
import inspect
import unittest

from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
					import torch
					from torch import nn

					from transformers import YolosForObjectDetection, YolosModel
					from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import AutoImageProcessor





class 				UpperCAmelCase :




	'''simple docstring'''





	def __init__(			self   :					Any				,A   :					Optional[Any]				,A   :					Optional[int]=13				,A   :					Dict=[30, 30]				,A   :					Tuple=2				,A   :					int=3				,A   :					Optional[int]=True				,A   :					Optional[int]=True				,A   :					str=32				,A   :					Optional[int]=5				,A   :					List[Any]=4				,A   :					Union[str, Any]=37				,A   :					int="gelu"				,A   :					Any=0.1				,A   :					Tuple=0.1				,A   :					Union[str, Any]=10				,A   :					Any=0.02				,A   :					List[str]=3				,A   :					Optional[Any]=None				,A   :					Any=8				,A   :					List[str]=10				,):
					__A       =					parent
					__A       =					batch_size
					__A       =					image_size
					__A       =					patch_size
					__A       =					num_channels
					__A       =					is_training
					__A       =					use_labels
					__A       =					hidden_size
					__A       =					num_hidden_layers
					__A       =					num_attention_heads
					__A       =					intermediate_size
					__A       =					hidden_act
					__A       =					hidden_dropout_prob
					__A       =					attention_probs_dropout_prob
					__A       =					type_sequence_label_size
					__A       =					initializer_range
					__A       =					num_labels
					__A       =					scope
					__A       =					n_targets
					__A       =					num_detection_tokens
					# we set the expected sequence length (which is used in several tests)
					# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
					__A       =					(image_size[1] // patch_size) * (image_size[0] // patch_size)
					__A       =					num_patches + 1 + self.num_detection_tokens






	def 							UpperCamelCase_    (			self   :					Any					):
					__A       =					floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]					)

					__A       =					None
					if self.use_labels:
									# labels is a list of Dict (each Dict being the labels for a given example in the batch)
									__A       =					[]
									for i in range(self.batch_size					):
													__A       =					{}
													__A       =					torch.randint(
													    high=self.num_labels				,size=(self.n_targets,)				,device=A					)
													__A       =					torch.rand(self.n_targets				,4				,device=A					)
													labels.append(A					)

					__A       =					self.get_config()

					return config, pixel_values, labels






	def 							UpperCamelCase_    (			self   :					Union[str, Any]					):
					return YolosConfig(
					    image_size=self.image_size				,patch_size=self.patch_size				,num_channels=self.num_channels				,hidden_size=self.hidden_size				,num_hidden_layers=self.num_hidden_layers				,num_attention_heads=self.num_attention_heads				,intermediate_size=self.intermediate_size				,hidden_act=self.hidden_act				,hidden_dropout_prob=self.hidden_dropout_prob				,attention_probs_dropout_prob=self.attention_probs_dropout_prob				,is_decoder=A				,initializer_range=self.initializer_range				,num_detection_tokens=self.num_detection_tokens				,num_labels=self.num_labels				,)






	def 							UpperCamelCase_    (			self   :					int				,A   :					Any				,A   :					Any				,A   :					str					):
					__A       =					YolosModel(config=A					)
					model.to(A					)
					model.eval()
					__A       =					model(A					)
					self.parent.assertEqual(
					    result.last_hidden_state.shape				,(self.batch_size, self.expected_seq_len, self.hidden_size)					)






	def 							UpperCamelCase_    (			self   :					Tuple				,A   :					List[Any]				,A   :					Union[str, Any]				,A   :					Optional[int]					):
					__A       =					YolosForObjectDetection(A					)
					model.to(A					)
					model.eval()

					__A       =					model(pixel_values=A					)
					__A       =					model(A					)

					self.parent.assertEqual(result.logits.shape				,(self.batch_size, self.num_detection_tokens, self.num_labels + 1)					)
					self.parent.assertEqual(result.pred_boxes.shape				,(self.batch_size, self.num_detection_tokens, 4)					)

