
import copy
import inspect

from transformers.testing_utils import require_torch, torch_device
from transformers.utils.backbone_utils import BackboneType





@require_torch
class 							UpperCAmelCase   :




	'''simple docstring'''





	snake_case_      					=  ()
	snake_case_      					=  True






	def        UpperCamelCase_						( self      :				Optional[Any]				):
					__A         =				self.config_class

					# test default config
					__A         =				config_class()
					self.assertIsNotNone(A				)
					__A         =				["stem"] + [f'''stage{idx}''' for idx in range(1							,len(config.depths				) + 1				)]
					self.assertEqual(config.stage_names							,A				)
					self.assertTrue(set(config.out_features				).issubset(set(config.stage_names				)				)				)

					# Test out_features and out_indices are correctly set
					# out_features and out_indices both None
					__A         =				config_class(out_features=A							,out_indices=A				)
					self.assertEqual(config.out_features							,[config.stage_names[-1]]				)
					self.assertEqual(config.out_indices							,[len(config.stage_names				) - 1]				)

					# out_features and out_indices both set
					__A         =				config_class(out_features=["stem", "stage1"]							,out_indices=[0, 1]				)
					self.assertEqual(config.out_features							,["stem", "stage1"]				)
					self.assertEqual(config.out_indices							,[0, 1]				)

					# Only out_features set
					__A         =				config_class(out_features=["stage1", "stage3"]				)
					self.assertEqual(config.out_features							,["stage1", "stage3"]				)
					self.assertEqual(config.out_indices							,[1, 3]				)

					# Only out_indices set
					__A         =				config_class(out_indices=[0, 2]				)
					self.assertEqual(config.out_features							,[config.stage_names[0], config.stage_names[2]]				)
					self.assertEqual(config.out_indices							,[0, 2]				)

					# Error raised when out_indices do not correspond to out_features
					with self.assertRaises(A				):
									__A         =				config_class(out_features=["stage1", "stage2"]							,out_indices=[0, 2]				)






	def        UpperCamelCase_						( self      :				List[str]				):
					__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      :				int				):
					__A    ,							__A         =				self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									__A         =				model_class(A				)
									self.assertEqual(len(model.channels				)							,len(config.out_features				)				)
									__A         =				model.num_features
									__A         =				[config.stage_names.index(A				) for feat in config.out_features]
									__A         =				[num_features[idx] for idx in out_indices]
									self.assertListEqual(model.channels							,A				)

									__A         =				copy.deepcopy(A				)
									__A         =				None
									__A         =				model_class(A				)
									self.assertEqual(len(model.channels				)							,1				)
									self.assertListEqual(model.channels							,[num_features[-1]]				)

									__A         =				copy.deepcopy(A				)
									__A         =				None
									__A         =				model_class(A				)
									self.assertEqual(len(model.channels				)							,1				)
									self.assertListEqual(model.channels							,[num_features[-1]]				)






	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				)
									model.to(A				)
									model.eval()
									__A         =				model(**A				)

									self.assertEqual(len(result.feature_maps				)							,len(config.out_features				)				)
									self.assertEqual(len(model.channels				)							,len(config.out_features				)				)
									self.assertEqual(len(result.feature_maps				)							,len(config.out_indices				)				)
									self.assertEqual(len(model.channels				)							,len(config.out_indices				)				)

									# Check output of last stage is taken if out_features=None, out_indices=None
									__A         =				copy.deepcopy(A				)
									__A         =				None
									__A         =				model_class(A				)
									model.to(A				)
									model.eval()
									__A         =				model(**A				)

									self.assertEqual(len(result.feature_maps				)							,1				)
									self.assertEqual(len(model.channels				)							,1				)

									__A         =				copy.deepcopy(A				)
									__A         =				None
									__A         =				model_class(A				)
									model.to(A				)
									model.eval()
									__A         =				model(**A				)

									self.assertEqual(len(result.feature_maps				)							,1				)
									self.assertEqual(len(model.channels				)							,1				)

