



'''simple docstring'''





import re
from pathlib import Path
from unittest import TestCase

import pytest






@pytest.mark.integration
class        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_     ):
  '''simple docstring'''




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Any:

      with open(__UpperCAmelCase  ,encoding="""utf-8"""      ) as input_file:
          lowerCAmelCase__ :  List[str]							      =					re.compile(R"""(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)"""      )
          lowerCAmelCase__ :  Any							      =					input_file.read()
          lowerCAmelCase__ :  Any							      =					regexp.search(__UpperCAmelCase      )

      return match




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Optional[int]:

      with open(__UpperCAmelCase  ,encoding="""utf-8"""      ) as input_file:
          lowerCAmelCase__ :  str							      =					re.compile(R"""#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()"""  ,re.DOTALL      )
          lowerCAmelCase__ :  Optional[int]							      =					input_file.read()
          # use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
          lowerCAmelCase__ :  Any							      =					regexp.finditer(__UpperCAmelCase      )

      lowerCAmelCase__ :  Union[str, Any]							      =					[match for match in matches if match is not None and match.group(1      ) is not None]
      return matches[0] if matches else None




  def 							UpperCAmelCase_	(    self      )    ->		List[Any]:
      lowerCAmelCase__ :  str							      =					Path("""./datasets"""      )
      lowerCAmelCase__ :  Dict							      =					list(dataset_paths.absolute().glob("""**/*.py"""      )      )

      for dataset in dataset_files:
          if self._no_encoding_on_file_open(str(__UpperCAmelCase      )      ):
              raise AssertionError(F"""open(...) must use utf-8 encoding in {dataset}"""      )






  def 							UpperCAmelCase_	(    self      )    ->		Tuple:
      lowerCAmelCase__ :  Union[str, Any]							      =					Path("""./datasets"""      )
      lowerCAmelCase__ :  Optional[int]							      =					list(dataset_paths.absolute().glob("""**/*.py"""      )      )

      for dataset in dataset_files:
          if self._no_print_statements(str(__UpperCAmelCase      )      ):
              raise AssertionError(F"""print statement found in {dataset}. Use datasets.logger/logging instead."""      )



"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


__A			   = {
    "configuration_speecht5": [
        "SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP",
        "SpeechT5Config",
        "SpeechT5HifiGanConfig",
    ],
    "feature_extraction_speecht5": ["SpeechT5FeatureExtractor"],
    "processing_speecht5": ["SpeechT5Processor"],
}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __A			   = ["SpeechT5Tokenizer"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __A			   = [
           "SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST",
           "SpeechT5ForSpeechToText",
           "SpeechT5ForSpeechToSpeech",
           "SpeechT5ForTextToSpeech",
           "SpeechT5Model",
           "SpeechT5PreTrainedModel",
           "SpeechT5HifiGan",
       ]

if TYPE_CHECKING:
       from .configuration_speechta import (
           SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
           SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
           SpeechTaConfig,
           SpeechTaHifiGanConfig,
       )
       from .feature_extraction_speechta import SpeechTaFeatureExtractor
       from .processing_speechta import SpeechTaProcessor

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_speechta import SpeechTaTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_speechta import (
                  SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
                  SpeechTaForSpeechToSpeech,
                  SpeechTaForSpeechToText,
                  SpeechTaForTextToSpeech,
                  SpeechTaHifiGan,
                  SpeechTaModel,
                  SpeechTaPreTrainedModel,
              )

else:
       import sys

       __A			   = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
