





'''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()

lowercase__		:     Any			=							logging.get_logger(__name__)


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







def    a__							(							lowercase   :	Any,   lowercase   :	List[str],   lowercase   :	List[str],   lowercase   :	int )						->						Tuple:





     """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:
          _UpperCamelCase							       =			TOKENIZER_CLASSES
     else:
          _UpperCamelCase							       =			{tokenizer_name: getattr(lowercase,   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(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:
                    _UpperCamelCase       ,							_UpperCamelCase							       =			checkpoint.split('''/''' )
                    _UpperCamelCase							       =			os.path.join(lowercase,   lowercase )
               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(lowercase )[-1][0]
                    if next_char == "/":
                         _UpperCamelCase							       =			os.path.join(lowercase,   lowercase )
                         _UpperCamelCase							       =			None

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

               _UpperCamelCase							       =			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__":
   lowercase__		:     Dict			=							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.',
   )
   lowercase__		:     List[str]			=							parser.parse_args()

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







_lowercase							:   Dict						=						{
    "Pillow": "Pillow",
    "accelerate": "accelerate>=0.11.0",
    "compel": "compel==0.1.8",
    "black": "black~=23.1",
    "datasets": "datasets",
    "filelock": "filelock",
    "flax": "flax>=0.4.1",
    "hf-doc-builder": "hf-doc-builder>=0.3.0",
    "huggingface-hub": "huggingface-hub>=0.13.2",
    "requests-mock": "requests-mock==1.10.0",
    "importlib_metadata": "importlib_metadata",
    "invisible-watermark": "invisible-watermark",
    "isort": "isort>=5.5.4",
    "jax": "jax>=0.2.8,!=0.3.2",
    "jaxlib": "jaxlib>=0.1.65",
    "Jinja2": "Jinja2",
    "k-diffusion": "k-diffusion>=0.0.12",
    "torchsde": "torchsde",
    "note_seq": "note_seq",
    "librosa": "librosa",
    "numpy": "numpy",
    "omegaconf": "omegaconf",
    "parameterized": "parameterized",
    "protobuf": "protobuf>=3.20.3,<4",
    "pytest": "pytest",
    "pytest-timeout": "pytest-timeout",
    "pytest-xdist": "pytest-xdist",
    "ruff": "ruff>=0.0.241",
    "safetensors": "safetensors",
    "sentencepiece": "sentencepiece>=0.1.91,!=0.1.92",
    "scipy": "scipy",
    "onnx": "onnx",
    "regex": "regex!=2019.12.17",
    "requests": "requests",
    "tensorboard": "tensorboard",
    "torch": "torch>=1.4",
    "torchvision": "torchvision",
    "transformers": "transformers>=4.25.1",
    "urllib3": "urllib3<=2.0.0",
}



