




"""simple docstring"""





import copy
import inspect

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

@require_torch
class 					__lowercase				:


				'''simple docstring'''


				_lowerCAmelCase			=()
				_lowerCAmelCase			=True

				def 					_lowerCamelCase    (      self      :   Dict					):
										snake_case__				:    Dict								=    self.config_class

										# test default config
										snake_case__				:    Optional[int]								=    config_class()
										self.assertIsNotNone(SCREAMING_SNAKE_CASE_					)
										snake_case__				:    Dict								=    ['''stem'''] + [F'''stage{idx}''' for idx in range(1					,len(config.depths					) + 1					)]
										self.assertEqual(config.stage_names					,SCREAMING_SNAKE_CASE_					)
										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
										snake_case__				:    str								=    config_class(out_features=SCREAMING_SNAKE_CASE_					,out_indices=SCREAMING_SNAKE_CASE_					)
										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
										snake_case__				:    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
										snake_case__				:    str								=    config_class(out_features=['''stage1''', '''stage3''']					)
										self.assertEqual(config.out_features					,['''stage1''', '''stage3''']					)
										self.assertEqual(config.out_indices					,[1, 3]					)

										# Only out_indices set
										snake_case__				:    Any								=    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(SCREAMING_SNAKE_CASE_					):
																snake_case__				:    Tuple								=    config_class(out_features=['''stage1''', '''stage2''']					,out_indices=[0, 2]					)

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

										for model_class in self.all_model_classes:
																snake_case__				:    List[str]								=    model_class(SCREAMING_SNAKE_CASE_					)
																snake_case__				:    str								=    inspect.signature(model.forward					)
																# signature.parameters is an OrderedDict => so arg_names order is deterministic
																snake_case__				:    str								=    [*signature.parameters.keys()]
																snake_case__				:    Any								=    ['''pixel_values''']
																self.assertListEqual(arg_names[:1]					,SCREAMING_SNAKE_CASE_					)

				def 					_lowerCamelCase    (      self      :   str					):
										snake_case__	,		snake_case__				:    Dict								=    self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																snake_case__				:    Any								=    model_class(SCREAMING_SNAKE_CASE_					)
																self.assertEqual(len(model.channels					)					,len(config.out_features					)					)
																snake_case__				:    List[Any]								=    model.num_features
																snake_case__				:    List[Any]								=    [config.stage_names.index(SCREAMING_SNAKE_CASE_					) for feat in config.out_features]
																snake_case__				:    Union[str, Any]								=    [num_features[idx] for idx in out_indices]
																self.assertListEqual(model.channels					,SCREAMING_SNAKE_CASE_					)

																snake_case__				:    Dict								=    copy.deepcopy(SCREAMING_SNAKE_CASE_					)
																snake_case__				:    Optional[int]								=    None
																snake_case__				:    int								=    model_class(SCREAMING_SNAKE_CASE_					)
																self.assertEqual(len(model.channels					)					,1					)
																self.assertListEqual(model.channels					,[num_features[-1]]					)

																snake_case__				:    List[str]								=    copy.deepcopy(SCREAMING_SNAKE_CASE_					)
																snake_case__				:    Dict								=    None
																snake_case__				:    Optional[Any]								=    model_class(SCREAMING_SNAKE_CASE_					)
																self.assertEqual(len(model.channels					)					,1					)
																self.assertListEqual(model.channels					,[num_features[-1]]					)

				def 					_lowerCamelCase    (      self      :   Union[str, Any]					):
										snake_case__	,		snake_case__				:    List[str]								=    self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																snake_case__				:    int								=    model_class(SCREAMING_SNAKE_CASE_					)
																model.to(SCREAMING_SNAKE_CASE_					)
																model.eval()
																snake_case__				:    Union[str, Any]								=    model(**SCREAMING_SNAKE_CASE_					)

