a filename is specified (instead of a file object),
the entire file is opened, read, and closed before parsing.
"""
try:
file_contents = file_or_filename.read()
except AttributeError:
with open(file_or_filename, "r") as f:
file_contents = f.read()
try:
return self.parseString(file_contents, parseAll)
except ParseBaseException as exc:
if ParserElement.verbose_stacktrace:
raise
else:
# catch and re-raise exception from here, clears out pyparsing internal stack trace
raise exc | [
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PaddlePaddle/Paddle | 1252f4bb3e574df80aa6d18c7ddae1b3a90bd81c | python/paddle/fluid/contrib/sparsity/utils.py | python | CheckMethod.get_checking_method | (mask_algo) | r"""
Get sparsity checking method by mask generating algorithm.
Args:
mask_algo (MaskAlgo): The algorithm of mask generating.
Returns:
CheckMethod: The corresponded sparsity checking method.
Examples:
.. code-block:: python
import numpy as np
from paddle.static.sparsity import MaskAlgo
from paddle.fluid.contrib.sparsity import CheckMethod
CheckMethod.get_checking_method(MaskAlgo.MASK_1D)
# CheckMethod.CHECK_1D
CheckMethod.get_checking_method(MaskAlgo.MASK_2D_GREEDY)
# CheckMethod.CHECK_2D
CheckMethod.get_checking_method(MaskAlgo.MASK_2D_BEST)
# CheckMethod.CHECK_2D | r"""
Get sparsity checking method by mask generating algorithm. | [
"r",
"Get",
"sparsity",
"checking",
"method",
"by",
"mask",
"generating",
"algorithm",
"."
] | def get_checking_method(mask_algo):
r"""
Get sparsity checking method by mask generating algorithm.
Args:
mask_algo (MaskAlgo): The algorithm of mask generating.
Returns:
CheckMethod: The corresponded sparsity checking method.
Examples:
.. code-block:: python
import numpy as np
from paddle.static.sparsity import MaskAlgo
from paddle.fluid.contrib.sparsity import CheckMethod
CheckMethod.get_checking_method(MaskAlgo.MASK_1D)
# CheckMethod.CHECK_1D
CheckMethod.get_checking_method(MaskAlgo.MASK_2D_GREEDY)
# CheckMethod.CHECK_2D
CheckMethod.get_checking_method(MaskAlgo.MASK_2D_BEST)
# CheckMethod.CHECK_2D
"""
assert isinstance(mask_algo, MaskAlgo), \
"mask_algo should be MaskAlgo type"
if mask_algo == MaskAlgo.MASK_1D:
return CheckMethod.CHECK_1D
else:
return CheckMethod.CHECK_2D | [
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ricardoquesada/Spidermonkey | 4a75ea2543408bd1b2c515aa95901523eeef7858 | build/pymake/pymake/data.py | python | Target.searchinlocs | (self, makefile, locs) | return None | Look in the given locations relative to the makefile working directory
for a file. Return a pair of the target and the mtime if found, None
if not. | Look in the given locations relative to the makefile working directory
for a file. Return a pair of the target and the mtime if found, None
if not. | [
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"mtime",
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] | def searchinlocs(self, makefile, locs):
"""
Look in the given locations relative to the makefile working directory
for a file. Return a pair of the target and the mtime if found, None
if not.
"""
for t in locs:
fspath = util.normaljoin(makefile.workdir, t).replace('\\', '/')
mtime = getmtime(fspath)
# _log.info("Searching %s ... checking %s ... mtime %r" % (t, fspath, mtime))
if mtime is not None:
return (t, mtime)
return None | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/packaging/py3/packaging/version.py | python | _parse_local_version | (local: str) | return None | Takes a string like abc.1.twelve and turns it into ("abc", 1, "twelve"). | Takes a string like abc.1.twelve and turns it into ("abc", 1, "twelve"). | [
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"(",
"abc",
"1",
"twelve",
")",
"."
] | def _parse_local_version(local: str) -> Optional[LocalType]:
"""
Takes a string like abc.1.twelve and turns it into ("abc", 1, "twelve").
"""
if local is not None:
return tuple(
part.lower() if not part.isdigit() else int(part)
for part in _local_version_separators.split(local)
)
return None | [
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mongodb/mongo | d8ff665343ad29cf286ee2cf4a1960d29371937b | buildscripts/packager.py | python | crossproduct | (*seqs) | Provide a generator for iterating all the tuples consisting of elements of seqs. | Provide a generator for iterating all the tuples consisting of elements of seqs. | [
"Provide",
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"generator",
"for",
"iterating",
"all",
"the",
"tuples",
"consisting",
"of",
"elements",
"of",
"seqs",
"."
] | def crossproduct(*seqs):
"""Provide a generator for iterating all the tuples consisting of elements of seqs."""
num_seqs = len(seqs)
if num_seqs == 0:
pass
elif num_seqs == 1:
for idx in seqs[0]:
yield [idx]
else:
for lst in crossproduct(*seqs[:-1]):
for idx in seqs[-1]:
lst2 = list(lst)
lst2.append(idx)
yield lst2 | [
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... | https://github.com/mongodb/mongo/blob/d8ff665343ad29cf286ee2cf4a1960d29371937b/buildscripts/packager.py#L443-L456 | ||
TimoSaemann/caffe-segnet-cudnn5 | abcf30dca449245e101bf4ced519f716177f0885 | scripts/cpp_lint.py | python | CheckCaffeDataLayerSetUp | (filename, clean_lines, linenum, error) | Except the base classes, Caffe DataLayer should define DataLayerSetUp
instead of LayerSetUp.
The base DataLayers define common SetUp steps, the subclasses should
not override them.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found. | Except the base classes, Caffe DataLayer should define DataLayerSetUp
instead of LayerSetUp.
The base DataLayers define common SetUp steps, the subclasses should
not override them.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found. | [
"Except",
"the",
"base",
"classes",
"Caffe",
"DataLayer",
"should",
"define",
"DataLayerSetUp",
"instead",
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".",
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"define",
"common",
"SetUp",
"steps",
"the",
"subclasses",
"should",
"not",
"override",
"them",
".",
... | def CheckCaffeDataLayerSetUp(filename, clean_lines, linenum, error):
"""Except the base classes, Caffe DataLayer should define DataLayerSetUp
instead of LayerSetUp.
The base DataLayers define common SetUp steps, the subclasses should
not override them.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum]
ix = line.find('DataLayer<Dtype>::LayerSetUp')
if ix >= 0 and (
line.find('void DataLayer<Dtype>::LayerSetUp') != -1 or
line.find('void ImageDataLayer<Dtype>::LayerSetUp') != -1 or
line.find('void MemoryDataLayer<Dtype>::LayerSetUp') != -1 or
line.find('void WindowDataLayer<Dtype>::LayerSetUp') != -1):
error(filename, linenum, 'caffe/data_layer_setup', 2,
'Except the base classes, Caffe DataLayer should define'
+ ' DataLayerSetUp instead of LayerSetUp. The base DataLayers'
+ ' define common SetUp steps, the subclasses should'
+ ' not override them.')
ix = line.find('DataLayer<Dtype>::DataLayerSetUp')
if ix >= 0 and (
line.find('void Base') == -1 and
line.find('void DataLayer<Dtype>::DataLayerSetUp') == -1 and
line.find('void ImageDataLayer<Dtype>::DataLayerSetUp') == -1 and
line.find('void MemoryDataLayer<Dtype>::DataLayerSetUp') == -1 and
line.find('void WindowDataLayer<Dtype>::DataLayerSetUp') == -1):
error(filename, linenum, 'caffe/data_layer_setup', 2,
'Except the base classes, Caffe DataLayer should define'
+ ' DataLayerSetUp instead of LayerSetUp. The base DataLayers'
+ ' define common SetUp steps, the subclasses should'
+ ' not override them.') | [
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... | https://github.com/TimoSaemann/caffe-segnet-cudnn5/blob/abcf30dca449245e101bf4ced519f716177f0885/scripts/cpp_lint.py#L1595-L1631 | ||
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numba/cgutils.py | python | snprintf_stackbuffer | (builder, bufsz, format, *args) | return buffer | Similar to `snprintf()` but the buffer is stack allocated to size *bufsz*.
Returns the buffer pointer as i8*. | Similar to `snprintf()` but the buffer is stack allocated to size *bufsz*. | [
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"but",
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"allocated",
"to",
"size",
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"bufsz",
"*",
"."
] | def snprintf_stackbuffer(builder, bufsz, format, *args):
"""Similar to `snprintf()` but the buffer is stack allocated to size *bufsz*.
Returns the buffer pointer as i8*.
"""
assert isinstance(bufsz, int)
spacety = ir.ArrayType(ir.IntType(8), bufsz)
space = alloca_once(builder, spacety, zfill=True)
buffer = builder.bitcast(space, voidptr_t)
snprintf(builder, buffer, intp_t(bufsz), format, *args)
return buffer | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/ops/variables.py | python | VariableMetaclass._variable_v1_call | (cls,
initial_value=None,
trainable=None,
collections=None,
validate_shape=True,
caching_device=None,
name=None,
variable_def=None,
dtype=None,
expected_shape=None,
import_scope=None,
constraint=None,
use_resource=None,
synchronization=VariableSynchronization.AUTO,
aggregation=VariableAggregation.NONE,
shape=None) | return previous_getter(
initial_value=initial_value,
trainable=trainable,
collections=collections,
validate_shape=validate_shape,
caching_device=caching_device,
name=name,
variable_def=variable_def,
dtype=dtype,
expected_shape=expected_shape,
import_scope=import_scope,
constraint=constraint,
use_resource=use_resource,
synchronization=synchronization,
aggregation=aggregation,
shape=shape) | Call on Variable class. Useful to force the signature. | Call on Variable class. Useful to force the signature. | [
"Call",
"on",
"Variable",
"class",
".",
"Useful",
"to",
"force",
"the",
"signature",
"."
] | def _variable_v1_call(cls,
initial_value=None,
trainable=None,
collections=None,
validate_shape=True,
caching_device=None,
name=None,
variable_def=None,
dtype=None,
expected_shape=None,
import_scope=None,
constraint=None,
use_resource=None,
synchronization=VariableSynchronization.AUTO,
aggregation=VariableAggregation.NONE,
shape=None):
"""Call on Variable class. Useful to force the signature."""
previous_getter = lambda **kwargs: default_variable_creator(None, **kwargs)
for _, getter in ops.get_default_graph()._variable_creator_stack: # pylint: disable=protected-access
previous_getter = _make_getter(getter, previous_getter)
# Reset `aggregation` that is explicitly set as `None` to the enum NONE.
if aggregation is None:
aggregation = VariableAggregation.NONE
return previous_getter(
initial_value=initial_value,
trainable=trainable,
collections=collections,
validate_shape=validate_shape,
caching_device=caching_device,
name=name,
variable_def=variable_def,
dtype=dtype,
expected_shape=expected_shape,
import_scope=import_scope,
constraint=constraint,
use_resource=use_resource,
synchronization=synchronization,
aggregation=aggregation,
shape=shape) | [
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HackWebRTC/webrtc | 7abfc990c00ab35090fff285fcf635d1d7892433 | tools_webrtc/network_emulator/network_emulator.py | python | _RunIpfwCommand | (command, fail_msg=None) | return output.strip() | Executes a command and prefixes the appropriate command for
Windows or Linux/UNIX.
