
"""simple docstring"""





import collections
import inspect
import unittest

from transformers import SwinConfig
from transformers.testing_utils import 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 SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel
    from transformers.models.swin.modeling_swin import SWIN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor


class A_      :




  '''simple docstring'''







  def __init__(						self							,  lowercase_							,  lowercase_=13							,  lowercase_=32							,  lowercase_=2							,  lowercase_=3							,  lowercase_=16							,  lowercase_=[1, 2, 1]							,  lowercase_=[2, 2, 4]							,  lowercase_=2							,  lowercase_=2.0							,  lowercase_=True							,  lowercase_=0.0							,  lowercase_=0.0							,  lowercase_=0.1							,  lowercase_="gelu"							,  lowercase_=False							,  lowercase_=True							,  lowercase_=0.02							,  lowercase_=1E-5							,  lowercase_=True							,  lowercase_=None							,  lowercase_=True							,  lowercase_=10							,  lowercase_=8							,  lowercase_=["stage1", "stage2"]							,  lowercase_=[1, 2]							,  ):

     """simple docstring"""
     UpperCAmelCase_   :   Union[str, Any]     	=       parent
     UpperCAmelCase_   :   List[Any]     	=       batch_size
     UpperCAmelCase_   :   Optional[int]     	=       image_size
     UpperCAmelCase_   :   Optional[int]     	=       patch_size
     UpperCAmelCase_   :   Optional[int]     	=       num_channels
     UpperCAmelCase_   :   int     	=       embed_dim
     UpperCAmelCase_   :   Any     	=       depths
     UpperCAmelCase_   :   Dict     	=       num_heads
     UpperCAmelCase_   :   int     	=       window_size
     UpperCAmelCase_   :   List[str]     	=       mlp_ratio
     UpperCAmelCase_   :   Any     	=       qkv_bias
     UpperCAmelCase_   :   Optional[Any]     	=       hidden_dropout_prob
     UpperCAmelCase_   :   str     	=       attention_probs_dropout_prob
     UpperCAmelCase_   :   List[Any]     	=       drop_path_rate
     UpperCAmelCase_   :   List[Any]     	=       hidden_act
     UpperCAmelCase_   :   Union[str, Any]     	=       use_absolute_embeddings
     UpperCAmelCase_   :   Optional[Any]     	=       patch_norm
     UpperCAmelCase_   :   str     	=       layer_norm_eps
     UpperCAmelCase_   :   int     	=       initializer_range
     UpperCAmelCase_   :   Any     	=       is_training
     UpperCAmelCase_   :   Optional[int]     	=       scope
     UpperCAmelCase_   :   int     	=       use_labels
     UpperCAmelCase_   :   Optional[Any]     	=       type_sequence_label_size
     UpperCAmelCase_   :   Tuple     	=       encoder_stride
     UpperCAmelCase_   :   Any     	=       out_features
     UpperCAmelCase_   :   Dict     	=       out_indices







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Tuple     	=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

     UpperCAmelCase_   :   Dict     	=       None
     if self.use_labels:
        UpperCAmelCase_   :   int     	=       ids_tensor([self.batch_size]							,  self.type_sequence_label_size    )

     UpperCAmelCase_   :   List[Any]     	=       self.get_config()

     return config, pixel_values, labels







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     return SwinConfig(
         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							,  window_size=self.window_size							,  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							,  use_absolute_embeddings=self.use_absolute_embeddings							,  path_norm=self.patch_norm							,  layer_norm_eps=self.layer_norm_eps							,  initializer_range=self.initializer_range							,  encoder_stride=self.encoder_stride							,  out_features=self.out_features							,  out_indices=self.out_indices							,  )







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_							,  lowercase_    ):

     """simple docstring"""
     UpperCAmelCase_   :   int     	=       SwinModel(config=lowercase_    )
     model.to(lowercase_    )
     model.eval()
     UpperCAmelCase_   :   Tuple     	=       model(lowercase_    )

     UpperCAmelCase_   :   Optional[Any]     	=       ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths    ) - 1))
     UpperCAmelCase_   :   str     	=       int(config.embed_dim * 2 ** (len(config.depths    ) - 1)    )

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







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_							,  lowercase_    ):

     """simple docstring"""
     UpperCAmelCase_   :   Any     	=       SwinBackbone(config=lowercase_    )
     model.to(lowercase_    )
     model.eval()
     UpperCAmelCase_   :   Optional[Any]     	=       model(lowercase_    )

     # 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], 16, 16]    )

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

     # verify backbone works with out_features=None
     UpperCAmelCase_   :   List[Any]     	=       None
     UpperCAmelCase_   :   Optional[int]     	=       SwinBackbone(config=lowercase_    )
     model.to(lowercase_    )
     model.eval()
     UpperCAmelCase_   :   List[Any]     	=       model(lowercase_    )

