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26,300 | tensorflow/datasets | tensorflow_datasets/core/features/text/subword_text_encoder.py | SubwordTextEncoder._id_to_subword | def _id_to_subword(self, subword_id):
"""Converts a subword integer ID to a subword string."""
if subword_id < 0 or subword_id >= (self.vocab_size - 1):
raise ValueError("Received id %d which is invalid. Ids must be within "
"[0, %d)." % (subword_id + 1, self.vocab_size))
if 0 ... | python | def _id_to_subword(self, subword_id):
"""Converts a subword integer ID to a subword string."""
if subword_id < 0 or subword_id >= (self.vocab_size - 1):
raise ValueError("Received id %d which is invalid. Ids must be within "
"[0, %d)." % (subword_id + 1, self.vocab_size))
if 0 ... | [
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26,301 | tensorflow/datasets | tensorflow_datasets/core/features/text/subword_text_encoder.py | SubwordTextEncoder._token_to_subwords | def _token_to_subwords(self, token):
"""Greedily split token into subwords."""
subwords = []
start = 0
while start < len(token):
subword = None
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candidate = token[start:end]
if (candidate ... | python | def _token_to_subwords(self, token):
"""Greedily split token into subwords."""
subwords = []
start = 0
while start < len(token):
subword = None
for end in range(
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candidate = token[start:end]
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26,302 | tensorflow/datasets | tensorflow_datasets/core/features/text/subword_text_encoder.py | SubwordTextEncoder._init_from_list | def _init_from_list(self, subwords):
"""Initializes the encoder from a list of subwords."""
subwords = [tf.compat.as_text(s) for s in subwords if s]
self._subwords = subwords
# Note that internally everything is 0-indexed. Padding is dealt with at the
# end of encode and the beginning of decode.
... | python | def _init_from_list(self, subwords):
"""Initializes the encoder from a list of subwords."""
subwords = [tf.compat.as_text(s) for s in subwords if s]
self._subwords = subwords
# Note that internally everything is 0-indexed. Padding is dealt with at the
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26,303 | tensorflow/datasets | tensorflow_datasets/core/features/text/subword_text_encoder.py | SubwordTextEncoder.save_to_file | def save_to_file(self, filename_prefix):
"""Save the vocabulary to a file."""
# Wrap in single quotes to make it easier to see the full subword when
# it has spaces and make it easier to search with ctrl+f.
filename = self._filename(filename_prefix)
lines = ["'%s'" % s for s in self._subwords]
s... | python | def save_to_file(self, filename_prefix):
"""Save the vocabulary to a file."""
# Wrap in single quotes to make it easier to see the full subword when
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filename = self._filename(filename_prefix)
lines = ["'%s'" % s for s in self._subwords]
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26,304 | tensorflow/datasets | tensorflow_datasets/core/features/text/subword_text_encoder.py | SubwordTextEncoder.load_from_file | def load_from_file(cls, filename_prefix):
"""Extracts list of subwords from file."""
filename = cls._filename(filename_prefix)
lines, _ = cls._read_lines_from_file(filename)
# Strip wrapping single quotes
vocab_list = [line[1:-1] for line in lines]
return cls(vocab_list=vocab_list) | python | def load_from_file(cls, filename_prefix):
"""Extracts list of subwords from file."""
filename = cls._filename(filename_prefix)
lines, _ = cls._read_lines_from_file(filename)
# Strip wrapping single quotes
vocab_list = [line[1:-1] for line in lines]
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26,305 | tensorflow/datasets | tensorflow_datasets/core/features/text/subword_text_encoder.py | SubwordTextEncoder.build_from_corpus | def build_from_corpus(cls,
corpus_generator,
target_vocab_size,
max_subword_length=20,
max_corpus_chars=None,
reserved_tokens=None):
"""Builds a `SubwordTextEncoder` based on the `corpus_generator... | python | def build_from_corpus(cls,
corpus_generator,
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max_subword_length=20,
max_corpus_chars=None,
reserved_tokens=None):
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26,306 | tensorflow/datasets | tensorflow_datasets/structured/higgs.py | Higgs._generate_examples | def _generate_examples(self, file_path):
"""Generate features given the directory path.
Args:
file_path: path where the csv file is stored
Yields:
The features, per row.
"""
fieldnames = [
'class_label', 'lepton_pT', 'lepton_eta', 'lepton_phi',
'missing_energy_magnitud... | python | def _generate_examples(self, file_path):
"""Generate features given the directory path.
Args:
file_path: path where the csv file is stored
Yields:
The features, per row.
"""
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26,307 | tensorflow/datasets | tensorflow_datasets/image/cats_vs_dogs.py | CatsVsDogs._generate_examples | def _generate_examples(self, archive):
"""Generate Cats vs Dogs images and labels given a directory path."""
num_skipped = 0
for fname, fobj in archive:
res = _NAME_RE.match(fname)
if not res: # README file, ...
continue
label = res.group(1).lower()
if tf.compat.as_bytes("JF... | python | def _generate_examples(self, archive):
"""Generate Cats vs Dogs images and labels given a directory path."""
num_skipped = 0
for fname, fobj in archive:
res = _NAME_RE.match(fname)
if not res: # README file, ...
continue
label = res.group(1).lower()
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26,308 | tensorflow/datasets | tensorflow_datasets/image/smallnorb.py | _load_chunk | def _load_chunk(dat_path, cat_path, info_path):
"""Loads a data chunk as specified by the paths.
Args:
dat_path: Path to dat file of the chunk.
cat_path: Path to cat file of the chunk.
info_path: Path to info file of the chunk.
Returns:
Tuple with the dat, cat, info_arrays.
"""
dat_array = r... | python | def _load_chunk(dat_path, cat_path, info_path):
"""Loads a data chunk as specified by the paths.
Args:
dat_path: Path to dat file of the chunk.
cat_path: Path to cat file of the chunk.
info_path: Path to info file of the chunk.
Returns:
Tuple with the dat, cat, info_arrays.
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26,309 | tensorflow/datasets | tensorflow_datasets/image/smallnorb.py | read_binary_matrix | def read_binary_matrix(filename):
"""Reads and returns binary formatted matrix stored in filename.
The file format is described on the data set page:
https://cs.nyu.edu/~ylclab/data/norb-v1.0-small/
Args:
filename: String with path to the file.
Returns:
Numpy array contained in the file.
"""
wi... | python | def read_binary_matrix(filename):
"""Reads and returns binary formatted matrix stored in filename.
The file format is described on the data set page:
https://cs.nyu.edu/~ylclab/data/norb-v1.0-small/
Args:
filename: String with path to the file.
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Numpy array contained in the file.
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26,310 | tensorflow/datasets | tensorflow_datasets/image/smallnorb.py | Smallnorb._generate_examples | def _generate_examples(self, dat_path, cat_path, info_path):
"""Generate examples for the Smallnorb dataset.
Args:
dat_path: Path to dat file of the chunk.
cat_path: Path to cat file of the chunk.
info_path: Path to info file of the chunk.
Yields:
Dictionaries with images and the d... | python | def _generate_examples(self, dat_path, cat_path, info_path):
"""Generate examples for the Smallnorb dataset.
Args:
dat_path: Path to dat file of the chunk.
cat_path: Path to cat file of the chunk.
info_path: Path to info file of the chunk.
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26,311 | tensorflow/datasets | tensorflow_datasets/core/dataset_utils.py | build_dataset | def build_dataset(instruction_dicts,
dataset_from_file_fn,
shuffle_files=False,
parallel_reads=64):
"""Constructs a `tf.data.Dataset` from TFRecord files.
Args:
instruction_dicts: `list` of {'filepath':, 'mask':, 'offset_mask':}
containing the informa... | python | def build_dataset(instruction_dicts,
dataset_from_file_fn,
shuffle_files=False,
parallel_reads=64):
"""Constructs a `tf.data.Dataset` from TFRecord files.
Args:
instruction_dicts: `list` of {'filepath':, 'mask':, 'offset_mask':}
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26,312 | tensorflow/datasets | tensorflow_datasets/core/dataset_utils.py | _build_instruction_ds | def _build_instruction_ds(instructions):
"""Create a dataset containing individual instruction for each shard.
