






from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


UpperCAmelCase__				   =		logging.get_logger(__name__)
def 						_a    (      a :Union[tf.Tensor, np.ndarray] )							->			List[int]:
 if isinstance(a	,							np.ndarray ):
  return list(tensor.shape )

 a		  =      tf.shape(a )

 if tensor.shape == tf.TensorShape(a ):
  return dynamic

 a		  =      tensor.shape.as_list()

 return [dynamic[i] if s is None else s for i, s in enumerate(a )]
def 						_a    (      a :tf.Tensor	,							a :Optional[int] = None	,							a :Optional[str] = None )							->			tf.Tensor:
 return tf.nn.softmax(logits=logits + 1e-9	,							axis=a	,							name=a )
def 						_a    (      a :Tuple	,							a :str	,							a :List[str]	,							a :str=1e-5	,							a :List[str]=-1 )							->			Any:
 # This is a very simplified functional layernorm, designed to duplicate
 # the functionality of PyTorch nn.functional.layer_norm when this is needed to port
 # models in Transformers.

 if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(a	,							a ):
  raise NotImplementedError('''Only 1D weight and bias tensors are supported for now, with only a single axis.''' )

 # Get mean and variance on the axis to be normalized
 a  ,     a		  =      tf.nn.moments(a	,							axes=[axis]	,							keepdims=a )

 if axis != -1:
  # Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
  # on every dimension except axis
  a		  =      [1] * inputs.shape.rank
  a		  =      shape_list(a )[axis]
  a		  =      tf.reshape(a	,							a )
  a		  =      tf.reshape(a	,							a )

 # Compute layer normalization using the batch_normalization
 # function.
 a		  =      tf.nn.batch_normalization(
     a	,							a	,							a	,							offset=a	,							scale=a	,							variance_epsilon=a	,							)
 return outputs
def 						_a    (      a :Optional[Any]	,							a :Dict=0	,							a :Any=-1 )							->			List[Any]:
 # Replicates the behavior of torch.flatten in TF

 # If end_dim or start_dim is negative, count them from the end
 if end_dim < 0:
  end_dim += input.shape.rank
 if start_dim < 0:
  start_dim += input.shape.rank

 if start_dim == end_dim:
  return input

 a		  =      tf.shape(a )
 a		  =      tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
 a		  =      tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]	,							axis=0 )
 return tf.reshape(a	,							a )
def 						_a    (      a :tf.Tensor )							->			tf.Tensor:
 if not isinstance(a	,							tf.Tensor ):
  a		  =      tf.convert_to_tensor(a )  # Catches stray NumPy inputs
 if encoder_attention_mask.shape.rank == 3:
  a		  =      encoder_attention_mask[:, None, :, :]
 if encoder_attention_mask.shape.rank == 2:
  a		  =      encoder_attention_mask[:, None, None, :]
 # T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
 # Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
 # /transformer/transformer_layers.py#L270
 # encoder_extended_attention_mask = (encoder_extended_attention_mask ==
 # encoder_extended_attention_mask.transpose(-1, -2))
 a		  =      (
     tf.cast(1	,							encoder_attention_mask.dtype ) - encoder_extended_attention_mask
 ) * encoder_extended_attention_mask.dtype.min

 return encoder_extended_attention_mask
def 						_a    (      a :tf.Tensor	,							a :int	,							a :str = "input_ids" )							->			None:
 tf.debugging.assert_less(
     a	,							tf.cast(a	,							dtype=tensor.dtype )	,							message=(
         F"""The maximum value of {tensor_name} ({tf.math.reduce_max(a )}) must be smaller than the embedding """
         F"""layer's input dimension ({embed_dim}). The likely cause is some problem at tokenization time."""
     )	,							)
def 						_a    (      a :Any	,							a :Optional[Any]	,							a :List[str] )							->			str:
 a		  =      64_512
 # Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
 # because in that case even chunking the array would not make the saving
 # possible.
 a		  =      [x for x in data if len(a ) > HDF5_OBJECT_HEADER_LIMIT]

 # Expecting this to never be true.
 if bad_attributes:
  raise RuntimeError(
      '''The following attributes cannot be saved to HDF5 file because '''
      F"""they are larger than {HDF5_OBJECT_HEADER_LIMIT} """
      F"""bytes: {bad_attributes}""" )

 a		  =      np.asarray(a )

 a		  =      1
 a		  =      np.array_split(a	,							a )

