




from ....configuration_utils import PretrainedConfig
from ....utils import logging


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

lowerCamelCase    :		List[Any]			   =       {
    "speechbrain/m-ctc-t-large": "https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json",
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}







class   A(						UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						'''mctct'''

 def __init__(    self :  List[Any]						,							A_ :  Dict=8065						,							A_ :  Optional[int]=1536						,							A_ :  List[Any]=36						,							A_ :  Optional[int]=6144						,							A_ :  Any=4						,							A_ :  Tuple=384						,							A_ :  str=920						,							A_ :  Optional[int]=1E-5						,							A_ :  List[str]=0.3						,							A_ :  Optional[Any]="relu"						,							A_ :  Union[str, Any]=0.02						,							A_ :  Any=0.3						,							A_ :  Optional[int]=0.3						,							A_ :  Dict=1						,							A_ :  Dict=0						,							A_ :  Optional[Any]=2						,							A_ :  int=1						,							A_ :  Optional[int]=0.3						,							A_ :  Optional[int]=1						,							A_ :  Any=(7,)						,							A_ :  Tuple=(3,)						,							A_ :  int=80						,							A_ :  Optional[int]=1						,							A_ :  Any=None						,							A_ :  str="sum"						,							A_ :  Dict=False						,							**A_ :  str						,							)       ->     Tuple:

       """simple docstring"""






       super().__init__(**A_						,							pad_token_id=A_						,							bos_token_id=A_						,							eos_token_id=A_  )
       lowerCamelCase_							=   vocab_size
       lowerCamelCase_							=   hidden_size
       lowerCamelCase_							=   num_hidden_layers
       lowerCamelCase_							=   intermediate_size
       lowerCamelCase_							=   num_attention_heads
       lowerCamelCase_							=   attention_head_dim
       lowerCamelCase_							=   max_position_embeddings
       lowerCamelCase_							=   layer_norm_eps
       lowerCamelCase_							=   layerdrop
       lowerCamelCase_							=   hidden_act
       lowerCamelCase_							=   initializer_range
       lowerCamelCase_							=   hidden_dropout_prob
       lowerCamelCase_							=   attention_probs_dropout_prob
       lowerCamelCase_							=   pad_token_id
       lowerCamelCase_							=   bos_token_id
       lowerCamelCase_							=   eos_token_id
       lowerCamelCase_							=   conv_glu_dim
       lowerCamelCase_							=   conv_dropout
       lowerCamelCase_							=   num_conv_layers
       lowerCamelCase_							=   input_feat_per_channel
       lowerCamelCase_							=   input_channels
       lowerCamelCase_							=   conv_channels
       lowerCamelCase_							=   ctc_loss_reduction
       lowerCamelCase_							=   ctc_zero_infinity

       # prevents config testing fail with exporting to json
       lowerCamelCase_							=   list(A_  )
       lowerCamelCase_							=   list(A_  )

       if len(self.conv_kernel  ) != self.num_conv_layers:
             raise ValueError(
                 'Configuration for convolutional module is incorrect. '
                 'It is required that `len(config.conv_kernel)` == `config.num_conv_layers` '
                 f"""but is `len(config.conv_kernel) = {len(self.conv_kernel  )}`, """
                 f"""`config.num_conv_layers = {self.num_conv_layers}`."""  )









import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

lowerCamelCase    :		Union[str, Any]			   =       logging.get_logger(__name__)


lowerCamelCase    :		str			   =       {name: getattr(transformers, name + "Fast") for name in SLOW_TO_FAST_CONVERTERS}

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Any     ,							lowercase     :	str     ,							lowercase     :	int     ,							lowercase     :	List[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:
            lowerCamelCase_							=   TOKENIZER_CLASSES
      else:
            lowerCamelCase_							=   {tokenizer_name: getattr(lowercase     ,							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(lowercase     ,							force_download=lowercase					)

                  # 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(lowercase     ,							lowercase					)
                  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(lowercase					)[-1][0]
                        if next_char == "/":
                              lowerCamelCase_							=   os.path.join(lowercase     ,							lowercase					)
                              lowerCamelCase_							=   None

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

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

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


if __name__ == "__main__":
 lowerCamelCase    :		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.",
 )
 lowerCamelCase    :		Union[str, Any]			   =       parser.parse_args()

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



