





from __future__ import annotations

import typing
from collections import Counter



def 				lowerCAmelCase_ (							A_):
    UpperCamelCase__:	Any					=     Counter()
    for base in range(1    ,max_perimeter + 1):
        for perpendicular in range(UpperCAmelCase__    ,max_perimeter + 1):
            UpperCamelCase__:	List[Any]					=     (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 				lowerCAmelCase_ (							A_ = 10_00):
    UpperCamelCase__:	Optional[int]					=     pythagorean_triple(UpperCAmelCase__)
    return triplets.most_common(1)[0][0]


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




'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available


__lowerCamelCase									=     {
    '''configuration_ernie''': ['''ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ErnieConfig''', '''ErnieOnnxConfig'''],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase									=     [
          '''ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''ErnieForCausalLM''',
          '''ErnieForMaskedLM''',
          '''ErnieForMultipleChoice''',
          '''ErnieForNextSentencePrediction''',
          '''ErnieForPreTraining''',
          '''ErnieForQuestionAnswering''',
          '''ErnieForSequenceClassification''',
          '''ErnieForTokenClassification''',
          '''ErnieModel''',
          '''ErniePreTrainedModel''',
      ]

if TYPE_CHECKING:
      from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_ernie import (
                ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
                ErnieForCausalLM,
                ErnieForMaskedLM,
                ErnieForMultipleChoice,
                ErnieForNextSentencePrediction,
                ErnieForPreTraining,
                ErnieForQuestionAnswering,
                ErnieForSequenceClassification,
                ErnieForTokenClassification,
                ErnieModel,
                ErniePreTrainedModel,
            )

else:
      import sys

      __lowerCamelCase									=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
