






"""simple docstring"""







import inspect
import unittest

from transformers import DataaVecVisionConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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 (
      MODEL_MAPPING,
      DataaVecVisionForImageClassification,
      DataaVecVisionForSemanticSegmentation,
      DataaVecVisionModel,
  )
  from transformers.models.dataavec.modeling_dataavec_vision import DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import BeitImageProcessor







class 					lowerCamelCase__					:






    def __init__( self	,A	,A=100	,A=13	,A=30	,A=2	,A=3	,A=True	,A=True	,A=32	,A=4	,A=4	,A=37	,A="gelu"	,A=0.1	,A=0.1	,A=10	,A=0.02	,A=3	,A=None	,A=[0, 1, 2, 3]	,):
          UpperCAmelCase							=       parent
          UpperCAmelCase							=       100
          UpperCAmelCase							=       batch_size
          UpperCAmelCase							=       image_size
          UpperCAmelCase							=       patch_size
          UpperCAmelCase							=       num_channels
          UpperCAmelCase							=       is_training
          UpperCAmelCase							=       use_labels
          UpperCAmelCase							=       hidden_size
          UpperCAmelCase							=       num_hidden_layers
          UpperCAmelCase							=       num_attention_heads
          UpperCAmelCase							=       intermediate_size
          UpperCAmelCase							=       hidden_act
          UpperCAmelCase							=       hidden_dropout_prob
          UpperCAmelCase							=       attention_probs_dropout_prob
          UpperCAmelCase							=       type_sequence_label_size
          UpperCAmelCase							=       initializer_range
          UpperCAmelCase							=       scope
          UpperCAmelCase							=       out_indices
          UpperCAmelCase							=       num_labels

          # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
          UpperCAmelCase							=       (image_size // patch_size) ** 2
          UpperCAmelCase							=       num_patches + 1






    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

          UpperCAmelCase							=       None
          UpperCAmelCase							=       None
          if self.use_labels:
                UpperCAmelCase							=       ids_tensor([self.batch_size]	,self.type_sequence_label_size					)
                UpperCAmelCase							=       ids_tensor([self.batch_size, self.image_size, self.image_size]	,self.num_labels					)

          UpperCAmelCase							=       self.get_config()

          return config, pixel_values, labels, pixel_labels






    def 				_UpperCamelCase							( self					):
          return DataaVecVisionConfig(
              vocab_size=self.vocab_size	,image_size=self.image_size	,patch_size=self.patch_size	,num_channels=self.num_channels	,hidden_size=self.hidden_size	,num_hidden_layers=self.num_hidden_layers	,num_attention_heads=self.num_attention_heads	,intermediate_size=self.intermediate_size	,hidden_act=self.hidden_act	,hidden_dropout_prob=self.hidden_dropout_prob	,attention_probs_dropout_prob=self.attention_probs_dropout_prob	,is_decoder=A	,initializer_range=self.initializer_range	,out_indices=self.out_indices	,)






    def 				_UpperCamelCase							( self	,A	,A	,A	,A					):
          UpperCAmelCase							=       DataaVecVisionModel(config=A					)
          model.to(A					)
          model.eval()
          UpperCAmelCase							=       model(A					)
          # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
          UpperCAmelCase							=       (self.image_size // self.patch_size) ** 2
          self.parent.assertEqual(result.last_hidden_state.shape	,(self.batch_size, num_patches + 1, self.hidden_size)					)






    def 				_UpperCamelCase							( self	,A	,A	,A	,A					):
          UpperCAmelCase							=       self.type_sequence_label_size
          UpperCAmelCase							=       DataaVecVisionForImageClassification(A					)
          model.to(A					)
          model.eval()
          UpperCAmelCase							=       model(A	,labels=A					)
          self.parent.assertEqual(result.logits.shape	,(self.batch_size, self.type_sequence_label_size)					)






    def 				_UpperCamelCase							( self	,A	,A	,A	,A					):
          UpperCAmelCase							=       self.num_labels
          UpperCAmelCase							=       DataaVecVisionForSemanticSegmentation(A					)
          model.to(A					)
          model.eval()
          UpperCAmelCase							=       model(A					)
          self.parent.assertEqual(
              result.logits.shape	,(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)					)
          UpperCAmelCase							=       model(A	,labels=A					)
          self.parent.assertEqual(
              result.logits.shape	,(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)					)






    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       self.prepare_config_and_inputs()
          UpperCAmelCase			,					UpperCAmelCase			,					UpperCAmelCase			,					UpperCAmelCase							=       config_and_inputs
          UpperCAmelCase							=       {"""pixel_values""": pixel_values}
          return config, inputs_dict







@require_torch
class 					lowerCamelCase__					(       snake_case    ,     snake_case    ,     unittest.TestCase ):
    SCREAMING_SNAKE_CASE		=			(
        (DataaVecVisionModel, DataaVecVisionForImageClassification, DataaVecVisionForSemanticSegmentation)
        if is_torch_available()
        else ()
    )
    SCREAMING_SNAKE_CASE		=			(
        {
            '''feature-extraction''': DataaVecVisionModel,
            '''image-classification''': DataaVecVisionForImageClassification,
            '''image-segmentation''': DataaVecVisionForSemanticSegmentation,
        }
        if is_torch_available()
        else {}
    )

