


'''simple docstring'''







def _UpperCamelCase			(	)				->						int:


      '''simple docstring'''



      for a in range(300       ):
            for b in range(a + 1 ,     400       ):
                  for c in range(b + 1 ,     500       ):
                        if (a + b + c) == 1000 and (a**2) + (b**2) == (c**2):
                              return a * b * c

      return -1







def _UpperCamelCase			(	)				->						Dict:


      '''simple docstring'''



      for a in range(300       ):
            for b in range(400       ):
                  UpperCamelCase__      =							1000 - a - b
                  if a < b < c and (a**2) + (b**2) == (c**2):
                        return a * b * c

      return -1







def _UpperCamelCase			(	)				->						int:


      '''simple docstring'''



      import timeit

      print(
          timeit.timeit("solution()" ,     setup="from __main__ import solution" ,     number=1000       )       )
      print(
          timeit.timeit(
              "solution_fast()" ,     setup="from __main__ import solution_fast" ,     number=1000       )       )


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






"""simple docstring"""




import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import MODEL_MAPPING, PoolFormerConfig, PoolFormerForImageClassification, PoolFormerModel
				from transformers.models.poolformer.modeling_poolformer import POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import PoolFormerImageProcessor
class      UpperCamelCase__(      __A			):





						def 					snake_case__						(							self  )       ->					int:
													A__						  =				self.config_class(**self.inputs_dict  )
													self.parent.assertTrue(hasattr(__UpperCAmelCase		,'hidden_sizes'  )  )
													self.parent.assertTrue(hasattr(__UpperCAmelCase		,'num_encoder_blocks'  )  )






class      UpperCamelCase__:





						def __init__(							self		,__UpperCAmelCase		,__UpperCAmelCase=13		,__UpperCAmelCase=64		,__UpperCAmelCase=3		,__UpperCAmelCase=4		,__UpperCAmelCase=[2, 2, 2, 2]		,__UpperCAmelCase=[8, 4, 2, 1]		,__UpperCAmelCase=[16, 32, 64, 1_28]		,__UpperCAmelCase=[1, 4, 8, 16]		,__UpperCAmelCase=False		,__UpperCAmelCase=True		,__UpperCAmelCase="gelu"		,__UpperCAmelCase=0.1		,__UpperCAmelCase=0.0_2		,__UpperCAmelCase=3		,__UpperCAmelCase=None		,)       ->					Tuple:
													A__						  =				parent
													A__						  =				batch_size
													A__						  =				image_size
													A__						  =				num_channels
													A__						  =				num_encoder_blocks
													A__						  =				sr_ratios
													A__						  =				depths
													A__						  =				hidden_sizes
													A__						  =				downsampling_rates
													A__						  =				is_training
													A__						  =				use_labels
													A__						  =				hidden_act
													A__						  =				hidden_dropout_prob
													A__						  =				initializer_range
													A__						  =				num_labels
													A__						  =				scope





						def 					snake_case__						(							self  )       ->					Tuple:
													A__						  =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

													A__						  =				None
													if self.use_labels:
																				A__						  =				ids_tensor([self.batch_size, self.image_size, self.image_size]		,self.num_labels  )

													A__						  =				PoolFormerConfig(
													    image_size=self.image_size		,num_channels=self.num_channels		,num_encoder_blocks=self.num_encoder_blocks		,depths=self.depths		,hidden_sizes=self.hidden_sizes		,hidden_act=self.hidden_act		,hidden_dropout_prob=self.hidden_dropout_prob		,initializer_range=self.initializer_range		,)

													return config, pixel_values, labels





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  )       ->					Union[str, Any]:
													A__						  =				PoolFormerModel(config=__UpperCAmelCase  )
													model.to(__UpperCAmelCase  )
													model.eval()
													A__						  =				model(__UpperCAmelCase  )
													A__						  =				A__						  =				self.image_size // 3_2.0
													self.parent.assertEqual(
													    result.last_hidden_state.shape		,(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)  )





						def 					snake_case__						(							self  )       ->					Any:
													A__						  =				self.prepare_config_and_inputs()
													A__  ,	A__  ,	A__						  =				config_and_inputs
													A__						  =				{'pixel_values': pixel_values}
													return config, inputs_dict






