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    SCREAMING_SNAKE_CASE__      :
  
  
  
  
  
      def __init__(   self		,     a		,     a=100		,     a=13		,     a=30		,     a=2		,     a=3		,     a=True		,     a=True		,     a=32		,     a=4		,     a=4		,     a=37		,     a="gelu"		,     a=0.1		,     a=0.1		,     a=10		,     a=0.02		,     a=3		,     a=None		,     a=[0, 1, 2, 3]		,     ):
           lowercase__					:     List[Any]   = parent
           lowercase__					:     Any   = 100
           lowercase__					:     str   = batch_size
           lowercase__					:     Optional[int]   = image_size
           lowercase__					:     Tuple   = patch_size
           lowercase__					:     List[str]   = num_channels
           lowercase__					:     Tuple   = is_training
           lowercase__					:     int   = use_labels
           lowercase__					:     Any   = hidden_size
           lowercase__					:     Tuple   = num_hidden_layers
           lowercase__					:     Optional[Any]   = num_attention_heads
           lowercase__					:     int   = intermediate_size
           lowercase__					:     int   = hidden_act
           lowercase__					:     Dict   = hidden_dropout_prob
           lowercase__					:     List[str]   = attention_probs_dropout_prob
           lowercase__					:     List[Any]   = type_sequence_label_size
           lowercase__					:     Any   = initializer_range
           lowercase__					:     Any   = scope
           lowercase__					:     List[Any]   = out_indices
           lowercase__					:     Union[str, Any]   = num_labels
  
           # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
           lowercase__					:     str   = (image_size // patch_size) ** 2
           lowercase__					:     Dict   = num_patches + 1
  
  
  
  
  
      def    snake_case_       (   self):
           lowercase__					:     Optional[int]   = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
  
           lowercase__					:     str   = None
           lowercase__					:     Dict   = None
           if self.use_labels:
                lowercase__					:     Optional[Any]   = ids_tensor([self.batch_size]		,     self.type_sequence_label_size)
                lowercase__					:     Any   = ids_tensor([self.batch_size, self.image_size, self.image_size]		,     self.num_labels)
  
           lowercase__					:     str   = self.get_config()
  
           return config, pixel_values, labels, pixel_labels
  
  
  
  
  
      def    snake_case_       (   self):
           return DataaVecVisionConfig(
               vocab_size=self.vocab_size		,     image_size=self.image_size		,     patch_size=self.patch_size		,     num_channels=self.num_channels		,     hidden_size=self.hidden_size		,     num_hidden_layers=self.num_hidden_layers		,     num_attention_heads=self.num_attention_heads		,     intermediate_size=self.intermediate_size		,     hidden_act=self.hidden_act		,     hidden_dropout_prob=self.hidden_dropout_prob		,     attention_probs_dropout_prob=self.attention_probs_dropout_prob		,     is_decoder=a		,     initializer_range=self.initializer_range		,     out_indices=self.out_indices		,     )
  
  
  
  
  
      def    snake_case_       (   self		,     a		,     a		,     a		,     a):
           lowercase__					:     str   = DataaVecVisionModel(config=a)
           model.to(a)
           model.eval()
           lowercase__					:     List[Any]   = model(a)
           # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
           lowercase__					:     Optional[int]   = (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    snake_case_       (   self		,     a		,     a		,     a		,     a):
           lowercase__					:     Tuple   = self.type_sequence_label_size
           lowercase__					:     List[str]   = DataaVecVisionForImageClassification(a)
           model.to(a)
           model.eval()
           lowercase__					:     Union[str, Any]   = model(a		,     labels=a)
           self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.type_sequence_label_size))
  
  
  
  
  
      def    snake_case_       (   self		,     a		,     a		,     a		,     a):
           lowercase__					:     Optional[Any]   = self.num_labels
           lowercase__					:     Optional[int]   = DataaVecVisionForSemanticSegmentation(a)
           model.to(a)
           model.eval()
           lowercase__					:     Union[str, Any]   = model(a)
           self.parent.assertEqual(
               result.logits.shape		,     (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2))
           lowercase__					:     int   = model(a		,     labels=a)
           self.parent.assertEqual(
               result.logits.shape		,     (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2))
  
  
  
  
  
      def    snake_case_       (   self):
           lowercase__					:     List[str]   = self.prepare_config_and_inputs()
           lowercase__   ,				lowercase__   ,				lowercase__   ,				lowercase__					:     Dict   = config_and_inputs
           lowercase__					:     Any   = {'pixel_values': pixel_values}
           return config, inputs_dict
  
  
  
