

'''simple docstring'''
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		,							snake_case		,							snake_case		,							snake_case		,							snake_case		,							snake_case		,							snake_case		,							snake_case		,							snake_case		,							snake_case		,							snake_case		,							snake_case		,							)      ->     Tuple:
   _lowercase      :				Optional[int]      					=       {
       "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      :				int      					=       input_paths_and_base_extractors[compression_format]
   if input_path is None:
      _lowercase      :				int      					=       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      :				List[str]      					=       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      :				List[str]      					=       file_path.read_text(encoding="utf-8"					)
   else:
      _lowercase      :				Dict      					=       output_path.read_text(encoding="utf-8"					)
   _lowercase      :				Union[str, Any]      					=       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		,							snake_case		,							snake_case		,							snake_case		,							snake_case		,							snake_case		,							snake_case		,							snake_case		,							snake_case		,							snake_case		,							snake_case		,							snake_case		,							)      ->     Tuple:
   _lowercase      :				Optional[Any]      					=       {
       "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      :				List[str]      					=       input_paths[compression_format]
   if input_path is None:
      _lowercase      :				Dict      					=       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      :				List[str]      					=       Extractor.infer_extractor_format(snake_case					)
   assert extractor_format is not None
   _lowercase      :				Optional[Any]      					=       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      :				List[str]      					=       file_path.read_text(encoding="utf-8"					)
   else:
      _lowercase      :				str      					=       output_path.read_text(encoding="utf-8"					)
   _lowercase      :				Optional[Any]      					=       text_file.read_text(encoding="utf-8"					)
   assert extracted_file_content == expected_file_content







@pytest.fixture
def  _A		(     snake_case		,							snake_case					)      ->     Dict:
   import tarfile

   _lowercase      :				Any      					=       tmp_path / "data_dot_dot"
   directory.mkdir()
   _lowercase      :				int      					=       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					)      ->     Tuple:
   import tarfile

   _lowercase      :				Tuple      					=       tmp_path / "data_sym_link"
   directory.mkdir()
   _lowercase      :				List[str]      					=       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		,							snake_case		,							snake_case		,							snake_case		,							snake_case		,							snake_case					)      ->     List[Any]:
   _lowercase      :				Tuple      					=       {
       "tar_file_with_dot_dot": tar_file_with_dot_dot,
       "tar_file_with_sym_link": tar_file_with_sym_link,
   }
   _lowercase      :				List[Any]      					=       insecure_tar_files[insecure_tar_file]
   _lowercase      :				int      					=       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					)      ->     List[Any]:
   # We should have less false positives than zipfile.is_zipfile
   # We do that by checking only the magic number
   _lowercase      :				Any      					=       tmpdir / "not_a_zip_file"
   # From: https://github.com/python/cpython/pull/5053
   _lowercase      :				Union[str, Any]      					=       (
       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









'''simple docstring'''
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer


class        a__  (							lowerCamelCase_  ):
   _SCREAMING_SNAKE_CASE   :					int     	=							'Wav2Vec2FeatureExtractor'
   _SCREAMING_SNAKE_CASE   :					List[str]     	=							'AutoTokenizer'


   def __init__(			self      ,    _UpperCamelCase      ,    _UpperCamelCase  ):





      """simple docstring"""





      super().__init__(_UpperCamelCase      ,    _UpperCamelCase  )
      _lowercase      :				List[Any]      					=       self.feature_extractor
      _lowercase      :				Optional[Any]      					=       False


   @classmethod
   def 						_lowerCamelCase       (			cls      ,    _UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      try:
         return super().from_pretrained(_UpperCamelCase      ,    **_UpperCamelCase  )
      except OSError:
         warnings.warn(
             f'''Loading a tokenizer inside {cls.__name__} from a config that does not'''
             " include a `tokenizer_class` attribute is deprecated and will be "
             "removed in v5. Please add `'tokenizer_class': 'Wav2Vec2CTCTokenizer'`"
             " attribute to either your `config.json` or `tokenizer_config.json` "
             "file to suppress this warning: "      ,    _UpperCamelCase      ,    )

         _lowercase      :				Dict      					=       WavaVecaFeatureExtractor.from_pretrained(_UpperCamelCase      ,    **_UpperCamelCase  )
         _lowercase      :				str      					=       WavaVecaCTCTokenizer.from_pretrained(_UpperCamelCase      ,    **_UpperCamelCase  )

         return cls(feature_extractor=_UpperCamelCase      ,    tokenizer=_UpperCamelCase  )


   def __call__(			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      if self._in_target_context_manager:
         return self.current_processor(*_UpperCamelCase      ,    **_UpperCamelCase  )

      if "raw_speech" in kwargs:
         warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."  )
         _lowercase      :				int      					=       kwargs.pop("raw_speech"  )
      else:
         _lowercase      :				List[Any]      					=       kwargs.pop("audio"      ,    _UpperCamelCase  )
      _lowercase      :				List[Any]      					=       kwargs.pop("sampling_rate"      ,    _UpperCamelCase  )
      _lowercase      :				Union[str, Any]      					=       kwargs.pop("text"      ,    _UpperCamelCase  )
      if len(_UpperCamelCase  ) > 0:
         _lowercase      :				int      					=       args[0]
         _lowercase      :				Any      					=       args[1:]

      if audio is None and text is None:
         raise ValueError("You need to specify either an `audio` or `text` input to process."  )

      if audio is not None:
         _lowercase      :				Dict      					=       self.feature_extractor(_UpperCamelCase      ,    *_UpperCamelCase      ,    sampling_rate=_UpperCamelCase      ,    **_UpperCamelCase  )
      if text is not None:
         _lowercase      :				Union[str, Any]      					=       self.tokenizer(_UpperCamelCase      ,    **_UpperCamelCase  )

      if text is None:
         return inputs
      elif audio is None:
         return encodings
      else:
         _lowercase      :				int      					=       encodings["input_ids"]
         return inputs


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      if self._in_target_context_manager:
         return self.current_processor.pad(*_UpperCamelCase      ,    **_UpperCamelCase  )

      _lowercase      :				List[Any]      					=       kwargs.pop("input_features"      ,    _UpperCamelCase  )
      _lowercase      :				Any      					=       kwargs.pop("labels"      ,    _UpperCamelCase  )
      if len(_UpperCamelCase  ) > 0:
         _lowercase      :				Any      					=       args[0]
         _lowercase      :				Any      					=       args[1:]

      if input_features is not None:
         _lowercase      :				Any      					=       self.feature_extractor.pad(_UpperCamelCase      ,    *_UpperCamelCase      ,    **_UpperCamelCase  )
      if labels is not None:
         _lowercase      :				int      					=       self.tokenizer.pad(_UpperCamelCase      ,    **_UpperCamelCase  )

      if labels is None:
         return input_features
      elif input_features is None:
         return labels
      else:
         _lowercase      :				Optional[Any]      					=       labels["input_ids"]
         return input_features


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      return self.tokenizer.batch_decode(*_UpperCamelCase      ,    **_UpperCamelCase  )


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      return self.tokenizer.decode(*_UpperCamelCase      ,    **_UpperCamelCase  )






   @contextmanager
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      warnings.warn(
          "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
          "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
          "your audio inputs, or in a separate call."  )
      _lowercase      :				Optional[Any]      					=       True
      _lowercase      :				Dict      					=       self.tokenizer
      yield
      _lowercase      :				List[str]      					=       self.feature_extractor
      _lowercase      :				List[str]      					=       False