Each instruction is a dict:
```
{
"filepath": tf.Tensor(shape=(), dtype=tf.string),
"mask_offset": tf.Tensor(shape=(), dtype=tf.int64),
"mask": tf.Tensor(shape=(100,), dtype=tf.bool)... | python | def _build_instruction_ds(instructions):
"""Create a dataset containing individual instruction for each shard.
Each instruction is a dict:
```
{
"filepath": tf.Tensor(shape=(), dtype=tf.string),
"mask_offset": tf.Tensor(shape=(), dtype=tf.int64),
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26,313 | tensorflow/datasets | tensorflow_datasets/core/dataset_utils.py | _build_mask_ds | def _build_mask_ds(mask, mask_offset):
"""Build the mask dataset to indicate which element to skip.
Args:
mask: `tf.Tensor`, binary mask to apply to all following elements. This
mask should have a length 100.
mask_offset: `tf.Tensor`, Integer specifying from how much the mask
should be shifted ... | python | def _build_mask_ds(mask, mask_offset):
"""Build the mask dataset to indicate which element to skip.
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mask: `tf.Tensor`, binary mask to apply to all following elements. This
mask should have a length 100.
mask_offset: `tf.Tensor`, Integer specifying from how much the mask
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26,314 | tensorflow/datasets | tensorflow_datasets/core/dataset_utils.py | _build_ds_from_instruction | def _build_ds_from_instruction(instruction, ds_from_file_fn):
"""Map an instruction to a real datasets for one particular shard.
Args:
instruction: A `dict` of `tf.Tensor` containing the instruction to load
the particular shard (filename, mask,...)
ds_from_file_fn: `fct`, function which returns the d... | python | def _build_ds_from_instruction(instruction, ds_from_file_fn):
"""Map an instruction to a real datasets for one particular shard.
Args:
instruction: A `dict` of `tf.Tensor` containing the instruction to load
the particular shard (filename, mask,...)
ds_from_file_fn: `fct`, function which returns the d... | [
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26,315 | tensorflow/datasets | tensorflow_datasets/core/dataset_utils.py | as_numpy | def as_numpy(dataset, graph=None):
"""Converts a `tf.data.Dataset` to an iterable of NumPy arrays.
`as_numpy` converts a possibly nested structure of `tf.data.Dataset`s
and `tf.Tensor`s to iterables of NumPy arrays and NumPy arrays, respectively.
Args:
dataset: a possibly nested structure of `tf.data.Data... | python | def as_numpy(dataset, graph=None):
"""Converts a `tf.data.Dataset` to an iterable of NumPy arrays.
`as_numpy` converts a possibly nested structure of `tf.data.Dataset`s
and `tf.Tensor`s to iterables of NumPy arrays and NumPy arrays, respectively.
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26,316 | tensorflow/datasets | tensorflow_datasets/image/shapes3d.py | _load_data | def _load_data(filepath):
"""Loads the images and latent values into Numpy arrays."""
with h5py.File(filepath, "r") as h5dataset:
image_array = np.array(h5dataset["images"])
# The 'label' data set in the hdf5 file actually contains the float values
# and not the class labels.
values_array = np.array... | python | def _load_data(filepath):
"""Loads the images and latent values into Numpy arrays."""
with h5py.File(filepath, "r") as h5dataset:
image_array = np.array(h5dataset["images"])
# The 'label' data set in the hdf5 file actually contains the float values
# and not the class labels.
values_array = np.array... | [
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26,317 | tensorflow/datasets | tensorflow_datasets/image/shapes3d.py | _discretize | def _discretize(a):
"""Discretizes array values to class labels."""
arr = np.asarray(a)
index = np.argsort(arr)
inverse_index = np.zeros(arr.size, dtype=np.intp)
inverse_index[index] = np.arange(arr.size, dtype=np.intp)
arr = arr[index]
obs = np.r_[True, arr[1:] != arr[:-1]]
return obs.cumsum()[inverse_... | python | def _discretize(a):
"""Discretizes array values to class labels."""
arr = np.asarray(a)
index = np.argsort(arr)
inverse_index = np.zeros(arr.size, dtype=np.intp)
inverse_index[index] = np.arange(arr.size, dtype=np.intp)
arr = arr[index]
obs = np.r_[True, arr[1:] != arr[:-1]]
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26,318 | tensorflow/datasets | tensorflow_datasets/image/shapes3d.py | Shapes3d._generate_examples | def _generate_examples(self, filepath):
"""Generate examples for the Shapes3d dataset.
Args:
filepath: path to the Shapes3d hdf5 file.
Yields:
Dictionaries with images and the different labels.
"""
# Simultaneously iterating through the different data sets in the hdf5
# file will b... | python | def _generate_examples(self, filepath):
"""Generate examples for the Shapes3d dataset.
Args:
filepath: path to the Shapes3d hdf5 file.
Yields:
Dictionaries with images and the different labels.
"""
# Simultaneously iterating through the different data sets in the hdf5
# file will b... | [
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26,319 | tensorflow/datasets | tensorflow_datasets/text/wikipedia.py | _parse_and_clean_wikicode | def _parse_and_clean_wikicode(raw_content):
"""Strips formatting and unwanted sections from raw page content."""
wikicode = tfds.core.lazy_imports.mwparserfromhell.parse(raw_content)
# Filters for references, tables, and file/image links.
re_rm_wikilink = re.compile(
"^(?:File|Image|Media):", flags=re.IG... | python | def _parse_and_clean_wikicode(raw_content):
"""Strips formatting and unwanted sections from raw page content."""
wikicode = tfds.core.lazy_imports.mwparserfromhell.parse(raw_content)
# Filters for references, tables, and file/image links.
re_rm_wikilink = re.compile(
"^(?:File|Image|Media):", flags=re.IG... | [
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26,320 | tensorflow/datasets | tensorflow_datasets/scripts/download_and_prepare.py | download_and_prepare | def download_and_prepare(builder):
"""Generate data for a given dataset."""
print("download_and_prepare for dataset {}...".format(builder.info.full_name))
dl_config = download_config()
if isinstance(builder, tfds.core.BeamBasedBuilder):
beam = tfds.core.lazy_imports.apache_beam
# TODO(b/129149715): Re... | python | def download_and_prepare(builder):
"""Generate data for a given dataset."""
print("download_and_prepare for dataset {}...".format(builder.info.full_name))
dl_config = download_config()
if isinstance(builder, tfds.core.BeamBasedBuilder):
beam = tfds.core.lazy_imports.apache_beam
# TODO(b/129149715): Re... | [
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26,321 | tensorflow/datasets | tensorflow_datasets/image/cifar.py | Cifar10._generate_examples | def _generate_examples(self, filepaths):
"""Generate CIFAR examples as dicts.
Shared across CIFAR-{10, 100}. Uses self._cifar_info as
configuration.
Args:
filepaths (list[str]): The files to use to generate the data.
Yields:
The cifar examples, as defined in the dataset info features.... | python | def _generate_examples(self, filepaths):
"""Generate CIFAR examples as dicts.
Shared across CIFAR-{10, 100}. Uses self._cifar_info as
configuration.
Args:
filepaths (list[str]): The files to use to generate the data.
Yields:
The cifar examples, as defined in the dataset info features.... | [
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26,322 | tensorflow/datasets | tensorflow_datasets/core/api_utils.py | disallow_positional_args | def disallow_positional_args(wrapped=None, allowed=None):
"""Requires function to be called using keyword arguments."""
# See
# https://wrapt.readthedocs.io/en/latest/decorators.html#decorators-with-optional-arguments
# for decorator pattern.
if wrapped is None:
return functools.partial(disallow_positiona... | python | def disallow_positional_args(wrapped=None, allowed=None):
"""Requires function to be called using keyword arguments."""
