

"""simple docstring"""




def 					lowerCAmelCase  (   __UpperCamelCase ):
 '''simple docstring'''





 UpperCAmelCase__   :					Dict   				=   word.upper()
 UpperCAmelCase__   :					Optional[Any]   				=   0
 UpperCAmelCase__   :					Any   				=   {}
 UpperCAmelCase__   :					Any   				=   []

 for letter in word:
  if letter not in letter_nums:
   UpperCAmelCase__   :					Optional[Any]   				=   str(__UpperCamelCase )
   next_num += 1
  word_pattern.append(letter_nums[letter] )
 return ".".join(__UpperCamelCase )


if __name__ == "__main__":
      import pprint
      import time

      __UpperCAmelCase	=			time.time()
      with open('dictionary.txt') as in_file:
            __UpperCAmelCase	=			in_file.read().splitlines()

      __UpperCAmelCase	=			{}
      for word in word_list:
            __UpperCAmelCase	=			get_word_pattern(word)
            if pattern in all_patterns:
                  all_patterns[pattern].append(word)
            else:
                  __UpperCAmelCase	=			[word]

      with open('word_patterns.txt', 'w') as out_file:
            out_file.write(pprint.pformat(all_patterns))

      __UpperCAmelCase	=			round(time.time() - start_time, 2)
      print(F"Done!  {len(all_patterns):,} word patterns found in {total_time} seconds.")
      # Done!  9,581 word patterns found in 0.58 seconds.








"""simple docstring"""




import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_reformer import ReformerTokenizer
else:
      __UpperCAmelCase	=			None


__UpperCAmelCase	=			logging.get_logger(__name__)


__UpperCAmelCase	=			'▁'

__UpperCAmelCase	=			{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

__UpperCAmelCase	=			{
    'vocab_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'
        )
    },
    'tokenizer_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json'
        )
    },
}

__UpperCAmelCase	=			{
    'google/reformer-crime-and-punishment': 52_4288,
}







class        __lowercase     (      __lowerCamelCase   ):
  snake_case_			    =    VOCAB_FILES_NAMES
  snake_case_			    =    PRETRAINED_VOCAB_FILES_MAP
  snake_case_			    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  snake_case_			    =    ["""input_ids""", """attention_mask"""]
  snake_case_			    =    ReformerTokenizer

  def __init__(					self						:     List[Any]   ,A						:     Dict=None   ,A						:     List[str]=None   ,A						:     Union[str, Any]="</s>"   ,A						:     List[str]="<unk>"   ,A						:     Tuple=[]   ,**A						:     Optional[int]   ,):






   '''simple docstring'''
   super().__init__(
       A   ,tokenizer_file=A   ,eos_token=A   ,unk_token=A   ,additional_special_tokens=A   ,**A   ,)

   UpperCAmelCase__   :					Tuple   				=   vocab_file
   UpperCAmelCase__   :					Optional[Any]   				=   False if not self.vocab_file else True




  def 				__lowercase    (					self						:     Any   ,A						:     str   ,A						:     Optional[str] = None      ):






   '''simple docstring'''
   if not self.can_save_slow_tokenizer:
    raise ValueError(
        """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
        """tokenizer."""      )

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

   if os.path.abspath(self.vocab_file      ) != os.path.abspath(A      ):
    copyfile(self.vocab_file   ,A      )

   return (out_vocab_file,)





