



"""simple docstring"""






from itertools import combinations, product
def      A      (			_A,     _A,     _A,     _A							):




					"""simple docstring"""
					if xa == ya == 0 or xa == ya == 0:
										return False
					snake_case_						:Any								=						xa * xa + ya * ya
					snake_case_						:str								=						xa * xa + ya * ya
					snake_case_						:Any								=						(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      A      (			_A = 50							):




					"""simple docstring"""
					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_tf_available,
    is_torch_available,
    is_vision_available,
)


__UpperCAmelCase      :      Optional[int]   					=					{
    'configuration_segformer': ['SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SegformerConfig', 'SegformerOnnxConfig']
}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__UpperCAmelCase      :      int   					=					['SegformerFeatureExtractor']
				__UpperCAmelCase      :      Dict   					=					['SegformerImageProcessor']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__UpperCAmelCase      :      Optional[Any]   					=					[
				    'SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'SegformerDecodeHead',
				    'SegformerForImageClassification',
				    'SegformerForSemanticSegmentation',
				    'SegformerLayer',
				    'SegformerModel',
				    'SegformerPreTrainedModel',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__UpperCAmelCase      :      Dict   					=					[
				    'TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFSegformerDecodeHead',
				    'TFSegformerForImageClassification',
				    'TFSegformerForSemanticSegmentation',
				    'TFSegformerModel',
				    'TFSegformerPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_segformer import SegformerFeatureExtractor
								from .image_processing_segformer import SegformerImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_segformer import (
								    SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SegformerDecodeHead,
								    SegformerForImageClassification,
								    SegformerForSemanticSegmentation,
								    SegformerLayer,
								    SegformerModel,
								    SegformerPreTrainedModel,
								)
				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_segformer import (
								    TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFSegformerDecodeHead,
								    TFSegformerForImageClassification,
								    TFSegformerForSemanticSegmentation,
								    TFSegformerModel,
								    TFSegformerPreTrainedModel,
								)

else:
				import sys

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