# See
# https://wrapt.readthedocs.io/en/latest/decorators.html#decorators-with-optional-arguments
# for decorator pattern.
if wrapped is None:
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26,323 | tensorflow/datasets | tensorflow_datasets/core/api_utils.py | _required_args | def _required_args(fn):
"""Returns arguments of fn with default=REQUIRED_ARG."""
spec = getargspec(fn)
if not spec.defaults:
return []
arg_names = spec.args[-len(spec.defaults):]
return [name for name, val in zip(arg_names, spec.defaults)
if val is REQUIRED_ARG] | python | def _required_args(fn):
"""Returns arguments of fn with default=REQUIRED_ARG."""
spec = getargspec(fn)
if not spec.defaults:
return []
arg_names = spec.args[-len(spec.defaults):]
return [name for name, val in zip(arg_names, spec.defaults)
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26,324 | tensorflow/datasets | tensorflow_datasets/core/utils/gcs_utils.py | download_gcs_file | def download_gcs_file(path, out_fname=None, prefix_filter=None):
"""Download a file from GCS, optionally to a file."""
url = posixpath.join(GCS_BUCKET, path)
if prefix_filter:
url += "?prefix=%s" % prefix_filter
stream = bool(out_fname)
resp = requests.get(url, stream=stream)
if not resp.ok:
raise V... | python | def download_gcs_file(path, out_fname=None, prefix_filter=None):
"""Download a file from GCS, optionally to a file."""
url = posixpath.join(GCS_BUCKET, path)
if prefix_filter:
url += "?prefix=%s" % prefix_filter
stream = bool(out_fname)
resp = requests.get(url, stream=stream)
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raise V... | [
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26,325 | tensorflow/datasets | tensorflow_datasets/core/utils/gcs_utils.py | gcs_files | def gcs_files(prefix_filter=None):
"""List all files in GCS bucket."""
top_level_xml_str = download_gcs_file("", prefix_filter=prefix_filter)
xml_root = ElementTree.fromstring(top_level_xml_str)
filenames = [el[0].text for el in xml_root if el.tag.endswith("Contents")]
return filenames | python | def gcs_files(prefix_filter=None):
"""List all files in GCS bucket."""
top_level_xml_str = download_gcs_file("", prefix_filter=prefix_filter)
xml_root = ElementTree.fromstring(top_level_xml_str)
filenames = [el[0].text for el in xml_root if el.tag.endswith("Contents")]
return filenames | [
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26,326 | tensorflow/datasets | tensorflow_datasets/core/utils/gcs_utils.py | gcs_dataset_info_files | def gcs_dataset_info_files(dataset_dir):
"""Return paths to GCS files in the given dataset directory."""
prefix = posixpath.join(GCS_DATASET_INFO_DIR, dataset_dir, "")
# Filter for this dataset
filenames = [el for el in gcs_files(prefix_filter=prefix)
if el.startswith(prefix) and len(el) > len(pr... | python | def gcs_dataset_info_files(dataset_dir):
"""Return paths to GCS files in the given dataset directory."""
prefix = posixpath.join(GCS_DATASET_INFO_DIR, dataset_dir, "")
# Filter for this dataset
filenames = [el for el in gcs_files(prefix_filter=prefix)
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26,327 | tensorflow/datasets | tensorflow_datasets/core/download/kaggle.py | _run_kaggle_command | def _run_kaggle_command(command_args, competition_name):
"""Run kaggle command with subprocess."""
try:
output = sp.check_output(command_args)
return tf.compat.as_text(output)
except sp.CalledProcessError as err:
output = err.output
_log_command_output(output, error=True)
if output.startswith(... | python | def _run_kaggle_command(command_args, competition_name):
"""Run kaggle command with subprocess."""
try:
output = sp.check_output(command_args)
return tf.compat.as_text(output)
except sp.CalledProcessError as err:
output = err.output
_log_command_output(output, error=True)
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26,328 | tensorflow/datasets | tensorflow_datasets/core/download/kaggle.py | KaggleCompetitionDownloader.competition_files | def competition_files(self):
"""List of competition files."""
command = [
"kaggle",
"datasets" if "/" in self._competition_name else "competitions",
"files",
"-v",
self._competition_name,
]
output = _run_kaggle_command(command, self._competition_name)
return s... | python | def competition_files(self):
"""List of competition files."""
command = [
"kaggle",
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output = _run_kaggle_command(command, self._competition_name)
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26,329 | tensorflow/datasets | tensorflow_datasets/core/download/kaggle.py | KaggleCompetitionDownloader.download_file | def download_file(self, fname, output_dir):
"""Downloads competition file to output_dir."""
if fname not in self.competition_files: # pylint: disable=unsupported-membership-test
raise ValueError("%s is not one of the competition's "
"files: %s" % (fname, self.competition_files))
... | python | def download_file(self, fname, output_dir):
"""Downloads competition file to output_dir."""
if fname not in self.competition_files: # pylint: disable=unsupported-membership-test
raise ValueError("%s is not one of the competition's "
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26,330 | tensorflow/datasets | tensorflow_datasets/image/flowers.py | TFFlowers._generate_examples | def _generate_examples(self, images_dir_path):
"""Generate flower images and labels given the image directory path.
Args:
images_dir_path: path to the directory where the images are stored.
Yields:
The image path and its corresponding label.
"""
parent_dir = tf.io.gfile.listdir(images_... | python | def _generate_examples(self, images_dir_path):
"""Generate flower images and labels given the image directory path.
Args:
images_dir_path: path to the directory where the images are stored.
Yields:
The image path and its corresponding label.
"""
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26,331 | tensorflow/datasets | tensorflow_datasets/core/download/checksums.py | _get_path | def _get_path(dataset_name):
"""Returns path to where checksums are stored for a given dataset."""
path = _checksum_paths().get(dataset_name, None)
if path:
return path
msg = ('No checksums file could be find for dataset %s. Please create one in '
'one of: %s') % (dataset_name, ', '.join(_CHECKSUM_... | python | def _get_path(dataset_name):
"""Returns path to where checksums are stored for a given dataset."""
path = _checksum_paths().get(dataset_name, None)
if path:
return path
msg = ('No checksums file could be find for dataset %s. Please create one in '
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26,332 | tensorflow/datasets | tensorflow_datasets/core/download/checksums.py | store_checksums | def store_checksums(dataset_name, sizes_checksums):
"""Store given checksums and sizes for specific dataset.
Content of file is never disgarded, only updated. This is to ensure that if
process is killed right after first download finishes, checksums registered
during previous runs aren't lost.
It is the res... | python | def store_checksums(dataset_name, sizes_checksums):
"""Store given checksums and sizes for specific dataset.
Content of file is never disgarded, only updated. This is to ensure that if
process is killed right after first download finishes, checksums registered
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26,333 | tensorflow/datasets | tensorflow_datasets/core/download/resource.py | _sanitize_url | def _sanitize_url(url, max_length):
"""Sanitize and shorten url to fit in max_length.
Function is stable: same input MUST ALWAYS give same result, accros changes
in code as well. Different URLs might give same result.
As much as possible, the extension should be kept.
Heuristics are applied to only keep use... | python | def _sanitize_url(url, max_length):
"""Sanitize and shorten url to fit in max_length.
Function is stable: same input MUST ALWAYS give same result, accros changes
in code as well. Different URLs might give same result.
As much as possible, the extension should be kept.
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26,334 | tensorflow/datasets | tensorflow_datasets/core/download/resource.py | get_dl_dirname | def get_dl_dirname(url):
"""Returns name of temp dir for given url."""
checksum = hashlib.sha256(tf.compat.as_bytes(url)).hexdigest()
return get_dl_fname(url, checksum) | python | def get_dl_dirname(url):
"""Returns name of temp dir for given url."""
checksum = hashlib.sha256(tf.compat.as_bytes(url)).hexdigest()
return get_dl_fname(url, checksum) | [
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26,335 | tensorflow/datasets | tensorflow_datasets/core/download/resource.py | _read_info | def _read_info(info_path):
"""Returns info dict or None."""
if not tf.io.gfile.exists(info_path):
return None
with tf.io.gfile.GFile(info_path) as info_f:
return json.load(info_f) | python | def _read_info(info_path):
"""Returns info dict or None."""
if not tf.io.gfile.exists(info_path):
return None
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return json.load(info_f) | [
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26,336 | tensorflow/datasets | tensorflow_datasets/core/download/resource.py | write_info_file | def write_info_file(resource, path, dataset_name, original_fname):
"""Write the INFO file next to local file.
Although the method is synchronized, there is still a risk two processes
running at the same time overlap here. Risk accepted, since potentially lost
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Although the method is synchronized, there is still a risk two processes
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26,337 | tensorflow/datasets | tensorflow_datasets/core/download/resource.py | get_extract_method | def get_extract_method(path):
"""Returns `ExtractMethod` to use on resource at path. Cannot be None."""
info_path = _get_info_path(path)
info = _read_info(info_path)
fname = info.get('original_fname', path) if info else path
return _guess_extract_method(fname) | python | def get_extract_method(path):
"""Returns `ExtractMethod` to use on resource at path. Cannot be None."""
info_path = _get_info_path(path)
info = _read_info(info_path)
fname = info.get('original_fname', path) if info else path
return _guess_extract_method(fname) | [
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26,338 | tensorflow/datasets | tensorflow_datasets/core/download/resource.py | Resource.exists_locally | def exists_locally(cls, path):
"""Returns whether the resource exists locally, at `resource.path`."""
