code: 
  
  
  import copy
  import inspect
  
  from transformers.testing_utils import require_torch, torch_device
  from transformers.utils.backbone_utils import BackboneType
  
  
  
  @require_torch
  class 			a      :
  		__lowercase													=		()
  		__lowercase													=		True
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			Any:
  						'''simple docstring'''
  
  
  						A__     :							List[str]   				=self.config_class
  
  						# test default config
  						A__     :							Tuple   				=config_class()
  						self.assertIsNotNone(__UpperCamelCase							)
  						A__     :							Any   				=['''stem'''] + [F'stage{idx}' for idx in range(1	,     len(config.depths							) + 1							)]
  						self.assertEqual(config.stage_names	,     __UpperCamelCase							)
  						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__     :							List[str]   				=config_class(out_features=__UpperCamelCase	,     out_indices=__UpperCamelCase							)
  						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__     :							List[Any]   				=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__     :							int   				=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__     :							str   				=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(__UpperCamelCase							):
  										A__     :							List[Any]   				=config_class(out_features=['''stage1''', '''stage2''']	,     out_indices=[0, 2]							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			int:
  						'''simple docstring'''
  
  
  						A__				,  A__     :							int   				=self.model_tester.prepare_config_and_inputs_for_common()
  
  						for model_class in self.all_model_classes:
  										A__     :							Dict   				=model_class(__UpperCamelCase							)
  										A__     :							Dict   				=inspect.signature(model.forward							)
  										# signature.parameters is an OrderedDict => so arg_names order is deterministic
  										A__     :							Tuple   				=[*signature.parameters.keys()]
  										A__     :							Optional[Any]   				=['''pixel_values''']
  										self.assertListEqual(arg_names[:1]	,     __UpperCamelCase							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			Tuple:
  						'''simple docstring'''
  
  
  						A__				,  A__     :							List[Any]   				=self.model_tester.prepare_config_and_inputs_for_common()
  
  						for model_class in self.all_model_classes:
  										A__     :							Dict   				=model_class(__UpperCamelCase							)
  										self.assertEqual(len(model.channels							)	,     len(config.out_features							)							)
  										A__     :							int   				=model.num_features
  										A__     :							List[str]   				=[config.stage_names.index(__UpperCamelCase							) for feat in config.out_features]
  										A__     :							List[Any]   				=[num_features[idx] for idx in out_indices]
  										self.assertListEqual(model.channels	,     __UpperCamelCase							)
  
  										A__     :							List[str]   				=copy.deepcopy(__UpperCamelCase							)
  										A__     :							Union[str, Any]   				=None
  										A__     :							Tuple   				=model_class(__UpperCamelCase							)
  										self.assertEqual(len(model.channels							)	,     1							)
  										self.assertListEqual(model.channels	,     [num_features[-1]]							)
  
  										A__     :							List[str]   				=copy.deepcopy(__UpperCamelCase							)
  										A__     :							Optional[Any]   				=None
  										A__     :							Union[str, Any]   				=model_class(__UpperCamelCase							)
  										self.assertEqual(len(model.channels							)	,     1							)
  										self.assertListEqual(model.channels	,     [num_features[-1]]							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			str:
  						'''simple docstring'''
  
  
  						A__				,  A__     :							Optional[int]   				=self.model_tester.prepare_config_and_inputs_for_common()
  
  						for model_class in self.all_model_classes:
  										A__     :							str   				=model_class(__UpperCamelCase							)
  										model.to(__UpperCamelCase							)
  										model.eval()
  										A__     :							Tuple   				=model(**__UpperCamelCase							)
  
  										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__     :							List[str]   				=copy.deepcopy(__UpperCamelCase							)
  										A__     :							int   				=None
  										A__     :							Dict   				=model_class(__UpperCamelCase							)
  										model.to(__UpperCamelCase							)
  										model.eval()
  										A__     :							Union[str, Any]   				=model(**__UpperCamelCase							)
  
  										self.assertEqual(len(result.feature_maps							)	,     1							)
  										self.assertEqual(len(model.channels							)	,     1							)
  
