code: 
  
  
  
  
  """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 			lowerCamelCase__    :
  
  
     def __init__(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE=13				, SCREAMING_SNAKE_CASE=32				, SCREAMING_SNAKE_CASE=2				, SCREAMING_SNAKE_CASE=3				, SCREAMING_SNAKE_CASE=16				, SCREAMING_SNAKE_CASE=[1, 2, 1]				, SCREAMING_SNAKE_CASE=[2, 2, 4]				, SCREAMING_SNAKE_CASE=2				, SCREAMING_SNAKE_CASE=2.0				, SCREAMING_SNAKE_CASE=True				, SCREAMING_SNAKE_CASE=0.0				, SCREAMING_SNAKE_CASE=0.0				, SCREAMING_SNAKE_CASE=0.1				, SCREAMING_SNAKE_CASE="gelu"				, SCREAMING_SNAKE_CASE=False				, SCREAMING_SNAKE_CASE=True				, SCREAMING_SNAKE_CASE=0.02				, SCREAMING_SNAKE_CASE=1E-5				, SCREAMING_SNAKE_CASE=True				, SCREAMING_SNAKE_CASE=None				, SCREAMING_SNAKE_CASE=True				, SCREAMING_SNAKE_CASE=10				, SCREAMING_SNAKE_CASE=8				, SCREAMING_SNAKE_CASE=["stage1", "stage2"]				, SCREAMING_SNAKE_CASE=[1, 2]				, ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
  
       snake_case		:						Tuple								=				parent
       snake_case		:						List[Any]								=				batch_size
       snake_case		:						str								=				image_size
       snake_case		:						List[str]								=				patch_size
       snake_case		:						int								=				num_channels
       snake_case		:						Optional[int]								=				embed_dim
       snake_case		:						Dict								=				depths
       snake_case		:						int								=				num_heads
       snake_case		:						List[Any]								=				window_size
       snake_case		:						Optional[Any]								=				mlp_ratio
       snake_case		:						str								=				qkv_bias
       snake_case		:						Dict								=				hidden_dropout_prob
       snake_case		:						Optional[Any]								=				attention_probs_dropout_prob
       snake_case		:						Union[str, Any]								=				drop_path_rate
       snake_case		:						str								=				hidden_act
       snake_case		:						Optional[Any]								=				use_absolute_embeddings
       snake_case		:						Union[str, Any]								=				patch_norm
       snake_case		:						Any								=				layer_norm_eps
       snake_case		:						Tuple								=				initializer_range
       snake_case		:						Optional[int]								=				is_training
       snake_case		:						Optional[Any]								=				scope
       snake_case		:						int								=				use_labels
       snake_case		:						Optional[Any]								=				type_sequence_label_size
       snake_case		:						Dict								=				encoder_stride
       snake_case		:						List[Any]								=				out_features
       snake_case		:						int								=				out_indices
  
  
     def 							lowerCamelCase_				(     self							):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
  
       snake_case		:						Optional[Any]								=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)
  
       snake_case		:						str								=				None
       if self.use_labels:
         snake_case		:						Optional[Any]								=				ids_tensor([self.batch_size]				, self.type_sequence_label_size							)
  
       snake_case		:						Tuple								=				self.get_config()
  
       return config, pixel_values, labels
  
  
     def 							lowerCamelCase_				(     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 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
  
       snake_case		:						int								=				SwinModel(config=snake_case_							)
       model.to(snake_case_							)
       model.eval()
       snake_case		:						Optional[int]								=				model(snake_case_							)
  
       snake_case		:						Union[str, Any]								=				((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths							) - 1))
       snake_case		:						Tuple								=				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 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
  
       snake_case		:						Tuple								=				SwinBackbone(config=snake_case_							)
       model.to(snake_case_							)
       model.eval()
       snake_case		:						Optional[Any]								=				model(snake_case_							)
  
       # 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
       snake_case		:						Optional[int]								=				None
       snake_case		:						List[str]								=				SwinBackbone(config=snake_case_							)
       model.to(snake_case_							)
       model.eval()
       snake_case		:						Optional[Any]								=				model(snake_case_							)
  