Args:
command: Command list to execute.
fail_msg: Message describing the error in case the command fails.
Raises:
NetworkEmulatorError: If command fails a message is set by the fail_msg
parameter. | Executes a command and prefixes the appropriate command for
Windows or Linux/UNIX. | [
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"and",
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"the",
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"command",
"for",
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"/",
"UNIX",
"."
] | def _RunIpfwCommand(command, fail_msg=None):
"""Executes a command and prefixes the appropriate command for
Windows or Linux/UNIX.
Args:
command: Command list to execute.
fail_msg: Message describing the error in case the command fails.
Raises:
NetworkEmulatorError: If command fails a message is set by the fail_msg
parameter.
"""
if sys.platform == 'win32':
ipfw_command = ['ipfw.exe']
else:
ipfw_command = ['sudo', '-n', 'ipfw']
cmd_list = ipfw_command[:] + [str(x) for x in command]
cmd_string = ' '.join(cmd_list)
logging.debug('Running command: %s', cmd_string)
process = subprocess.Popen(cmd_list, stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
output, error = process.communicate()
if process.returncode != 0:
raise NetworkEmulatorError(fail_msg, cmd_string, process.returncode, output,
error)
return output.strip() | [
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adobe/chromium | cfe5bf0b51b1f6b9fe239c2a3c2f2364da9967d7 | third_party/mesa/MesaLib/src/gallium/auxiliary/util/u_format_pack.py | python | generate_format_type | (format) | Generate a structure that describes the format. | Generate a structure that describes the format. | [
"Generate",
"a",
"structure",
"that",
"describes",
"the",
"format",
"."
] | def generate_format_type(format):
'''Generate a structure that describes the format.'''
assert format.layout == PLAIN
print 'union util_format_%s {' % format.short_name()
if format.block_size() in (8, 16, 32, 64):
print ' uint%u_t value;' % (format.block_size(),)
use_bitfields = False
for channel in format.channels:
if channel.size % 8 or not is_pot(channel.size):
use_bitfields = True
print ' struct {'
for channel in format.channels:
if use_bitfields:
if channel.type == VOID:
if channel.size:
print ' unsigned %s:%u;' % (channel.name, channel.size)
elif channel.type == UNSIGNED:
print ' unsigned %s:%u;' % (channel.name, channel.size)
elif channel.type in (SIGNED, FIXED):
print ' int %s:%u;' % (channel.name, channel.size)
elif channel.type == FLOAT:
if channel.size == 64:
print ' double %s;' % (channel.name)
elif channel.size == 32:
print ' float %s;' % (channel.name)
else:
print ' unsigned %s:%u;' % (channel.name, channel.size)
else:
assert 0
else:
assert channel.size % 8 == 0 and is_pot(channel.size)
if channel.type == VOID:
if channel.size:
print ' uint%u_t %s;' % (channel.size, channel.name)
elif channel.type == UNSIGNED:
print ' uint%u_t %s;' % (channel.size, channel.name)
elif channel.type in (SIGNED, FIXED):
print ' int%u_t %s;' % (channel.size, channel.name)
elif channel.type == FLOAT:
if channel.size == 64:
print ' double %s;' % (channel.name)
elif channel.size == 32:
print ' float %s;' % (channel.name)
elif channel.size == 16:
print ' uint16_t %s;' % (channel.name)
else:
assert 0
else:
assert 0
print ' } chan;'
print '};'
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cvxpy/cvxpy | 5165b4fb750dfd237de8659383ef24b4b2e33aaf | cvxpy/lin_ops/lin_utils.py | python | replace_new_vars | (expr, id_to_new_var) | Replaces the given variables in the expression.
Parameters
----------
expr : LinOp
The expression to replace variables in.
id_to_new_var : dict
A map of id to new variable.
Returns
-------
LinOp
An LinOp identical to expr, but with the given variables replaced. | Replaces the given variables in the expression. | [
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Parameters
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expr : LinOp
The expression to replace variables in.
id_to_new_var : dict
A map of id to new variable.
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-------
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An LinOp identical to expr, but with the given variables replaced.
"""
if expr.type == lo.VARIABLE and expr.data in id_to_new_var:
return id_to_new_var[expr.data]
else:
new_args = []
for arg in expr.args:
new_args.append(
replace_new_vars(arg, id_to_new_var)
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return lo.LinOp(expr.type, expr.shape, new_args, expr.data) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/richtext.py | python | RichTextCtrl.KeyboardNavigate | (*args, **kwargs) | return _richtext.RichTextCtrl_KeyboardNavigate(*args, **kwargs) | KeyboardNavigate(self, int keyCode, int flags) -> bool | KeyboardNavigate(self, int keyCode, int flags) -> bool | [
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ricardoquesada/Spidermonkey | 4a75ea2543408bd1b2c515aa95901523eeef7858 | python/mozbuild/mozpack/chrome/manifest.py | python | ManifestEntry.__init__ | (self, base, *flags) | Initialize a manifest entry with the given base path and flags. | Initialize a manifest entry with the given base path and flags. | [
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Initialize a manifest entry with the given base path and flags.
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eclipse/sumo | 7132a9b8b6eea734bdec38479026b4d8c4336d03 | tools/traci/_vehicle.py | python | VehicleDomain.setMaxSpeed | (self, vehID, speed) | setMaxSpeed(string, double) -> None
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"""setMaxSpeed(string, double) -> None
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/cmd.py | python | Cmd.__init__ | (self, completekey='tab', stdin=None, stdout=None) | Instantiate a line-oriented interpreter framework.
The optional argument 'completekey' is the readline name of a
completion key; it defaults to the Tab key. If completekey is
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"""Instantiate a line-oriented interpreter framework.
The optional argument 'completekey' is the readline name of a
completion key; it defaults to the Tab key. If completekey is
not None and the readline module is available, command completion
is done automatically. The optional arguments stdin and stdout
specify alternate input and output file objects; if not specified,
sys.stdin and sys.stdout are used.
"""
import sys
if stdin is not None:
self.stdin = stdin
else:
self.stdin = sys.stdin
if stdout is not None:
self.stdout = stdout
else:
self.stdout = sys.stdout
self.cmdqueue = []
self.completekey = completekey | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/richtext.py | python | RichTextPrinting.SetFooterText | (*args, **kwargs) | return _richtext.RichTextPrinting_SetFooterText(*args, **kwargs) | SetFooterText(self, String text, int page=RICHTEXT_PAGE_ALL, int location=RICHTEXT_PAGE_CENTRE) | SetFooterText(self, String text, int page=RICHTEXT_PAGE_ALL, int location=RICHTEXT_PAGE_CENTRE) | [
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return _richtext.RichTextPrinting_SetFooterText(*args, **kwargs) | [
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snap-stanford/snap-python | d53c51b0a26aa7e3e7400b014cdf728948fde80a | setup/snap.py | python | TCnCom.SaveTxt | (*args) | return _snap.TCnCom_SaveTxt(*args) | SaveTxt(TCnComV const & CnComV, TStr FNm, TStr Desc=TStr())
Parameters:
CnComV: TCnComV const &
FNm: TStr const &
Desc: TStr const &
SaveTxt(TCnComV const & CnComV, TStr FNm)
Parameters:
CnComV: TCnComV const &
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"""
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return _snap.TCnCom_SaveTxt(*args) | [
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adobe/chromium | cfe5bf0b51b1f6b9fe239c2a3c2f2364da9967d7 | third_party/closure_linter/closure_linter/tokenutil.py | python | InsertTokensAfter | (new_tokens, token) | Insert multiple tokens after token.
Args:
new_tokens: An array of tokens to be added to the stream
token: A token already in the stream | Insert multiple tokens after token. | [
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Args:
new_tokens: An array of tokens to be added to the stream
token: A token already in the stream
"""
# TODO(user): It would be nicer to have InsertTokenAfter defer to here
# instead of vice-versa.
current_token = token
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InsertTokenAfter(new_token, current_token)
current_token = new_token | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_core.py | python | Rect2D.GetRightBottom | (*args, **kwargs) | return _core_.Rect2D_GetRightBottom(*args, **kwargs) | GetRightBottom(self) -> Point2D | GetRightBottom(self) -> Point2D | [
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oracle/graaljs | 36a56e8e993d45fc40939a3a4d9c0c24990720f1 | graal-nodejs/deps/npm/node_modules/node-gyp/gyp/pylib/gyp/MSVSProject.py | python | Tool.__init__ | (self, name, attrs=None) | Initializes the tool.
Args:
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name: Tool name.
attrs: Dict of tool attributes; may be None.
"""
self._attrs = attrs or {}
self._attrs["Name"] = name | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/framework/subscribe.py | python | _scoped_subscribe | (tensor, side_effects, control_cache) | Helper method that subscribes a single tensor to a list of side_effects.
This is a thin wrapper around `_subscribe` and ensures that the side effect
ops are added within the same device and control flow context of the
subscribed tensor.
Args:
tensor: The `tf.Tensor` to be subscribed.
side_effects: List of side_effect functions, see subscribe for details.
control_cache: `_ControlOutputCache` helper to get control_outputs faster.
Returns:
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This is a thin wrapper around `_subscribe` and ensures that the side effect
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Args:
tensor: The `tf.Tensor` to be subscribed.
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with ops.device(tensor.device):
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pytorch/pytorch | 7176c92687d3cc847cc046bf002269c6949a21c2 | torch/ao/quantization/fx/lower_to_qnnpack.py | python | lower_to_qnnpack | (model: QuantizedGraphModule) | return _lower_to_native_backend(model) | Lower a quantized reference model (with reference quantized operator patterns)
to qnnpack | Lower a quantized reference model (with reference quantized operator patterns)
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""" Lower a quantized reference model (with reference quantized operator patterns)
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return _lower_to_native_backend(model) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_controls.py | python | ListCtrl.GetItemFont | (*args, **kwargs) | return _controls_.ListCtrl_GetItemFont(*args, **kwargs) | GetItemFont(self, long item) -> Font | GetItemFont(self, long item) -> Font | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/build/waf-1.7.13/waflib/Tools/c_config.py | python | check_cc | (self, *k, **kw) | return self.check(*k, **kw) | Same as :py:func:`waflib.Tools.c_config.check` but default to the *c* programming language | Same as :py:func:`waflib.Tools.c_config.check` but default to the *c* programming language | [
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"""
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kw['compiler'] = 'c'
return self.check(*k, **kw) | [
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/lib-tk/Tkinter.py | python | Misc.selection_own | (self, **kw) | Become owner of X selection.
A keyword parameter selection specifies the name of
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self.tk.call(('selection', 'own') +
self._options(kw) + (self._w,)) | [
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miyosuda/TensorFlowAndroidDemo | 35903e0221aa5f109ea2dbef27f20b52e317f42d | jni-build/jni/include/external/bazel_tools/third_party/py/gflags/__init__.py | python | FlagValues.FindModuleIdDefiningFlag | (self, flagname, default=None) | return default | Return the ID of the module defining this flag, or default.
