



"""simple docstring"""
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments

def    _snake_case		(		):
 UpperCAmelCase			:      Optional[int]			=			HfArgumentParser(UpperCamelCase		)
 UpperCAmelCase			:      Tuple			=			parser.parse_args_into_dataclasses()[0]
 UpperCAmelCase			:      str			=			TensorFlowBenchmark(args=UpperCamelCase		)
 try:
  UpperCAmelCase			:      Any			=			parser.parse_args_into_dataclasses()[0]
 except ValueError as e:
  UpperCAmelCase			:      str			=			"""Arg --no_{0} is no longer used, please use --no-{0} instead."""
  UpperCAmelCase			:      Dict			=			""" """.join(str(UpperCamelCase		).split(""" """		)[:-1]		)
  UpperCAmelCase			:      Union[str, Any]			=			""""""
  UpperCAmelCase			:      Dict			=			eval(str(UpperCamelCase		).split(""" """		)[-1]		)
  UpperCAmelCase			:      Tuple			=			[]
  for arg in depreciated_args:
   # arg[2:] removes '--'
   if arg[2:] in TensorFlowBenchmark.deprecated_args:
    # arg[5:] removes '--no_'
    full_error_msg += arg_error_msg.format(arg[5:]		)
   else:
    wrong_args.append(UpperCamelCase		)
  if len(UpperCamelCase		) > 0:
   UpperCAmelCase			:      List[str]			=			full_error_msg + begin_error_msg + str(UpperCamelCase		)
  raise ValueError(UpperCamelCase		)
 benchmark.run()


if __name__ == "__main__":
      main()





"""simple docstring"""
from typing import TYPE_CHECKING

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


A:     Union[str, Any]										=	{
    "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:     List[str]										=	["SpeechT5Tokenizer"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:     Dict										=	[
          "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:     Dict										=	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