       # 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 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
  
       snake_case		:						List[str]								=				SwinForMaskedImageModeling(config=snake_case_							)
       model.to(snake_case_							)
       model.eval()
       snake_case		:						Any								=				model(snake_case_							)
       self.parent.assertEqual(
           result.logits.shape				, (self.batch_size, self.num_channels, self.image_size, self.image_size)							)
  
       # test greyscale images
       snake_case		:						Union[str, Any]								=				1
       snake_case		:						int								=				SwinForMaskedImageModeling(snake_case_							)
       model.to(snake_case_							)
       model.eval()
  
       snake_case		:						Dict								=				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
       snake_case		:						Optional[Any]								=				model(snake_case_							)
       self.parent.assertEqual(result.logits.shape				, (self.batch_size, 1, self.image_size, self.image_size)							)
  
  
     def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
  
       snake_case		:						Union[str, Any]								=				self.type_sequence_label_size
       snake_case		:						Optional[Any]								=				SwinForImageClassification(snake_case_							)
       model.to(snake_case_							)
       model.eval()
       snake_case		:						str								=				model(snake_case_				, labels=snake_case_							)
       self.parent.assertEqual(result.logits.shape				, (self.batch_size, self.type_sequence_label_size)							)
  
       # test greyscale images
       snake_case		:						int								=				1
       snake_case		:						List[Any]								=				SwinForImageClassification(snake_case_							)
       model.to(snake_case_							)
       model.eval()
  
       snake_case		:						Optional[int]								=				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
       snake_case		:						Union[str, Any]								=				model(snake_case_							)
       self.parent.assertEqual(result.logits.shape				, (self.batch_size, self.type_sequence_label_size)							)
  
  
  
     def 							lowerCamelCase_				(     self							):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
  
       snake_case		:						Optional[int]								=				self.prepare_config_and_inputs()
       (
           snake_case							
       )		:						Optional[Any]								=				config_and_inputs
       snake_case		:						Optional[Any]								=				{'''pixel_values''': pixel_values}
       return config, inputs_dict
  
  
  
  
  
  
  
  @require_torch
  class 			lowerCamelCase__    (  UpperCamelCase__				,  UpperCamelCase__				,  unittest.TestCase			):
     a__ :      Optional[Any]								=     (
         (
             SwinModel,
             SwinBackbone,
             SwinForImageClassification,
             SwinForMaskedImageModeling,
         )
         if is_torch_available()
         else ()
     )
     a__ :      Any								=     (
         {"""feature-extraction""": SwinModel, """image-classification""": SwinForImageClassification}
         if is_torch_available()
         else {}
     )
     a__ :      Union[str, Any]								=     True
  
     a__ :      Any								=     False
     a__ :      List[str]								=     False
     a__ :      Union[str, Any]								=     False
  
  
     def 							lowerCamelCase_				(     self							):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
  
       snake_case		:						Optional[int]								=				SwinModelTester(self							)
       snake_case		:						Dict								=				ConfigTester(self				, config_class=snake_case_				, embed_dim=37							)
  
  
     def 							lowerCamelCase_				(     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 							lowerCamelCase_				(     self							):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
  
       return
  
  
     def 							lowerCamelCase_				(     self							):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
  
       snake_case		:						Union[str, Any]								=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*snake_case_							)
  
  
     @unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0."							)
     def 							lowerCamelCase_				(     self							):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
  
       pass
  
  
     def 							lowerCamelCase_				(     self							):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
  
       super().test_training_gradient_checkpointing()
  
  
     def 							lowerCamelCase_				(     self							):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
  
       snake_case		:						Optional[int]								=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_backbone(*snake_case_							)
  
  
     def 							lowerCamelCase_				(     self							):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
  
       snake_case		:						List[str]								=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_image_modeling(*snake_case_							)
  
  
     def 							lowerCamelCase_				(     self							):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
  
       snake_case		:						Tuple								=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*snake_case_							)
  
