
"""simple docstring"""




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

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


if is_sentencepiece_available():
  from .tokenization_albert import AlbertTokenizer
else:
  A__ :       str				= None

A__ :       str				= logging.get_logger(__name__)
A__ :       Tuple				= {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

A__ :       Optional[Any]				= {
    'vocab_file': {
        'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/spiece.model',
        'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/spiece.model',
        'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model',
        'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model',
        'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/spiece.model',
        'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/spiece.model',
        'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model',
        'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json',
        'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json',
        'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json',
        'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json',
        'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json',
        'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json',
        'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json',
        'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json',
    },
}

A__ :       int				= {
    'albert-base-v1': 5_1_2,
    'albert-large-v1': 5_1_2,
    'albert-xlarge-v1': 5_1_2,
    'albert-xxlarge-v1': 5_1_2,
    'albert-base-v2': 5_1_2,
    'albert-large-v2': 5_1_2,
    'albert-xlarge-v2': 5_1_2,
    'albert-xxlarge-v2': 5_1_2,
}

A__ :       List[str]				= '▁'


class       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       UpperCamelCase_ =		VOCAB_FILES_NAMES
       UpperCamelCase_ =		PRETRAINED_VOCAB_FILES_MAP
       UpperCamelCase_ =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCamelCase_ =		AlbertTokenizer
       def __init__(  self			,					A_=None			,					A_=None			,					A_=True			,					A_=True			,					A_=False			,					A_="[CLS]"			,					A_="[SEP]"			,					A_="<unk>"			,					A_="[SEP]"			,					A_="<pad>"			,					A_="[CLS]"			,					A_="[MASK]"			,					**A_			,					)  ->						Any:





         """simple docstring"""




         _lowercase:      str   			=  (
             AddedToken(A_			,					lstrip=A_			,					rstrip=A_			,					normalized=A_							)
             if isinstance(A_			,					A_							)
             else mask_token
         )

         super().__init__(
             A_			,					tokenizer_file=A_			,					do_lower_case=A_			,					remove_space=A_			,					keep_accents=A_			,					bos_token=A_			,					eos_token=A_			,					unk_token=A_			,					sep_token=A_			,					pad_token=A_			,					cls_token=A_			,					mask_token=A_			,					**A_			,					)

         _lowercase:      List[str]   			=  do_lower_case
         _lowercase:      List[Any]   			=  remove_space
         _lowercase:      Dict   			=  keep_accents
         _lowercase:      str   			=  vocab_file
         _lowercase:      Dict   			=  False if not self.vocab_file else True
       def 				lowercase_				(  self			,					A_			,					A_ = None							)  ->						List[int]:





         """simple docstring"""




         _lowercase:      str   			=  [self.sep_token_id]
         _lowercase:      str   			=  [self.cls_token_id]
         if token_ids_a is None:
           return cls + token_ids_a + sep
         return cls + token_ids_a + sep + token_ids_a + sep
       def 				lowercase_				(  self			,					A_			,					A_ = None							)  ->						List[int]:





         """simple docstring"""




         _lowercase:      List[Any]   			=  [self.sep_token_id]
         _lowercase:      Union[str, Any]   			=  [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 				lowercase_				(  self			,					A_			,					A_ = None							)  ->						Tuple[str]:





         """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
         _lowercase:      List[str]   			=  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"""




from __future__ import annotations

import typing
from collections import Counter




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      typing.Counter[int]   			=  Counter()
  for base in range(1			,					max_perimeter + 1    ):
    for perpendicular in range(_UpperCamelCase			,					max_perimeter + 1    ):
      _lowercase:      str   			=  (base * base + perpendicular * perpendicular) ** 0.5
      if hypotenuse == int(_UpperCamelCase    ):
        _lowercase:      str   			=  int(base + perpendicular + hypotenuse    )
        if perimeter > max_perimeter:
          continue
        triplets[perimeter] += 1
  return triplets




def 			_lowerCAmelCase		(		_UpperCamelCase = 1_000    ):






  """simple docstring"""



  _lowercase:      int   			=  pythagorean_triple(_UpperCamelCase    )
  return triplets.most_common(1    )[0][0]


if __name__ == "__main__":
  print(f"""Perimeter {solution()} has maximum solutions""")