# If INFO file doesn't exist, consider resource does NOT exist, as it would
# prevent guessing the `extract_method`.
return (tf.io.gfile.exists(path) and
tf.io.gfile.exists(_get_info_path(pa... | python | def exists_locally(cls, path):
"""Returns whether the resource exists locally, at `resource.path`."""
# If INFO file doesn't exist, consider resource does NOT exist, as it would
# prevent guessing the `extract_method`.
return (tf.io.gfile.exists(path) and
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26,339 | tensorflow/datasets | tensorflow_datasets/core/features/text_feature.py | Text.maybe_build_from_corpus | def maybe_build_from_corpus(self, corpus_generator, **kwargs):
"""Call SubwordTextEncoder.build_from_corpus is encoder_cls is such."""
if self._encoder_cls is not text_lib.SubwordTextEncoder:
return
if self.encoder:
return
vocab_size = self._encoder_config.vocab_size
self.encoder = text... | python | def maybe_build_from_corpus(self, corpus_generator, **kwargs):
"""Call SubwordTextEncoder.build_from_corpus is encoder_cls is such."""
if self._encoder_cls is not text_lib.SubwordTextEncoder:
return
if self.encoder:
return
vocab_size = self._encoder_config.vocab_size
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26,340 | tensorflow/datasets | tensorflow_datasets/core/naming.py | sharded_filenames | def sharded_filenames(filename_prefix, num_shards):
"""Sharded filenames given prefix and number of shards."""
shard_suffix = "%05d-of-%05d"
return [
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] | python | def sharded_filenames(filename_prefix, num_shards):
"""Sharded filenames given prefix and number of shards."""
shard_suffix = "%05d-of-%05d"
return [
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26,341 | tensorflow/datasets | tensorflow_datasets/image/omniglot.py | _walk_omniglot_dir | def _walk_omniglot_dir(directory):
"""Walk an Omniglot directory and yield examples."""
directory = os.path.join(directory, tf.io.gfile.listdir(directory)[0])
alphabets = sorted(tf.io.gfile.listdir(directory))
for alphabet in alphabets:
alphabet_dir = os.path.join(directory, alphabet)
characters = sorte... | python | def _walk_omniglot_dir(directory):
"""Walk an Omniglot directory and yield examples."""
directory = os.path.join(directory, tf.io.gfile.listdir(directory)[0])
alphabets = sorted(tf.io.gfile.listdir(directory))
for alphabet in alphabets:
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26,342 | tensorflow/datasets | tensorflow_datasets/image/omniglot.py | _get_names | def _get_names(dirs):
"""Get alphabet and label names, union across all dirs."""
alphabets = set()
label_names = {}
for d in dirs:
for example in _walk_omniglot_dir(d):
alphabet, alphabet_char_id, label, _ = example
alphabets.add(alphabet)
label_name = "%s_%d" % (alphabet, alphabet_char_id... | python | def _get_names(dirs):
"""Get alphabet and label names, union across all dirs."""
alphabets = set()
label_names = {}
for d in dirs:
for example in _walk_omniglot_dir(d):
alphabet, alphabet_char_id, label, _ = example
alphabets.add(alphabet)
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26,343 | tensorflow/datasets | tensorflow_datasets/core/units.py | size_str | def size_str(size_in_bytes):
"""Returns a human readable size string.
If size_in_bytes is None, then returns "?? GiB".
For example `size_str(1.5 * tfds.units.GiB) == "1.50 GiB"`.
Args:
size_in_bytes: `int` or `None`, the size, in bytes, that we want to
format as a human-readable size string.
"""
... | python | def size_str(size_in_bytes):
"""Returns a human readable size string.
If size_in_bytes is None, then returns "?? GiB".
For example `size_str(1.5 * tfds.units.GiB) == "1.50 GiB"`.
Args:
size_in_bytes: `int` or `None`, the size, in bytes, that we want to
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26,344 | tensorflow/datasets | tensorflow_datasets/core/download/downloader.py | _Downloader.tqdm | def tqdm(self):
"""Add a progression bar for the current download."""
async_tqdm = utils.async_tqdm
with async_tqdm(total=0, desc='Dl Completed...', unit=' url') as pbar_url:
with async_tqdm(total=0, desc='Dl Size...', unit=' MiB') as pbar_dl_size:
self._pbar_url = pbar_url
self._pbar_... | python | def tqdm(self):
"""Add a progression bar for the current download."""
async_tqdm = utils.async_tqdm
with async_tqdm(total=0, desc='Dl Completed...', unit=' url') as pbar_url:
with async_tqdm(total=0, desc='Dl Size...', unit=' MiB') as pbar_dl_size:
self._pbar_url = pbar_url
self._pbar_... | [
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26,345 | tensorflow/datasets | tensorflow_datasets/core/download/downloader.py | _Downloader.download | def download(self, url, destination_path):
"""Download url to given path.
Returns Promise -> sha256 of downloaded file.
Args:
url: address of resource to download.
destination_path: `str`, path to directory where to download the resource.
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Promise obj -> (`str`, int): (downl... | python | def download(self, url, destination_path):
"""Download url to given path.
Returns Promise -> sha256 of downloaded file.
Args:
url: address of resource to download.
destination_path: `str`, path to directory where to download the resource.
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26,346 | tensorflow/datasets | tensorflow_datasets/core/download/downloader.py | _Downloader._sync_kaggle_download | def _sync_kaggle_download(self, kaggle_url, destination_path):
"""Download with Kaggle API."""
kaggle_file = kaggle.KaggleFile.from_url(kaggle_url)
downloader = self.kaggle_downloader(kaggle_file.competition)
filepath = downloader.download_file(kaggle_file.filename, destination_path)
dl_size = tf.i... | python | def _sync_kaggle_download(self, kaggle_url, destination_path):
"""Download with Kaggle API."""
kaggle_file = kaggle.KaggleFile.from_url(kaggle_url)
downloader = self.kaggle_downloader(kaggle_file.competition)
filepath = downloader.download_file(kaggle_file.filename, destination_path)
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26,347 | tensorflow/datasets | tensorflow_datasets/core/download/downloader.py | _Downloader._get_drive_url | def _get_drive_url(self, url, session):
"""Returns url, possibly with confirmation token."""
response = session.get(url, stream=True)
if response.status_code != 200:
raise DownloadError(
'Failed to get url %s. HTTP code: %d.' % (url, response.status_code))
for k, v in response.cookies.it... | python | def _get_drive_url(self, url, session):
"""Returns url, possibly with confirmation token."""
response = session.get(url, stream=True)
if response.status_code != 200:
raise DownloadError(
'Failed to get url %s. HTTP code: %d.' % (url, response.status_code))
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26,348 | tensorflow/datasets | tensorflow_datasets/core/download/downloader.py | _Downloader._sync_download | def _sync_download(self, url, destination_path):
"""Synchronous version of `download` method."""
proxies = {
'http': os.environ.get('TFDS_HTTP_PROXY', None),
'https': os.environ.get('TFDS_HTTPS_PROXY', None),
'ftp': os.environ.get('TFDS_FTP_PROXY', None)
}
if kaggle.KaggleFile.is... | python | def _sync_download(self, url, destination_path):
"""Synchronous version of `download` method."""
proxies = {
'http': os.environ.get('TFDS_HTTP_PROXY', None),
'https': os.environ.get('TFDS_HTTPS_PROXY', None),
'ftp': os.environ.get('TFDS_FTP_PROXY', None)
}
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26,349 | tensorflow/datasets | tensorflow_datasets/image/diabetic_retinopathy_detection.py | DiabeticRetinopathyDetection._generate_examples | def _generate_examples(self, images_dir_path, csv_path=None, csv_usage=None):
"""Yields Example instances from given CSV.
Args:
images_dir_path: path to dir in which images are stored.
csv_path: optional, path to csv file with two columns: name of image and
label. If not provided, just scan... | python | def _generate_examples(self, images_dir_path, csv_path=None, csv_usage=None):
"""Yields Example instances from given CSV.