  
     @unittest.skip(reason="Swin does not use inputs_embeds"							)
     def 							lowerCamelCase_				(     self							):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
  
       pass
  
  
     @unittest.skip(reason="Swin Transformer does not use feedforward chunking"							)
     def 							lowerCamelCase_				(     self							):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
  
       pass
  
  
     def 							lowerCamelCase_				(     self							):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
  
       snake_case		:						Optional[int]								=				self.model_tester.prepare_config_and_inputs_for_common()
  
       for model_class in self.all_model_classes:
         snake_case		:						int								=				model_class(snake_case_							)
         self.assertIsInstance(model.get_input_embeddings()				, (nn.Module)							)
         snake_case		:						Optional[int]								=				model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(snake_case_				, nn.Linear							)							)
  
  
     def 							lowerCamelCase_				(     self							):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
  
       snake_case		:						Dict								=				self.model_tester.prepare_config_and_inputs_for_common()
  
       for model_class in self.all_model_classes:
         snake_case		:						int								=				model_class(snake_case_							)
         snake_case		:						Optional[Any]								=				inspect.signature(model.forward							)
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         snake_case		:						Optional[int]								=				[*signature.parameters.keys()]
  
         snake_case		:						Dict								=				['''pixel_values''']
         self.assertListEqual(arg_names[:1]				, snake_case_							)
  
  
     def 							lowerCamelCase_				(     self							):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
  
       snake_case		:						Tuple								=				self.model_tester.prepare_config_and_inputs_for_common()
       snake_case		:						Union[str, Any]								=				True
  
       for model_class in self.all_model_classes:
         snake_case		:						Dict								=				True
         snake_case		:						Union[str, Any]								=				False
         snake_case		:						Optional[int]								=				True
         snake_case		:						Optional[int]								=				model_class(snake_case_							)
         model.to(snake_case_							)
         model.eval()
         with torch.no_grad():
           snake_case		:						Union[str, Any]								=				model(**self._prepare_for_class(snake_case_				, snake_case_							)							)
         snake_case		:						int								=				outputs.attentions
         snake_case		:						Union[str, Any]								=				len(self.model_tester.depths							)
         self.assertEqual(len(snake_case_							)				, snake_case_							)
  
         # check that output_attentions also work using config
         del inputs_dict["output_attentions"]
         snake_case		:						Optional[Any]								=				True
         snake_case		:						int								=				config.window_size**2
         snake_case		:						str								=				model_class(snake_case_							)
         model.to(snake_case_							)
         model.eval()
         with torch.no_grad():
           snake_case		:						List[str]								=				model(**self._prepare_for_class(snake_case_				, snake_case_							)							)
         snake_case		:						Dict								=				outputs.attentions
         self.assertEqual(len(snake_case_							)				, snake_case_							)
  
         self.assertListEqual(
             list(attentions[0].shape[-3:]							)				, [self.model_tester.num_heads[0], window_size_squared, window_size_squared]				, )
         snake_case		:						Union[str, Any]								=				len(snake_case_							)
  
         # Check attention is always last and order is fine
         snake_case		:						str								=				True
         snake_case		:						Union[str, Any]								=				True
         snake_case		:						Union[str, Any]								=				model_class(snake_case_							)
         model.to(snake_case_							)
         model.eval()
         with torch.no_grad():
           snake_case		:						Union[str, Any]								=				model(**self._prepare_for_class(snake_case_				, snake_case_							)							)
  
         # also another +1 for reshaped_hidden_states
         snake_case		:						Optional[int]								=				1 if model_class.__name__ == '''SwinBackbone''' else 2
         self.assertEqual(out_len + added_hidden_states				, len(snake_case_							)							)
  
         snake_case		:						Dict								=				outputs.attentions
  
         self.assertEqual(len(snake_case_							)				, snake_case_							)
  
         self.assertListEqual(
             list(self_attentions[0].shape[-3:]							)				, [self.model_tester.num_heads[0], window_size_squared, window_size_squared]				, )
  
  
     def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
  
       snake_case		:						List[str]								=				model_class(snake_case_							)
       model.to(snake_case_							)
       model.eval()
  
       with torch.no_grad():
         snake_case		:						Any								=				model(**self._prepare_for_class(snake_case_				, snake_case_							)							)
  
       snake_case		:						Tuple								=				outputs.hidden_states
  
       snake_case		:						str								=				getattr(
           self.model_tester				, "expected_num_hidden_layers"				, len(self.model_tester.depths							) + 1							)
       self.assertEqual(len(snake_case_							)				, snake_case_							)
  