 # This will never loop forever thanks to the test above.
 while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data ):
  num_chunks += 1
  a		  =      np.array_split(a	,							a )

 if num_chunks > 1:
  for chunk_id, chunk_data in enumerate(a ):
   a		  =      chunk_data
 else:
  a		  =      data
def 						_a    (      a :Optional[Any]	,							a :Dict )							->			Optional[int]:
 if name in group.attrs:
  a		  =      [n.decode('''utf8''' ) if hasattr(a	,							'''decode''' ) else n for n in group.attrs[name]]
 else:
  a		  =      []
  a		  =      0
  while "%s%d" % (name, chunk_id) in group.attrs:
   data.extend(
       [n.decode('''utf8''' ) if hasattr(a	,							'''decode''' ) else n for n in group.attrs['''%s%d''' % (name, chunk_id)]] )
   chunk_id += 1
 return data

def 						_a    (      a :Any )							->			str:

 def _expand_single_ad_tensor(a :Optional[int] ):
  if isinstance(a	,							tf.Tensor ) and t.shape.rank == 1:
   return tf.expand_dims(a	,							axis=-1 )
  return t

 return tf.nest.map_structure(_expand_single_ad_tensor	,							a )




import os
import zipfile

import pytest

from datasets.utils.extract import (
    BzipaExtractor,
    Extractor,
    GzipExtractor,
    LzaExtractor,
    SevenZipExtractor,
    TarExtractor,
    XzExtractor,
    ZipExtractor,
    ZstdExtractor,
)

from .utils import require_lza, require_pyazr, require_zstandard




@pytest.mark.parametrize(
    '''compression_format, is_archive'''						,   [
        ('''7z''', True),
        ('''bz2''', False),
        ('''gzip''', False),
        ('''lz4''', False),
        ('''tar''', True),
        ('''xz''', False),
        ('''zip''', True),
        ('''zstd''', False),
    ]						,   )
def 				a						(							snake_case__:					int						,   snake_case__:					Tuple						,   snake_case__:					Dict						,   snake_case__:					Dict						,   snake_case__:					List[Any]						,   snake_case__:					int						,   snake_case__:					List[Any]						,   snake_case__:					Optional[int]						,   snake_case__:					str						,   snake_case__:					Union[str, Any]						,   snake_case__:					List[str]						,   snake_case__:					int						,   ):


				'''simple docstring'''
				lowercase_       =					{
				    '''7z''': (seven_zip_file, SevenZipExtractor),
				    '''bz2''': (bza_file, BzipaExtractor),
				    '''gzip''': (gz_file, GzipExtractor),
				    '''lz4''': (lza_file, LzaExtractor),
				    '''tar''': (tar_file, TarExtractor),
				    '''xz''': (xz_file, XzExtractor),
				    '''zip''': (zip_file, ZipExtractor),
				    '''zstd''': (zstd_file, ZstdExtractor),
				}
				lowercase_  ,	lowercase_       =					input_paths_and_base_extractors[compression_format]
				if input_path is None:
								lowercase_       =					F'''for \'{compression_format}\' compression_format, '''
								if compression_format == "7z":
												reason += require_pyazr.kwargs["reason"]
								elif compression_format == "lz4":
												reason += require_lza.kwargs["reason"]
								elif compression_format == "zstd":
												reason += require_zstandard.kwargs["reason"]
								pytest.skip(snake_case__					)
				assert base_extractor.is_extractable(snake_case__					)
				lowercase_       =					tmp_path / ('''extracted''' if is_archive else '''extracted.txt''')
				base_extractor.extract(snake_case__						,   snake_case__					)
				if is_archive:
								assert output_path.is_dir()
								for file_path in output_path.iterdir():
												assert file_path.name == text_file.name
												lowercase_       =					file_path.read_text(encoding='''utf-8'''					)
				else:
								lowercase_       =					output_path.read_text(encoding='''utf-8'''					)
				lowercase_       =					text_file.read_text(encoding='''utf-8'''					)
				assert extracted_file_content == expected_file_content




@pytest.mark.parametrize(
    '''compression_format, is_archive'''						,   [
        ('''7z''', True),
        ('''bz2''', False),
        ('''gzip''', False),
        ('''lz4''', False),
        ('''tar''', True),
        ('''xz''', False),
        ('''zip''', True),
        ('''zstd''', False),
    ]						,   )
def 				a						(							snake_case__:					List[Any]						,   snake_case__:					int						,   snake_case__:					Optional[int]						,   snake_case__:					Union[str, Any]						,   snake_case__:					List[Any]						,   snake_case__:					Tuple						,   snake_case__:					Optional[int]						,   snake_case__:					List[str]						,   snake_case__:					Union[str, Any]						,   snake_case__:					Tuple						,   snake_case__:					int						,   snake_case__:					Optional[int]						,   ):