@require_torch
class      UpperCamelCase__(      __A		,    __A		,    unittest.TestCase			):
						lowerCAmelCase__	:    Optional[int]    				=		(PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
						lowerCAmelCase__	:    Optional[Any]    				=		(
						    {'feature-extraction': PoolFormerModel, 'image-classification': PoolFormerForImageClassification}
						    if is_torch_available()
						    else {}
						)

						lowerCAmelCase__	:    Any    				=		False
						lowerCAmelCase__	:    str    				=		False
						lowerCAmelCase__	:    Any    				=		False
						lowerCAmelCase__	:    str    				=		False
						lowerCAmelCase__	:    Optional[int]    				=		False





						def 					snake_case__						(							self  )       ->					Any:
													A__						  =				PoolFormerModelTester(self  )
													A__						  =				PoolFormerConfigTester(self		,config_class=__UpperCAmelCase  )





						def 					snake_case__						(							self  )       ->					int:
													self.config_tester.run_common_tests()





						def 					snake_case__						(							self  )       ->					Tuple:
													A__						  =				self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*__UpperCAmelCase  )





						@unittest.skip('PoolFormer does not use inputs_embeds'  )
						def 					snake_case__						(							self  )       ->					int:
													pass





						@unittest.skip('PoolFormer does not have get_input_embeddings method and get_output_embeddings methods'  )
						def 					snake_case__						(							self  )       ->					List[str]:
													pass





						def 					snake_case__						(							self  )       ->					List[Any]:
													def check_hidden_states_output(__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  ):
																				A__						  =				model_class(__UpperCAmelCase  )
																				model.to(__UpperCAmelCase  )
																				model.eval()

																				with torch.no_grad():
																											A__						  =				model(**self._prepare_for_class(__UpperCAmelCase		,__UpperCAmelCase  )  )

																				A__						  =				outputs.hidden_states

																				A__						  =				self.model_tester.num_encoder_blocks
																				self.assertEqual(len(__UpperCAmelCase  )		,__UpperCAmelCase  )

																				# verify the first hidden states (first block)
																				self.assertListEqual(
																				    list(hidden_states[0].shape[-3:]  )		,[
																				        self.model_tester.hidden_sizes[0],
																				        self.model_tester.image_size // 4,
																				        self.model_tester.image_size // 4,
																				    ]		,)

													A__  ,	A__						  =				self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				A__						  =				True
																				check_hidden_states_output(__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  )

																				# check that output_hidden_states also work using config
																				del inputs_dict["output_hidden_states"]
																				A__						  =				True

																				check_hidden_states_output(__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  )





						def 					snake_case__						(							self  )       ->					Union[str, Any]:
													if not self.model_tester.is_training:
																				return

													A__  ,	A__						  =				self.model_tester.prepare_config_and_inputs_for_common()
													A__						  =				True

													for model_class in self.all_model_classes:
																				if model_class in get_values(__UpperCAmelCase  ):
																											continue
																				A__						  =				model_class(__UpperCAmelCase  )
																				model.to(__UpperCAmelCase  )
																				model.train()
																				A__						  =				self._prepare_for_class(__UpperCAmelCase		,__UpperCAmelCase		,return_labels=__UpperCAmelCase  )
																				A__						  =				model(**__UpperCAmelCase  ).loss
																				loss.backward()





						def 					snake_case__						(							self  )       ->					Union[str, Any]:
													A__  ,	A__						  =				self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				A__						  =				model_class(__UpperCAmelCase  )
																				A__						  =				inspect.signature(model.forward  )
																				# signature.parameters is an OrderedDict => so arg_names order is deterministic
																				A__						  =				[*signature.parameters.keys()]

																				A__						  =				['pixel_values']
																				self.assertListEqual(arg_names[:1]		,__UpperCAmelCase  )





						@slow
						def 					snake_case__						(							self  )       ->					Union[str, Any]:
													for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				A__						  =				PoolFormerModel.from_pretrained(__UpperCAmelCase  )
																				self.assertIsNotNone(__UpperCAmelCase  )






def 	UpperCAmelCase       (						):






							"""simple docstring"""





							A__						  =				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
							return image


@require_torch
class      UpperCamelCase__(      unittest.TestCase			):





						@slow
						def 					snake_case__						(							self  )       ->					Union[str, Any]:
													A__						  =				PoolFormerImageProcessor()
													A__						  =				PoolFormerForImageClassification.from_pretrained('sail/poolformer_s12'  ).to(__UpperCAmelCase  )

													A__						  =				image_processor(images=prepare_img()		,return_tensors='pt'  ).to(__UpperCAmelCase  )

													# forward pass
													with torch.no_grad():
																				A__						  =				model(**__UpperCAmelCase  )

													# verify the logits
													A__						  =				torch.Size((1, 10_00)  )
													self.assertEqual(outputs.logits.shape		,__UpperCAmelCase  )

													A__						  =				torch.tensor([-0.6_1_1_3, 0.1_6_8_5, -0.0_4_9_2]  ).to(__UpperCAmelCase  )
													self.assertTrue(torch.allclose(outputs.logits[0, :3]		,__UpperCAmelCase		,atol=1e-4  )  )