       # Swin has a different seq_length
       snake_case		:						int								=				(
           config.patch_size
           if isinstance(config.patch_size				, collections.abc.Iterable							)
           else (config.patch_size, config.patch_size)
       )
  
       snake_case		:						List[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":
         snake_case		:						List[Any]								=				outputs.reshaped_hidden_states
         self.assertEqual(len(snake_case_							)				, snake_case_							)
  
         snake_case		:						List[str]								=				reshaped_hidden_states[0].shape
         snake_case		:						Dict								=				(
             reshaped_hidden_states[0].view(snake_case_				, snake_case_				, height * width							).permute(0				, 2				, 1							)
         )
         self.assertListEqual(
             list(reshaped_hidden_states.shape[-2:]							)				, [num_patches, self.model_tester.embed_dim]				, )
  
  
     def 							lowerCamelCase_				(     self							):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
  
       snake_case		:						Tuple								=				self.model_tester.prepare_config_and_inputs_for_common()
  
       snake_case		:						Optional[Any]								=				(
           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:
         snake_case		:						Optional[Any]								=				True
         self.check_hidden_states_output(snake_case_				, snake_case_				, snake_case_				, snake_case_							)
  
         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         snake_case		:						int								=				True
  
         self.check_hidden_states_output(snake_case_				, snake_case_				, snake_case_				, snake_case_							)
  
  
     def 							lowerCamelCase_				(     self							):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
  
       snake_case		:						Union[str, Any]								=				self.model_tester.prepare_config_and_inputs_for_common()
       snake_case		:						int								=				3
  
       snake_case		:						Optional[int]								=				(
           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)
       )
       snake_case		:						Dict								=				(
           config.patch_size
           if isinstance(config.patch_size				, collections.abc.Iterable							)
           else (config.patch_size, config.patch_size)
       )
  
       snake_case		:						Tuple								=				image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
       snake_case		:						List[Any]								=				image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
  
       for model_class in self.all_model_classes:
         snake_case		:						List[Any]								=				True
         self.check_hidden_states_output(snake_case_				, snake_case_				, snake_case_				, (padded_height, padded_width)							)
  
         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         snake_case		:						Union[str, Any]								=				True
         self.check_hidden_states_output(snake_case_				, snake_case_				, snake_case_				, (padded_height, padded_width)							)
  
  
     @slow
     def 							lowerCamelCase_				(     self							):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
  
       for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         snake_case		:						str								=				SwinModel.from_pretrained(snake_case_							)
         self.assertIsNotNone(snake_case_							)
  
  
  
     def 							lowerCamelCase_				(     self							):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
  
       snake_case		:						List[Any]								=				self.model_tester.prepare_config_and_inputs_for_common()
  
       snake_case		:						Any								=				_config_zero_init(snake_case_							)
       for model_class in self.all_model_classes:
         snake_case		:						List[str]								=				model_class(config=snake_case_							)
         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 			lowerCamelCase__    (  unittest.TestCase			):
  
  
     @cached_property
     def 							lowerCamelCase_				(     self							):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
  
       return (
           AutoImageProcessor.from_pretrained("microsoft/swin-tiny-patch4-window7-224"							)
           if is_vision_available()
           else None
       )
  
  
  
     @slow
     def 							lowerCamelCase_				(     self							):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
  
       snake_case		:						Optional[int]								=				SwinForImageClassification.from_pretrained("microsoft/swin-tiny-patch4-window7-224"							).to(snake_case_							)
       snake_case		:						List[str]								=				self.default_image_processor
  
       snake_case		:						str								=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
       snake_case		:						List[Any]								=				image_processor(images=snake_case_				, return_tensors="pt"							).to(snake_case_							)
  
