code: 
  
  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 					A_   :
  
  
      '''simple docstring'''
  
  
  
  
  
  
      def __init__(self     ,      lowercase__     ,      lowercase__=100     ,      lowercase__=13     ,      lowercase__=30     ,      lowercase__=2     ,      lowercase__=3     ,      lowercase__=True     ,      lowercase__=True     ,      lowercase__=32     ,      lowercase__=4     ,      lowercase__=4     ,      lowercase__=37     ,      lowercase__="gelu"     ,      lowercase__=0.1     ,      lowercase__=0.1     ,      lowercase__=10     ,      lowercase__=0.02     ,      lowercase__=3     ,      lowercase__=None     ,      lowercase__=[0, 1, 2, 3]     ,      )     ->      Optional[int]:
          __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      lowerCAmelCase_						(self  )     ->      Optional[int]:
          __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      lowerCAmelCase_						(self  )     ->      Tuple:
          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=lowercase__     ,      initializer_range=self.initializer_range     ,      out_indices=self.out_indices     ,      )
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__  )     ->      str:
          __UpperCAmelCase									=			DataaVecVisionModel(config=lowercase__  )
          model.to(lowercase__  )
          model.eval()
          __UpperCAmelCase									=			model(lowercase__  )
          # 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      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__  )     ->      int:
          __UpperCAmelCase									=			self.type_sequence_label_size
          __UpperCAmelCase									=			DataaVecVisionForImageClassification(lowercase__  )
          model.to(lowercase__  )
          model.eval()
          __UpperCAmelCase									=			model(lowercase__     ,      labels=lowercase__  )
          self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.type_sequence_label_size)  )
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__  )     ->      Optional[Any]:
          __UpperCAmelCase									=			self.num_labels
          __UpperCAmelCase									=			DataaVecVisionForSemanticSegmentation(lowercase__  )
          model.to(lowercase__  )
          model.eval()
          __UpperCAmelCase									=			model(lowercase__  )
          self.parent.assertEqual(
              result.logits.shape     ,      (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)  )
          __UpperCAmelCase									=			model(lowercase__     ,      labels=lowercase__  )
          self.parent.assertEqual(
              result.logits.shape     ,      (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)  )
  
  
  
  
  
  
      def      lowerCAmelCase_						(self  )     ->      Union[str, Any]:
          __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 					A_   (      _a ,     _a ,     unittest.TestCase						):
  
  
      '''simple docstring'''
  
  
  
  
  
  
      a__     							=       (
          (DataaVecVisionModel, DataaVecVisionForImageClassification, DataaVecVisionForSemanticSegmentation)
          if is_torch_available()
          else ()
      )
      a__     							=       (
          {
              "feature-extraction": DataaVecVisionModel,
              "image-classification": DataaVecVisionForImageClassification,
              "image-segmentation": DataaVecVisionForSemanticSegmentation,
          }
          if is_torch_available()
          else {}
      )
  
      a__     							=       False
      a__     							=       False
      a__     							=       False
  
  
      def      lowerCAmelCase_						(self  )     ->      int:
          __UpperCAmelCase									=			DataaVecVisionModelTester(self  )
          __UpperCAmelCase									=			ConfigTester(
              self     ,      config_class=lowercase__     ,      has_text_modality=lowercase__     ,      hidden_size=37  )
  
  
      def      lowerCAmelCase_						(self  )     ->      List[str]:
          self.config_tester.run_common_tests()
  
  
      def      lowerCAmelCase_						(self  )     ->      Any:
          # 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      lowerCAmelCase_						(self  )     ->      Optional[Any]:
          pass
  
  
      def      lowerCAmelCase_						(self  )     ->      Optional[int]:
          __UpperCAmelCase					,     __UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in self.all_model_classes:
              __UpperCAmelCase									=			model_class(lowercase__  )
              self.assertIsInstance(model.get_input_embeddings()     ,      (nn.Module)  )
              __UpperCAmelCase									=			model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(lowercase__     ,      nn.Linear  )  )
  
  
      def      lowerCAmelCase_						(self  )     ->      Dict:
          __UpperCAmelCase					,     __UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in self.all_model_classes:
              __UpperCAmelCase									=			model_class(lowercase__  )
              __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]     ,      lowercase__  )
  
  
      def      lowerCAmelCase_						(self  )     ->      int:
          __UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowercase__  )
  
  
      def      lowerCAmelCase_						(self  )     ->      Union[str, Any]:
          __UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_segmentation(*lowercase__  )
  
  
      def      lowerCAmelCase_						(self  )     ->      Dict:
          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(lowercase__  )]:
                  continue
  