Args:
flagname: Name of the flag to lookup.
default: Value to return if flagname is not defined. Defaults
to None.
Returns:
The ID of the module which registered the flag with this name.
If no such module exists (i.e. no flag with this name exists),
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"""Return the ID of the module defining this flag, or default.
Args:
flagname: Name of the flag to lookup.
default: Value to return if flagname is not defined. Defaults
to None.
Returns:
The ID of the module which registered the flag with this name.
If no such module exists (i.e. no flag with this name exists),
we return default.
"""
for module_id, flags in self.FlagsByModuleIdDict().iteritems():
for flag in flags:
if flag.name == flagname or flag.short_name == flagname:
return module_id
return default | [
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/lib2to3/fixer_util.py | python | find_binding | (name, node, package=None) | return None | Returns the node which binds variable name, otherwise None.
If optional argument package is supplied, only imports will
be returned.
See test cases for examples. | Returns the node which binds variable name, otherwise None.
If optional argument package is supplied, only imports will
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""" Returns the node which binds variable name, otherwise None.
If optional argument package is supplied, only imports will
be returned.
See test cases for examples."""
for child in node.children:
ret = None
if child.type == syms.for_stmt:
if _find(name, child.children[1]):
return child
n = find_binding(name, make_suite(child.children[-1]), package)
if n: ret = n
elif child.type in (syms.if_stmt, syms.while_stmt):
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if n: ret = n
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if n:
ret = n
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# i+3 is the colon, i+4 is the suite
n = find_binding(name, make_suite(child.children[i+4]), package)
if n: ret = n
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ret = child
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0vercl0k/blazefox | 0ffeddfc1de3acb2254c505388e4cf9ab9d1f0a7 | src/js/util/make_unicode.py | python | read_unicode_data | (unicode_data) | If you want to understand how this wonderful file format works checkout
Unicode Standard Annex #44 - Unicode Character Database
http://www.unicode.org/reports/tr44/ | If you want to understand how this wonderful file format works checkout
Unicode Standard Annex #44 - Unicode Character Database
http://www.unicode.org/reports/tr44/ | [
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"""
If you want to understand how this wonderful file format works checkout
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http://www.unicode.org/reports/tr44/
"""
reader = csv.reader(unicode_data, delimiter=str(';'))
while True:
row = next(reader)
name = row[1]
# We need to expand the UAX #44 4.2.3 Code Point Range
if name.startswith('<') and name.endswith('First>'):
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yield row
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FreeCAD/FreeCAD | ba42231b9c6889b89e064d6d563448ed81e376ec | src/Mod/Draft/draftguitools/gui_trackers.py | python | gridTracker.update | (self) | Redraw the grid. | Redraw the grid. | [
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] | def update(self):
"""Redraw the grid."""
# Resize the grid to make sure it fits
# an exact pair number of main lines
numlines = self.numlines // self.mainlines // 2 * 2 * self.mainlines
bound = (numlines // 2) * self.space
border = (numlines//2 + self.mainlines/2) * self.space
cursor = self.mainlines//4 * self.space
pts = []
mpts = []
apts = []
cpts = []
for i in range(numlines + 1):
curr = -bound + i * self.space
z = 0
if i / float(self.mainlines) == i // self.mainlines:
if round(curr, 4) == 0:
apts.extend([[-bound, curr, z], [bound, curr, z]])
apts.extend([[curr, -bound, z], [curr, bound, z]])
else:
mpts.extend([[-bound, curr, z], [bound, curr, z]])
mpts.extend([[curr, -bound, z], [curr, bound, z]])
cpts.extend([[-border,curr,z], [-border+cursor,curr,z]])
cpts.extend([[border-cursor,curr,z], [border,curr,z]])
cpts.extend([[curr,-border,z], [curr,-border+cursor,z]])
cpts.extend([[curr,border-cursor,z], [curr,border,z]])
else:
pts.extend([[-bound, curr, z], [bound, curr, z]])
pts.extend([[curr, -bound, z], [curr, bound, z]])
if pts != self.pts:
idx = []
midx = []
#aidx = []
cidx = []
for p in range(0, len(pts), 2):
idx.append(2)
for mp in range(0, len(mpts), 2):
midx.append(2)
#for ap in range(0, len(apts), 2):
# aidx.append(2)
for cp in range(0, len(cpts),2):
cidx.append(2)
if Draft.getParam("gridBorder", True):
# extra border
border = (numlines//2 + self.mainlines/2) * self.space
mpts.extend([[-border, -border, z], [border, -border, z], [border, border, z], [-border, border, z], [-border, -border, z]])
midx.append(5)
# cursors
mpts.extend(cpts)
midx.extend(cidx)
# texts
self.font.size = self.space*(self.mainlines//4) or 1
self.font.name = Draft.getParam("textfont","Sans")
txt = FreeCAD.Units.Quantity(self.space*self.mainlines,FreeCAD.Units.Length).UserString
self.text1.string = txt
self.text2.string = txt
self.textpos1.translation.setValue((-bound+self.space,-border+self.space,z))
self.textpos2.translation.setValue((-bound-self.space,-bound+self.space,z))
else:
self.text1.string = " "
self.text2.string = " "
self.lines1.numVertices.deleteValues(0)
self.lines2.numVertices.deleteValues(0)
#self.lines3.numVertices.deleteValues(0)
self.coords1.point.setValues(pts)
self.lines1.numVertices.setValues(idx)
self.coords2.point.setValues(mpts)
self.lines2.numVertices.setValues(midx)
self.coords3.point.setValues(apts)
#self.lines3.numVertices.setValues(aidx)
self.pts = pts
self.displayHumanFigure()
self.setAxesColor() | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/setuptools/py3/setuptools/_distutils/command/install.py | python | install.convert_paths | (self, *names) | Call `convert_path` over `names`. | Call `convert_path` over `names`. | [
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] | def convert_paths(self, *names):
"""Call `convert_path` over `names`."""
for name in names:
attr = "install_" + name
setattr(self, attr, convert_path(getattr(self, attr))) | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/ops/math_grad.py | python | _XLog1pyGrad | (op, grad) | Returns gradient of xlog1py(x, y) with respect to x and y. | Returns gradient of xlog1py(x, y) with respect to x and y. | [
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"""Returns gradient of xlog1py(x, y) with respect to x and y."""
x = op.inputs[0]
y = op.inputs[1]
sx = array_ops.shape(x)
sy = array_ops.shape(y)
rx, ry = gen_array_ops.broadcast_gradient_args(sx, sy)
with ops.control_dependencies([grad]):
not_zero_x = math_ops.cast(
math_ops.not_equal(x, math_ops.cast(0., dtype=x.dtype)), dtype=x.dtype)
partial_x = gen_math_ops.xlog1py(not_zero_x, y)
partial_y = gen_math_ops.xdivy(x, y + 1.)
return (array_ops.reshape(math_ops.reduce_sum(partial_x * grad, rx), sx),
array_ops.reshape(math_ops.reduce_sum(partial_y * grad, ry), sy)) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/prompt-toolkit/py3/prompt_toolkit/key_binding/key_processor.py | python | KeyProcessor.feed_multiple | (self, key_presses: List[KeyPress], first: bool = False) | :param first: If true, insert before everything else. | :param first: If true, insert before everything else. | [
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"""
:param first: If true, insert before everything else.
"""
if first:
self.input_queue.extendleft(reversed(key_presses))
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self.input_queue.extend(key_presses) | [
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ROCmSoftwarePlatform/hipCaffe | 4ec5d482515cce532348553b6db6d00d015675d5 | python/caffe/io.py | python | Transformer.set_raw_scale | (self, in_, scale) | Set the scale of raw features s.t. the input blob = input * scale.
While Python represents images in [0, 1], certain Caffe models
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in_ : which input to assign this scale factor
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"""
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self.__check_input(in_)
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bigartm/bigartm | 47e37f982de87aa67bfd475ff1f39da696b181b3 | 3rdparty/protobuf-3.0.0/python/google/protobuf/internal/well_known_types.py | python | _FieldMaskTree.IntersectPath | (self, path, intersection) | Calculates the intersection part of a field path with this tree.
Args:
path: The field path to calculates.
intersection: The out tree to record the intersection part. | Calculates the intersection part of a field path with this tree. | [
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Args:
path: The field path to calculates.
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"""
node = self._root
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node = node[name]
intersection.AddLeafNodes(path, node) | [
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y123456yz/reading-and-annotate-mongodb-3.6 | 93280293672ca7586dc24af18132aa61e4ed7fcf | mongo/src/third_party/scons-2.5.0/scons-local-2.5.0/SCons/Builder.py | python | _node_errors | (builder, env, tlist, slist) | Validate that the lists of target and source nodes are
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"""
# First, figure out if there are any errors in the way the targets
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if t.side_effect:
raise UserError("Multiple ways to build the same target were specified for: %s" % t)
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action = t.builder.action
t_contents = t.builder.action.get_contents(tlist, slist, t.env)
contents = builder.action.get_contents(tlist, slist, env)
if t_contents == contents:
msg = "Two different environments were specified for target %s,\n\tbut they appear to have the same action: %s" % (t, action.genstring(tlist, slist, t.env))
SCons.Warnings.warn(SCons.Warnings.DuplicateEnvironmentWarning, msg)
else:
msg = "Two environments with different actions were specified for the same target: %s\n(action 1: %s)\n(action 2: %s)" % (t,t_contents,contents)
raise UserError(msg)
if builder.multi:
if t.builder != builder:
msg = "Two different builders (%s and %s) were specified for the same target: %s" % (t.builder.get_name(env), builder.get_name(env), t)
raise UserError(msg)
# TODO(batch): list constructed each time!
if t.get_executor().get_all_targets() != tlist:
msg = "Two different target lists have a target in common: %s (from %s and from %s)" % (t, list(map(str, t.get_executor().get_all_targets())), list(map(str, tlist)))
raise UserError(msg)
elif t.sources != slist:
msg = "Multiple ways to build the same target were specified for: %s (from %s and from %s)" % (t, list(map(str, t.sources)), list(map(str, slist)))
raise UserError(msg)
if builder.single_source:
if len(slist) > 1:
raise UserError("More than one source given for single-source builder: targets=%s sources=%s" % (list(map(str,tlist)), list(map(str,slist)))) | [
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qgis/QGIS | 15a77662d4bb712184f6aa60d0bd663010a76a75 | python/core/additions/qgssettings.py | python | _qgssettings_enum_value | (self, key, enumDefaultValue, section=QgsSettings.NoSection) | return enu_val | Return the setting value for a setting based on an enum.
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"... | def _qgssettings_enum_value(self, key, enumDefaultValue, section=QgsSettings.NoSection):
"""
Return the setting value for a setting based on an enum.
This forces the output to be a valid and existing entry of the enum.
Hence if the setting value is incorrect, the given default value is returned.
:param self: the QgsSettings object
:param key: the setting key
:param enumDefaultValue: the default value as an enum value
:param section: optional section
:return: the setting value
.. note:: The enum needs to be declared with Q_ENUM.
