






"""simple docstring"""







import inspect
import unittest

from transformers import ResNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
  import jax
  import jax.numpy as jnp

  from transformers.models.resnet.modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel

if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor







class 					lowerCamelCase__					(       unittest.TestCase ):






    def __init__( self	,A	,A=3	,A=32	,A=3	,A=10	,A=[10, 20, 30, 40]	,A=[1, 1, 2, 1]	,A=True	,A=True	,A="relu"	,A=3	,A=None	,):
          UpperCAmelCase							=       parent
          UpperCAmelCase							=       batch_size
          UpperCAmelCase							=       image_size
          UpperCAmelCase							=       num_channels
          UpperCAmelCase							=       embeddings_size
          UpperCAmelCase							=       hidden_sizes
          UpperCAmelCase							=       depths
          UpperCAmelCase							=       is_training
          UpperCAmelCase							=       use_labels
          UpperCAmelCase							=       hidden_act
          UpperCAmelCase							=       num_labels
          UpperCAmelCase							=       scope
          UpperCAmelCase							=       len(A					)






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

          UpperCAmelCase							=       self.get_config()

          return config, pixel_values






    def 				_UpperCamelCase							( self					):
          return ResNetConfig(
              num_channels=self.num_channels	,embeddings_size=self.embeddings_size	,hidden_sizes=self.hidden_sizes	,depths=self.depths	,hidden_act=self.hidden_act	,num_labels=self.num_labels	,image_size=self.image_size	,)






    def 				_UpperCamelCase							( self	,A	,A					):
          UpperCAmelCase							=       FlaxResNetModel(config=A					)
          UpperCAmelCase							=       model(A					)

          # Output shape (b, c, h, w)
          self.parent.assertEqual(
              result.last_hidden_state.shape	,(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)	,)






    def 				_UpperCamelCase							( self	,A	,A					):
          UpperCAmelCase							=       self.num_labels
          UpperCAmelCase							=       FlaxResNetForImageClassification(config=A					)
          UpperCAmelCase							=       model(A					)
          self.parent.assertEqual(result.logits.shape	,(self.batch_size, self.num_labels)					)






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







@require_flax
class 					lowerCamelCase__					(       snake_case    ,     unittest.TestCase ):
    SCREAMING_SNAKE_CASE		=			(FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

    SCREAMING_SNAKE_CASE		=			False
    SCREAMING_SNAKE_CASE		=			False
    SCREAMING_SNAKE_CASE		=			False






    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       FlaxResNetModelTester(self					)
          UpperCAmelCase							=       ConfigTester(self	,config_class=A	,has_text_modality=A					)






    def 				_UpperCamelCase							( 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 				_UpperCamelCase							( self					):
          return






    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_classification(*A					)






    @unittest.skip(reason="""ResNet does not use inputs_embeds"""					)
    def 				_UpperCamelCase							( self					):
          pass






    @unittest.skip(reason="""ResNet does not support input and output embeddings"""					)
    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					)
                UpperCAmelCase							=       inspect.signature(model.__call__					)
                # 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					):
          def check_hidden_states_output(A	,A	,A					):
                UpperCAmelCase							=       model_class(A					)

                UpperCAmelCase							=       model(**self._prepare_for_class(A	,A					)					)

                UpperCAmelCase							=       outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                UpperCAmelCase							=       self.model_tester.num_stages
                self.assertEqual(len(A					)	,expected_num_stages + 1					)






    @unittest.skip(reason="""ResNet does not use feedforward chunking"""					)
    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:
                with self.subTest(model_class.__name__					):
                      UpperCAmelCase							=       self._prepare_for_class(A	,A					)
                      UpperCAmelCase							=       model_class(A					)

                      @jax.jit
                      def model_jitted(A	,**A					):
                            return model(pixel_values=A	,**A					)

                      with self.subTest("""JIT Enabled"""					):
                            UpperCAmelCase							=       model_jitted(**A					).to_tuple()

                      with self.subTest("""JIT Disabled"""					):
                            with jax.disable_jit():
                                  UpperCAmelCase							=       model_jitted(**A					).to_tuple()

                      self.assertEqual(len(A					)	,len(A					)					)
                      for jitted_output, output in zip(A	,A					):
                            self.assertEqual(jitted_output.shape	,output.shape					)







def _a (		):







      """simple docstring"""






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







@require_flax
class 					lowerCamelCase__					(       unittest.TestCase ):






    @cached_property
    def 				_UpperCamelCase							( self					):
          return AutoImageProcessor.from_pretrained("""microsoft/resnet-50"""					) if is_vision_available() else None






    @slow
    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       FlaxResNetForImageClassification.from_pretrained("""microsoft/resnet-50"""					)

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

          UpperCAmelCase							=       model(**A					)

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

          UpperCAmelCase							=       jnp.array([-11.1069, -9.7877, -8.3777]					)

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











"""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()