     # 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 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_							,  lowercase_    ):

     """simple docstring"""
     UpperCAmelCase_   :   Tuple     	=       SwinForMaskedImageModeling(config=lowercase_    )
     model.to(lowercase_    )
     model.eval()
     UpperCAmelCase_   :   str     	=       model(lowercase_    )
     self.parent.assertEqual(
         result.logits.shape							,  (self.batch_size, self.num_channels, self.image_size, self.image_size)    )

     # test greyscale images
     UpperCAmelCase_   :   List[str]     	=       1
     UpperCAmelCase_   :   Any     	=       SwinForMaskedImageModeling(lowercase_    )
     model.to(lowercase_    )
     model.eval()

     UpperCAmelCase_   :   Tuple     	=       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
     UpperCAmelCase_   :   str     	=       model(lowercase_    )
     self.parent.assertEqual(result.logits.shape							,  (self.batch_size, 1, self.image_size, self.image_size)    )







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_							,  lowercase_    ):

     """simple docstring"""
     UpperCAmelCase_   :   Union[str, Any]     	=       self.type_sequence_label_size
     UpperCAmelCase_   :   Optional[int]     	=       SwinForImageClassification(lowercase_    )
     model.to(lowercase_    )
     model.eval()
     UpperCAmelCase_   :   int     	=       model(lowercase_							,  labels=lowercase_    )
     self.parent.assertEqual(result.logits.shape							,  (self.batch_size, self.type_sequence_label_size)    )

     # test greyscale images
     UpperCAmelCase_   :   List[str]     	=       1
     UpperCAmelCase_   :   str     	=       SwinForImageClassification(lowercase_    )
     model.to(lowercase_    )
     model.eval()

     UpperCAmelCase_   :   Dict     	=       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
     UpperCAmelCase_   :   Dict     	=       model(lowercase_    )
     self.parent.assertEqual(result.logits.shape							,  (self.batch_size, self.type_sequence_label_size)    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Dict     	=       self.prepare_config_and_inputs()
     (
         (
         UpperCAmelCase_
     )	,  (
         UpperCAmelCase_
     )	,  (
         UpperCAmelCase_
     )	,  
     )   :   Optional[int]     	=       config_and_inputs
     UpperCAmelCase_   :   str     	=       {"pixel_values": pixel_values}
     return config, inputs_dict




@require_torch
class A_      (lowercase__							,lowercase__							,unittest.TestCase ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__     :		Any    	=						(
      (
          SwinModel,
          SwinBackbone,
          SwinForImageClassification,
          SwinForMaskedImageModeling,
      )
      if is_torch_available()
      else ()
  )
  SCREAMING_SNAKE_CASE__     :		int    	=						(
      {"""feature-extraction""": SwinModel, """image-classification""": SwinForImageClassification}
      if is_torch_available()
      else {}
  )
  SCREAMING_SNAKE_CASE__     :		Optional[int]    	=						True

  SCREAMING_SNAKE_CASE__     :		Optional[int]    	=						False
  SCREAMING_SNAKE_CASE__     :		Dict    	=						False
  SCREAMING_SNAKE_CASE__     :		List[Any]    	=						False







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   int     	=       SwinModelTester(self    )
     UpperCAmelCase_   :   Optional[int]     	=       ConfigTester(self							,  config_class=lowercase_							,  embed_dim=37    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     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    ):

     """simple docstring"""
     return







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Optional[int]     	=       self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowercase_    )







  @unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0."    )
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     pass







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     super().test_training_gradient_checkpointing()







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   int     	=       self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_backbone(*lowercase_    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   int     	=       self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_image_modeling(*lowercase_    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Tuple     	=       self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*lowercase_    )







  @unittest.skip(reason="Swin does not use inputs_embeds"    )
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     pass







  @unittest.skip(reason="Swin Transformer does not use feedforward chunking"    )
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     pass







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_	,  UpperCAmelCase_   :   Any     	=       self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        UpperCAmelCase_   :   Tuple     	=       model_class(lowercase_    )
        self.assertIsInstance(model.get_input_embeddings()							,  (nn.Module)    )
        UpperCAmelCase_   :   Optional[Any]     	=       model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(lowercase_							,  nn.Linear    )    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_	,  UpperCAmelCase_   :   Optional[int]     	=       self.model_tester.prepare_config_and_inputs_for_common()