"""
meta_enum = metaEnumFromValue(enumDefaultValue)
if meta_enum is None or not meta_enum.isValid():
# this should not happen
raise ValueError("could not get the meta enum for given enum default value (type: {})"
.format(enumDefaultValue.__class__))
str_val = self.value(key, meta_enum.valueToKey(enumDefaultValue), str, section)
# need a new meta enum as QgsSettings.value is making a copy and leads to seg fault (probably a PyQt issue)
meta_enum_2 = metaEnumFromValue(enumDefaultValue)
(enu_val, ok) = meta_enum_2.keyToValue(str_val)
if not ok:
enu_val = enumDefaultValue
else:
# cast to the enum class
enu_val = enumDefaultValue.__class__(enu_val)
return enu_val | [
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eyllanesc/stackoverflow | 3837cc9ff94541bf5a500aac1b6182f53669537d | questions/55241644/fakeuic/__init__.py | python | compileUi | (uifile, pyfile, execute=False, indent=4, from_imports=False, resource_suffix='_rc', import_from='.') | compileUi(uifile, pyfile, execute=False, indent=4, from_imports=False, resource_suffix='_rc', import_from='.')
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uifile is a file name or file-like object containing the .ui file.
pyfile is the file-like object to which the Python code will be written to.
execute is optionally set to generate extra Python code that allows the
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indent is the optional indentation width using spaces. If it is 0 then a
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resource_suffix is the suffix appended to the basename of any resource file
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file specified a resource file called foo.qrc then the corresponding Python
module is foo_rc.
import_from is optionally set to the package used for relative import
statements. The default is ``'.'``. | compileUi(uifile, pyfile, execute=False, indent=4, from_imports=False, resource_suffix='_rc', import_from='.') | [
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"""compileUi(uifile, pyfile, execute=False, indent=4, from_imports=False, resource_suffix='_rc', import_from='.')
Creates a Python module from a Qt Designer .ui file.
uifile is a file name or file-like object containing the .ui file.
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file specified a resource file called foo.qrc then the corresponding Python
module is foo_rc.
import_from is optionally set to the package used for relative import
statements. The default is ``'.'``.
"""
from PySide2.QtCore import PYQT_VERSION_STR
try:
uifname = uifile.name
except AttributeError:
uifname = uifile
indenter.indentwidth = indent
pyfile.write(_header % (uifname, PYQT_VERSION_STR))
winfo = compiler.UICompiler().compileUi(uifile, pyfile, from_imports, resource_suffix, import_from)
if execute:
indenter.write_code(_display_code % winfo) | [
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microsoft/TSS.MSR | 0f2516fca2cd9929c31d5450e39301c9bde43688 | TSS.Py/src/TpmTypes.py | python | TPM2_SequenceComplete_REQUEST.__init__ | (self, sequenceHandle = TPM_HANDLE(), buffer = None, hierarchy = TPM_HANDLE()) | This command adds the last part of data, if any, to a hash/HMAC
sequence and returns the result.
Attributes:
sequenceHandle (TPM_HANDLE): Authorization for the sequence
Auth Index: 1
Auth Role: USER
buffer (bytes): Data to be added to the hash/HMAC
hierarchy (TPM_HANDLE): Hierarchy of the ticket for a hash | This command adds the last part of data, if any, to a hash/HMAC
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sequenceHandle (TPM_HANDLE): Authorization for the sequence
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buffer (bytes): Data to be added to the hash/HMAC
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self.sequenceHandle = sequenceHandle
self.buffer = buffer
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/dataview.py | python | DataViewCtrl.EnsureVisible | (*args, **kwargs) | return _dataview.DataViewCtrl_EnsureVisible(*args, **kwargs) | EnsureVisible(self, DataViewItem item, DataViewColumn column=None) | EnsureVisible(self, DataViewItem item, DataViewColumn column=None) | [
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"""EnsureVisible(self, DataViewItem item, DataViewColumn column=None)"""
return _dataview.DataViewCtrl_EnsureVisible(*args, **kwargs) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/site-packages/pip/_vendor/urllib3/util/ssl_.py | python | create_urllib3_context | (
ssl_version=None, cert_reqs=None, options=None, ciphers=None
) | return context | All arguments have the same meaning as ``ssl_wrap_socket``.
By default, this function does a lot of the same work that
``ssl.create_default_context`` does on Python 3.4+. It:
- Disables SSLv2, SSLv3, and compression
- Sets a restricted set of server ciphers
If you wish to enable SSLv3, you can do::
from pip._vendor.urllib3.util import ssl_
context = ssl_.create_urllib3_context()
context.options &= ~ssl_.OP_NO_SSLv3
You can do the same to enable compression (substituting ``COMPRESSION``
for ``SSLv3`` in the last line above).
:param ssl_version:
The desired protocol version to use. This will default to
PROTOCOL_SSLv23 which will negotiate the highest protocol that both
the server and your installation of OpenSSL support.
:param cert_reqs:
Whether to require the certificate verification. This defaults to
``ssl.CERT_REQUIRED``.
:param options:
Specific OpenSSL options. These default to ``ssl.OP_NO_SSLv2``,
``ssl.OP_NO_SSLv3``, ``ssl.OP_NO_COMPRESSION``, and ``ssl.OP_NO_TICKET``.
:param ciphers:
Which cipher suites to allow the server to select.
:returns:
Constructed SSLContext object with specified options
:rtype: SSLContext | All arguments have the same meaning as ``ssl_wrap_socket``. | [
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ssl_version=None, cert_reqs=None, options=None, ciphers=None
):
"""All arguments have the same meaning as ``ssl_wrap_socket``.
By default, this function does a lot of the same work that
``ssl.create_default_context`` does on Python 3.4+. It:
- Disables SSLv2, SSLv3, and compression
- Sets a restricted set of server ciphers
If you wish to enable SSLv3, you can do::
from pip._vendor.urllib3.util import ssl_
context = ssl_.create_urllib3_context()
context.options &= ~ssl_.OP_NO_SSLv3
You can do the same to enable compression (substituting ``COMPRESSION``
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:param ssl_version:
The desired protocol version to use. This will default to
PROTOCOL_SSLv23 which will negotiate the highest protocol that both
the server and your installation of OpenSSL support.
:param cert_reqs:
Whether to require the certificate verification. This defaults to
``ssl.CERT_REQUIRED``.
:param options:
Specific OpenSSL options. These default to ``ssl.OP_NO_SSLv2``,
``ssl.OP_NO_SSLv3``, ``ssl.OP_NO_COMPRESSION``, and ``ssl.OP_NO_TICKET``.
:param ciphers:
Which cipher suites to allow the server to select.
:returns:
Constructed SSLContext object with specified options
:rtype: SSLContext
"""
context = SSLContext(ssl_version or PROTOCOL_TLS)
context.set_ciphers(ciphers or DEFAULT_CIPHERS)
# Setting the default here, as we may have no ssl module on import
cert_reqs = ssl.CERT_REQUIRED if cert_reqs is None else cert_reqs
if options is None:
options = 0
# SSLv2 is easily broken and is considered harmful and dangerous
options |= OP_NO_SSLv2
# SSLv3 has several problems and is now dangerous
options |= OP_NO_SSLv3
# Disable compression to prevent CRIME attacks for OpenSSL 1.0+
# (issue #309)
options |= OP_NO_COMPRESSION
# TLSv1.2 only. Unless set explicitly, do not request tickets.
# This may save some bandwidth on wire, and although the ticket is encrypted,
# there is a risk associated with it being on wire,
# if the server is not rotating its ticketing keys properly.
options |= OP_NO_TICKET
context.options |= options
# Enable post-handshake authentication for TLS 1.3, see GH #1634. PHA is
# necessary for conditional client cert authentication with TLS 1.3.
# The attribute is None for OpenSSL <= 1.1.0 or does not exist in older
# versions of Python. We only enable on Python 3.7.4+ or if certificate
# verification is enabled to work around Python issue #37428
# See: https://bugs.python.org/issue37428
if (cert_reqs == ssl.CERT_REQUIRED or sys.version_info >= (3, 7, 4)) and getattr(
context, "post_handshake_auth", None
) is not None:
context.post_handshake_auth = True
context.verify_mode = cert_reqs
if (
getattr(context, "check_hostname", None) is not None
): # Platform-specific: Python 3.2
# We do our own verification, including fingerprints and alternative
# hostnames. So disable it here
context.check_hostname = False
# Enable logging of TLS session keys via defacto standard environment variable
# 'SSLKEYLOGFILE', if the feature is available (Python 3.8+). Skip empty values.
if hasattr(context, "keylog_filename"):
sslkeylogfile = os.environ.get("SSLKEYLOGFILE")
if sslkeylogfile:
context.keylog_filename = sslkeylogfile
return context | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/AWSPythonSDK/1.5.8/botocore/vendored/requests/models.py | python | Response.is_permanent_redirect | (self) | return ('location' in self.headers and self.status_code in (codes.moved_permanently, codes.permanent_redirect)) | True if this Response one of the permanant versions of redirect | True if this Response one of the permanant versions of redirect | [
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"""True if this Response one of the permanant versions of redirect"""
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krishauser/Klampt | 972cc83ea5befac3f653c1ba20f80155768ad519 | Python/klampt/robotsim.py | python | RobotModel.velocityFromDrivers | (self, driverVelocities: Vector) | return _robotsim.RobotModel_velocityFromDrivers(self, driverVelocities) | r"""
Converts a list of driver velocities (length numDrivers()) to a full velocity
vector (length numLinks()).
Args:
driverVelocities (:obj:`list of floats`) | r"""
Converts a list of driver velocities (length numDrivers()) to a full velocity
vector (length numLinks()). | [
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r"""
Converts a list of driver velocities (length numDrivers()) to a full velocity
vector (length numLinks()).
Args:
driverVelocities (:obj:`list of floats`)
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return _robotsim.RobotModel_velocityFromDrivers(self, driverVelocities) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/scipy/optimize/optimize.py | python | rosen_der | (x) | return der | The derivative (i.e. gradient) of the Rosenbrock function.
Parameters
----------
x : array_like
1-D array of points at which the derivative is to be computed.
Returns
-------
rosen_der : (N,) ndarray
The gradient of the Rosenbrock function at `x`.
See Also
--------
rosen, rosen_hess, rosen_hess_prod | The derivative (i.e. gradient) of the Rosenbrock function. | [
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"""
The derivative (i.e. gradient) of the Rosenbrock function.
Parameters
----------
x : array_like
1-D array of points at which the derivative is to be computed.
Returns
-------
rosen_der : (N,) ndarray
The gradient of the Rosenbrock function at `x`.
See Also
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rosen, rosen_hess, rosen_hess_prod
"""
x = asarray(x)
xm = x[1:-1]
xm_m1 = x[:-2]
xm_p1 = x[2:]
der = numpy.zeros_like(x)
der[1:-1] = (200 * (xm - xm_m1**2) -
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der[0] = -400 * x[0] * (x[1] - x[0]**2) - 2 * (1 - x[0])
der[-1] = 200 * (x[-1] - x[-2]**2)
return der | [
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rtbkit/rtbkit | 502d06acc3f8d90438946b6ae742190f2f4b4fbb | jml-build/jmlbuild.py | python | next_line | (stream) | return result | Returns the next line that needs parsing. The returned line is guaranteed to
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"""
result = ""
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if len(line) == 0: break
line = strip_line(line)
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result += strip_line(line[:-1])
continue
result += line
break
if len(result) == 0:
print_dbg("\tEOF")
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print_dbg("\tline: " + result)
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/richtext.py | python | RichTextCtrl.SetInternalSelectionRange | (*args, **kwargs) | return _richtext.RichTextCtrl_SetInternalSelectionRange(*args, **kwargs) | SetInternalSelectionRange(self, RichTextRange range)
Set the selection range in character positions. The range is in
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"""
SetInternalSelectionRange(self, RichTextRange range)
Set the selection range in character positions. The range is in
internal format, i.e. a single character selection is denoted by (n,n).