       # forward pass
       with torch.no_grad():
         snake_case		:						Optional[int]								=				model(**snake_case_							)
  
       # verify the logits
       snake_case		:						List[Any]								=				torch.Size((1, 1_000)							)
       self.assertEqual(outputs.logits.shape				, snake_case_							)
       snake_case		:						Tuple								=				torch.tensor([-0.09_48, -0.64_54, -0.09_21]							).to(snake_case_							)
       self.assertTrue(torch.allclose(outputs.logits[0, :3]				, snake_case_				, atol=1E-4							)							)
  
  
  
  
  
  
  
  @require_torch
  class 			lowerCamelCase__    (  unittest.TestCase				,  UpperCamelCase__			):
     a__ :      Optional[int]								=     (SwinBackbone,) if is_torch_available() else ()
     a__ :      Any								=     SwinConfig
  
  
     def 							lowerCamelCase_				(     self							):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
  
       snake_case		:						Tuple								=				SwinModelTester(self							)
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  import numpy as np
  from cva import destroyAllWindows, imread, imshow, waitKey
  class 			lowerCamelCase__    :
  
  
  			def __init__(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						int								=				0
  					# max greyscale value for #FFFFFF
  					snake_case		:						List[str]								=				int(self.get_greyscale(255				, 255				, 255							)							)
  
  					if not self.min_threshold < threshold < self.max_threshold:
  							snake_case		:						Union[str, Any]								=				F'''Factor value should be from 0 to {self.max_threshold}'''
  							raise ValueError(SCREAMING_SNAKE_CASE							)
  
  					snake_case		:						str								=				input_img
  					snake_case		:						Optional[Any]								=				threshold
  					snake_case			,				snake_case		:						List[str]								=				self.input_img.shape[1], self.input_img.shape[0]
  
  					# error table size (+4 columns and +1 row) greater than input image because of
  					# lack of if statements
  					snake_case		:						Union[str, Any]								=				[
  					    [0 for _ in range(self.height + 4							)] for __ in range(self.width + 1							)
  					]
  					snake_case		:						Tuple								=				np.ones((self.width, self.height, 3)				, np.uinta							) * 255
  
  
  			@classmethod
  			def 							lowerCamelCase_				(     cls				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					return 0.1_14 * blue + 0.5_87 * green + 0.21_26 * red
  
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					for y in range(self.height							):
  							for x in range(self.width							):
  									snake_case		:						Dict								=				int(self.get_greyscale(*self.input_img[y][x]							)							)
  									if self.threshold > greyscale + self.error_table[y][x]:
  											snake_case		:						List[str]								=				(0, 0, 0)
  											snake_case		:						List[Any]								=				greyscale + self.error_table[x][y]
  									else:
  											snake_case		:						int								=				(255, 255, 255)
  											snake_case		:						Tuple								=				greyscale + self.error_table[x][y] - 255
  									self.error_table[y][x + 1] += int(8 / 32 * current_error							)
  									self.error_table[y][x + 2] += int(4 / 32 * current_error							)
  									self.error_table[y + 1][x] += int(8 / 32 * current_error							)
  									self.error_table[y + 1][x + 1] += int(4 / 32 * current_error							)
  									self.error_table[y + 1][x + 2] += int(2 / 32 * current_error							)
  									self.error_table[y + 1][x - 1] += int(4 / 32 * current_error							)
  									self.error_table[y + 1][x - 2] += int(2 / 32 * current_error							)
  
  
  if __name__ == "__main__":
  							# create Burke's instances with original images in greyscale
  							__A 		=			[
  							    Burkes(imread("image_data/lena.jpg", 1), threshold)
  							    for threshold in (1, 126, 130, 140)
  							]
  
  							for burkes in burkes_instances:
  														burkes.process()
  
  							for burkes in burkes_instances:
  														imshow(
  														    f'Original image with dithering threshold: {burkes.threshold}',
  														    burkes.output_img,
  														)
  
  							waitKey(0)
  							destroyAllWindows()
  
  