

import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

__a    :List[str]      =			logging.get_logger(__name__)


__a    :Dict      =			{name: getattr(transformers, name + 'Fast') for name in SLOW_TO_FAST_CONVERTERS}

def       __snake_case						( __UpperCamelCase	: Optional[int]				,__UpperCamelCase	: int				,__UpperCamelCase	: List[str]				,__UpperCamelCase	: Union[str, Any]    ):



      """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:
            A_						  =				TOKENIZER_CLASSES
      else:
            A_						  =				{tokenizer_name: getattr(__UpperCamelCase				,tokenizer_name + "Fast"    )}

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

      for tokenizer_name in tokenizer_names:
            A_						  =				TOKENIZER_CLASSES[tokenizer_name]

            A_						  =				True
            if checkpoint_name is None:
                  A_						  =				list(tokenizer_class.max_model_input_sizes.keys()    )
            else:
                  A_						  =				[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
                  A_						  =				tokenizer_class.from_pretrained(__UpperCamelCase				,force_download=__UpperCamelCase    )

                  # 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:
                        A_  ,     A_						  =				checkpoint.split("/"    )
                        A_						  =				os.path.join(__UpperCamelCase				,__UpperCamelCase    )
                  elif add_prefix:
                        A_						  =				checkpoint
                        A_						  =				dump_path
                  else:
                        A_						  =				None
                        A_						  =				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]:
                        A_						  =				list(tokenizer.pretrained_vocab_files_map.values()    )[0][checkpoint]
                        A_						  =				file_path.split(__UpperCamelCase    )[-1][0]
                        if next_char == "/":
                              A_						  =				os.path.join(__UpperCamelCase				,__UpperCamelCase    )
                              A_						  =				None

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

                  A_						  =				tokenizer.save_pretrained(
                      __UpperCamelCase				,legacy_format=__UpperCamelCase				,filename_prefix=__UpperCamelCase    )
                  logger.info(f'''=> File names {file_names}'''    )

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


if __name__ == "__main__":
      __a    :Optional[int]      =			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    :List[str]      =			parser.parse_args()

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



import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowerCAmelCase__					)
class 						lowerCamelCase_							(					lowerCAmelCase__					):



					'''simple docstring'''







					__UpperCAmelCase							  =					field(default="automatic-speech-recognition"  ,  metadata={"include_in_asdict_even_if_is_default": True}					)
					__UpperCAmelCase							  =					Features({"audio": Audio()}					)
					__UpperCAmelCase							  =					Features({"transcription": Value("string"					)}					)
					__UpperCAmelCase							  =					"audio"
					__UpperCAmelCase							  =					"transcription"






					def A   (					self			,							snake_case_    ) ->      Any:







								'''simple docstring'''

								if self.audio_column not in features:
											raise ValueError(F'Column {self.audio_column} is not present in features.'    )
								if not isinstance(features[self.audio_column]			,							snake_case_    ):
											raise ValueError(F'Column {self.audio_column} is not an Audio type.'    )
								__lowercase			       = copy.deepcopy(self    )
								__lowercase			       = self.input_schema.copy()
								__lowercase			       = features[self.audio_column]
								__lowercase			       = input_schema
								return task_template






					@property
					def A   (					self    ) ->      Dict[str, str]:







								'''simple docstring'''

								return {self.audio_column: "audio", self.transcription_column: "transcription"}


