



"""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__		:				Optional[Any]          =	logging.get_logger(__name__)


lowercase__		:				List[str]          =	{name: getattr(transformers, name + """Fast""") for name in SLOW_TO_FAST_CONVERTERS}

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Dict    ,   lowerCAmelCase__      :				Any    ,   lowerCAmelCase__      :				Optional[int]    ,   lowerCAmelCase__      :				Union[str, Any]						)						->   int:

  """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_   :				Optional[Any]								= TOKENIZER_CLASSES
  else:
    lowerCAmelCase_   :				List[Any]								= {tokenizer_name: getattr(lowerCamelCase__    ,   tokenizer_name + 'Fast'						)}

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

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

    lowerCAmelCase_   :				Union[str, Any]								= True
    if checkpoint_name is None:
      lowerCAmelCase_   :				Optional[int]								= list(tokenizer_class.max_model_input_sizes.keys()						)
    else:
      lowerCAmelCase_   :				Dict								= [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_   :				Optional[Any]								= tokenizer_class.from_pretrained(lowerCamelCase__    ,   force_download=lowerCamelCase__						)

      # 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_   :				Optional[Any]								= checkpoint.split('/'						)
        lowerCAmelCase_   :				Optional[int]								= os.path.join(lowerCamelCase__    ,   lowerCamelCase__						)
      elif add_prefix:
        lowerCAmelCase_   :				Union[str, Any]								= checkpoint
        lowerCAmelCase_   :				List[str]								= dump_path
      else:
        lowerCAmelCase_   :				Optional[Any]								= None
        lowerCAmelCase_   :				List[str]								= 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_   :				int								= list(tokenizer.pretrained_vocab_files_map.values()						)[0][checkpoint]
        lowerCAmelCase_   :				List[Any]								= file_path.split(lowerCamelCase__						)[-1][0]
        if next_char == "/":
          lowerCAmelCase_   :				Any								= os.path.join(lowerCamelCase__    ,   lowerCamelCase__						)
          lowerCAmelCase_   :				Union[str, Any]								= None

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

      lowerCAmelCase_   :				List[Any]								= tokenizer.save_pretrained(
          lowerCamelCase__    ,   legacy_format=lowerCamelCase__    ,   filename_prefix=lowerCamelCase__						)
      logger.info(f"=> File names {file_names}"						)

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


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

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










'''simple docstring'''




import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save



def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__=10_24						,      lowerCamelCase__=10_24						,      lowerCamelCase__=False						,      **lowerCamelCase__    ):
			'''simple docstring'''





			A_	:						Any	=     AutoTokenizer.from_pretrained(lowerCamelCase__    )
			A_	:						Union[str, Any]	=     SeqaSeqDataset(lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      type_path="""train"""						,      **lowerCamelCase__    )
			A_	:						Optional[Any]	=     tok.pad_token_id

			def get_lens(lowerCamelCase__    ):
						A_	:						int	=     tqdm(
						    DataLoader(lowerCamelCase__						,      batch_size=5_12						,      num_workers=8						,      shuffle=lowerCamelCase__						,      collate_fn=ds.collate_fn    )						,      desc=str(ds.len_file    )						,      )
						A_	:						int	=     []
						for batch in dl:
									A_	:						str	=     batch["""input_ids"""].ne(lowerCamelCase__    ).sum(1    ).tolist()
									A_	:						Tuple	=     batch["""labels"""].ne(lowerCamelCase__    ).sum(1    ).tolist()
									if consider_target:
												for src, tgt in zip(lowerCamelCase__						,      lowerCamelCase__    ):
															max_lens.append(max(lowerCamelCase__						,      lowerCamelCase__    )    )
									else:
												max_lens.extend(lowerCamelCase__    )
						return max_lens

			A_	:						str	=     get_lens(lowerCamelCase__    )
			A_	:						Dict	=     SeqaSeqDataset(lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      type_path="""val"""						,      **lowerCamelCase__    )
			A_	:						List[Any]	=     get_lens(lowerCamelCase__    )
			pickle_save(lowerCamelCase__						,      train_ds.len_file    )
			pickle_save(lowerCamelCase__						,      val_ds.len_file    )


if __name__ == "__main__":
						fire.Fire(save_len_file)