


"""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     A:

  """simple docstring"""





  A         =       ()
  A         =       True
  def      _UpperCamelCase(   self      )			->  int:

     """simple docstring"""



     _UpperCamelCase				:Optional[int]				    =  self.config_class

     # test default config
     _UpperCamelCase				:Dict				    =  config_class()
     self.assertIsNotNone(SCREAMING_SNAKE_CASE__      )
     _UpperCamelCase				:List[str]				    =  ['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
     _UpperCamelCase				:Dict				    =  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
     _UpperCamelCase				:str				    =  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				:Dict				    =  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				:Dict				    =  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__      ):
        _UpperCamelCase				:Tuple				    =  config_class(out_features=['''stage1''', '''stage2''']				,							out_indices=[0, 2]      )
  def      _UpperCamelCase(   self      )			->  Tuple:

     """simple docstring"""



     _UpperCamelCase				:Dict				    =  self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        _UpperCamelCase				:Tuple				    =  model_class(SCREAMING_SNAKE_CASE__      )
        _UpperCamelCase				:str				    =  inspect.signature(model.forward      )
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        _UpperCamelCase				:Dict				    =  [*signature.parameters.keys()]
        _UpperCamelCase				:Union[str, Any]				    =  ['pixel_values']
        self.assertListEqual(arg_names[:1]				,							SCREAMING_SNAKE_CASE__      )
  def      _UpperCamelCase(   self      )			->  Optional[int]:

     """simple docstring"""



     _UpperCamelCase				:Tuple				    =  self.model_tester.prepare_config_and_inputs_for_common()

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

        _UpperCamelCase				:Any				    =  copy.deepcopy(SCREAMING_SNAKE_CASE__      )
        _UpperCamelCase				:Optional[Any]				    =  None
        _UpperCamelCase				:Optional[int]				    =  model_class(SCREAMING_SNAKE_CASE__      )
        self.assertEqual(len(model.channels      )				,							1      )
        self.assertListEqual(model.channels				,							[num_features[-1]]      )

        _UpperCamelCase				:Dict				    =  copy.deepcopy(SCREAMING_SNAKE_CASE__      )
        _UpperCamelCase				:Optional[int]				    =  None
        _UpperCamelCase				:int				    =  model_class(SCREAMING_SNAKE_CASE__      )
        self.assertEqual(len(model.channels      )				,							1      )
        self.assertListEqual(model.channels				,							[num_features[-1]]      )
  def      _UpperCamelCase(   self      )			->  Optional[Any]:

     """simple docstring"""



     _UpperCamelCase				:List[str]				    =  self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        _UpperCamelCase				:int				    =  model_class(SCREAMING_SNAKE_CASE__      )
        model.to(SCREAMING_SNAKE_CASE__      )
        model.eval()
        _UpperCamelCase				:int				    =  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
        _UpperCamelCase				:List[str]				    =  copy.deepcopy(SCREAMING_SNAKE_CASE__      )
        _UpperCamelCase				:Any				    =  None
        _UpperCamelCase				:Optional[int]				    =  model_class(SCREAMING_SNAKE_CASE__      )
        model.to(SCREAMING_SNAKE_CASE__      )
        model.eval()
        _UpperCamelCase				:Any				    =  model(**SCREAMING_SNAKE_CASE__      )

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

        _UpperCamelCase				:Any				    =  copy.deepcopy(SCREAMING_SNAKE_CASE__      )
        _UpperCamelCase				:Dict				    =  None
        _UpperCamelCase				:int				    =  model_class(SCREAMING_SNAKE_CASE__      )
        model.to(SCREAMING_SNAKE_CASE__      )
        model.eval()
        _UpperCamelCase				:int				    =  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
        _UpperCamelCase				:Union[str, Any]				    =  copy.deepcopy(SCREAMING_SNAKE_CASE__      )
        _UpperCamelCase				:Tuple				    =  False
        _UpperCamelCase				:Any				    =  model_class(SCREAMING_SNAKE_CASE__      )
        model.to(SCREAMING_SNAKE_CASE__      )
        model.eval()
        _UpperCamelCase				:Any				    =  model(**SCREAMING_SNAKE_CASE__      )
  def      _UpperCamelCase(   self      )			->  List[Any]:

     """simple docstring"""



     _UpperCamelCase				:List[Any]				    =  self.model_tester.prepare_config_and_inputs_for_common()

     for backbone_class in self.all_model_classes:
        _UpperCamelCase				: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      _UpperCamelCase(   self      )			->  Dict:

     """simple docstring"""



     _UpperCamelCase				:Optional[int]				    =  self.model_tester.prepare_config_and_inputs_for_common()
     _UpperCamelCase				:Any				    =  inputs_dict['pixel_values'].shape[0]

     for backbone_class in self.all_model_classes:
        _UpperCamelCase				:List[Any]				    =  backbone_class(SCREAMING_SNAKE_CASE__      )
        backbone.to(SCREAMING_SNAKE_CASE__      )
        backbone.eval()

        _UpperCamelCase				:Union[str, Any]				    =  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
        _UpperCamelCase				:List[str]				    =  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:
           _UpperCamelCase				:Union[str, Any]				    =  backbone(**SCREAMING_SNAKE_CASE__				,							output_attentions=SCREAMING_SNAKE_CASE__      )
           self.assertIsNotNone(outputs.attentions      )





"""simple docstring"""






import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey

from digital_image_processing.filters.convolve import img_convolve





def 							A_					(   snake_case__		)				->     str:
			_UpperCamelCase				:List[Any]				    =  np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]		)
			_UpperCamelCase				:Dict				    =  np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]		)

			_UpperCamelCase				:List[str]				    =  np.abs(img_convolve(snake_case__					,				snake_case__		)		)
			_UpperCamelCase				:Dict				    =  np.abs(img_convolve(snake_case__					,				snake_case__		)		)
			# modify the pix within [0, 255]
			_UpperCamelCase				:List[Any]				    =  dst_x * 2_55 / np.max(snake_case__		)
			_UpperCamelCase				:Tuple				    =  dst_y * 2_55 / np.max(snake_case__		)

			_UpperCamelCase				:Tuple				    =  np.sqrt((np.square(snake_case__		)) + (np.square(snake_case__		))		)
			_UpperCamelCase				:Any				    =  dst_xy * 2_55 / np.max(snake_case__		)
			_UpperCamelCase				:Optional[int]				    =  dst_xy.astype(np.uinta		)

			_UpperCamelCase				:str				    =  np.arctana(snake_case__					,				snake_case__		)
			return dst, theta


if __name__ == "__main__":
					# read original image
					UpperCamelCase__							:List[str]       =  imread("""../image_data/lena.jpg""")
					# turn image in gray scale value
					UpperCamelCase__							:Union[str, Any]       =  cvtColor(img, COLOR_BGR2GRAY)

					UpperCamelCase__      , UpperCamelCase__							:Optional[int]       =  sobel_filter(gray)

					# show result images
					imshow("""sobel filter""", sobel_grad)
					imshow("""sobel theta""", sobel_theta)
					waitKey(0)

