



'''simple docstring'''
from __future__ import annotations

import typing
from collections import Counter






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






def A__   (					UpperCAmelCase_ = 1_0_0_0							):
      _UpperCamelCase :		Any			    =       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 ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable


snake_case_			:     Any  	=  {
    'configuration_gpt_neox_japanese': ['GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoXJapaneseConfig'],
    'tokenization_gpt_neox_japanese': ['GPTNeoXJapaneseTokenizer'],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_			:     Optional[int]  	=  [
					    'GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'GPTNeoXJapaneseForCausalLM',
					    'GPTNeoXJapaneseLayer',
					    'GPTNeoXJapaneseModel',
					    'GPTNeoXJapanesePreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
					from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_gpt_neox_japanese import (
										    GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GPTNeoXJapaneseForCausalLM,
										    GPTNeoXJapaneseLayer,
										    GPTNeoXJapaneseModel,
										    GPTNeoXJapanesePreTrainedModel,
										)


else:
					import sys

					snake_case_			:     Any  	=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