              __UpperCAmelCase									=			model_class(lowercase__  )
              model.to(lowercase__  )
              model.train()
              __UpperCAmelCase									=			self._prepare_for_class(lowercase__     ,      lowercase__     ,      return_labels=lowercase__  )
              __UpperCAmelCase									=			model(**lowercase__  ).loss
              loss.backward()
  
  
      def      lowerCAmelCase_						(self  )     ->      Any:
          __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(lowercase__  )] 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=lowercase__  ).long()
              __UpperCAmelCase									=			model_class(lowercase__  )
              model.gradient_checkpointing_enable()
              model.to(lowercase__  )
              model.train()
              __UpperCAmelCase									=			self._prepare_for_class(lowercase__     ,      lowercase__     ,      return_labels=lowercase__  )
              __UpperCAmelCase									=			model(**lowercase__  ).loss
              loss.backward()
  
  
      def      lowerCAmelCase_						(self  )     ->      List[Any]:
          __UpperCAmelCase					,     __UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs_for_common()
  
          __UpperCAmelCase									=			_config_zero_init(lowercase__  )
          for model_class in self.all_model_classes:
              __UpperCAmelCase									=			model_class(config=lowercase__  )
              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      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__=2E-4     ,      lowercase__="outputs"     ,      lowercase__=None  )     ->      List[str]:
          # We override with a slightly higher tol value, as semseg models tend to diverge a bit more
          super().check_pt_tf_outputs(lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__  )
  
  
      def      lowerCAmelCase_						(self  )     ->      List[Any]:
          __UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*lowercase__  )
  
  
  
  
  
  
      @slow
      def      lowerCAmelCase_						(self  )     ->      Optional[Any]:
          for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              __UpperCAmelCase									=			DataaVecVisionModel.from_pretrained(lowercase__  )
              self.assertIsNotNone(lowercase__  )
  
  
  
  
  
  
  def       __a       (   )      ->     Union[str, Any]:
  
  
  
  
  
  
      '''simple docstring'''
      __UpperCAmelCase									=			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
      return image
  @require_torch
  @require_vision
  class 					A_   (      unittest.TestCase						):
  
  
      '''simple docstring'''
  
  
  
  
  
  
      @cached_property
      def      lowerCAmelCase_						(self  )     ->      Optional[int]:
          return (
              BeitImageProcessor.from_pretrained('''facebook/data2vec-vision-base-ft1k'''  ) if is_vision_available() else None
          )
  
  
  
  
  
  
      @slow
      def      lowerCAmelCase_						(self  )     ->      Tuple:
          __UpperCAmelCase									=			DataaVecVisionForImageClassification.from_pretrained('''facebook/data2vec-vision-base-ft1k'''  ).to(
              lowercase__  )
  
          __UpperCAmelCase									=			self.default_image_processor
          __UpperCAmelCase									=			prepare_img()
          __UpperCAmelCase									=			image_processor(images=lowercase__     ,      return_tensors='''pt'''  ).to(lowercase__  )
  
          # forward pass
          with torch.no_grad():
              __UpperCAmelCase									=			model(**lowercase__  )
          __UpperCAmelCase									=			outputs.logits
  
          # verify the logits
          __UpperCAmelCase									=			torch.Size((1, 1_000)  )
          self.assertEqual(logits.shape     ,      lowercase__  )
  
          __UpperCAmelCase									=			torch.tensor([0.3277, -0.1395, 0.0911]  ).to(lowercase__  )
  
          self.assertTrue(torch.allclose(logits[0, :3]     ,      lowercase__     ,      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()     ,      lowercase__  )
  
  
  
  
  
style_context: 
  
  import unittest
  
  import numpy as np
  
  from transformers.testing_utils import require_torch, require_vision
  from transformers.utils import is_torch_available, is_vision_available
  
  from ...test_image_processing_common import ImageProcessingSavingTestMixin
  
  
  if is_torch_available():
         import torch
  
  if is_vision_available():
         from PIL import Image
  
         from transformers import SwinaSRImageProcessor
         from transformers.image_transforms import get_image_size
  class 					A_   (      unittest.TestCase						):
  
  
      '''simple docstring'''
  
  
  
  
  
  
      def __init__(self     ,      lowercase__     ,      lowercase__=7     ,      lowercase__=3     ,      lowercase__=18     ,      lowercase__=30     ,      lowercase__=400     ,      lowercase__=True     ,      lowercase__=1 / 255     ,      lowercase__=True     ,      lowercase__=8     ,      )     ->      List[str]:
          __UpperCAmelCase									=			parent
          __UpperCAmelCase									=			batch_size
          __UpperCAmelCase									=			num_channels
          __UpperCAmelCase									=			image_size
          __UpperCAmelCase									=			min_resolution
          __UpperCAmelCase									=			max_resolution
          __UpperCAmelCase									=			do_rescale
          __UpperCAmelCase									=			rescale_factor
          __UpperCAmelCase									=			do_pad
          __UpperCAmelCase									=			pad_size
  
  
      def      lowerCAmelCase_						(self  )     ->      int:
          return {
              "do_rescale": self.do_rescale,
              "rescale_factor": self.rescale_factor,
              "do_pad": self.do_pad,
              "pad_size": self.pad_size,
          }
  
  
  