				'''simple docstring'''
				lowercase_       =					{
				    '''7z''': seven_zip_file,
				    '''bz2''': bza_file,
				    '''gzip''': gz_file,
				    '''lz4''': lza_file,
				    '''tar''': tar_file,
				    '''xz''': xz_file,
				    '''zip''': zip_file,
				    '''zstd''': zstd_file,
				}
				lowercase_       =					input_paths[compression_format]
				if input_path is None:
								lowercase_       =					F'''for \'{compression_format}\' compression_format, '''
								if compression_format == "7z":
												reason += require_pyazr.kwargs["reason"]
								elif compression_format == "lz4":
												reason += require_lza.kwargs["reason"]
								elif compression_format == "zstd":
												reason += require_zstandard.kwargs["reason"]
								pytest.skip(snake_case__					)
				lowercase_       =					Extractor.infer_extractor_format(snake_case__					)
				assert extractor_format is not None
				lowercase_       =					tmp_path / ('''extracted''' if is_archive else '''extracted.txt''')
				Extractor.extract(snake_case__						,   snake_case__						,   snake_case__					)
				if is_archive:
								assert output_path.is_dir()
								for file_path in output_path.iterdir():
												assert file_path.name == text_file.name
												lowercase_       =					file_path.read_text(encoding='''utf-8'''					)
				else:
								lowercase_       =					output_path.read_text(encoding='''utf-8'''					)
				lowercase_       =					text_file.read_text(encoding='''utf-8'''					)
				assert extracted_file_content == expected_file_content




@pytest.fixture
def 				a						(							snake_case__:					Union[str, Any]						,   snake_case__:					List[Any]					):


				'''simple docstring'''
				import tarfile

				lowercase_       =					tmp_path / '''data_dot_dot'''
				directory.mkdir()
				lowercase_       =					directory / '''tar_file_with_dot_dot.tar'''
				with tarfile.TarFile(snake_case__						,   '''w'''					) as f:
								f.add(snake_case__						,   arcname=os.path.join('''..'''						,   text_file.name					)					)
				return path




@pytest.fixture
def 				a						(							snake_case__:					int					):


				'''simple docstring'''
				import tarfile

				lowercase_       =					tmp_path / '''data_sym_link'''
				directory.mkdir()
				lowercase_       =					directory / '''tar_file_with_sym_link.tar'''
				os.symlink('''..'''						,   directory / '''subdir'''						,   target_is_directory=snake_case__					)
				with tarfile.TarFile(snake_case__						,   '''w'''					) as f:
								f.add(str(directory / '''subdir'''					)						,   arcname='''subdir'''					)  # str required by os.readlink on Windows and Python < 3.8
				return path




@pytest.mark.parametrize(
    '''insecure_tar_file, error_log'''						,   [('''tar_file_with_dot_dot''', '''illegal path'''), ('''tar_file_with_sym_link''', '''Symlink''')]						,   )
def 				a						(							snake_case__:					List[Any]						,   snake_case__:					Optional[int]						,   snake_case__:					List[str]						,   snake_case__:					List[str]						,   snake_case__:					int						,   snake_case__:					Optional[Any]					):


				'''simple docstring'''
				lowercase_       =					{
				    '''tar_file_with_dot_dot''': tar_file_with_dot_dot,
				    '''tar_file_with_sym_link''': tar_file_with_sym_link,
				}
				lowercase_       =					insecure_tar_files[insecure_tar_file]
				lowercase_       =					tmp_path / '''extracted'''
				TarExtractor.extract(snake_case__						,   snake_case__					)
				assert caplog.text
				for record in caplog.records:
								assert record.levelname == "ERROR"
								assert error_log in record.msg




def 				a						(							snake_case__:					Optional[int]					):


				'''simple docstring'''
				# We should have less false positives than zipfile.is_zipfile
				# We do that by checking only the magic number
				lowercase_       =					tmpdir / '''not_a_zip_file'''
				# From: https://github.com/python/cpython/pull/5053
				lowercase_       =					(
				    B'''\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x01\x00\x00'''
				    B'''\x00\x02\x08\x06\x00\x00\x00\x99\x81\xb6\'\x00\x00\x00\x15I'''
				    B'''DATx\x01\x01\n\x00\xf5\xff\x00PK\x05\x06\x00PK\x06\x06\x07'''
				    B'''\xac\x01N\xc6|a\r\x00\x00\x00\x00IEND\xaeB`\x82'''
				)
				with not_a_zip_file.open('''wb'''					) as f:
								f.write(snake_case__					)
				assert zipfile.is_zipfile(str(snake_case__					)					)  # is a false positive for `zipfile`
				assert not ZipExtractor.is_extractable(snake_case__					)  # but we're right




