




from __future__ import annotations

import typing
from collections import Counter





def        __UpperCAmelCase						(							lowerCamelCase_ :     int  )       ->  typing.Counter[int]:







			"""simple docstring"""





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





def        __UpperCAmelCase						(							lowerCamelCase_ :     int = 10_00  )       ->  int:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      pythagorean_triple(lowerCamelCase_  )
			return triplets.most_common(1  )[0][0]


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









from typing import TYPE_CHECKING

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


UpperCamelCase__ :   Optional[int]						=							{
    '''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}
try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__ :   Tuple						=							['''BloomTokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__ :   Any						=							[
						    '''BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''BloomForCausalLM''',
						    '''BloomModel''',
						    '''BloomPreTrainedModel''',
						    '''BloomForSequenceClassification''',
						    '''BloomForTokenClassification''',
						    '''BloomForQuestionAnswering''',
						]

if TYPE_CHECKING:
						from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_bloom_fast import BloomTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_bloom import (
												    BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BloomForCausalLM,
												    BloomForQuestionAnswering,
												    BloomForSequenceClassification,
												    BloomForTokenClassification,
												    BloomModel,
												    BloomPreTrainedModel,
												)

else:
						import sys

						UpperCamelCase__ :   Tuple						=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



