

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

import typing
from collections import Counter


def  a__       (    _SCREAMING_SNAKE_CASE   :       int				)     ->					typing.Counter[int]:


	"""simple docstring"""





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


def  a__       (    _SCREAMING_SNAKE_CASE   :       int = 10_00				)     ->					int:


	"""simple docstring"""





	UpperCAmelCase_     :      Dict						=  pythagorean_triple(_SCREAMING_SNAKE_CASE				)
	return triplets.most_common(1				)[0][0]


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









'''simple docstring'''





from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A__    :   List[str]           =					{
    '''configuration_autoformer''': [
        '''AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''AutoformerConfig''',
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A__    :   Optional[Any]           =					[
        '''AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''AutoformerForPrediction''',
        '''AutoformerModel''',
        '''AutoformerPreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_autoformer import (
        AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
        AutoformerConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_autoformer import (
            AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            AutoformerForPrediction,
            AutoformerModel,
            AutoformerPreTrainedModel,
        )

else:
    import sys

    A__    :   Optional[int]           =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



