



"""simple docstring"""
import collections
import inspect
import unittest

from transformers import DinatConfig
from transformers.testing_utils import require_natten, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch
			from torch import nn

			from transformers import DinatBackbone, DinatForImageClassification, DinatModel
			from transformers.models.dinat.modeling_dinat import DINAT_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
			from PIL import Image

			from transformers import AutoImageProcessor


class 							lowerCamelCase_:





					'''simple docstring'''



					def __init__(			self ,				lowerCamelCase__ ,				lowerCamelCase__=1_3 ,				lowerCamelCase__=6_4 ,				lowerCamelCase__=4 ,				lowerCamelCase__=3 ,				lowerCamelCase__=1_6 ,				lowerCamelCase__=[1, 2, 1] ,				lowerCamelCase__=[2, 4, 8] ,				lowerCamelCase__=3 ,				lowerCamelCase__=[[3], [1, 2], [1]] ,				lowerCamelCase__=2.0 ,				lowerCamelCase__=True ,				lowerCamelCase__=0.0 ,				lowerCamelCase__=0.0 ,				lowerCamelCase__=0.1 ,				lowerCamelCase__="gelu" ,				lowerCamelCase__=True ,				lowerCamelCase__=0.0_2 ,				lowerCamelCase__=1e-5 ,				lowerCamelCase__=True ,				lowerCamelCase__=None ,				lowerCamelCase__=True ,				lowerCamelCase__=1_0 ,				lowerCamelCase__=["stage1", "stage2"] ,				lowerCamelCase__=[1, 2] ,				):
									_lowerCamelCase					     = parent
									_lowerCamelCase					     = batch_size
									_lowerCamelCase					     = image_size
									_lowerCamelCase					     = patch_size
									_lowerCamelCase					     = num_channels
									_lowerCamelCase					     = embed_dim
									_lowerCamelCase					     = depths
									_lowerCamelCase					     = num_heads
									_lowerCamelCase					     = kernel_size
									_lowerCamelCase					     = dilations
									_lowerCamelCase					     = mlp_ratio
									_lowerCamelCase					     = qkv_bias
									_lowerCamelCase					     = hidden_dropout_prob
									_lowerCamelCase					     = attention_probs_dropout_prob
									_lowerCamelCase					     = drop_path_rate
									_lowerCamelCase					     = hidden_act
									_lowerCamelCase					     = patch_norm
									_lowerCamelCase					     = layer_norm_eps
									_lowerCamelCase					     = initializer_range
									_lowerCamelCase					     = is_training
									_lowerCamelCase					     = scope
									_lowerCamelCase					     = use_labels
									_lowerCamelCase					     = num_labels
									_lowerCamelCase					     = out_features
									_lowerCamelCase					     = out_indices

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

									_lowerCamelCase					     = None
									if self.use_labels:
													_lowerCamelCase					     = ids_tensor([self.batch_size] ,				self.num_labels      )

									_lowerCamelCase					     = self.get_config()

									return config, pixel_values, labels

					def 	snake_case__					(			self      ):
									return DinatConfig(
									    num_labels=self.num_labels ,				image_size=self.image_size ,				patch_size=self.patch_size ,				num_channels=self.num_channels ,				embed_dim=self.embed_dim ,				depths=self.depths ,				num_heads=self.num_heads ,				kernel_size=self.kernel_size ,				dilations=self.dilations ,				mlp_ratio=self.mlp_ratio ,				qkv_bias=self.qkv_bias ,				hidden_dropout_prob=self.hidden_dropout_prob ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob ,				drop_path_rate=self.drop_path_rate ,				hidden_act=self.hidden_act ,				patch_norm=self.patch_norm ,				layer_norm_eps=self.layer_norm_eps ,				initializer_range=self.initializer_range ,				out_features=self.out_features ,				out_indices=self.out_indices ,				)

					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									_lowerCamelCase					     = DinatModel(config=lowerCamelCase__      )
									model.to(lowerCamelCase__      )
									model.eval()
									_lowerCamelCase					     = model(lowerCamelCase__      )

									_lowerCamelCase					     = _lowerCamelCase					     = (config.image_size // config.patch_size) // (2 ** (len(config.depths      ) - 1))
									_lowerCamelCase					     = int(config.embed_dim * 2 ** (len(config.depths      ) - 1)      )

									self.parent.assertEqual(
									    result.last_hidden_state.shape ,				(self.batch_size, expected_height, expected_width, expected_dim)      )

					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									_lowerCamelCase					     = DinatForImageClassification(lowerCamelCase__      )
									model.to(lowerCamelCase__      )
									model.eval()
									_lowerCamelCase					     = model(lowerCamelCase__ ,				labels=lowerCamelCase__      )
									self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_labels)      )

									# test greyscale images
									_lowerCamelCase					     = 1
									_lowerCamelCase					     = DinatForImageClassification(lowerCamelCase__      )
									model.to(lowerCamelCase__      )
									model.eval()

