






'''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:
  SCREAMING_SNAKE_CASE_				      =							None


SCREAMING_SNAKE_CASE_				      =							logging.get_logger(__name__)


SCREAMING_SNAKE_CASE_				      =							"▁"

SCREAMING_SNAKE_CASE_				      =							{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

SCREAMING_SNAKE_CASE_				      =							{
    "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"
        )
    },
}

SCREAMING_SNAKE_CASE_				      =							{
    "google/reformer-crime-and-punishment": 5_2_4_2_8_8,
}




class 							lowerCAmelCase					(							lowerCamelCase__   ):







   """simple docstring"""







   _A						=   VOCAB_FILES_NAMES
   _A						=   PRETRAINED_VOCAB_FILES_MAP
   _A						=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _A						=   ['input_ids', 'attention_mask']
   _A						=   ReformerTokenizer



   def __init__( self   ,   _A=None   ,   _A=None   ,   _A="</s>"   ,   _A="<unk>"   ,   _A=[]   ,   **_A   ,   )    ->   List[Any]:
    super().__init__(
        _A   ,   tokenizer_file=_A   ,   eos_token=_A   ,   unk_token=_A   ,   additional_special_tokens=_A   ,   **_A   ,   )

    __a    :	str									=						vocab_file
    __a    :	int									=						False if not self.vocab_file else True



   def    __magic_name__    ( self   ,   _A   ,   _A = None			)    ->   Tuple[str]:
    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
    __a    :	Any									=						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,)












'''simple docstring'''


import math
from timeit import timeit






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__		):
 __a    :	Any									=						0
 __a    :	List[Any]									=						abs(SCREAMING_SNAKE_CASE__		)
 while True:
  __a    :	int									=						n // 10
  digits += 1
  if n == 0:
   break
 return digits






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__		):
 return 1 if n == 0 else math.floor(math.log(abs(SCREAMING_SNAKE_CASE__		)       ,		10		) + 1		)






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__		):
 return len(str(abs(SCREAMING_SNAKE_CASE__		)		)		)






def   lowerCAmelCase__			(					):
 from collections.abc import Callable

 def benchmark_a_function(SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		) -> None:
  __a    :	Tuple									=						f'''{func.__name__}({value})'''
  __a    :	List[Any]									=						timeit(f'''__main__.{call}'''       ,		setup='import __main__'		)
  print(f'''{call}: {func(SCREAMING_SNAKE_CASE__		)} -- {timing} seconds'''		)

 for value in (262144, 1125899906842624, 1267650600228229401496703205376):
  for func in (num_digits, num_digits_fast, num_digits_faster):
   benchmark_a_function(SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		)
  print()


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  benchmark()