Args:
images_dir_path: path to dir in which images are stored.
csv_path: optional, path to csv file with two columns: name of image and
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26,350 | tensorflow/datasets | tensorflow_datasets/core/dataset_builder.py | FileAdapterBuilder._slice_split_info_to_instruction_dicts | def _slice_split_info_to_instruction_dicts(self, list_sliced_split_info):
"""Return the list of files and reading mask of the files to read."""
instruction_dicts = []
for sliced_split_info in list_sliced_split_info:
mask = splits_lib.slice_to_percent_mask(sliced_split_info.slice_value)
# Comput... | python | def _slice_split_info_to_instruction_dicts(self, list_sliced_split_info):
"""Return the list of files and reading mask of the files to read."""
instruction_dicts = []
for sliced_split_info in list_sliced_split_info:
mask = splits_lib.slice_to_percent_mask(sliced_split_info.slice_value)
# Comput... | [
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26,351 | tensorflow/datasets | tensorflow_datasets/core/dataset_builder.py | FileAdapterBuilder._build_split_filenames | def _build_split_filenames(self, split_info_list):
"""Construct the split filenames associated with the split info.
The filenames correspond to the pre-processed datasets files present in
the root directory of the dataset.
Args:
split_info_list: (list[SplitInfo]) List of split from which generat... | python | def _build_split_filenames(self, split_info_list):
"""Construct the split filenames associated with the split info.
The filenames correspond to the pre-processed datasets files present in
the root directory of the dataset.
Args:
split_info_list: (list[SplitInfo]) List of split from which generat... | [
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26,352 | tensorflow/datasets | tensorflow_datasets/video/moving_mnist.py | MovingMnist._generate_examples | def _generate_examples(self, data_path):
"""Generate MovingMnist sequences.
Args:
data_path (str): Path to the data file
Yields:
20 x 64 x 64 x 1 uint8 numpy arrays
"""
with tf.io.gfile.GFile(data_path, "rb") as fp:
images = np.load(fp)
images = np.transpose(images, (1, 0, 2,... | python | def _generate_examples(self, data_path):
"""Generate MovingMnist sequences.
Args:
data_path (str): Path to the data file
Yields:
20 x 64 x 64 x 1 uint8 numpy arrays
"""
with tf.io.gfile.GFile(data_path, "rb") as fp:
images = np.load(fp)
images = np.transpose(images, (1, 0, 2,... | [
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26,353 | tensorflow/datasets | tensorflow_datasets/video/starcraft.py | StarcraftVideo._parse_single_video | def _parse_single_video(self, example_proto):
"""Parses single video from the input tfrecords.
Args:
example_proto: tfExample proto with a single video.
Returns:
dict with all frames, positions and actions.
"""
context_features = {
"game_duration_loops": tf.io.FixedLenFeature([... | python | def _parse_single_video(self, example_proto):
"""Parses single video from the input tfrecords.
Args:
example_proto: tfExample proto with a single video.
Returns:
dict with all frames, positions and actions.
"""
context_features = {
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26,354 | tensorflow/datasets | tensorflow_datasets/image/dsprites.py | Dsprites._generate_examples | def _generate_examples(self, filepath):
"""Generates examples for the dSprites data set.
Args:
filepath: path to the dSprites hdf5 file.
Yields:
Dictionaries with images, latent classes, and latent values.
"""
# Simultaneously iterating through the different data sets in the hdf5
#... | python | def _generate_examples(self, filepath):
"""Generates examples for the dSprites data set.
Args:
filepath: path to the dSprites hdf5 file.
Yields:
Dictionaries with images, latent classes, and latent values.
"""
# Simultaneously iterating through the different data sets in the hdf5
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26,355 | tensorflow/datasets | tensorflow_datasets/image/open_images.py | _load_objects | def _load_objects(csv_paths, csv_positions, prefix):
"""Returns objects listed within given CSV files."""
logging.info('Loading CSVs %s from positions %s with prefix %s',
csv_paths, csv_positions, prefix)
objects = collections.defaultdict(list)
for i, labels_path in enumerate(csv_paths):
with... | python | def _load_objects(csv_paths, csv_positions, prefix):
"""Returns objects listed within given CSV files."""
logging.info('Loading CSVs %s from positions %s with prefix %s',
csv_paths, csv_positions, prefix)
objects = collections.defaultdict(list)
for i, labels_path in enumerate(csv_paths):
with... | [
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26,356 | tensorflow/datasets | tensorflow_datasets/image/open_images.py | _load_bboxes | def _load_bboxes(csv_path, csv_positions, prefix):
"""Returns bounded boxes listed within given CSV file."""
logging.info('Loading CSVs %s from positions %s with prefix %s',
csv_path, csv_positions, prefix)
boxes = collections.defaultdict(list)
with tf.io.gfile.GFile(csv_path) as csv_f:
if cs... | python | def _load_bboxes(csv_path, csv_positions, prefix):
"""Returns bounded boxes listed within given CSV file."""
logging.info('Loading CSVs %s from positions %s with prefix %s',
csv_path, csv_positions, prefix)
boxes = collections.defaultdict(list)
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26,357 | tensorflow/datasets | tensorflow_datasets/text/imdb.py | IMDBReviews._generate_examples | def _generate_examples(self, archive, directory):
"""Generate IMDB examples."""
reg = re.compile(os.path.join("^%s" % directory, "(?P<label>neg|pos)", ""))
for path, imdb_f in archive:
res = reg.match(path)
if not res:
continue
text = imdb_f.read().strip()
yield {
"... | python | def _generate_examples(self, archive, directory):
"""Generate IMDB examples."""
reg = re.compile(os.path.join("^%s" % directory, "(?P<label>neg|pos)", ""))
for path, imdb_f in archive:
res = reg.match(path)
if not res:
continue
text = imdb_f.read().strip()
yield {
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... | Generate IMDB examples. | [
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] | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/text/imdb.py#L146-L157 |
26,358 | tensorflow/datasets | tensorflow_datasets/text/cnn_dailymail.py | _get_url_hashes | def _get_url_hashes(path):
"""Get hashes of urls in file."""
urls = _read_text_file(path)
def url_hash(u):
h = hashlib.sha1()
try:
u = u.encode('utf-8')
except UnicodeDecodeError:
logging.error('Cannot hash url: %s', u)
h.update(u)
return h.hexdigest()
return {url_hash(u): True f... | python | def _get_url_hashes(path):
"""Get hashes of urls in file."""
urls = _read_text_file(path)
def url_hash(u):
h = hashlib.sha1()
try:
u = u.encode('utf-8')
except UnicodeDecodeError:
logging.error('Cannot hash url: %s', u)
h.update(u)
return h.hexdigest()
return {url_hash(u): True f... | [
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26,359 | tensorflow/datasets | tensorflow_datasets/text/cnn_dailymail.py | _find_files | def _find_files(dl_paths, publisher, url_dict):
"""Find files corresponding to urls."""
if publisher == 'cnn':
top_dir = os.path.join(dl_paths['cnn_stories'], 'cnn', 'stories')
elif publisher == 'dm':
top_dir = os.path.join(dl_paths['dm_stories'], 'dailymail', 'stories')
else:
logging.fatal('Unsuppo... | python | def _find_files(dl_paths, publisher, url_dict):
"""Find files corresponding to urls."""
if publisher == 'cnn':
top_dir = os.path.join(dl_paths['cnn_stories'], 'cnn', 'stories')
elif publisher == 'dm':
top_dir = os.path.join(dl_paths['dm_stories'], 'dailymail', 'stories')
else:
logging.fatal('Unsuppo... | [
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26,360 | tensorflow/datasets | tensorflow_datasets/text/cnn_dailymail.py | _subset_filenames | def _subset_filenames(dl_paths, split):
"""Get filenames for a particular split."""
assert isinstance(dl_paths, dict), dl_paths
# Get filenames for a split.
if split == tfds.Split.TRAIN:
urls = _get_url_hashes(dl_paths['train_urls'])
elif split == tfds.Split.VALIDATION:
urls = _get_url_hashes(dl_paths... | python | def _subset_filenames(dl_paths, split):
"""Get filenames for a particular split."""