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

        UpperCAmelCase_   :   Optional[Any]     	=       ["pixel_values"]
        self.assertListEqual(arg_names[:1]							,  lowercase_    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_	,  UpperCAmelCase_   :   List[Any]     	=       self.model_tester.prepare_config_and_inputs_for_common()
     UpperCAmelCase_   :   str     	=       True

     for model_class in self.all_model_classes:
        UpperCAmelCase_   :   Dict     	=       True
        UpperCAmelCase_   :   Union[str, Any]     	=       False
        UpperCAmelCase_   :   Optional[Any]     	=       True
        UpperCAmelCase_   :   int     	=       model_class(lowercase_    )
        model.to(lowercase_    )
        model.eval()
        with torch.no_grad():
           UpperCAmelCase_   :   Union[str, Any]     	=       model(**self._prepare_for_class(lowercase_							,  lowercase_    )    )
        UpperCAmelCase_   :   int     	=       outputs.attentions
        UpperCAmelCase_   :   Dict     	=       len(self.model_tester.depths    )
        self.assertEqual(len(lowercase_    )							,  lowercase_    )

        # check that output_attentions also work using config
        del inputs_dict["output_attentions"]
        UpperCAmelCase_   :   Tuple     	=       True
        UpperCAmelCase_   :   Optional[int]     	=       config.window_size**2
        UpperCAmelCase_   :   Optional[Any]     	=       model_class(lowercase_    )
        model.to(lowercase_    )
        model.eval()
        with torch.no_grad():
           UpperCAmelCase_   :   int     	=       model(**self._prepare_for_class(lowercase_							,  lowercase_    )    )
        UpperCAmelCase_   :   Optional[Any]     	=       outputs.attentions
        self.assertEqual(len(lowercase_    )							,  lowercase_    )

        self.assertListEqual(
            list(attentions[0].shape[-3:]    )							,  [self.model_tester.num_heads[0], window_size_squared, window_size_squared]							,  )
        UpperCAmelCase_   :   str     	=       len(lowercase_    )

        # Check attention is always last and order is fine
        UpperCAmelCase_   :   str     	=       True
        UpperCAmelCase_   :   Dict     	=       True
        UpperCAmelCase_   :   Optional[int]     	=       model_class(lowercase_    )
        model.to(lowercase_    )
        model.eval()
        with torch.no_grad():
           UpperCAmelCase_   :   Tuple     	=       model(**self._prepare_for_class(lowercase_							,  lowercase_    )    )

        # also another +1 for reshaped_hidden_states
        UpperCAmelCase_   :   Dict     	=       1 if model_class.__name__ == "SwinBackbone" else 2
        self.assertEqual(out_len + added_hidden_states							,  len(lowercase_    )    )

        UpperCAmelCase_   :   Tuple     	=       outputs.attentions

        self.assertEqual(len(lowercase_    )							,  lowercase_    )

        self.assertListEqual(
            list(self_attentions[0].shape[-3:]    )							,  [self.model_tester.num_heads[0], window_size_squared, window_size_squared]							,  )







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_    ):

     """simple docstring"""
     UpperCAmelCase_   :   int     	=       model_class(lowercase_    )
     model.to(lowercase_    )
     model.eval()

     with torch.no_grad():
        UpperCAmelCase_   :   Optional[Any]     	=       model(**self._prepare_for_class(lowercase_							,  lowercase_    )    )

     UpperCAmelCase_   :   Tuple     	=       outputs.hidden_states

     UpperCAmelCase_   :   Optional[Any]     	=       getattr(
         self.model_tester							,  "expected_num_hidden_layers"							,  len(self.model_tester.depths    ) + 1    )
     self.assertEqual(len(lowercase_    )							,  lowercase_    )

     # Swin has a different seq_length
     UpperCAmelCase_   :   Tuple     	=       (
         config.patch_size
         if isinstance(config.patch_size							,  collections.abc.Iterable    )
         else (config.patch_size, config.patch_size)
     )

     UpperCAmelCase_   :   Any     	=       (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

     self.assertListEqual(
         list(hidden_states[0].shape[-2:]    )							,  [num_patches, self.model_tester.embed_dim]							,  )

     if not model_class.__name__ == "SwinBackbone":
        UpperCAmelCase_   :   List[str]     	=       outputs.reshaped_hidden_states
        self.assertEqual(len(lowercase_    )							,  lowercase_    )

        UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_   :   List[str]     	=       reshaped_hidden_states[0].shape
        UpperCAmelCase_   :   List[Any]     	=       (
            reshaped_hidden_states[0].view(lowercase_							,  lowercase_							,  height * width    ).permute(0							,  2							,  1    )
        )
        self.assertListEqual(
            list(reshaped_hidden_states.shape[-2:]    )							,  [num_patches, self.model_tester.embed_dim]							,  )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_	,  UpperCAmelCase_   :   int     	=       self.model_tester.prepare_config_and_inputs_for_common()

     UpperCAmelCase_   :   Tuple     	=       (
         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:
        UpperCAmelCase_   :   Optional[int]     	=       True
        self.check_hidden_states_output(lowercase_							,  lowercase_							,  lowercase_							,  lowercase_    )