					__A       =					model(pixel_values=A				,labels=A					)

					self.parent.assertEqual(result.loss.shape				,()					)
					self.parent.assertEqual(result.logits.shape				,(self.batch_size, self.num_detection_tokens, self.num_labels + 1)					)
					self.parent.assertEqual(result.pred_boxes.shape				,(self.batch_size, self.num_detection_tokens, 4)					)







	def 							UpperCamelCase_    (			self   :					Tuple					):
					__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 (    __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     unittest.TestCase    ):




	'''simple docstring'''





	snake_case_          =	(YolosModel, YolosForObjectDetection) if is_torch_available() else ()
	snake_case_          =	(
	    {"feature-extraction": YolosModel, "object-detection": YolosForObjectDetection} if is_torch_available() else {}
	)

	snake_case_          =	False
	snake_case_          =	False
	snake_case_          =	False
	snake_case_          =	False






	def 							UpperCamelCase_    (			self   :					Optional[int]				,A   :					Optional[int]				,A   :					str				,A   :					Tuple=False					):
					__A       =					super()._prepare_for_class(A				,A				,return_labels=A					)

					if return_labels:
									if model_class.__name__ == "YolosForObjectDetection":
													__A       =					[]
													for i in range(self.model_tester.batch_size					):
																	__A       =					{}
																	__A       =					torch.ones(
																	    size=(self.model_tester.n_targets,)				,device=A				,dtype=torch.long					)
																	__A       =					torch.ones(
																	    self.model_tester.n_targets				,4				,device=A				,dtype=torch.float					)
																	labels.append(A					)
													__A       =					labels

					return inputs_dict






	def 							UpperCamelCase_    (			self   :					List[Any]					):
					__A       =					YolosModelTester(self					)
					__A       =					ConfigTester(self				,config_class=A				,has_text_modality=A				,hidden_size=37					)






	def 							UpperCamelCase_    (			self   :					int					):
					self.config_tester.run_common_tests()






	def 							UpperCamelCase_    (			self   :					List[str]					):
					# YOLOS does not use inputs_embeds
					pass






	def 							UpperCamelCase_    (			self   :					Optional[Any]					):
					__A     ,				__A       =					self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									__A       =					model_class(A					)
									self.assertIsInstance(model.get_input_embeddings()				,(nn.Module)					)
									__A       =					model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(A				,nn.Linear					)					)






	def 							UpperCamelCase_    (			self   :					int					):
					__A     ,				__A       =					self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									__A       =					model_class(A					)
									__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]				,A					)






	def 							UpperCamelCase_    (			self   :					Any					):
					__A       =					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*A					)






	def 							UpperCamelCase_    (			self   :					Optional[int]					):
					__A     ,				__A       =					self.model_tester.prepare_config_and_inputs_for_common()
					__A       =					True

					# in YOLOS, the seq_len is different
					__A       =					self.model_tester.expected_seq_len
					for model_class in self.all_model_classes:
									__A       =					True
									__A       =					False
									__A       =					True
									__A       =					model_class(A					)
									model.to(A					)
									model.eval()
									with torch.no_grad():
													__A       =					model(**self._prepare_for_class(A				,A					)					)
									__A       =					outputs.attentions
									self.assertEqual(len(A					)				,self.model_tester.num_hidden_layers					)

									# check that output_attentions also work using config
									del inputs_dict["output_attentions"]
									__A       =					True
									__A       =					model_class(A					)
									model.to(A					)
									model.eval()
									with torch.no_grad():
													__A       =					model(**self._prepare_for_class(A				,A					)					)
									__A       =					outputs.attentions
									self.assertEqual(len(A					)				,self.model_tester.num_hidden_layers					)

									self.assertListEqual(
									    list(attentions[0].shape[-3:]					)				,[self.model_tester.num_attention_heads, seq_len, seq_len]				,)
									__A       =					len(A					)

									# Check attention is always last and order is fine
									__A       =					True
									__A       =					True
									__A       =					model_class(A					)
									model.to(A					)
									model.eval()
									with torch.no_grad():
													__A       =					model(**self._prepare_for_class(A				,A					)					)

