






"""simple docstring"""




import os

from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home


UpperCAmelCase_       :							int             =				HUGGINGFACE_HUB_CACHE


UpperCAmelCase_       :							Optional[Any]             =				'''config.json'''
UpperCAmelCase_       :							List[str]             =				'''diffusion_pytorch_model.bin'''
UpperCAmelCase_       :							Dict             =				'''diffusion_flax_model.msgpack'''
UpperCAmelCase_       :							List[Any]             =				'''model.onnx'''
UpperCAmelCase_       :							Tuple             =				'''diffusion_pytorch_model.safetensors'''
UpperCAmelCase_       :							Dict             =				'''weights.pb'''
UpperCAmelCase_       :							Tuple             =				'''https://huggingface.co'''
UpperCAmelCase_       :							Any             =				default_cache_path
UpperCAmelCase_       :							int             =				'''diffusers_modules'''
UpperCAmelCase_       :							Any             =				os.getenv('''HF_MODULES_CACHE''', os.path.join(hf_cache_home, '''modules'''))
UpperCAmelCase_       :							Dict             =				['''fp16''', '''non-ema''']
UpperCAmelCase_       :							List[str]             =				'''.self_attn'''








'''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 							=  logging.get_logger(__name__)


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


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''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	:       Tuple      =							TOKENIZER_CLASSES
     else:
          __A	:       Dict      =							{tokenizer_name: getattr(SCREAMING_SNAKE_CASE       ,					tokenizer_name + "Fast"							)}

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

     for tokenizer_name in tokenizer_names:
          __A	:       int      =							TOKENIZER_CLASSES[tokenizer_name]

          __A	:       Union[str, Any]      =							True
          if checkpoint_name is None:
               __A	:       Dict      =							list(tokenizer_class.max_model_input_sizes.keys()							)
          else:
               __A	:       str      =							[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	:       List[str]      =							tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE       ,					force_download=SCREAMING_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:
                    __A  ,__A	:       Optional[Any]      =							checkpoint.split("/"							)
                    __A	:       str      =							os.path.join(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)
               elif add_prefix:
                    __A	:       Optional[int]      =							checkpoint
                    __A	:       Any      =							dump_path
               else:
                    __A	:       int      =							None
                    __A	:       Optional[int]      =							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	:       Any      =							list(tokenizer.pretrained_vocab_files_map.values()							)[0][checkpoint]
                    __A	:       Optional[int]      =							file_path.split(SCREAMING_SNAKE_CASE							)[-1][0]
                    if next_char == "/":
                         __A	:       Union[str, Any]      =							os.path.join(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)
                         __A	:       Union[str, Any]      =							None

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

               __A	:       Tuple      =							tokenizer.save_pretrained(
                   SCREAMING_SNAKE_CASE       ,					legacy_format=SCREAMING_SNAKE_CASE       ,					filename_prefix=SCREAMING_SNAKE_CASE							)
               logger.info(f"=> File names {file_names}"							)

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


if __name__ == "__main__":
      _UpperCamelCase 							=  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 							=  parser.parse_args()

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