  										A__     :							Dict   				=copy.deepcopy(__UpperCamelCase							)
  										A__     :							Dict   				=None
  										A__     :							str   				=model_class(__UpperCamelCase							)
  										model.to(__UpperCamelCase							)
  										model.eval()
  										A__     :							Union[str, Any]   				=model(**__UpperCamelCase							)
  
  										self.assertEqual(len(result.feature_maps							)	,     1							)
  										self.assertEqual(len(model.channels							)	,     1							)
  
  										# Check backbone can be initialized with fresh weights
  										A__     :							str   				=copy.deepcopy(__UpperCamelCase							)
  										A__     :							Union[str, Any]   				=False
  										A__     :							int   				=model_class(__UpperCamelCase							)
  										model.to(__UpperCamelCase							)
  										model.eval()
  										A__     :							Optional[Any]   				=model(**__UpperCamelCase							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			List[Any]:
  						'''simple docstring'''
  
  
  						A__				,  A__     :							Dict   				=self.model_tester.prepare_config_and_inputs_for_common()
  
  						for backbone_class in self.all_model_classes:
  										A__     :							List[Any]   				=backbone_class(__UpperCamelCase							)
  
  										self.assertTrue(hasattr(__UpperCamelCase	,     '''backbone_type'''							)							)
  										self.assertTrue(hasattr(__UpperCamelCase	,     '''stage_names'''							)							)
  										self.assertTrue(hasattr(__UpperCamelCase	,     '''num_features'''							)							)
  										self.assertTrue(hasattr(__UpperCamelCase	,     '''out_indices'''							)							)
  										self.assertTrue(hasattr(__UpperCamelCase	,     '''out_features'''							)							)
  										self.assertTrue(hasattr(__UpperCamelCase	,     '''out_feature_channels'''							)							)
  										self.assertTrue(hasattr(__UpperCamelCase	,     '''channels'''							)							)
  
  										self.assertIsInstance(backbone.backbone_type	,     __UpperCamelCase							)
  										# 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       lowerCAmelCase_  (  self							)->			str:
  						'''simple docstring'''
  
  
  						A__				,  A__     :							str   				=self.model_tester.prepare_config_and_inputs_for_common()
  						A__     :							Any   				=inputs_dict['''pixel_values'''].shape[0]
  
  						for backbone_class in self.all_model_classes:
  										A__     :							Optional[int]   				=backbone_class(__UpperCamelCase							)
  										backbone.to(__UpperCamelCase							)
  										backbone.eval()
  
  										A__     :							List[Any]   				=backbone(**__UpperCamelCase							)
  
  										# Test default outputs and verify feature maps
  										self.assertIsInstance(outputs.feature_maps	,     __UpperCamelCase							)
  										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__     :							List[Any]   				=backbone(**__UpperCamelCase	,     output_hidden_states=__UpperCamelCase							)
  										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__     :							int   				=backbone(**__UpperCamelCase	,     output_attentions=__UpperCamelCase							)
  														self.assertIsNotNone(outputs.attentions							)
  
  
  
  
style_context: 
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							Dict:
  				A__     :							Dict   				=len(snake_case_	)
  				for i in range(length - 1	):
  								A__     :							List[Any]   				=False
  								for j in range(length - 1 - i	):
  												if collection[j] > collection[j + 1]:
  																A__     :							Dict   				=True
  																A__				,  A__     :							Union[str, Any]   				=collection[j + 1], collection[j]
  								if not swapped:
  												break  # Stop iteration if the collection is sorted.
  				return collection
  
  
  if __name__ == "__main__":
  						import doctest
  						import time
  
  						doctest.testmod()
  
  						__lowerCamelCase       :       Union[str, Any]				   =			input("Enter numbers separated by a comma:").strip()
  						__lowerCamelCase       :       Optional[int]				   =			[int(item) for item in user_input.split(",")]
  						__lowerCamelCase       :       Optional[int]				   =			time.process_time()
  						print(*bubble_sort(unsorted), sep=",")
  						print(F"Processing time: {(time.process_time() - start)%1E9 + 7}")
  
  
  
  