






"""simple docstring"""




import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer


UpperCAmelCase_       :							List[Any]             =				logging.get_logger(__name__)

UpperCAmelCase_       :							List[Any]             =				{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase_       :							Union[str, Any]             =				{
    '''vocab_file''': {
        '''yjernite/retribert-base-uncased''': (
            '''https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''yjernite/retribert-base-uncased''': (
            '''https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json'''
        ),
    },
}

UpperCAmelCase_       :							Tuple             =				{
    '''yjernite/retribert-base-uncased''': 5_1_2,
}


UpperCAmelCase_       :							str             =				{
    '''yjernite/retribert-base-uncased''': {'''do_lower_case''': True},
}


class 	__UpperCAmelCase		(				_lowerCamelCase	):


	'''simple docstring'''





	lowercase		:	Dict							     =				VOCAB_FILES_NAMES
	lowercase		:	Tuple							     =				PRETRAINED_VOCAB_FILES_MAP
	lowercase		:	Union[str, Any]							     =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowercase		:	List[str]							     =				PRETRAINED_INIT_CONFIGURATION
	lowercase		:	Dict							     =				RetriBertTokenizer
	lowercase		:	Optional[int]							     =				["input_ids", "attention_mask"]


	def __init__(     self      ,      _A=None      ,      _A=None      ,      _A=True      ,      _A="[UNK]"      ,      _A="[SEP]"      ,      _A="[PAD]"      ,      _A="[CLS]"      ,      _A="[MASK]"      ,      _A=True      ,      _A=None      ,      **_A      ,      ):



		'''simple docstring'''

		super().__init__(
		    _A      ,      tokenizer_file=_A      ,      do_lower_case=_A      ,      unk_token=_A      ,      sep_token=_A      ,      pad_token=_A      ,      cls_token=_A      ,      mask_token=_A      ,      tokenize_chinese_chars=_A      ,      strip_accents=_A      ,      **_A      ,      )

		_SCREAMING_SNAKE_CASE						=json.loads(self.backend_tokenizer.normalizer.__getstate__()					)
		if (
		    normalizer_state.get('''lowercase'''      ,      _A					) != do_lower_case
		    or normalizer_state.get('''strip_accents'''      ,      _A					) != strip_accents
		    or normalizer_state.get('''handle_chinese_chars'''      ,      _A					) != tokenize_chinese_chars
		):
			_SCREAMING_SNAKE_CASE						=getattr(_A      ,      normalizer_state.pop('''type'''					)					)
			_SCREAMING_SNAKE_CASE						=do_lower_case
			_SCREAMING_SNAKE_CASE						=strip_accents
			_SCREAMING_SNAKE_CASE						=tokenize_chinese_chars
			_SCREAMING_SNAKE_CASE						=normalizer_class(**_A					)

		_SCREAMING_SNAKE_CASE						=do_lower_case


	def 				UpperCamelCase_  (     self      ,      _A      ,      _A=None					):



		'''simple docstring'''

		_SCREAMING_SNAKE_CASE						=[self.cls_token_id] + token_ids_a + [self.sep_token_id]

		if token_ids_a:
			output += token_ids_a + [self.sep_token_id]

		return output


	def 				UpperCamelCase_  (     self      ,      _A      ,      _A = None					):



		'''simple docstring'''

		_SCREAMING_SNAKE_CASE						=[self.sep_token_id]
		_SCREAMING_SNAKE_CASE						=[self.cls_token_id]
		if token_ids_a is None:
			return len(cls + token_ids_a + sep					) * [0]
		return len(cls + token_ids_a + sep					) * [0] + len(token_ids_a + sep					) * [1]







	def 				UpperCamelCase_  (     self      ,      _A      ,      _A = None					):



		'''simple docstring'''

		_SCREAMING_SNAKE_CASE						=self._tokenizer.model.save(_A      ,      name=_A					)
		return tuple(_A					)








'''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)