  
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__=False     ,      lowercase__=False     ,      lowercase__=False  )     ->      Tuple:
          assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
  
          if equal_resolution:
              __UpperCAmelCase									=			[]
              for i in range(self.batch_size  ):
                  image_inputs.append(
                      np.random.randint(
                          255     ,      size=(self.num_channels, self.max_resolution, self.max_resolution)     ,      dtype=np.uinta  )  )
          else:
              __UpperCAmelCase									=			[]
              for i in range(self.batch_size  ):
                  __UpperCAmelCase					,     __UpperCAmelCase									=			np.random.choice(np.arange(self.min_resolution     ,      self.max_resolution  )     ,      2  )
                  image_inputs.append(np.random.randint(255     ,      size=(self.num_channels, width, height)     ,      dtype=np.uinta  )  )
  
          if not numpify and not torchify:
              # PIL expects the channel dimension as last dimension
              __UpperCAmelCase									=			[Image.fromarray(np.moveaxis(lowercase__     ,      0     ,      -1  )  ) for x in image_inputs]
  
          if torchify:
              __UpperCAmelCase									=			[torch.from_numpy(lowercase__  ) for x in image_inputs]
  
          return image_inputs
  
  
  
  
  
  
  @require_torch
  @require_vision
  class 					A_   (      _a ,     unittest.TestCase						):
  
  
      '''simple docstring'''
  
  
  
  
  
  
      a__     							=       SwinaSRImageProcessor if is_vision_available() else None
  
  
      def      lowerCAmelCase_						(self  )     ->      str:
          __UpperCAmelCase									=			SwinaSRImageProcessingTester(self  )
  
  
      @property
      def      lowerCAmelCase_						(self  )     ->      str:
          return self.image_processor_tester.prepare_image_processor_dict()
  
  
      def      lowerCAmelCase_						(self  )     ->      List[Any]:
          __UpperCAmelCase									=			self.image_processing_class(**self.image_processor_dict  )
          self.assertTrue(hasattr(lowercase__     ,      '''do_rescale'''  )  )
          self.assertTrue(hasattr(lowercase__     ,      '''rescale_factor'''  )  )
          self.assertTrue(hasattr(lowercase__     ,      '''do_pad'''  )  )
          self.assertTrue(hasattr(lowercase__     ,      '''pad_size'''  )  )
  
  
      def      lowerCAmelCase_						(self  )     ->      Optional[Any]:
          pass
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__  )     ->      int:
          __UpperCAmelCase					,     __UpperCAmelCase									=			get_image_size(lowercase__  )
          __UpperCAmelCase									=			self.image_processor_tester.pad_size
  
          __UpperCAmelCase									=			(old_height // size + 1) * size - old_height
          __UpperCAmelCase									=			(old_width // size + 1) * size - old_width
          return old_height + pad_height, old_width + pad_width
  
  
      def      lowerCAmelCase_						(self  )     ->      Optional[int]:
          # Initialize image_processor
          __UpperCAmelCase									=			self.image_processing_class(**self.image_processor_dict  )
          # create random PIL images
          __UpperCAmelCase									=			self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__  )
          for image in image_inputs:
              self.assertIsInstance(lowercase__     ,      Image.Image  )
  
          # Test not batched input
          __UpperCAmelCase									=			image_processor(image_inputs[0]     ,      return_tensors='''pt'''  ).pixel_values
          __UpperCAmelCase					,     __UpperCAmelCase									=			self.calculate_expected_size(np.array(image_inputs[0]  )  )
          self.assertEqual(
              encoded_images.shape     ,      (
                  1,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )     ,      )
  
  
      def      lowerCAmelCase_						(self  )     ->      str:
          # Initialize image_processor
          __UpperCAmelCase									=			self.image_processing_class(**self.image_processor_dict  )
          # create random numpy tensors
          __UpperCAmelCase									=			self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__     ,      numpify=lowercase__  )
          for image in image_inputs:
              self.assertIsInstance(lowercase__     ,      np.ndarray  )
  
          # Test not batched input
          __UpperCAmelCase									=			image_processor(image_inputs[0]     ,      return_tensors='''pt'''  ).pixel_values
          __UpperCAmelCase					,     __UpperCAmelCase									=			self.calculate_expected_size(image_inputs[0]  )
          self.assertEqual(
              encoded_images.shape     ,      (
                  1,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )     ,      )
  
  
  
  
  
  
      def      lowerCAmelCase_						(self  )     ->      int:
          # Initialize image_processor
          __UpperCAmelCase									=			self.image_processing_class(**self.image_processor_dict  )
          # create random PyTorch tensors
          __UpperCAmelCase									=			self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__     ,      torchify=lowercase__  )
          for image in image_inputs:
              self.assertIsInstance(lowercase__     ,      torch.Tensor  )
  
          # Test not batched input
          __UpperCAmelCase									=			image_processor(image_inputs[0]     ,      return_tensors='''pt'''  ).pixel_values
          __UpperCAmelCase					,     __UpperCAmelCase									=			self.calculate_expected_size(image_inputs[0]  )
          self.assertEqual(
              encoded_images.shape     ,      (
                  1,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )     ,      )
  
  
  
  
  