





'''simple docstring'''






import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

A__							:  Optional[Any]    =logging.get_logger(__name__)


A__							:  Union[str, Any]    ={name: getattr(transformers, name + '''Fast''') for name in SLOW_TO_FAST_CONVERTERS}
def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase    ):





      """simple docstring"""


      if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
            raise ValueError(f"Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys()    )}."    )

      if tokenizer_name is None:
            _lowerCAmelCase					=				TOKENIZER_CLASSES
      else:
            _lowerCAmelCase					=				{tokenizer_name: getattr(lowerCAmelCase				,      tokenizer_name + """Fast"""    )}

      logger.info(f"Loading tokenizer classes: {tokenizer_names}"    )

      for tokenizer_name in tokenizer_names:
            _lowerCAmelCase					=				TOKENIZER_CLASSES[tokenizer_name]

            _lowerCAmelCase					=				True
            if checkpoint_name is None:
                  _lowerCAmelCase					=				list(tokenizer_class.max_model_input_sizes.keys()    )
            else:
                  _lowerCAmelCase					=				[checkpoint_name]

            logger.info(f"For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}"    )

            for checkpoint in checkpoint_names:
                  logger.info(f"Loading {tokenizer_class.__class__.__name__} {checkpoint}"    )

                  # Load tokenizer
                  _lowerCAmelCase					=				tokenizer_class.from_pretrained(lowerCAmelCase				,      force_download=lowerCAmelCase    )

                  # Save fast tokenizer
                  logger.info(f"Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}"    )

                  # For organization names we create sub-directories
                  if "/" in checkpoint:
                        _lowerCAmelCase	,				_lowerCAmelCase					=				checkpoint.split("""/"""    )
                        _lowerCAmelCase					=				os.path.join(lowerCAmelCase				,      lowerCAmelCase    )
                  elif add_prefix:
                        _lowerCAmelCase					=				checkpoint
                        _lowerCAmelCase					=				dump_path
                  else:
                        _lowerCAmelCase					=				None
                        _lowerCAmelCase					=				dump_path

                  logger.info(f"=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}"    )

                  if checkpoint in list(tokenizer.pretrained_vocab_files_map.values()    )[0]:
                        _lowerCAmelCase					=				list(tokenizer.pretrained_vocab_files_map.values()    )[0][checkpoint]
                        _lowerCAmelCase					=				file_path.split(lowerCAmelCase    )[-1][0]
                        if next_char == "/":
                              _lowerCAmelCase					=				os.path.join(lowerCAmelCase				,      lowerCAmelCase    )
                              _lowerCAmelCase					=				None

                        logger.info(f"=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}"    )

                  _lowerCAmelCase					=				tokenizer.save_pretrained(
                      lowerCAmelCase				,      legacy_format=lowerCAmelCase				,      filename_prefix=lowerCAmelCase    )
                  logger.info(f"=> File names {file_names}"    )

                  for file_name in file_names:
                        if not file_name.endswith("""tokenizer.json"""    ):
                              os.remove(lowerCAmelCase    )
                              logger.info(f"=> removing {file_name}"    )


if __name__ == "__main__":
      A__							:  Tuple    =argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--dump_path''', default=None, type=str, required=True, help='''Path to output generated fast tokenizer files.'''
      )
      parser.add_argument(
          '''--tokenizer_name''',
          default=None,
          type=str,
          help=(
              F"""Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will """
              '''download and convert all the checkpoints from AWS.'''
          ),
      )
      parser.add_argument(
          '''--checkpoint_name''',
          default=None,
          type=str,
          help='''Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.''',
      )
      parser.add_argument(
          '''--force_download''',
          action='''store_true''',
          help='''Re-download checkpoints.''',
      )
      A__							:  int    =parser.parse_args()

      convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)













'''simple docstring'''






from typing import TYPE_CHECKING

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


A__							:  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__							:  Union[str, Any]    =['''SpeechT5Tokenizer''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__							:  List[Any]    =[
          '''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__							:  Tuple    =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