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return _richtext.RichTextCtrl_SetInternalSelectionRange(*args, **kwargs) | [
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openvinotoolkit/openvino | dedcbeafa8b84cccdc55ca64b8da516682b381c7 | tools/mo/openvino/tools/mo/utils/model_analysis.py | python | AnalyzeAction.run_after | (self) | return [LoadFinish] | Returns list of replacer classes which this replacer must be run after.
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Returns list of replacer classes which this replacer must be run after.
:return: list of classes
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/ultimatelistctrl.py | python | UltimateListTextCtrl.AcceptChanges | (self) | return True | Accepts/refuses the changes made by the user. | Accepts/refuses the changes made by the user. | [
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""" Accepts/refuses the changes made by the user. """
value = self.GetValue()
if value == self._startValue:
# nothing changed, always accept
# when an item remains unchanged, the owner
# needs to be notified that the user decided
# not to change the tree item label, and that
# the edit has been cancelled
self._owner.OnRenameCancelled(self._itemEdited)
return True
if not self._owner.OnRenameAccept(self._itemEdited, value):
# vetoed by the user
return False
# accepted, do rename the item
self._owner.SetItemText(self._itemEdited, value)
if value.count("\n") != self._startValue.count("\n"):
self._owner.ResetLineDimensions()
self._owner.Refresh()
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learnforpractice/pyeos | 4f04eb982c86c1fdb413084af77c713a6fda3070 | libraries/vm/vm_cpython_ss/lib/codecs.py | python | Codec.decode | (self, input, errors='strict') | Decodes the object input and returns a tuple (output
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hughperkins/tf-coriander | 970d3df6c11400ad68405f22b0c42a52374e94ca | tensorflow/python/training/supervisor.py | python | Supervisor.start_queue_runners | (self, sess, queue_runners=None) | return threads | Start threads for `QueueRunners`.
Note that the queue runners collected in the graph key `QUEUE_RUNNERS`
are already started automatically when you create a session with the
supervisor, so unless you have non-collected queue runners to start
you do not need to call this explicitly.
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sess: A `Session`.
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"""Start threads for `QueueRunners`.
Note that the queue runners collected in the graph key `QUEUE_RUNNERS`
are already started automatically when you create a session with the
supervisor, so unless you have non-collected queue runners to start
you do not need to call this explicitly.
Args:
sess: A `Session`.
queue_runners: A list of `QueueRunners`. If not specified, we'll use the
list of queue runners gathered in the graph under the key
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Returns:
The list of threads started for the `QueueRunners`.
"""
if queue_runners is None:
queue_runners = self._graph.get_collection(ops.GraphKeys.QUEUE_RUNNERS)
threads = []
for qr in queue_runners:
threads.extend(qr.create_threads(sess, coord=self._coord, daemon=True,
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/ops/summary_ops_v2.py | python | audio | (name, tensor, sample_rate, max_outputs, family=None, step=None) | return summary_writer_function(name, tensor, function, family=family) | Writes an audio summary if possible. | Writes an audio summary if possible. | [
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"""Writes an audio summary if possible."""
def function(tag, scope):
# Note the identity to move the tensor to the CPU.
return gen_summary_ops.write_audio_summary(
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max_outputs=max_outputs,
name=scope)
return summary_writer_function(name, tensor, function, family=family) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/AWSPythonSDK/1.5.8/docutils/utils/math/math2html.py | python | Trace.error | (cls, message) | Show an error message | Show an error message | [
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"Show an error message"
message = '* ' + message
if Trace.prefix and Trace.showlinesmode:
message = Trace.prefix + message
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/ops/sparse_grad.py | python | _SparseSoftmaxGrad | (op, grad) | return [None, grad_x, None] | Gradients for SparseSoftmax.
The calculation is the same as SoftmaxGrad:
grad_x = grad_softmax * softmax - sum(grad_softmax * softmax) * softmax
where we now only operate on the non-zero values present in the SparseTensors.
Args:
op: the SparseSoftmax op.
grad: the upstream gradient w.r.t. the non-zero SparseSoftmax output values.
Returns:
Gradients w.r.t. the input (sp_indices, sp_values, sp_shape). | Gradients for SparseSoftmax. | [
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"""Gradients for SparseSoftmax.
The calculation is the same as SoftmaxGrad:
grad_x = grad_softmax * softmax - sum(grad_softmax * softmax) * softmax
where we now only operate on the non-zero values present in the SparseTensors.
Args:
op: the SparseSoftmax op.
grad: the upstream gradient w.r.t. the non-zero SparseSoftmax output values.
Returns:
Gradients w.r.t. the input (sp_indices, sp_values, sp_shape).
"""
indices, shape = op.inputs[0], op.inputs[2]
out_vals = op.outputs[0]
sp_output = sparse_tensor.SparseTensor(indices, out_vals, shape)
sp_grad = sparse_tensor.SparseTensor(indices, grad, shape)
sp_product = sparse_tensor.SparseTensor(indices,
sp_output.values * sp_grad.values,
shape)
# [..., B, 1], dense.
sum_reduced = -sparse_ops.sparse_reduce_sum(sp_product, [-1], keepdims=True)
# sparse [..., B, C] + dense [..., B, 1] with broadcast; outputs sparse.
sp_sum = sparse_ops.sparse_dense_cwise_add(sp_grad, sum_reduced)
grad_x = sp_sum.values * sp_output.values
return [None, grad_x, None] | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scikit-learn/py3/sklearn/linear_model/_omp.py | python | OrthogonalMatchingPursuit.fit | (self, X, y) | return self | Fit the model using X, y as training data.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Training data.
y : array-like, shape (n_samples,) or (n_samples, n_targets)
Target values. Will be cast to X's dtype if necessary
Returns
-------
self : object
returns an instance of self. | Fit the model using X, y as training data. | [
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"""Fit the model using X, y as training data.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Training data.
y : array-like, shape (n_samples,) or (n_samples, n_targets)
Target values. Will be cast to X's dtype if necessary
Returns
-------
self : object
returns an instance of self.
"""
X, y = check_X_y(X, y, multi_output=True, y_numeric=True)
n_features = X.shape[1]
X, y, X_offset, y_offset, X_scale, Gram, Xy = \
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self.fit_intercept, copy=True)
if y.ndim == 1:
y = y[:, np.newaxis]
if self.n_nonzero_coefs is None and self.tol is None:
# default for n_nonzero_coefs is 0.1 * n_features
# but at least one.
self.n_nonzero_coefs_ = max(int(0.1 * n_features), 1)
else:
self.n_nonzero_coefs_ = self.n_nonzero_coefs
if Gram is False:
coef_, self.n_iter_ = orthogonal_mp(
X, y, self.n_nonzero_coefs_, self.tol,
precompute=False, copy_X=True,
return_n_iter=True)
else:
norms_sq = np.sum(y ** 2, axis=0) if self.tol is not None else None
coef_, self.n_iter_ = orthogonal_mp_gram(
Gram, Xy=Xy, n_nonzero_coefs=self.n_nonzero_coefs_,
tol=self.tol, norms_squared=norms_sq,
copy_Gram=True, copy_Xy=True,
return_n_iter=True)
self.coef_ = coef_.T
self._set_intercept(X_offset, y_offset, X_scale)
return self | [
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/python/framework/meta_graph.py | python | read_meta_graph_file | (filename) | return meta_graph_def | Reads a file containing `MetaGraphDef` and returns the protocol buffer.
Args:
filename: `meta_graph_def` filename including the path.
Returns:
A `MetaGraphDef` protocol buffer.
Raises:
IOError: If the file doesn't exist, or cannot be successfully parsed. | Reads a file containing `MetaGraphDef` and returns the protocol buffer. | [
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"""Reads a file containing `MetaGraphDef` and returns the protocol buffer.
Args:
filename: `meta_graph_def` filename including the path.
Returns:
A `MetaGraphDef` protocol buffer.
Raises:
IOError: If the file doesn't exist, or cannot be successfully parsed.
"""
meta_graph_def = meta_graph_pb2.MetaGraphDef()
if not file_io.file_exists(filename):
raise IOError("File %s does not exist." % filename)
# First try to read it as a binary file.
file_content = file_io.FileIO(filename, "rb").read()
try:
meta_graph_def.ParseFromString(file_content)
return meta_graph_def
except Exception: # pylint: disable=broad-except
pass
# Next try to read it as a text file.
try:
text_format.Merge(file_content.decode("utf-8"), meta_graph_def)
except text_format.ParseError as e:
raise IOError("Cannot parse file %s: %s." % (filename, str(e)))
return meta_graph_def | [
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/smtplib.py | python | SMTP.help | (self, args='') | return self.getreply()[1] | SMTP 'help' command.
Returns help text from server. | SMTP 'help' command.
Returns help text from server. | [
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Returns help text from server."""
self.putcmd("help", args)
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mhammond/pywin32 | 44afd86ba8485194df93234639243252deeb40d5 | com/win32com/client/__init__.py | python | GetActiveObject | (Class, clsctx=pythoncom.CLSCTX_ALL) | return __WrapDispatch(dispatch, Class, resultCLSID=resultCLSID, clsctx=clsctx) | Python friendly version of GetObject's ProgID/CLSID functionality. | Python friendly version of GetObject's ProgID/CLSID functionality. | [
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] | def GetActiveObject(Class, clsctx=pythoncom.CLSCTX_ALL):
"""
Python friendly version of GetObject's ProgID/CLSID functionality.