									_lowerCamelCase					     = floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
									_lowerCamelCase					     = model(lowerCamelCase__      )
									self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_labels)      )

					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									_lowerCamelCase					     = DinatBackbone(config=lowerCamelCase__      )
									model.to(lowerCamelCase__      )
									model.eval()
									_lowerCamelCase					     = model(lowerCamelCase__      )

									# verify hidden states
									self.parent.assertEqual(len(result.feature_maps      ) ,				len(config.out_features      )      )
									self.parent.assertListEqual(list(result.feature_maps[0].shape      ) ,				[self.batch_size, model.channels[0], 1_6, 1_6]      )

									# verify channels
									self.parent.assertEqual(len(model.channels      ) ,				len(config.out_features      )      )

									# verify backbone works with out_features=None
									_lowerCamelCase					     = None
									_lowerCamelCase					     = DinatBackbone(config=lowerCamelCase__      )
									model.to(lowerCamelCase__      )
									model.eval()
									_lowerCamelCase					     = model(lowerCamelCase__      )

									# verify feature maps
									self.parent.assertEqual(len(result.feature_maps      ) ,				1      )
									self.parent.assertListEqual(list(result.feature_maps[0].shape      ) ,				[self.batch_size, model.channels[-1], 4, 4]      )

									# verify channels
									self.parent.assertEqual(len(model.channels      ) ,				1      )





					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = self.prepare_config_and_inputs()
									_lowerCamelCase     ,   _lowerCamelCase     ,   _lowerCamelCase					     = config_and_inputs
									_lowerCamelCase					     = {'''pixel_values''': pixel_values}
									return config, inputs_dict


@require_natten
@require_torch
class 							lowerCamelCase_(      A__, A__, unittest.TestCase			):





					'''simple docstring'''



					lowercase__      :				List[Any]        =       (
					    (
					        DinatModel,
					        DinatForImageClassification,
					        DinatBackbone,
					    )
					    if is_torch_available()
					    else ()
					)
					lowercase__      :				List[Any]        =       (
					    {'feature-extraction': DinatModel, 'image-classification': DinatForImageClassification}
					    if is_torch_available()
					    else {}
					)
					lowercase__      :				Dict        =       False

					lowercase__      :				Optional[int]        =       False
					lowercase__      :				Tuple        =       False
					lowercase__      :				Optional[int]        =       False
					lowercase__      :				Optional[Any]        =       False

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = DinatModelTester(self      )
									_lowerCamelCase					     = ConfigTester(self ,				config_class=lowerCamelCase__ ,				embed_dim=3_7      )

					def 	snake_case__					(			self      ):
									self.create_and_test_config_common_properties()
									self.config_tester.create_and_test_config_to_json_string()
									self.config_tester.create_and_test_config_to_json_file()
									self.config_tester.create_and_test_config_from_and_save_pretrained()
									self.config_tester.create_and_test_config_with_num_labels()
									self.config_tester.check_config_can_be_init_without_params()
									self.config_tester.check_config_arguments_init()

					def 	snake_case__					(			self      ):
									return

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*lowerCamelCase__      )

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__      )

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_backbone(*lowerCamelCase__      )

					@unittest.skip(reason='''Dinat does not use inputs_embeds'''      )
					def 	snake_case__					(			self      ):
									pass

					@unittest.skip(reason='''Dinat does not use feedforward chunking'''      )
					def 	snake_case__					(			self      ):
									pass

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

									for model_class in self.all_model_classes:
													_lowerCamelCase					     = model_class(lowerCamelCase__      )
													self.assertIsInstance(model.get_input_embeddings() ,				(nn.Module)      )
													_lowerCamelCase					     = model.get_output_embeddings()
													self.assertTrue(x is None or isinstance(lowerCamelCase__ ,				nn.Linear      )      )

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

									for model_class in self.all_model_classes:
													_lowerCamelCase					     = model_class(lowerCamelCase__      )
													_lowerCamelCase					     = inspect.signature(model.forward      )
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													_lowerCamelCase					     = [*signature.parameters.keys()]

													_lowerCamelCase					     = ['''pixel_values''']
													self.assertListEqual(arg_names[:1] ,				lowerCamelCase__      )

					def 	snake_case__					(			self      ):
									self.skipTest('''Dinat\'s attention operation is handled entirely by NATTEN.'''      )

					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									_lowerCamelCase					     = model_class(lowerCamelCase__      )
									model.to(lowerCamelCase__      )
									model.eval()

									with torch.no_grad():
													_lowerCamelCase					     = model(**self._prepare_for_class(lowerCamelCase__ ,				lowerCamelCase__      )      )

									_lowerCamelCase					     = outputs.hidden_states

									_lowerCamelCase					     = getattr(
									    self.model_tester ,				'''expected_num_hidden_layers''' ,				len(self.model_tester.depths      ) + 1      )
									self.assertEqual(len(lowerCamelCase__      ) ,				lowerCamelCase__      )

