




"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


lowerCAmelCase__							=				{
    'configuration_speecht5': [
        'SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP',
        'SpeechT5Config',
        'SpeechT5HifiGanConfig',
    ],
    'feature_extraction_speecht5': ['SpeechT5FeatureExtractor'],
    'processing_speecht5': ['SpeechT5Processor'],
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__							=				['SpeechT5Tokenizer']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__							=				[
						    'SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'SpeechT5ForSpeechToText',
						    'SpeechT5ForSpeechToSpeech',
						    'SpeechT5ForTextToSpeech',
						    'SpeechT5Model',
						    'SpeechT5PreTrainedModel',
						    'SpeechT5HifiGan',
						]

if TYPE_CHECKING:
						from .configuration_speechta import (
						    SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
						    SpeechTaConfig,
						    SpeechTaHifiGanConfig,
						)
						from .feature_extraction_speechta import SpeechTaFeatureExtractor
						from .processing_speechta import SpeechTaProcessor

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_speechta import SpeechTaTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_speechta import (
												    SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
												    SpeechTaForSpeechToSpeech,
												    SpeechTaForSpeechToText,
												    SpeechTaForTextToSpeech,
												    SpeechTaHifiGan,
												    SpeechTaModel,
												    SpeechTaPreTrainedModel,
												)

else:
						import sys

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

"""simple docstring"""




from __future__ import annotations

import typing
from collections import Counter



def        _lowerCamelCase ( __a    ):
		SCREAMING_SNAKE_CASE_						 = Counter()
		for base in range(1,							max_perimeter + 1    ):
				for perpendicular in range(__a,							max_perimeter + 1    ):
						SCREAMING_SNAKE_CASE_						 = (base * base + perpendicular * perpendicular) ** 0.5
						if hypotenuse == int(__a    ):
								SCREAMING_SNAKE_CASE_						 = int(base + perpendicular + hypotenuse    )
								if perimeter > max_perimeter:
										continue
								triplets[perimeter] += 1
		return triplets



def        _lowerCamelCase ( __a = 1_000    ):
		SCREAMING_SNAKE_CASE_						 = pythagorean_triple(__a    )
		return triplets.most_common(1    )[0][0]


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