assert isinstance(dl_paths, dict), dl_paths
# Get filenames for a split.
if split == tfds.Split.TRAIN:
urls = _get_url_hashes(dl_paths['train_urls'])
elif split == tfds.Split.VALIDATION:
urls = _get_url_hashes(dl_paths... | [
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26,361 | s0md3v/Photon | plugins/exporter.py | exporter | def exporter(directory, method, datasets):
"""Export the results."""
if method.lower() == 'json':
# Convert json_dict to a JSON styled string
json_string = json.dumps(datasets, indent=4)
savefile = open('{}/exported.json'.format(directory), 'w+')
savefile.write(json_string)
... | python | def exporter(directory, method, datasets):
"""Export the results."""
if method.lower() == 'json':
# Convert json_dict to a JSON styled string
json_string = json.dumps(datasets, indent=4)
savefile = open('{}/exported.json'.format(directory), 'w+')
savefile.write(json_string)
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26,362 | s0md3v/Photon | plugins/wayback.py | time_machine | def time_machine(host, mode):
"""Query archive.org."""
now = datetime.datetime.now()
to = str(now.year) + str(now.day) + str(now.month)
if now.month > 6:
fro = str(now.year) + str(now.day) + str(now.month - 6)
else:
fro = str(now.year - 1) + str(now.day) + str(now.month + 6)
url = "htt... | python | def time_machine(host, mode):
"""Query archive.org."""
now = datetime.datetime.now()
to = str(now.year) + str(now.day) + str(now.month)
if now.month > 6:
fro = str(now.year) + str(now.day) + str(now.month - 6)
else:
fro = str(now.year - 1) + str(now.day) + str(now.month + 6)
url = "htt... | [
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26,363 | s0md3v/Photon | core/zap.py | zap | def zap(input_url, archive, domain, host, internal, robots, proxies):
"""Extract links from robots.txt and sitemap.xml."""
if archive:
print('%s Fetching URLs from archive.org' % run)
if False:
archived_urls = time_machine(domain, 'domain')
else:
archived_urls = t... | python | def zap(input_url, archive, domain, host, internal, robots, proxies):
"""Extract links from robots.txt and sitemap.xml."""
if archive:
print('%s Fetching URLs from archive.org' % run)
if False:
archived_urls = time_machine(domain, 'domain')
else:
archived_urls = t... | [
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26,364 | s0md3v/Photon | core/requester.py | requester | def requester(
url,
main_url=None,
delay=0,
cook=None,
headers=None,
timeout=10,
host=None,
proxies=[None],
user_agents=[None],
failed=None,
processed=None
):
"""Handle the requests and return the response body."""
cook ... | python | def requester(
url,
main_url=None,
delay=0,
cook=None,
headers=None,
timeout=10,
host=None,
proxies=[None],
user_agents=[None],
failed=None,
processed=None
):
"""Handle the requests and return the response body."""
cook ... | [
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26,365 | s0md3v/Photon | photon.py | intel_extractor | def intel_extractor(url, response):
"""Extract intel from the response body."""
for rintel in rintels:
res = re.sub(r'<(script).*?</\1>(?s)', '', response)
res = re.sub(r'<[^<]+?>', '', res)
matches = rintel[0].findall(res)
if matches:
for match in matches:
... | python | def intel_extractor(url, response):
"""Extract intel from the response body."""
for rintel in rintels:
res = re.sub(r'<(script).*?</\1>(?s)', '', response)
res = re.sub(r'<[^<]+?>', '', res)
matches = rintel[0].findall(res)
if matches:
for match in matches:
... | [
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26,366 | s0md3v/Photon | photon.py | js_extractor | def js_extractor(response):
"""Extract js files from the response body"""
# Extract .js files
matches = rscript.findall(response)
for match in matches:
match = match[2].replace('\'', '').replace('"', '')
verb('JS file', match)
bad_scripts.add(match) | python | def js_extractor(response):
"""Extract js files from the response body"""
# Extract .js files
matches = rscript.findall(response)
for match in matches:
match = match[2].replace('\'', '').replace('"', '')
verb('JS file', match)
bad_scripts.add(match) | [
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26,367 | s0md3v/Photon | photon.py | extractor | def extractor(url):
"""Extract details from the response body."""
response = requester(url, main_url, delay, cook, headers, timeout, host, proxies, user_agents, failed, processed)
if clone:
mirror(url, response)
matches = rhref.findall(response)
for link in matches:
# Remove e... | python | def extractor(url):
"""Extract details from the response body."""
response = requester(url, main_url, delay, cook, headers, timeout, host, proxies, user_agents, failed, processed)
if clone:
mirror(url, response)
matches = rhref.findall(response)
for link in matches:
# Remove e... | [
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26,368 | s0md3v/Photon | photon.py | jscanner | def jscanner(url):
"""Extract endpoints from JavaScript code."""
response = requester(url, main_url, delay, cook, headers, timeout, host, proxies, user_agents, failed, processed)
# Extract URLs/endpoints
matches = rendpoint.findall(response)
# Iterate over the matches, match is a tuple
for... | python | def jscanner(url):
"""Extract endpoints from JavaScript code."""
response = requester(url, main_url, delay, cook, headers, timeout, host, proxies, user_agents, failed, processed)
# Extract URLs/endpoints
matches = rendpoint.findall(response)
# Iterate over the matches, match is a tuple
for... | [
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26,369 | s0md3v/Photon | core/updater.py | updater | def updater():
"""Update the current installation.
git clones the latest version and merges it with the current directory.
"""
print('%s Checking for updates' % run)
# Changes must be separated by ;
changes = '''major bug fixes;removed ninja mode;dropped python < 3.2 support;fixed unicode outpu... | python | def updater():
"""Update the current installation.
git clones the latest version and merges it with the current directory.
"""
print('%s Checking for updates' % run)
# Changes must be separated by ;
changes = '''major bug fixes;removed ninja mode;dropped python < 3.2 support;fixed unicode outpu... | [
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26,370 | s0md3v/Photon | plugins/find_subdomains.py | find_subdomains | def find_subdomains(domain):
"""Find subdomains according to the TLD."""
result = set()
response = get('https://findsubdomains.com/subdomains-of/' + domain).text
matches = findall(r'(?s)<div class="domains js-domain-name">(.*?)</div>', response)
for match in matches:
result.add(match.replace... | python | def find_subdomains(domain):
"""Find subdomains according to the TLD."""
result = set()
response = get('https://findsubdomains.com/subdomains-of/' + domain).text
matches = findall(r'(?s)<div class="domains js-domain-name">(.*?)</div>', response)
for match in matches:
result.add(match.replace... | [
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26,371 | s0md3v/Photon | core/flash.py | flash | def flash(function, links, thread_count):
"""Process the URLs and uses a threadpool to execute a function."""
# Convert links (set) to list
links = list(links)
threadpool = concurrent.futures.ThreadPoolExecutor(
max_workers=thread_count)
futures = (threadpool.submit(function, link) for l... | python | def flash(function, links, thread_count):
"""Process the URLs and uses a threadpool to execute a function."""
# Convert links (set) to list
links = list(links)
threadpool = concurrent.futures.ThreadPoolExecutor(
max_workers=thread_count)
futures = (threadpool.submit(function, link) for l... | [
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26,372 | s0md3v/Photon | core/utils.py | regxy | def regxy(pattern, response, supress_regex, custom):
"""Extract a string based on regex pattern supplied by user."""
try:
matches = re.findall(r'%s' % pattern, response)
for match in matches:
verb('Custom regex', match)
custom.add(match)
except:
supress_regex ... | python | def regxy(pattern, response, supress_regex, custom):
"""Extract a string based on regex pattern supplied by user."""
try:
matches = re.findall(r'%s' % pattern, response)
for match in matches:
verb('Custom regex', match)
custom.add(match)
except:
supress_regex ... | [
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26,373 | s0md3v/Photon | core/utils.py | is_link | def is_link(url, processed, files):
"""
Determine whether or not a link should be crawled
A url should not be crawled if it
- Is a file
- Has already been crawled
Args:
url: str Url to be processed
processed: list[str] List of urls that have already been crawled
Ret... | python | def is_link(url, processed, files):
"""
Determine whether or not a link should be crawled
A url should not be crawled if it
- Is a file
- Has already been crawled
Args:
url: str Url to be processed
processed: list[str] List of urls that have already been crawled
Ret... | [
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26,374 | s0md3v/Photon | core/utils.py | remove_regex | def remove_regex(urls, regex):
"""
Parse a list for non-matches to a regex.