									__A       =					1
									self.assertEqual(out_len + added_hidden_states				,len(A					)					)

									__A       =					outputs.attentions

									self.assertEqual(len(A					)				,self.model_tester.num_hidden_layers					)
									self.assertListEqual(
									    list(self_attentions[0].shape[-3:]					)				,[self.model_tester.num_attention_heads, seq_len, seq_len]				,)






	def 							UpperCamelCase_    (			self   :					int					):
					def check_hidden_states_output(A   :					str				,A   :					Dict				,A   :					List[Any]					):
									__A       =					model_class(A					)
									model.to(A					)
									model.eval()

									with torch.no_grad():
													__A       =					model(**self._prepare_for_class(A				,A					)					)

									__A       =					outputs.hidden_states

									__A       =					getattr(
									    self.model_tester				,"expected_num_hidden_layers"				,self.model_tester.num_hidden_layers + 1					)
									self.assertEqual(len(A					)				,A					)

									# YOLOS has a different seq_length
									__A       =					self.model_tester.expected_seq_len

									self.assertListEqual(
									    list(hidden_states[0].shape[-2:]					)				,[seq_length, self.model_tester.hidden_size]				,)

					__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(A				,A				,A					)

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

									check_hidden_states_output(A				,A				,A					)






	def 							UpperCamelCase_    (			self   :					Optional[Any]					):
					__A       =					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_object_detection(*A					)







	@slow
	def 							UpperCamelCase_    (			self   :					Any					):
					for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__A       =					YolosModel.from_pretrained(A					)
									self.assertIsNotNone(A					)





def 		UpperCAmelCase     (      )						->      Tuple:







				"""simple docstring"""



				__A       =					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
				return image





@require_torch
@require_vision
class 				UpperCAmelCase (    unittest.TestCase    ):




	'''simple docstring'''





	@cached_property
	def 							UpperCamelCase_    (			self   :					Any					):
					return AutoImageProcessor.from_pretrained("hustvl/yolos-small"					) if is_vision_available() else None







	@slow
	def 							UpperCamelCase_    (			self   :					List[Any]					):
					__A       =					YolosForObjectDetection.from_pretrained("hustvl/yolos-small"					).to(A					)

					__A       =					self.default_image_processor
					__A       =					prepare_img()
					__A       =					image_processor(images=A				,return_tensors="pt"					).to(A					)

					# forward pass
					with torch.no_grad():
									__A       =					model(inputs.pixel_values					)

					# verify outputs
					__A       =					torch.Size((1, 1_00, 92)					)
					self.assertEqual(outputs.logits.shape				,A					)

					__A       =					torch.tensor(
					    [[-24.02_48, -10.30_24, -14.82_90], [-42.03_92, -16.82_00, -27.43_34], [-27.27_43, -11.81_54, -18.71_48]]				,device=A				,)
					__A       =					torch.tensor(
					    [[0.25_59, 0.54_55, 0.47_06], [0.29_89, 0.72_79, 0.18_75], [0.77_32, 0.40_17, 0.44_62]]				,device=A					)
					self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]				,A				,atol=1E-4					)					)
					self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3]				,A				,atol=1E-4					)					)

					# verify postprocessing
					__A       =					image_processor.post_process_object_detection(
					    A				,threshold=0.3				,target_sizes=[image.size[::-1]]					)[0]
					__A       =					torch.tensor([0.99_94, 0.97_90, 0.99_64, 0.99_72, 0.98_61]					).to(A					)
					__A       =					[75, 75, 17, 63, 17]
					__A       =					torch.tensor([3_35.06_09, 79.38_48, 3_75.42_16, 1_87.24_95]					).to(A					)

					self.assertEqual(len(results["scores"]					)				,5					)
					self.assertTrue(torch.allclose(results["scores"]				,A				,atol=1E-4					)					)
					self.assertSequenceEqual(results["labels"].tolist()				,A					)
					self.assertTrue(torch.allclose(results["boxes"][0, :]				,A					)					)




'''simple docstring'''

def _SCREAMING_SNAKE_CASE (A = 1_000			)			->			int:






       """simple docstring"""







       return sum(2 * a * ((a - 1) // 2) for a in range(3    ,					n + 1			)			)


if __name__ == "__main__":
 print(solution())




