






import copy
import inspect

from transformers.testing_utils import require_torch, torch_device
from transformers.utils.backbone_utils import BackboneType
@require_torch
class 							A_      :






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:     Tuple	=   ()
 _SCREAMING_SNAKE_CASE							:     Optional[Any]	=   True


 def 	snake_case__			(		self)	->						Optional[int]:

     """simple docstring"""



     _UpperCAmelCase			: Union[str, Any]     					=			self.config_class

     # test default config
     _UpperCAmelCase			: Optional[int]     					=			config_class()
     self.assertIsNotNone(_A)
     _UpperCAmelCase			: str     					=			['''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
     _UpperCAmelCase			: Optional[int]     					=			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
     _UpperCAmelCase			: 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
     _UpperCAmelCase			: Tuple     					=			config_class(out_features=['''stage1''', '''stage3'''])
     self.assertEqual(config.out_features							,					['''stage1''', '''stage3'''])
     self.assertEqual(config.out_indices							,					[1, 3])

     # Only out_indices set
     _UpperCAmelCase			: List[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(_A):
         _UpperCAmelCase			: Optional[Any]     					=			config_class(out_features=['''stage1''', '''stage2''']							,					out_indices=[0, 2])


 def 	snake_case__			(		self)	->						List[str]:

     """simple docstring"""



     _UpperCAmelCase					,	_UpperCAmelCase			: Optional[Any]     					=			self.model_tester.prepare_config_and_inputs_for_common()

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


 def 	snake_case__			(		self)	->						Optional[int]:

     """simple docstring"""



     _UpperCAmelCase					,	_UpperCAmelCase			: Dict     					=			self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         _UpperCAmelCase			: Optional[Any]     					=			model_class(_A)
         self.assertEqual(len(model.channels)							,					len(config.out_features))
         _UpperCAmelCase			: Dict     					=			model.num_features
         _UpperCAmelCase			: int     					=			[config.stage_names.index(_A) for feat in config.out_features]
         _UpperCAmelCase			: Dict     					=			[num_features[idx] for idx in out_indices]
         self.assertListEqual(model.channels							,					_A)

         _UpperCAmelCase			: List[Any]     					=			copy.deepcopy(_A)
         _UpperCAmelCase			: List[Any]     					=			None
         _UpperCAmelCase			: Any     					=			model_class(_A)
         self.assertEqual(len(model.channels)							,					1)
         self.assertListEqual(model.channels							,					[num_features[-1]])

         _UpperCAmelCase			: List[Any]     					=			copy.deepcopy(_A)
         _UpperCAmelCase			: Tuple     					=			None
         _UpperCAmelCase			: int     					=			model_class(_A)
         self.assertEqual(len(model.channels)							,					1)
         self.assertListEqual(model.channels							,					[num_features[-1]])


 def 	snake_case__			(		self)	->						Tuple:

     """simple docstring"""



     _UpperCAmelCase					,	_UpperCAmelCase			: Dict     					=			self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         _UpperCAmelCase			: Union[str, Any]     					=			model_class(_A)
         model.to(_A)
         model.eval()
         _UpperCAmelCase			: Union[str, Any]     					=			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
         _UpperCAmelCase			: Tuple     					=			copy.deepcopy(_A)
         _UpperCAmelCase			: Optional[Any]     					=			None
         _UpperCAmelCase			: Tuple     					=			model_class(_A)
         model.to(_A)
         model.eval()
         _UpperCAmelCase			: Tuple     					=			model(**_A)

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

         _UpperCAmelCase			: Any     					=			copy.deepcopy(_A)
         _UpperCAmelCase			: List[str]     					=			None
         _UpperCAmelCase			: Optional[int]     					=			model_class(_A)
         model.to(_A)
         model.eval()
         _UpperCAmelCase			: Optional[Any]     					=			model(**_A)

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

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


 def 	snake_case__			(		self)	->						Optional[int]:

     """simple docstring"""



     _UpperCAmelCase					,	_UpperCAmelCase			: str     					=			self.model_tester.prepare_config_and_inputs_for_common()

     for backbone_class in self.all_model_classes:
         _UpperCAmelCase			: Optional[int]     					=			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 	snake_case__			(		self)	->						Dict:

     """simple docstring"""



     _UpperCAmelCase					,	_UpperCAmelCase			: Dict     					=			self.model_tester.prepare_config_and_inputs_for_common()
     _UpperCAmelCase			: Dict     					=			inputs_dict['''pixel_values'''].shape[0]

     for backbone_class in self.all_model_classes:
         _UpperCAmelCase			: str     					=			backbone_class(_A)
         backbone.to(_A)
         backbone.eval()

         _UpperCAmelCase			: Optional[Any]     					=			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
         _UpperCAmelCase			: Union[str, Any]     					=			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:
             _UpperCAmelCase			: int     					=			backbone(**_A							,					output_attentions=_A)
             self.assertIsNotNone(outputs.attentions)












def        _lowerCamelCase		(  __A      :							dict						,						__A      :							int	) ->   str:
    if not root or root not in binary_tree_mirror_dictionary:
        return
    _UpperCAmelCase					,	_UpperCAmelCase			: int     					=			binary_tree_mirror_dictionary[root][:2]
    _UpperCAmelCase			: int     					=			[right_child, left_child]
    binary_tree_mirror_dict(__A						,						__A	)
    binary_tree_mirror_dict(__A						,						__A	)

def        _lowerCamelCase		(  __A      :							dict						,						__A      :							int = 1	) ->   dict:
    if not binary_tree:
        raise ValueError('''binary tree cannot be empty'''	)
    if root not in binary_tree:
        _UpperCAmelCase			: Dict     					=			f'''root {root} is not present in the binary_tree'''
        raise ValueError(__A	)
    _UpperCAmelCase			: int     					=			dict(__A	)
    binary_tree_mirror_dict(__A						,						__A	)
    return binary_tree_mirror_dictionary


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE								=  {1: [2, 3], 2: [4, 5], 3: [6, 7], 7: [8, 9]}
  print(F'Binary tree: {binary_tree}')
  SCREAMING_SNAKE_CASE								=  binary_tree_mirror(binary_tree, 5)
  print(F'Binary tree mirror: {binary_tree_mirror_dictionary}')