Args:
urls: iterable of urls
regex: string regex to be parsed for
Returns:
list of strings not matching regex
"""
if not regex:
return urls
# To avoid iterating over the characters... | python | def remove_regex(urls, regex):
"""
Parse a list for non-matches to a regex.
Args:
urls: iterable of urls
regex: string regex to be parsed for
Returns:
list of strings not matching regex
"""
if not regex:
return urls
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26,375 | s0md3v/Photon | core/utils.py | writer | def writer(datasets, dataset_names, output_dir):
"""Write the results."""
for dataset, dataset_name in zip(datasets, dataset_names):
if dataset:
filepath = output_dir + '/' + dataset_name + '.txt'
with open(filepath, 'w+') as out_file:
joined = '\n'.join(dataset)
... | python | def writer(datasets, dataset_names, output_dir):
"""Write the results."""
for dataset, dataset_name in zip(datasets, dataset_names):
if dataset:
filepath = output_dir + '/' + dataset_name + '.txt'
with open(filepath, 'w+') as out_file:
joined = '\n'.join(dataset)
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26,376 | s0md3v/Photon | core/utils.py | timer | def timer(diff, processed):
"""Return the passed time."""
# Changes seconds into minutes and seconds
minutes, seconds = divmod(diff, 60)
try:
# Finds average time taken by requests
time_per_request = diff / float(len(processed))
except ZeroDivisionError:
time_per_request = 0
... | python | def timer(diff, processed):
"""Return the passed time."""
# Changes seconds into minutes and seconds
minutes, seconds = divmod(diff, 60)
try:
# Finds average time taken by requests
time_per_request = diff / float(len(processed))
except ZeroDivisionError:
time_per_request = 0
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26,377 | s0md3v/Photon | core/utils.py | entropy | def entropy(string):
"""Calculate the entropy of a string."""
entropy = 0
for number in range(256):
result = float(string.encode('utf-8').count(
chr(number))) / len(string.encode('utf-8'))
if result != 0:
entropy = entropy - result * math.log(result, 2)
return ent... | python | def entropy(string):
"""Calculate the entropy of a string."""
entropy = 0
for number in range(256):
result = float(string.encode('utf-8').count(
chr(number))) / len(string.encode('utf-8'))
if result != 0:
entropy = entropy - result * math.log(result, 2)
return ent... | [
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26,378 | s0md3v/Photon | core/utils.py | extract_headers | def extract_headers(headers):
"""This function extracts valid headers from interactive input."""
sorted_headers = {}
matches = re.findall(r'(.*):\s(.*)', headers)
for match in matches:
header = match[0]
value = match[1]
try:
if value[-1] == ',':
value ... | python | def extract_headers(headers):
"""This function extracts valid headers from interactive input."""
sorted_headers = {}
matches = re.findall(r'(.*):\s(.*)', headers)
for match in matches:
header = match[0]
value = match[1]
try:
if value[-1] == ',':
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26,379 | s0md3v/Photon | core/utils.py | top_level | def top_level(url, fix_protocol=True):
"""Extract the top level domain from an URL."""
ext = tld.get_tld(url, fix_protocol=fix_protocol)
toplevel = '.'.join(urlparse(url).netloc.split('.')[-2:]).split(
ext)[0] + ext
return toplevel | python | def top_level(url, fix_protocol=True):
"""Extract the top level domain from an URL."""
ext = tld.get_tld(url, fix_protocol=fix_protocol)
toplevel = '.'.join(urlparse(url).netloc.split('.')[-2:]).split(
ext)[0] + ext
return toplevel | [
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26,380 | s0md3v/Photon | core/prompt.py | prompt | def prompt(default=None):
"""Present the user a prompt."""
editor = 'nano'
with tempfile.NamedTemporaryFile(mode='r+') as tmpfile:
if default:
tmpfile.write(default)
tmpfile.flush()
child_pid = os.fork()
is_child = child_pid == 0
if is_child:
... | python | def prompt(default=None):
"""Present the user a prompt."""
editor = 'nano'
with tempfile.NamedTemporaryFile(mode='r+') as tmpfile:
if default:
tmpfile.write(default)
tmpfile.flush()
child_pid = os.fork()
is_child = child_pid == 0
if is_child:
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26,381 | QUANTAXIS/QUANTAXIS | QUANTAXIS/QAApplication/QATradeRealtime.py | QA_RealTrade.run | def run(self):
"""generator driven data flow
"""
# 如果出现了日期的改变 才会进行结算的事件
_date = None
while QA_util_if_tradetime(self.now):
for data in self.ingest_data: # 对于在ingest_data中的数据
# <class 'QUANTAXIS.QAData.QADataStruct.QA_DataStruct_Stock_day'>
... | python | def run(self):
"""generator driven data flow
"""
# 如果出现了日期的改变 才会进行结算的事件
_date = None
while QA_util_if_tradetime(self.now):
for data in self.ingest_data: # 对于在ingest_data中的数据
# <class 'QUANTAXIS.QAData.QADataStruct.QA_DataStruct_Stock_day'>
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26,382 | QUANTAXIS/QUANTAXIS | QUANTAXIS/QAARP/QAAccount.py | QA_Account.message | def message(self):
'the standard message which can be transfer'
return {
'source':
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'frequence':
self.frequence,
'account_cookie':
self.account_cookie,
'portfolio_cookie':
self.portfolio_cookie,
... | python | def message(self):
'the standard message which can be transfer'
return {
'source':
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'frequence':
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'account_cookie':
self.account_cookie,
'portfolio_cookie':
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26,383 | QUANTAXIS/QUANTAXIS | QUANTAXIS/QAFetch/QACrawler.py | QA_fetch_get_sh_margin | def QA_fetch_get_sh_margin(date):
"""return shanghai margin data
Arguments:
date {str YYYY-MM-DD} -- date format
Returns:
pandas.DataFrame -- res for margin data
"""
if date in trade_date_sse:
data= pd.read_excel(_sh_url.format(QA_util_date_str2int
... | python | def QA_fetch_get_sh_margin(date):
"""return shanghai margin data
Arguments:
date {str YYYY-MM-DD} -- date format
Returns:
pandas.DataFrame -- res for margin data
"""
if date in trade_date_sse:
data= pd.read_excel(_sh_url.format(QA_util_date_str2int
... | [
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26,384 | QUANTAXIS/QUANTAXIS | QUANTAXIS/QAFetch/QACrawler.py | QA_fetch_get_sz_margin | def QA_fetch_get_sz_margin(date):
"""return shenzhen margin data
Arguments:
date {str YYYY-MM-DD} -- date format
Returns:
pandas.DataFrame -- res for margin data
"""
if date in trade_date_sse:
return pd.read_excel(_sz_url.format(date)).assign(date=date).assign(sse='sz') | python | def QA_fetch_get_sz_margin(date):
"""return shenzhen margin data
Arguments:
date {str YYYY-MM-DD} -- date format
Returns:
pandas.DataFrame -- res for margin data
"""
if date in trade_date_sse:
return pd.read_excel(_sz_url.format(date)).assign(date=date).assign(sse='sz') | [
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26,385 | QUANTAXIS/QUANTAXIS | QUANTAXIS/QAData/base_datastruct.py | _quotation_base.kline_echarts | def kline_echarts(self, code=None):
def kline_formater(param):
return param.name + ':' + vars(param)
"""plot the market_data"""
if code is None:
path_name = '.' + os.sep + 'QA_' + self.type + \
'_codepackage_' + self.if_fq + '.html'
kline = K... | python | def kline_echarts(self, code=None):
def kline_formater(param):
return param.name + ':' + vars(param)
"""plot the market_data"""
if code is None:
path_name = '.' + os.sep + 'QA_' + self.type + \
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kline = K... | [
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26,386 | QUANTAXIS/QUANTAXIS | QUANTAXIS/QAData/dsmethods.py | from_tushare | def from_tushare(dataframe, dtype='day'):
"""dataframe from tushare
Arguments:
dataframe {[type]} -- [description]
Returns:
[type] -- [description]
"""
if dtype in ['day']:
return QA_DataStruct_Stock_day(
dataframe.assign(date=pd.to_datetime(dataframe.date)
... | python | def from_tushare(dataframe, dtype='day'):
"""dataframe from tushare
Arguments:
dataframe {[type]} -- [description]
Returns:
[type] -- [description]
"""
if dtype in ['day']:
return QA_DataStruct_Stock_day(
dataframe.assign(date=pd.to_datetime(dataframe.date)
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26,387 | QUANTAXIS/QUANTAXIS | QUANTAXIS/QASetting/cache.py | Cache._create | def _create(self, cache_file):
"""Create the tables needed to store the information."""