"""
resultCLSID = pywintypes.IID(Class)
dispatch = pythoncom.GetActiveObject(resultCLSID)
dispatch = dispatch.QueryInterface(pythoncom.IID_IDispatch)
return __WrapDispatch(dispatch, Class, resultCLSID=resultCLSID, clsctx=clsctx) | [
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tfwu/FaceDetection-ConvNet-3D | f9251c48eb40c5aec8fba7455115c355466555be | python/build/lib.linux-x86_64-2.7/mxnet/kvstore.py | python | _ctype_key_value | (keys, vals) | Return ctype arrays for the key-value args, for internal use | Return ctype arrays for the key-value args, for internal use | [
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"""
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"""
if isinstance(keys, int):
if isinstance(vals, NDArray):
return (c_array(ctypes.c_int, [keys]),
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else:
for value in vals:
assert(isinstance(value, NDArray))
return (c_array(ctypes.c_int, [keys] * len(vals)),
c_array(NDArrayHandle, [value.handle for value in vals]))
else:
assert(len(keys) == len(vals))
for k in keys:
assert(isinstance(k, int))
c_keys = []
c_vals = []
for i in range(len(keys)):
c_key_i, c_val_i = _ctype_key_value(keys[i], vals[i])
c_keys += c_key_i
c_vals += c_val_i
return (c_array(ctypes.c_int, c_keys), c_array(NDArrayHandle, c_vals)) | [
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generalized-intelligence/GAAS | 29ab17d3e8a4ba18edef3a57c36d8db6329fac73 | algorithms/src/SystemManagement/json_request_response_lib/src/third_party/nlohmann_json/third_party/cpplint/cpplint.py | python | _Quiet | () | return _cpplint_state.quiet | Return's the module's quiet setting. | Return's the module's quiet setting. | [
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qgis/QGIS | 15a77662d4bb712184f6aa60d0bd663010a76a75 | python/plugins/db_manager/dlg_create_table.py | python | DlgCreateTable.updatePkeyCombo | (self, selRow=None) | called when list of columns changes. if 'sel' is None, it keeps current index | called when list of columns changes. if 'sel' is None, it keeps current index | [
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""" called when list of columns changes. if 'sel' is None, it keeps current index """
if selRow is None:
selRow = self.cboPrimaryKey.currentIndex()
self.cboPrimaryKey.clear()
m = self.fields.model()
for row in range(m.rowCount()):
name = m.data(m.index(row, 0))
self.cboPrimaryKey.addItem(name)
self.cboPrimaryKey.setCurrentIndex(selRow) | [
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... | https://github.com/qgis/QGIS/blob/15a77662d4bb712184f6aa60d0bd663010a76a75/python/plugins/db_manager/dlg_create_table.py#L160-L173 | ||
bh107/bohrium | 5b83e7117285fefc7779ed0e9acb0f8e74c7e068 | bridge/bh107/bh107/random.py | python | RandomState.rand | (self, *shape) | return self.random_sample(shape) | Random values in a given shape.
Create an array of the given shape and propagate it with
random samples from a uniform distribution
over ``[0, 1)``.
Parameters
----------
d0, d1, ..., dn : int, optional
The dimensions of the returned array, should all be positive.
If no argument is given a single Python float is returned.
Returns
-------
out : BhArray, shape ``(d0, d1, ..., dn)``
Random values.
See Also
--------
random
Notes
-----
This is a convenience function. If you want an interface that
takes a shape-tuple as the first argument, refer to
np.random.random_sample .
Examples
--------
>>> np.random.rand(3,2)
array([[ 0.14022471, 0.96360618], #random
[ 0.37601032, 0.25528411], #random
[ 0.49313049, 0.94909878]]) #random | Random values in a given shape. | [
"Random",
"values",
"in",
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"given",
"shape",
"."
] | def rand(self, *shape):
"""Random values in a given shape.
Create an array of the given shape and propagate it with
random samples from a uniform distribution
over ``[0, 1)``.
Parameters
----------
d0, d1, ..., dn : int, optional
The dimensions of the returned array, should all be positive.
If no argument is given a single Python float is returned.
Returns
-------
out : BhArray, shape ``(d0, d1, ..., dn)``
Random values.
See Also
--------
random
Notes
-----
This is a convenience function. If you want an interface that
takes a shape-tuple as the first argument, refer to
np.random.random_sample .
Examples
--------
>>> np.random.rand(3,2)
array([[ 0.14022471, 0.96360618], #random
[ 0.37601032, 0.25528411], #random
[ 0.49313049, 0.94909878]]) #random
"""
return self.random_sample(shape) | [
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KhronosGroup/Vulkan-Samples | 11a0eeffa223e3c049780fd783900da0bfe50431 | .github/docker/scripts/run-clang-tidy.py | python | apply_fixes | (args, tmpdir) | Calls clang-apply-fixes on a given directory. | Calls clang-apply-fixes on a given directory. | [
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invocation = [args.clang_apply_replacements_binary]
if args.format:
invocation.append('-format')
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invocation.append('-style=' + args.style)
invocation.append(tmpdir)
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/pdfviewer/viewer.py | python | pdfViewer.UsePrintDirect | (self) | return self._usePrintDirect | Property to control to use either Cairo (via a page buffer) or
dcGraphicsContext depending. | Property to control to use either Cairo (via a page buffer) or
dcGraphicsContext depending. | [
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"""
Property to control to use either Cairo (via a page buffer) or
dcGraphicsContext depending.
"""
return self._usePrintDirect | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/stc.py | python | StyledTextCtrl.MarkerNext | (*args, **kwargs) | return _stc.StyledTextCtrl_MarkerNext(*args, **kwargs) | MarkerNext(self, int lineStart, int markerMask) -> int
Find the next line after lineStart that includes a marker in mask. | MarkerNext(self, int lineStart, int markerMask) -> int | [
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"(",
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] | def MarkerNext(*args, **kwargs):
"""
MarkerNext(self, int lineStart, int markerMask) -> int
Find the next line after lineStart that includes a marker in mask.
"""
return _stc.StyledTextCtrl_MarkerNext(*args, **kwargs) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/animate.py | python | AnimationCtrlBase.Stop | (*args, **kwargs) | return _animate.AnimationCtrlBase_Stop(*args, **kwargs) | Stop(self) | Stop(self) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/pydoc.py | python | writedoc | (thing, forceload=0) | Write HTML documentation to a file in the current directory. | Write HTML documentation to a file in the current directory. | [
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"a",
"file",
"in",
"the",
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"directory",
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] | def writedoc(thing, forceload=0):
"""Write HTML documentation to a file in the current directory."""
try:
object, name = resolve(thing, forceload)
page = html.page(describe(object), html.document(object, name))
with open(name + '.html', 'w', encoding='utf-8') as file:
file.write(page)
print('wrote', name + '.html')
except (ImportError, ErrorDuringImport) as value:
print(value) | [
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D-X-Y/caffe-faster-rcnn | eb50c97ff48f3df115d0e85fe0a32b0c7e2aa4cb | python/caffe/draw.py | python | get_layer_label | (layer, rankdir) | return node_label | Define node label based on layer type.
Parameters
----------
layer : ?
rankdir : {'LR', 'TB', 'BT'}
Direction of graph layout.
Returns
-------
string :
A label for the current layer | Define node label based on layer type. | [
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"."
] | def get_layer_label(layer, rankdir):
"""Define node label based on layer type.
Parameters
----------
layer : ?
rankdir : {'LR', 'TB', 'BT'}
Direction of graph layout.
Returns
-------
string :
A label for the current layer
"""
if rankdir in ('TB', 'BT'):
# If graph orientation is vertical, horizontal space is free and
# vertical space is not; separate words with spaces
separator = ' '
else:
# If graph orientation is horizontal, vertical space is free and
# horizontal space is not; separate words with newlines
separator = '\\n'
if layer.type == 'Convolution' or layer.type == 'Deconvolution':
# Outer double quotes needed or else colon characters don't parse
# properly
node_label = '"%s%s(%s)%skernel size: %d%sstride: %d%spad: %d"' %\
(layer.name,
separator,
layer.type,
separator,
layer.convolution_param.kernel_size[0] if len(layer.convolution_param.kernel_size) else 1,
separator,
layer.convolution_param.stride[0] if len(layer.convolution_param.stride) else 1,
separator,
layer.convolution_param.pad[0] if len(layer.convolution_param.pad) else 0)
elif layer.type == 'Pooling':
pooling_types_dict = get_pooling_types_dict()
node_label = '"%s%s(%s %s)%skernel size: %d%sstride: %d%spad: %d"' %\
(layer.name,
separator,
pooling_types_dict[layer.pooling_param.pool],
layer.type,
separator,
layer.pooling_param.kernel_size,
separator,
layer.pooling_param.stride,
separator,
layer.pooling_param.pad)
else:
node_label = '"%s%s(%s)"' % (layer.name, separator, layer.type)
return node_label | [
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carla-simulator/carla | 8854804f4d7748e14d937ec763a2912823a7e5f5 | PythonAPI/docs/doc_gen.py | python | Documentation.gen_body | (self) | return md.data().strip() | Generates the documentation body | Generates the documentation body | [
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] | def gen_body(self):
"""Generates the documentation body"""
md = MarkdownFile()
md.first_title()
md.textn(
"This reference contains all the details the Python API. To consult a previous reference for a specific CARLA release, change the documentation version using the panel in the bottom right corner.<br>"
+"This will change the whole documentation to a previous state. Remember that the <i>latest</i> version is the `dev` branch and may show features not available in any packaged versions of CARLA.<hr>")
for module_name in sorted(self.master_dict):
module = self.master_dict[module_name]
module_key = module_name
# Generate class doc (if any)
if valid_dic_val(module, 'classes'):
for cl in sorted(module['classes'], key = lambda i: i['class_name']):
class_name = cl['class_name']
class_key = join([module_key, class_name], '.')
current_title = module_name+'.'+class_name
md.title(2, join([current_title,'<a name="',current_title,'"></a>']))
# Inheritance
if valid_dic_val(cl, 'parent'):
inherits = italic(create_hyperlinks(cl['parent']))
md.inherit_join(inherits)
# Class main doc
if valid_dic_val(cl, 'doc'):
md.textn(create_hyperlinks(md.prettify_doc(cl['doc'])))
# Generate instance variable doc (if any)
if valid_dic_val(cl, 'instance_variables'):
md.title(3, 'Instance Variables')
for inst_var in cl['instance_variables']:
add_doc_inst_var(md, inst_var, class_key)
# Generate method doc (if any)
if valid_dic_val(cl, 'methods'):
method_list = list()
dunder_list = list()
get_list = list()
set_list = list()
for method in sorted(cl['methods'], key = lambda i: i['def_name']):
method_name = method['def_name']
if method_name[0] == '_' and method_name != '__init__':
dunder_list.append(method)
elif method_name[:4] == 'get_':
get_list.append(method)
elif method_name[:4] == 'set_':
set_list.append(method)
else:
method_list.append(method)
md.title(3, 'Methods')
for method in method_list:
add_doc_method(md, method, class_key)
if len(get_list)>0:
md.title(5, 'Getters')
for method in get_list:
add_doc_getter_setter(md, method, class_key, True, set_list)
if len(set_list)>0:
md.title(5, 'Setters')
for method in set_list:
add_doc_getter_setter(md, method, class_key, False, get_list)
if len(dunder_list)>0:
md.title(5, 'Dunder methods')
for method in dunder_list:
add_doc_dunder(md, method, class_key)
md.separator()
append_code_snipets(md)
append_snipet_button_script(md)
return md.data().strip() | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pyparsing/py3/pyparsing/helpers.py | python | match_previous_expr | (expr: ParserElement) | return rep | Helper to define an expression that is indirectly defined from
the tokens matched in a previous expression, that is, it looks for
a 'repeat' of a previous expression. For example::
first = Word(nums)
second = match_previous_expr(first)
match_expr = first + ":" + second
will match ``"1:1"``, but not ``"1:2"``. Because this
matches by expressions, will *not* match the leading ``"1:1"``
in ``"1:10"``; the expressions are evaluated first, and then
compared, so ``"1"`` is compared with ``"10"``. Do *not* use
with packrat parsing enabled. | Helper to define an expression that is indirectly defined from
the tokens matched in a previous expression, that is, it looks for
a 'repeat' of a previous expression. For example:: | [
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"""Helper to define an expression that is indirectly defined from
the tokens matched in a previous expression, that is, it looks for
a 'repeat' of a previous expression. For example::
first = Word(nums)
second = match_previous_expr(first)
match_expr = first + ":" + second
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in ``"1:10"``; the expressions are evaluated first, and then
compared, so ``"1"`` is compared with ``"10"``. Do *not* use
with packrat parsing enabled.