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        UpperCAmelCase_   :   Any     	=       True

        self.check_hidden_states_output(lowercase_							,  lowercase_							,  lowercase_							,  lowercase_    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_	,  UpperCAmelCase_   :   Optional[Any]     	=       self.model_tester.prepare_config_and_inputs_for_common()
     UpperCAmelCase_   :   str     	=       3

     UpperCAmelCase_   :   Dict     	=       (
         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)
     )
     UpperCAmelCase_   :   Dict     	=       (
         config.patch_size
         if isinstance(config.patch_size							,  collections.abc.Iterable    )
         else (config.patch_size, config.patch_size)
     )

     UpperCAmelCase_   :   Any     	=       image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
     UpperCAmelCase_   :   Optional[int]     	=       image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

     for model_class in self.all_model_classes:
        UpperCAmelCase_   :   Tuple     	=       True
        self.check_hidden_states_output(lowercase_							,  lowercase_							,  lowercase_							,  (padded_height, padded_width)    )

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        UpperCAmelCase_   :   List[str]     	=       True
        self.check_hidden_states_output(lowercase_							,  lowercase_							,  lowercase_							,  (padded_height, padded_width)    )







  @slow
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        UpperCAmelCase_   :   Tuple     	=       SwinModel.from_pretrained(lowercase_    )
        self.assertIsNotNone(lowercase_    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_	,  UpperCAmelCase_   :   int     	=       self.model_tester.prepare_config_and_inputs_for_common()

     UpperCAmelCase_   :   Optional[int]     	=       _config_zero_init(lowercase_    )
     for model_class in self.all_model_classes:
        UpperCAmelCase_   :   Union[str, Any]     	=       model_class(config=lowercase_    )
        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_vision
@require_torch
class A_      (unittest.TestCase ):




  '''simple docstring'''







  @cached_property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     return (
         AutoImageProcessor.from_pretrained("microsoft/swin-tiny-patch4-window7-224"    )
         if is_vision_available()
         else None
     )







  @slow
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   str     	=       SwinForImageClassification.from_pretrained("microsoft/swin-tiny-patch4-window7-224"    ).to(lowercase_    )
     UpperCAmelCase_   :   str     	=       self.default_image_processor

     UpperCAmelCase_   :   Any     	=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
     UpperCAmelCase_   :   Optional[Any]     	=       image_processor(images=lowercase_							,  return_tensors="pt"    ).to(lowercase_    )

     # forward pass
     with torch.no_grad():
        UpperCAmelCase_   :   List[str]     	=       model(**lowercase_    )

     # verify the logits
     UpperCAmelCase_   :   Union[str, Any]     	=       torch.Size((1, 1000)    )
     self.assertEqual(outputs.logits.shape							,  lowercase_    )
     UpperCAmelCase_   :   Any     	=       torch.tensor([-0.09_48, -0.64_54, -0.09_21]    ).to(lowercase_    )
     self.assertTrue(torch.allclose(outputs.logits[0, :3]							,  lowercase_							,  atol=1E-4    )    )




@require_torch
class A_      (unittest.TestCase							,lowercase__ ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__     :		List[Any]    	=						(SwinBackbone,) if is_torch_available() else ()
  SCREAMING_SNAKE_CASE__     :		List[str]    	=						SwinConfig







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   str     	=       SwinModelTester(self    )




"""simple docstring"""





import cva
import numpy as np







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):

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







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   Optional[int]     	=       image[x_coordinate][y_coordinate]
   UpperCAmelCase_   :   int     	=       [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_   :   List[Any]     	=       [
       get_neighbors_pixel(__lowerCamelCase,						x_coordinate - 1,						y_coordinate + 1,						__lowerCamelCase    ),
       get_neighbors_pixel(__lowerCamelCase,						__lowerCamelCase,						y_coordinate + 1,						__lowerCamelCase    ),
       get_neighbors_pixel(__lowerCamelCase,						x_coordinate - 1,						__lowerCamelCase,						__lowerCamelCase    ),
       get_neighbors_pixel(__lowerCamelCase,						x_coordinate + 1,						y_coordinate + 1,						__lowerCamelCase    ),
       get_neighbors_pixel(__lowerCamelCase,						x_coordinate + 1,						__lowerCamelCase,						__lowerCamelCase    ),
       get_neighbors_pixel(__lowerCamelCase,						x_coordinate + 1,						y_coordinate - 1,						__lowerCamelCase    ),
       get_neighbors_pixel(__lowerCamelCase,						__lowerCamelCase,						y_coordinate - 1,						__lowerCamelCase    ),
       get_neighbors_pixel(__lowerCamelCase,						x_coordinate - 1,						y_coordinate - 1,						__lowerCamelCase    ),
   ]

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


if __name__ == "__main__":
    # Reading the image and converting it to grayscale.
    _a									=				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
    _a									=				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]):
            _a									=				local_binary_value(image, i, j)

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