									# Dinat has a different seq_length
									_lowerCamelCase					     = (
									    config.patch_size
									    if isinstance(config.patch_size ,				collections.abc.Iterable      )
									    else (config.patch_size, config.patch_size)
									)

									_lowerCamelCase					     = image_size[0] // patch_size[0]
									_lowerCamelCase					     = image_size[1] // patch_size[1]

									self.assertListEqual(
									    list(hidden_states[0].shape[-3:]      ) ,				[height, width, self.model_tester.embed_dim] ,				)

									if model_class.__name__ != "DinatBackbone":
													_lowerCamelCase					     = outputs.reshaped_hidden_states
													self.assertEqual(len(lowerCamelCase__      ) ,				lowerCamelCase__      )

													_lowerCamelCase     ,   _lowerCamelCase     ,   _lowerCamelCase     ,   _lowerCamelCase					     = reshaped_hidden_states[0].shape
													_lowerCamelCase					     = (
													    reshaped_hidden_states[0].view(lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      ).permute(0 ,				2 ,				3 ,				1      )
													)
													self.assertListEqual(
													    list(reshaped_hidden_states.shape[-3:]      ) ,				[height, width, self.model_tester.embed_dim] ,				)

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

									_lowerCamelCase					     = (
									    self.model_tester.image_size
									    if isinstance(self.model_tester.image_size ,				collections.abc.Iterable      )
									    else (self.model_tester.image_size, self.model_tester.image_size)
									)

									for model_class in self.all_model_classes:
													_lowerCamelCase					     = True
													self.check_hidden_states_output(lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      )

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													_lowerCamelCase					     = True

													self.check_hidden_states_output(lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      )

					@slow
					def 	snake_case__					(			self      ):
									for model_name in DINAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													_lowerCamelCase					     = DinatModel.from_pretrained(lowerCamelCase__      )
													self.assertIsNotNone(lowerCamelCase__      )





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

									_lowerCamelCase					     = _config_zero_init(lowerCamelCase__      )
									for model_class in self.all_model_classes:
													_lowerCamelCase					     = model_class(config=lowerCamelCase__      )
													for name, param in model.named_parameters():
																	if "embeddings" not in name and param.requires_grad:
																					self.assertIn(
																					    ((param.data.mean() * 1e9).round() / 1e9).item() ,				[0.0, 1.0] ,				msg=F"""Parameter {name} of model {model_class} seems not properly initialized""" ,				)


@require_natten
@require_vision
@require_torch
class 							lowerCamelCase_(      unittest.TestCase			):





					'''simple docstring'''



					@cached_property
					def 	snake_case__					(			self      ):
									return AutoImageProcessor.from_pretrained('''shi-labs/dinat-mini-in1k-224'''      ) if is_vision_available() else None





					@slow
					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = DinatForImageClassification.from_pretrained('''shi-labs/dinat-mini-in1k-224'''      ).to(lowerCamelCase__      )
									_lowerCamelCase					     = self.default_image_processor

									_lowerCamelCase					     = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
									_lowerCamelCase					     = image_processor(images=lowerCamelCase__ ,				return_tensors='''pt'''      ).to(lowerCamelCase__      )

									# forward pass
									with torch.no_grad():
													_lowerCamelCase					     = model(**lowerCamelCase__      )

									# verify the logits
									_lowerCamelCase					     = torch.Size((1, 1_0_0_0)      )
									self.assertEqual(outputs.logits.shape ,				lowerCamelCase__      )
									_lowerCamelCase					     = torch.tensor([-0.1_5_4_5, -0.7_6_6_7, 0.4_6_4_2]      ).to(lowerCamelCase__      )
									self.assertTrue(torch.allclose(outputs.logits[0, :3] ,				lowerCamelCase__ ,				atol=1e-4      )      )


@require_torch
@require_natten
class 							lowerCamelCase_(      unittest.TestCase, A__			):





					'''simple docstring'''



					lowercase__      :				int        =       (DinatBackbone,) if is_torch_available() else ()
					lowercase__      :				Optional[int]        =       DinatConfig

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = DinatModelTester(self      )








"""simple docstring"""







import cva
import numpy as np

def _a (		_snake_case      ,       _snake_case      ,       _snake_case      ,       _snake_case							):







      """simple docstring"""






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

def _a (		_snake_case      ,       _snake_case      ,       _snake_case							):







      """simple docstring"""






      UpperCAmelCase							=       image[x_coordinate][y_coordinate]
      UpperCAmelCase							=       [1, 2, 4, 8, 16, 32, 64, 128]

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

      # Starting from the top right, assigning value to pixels clockwise
      UpperCAmelCase							=       [
          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.
  _UpperCamelCase     			=  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
  _UpperCamelCase     			=  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]):
      _UpperCamelCase     			=  local_binary_value(image, i, j)

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