									# Check backbone can be initialized with fresh weights
									__A         =				copy.deepcopy(A				)
									__A         =				False
									__A         =				model_class(A				)
									model.to(A				)
									model.eval()
									__A         =				model(**A				)






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

					for backbone_class in self.all_model_classes:
									__A         =				backbone_class(A				)

									self.assertTrue(hasattr(A							,"backbone_type"				)				)
									self.assertTrue(hasattr(A							,"stage_names"				)				)
									self.assertTrue(hasattr(A							,"num_features"				)				)
									self.assertTrue(hasattr(A							,"out_indices"				)				)
									self.assertTrue(hasattr(A							,"out_features"				)				)
									self.assertTrue(hasattr(A							,"out_feature_channels"				)				)
									self.assertTrue(hasattr(A							,"channels"				)				)

									self.assertIsInstance(backbone.backbone_type							,A				)
									# Verify num_features has been initialized in the backbone init
									self.assertIsNotNone(backbone.num_features				)
									self.assertTrue(len(backbone.channels				) == len(backbone.out_indices				)				)
									self.assertTrue(len(backbone.stage_names				) == len(backbone.num_features				)				)
									self.assertTrue(len(backbone.channels				) <= len(backbone.num_features				)				)
									self.assertTrue(len(backbone.out_feature_channels				) == len(backbone.stage_names				)				)







	def        UpperCamelCase_						( self      :				int				):
					__A    ,							__A         =				self.model_tester.prepare_config_and_inputs_for_common()
					__A         =				inputs_dict["pixel_values"].shape[0]

					for backbone_class in self.all_model_classes:
									__A         =				backbone_class(A				)
									backbone.to(A				)
									backbone.eval()

									__A         =				backbone(**A				)

									# Test default outputs and verify feature maps
									self.assertIsInstance(outputs.feature_maps							,A				)
									self.assertTrue(len(outputs.feature_maps				) == len(backbone.channels				)				)
									for feature_map, n_channels in zip(outputs.feature_maps							,backbone.channels				):
													self.assertTrue(feature_map.shape[:2]							,(batch_size, n_channels)				)
									self.assertIsNone(outputs.hidden_states				)
									self.assertIsNone(outputs.attentions				)

									# Test output_hidden_states=True
									__A         =				backbone(**A							,output_hidden_states=A				)
									self.assertIsNotNone(outputs.hidden_states				)
									self.assertTrue(len(outputs.hidden_states				)							,len(backbone.stage_names				)				)
									for hidden_state, n_channels in zip(outputs.hidden_states							,backbone.channels				):
													self.assertTrue(hidden_state.shape[:2]							,(batch_size, n_channels)				)

									# Test output_attentions=True
									if self.has_attentions:
													__A         =				backbone(**A							,output_attentions=A				)
													self.assertIsNotNone(outputs.attentions				)


from typing import Any





class 							UpperCAmelCase   :




	'''simple docstring'''





	def __init__( self      :				Union[str, Any]				):
					__A         =				[]
					__A         =				0






	def __str__( self      :				Tuple				):
					__A         =				"<" + str(self.stack				)[1:-1] + ">"
					return printed






	def        UpperCamelCase_						( self      :				Optional[int]							,A      :				Any				):
					self.stack.append(A				)
					__A         =				self.length + 1






	def        UpperCamelCase_						( self      :				Any				):
					self.rotate(1				)
					__A         =				self.stack[self.length - 1]
					__A         =				self.stack[:-1]
					self.rotate(self.length - 1				)
					__A         =				self.length - 1
					return dequeued






	def        UpperCamelCase_						( self      :				Tuple							,A      :				int				):
					for _ in range(A				):
									__A         =				self.stack[0]
									__A         =				self.stack[1:]
									self.put(A				)
									__A         =				self.length - 1






	def        UpperCamelCase_						( self      :				Optional[Any]				):
					__A         =				self.get()
					self.put(A				)
					self.rotate(self.length - 1				)
					return front






	def        UpperCamelCase_						( self      :				Optional[Any]				):
					return self.length
