
"""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()

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


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






def 							A		(       snake_case   :Any       ,      snake_case   :Optional[Any]       ,      snake_case   :Tuple       ,      snake_case   :Union[str, Any]	)   ->			Optional[Any]:
 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:
  __UpperCamelCase       						= TOKENIZER_CLASSES
 else:
  __UpperCamelCase       						= {tokenizer_name: getattr(snake_case       ,      tokenizer_name + 'Fast'	)}

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

 for tokenizer_name in tokenizer_names:
  __UpperCamelCase       						= TOKENIZER_CLASSES[tokenizer_name]

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

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

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

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

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


if __name__ == "__main__":
      UpperCamelCase			:       Any									=				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.",
      )
      UpperCamelCase			:       Optional[int]									=				parser.parse_args()

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





"""simple docstring"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCamelCase			:       int									=				logging.get_logger(__name__)

UpperCamelCase			:       int									=				{
    "google/bit-50": "https://huggingface.co/google/bit-50/resolve/main/config.json",
}

class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ,						__SCREAMING_SNAKE_CASE    ):
     lowercase		     =		"bit"
     lowercase		     =		["preactivation", "bottleneck"]
     lowercase		     =		["SAME", "VALID"]





     def __init__(			self			,  __UpperCAmelCase=3			,  __UpperCAmelCase=64			,  __UpperCAmelCase=[256, 512, 1024, 2048]			,  __UpperCAmelCase=[3, 4, 6, 3]			,  __UpperCAmelCase="preactivation"			,  __UpperCAmelCase="relu"			,  __UpperCAmelCase=None			,  __UpperCAmelCase=32			,  __UpperCAmelCase=0.0			,  __UpperCAmelCase=False			,  __UpperCAmelCase=32			,  __UpperCAmelCase=1			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  **__UpperCAmelCase			,  ):

      '''simple docstring'''




      super().__init__(**__UpperCAmelCase   )
      if layer_type not in self.layer_types:
       raise ValueError(F'layer_type={layer_type} is not one of {",".join(self.layer_types   )}'   )
      if global_padding is not None:
       if global_padding.upper() in self.supported_padding:
        __UpperCamelCase       						= global_padding.upper()
       else:
        raise ValueError(F'Padding strategy {global_padding} not supported'   )
      __UpperCamelCase       						= num_channels
      __UpperCamelCase       						= embedding_size
      __UpperCamelCase       						= hidden_sizes
      __UpperCamelCase       						= depths
      __UpperCamelCase       						= layer_type
      __UpperCamelCase       						= hidden_act
      __UpperCamelCase       						= global_padding
      __UpperCamelCase       						= num_groups
      __UpperCamelCase       						= drop_path_rate
      __UpperCamelCase       						= embedding_dynamic_padding
      __UpperCamelCase       						= output_stride
      __UpperCamelCase       						= width_factor

      __UpperCamelCase       						= ['stem'] + [F'stage{idx}' for idx in range(1			,  len(__UpperCAmelCase   ) + 1   )]
      __UpperCamelCase						,			__UpperCamelCase       						= get_aligned_output_features_output_indices(
          out_features=__UpperCAmelCase			,  out_indices=__UpperCAmelCase			,  stage_names=self.stage_names   )