conn = sqlite3.connect(cache_file)
cur = conn.cursor()
cur.execute("PRAGMA foreign_keys = ON")
cur.execute('''
CREATE TABLE jobs(
hash TEXT NOT NULL UNIQUE PRIMARY ... | python | def _create(self, cache_file):
"""Create the tables needed to store the information."""
conn = sqlite3.connect(cache_file)
cur = conn.cursor()
cur.execute("PRAGMA foreign_keys = ON")
cur.execute('''
CREATE TABLE jobs(
hash TEXT NOT NULL UNIQUE PRIMARY ... | [
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26,388 | QUANTAXIS/QUANTAXIS | QUANTAXIS/QASU/main.py | QA_SU_save_stock_info | def QA_SU_save_stock_info(engine, client=DATABASE):
"""save stock info
Arguments:
engine {[type]} -- [description]
Keyword Arguments:
client {[type]} -- [description] (default: {DATABASE})
"""
engine = select_save_engine(engine)
engine.QA_SU_save_stock_info(client=client) | python | def QA_SU_save_stock_info(engine, client=DATABASE):
"""save stock info
Arguments:
engine {[type]} -- [description]
Keyword Arguments:
client {[type]} -- [description] (default: {DATABASE})
"""
engine = select_save_engine(engine)
engine.QA_SU_save_stock_info(client=client) | [
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26,389 | QUANTAXIS/QUANTAXIS | QUANTAXIS/QAFetch/QAQuery.py | QA_fetch_risk | def QA_fetch_risk(message={}, params={"_id": 0, 'assets': 0, 'timeindex': 0, 'totaltimeindex': 0, 'benchmark_assets': 0, 'month_profit': 0}, db=DATABASE):
"""get the risk message
Arguments:
query_mes {[type]} -- [description]
Keyword Arguments:
collection {[type]} -- [description] (default... | python | def QA_fetch_risk(message={}, params={"_id": 0, 'assets': 0, 'timeindex': 0, 'totaltimeindex': 0, 'benchmark_assets': 0, 'month_profit': 0}, db=DATABASE):
"""get the risk message
Arguments:
query_mes {[type]} -- [description]
Keyword Arguments:
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26,390 | QUANTAXIS/QUANTAXIS | QUANTAXIS/QAFetch/QAQuery.py | QA_fetch_user | def QA_fetch_user(user_cookie, db=DATABASE):
"""
get the user
Arguments:
user_cookie : str the unique cookie_id for a user
Keyword Arguments:
db: database for query
Returns:
list --- [ACCOUNT]
"""
collection = DATABASE.account
return [res for res in collection... | python | def QA_fetch_user(user_cookie, db=DATABASE):
"""
get the user
Arguments:
user_cookie : str the unique cookie_id for a user
Keyword Arguments:
db: database for query
Returns:
list --- [ACCOUNT]
"""
collection = DATABASE.account
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26,391 | QUANTAXIS/QUANTAXIS | QUANTAXIS/QACmd/__init__.py | CLI.do_shell | def do_shell(self, arg):
"run a shell commad"
print(">", arg)
sub_cmd = subprocess.Popen(arg, shell=True, stdout=subprocess.PIPE)
print(sub_cmd.communicate()[0]) | python | def do_shell(self, arg):
"run a shell commad"
print(">", arg)
sub_cmd = subprocess.Popen(arg, shell=True, stdout=subprocess.PIPE)
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26,392 | gunthercox/ChatterBot | chatterbot/chatterbot.py | ChatBot.get_response | def get_response(self, statement=None, **kwargs):
"""
Return the bot's response based on the input.
:param statement: An statement object or string.
:returns: A response to the input.
:rtype: Statement
:param additional_response_selection_parameters: Parameters to pass ... | python | def get_response(self, statement=None, **kwargs):
"""
Return the bot's response based on the input.
:param statement: An statement object or string.
:returns: A response to the input.
:rtype: Statement
:param additional_response_selection_parameters: Parameters to pass ... | [
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26,393 | gunthercox/ChatterBot | chatterbot/chatterbot.py | ChatBot.generate_response | def generate_response(self, input_statement, additional_response_selection_parameters=None):
"""
Return a response based on a given input statement.
:param input_statement: The input statement to be processed.
"""
Statement = self.storage.get_object('statement')
results... | python | def generate_response(self, input_statement, additional_response_selection_parameters=None):
"""
Return a response based on a given input statement.
:param input_statement: The input statement to be processed.
"""
Statement = self.storage.get_object('statement')
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26,394 | gunthercox/ChatterBot | chatterbot/chatterbot.py | ChatBot.learn_response | def learn_response(self, statement, previous_statement=None):
"""
Learn that the statement provided is a valid response.
"""
if not previous_statement:
previous_statement = statement.in_response_to
if not previous_statement:
previous_statement = self.get_... | python | def learn_response(self, statement, previous_statement=None):
"""
Learn that the statement provided is a valid response.
"""
if not previous_statement:
previous_statement = statement.in_response_to
if not previous_statement:
previous_statement = self.get_... | [
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26,395 | gunthercox/ChatterBot | chatterbot/utils.py | import_module | def import_module(dotted_path):
"""
Imports the specified module based on the
dot notated import path for the module.
"""
import importlib
module_parts = dotted_path.split('.')
module_path = '.'.join(module_parts[:-1])
module = importlib.import_module(module_path)
return getattr(mo... | python | def import_module(dotted_path):
"""
Imports the specified module based on the
dot notated import path for the module.
"""
import importlib
module_parts = dotted_path.split('.')
module_path = '.'.join(module_parts[:-1])
module = importlib.import_module(module_path)
return getattr(mo... | [
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26,396 | gunthercox/ChatterBot | chatterbot/utils.py | validate_adapter_class | def validate_adapter_class(validate_class, adapter_class):
"""
Raises an exception if validate_class is not a
subclass of adapter_class.
:param validate_class: The class to be validated.
:type validate_class: class
:param adapter_class: The class type to check against.
:type adapter_class:... | python | def validate_adapter_class(validate_class, adapter_class):
"""
Raises an exception if validate_class is not a
subclass of adapter_class.
:param validate_class: The class to be validated.
:type validate_class: class
:param adapter_class: The class type to check against.
:type adapter_class:... | [
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26,397 | gunthercox/ChatterBot | chatterbot/utils.py | get_response_time | def get_response_time(chatbot, statement='Hello'):
"""
Returns the amount of time taken for a given
chat bot to return a response.
:param chatbot: A chat bot instance.
:type chatbot: ChatBot
:returns: The response time in seconds.
:rtype: float
"""
import time
start_time = tim... | python | def get_response_time(chatbot, statement='Hello'):
"""
Returns the amount of time taken for a given
chat bot to return a response.
:param chatbot: A chat bot instance.
:type chatbot: ChatBot
:returns: The response time in seconds.
:rtype: float
"""
import time
start_time = tim... | [
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26,398 | gunthercox/ChatterBot | chatterbot/logic/unit_conversion.py | UnitConversion.get_valid_units | def get_valid_units(self, ureg, from_unit, target_unit):
"""
Returns the firt match `pint.unit.Unit` object for from_unit and
target_unit strings from a possible variation of metric unit names
supported by pint library.
:param ureg: unit registry which units are defined and hand... | python | def get_valid_units(self, ureg, from_unit, target_unit):
"""
Returns the firt match `pint.unit.Unit` object for from_unit and
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26,399 | gunthercox/ChatterBot | chatterbot/logic/unit_conversion.py | UnitConversion.handle_matches | def handle_matches(self, match):
"""
Returns a response statement from a matched input statement.
:param match: It is a valid matched pattern from the input statement
:type: `_sre.SRE_Match`
"""
response = Statement(text='')
from_parsed = match.group("from")
... | python | def handle_matches(self, match):
"""
Returns a response statement from a matched input statement.
:param match: It is a valid matched pattern from the input statement
:type: `_sre.SRE_Match`
"""
response = Statement(text='')
from_parsed = match.group("from")
... | [
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