'''simple docstring'''




from __future__ import annotations

import typing
from collections import Counter

def 	UpperCamelCase_							(					_UpperCAmelCase  :      int    )    ->    typing.Counter[int]:


       """simple docstring"""







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

def 	UpperCamelCase_							(					_UpperCAmelCase  :      int = 1_000    )    ->    int:


       """simple docstring"""







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


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









"""simple docstring"""



import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin

class        a     (   a_	):
    UpperCAmelCase_ :     List[Any]	     =["image_processor", "tokenizer"]
    UpperCAmelCase_ :     str	     ="AutoImageProcessor"
    UpperCAmelCase_ :     Any	     ="AutoTokenizer"







    def __init__(			self							,		_lowerCamelCase=None							,		_lowerCamelCase=None							,		**_lowerCamelCase       ):
      lowercase							    =       None
      if "feature_extractor" in kwargs:
        warnings.warn(
            'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
            ' instead.'							,		_lowerCamelCase							,		)
        lowercase							    =       kwargs.pop('feature_extractor'       )

      lowercase							    =       image_processor if image_processor is not None else feature_extractor
      if image_processor is None:
        raise ValueError('You need to specify an `image_processor`.'       )
      if tokenizer is None:
        raise ValueError('You need to specify a `tokenizer`.'       )

      super().__init__(_lowerCamelCase							,		_lowerCamelCase       )
      lowercase							    =       self.image_processor
      lowercase							    =       False







    def __call__(			self							,		*_lowerCamelCase							,		**_lowerCamelCase       ):
      # For backward compatibility
      if self._in_target_context_manager:
        return self.current_processor(*_lowerCamelCase							,		**_lowerCamelCase       )

      lowercase							    =       kwargs.pop('images'							,		_lowerCamelCase       )
      lowercase							    =       kwargs.pop('text'							,		_lowerCamelCase       )
      if len(_lowerCamelCase       ) > 0:
        lowercase							    =       args[0]
        lowercase							    =       args[1:]

      if images is None and text is None:
        raise ValueError('You need to specify either an `images` or `text` input to process.'       )

      if images is not None:
        lowercase							    =       self.image_processor(_lowerCamelCase							,		*_lowerCamelCase							,		**_lowerCamelCase       )
      if text is not None:
        lowercase							    =       self.tokenizer(_lowerCamelCase							,		**_lowerCamelCase       )

      if text is None:
        return inputs
      elif images is None:
        return encodings
      else:
        lowercase							    =       encodings['input_ids']
        return inputs







    def 			UpperCamelCase_      (			self							,		*_lowerCamelCase							,		**_lowerCamelCase       ):
      return self.tokenizer.batch_decode(*_lowerCamelCase							,		**_lowerCamelCase       )







    def 			UpperCamelCase_      (			self							,		*_lowerCamelCase							,		**_lowerCamelCase       ):
      return self.tokenizer.decode(*_lowerCamelCase							,		**_lowerCamelCase       )







    @contextmanager
    def 			UpperCamelCase_      (			self       ):
      warnings.warn(
          '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
          'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
          'your images inputs, or in a separate call.'       )
      lowercase							    =       True
      lowercase							    =       self.tokenizer
      yield
      lowercase							    =       self.image_processor
      lowercase							    =       False







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase=False							,		_lowerCamelCase=None       ):
      if added_vocab is None:
        lowercase							    =       self.tokenizer.get_added_vocab()

      lowercase							    =       {}

      while tokens:
        lowercase							    =       re.search(R'<s_(.*?)>'							,		_lowerCamelCase							,		re.IGNORECASE       )
        if start_token is None:
          break
        lowercase							    =       start_token.group(1       )
        lowercase							    =       re.search(RF'</s_{key}>'							,		_lowerCamelCase							,		re.IGNORECASE       )
        lowercase							    =       start_token.group()
        if end_token is None:
          lowercase							    =       tokens.replace(_lowerCamelCase							,		''       )
        else:
          lowercase							    =       end_token.group()
          lowercase							    =       re.escape(_lowerCamelCase       )
          lowercase							    =       re.escape(_lowerCamelCase       )
          lowercase							    =       re.search(F'{start_token_escaped}(.*?){end_token_escaped}'							,		_lowerCamelCase							,		re.IGNORECASE       )
          if content is not None:
            lowercase							    =       content.group(1       ).strip()
            if r"<s_" in content and r"</s_" in content:  # non-leaf node
              lowercase							    =       self.tokenajson(_lowerCamelCase							,		is_inner_value=_lowerCamelCase							,		added_vocab=_lowerCamelCase       )
              if value:
                if len(_lowerCamelCase       ) == 1:
                  lowercase							    =       value[0]
                lowercase							    =       value
            else:  # leaf nodes
              lowercase							    =       []
              for leaf in content.split(R'<sep/>'       ):
                lowercase							    =       leaf.strip()
                if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
                  lowercase							    =       leaf[1:-2]  # for categorical special tokens
                output[key].append(_lowerCamelCase       )
              if len(output[key]       ) == 1:
                lowercase							    =       output[key][0]

          lowercase							    =       tokens[tokens.find(_lowerCamelCase       ) + len(_lowerCamelCase       ) :].strip()
          if tokens[:6] == r"<sep/>":  # non-leaf nodes
            return [output] + self.tokenajson(tokens[6:]							,		is_inner_value=_lowerCamelCase							,		added_vocab=_lowerCamelCase       )

      if len(_lowerCamelCase       ):
        return [output] if is_inner_value else output
      else:
        return [] if is_inner_value else {"text_sequence": tokens}







    @property
    def 			UpperCamelCase_      (			self       ):
      warnings.warn(
          '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'							,		_lowerCamelCase							,		)
      return self.image_processor_class







    @property
    def 			UpperCamelCase_      (			self       ):
      warnings.warn(
          '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'							,		_lowerCamelCase							,		)
      return self.image_processor


