





"""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"""






							SCREAMING_SNAKE_CASE_			   =     ()
							SCREAMING_SNAKE_CASE_			   =     True





							def    UpperCAmelCase__							(						self						:int ):





										"""simple docstring"""

										lowerCamelCase__			:  Optional[int]  =self.config_class

										# test default config
										lowerCamelCase__			:  Dict  =config_class()
										self.assertIsNotNone(lowerCamelCase_ )
										lowerCamelCase__			:  List[str]  =['stem'] + [f"""stage{idx}""" for idx in range(1  ,							len(config.depths ) + 1 )]
										self.assertEqual(config.stage_names  ,							lowerCamelCase_ )
										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
										lowerCamelCase__			:  Dict  =config_class(out_features=lowerCamelCase_  ,							out_indices=lowerCamelCase_ )
										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
										lowerCamelCase__			:  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
										lowerCamelCase__			:  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
										lowerCamelCase__			:  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(lowerCamelCase_ ):
													lowerCamelCase__			:  Tuple  =config_class(out_features=['stage1', 'stage2']  ,							out_indices=[0, 2] )





							def    UpperCAmelCase__							(						self						:Any ):





										"""simple docstring"""

										lowerCamelCase__			:  Dict  =self.model_tester.prepare_config_and_inputs_for_common()

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





							def    UpperCAmelCase__							(						self						:Optional[Any] ):





										"""simple docstring"""

										lowerCamelCase__			:  Tuple  =self.model_tester.prepare_config_and_inputs_for_common()

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

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

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





							def    UpperCAmelCase__							(						self						:List[str] ):





										"""simple docstring"""

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

										for model_class in self.all_model_classes:
													lowerCamelCase__			:  int  =model_class(lowerCamelCase_ )
													model.to(lowerCamelCase_ )
													model.eval()
													lowerCamelCase__			:  int  =model(**lowerCamelCase_ )

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

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

													lowerCamelCase__			:  Any  =copy.deepcopy(lowerCamelCase_ )
													lowerCamelCase__			:  Dict  =None
													lowerCamelCase__			:  int  =model_class(lowerCamelCase_ )
													model.to(lowerCamelCase_ )
													model.eval()
													lowerCamelCase__			:  int  =model(**lowerCamelCase_ )

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

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





							def    UpperCAmelCase__							(						self						:Optional[int] ):





										"""simple docstring"""

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

										for backbone_class in self.all_model_classes:
													lowerCamelCase__			:  int  =backbone_class(lowerCamelCase_ )

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

													self.assertIsInstance(backbone.backbone_type  ,							lowerCamelCase_ )
													# 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"""

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

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

													lowerCamelCase__			:  Union[str, Any]  =backbone(**lowerCamelCase_ )

													# Test default outputs and verify feature maps
													self.assertIsInstance(outputs.feature_maps  ,							lowerCamelCase_ )
													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
													lowerCamelCase__			:  List[str]  =backbone(**lowerCamelCase_  ,							output_hidden_states=lowerCamelCase_ )
													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:
																lowerCamelCase__			:  Union[str, Any]  =backbone(**lowerCamelCase_  ,							output_attentions=lowerCamelCase_ )
																self.assertIsNotNone(outputs.attentions )

"""simple docstring"""


import cva
import numpy as np






def      lowerCAmelCase_     (	snake_case_	:    np.ndarray							,					snake_case_	:    int							,					snake_case_	:    int							,					snake_case_	:    int    )     ->int:

			try:
						return int(image[x_coordinate][y_coordinate] >= center    )
			except (IndexError, TypeError):
						return 0






def      lowerCAmelCase_     (	snake_case_	:    np.ndarray							,					snake_case_	:    int							,					snake_case_	:    int    )     ->int:
			lowerCamelCase__			:  Optional[int]  =image[x_coordinate][y_coordinate]
			lowerCamelCase__			:  int  =[1, 2, 4, 8, 1_6, 3_2, 6_4, 1_2_8]

			# skip get_neighbors_pixel if center is null
			if center is None:
						return 0

			# Starting from the top right, assigning value to pixels clockwise
			lowerCamelCase__			:  int  =[
			    get_neighbors_pixel(snake_case_							,					x_coordinate - 1							,					y_coordinate + 1							,					snake_case_    ),
			    get_neighbors_pixel(snake_case_							,					snake_case_							,					y_coordinate + 1							,					snake_case_    ),
			    get_neighbors_pixel(snake_case_							,					x_coordinate - 1							,					snake_case_							,					snake_case_    ),
			    get_neighbors_pixel(snake_case_							,					x_coordinate + 1							,					y_coordinate + 1							,					snake_case_    ),
			    get_neighbors_pixel(snake_case_							,					x_coordinate + 1							,					snake_case_							,					snake_case_    ),
			    get_neighbors_pixel(snake_case_							,					x_coordinate + 1							,					y_coordinate - 1							,					snake_case_    ),
			    get_neighbors_pixel(snake_case_							,					snake_case_							,					y_coordinate - 1							,					snake_case_    ),
			    get_neighbors_pixel(snake_case_							,					x_coordinate - 1							,					y_coordinate - 1							,					snake_case_    ),
			]

			# Converting the binary value to decimal.
			return sum(
			    binary_value * power for binary_value, power in zip(snake_case_							,					snake_case_    )    )


if __name__ == "__main__":
							# Reading the image and converting it to grayscale.
							lowerCAmelCase			=		cva.imread(
							    """digital_image_processing/image_data/lena.jpg""", cva.IMREAD_GRAYSCALE
							)

							# Create a numpy array as the same height and width of read image
							lowerCAmelCase			=		np.zeros((image.shape[0], image.shape[1]))

							# Iterating through the image and calculating the
							# local binary pattern value for each pixel.
							for i in range(0, image.shape[0]):
														for j in range(0, image.shape[1]):
																					lowerCAmelCase			=		local_binary_value(image, i, j)

							cva.imshow("""local binary pattern""", lbp_image)
							cva.waitKey(0)
							cva.destroyAllWindows()