
'''simple docstring'''




from itertools import combinations, product






def     snake_case__							(      _A: List[str]				,       _A: Optional[int]				,       _A: str				,       _A: List[str]			)   ->     str:

		'''simple docstring'''


		if xa == ya == 0 or xa == ya == 0:
				return False
		lowerCAmelCase        =  xa * xa + ya * ya
		lowerCAmelCase        =  xa * xa + ya * ya
		lowerCAmelCase        =  (xa - xa) * (xa - xa) + (ya - ya) * (ya - ya)
		return (
		    a_square + b_square == c_square
		    or a_square + c_square == b_square
		    or b_square + c_square == a_square
		)






def     snake_case__							(      _A: List[Any] = 50			)   ->     Tuple:

		'''simple docstring'''


		return sum(
		    1
		    for pta, pta in combinations(product(range(limit + 1			)				,       repeat=2			)				,       2			)
		    if is_right(*lowerCamelCase_				,       *lowerCamelCase_			)			)


if __name__ == "__main__":
						print(f'{solution() = }')





'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowercase				   =  {
    '''configuration_bart''': ['''BART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BartConfig''', '''BartOnnxConfig'''],
    '''tokenization_bart''': ['''BartTokenizer'''],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase				   =  ['''BartTokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase				   =  [
						    '''BART_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''BartForCausalLM''',
						    '''BartForConditionalGeneration''',
						    '''BartForQuestionAnswering''',
						    '''BartForSequenceClassification''',
						    '''BartModel''',
						    '''BartPretrainedModel''',
						    '''PretrainedBartModel''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase				   =  [
						    '''TFBartForConditionalGeneration''',
						    '''TFBartForSequenceClassification''',
						    '''TFBartModel''',
						    '''TFBartPretrainedModel''',
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase				   =  [
						    '''FlaxBartDecoderPreTrainedModel''',
						    '''FlaxBartForCausalLM''',
						    '''FlaxBartForConditionalGeneration''',
						    '''FlaxBartForQuestionAnswering''',
						    '''FlaxBartForSequenceClassification''',
						    '''FlaxBartModel''',
						    '''FlaxBartPreTrainedModel''',
						]

if TYPE_CHECKING:
						from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
						from .tokenization_bart import BartTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_bart_fast import BartTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_bart import (
												    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BartForCausalLM,
												    BartForConditionalGeneration,
												    BartForQuestionAnswering,
												    BartForSequenceClassification,
												    BartModel,
												    BartPretrainedModel,
												    PretrainedBartModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_bart import (
												    TFBartForConditionalGeneration,
												    TFBartForSequenceClassification,
												    TFBartModel,
												    TFBartPretrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_bart import (
												    FlaxBartDecoderPreTrainedModel,
												    FlaxBartForCausalLM,
												    FlaxBartForConditionalGeneration,
												    FlaxBartForQuestionAnswering,
												    FlaxBartForSequenceClassification,
												    FlaxBartModel,
												    FlaxBartPreTrainedModel,
												)

else:
						import sys

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