"""
rep = Forward()
e2 = expr.copy()
rep <<= e2
def copy_token_to_repeater(s, l, t):
matchTokens = _flatten(t.as_list())
def must_match_these_tokens(s, l, t):
theseTokens = _flatten(t.as_list())
if theseTokens != matchTokens:
raise ParseException(s, l, "Expected {}, found{}".format(matchTokens, theseTokens))
rep.set_parse_action(must_match_these_tokens, callDuringTry=True)
expr.add_parse_action(copy_token_to_repeater, callDuringTry=True)
rep.set_name("(prev) " + str(expr))
return rep | [
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kamyu104/LeetCode-Solutions | 77605708a927ea3b85aee5a479db733938c7c211 | Python/escape-the-ghosts.py | python | Solution.escapeGhosts | (self, ghosts, target) | return all(total < abs(target[0]-i)+abs(target[1]-j) for i, j in ghosts) | :type ghosts: List[List[int]]
:type target: List[int]
:rtype: bool | :type ghosts: List[List[int]]
:type target: List[int]
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"""
:type ghosts: List[List[int]]
:type target: List[int]
:rtype: bool
"""
total = abs(target[0])+abs(target[1])
return all(total < abs(target[0]-i)+abs(target[1]-j) for i, j in ghosts) | [
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google/llvm-propeller | 45c226984fe8377ebfb2ad7713c680d652ba678d | llvm/utils/llvm-build/llvmbuild/main.py | python | LLVMProjectInfo.write_cmake_exports_fragment | (self, output_path, enabled_optional_components) | write_cmake_exports_fragment(output_path) -> None
Generate a CMake fragment which includes LLVMBuild library
dependencies expressed similarly to how CMake would write
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"""
write_cmake_exports_fragment(output_path) -> None
Generate a CMake fragment which includes LLVMBuild library
dependencies expressed similarly to how CMake would write
them via install(EXPORT).
"""
dependencies = list(self.get_fragment_dependencies())
# Write out the CMake exports fragment.
make_install_dir(os.path.dirname(output_path))
f = open(output_path, 'w')
f.write("""\
# Explicit library dependency information.
#
# The following property assignments tell CMake about link
# dependencies of libraries imported from LLVM.
""")
self.foreach_cmake_library(
lambda ci:
f.write("""\
set_property(TARGET %s PROPERTY IMPORTED_LINK_INTERFACE_LIBRARIES %s)\n""" % (
ci.get_prefixed_library_name(), " ".join(sorted(
dep.get_prefixed_library_name()
for dep in self.get_required_libraries_for_component(ci)))))
,
enabled_optional_components,
skip_disabled = True,
skip_not_installed = True # Do not export internal libraries like gtest
)
f.close() | [
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ApolloAuto/apollo-platform | 86d9dc6743b496ead18d597748ebabd34a513289 | ros/third_party/lib_x86_64/python2.7/dist-packages/numpy/lib/function_base.py | python | unique | (x) | This function is deprecated. Use numpy.lib.arraysetops.unique()
instead. | This function is deprecated. Use numpy.lib.arraysetops.unique()
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"""
This function is deprecated. Use numpy.lib.arraysetops.unique()
instead.
"""
try:
tmp = x.flatten()
if tmp.size == 0:
return tmp
tmp.sort()
idx = concatenate(([True], tmp[1:]!=tmp[:-1]))
return tmp[idx]
except AttributeError:
items = sorted(set(x))
return asarray(items) | [
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... | https://github.com/ApolloAuto/apollo-platform/blob/86d9dc6743b496ead18d597748ebabd34a513289/ros/third_party/lib_x86_64/python2.7/dist-packages/numpy/lib/function_base.py#L1262-L1276 | ||
PaddlePaddle/Paddle | 1252f4bb3e574df80aa6d18c7ddae1b3a90bd81c | python/paddle/distributed/auto_parallel/operators/dist_reshape.py | python | DistributedReshapeImpl0.forward | (ctx, *args, **kwargs) | kwargs: inputname_mapping & outputname_mapping | kwargs: inputname_mapping & outputname_mapping | [
"kwargs",
":",
"inputname_mapping",
"&",
"outputname_mapping"
] | def forward(ctx, *args, **kwargs):
"""
kwargs: inputname_mapping & outputname_mapping
"""
dist_op_context = ctx.dist_op_context
main_block = dist_op_context.get_dst_main_program().global_block()
src_op = dist_op_context.get_cur_src_op()
rank_id = dist_op_context.get_rank_id()
op_dist_attr = ctx.get_op_dist_attr_for_program(src_op)
assert op_dist_attr is not None, "backward op [{}] don't have dist attribute !".format(
str(src_op))
# check validation of inputs / outputs
for input_name in src_op.desc.input_names():
assert input_name in kwargs, "input [{}] is not given".format(
input_name)
assert len(kwargs[input_name]) == len(
src_op.desc.input(input_name)
), "number of tensor for input [{}] is not match".format(input_name)
for output_name in src_op.desc.output_names():
assert output_name in kwargs, "input [{}] is not given".format(
output_name)
assert len(kwargs[output_name]) == len(
src_op.desc.output(output_name)
), "number of tensor for input [{}] is not match".format(
output_name)
X_var = main_block.var(kwargs['X'][0])
Out_var = main_block.var(kwargs['Out'][0])
XShape_var = main_block.var(kwargs['XShape'][0])
shape_list = src_op.desc.attr("shape")
ShapeTensor_var_list = []
for name in kwargs['ShapeTensor']:
ShapeTensor_var_list.append(name)
Shape_var_list = []
for name in kwargs['Shape']:
Shape_var_list.append(name)
# got dist attribute info
dim_mapping = op_dist_attr.get_output_dims_mapping(Out_var.name)
process_mesh_shape = op_dist_attr.process_mesh.topology
# modify target shape
for idx, axis in enumerate(dim_mapping):
if axis >= 0:
if len(shape_list) > idx:
shape_list[idx] = shape_list[idx] // process_mesh_shape[
axis]
# create op
new_op_desc = main_block.desc.append_op()
new_op_desc.copy_from(src_op.desc)
set_dist_op_desc_original_id(new_op_desc, src_op.desc, ctx)
new_op_desc.set_input('ShapeTensor', ShapeTensor_var_list)
new_op_desc.set_input('Shape', Shape_var_list)
new_op_desc.set_input('X', [X_var.name])
new_op_desc.set_output('XShape', [XShape_var.name])
new_op_desc.set_output('Out', [Out_var.name])
new_op_desc._set_attr('shape', shape_list)
main_block._sync_with_cpp() | [
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... | https://github.com/PaddlePaddle/Paddle/blob/1252f4bb3e574df80aa6d18c7ddae1b3a90bd81c/python/paddle/distributed/auto_parallel/operators/dist_reshape.py#L127-L188 | ||
NVIDIAGameWorks/kaolin | e5148d05e9c1e2ce92a07881ce3593b1c5c3f166 | kaolin/render/mesh/deftet.py | python | _base_naive_deftet_render | (
pixel_coords, # (2,)
render_range, # (2,)
face_vertices_z, # (num_faces, 3)
face_vertices_min, # (num_faces, 2)
face_vertices_max, # (num_faces, 2)
ax, ay, m, p, n, q, k3) | return in_bbox_idx[selected_mask][in_render_range_mask][order] | Base function for :func:`_naive_deftet_sparse_render`
non-batched and for a single pixel
This is because most operations are vectorized on faces
but then only few outputs of those vectorized operations
are used (so it's the memory is only used temporarily). | Base function for :func:`_naive_deftet_sparse_render`
non-batched and for a single pixel | [
"Base",
"function",
"for",
":",
"func",
":",
"_naive_deftet_sparse_render",
"non",
"-",
"batched",
"and",
"for",
"a",
"single",
"pixel"
] | def _base_naive_deftet_render(
pixel_coords, # (2,)
render_range, # (2,)
face_vertices_z, # (num_faces, 3)
face_vertices_min, # (num_faces, 2)
face_vertices_max, # (num_faces, 2)
ax, ay, m, p, n, q, k3): # int
"""Base function for :func:`_naive_deftet_sparse_render`
non-batched and for a single pixel
This is because most operations are vectorized on faces
but then only few outputs of those vectorized operations
are used (so it's the memory is only used temporarily).
"""
in_bbox_mask = torch.logical_and(
pixel_coords.unsqueeze(0) > face_vertices_min,
pixel_coords.unsqueeze(0) < face_vertices_max)
in_bbox_mask = torch.logical_and(in_bbox_mask[:, 0],
in_bbox_mask[:, 1])
in_bbox_idx = torch.where(in_bbox_mask)[0]
ax = ax[in_bbox_idx]
ay = ay[in_bbox_idx]
m = m[in_bbox_idx]
p = p[in_bbox_idx]
n = n[in_bbox_idx]
q = q[in_bbox_idx]
k3 = k3[in_bbox_idx]
s = pixel_coords[0] - ax
t = pixel_coords[1] - ay
k1 = s * q - n * t
k2 = m * t - s * p
w1 = k1 / (k3 + NAIVE_EPS)
w2 = k2 / (k3 + NAIVE_EPS)
w0 = 1. - w1 - w2
selected_mask = (w0 >= -NAIVE_EPS) & (w1 >= -NAIVE_EPS) & (w2 >= -NAIVE_EPS)
selected_face_vertices_z = face_vertices_z[in_bbox_idx][selected_mask]
selected_weights = torch.stack([
w0[selected_mask], w1[selected_mask], w2[selected_mask]], dim=-1)
pixel_depth = torch.sum(selected_weights * face_vertices_z[in_bbox_idx][selected_mask],
dim=-1)
in_render_range_mask = torch.logical_and(
pixel_depth > render_range[0],
pixel_depth < render_range[1]
)
order = torch.argsort(pixel_depth[in_render_range_mask],
descending=True, dim=0)
return in_bbox_idx[selected_mask][in_render_range_mask][order] | [
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"ax",
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",",... | https://github.com/NVIDIAGameWorks/kaolin/blob/e5148d05e9c1e2ce92a07881ce3593b1c5c3f166/kaolin/render/mesh/deftet.py#L27-L74 | |
turi-code/SFrame | 796b9bdfb2fa1b881d82080754643c7e68629cd2 | oss_src/unity/python/sframe/data_structures/sketch.py | python | Sketch.num_elements_processed | (self) | Returns the number of elements processed so far.
If the sketch is created with background == False (default), this will
always return the length of the input array. Otherwise, this will
return the number of elements processed so far. | Returns the number of elements processed so far.