																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
																snake_case__				:    Optional[int]								=    copy.deepcopy(SCREAMING_SNAKE_CASE_					)
																snake_case__				:    Tuple								=    None
																snake_case__				:    List[Any]								=    model_class(SCREAMING_SNAKE_CASE_					)
																model.to(SCREAMING_SNAKE_CASE_					)
																model.eval()
																snake_case__				:    List[Any]								=    model(**SCREAMING_SNAKE_CASE_					)

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

																snake_case__				:    Optional[int]								=    copy.deepcopy(SCREAMING_SNAKE_CASE_					)
																snake_case__				:    Optional[Any]								=    None
																snake_case__				:    Union[str, Any]								=    model_class(SCREAMING_SNAKE_CASE_					)
																model.to(SCREAMING_SNAKE_CASE_					)
																model.eval()
																snake_case__				:    Dict								=    model(**SCREAMING_SNAKE_CASE_					)

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

																# Check backbone can be initialized with fresh weights
																snake_case__				:    List[Any]								=    copy.deepcopy(SCREAMING_SNAKE_CASE_					)
																snake_case__				:    Optional[Any]								=    False
																snake_case__				:    List[Any]								=    model_class(SCREAMING_SNAKE_CASE_					)
																model.to(SCREAMING_SNAKE_CASE_					)
																model.eval()
																snake_case__				:    List[str]								=    model(**SCREAMING_SNAKE_CASE_					)

				def 					_lowerCamelCase    (      self      :   int					):
										snake_case__	,		snake_case__				:    Any								=    self.model_tester.prepare_config_and_inputs_for_common()

										for backbone_class in self.all_model_classes:
																snake_case__				:    int								=    backbone_class(SCREAMING_SNAKE_CASE_					)

																self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_					,'''backbone_type'''					)					)
																self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_					,'''stage_names'''					)					)
																self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_					,'''num_features'''					)					)
																self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_					,'''out_indices'''					)					)
																self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_					,'''out_features'''					)					)
																self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_					,'''out_feature_channels'''					)					)
																self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_					,'''channels'''					)					)

																self.assertIsInstance(backbone.backbone_type					,SCREAMING_SNAKE_CASE_					)
																# 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      :   Dict					):
										snake_case__	,		snake_case__				:    List[str]								=    self.model_tester.prepare_config_and_inputs_for_common()
										snake_case__				:    List[str]								=    inputs_dict['''pixel_values'''].shape[0]

										for backbone_class in self.all_model_classes:
																snake_case__				:    int								=    backbone_class(SCREAMING_SNAKE_CASE_					)
																backbone.to(SCREAMING_SNAKE_CASE_					)
																backbone.eval()

																snake_case__				:    List[str]								=    backbone(**SCREAMING_SNAKE_CASE_					)

																# Test default outputs and verify feature maps
																self.assertIsInstance(outputs.feature_maps					,SCREAMING_SNAKE_CASE_					)
																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
																snake_case__				:    Tuple								=    backbone(**SCREAMING_SNAKE_CASE_					,output_hidden_states=SCREAMING_SNAKE_CASE_					)
																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:
																						snake_case__				:    List[Any]								=    backbone(**SCREAMING_SNAKE_CASE_					,output_attentions=SCREAMING_SNAKE_CASE_					)
																						self.assertIsNotNone(outputs.attentions					)



def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase						):
					sequence.append(__lowerCamelCase						)

					__a							=   0
					while sequence[index] != target:
										index += 1

					sequence.pop()

					if index == len(__lowerCamelCase						):
										return None

					return index


if __name__ == "__main__":
	lowerCamelCase_				:		List[str]       						=       input("""Enter numbers separated by comma:\n""").strip()
	lowerCamelCase_				:		str       						=       [int(item) for item in user_input.split(""",""")]

	lowerCamelCase_				:		Tuple       						=       input("""Enter a single number to be found in the list:\n""")
	lowerCamelCase_				:		Optional[int]       						=       int(target_input)
	lowerCamelCase_				:		List[Any]       						=       sentinel_linear_search(sequence, target)
	if result is not None:
		print(F'''{target} found at positions: {result}''')
	else:
		print("""Not found""")