    SCREAMING_SNAKE_CASE		=			False
    SCREAMING_SNAKE_CASE		=			False
    SCREAMING_SNAKE_CASE		=			False






    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       DataaVecVisionModelTester(self					)
          UpperCAmelCase							=       ConfigTester(
              self	,config_class=A	,has_text_modality=A	,hidden_size=37					)






    def 				_UpperCamelCase							( self					):
          self.config_tester.run_common_tests()






    def 				_UpperCamelCase							( self					):
          # Data2VecVision does not use inputs_embeds
          pass






    @require_torch_multi_gpu
    @unittest.skip(
        reason="""Data2VecVision has some layers using `add_module` which doesn't work well with `nn.DataParallel`"""					)
    def 				_UpperCamelCase							( self					):
          pass






    def 				_UpperCamelCase							( self					):
          UpperCAmelCase			,					UpperCAmelCase							=       self.model_tester.prepare_config_and_inputs_for_common()

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






    def 				_UpperCamelCase							( self					):
          UpperCAmelCase			,					UpperCAmelCase							=       self.model_tester.prepare_config_and_inputs_for_common()

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

                UpperCAmelCase							=       ["""pixel_values"""]
                self.assertListEqual(arg_names[:1]	,A					)






    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*A					)






    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_segmentation(*A					)






    def 				_UpperCamelCase							( self					):
          if not self.model_tester.is_training:
                return

          UpperCAmelCase			,					UpperCAmelCase							=       self.model_tester.prepare_config_and_inputs_for_common()
          UpperCAmelCase							=       True

          for model_class in self.all_model_classes:
                if model_class in [*get_values(A					)]:
                      continue

                UpperCAmelCase							=       model_class(A					)
                model.to(A					)
                model.train()
                UpperCAmelCase							=       self._prepare_for_class(A	,A	,return_labels=A					)
                UpperCAmelCase							=       model(**A					).loss
                loss.backward()






    def 				_UpperCamelCase							( self					):
          UpperCAmelCase			,					UpperCAmelCase							=       self.model_tester.prepare_config_and_inputs_for_common()
          if not self.model_tester.is_training:
                return

          UpperCAmelCase							=       False
          UpperCAmelCase							=       True

          for model_class in self.all_model_classes:
                if model_class in [*get_values(A					)] or not model_class.supports_gradient_checkpointing:
                      continue
                # TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING
                # this can then be incorporated into _prepare_for_class in test_modeling_common.py
                elif model_class.__name__ == "Data2VecVisionForSemanticSegmentation":
                      UpperCAmelCase			,					UpperCAmelCase			,					UpperCAmelCase			,					UpperCAmelCase							=       inputs_dict["""pixel_values"""].shape
                      UpperCAmelCase							=       torch.zeros(
                          [self.model_tester.batch_size, height, width]	,device=A					).long()
                UpperCAmelCase							=       model_class(A					)
                model.gradient_checkpointing_enable()
                model.to(A					)
                model.train()
                UpperCAmelCase							=       self._prepare_for_class(A	,A	,return_labels=A					)
                UpperCAmelCase							=       model(**A					).loss
                loss.backward()






    def 				_UpperCamelCase							( self					):
          UpperCAmelCase			,					UpperCAmelCase							=       self.model_tester.prepare_config_and_inputs_for_common()

          UpperCAmelCase							=       _config_zero_init(A					)
          for model_class in self.all_model_classes:
                UpperCAmelCase							=       model_class(config=A					)
                for name, param in model.named_parameters():
                      # we skip lambda parameters as these require special initial values
                      # determined by config.layer_scale_init_value
                      if "lambda" in name:
                            continue
                      if 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'''	,)






    def 				_UpperCamelCase							( self	,A	,A	,A	,A=2e-4	,A="outputs"	,A=None					):
          # We override with a slightly higher tol value, as semseg models tend to diverge a bit more
          super().check_pt_tf_outputs(A	,A	,A	,A	,A	,A					)






    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*A					)






    @slow
    def 				_UpperCamelCase							( self					):
          for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                UpperCAmelCase							=       DataaVecVisionModel.from_pretrained(A					)
                self.assertIsNotNone(A					)







def _a (		):







      """simple docstring"""






      UpperCAmelCase							=       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
      return image







@require_torch
@require_vision
class 					lowerCamelCase__					(       unittest.TestCase ):






    @cached_property
    def 				_UpperCamelCase							( self					):
          return (
              BeitImageProcessor.from_pretrained("""facebook/data2vec-vision-base-ft1k"""					) if is_vision_available() else None
          )






    @slow
    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       DataaVecVisionForImageClassification.from_pretrained("""facebook/data2vec-vision-base-ft1k"""					).to(
              A					)

          UpperCAmelCase							=       self.default_image_processor
          UpperCAmelCase							=       prepare_img()
          UpperCAmelCase							=       image_processor(images=A	,return_tensors="""pt"""					).to(A					)

          # forward pass
          with torch.no_grad():
                UpperCAmelCase							=       model(**A					)
          UpperCAmelCase							=       outputs.logits

          # verify the logits
          UpperCAmelCase							=       torch.Size((1, 1_000)					)
          self.assertEqual(logits.shape	,A					)

          UpperCAmelCase							=       torch.tensor([0.3277, -0.1395, 0.0911]					).to(A					)

          self.assertTrue(torch.allclose(logits[0, :3]	,A	,atol=1e-4					)					)

          UpperCAmelCase							=       [model.config.labelaid[i] for i in ["""remote control, remote""", """tabby, tabby cat"""]]
          self.assertEqual(logits[0].topk(2					).indices.cpu().tolist()	,A					)











"""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()