If the sketch is created with background == False (default), this will
always return the length of the input array. Otherwise, this will
return the number of elements processed so far. | [
"Returns",
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"(",
"default",
")",
"this",
"will",
"always",
"return",
"the",
"length",
"of",
"the",
"input",
"array... | def num_elements_processed(self):
"""
Returns the number of elements processed so far.
If the sketch is created with background == False (default), this will
always return the length of the input array. Otherwise, this will
return the number of elements processed so far.
"""
with cython_context():
return self.__proxy__.num_elements_processed() | [
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"(",
")"
] | https://github.com/turi-code/SFrame/blob/796b9bdfb2fa1b881d82080754643c7e68629cd2/oss_src/unity/python/sframe/data_structures/sketch.py#L499-L507 | ||
mhammond/pywin32 | 44afd86ba8485194df93234639243252deeb40d5 | Pythonwin/pywin/framework/app.py | python | CApp.OnRClick | (self, params) | return 0 | Handle right click message | Handle right click message | [
"Handle",
"right",
"click",
"message"
] | def OnRClick(self, params):
"Handle right click message"
# put up the entire FILE menu!
menu = win32ui.LoadMenu(win32ui.IDR_TEXTTYPE).GetSubMenu(0)
menu.TrackPopupMenu(params[5]) # track at mouse position.
return 0 | [
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"(",
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")",
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"[",... | https://github.com/mhammond/pywin32/blob/44afd86ba8485194df93234639243252deeb40d5/Pythonwin/pywin/framework/app.py#L278-L283 | |
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/calendar.py | python | HTMLCalendar.formatmonthname | (self, theyear, themonth, withyear=True) | return '<tr><th colspan="7" class="month">%s</th></tr>' % s | Return a month name as a table row. | Return a month name as a table row. | [
"Return",
"a",
"month",
"name",
"as",
"a",
"table",
"row",
"."
] | def formatmonthname(self, theyear, themonth, withyear=True):
"""
Return a month name as a table row.
"""
if withyear:
s = '%s %s' % (month_name[themonth], theyear)
else:
s = '%s' % month_name[themonth]
return '<tr><th colspan="7" class="month">%s</th></tr>' % s | [
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"'%s... | https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/tools/python/src/Lib/calendar.py#L414-L422 | |
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/grid.py | python | Grid.SetColMinimalAcceptableWidth | (*args, **kwargs) | return _grid.Grid_SetColMinimalAcceptableWidth(*args, **kwargs) | SetColMinimalAcceptableWidth(self, int width) | SetColMinimalAcceptableWidth(self, int width) | [
"SetColMinimalAcceptableWidth",
"(",
"self",
"int",
"width",
")"
] | def SetColMinimalAcceptableWidth(*args, **kwargs):
"""SetColMinimalAcceptableWidth(self, int width)"""
return _grid.Grid_SetColMinimalAcceptableWidth(*args, **kwargs) | [
"def",
"SetColMinimalAcceptableWidth",
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"(",
"*",
"args",
",",
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")"
] | https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/osx_cocoa/grid.py#L1918-L1920 | |
Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/ops/nn_impl.py | python | sufficient_statistics_v2 | (x, axes, shift=None, keepdims=False, name=None) | return sufficient_statistics(
x=x, axes=axes, shift=shift, keep_dims=keepdims, name=name) | Calculate the sufficient statistics for the mean and variance of `x`.
These sufficient statistics are computed using the one pass algorithm on
an input that's optionally shifted. See:
https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Computing_shifted_data
Args:
x: A `Tensor`.
axes: Array of ints. Axes along which to compute mean and variance.
shift: A `Tensor` containing the value by which to shift the data for
numerical stability, or `None` if no shift is to be performed. A shift
close to the true mean provides the most numerically stable results.
keepdims: produce statistics with the same dimensionality as the input.
name: Name used to scope the operations that compute the sufficient stats.
Returns:
Four `Tensor` objects of the same type as `x`:
* the count (number of elements to average over).
* the (possibly shifted) sum of the elements in the array.
* the (possibly shifted) sum of squares of the elements in the array.
* the shift by which the mean must be corrected or None if `shift` is None. | Calculate the sufficient statistics for the mean and variance of `x`. | [
"Calculate",
"the",
"sufficient",
"statistics",
"for",
"the",
"mean",
"and",
"variance",
"of",
"x",
"."
] | def sufficient_statistics_v2(x, axes, shift=None, keepdims=False, name=None):
"""Calculate the sufficient statistics for the mean and variance of `x`.
These sufficient statistics are computed using the one pass algorithm on
an input that's optionally shifted. See:
https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Computing_shifted_data
Args:
x: A `Tensor`.
axes: Array of ints. Axes along which to compute mean and variance.
shift: A `Tensor` containing the value by which to shift the data for
numerical stability, or `None` if no shift is to be performed. A shift
close to the true mean provides the most numerically stable results.
keepdims: produce statistics with the same dimensionality as the input.
name: Name used to scope the operations that compute the sufficient stats.
Returns:
Four `Tensor` objects of the same type as `x`:
* the count (number of elements to average over).
* the (possibly shifted) sum of the elements in the array.
* the (possibly shifted) sum of squares of the elements in the array.
* the shift by which the mean must be corrected or None if `shift` is None.
"""
return sufficient_statistics(
x=x, axes=axes, shift=shift, keep_dims=keepdims, name=name) | [
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FreeCAD/FreeCAD | ba42231b9c6889b89e064d6d563448ed81e376ec | src/Mod/Draft/DraftGui.py | python | DraftToolBar.getDefaultColor | (self,type,rgb=False) | gets color from the preferences or toolbar | gets color from the preferences or toolbar | [
"gets",
"color",
"from",
"the",
"preferences",
"or",
"toolbar"
] | def getDefaultColor(self,type,rgb=False):
"""gets color from the preferences or toolbar"""
r = 0
g = 0
b = 0
if type == "snap":
color = Draft.getParam("snapcolor",4294967295)
r = ((color>>24)&0xFF)/255
g = ((color>>16)&0xFF)/255
b = ((color>>8)&0xFF)/255
elif type == "ui":
print("draft: deprecation warning: Do not use getDefaultColor(\"ui\") anymore - use getDefaultColor(\"line\") instead.")
r = float(self.color.red()/255.0)
g = float(self.color.green()/255.0)
b = float(self.color.blue()/255.0)
elif type == "line":
color = FreeCAD.ParamGet("User parameter:BaseApp/Preferences/View")\
.GetUnsigned("DefaultShapeLineColor",255)
r = ((color>>24)&0xFF)/255
g = ((color>>16)&0xFF)/255
b = ((color>>8)&0xFF)/255
elif type == "text":
color = FreeCAD.ParamGet("User parameter:BaseApp/Preferences/Mod/Draft")\
.GetUnsigned("DefaultTextColor",255)
r = ((color>>24)&0xFF)/255
g = ((color>>16)&0xFF)/255
b = ((color>>8)&0xFF)/255
elif type == "face":
color = FreeCAD.ParamGet("User parameter:BaseApp/Preferences/View")\
.GetUnsigned("DefaultShapeColor",4294967295)
r = ((color>>24)&0xFF)/255
g = ((color>>16)&0xFF)/255
b = ((color>>8)&0xFF)/255
elif type == "constr":
color = Draft.getParam("constructioncolor",746455039)
r = ((color>>24)&0xFF)/255
g = ((color>>16)&0xFF)/255
b = ((color>>8)&0xFF)/255
else:
print("draft: error: couldn't get a color for ",type," type.")
if rgb:
return("rgb("+str(int(r*255))+","+str(int(g*255))+","+str(int(b*255))+")")
else:
return (r,g,b) | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/framework/tensor_spec.py | python | BoundedTensorSpec.__init__ | (self, shape, dtype, minimum, maximum, name=None) | Initializes a new `BoundedTensorSpec`.
Args:
shape: Value convertible to `tf.TensorShape`. The shape of the tensor.
dtype: Value convertible to `tf.DType`. The type of the tensor values.
minimum: Number or sequence specifying the minimum element bounds
(inclusive). Must be broadcastable to `shape`.
maximum: Number or sequence specifying the maximum element bounds
(inclusive). Must be broadcastable to `shape`.
name: Optional string containing a semantic name for the corresponding
array. Defaults to `None`.
Raises:
ValueError: If `minimum` or `maximum` are not provided or not
broadcastable to `shape`.
TypeError: If the shape is not an iterable or if the `dtype` is an invalid
numpy dtype. | Initializes a new `BoundedTensorSpec`. | [
"Initializes",
"a",
"new",
"BoundedTensorSpec",
"."
] | def __init__(self, shape, dtype, minimum, maximum, name=None):
"""Initializes a new `BoundedTensorSpec`.
Args:
shape: Value convertible to `tf.TensorShape`. The shape of the tensor.
dtype: Value convertible to `tf.DType`. The type of the tensor values.
minimum: Number or sequence specifying the minimum element bounds
(inclusive). Must be broadcastable to `shape`.
maximum: Number or sequence specifying the maximum element bounds
(inclusive). Must be broadcastable to `shape`.
name: Optional string containing a semantic name for the corresponding
array. Defaults to `None`.
Raises:
ValueError: If `minimum` or `maximum` are not provided or not
broadcastable to `shape`.
TypeError: If the shape is not an iterable or if the `dtype` is an invalid
numpy dtype.
"""
super(BoundedTensorSpec, self).__init__(shape, dtype, name)
if minimum is None or maximum is None:
raise ValueError("minimum and maximum must be provided; but saw "
"'%s' and '%s'" % (minimum, maximum))
try:
minimum_shape = np.shape(minimum)
common_shapes.broadcast_shape(
tensor_shape.TensorShape(minimum_shape), self.shape)
except ValueError as exception:
raise ValueError("minimum is not compatible with shape. "
"Message: {!r}.".format(exception))
try:
maximum_shape = np.shape(maximum)
common_shapes.broadcast_shape(
tensor_shape.TensorShape(maximum_shape), self.shape)
except ValueError as exception:
raise ValueError("maximum is not compatible with shape. "
"Message: {!r}.".format(exception))
self._minimum = np.array(minimum, dtype=self.dtype.as_numpy_dtype())
self._minimum.setflags(write=False)
self._maximum = np.array(maximum, dtype=self.dtype.as_numpy_dtype())
self._maximum.setflags(write=False) | [
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... | https://github.com/Xilinx/Vitis-AI/blob/fc74d404563d9951b57245443c73bef389f3657f/tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/framework/tensor_spec.py#L208-L253 | ||
hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/gsutil/third_party/boto/boto/route53/zone.py | python | Zone.get_records | (self) | return self.route53connection.get_all_rrsets(self.id) | Return a ResourceRecordsSets for all of the records in this zone. | Return a ResourceRecordsSets for all of the records in this zone. | [
"Return",
"a",
"ResourceRecordsSets",
"for",
"all",
"of",
"the",
"records",
"in",
"this",
"zone",
"."
] | def get_records(self):
"""
Return a ResourceRecordsSets for all of the records in this zone.
"""
return self.route53connection.get_all_rrsets(self.id) | [
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] | https://github.com/hanpfei/chromium-net/blob/392cc1fa3a8f92f42e4071ab6e674d8e0482f83f/third_party/catapult/third_party/gsutil/third_party/boto/boto/route53/zone.py#L402-L406 |
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