
"""simple docstring"""


from itertools import combinations, product






def        lowerCAmelCase_       (		snake_case_		:      int							,   snake_case_		:      Union[str, Any]							,   snake_case_		:      int							,   snake_case_		:      int      )       ->Optional[Any]:



			if xa == ya == 0 or xa == ya == 0:
						return False
			lowerCamelCase__     :					List[Any]		=xa * xa + ya * ya
			lowerCamelCase__     :					Dict		=xa * xa + ya * ya
			lowerCamelCase__     :					str		=(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        lowerCAmelCase_       (		snake_case_		:      Tuple = 5_0      )       ->Optional[Any]:
			return sum(
			    1
			    for pta, pta in combinations(product(range(limit + 1      )							,   repeat=2      )							,   2      )
			    if is_right(*__A							,   *__A      )      )


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

"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase								=						{
    """configuration_decision_transformer""": [
        """DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """DecisionTransformerConfig""",
    ],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase								=						[
							    """DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """DecisionTransformerGPT2Model""",
							    """DecisionTransformerGPT2PreTrainedModel""",
							    """DecisionTransformerModel""",
							    """DecisionTransformerPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_decision_transformer import (
							    DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    DecisionTransformerConfig,
							)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_decision_transformer import (
														    DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DecisionTransformerGPTaModel,
														    DecisionTransformerGPTaPreTrainedModel,
														    DecisionTransformerModel,
														    DecisionTransformerPreTrainedModel,
														)


else:
							import sys

							lowerCAmelCase								=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)