





import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

SCREAMING_SNAKE_CASE__							:      str         =    logging.get_logger(__name__)


SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    {name: getattr(transformers, name + """Fast""") for name in SLOW_TO_FAST_CONVERTERS}






def    _A	(   lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase ):
   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__						:    List[str]   		=	TOKENIZER_CLASSES
   else:
      a__						:    Dict   		=	{tokenizer_name: getattr(lowerCamelCase				,			tokenizer_name + "Fast" )}

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

   for tokenizer_name in tokenizer_names:
      a__						:    Dict   		=	TOKENIZER_CLASSES[tokenizer_name]

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

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

         a__						:    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__":
  SCREAMING_SNAKE_CASE__							:      List[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.""",
  )
  SCREAMING_SNAKE_CASE__							:      List[str]         =    parser.parse_args()

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








import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE__							:      Tuple         =    logging.get_logger(__name__)


SCREAMING_SNAKE_CASE__							:      int         =    """▁"""

SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    {"""vocab_file""": """spiece.model"""}

SCREAMING_SNAKE_CASE__							:      Optional[int]         =    {
    """vocab_file""": {
        """google/reformer-crime-and-punishment""": (
            """https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"""
        )
    }
}

SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    {
    """google/reformer-crime-and-punishment""": 5_2_4_2_8_8,
}







class 	__lowerCAmelCase      (       _UpperCamelCase	):
   _UpperCamelCase			:    List[Any]  		=					VOCAB_FILES_NAMES
   _UpperCamelCase			:    Any  		=					PRETRAINED_VOCAB_FILES_MAP
   _UpperCamelCase			:    Any  		=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCamelCase			:    Tuple  		=					["""input_ids""", """attention_mask"""]





   def __init__(			self       ,      snake_case       ,      snake_case="</s>"       ,      snake_case="<unk>"       ,      snake_case=[]       ,      snake_case = None       ,      **snake_case       ,      )					->	None:
      """simple docstring"""





      a__						:    Optional[Any]   		=	{} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          eos_token=snake_case       ,      unk_token=snake_case       ,      additional_special_tokens=snake_case       ,      sp_model_kwargs=self.sp_model_kwargs       ,      **snake_case       ,      )

      a__						:    Union[str, Any]   		=	vocab_file
      a__						:    Optional[Any]   		=	spm.SentencePieceProcessor(**self.sp_model_kwargs   )
      self.sp_model.Load(snake_case   )





   @property
   def 						_snake_case			(			self   )					->	str:
      """simple docstring"""





      return self.sp_model.get_piece_size()





   def 						_snake_case			(			self   )					->	Dict[str, int]:
      """simple docstring"""





      a__						:    Dict   		=	{self.convert_ids_to_tokens(snake_case   ): i for i in range(self.vocab_size   )}
      vocab.update(self.added_tokens_encoder   )
      return vocab





   def __getstate__(			self   )					->	Any:
      """simple docstring"""





      a__						:    Tuple   		=	self.__dict__.copy()
      a__						:    Optional[int]   		=	None
      return state





   def __setstate__(			self       ,      snake_case   )					->	Union[str, Any]:
      """simple docstring"""





      a__						:    List[Any]   		=	d

      # for backward compatibility
      if not hasattr(self       ,      "sp_model_kwargs"   ):
         a__						:    List[Any]   		=	{}

      a__						:    int   		=	spm.SentencePieceProcessor(**self.sp_model_kwargs   )
      self.sp_model.Load(self.vocab_file   )





   def 						_snake_case			(			self       ,      snake_case   )					->	List[str]:
      """simple docstring"""





      return self.sp_model.encode(snake_case       ,      out_type=snake_case   )





   def 						_snake_case			(			self       ,      snake_case   )					->	Union[str, Any]:
      """simple docstring"""





      return self.sp_model.piece_to_id(snake_case   )





   def 						_snake_case			(			self       ,      snake_case   )					->	Any:
      """simple docstring"""





      if index < self.sp_model.get_piece_size():
         a__						:    Optional[Any]   		=	self.sp_model.IdToPiece(snake_case   )
      return token





   def 						_snake_case			(			self       ,      snake_case   )					->	Any:
      """simple docstring"""





      a__						:    int   		=	[]
      a__						:    Dict   		=	""
      for token in tokens:
         # make sure that special tokens are not decoded using sentencepiece model
         if token in self.all_special_tokens:
            out_string += self.sp_model.decode(snake_case   ) + token
            a__						:    Dict   		=	[]
         else:
            current_sub_tokens.append(snake_case   )
      out_string += self.sp_model.decode(snake_case   )
      return out_string.strip()





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case = None   )					->	Tuple[str]:
      """simple docstring"""





      if not os.path.isdir(snake_case   ):
         logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""   )
         return
      a__						:    Tuple   		=	os.path.join(
          snake_case       ,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]   )

      if os.path.abspath(self.vocab_file   ) != os.path.abspath(snake_case   ) and os.path.isfile(self.vocab_file   ):
         copyfile(self.vocab_file       ,      snake_case   )
      elif not os.path.isfile(self.vocab_file   ):
         with open(snake_case       ,      "wb"   ) as fi:
            a__						:    str   		=	self.sp_model.serialized_model_proto()
            fi.write(snake_case   )

      return (out_vocab_file,)