  @require_torch
  class    SCREAMING_SNAKE_CASE__      (__snake_case	, __snake_case	, unittest.TestCase  ):
      __lowerCamelCase		: Optional[Any]														=  (
          (DataaVecVisionModel, DataaVecVisionForImageClassification, DataaVecVisionForSemanticSegmentation)
          if is_torch_available()
          else ()
      )
      __lowerCamelCase		: Tuple														=  (
          {
              """feature-extraction""": DataaVecVisionModel,
              """image-classification""": DataaVecVisionForImageClassification,
              """image-segmentation""": DataaVecVisionForSemanticSegmentation,
          }
          if is_torch_available()
          else {}
      )
  
      __lowerCamelCase		: List[str]														=  False
      __lowerCamelCase		: int														=  False
      __lowerCamelCase		: List[str]														=  False
  
  
  
  
  
      def    snake_case_       (   self):
           lowercase__					:     List[str]   = DataaVecVisionModelTester(self)
           lowercase__					:     Tuple   = ConfigTester(
               self		,     config_class=a		,     has_text_modality=a		,     hidden_size=37)
  
  
  
  
  
      def    snake_case_       (   self):
           self.config_tester.run_common_tests()
  
  
  
  
  
      def    snake_case_       (   self):
           # Data2VecVision does not use inputs_embeds
           pass
  
  
  
  
  
      @require_torch_multi_gpu
      @unittest.skip(
          reason='Data2VecVision has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`')
      def    snake_case_       (   self):
           pass
  
  
  
  
  
      def    snake_case_       (   self):
           lowercase__   ,				lowercase__					:     Any   = self.model_tester.prepare_config_and_inputs_for_common()
  
           for model_class in self.all_model_classes:
                lowercase__					:     Dict   = model_class(a)
                self.assertIsInstance(model.get_input_embeddings()		,     (nn.Module))
                lowercase__					:     str   = model.get_output_embeddings()
                self.assertTrue(x is None or isinstance(a		,     nn.Linear))
  
  
  
  
  
      def    snake_case_       (   self):
           lowercase__   ,				lowercase__					:     Union[str, Any]   = self.model_tester.prepare_config_and_inputs_for_common()
  
           for model_class in self.all_model_classes:
                lowercase__					:     int   = model_class(a)
                lowercase__					:     Any   = inspect.signature(model.forward)
                # signature.parameters is an OrderedDict => so arg_names order is deterministic
                lowercase__					:     Any   = [*signature.parameters.keys()]
  
                lowercase__					:     Optional[Any]   = ['pixel_values']
                self.assertListEqual(arg_names[:1]		,     a)
  
  
  
  
  
      def    snake_case_       (   self):
           lowercase__					:     Tuple   = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*a)
  
  
  
  
  
      def    snake_case_       (   self):
           lowercase__					:     Any   = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_segmentation(*a)
  
  
  
  
  
      def    snake_case_       (   self):
           if not self.model_tester.is_training:
                return
  
           lowercase__   ,				lowercase__					:     Dict   = self.model_tester.prepare_config_and_inputs_for_common()
           lowercase__					:     Any   = True
  
           for model_class in self.all_model_classes:
                if model_class in [*get_values(a)]:
                     continue
  
                lowercase__					:     List[str]   = model_class(a)
                model.to(a)
                model.train()
                lowercase__					:     Any   = self._prepare_for_class(a		,     a		,     return_labels=a)
                lowercase__					:     Optional[int]   = model(**a).loss
                loss.backward()
  
  
  
  
  
      def    snake_case_       (   self):
           lowercase__   ,				lowercase__					:     Tuple   = self.model_tester.prepare_config_and_inputs_for_common()
           if not self.model_tester.is_training:
                return
  
           lowercase__					:     str   = False
           lowercase__					:     List[Any]   = True
  
           for model_class in self.all_model_classes:
                if model_class in [*get_values(a)] or not model_class.supports_gradient_checkpointing:
                     continue
                # TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING
                # this can then be incorporated into _prepare_for_class in test_modeling_common.py
                elif model_class.__name__ == "Data2VecVisionForSemanticSegmentation":
                     lowercase__   ,				lowercase__   ,				lowercase__   ,				lowercase__					:     int   = inputs_dict['pixel_values'].shape
                     lowercase__					:     Any   = torch.zeros(
                         [self.model_tester.batch_size, height, width]		,     device=a).long()
                lowercase__					:     Tuple   = model_class(a)
                model.gradient_checkpointing_enable()
                model.to(a)
                model.train()
                lowercase__					:     Union[str, Any]   = self._prepare_for_class(a		,     a		,     return_labels=a)
                lowercase__					:     Optional[int]   = model(**a).loss
                loss.backward()
  
  
  
  
  
      def    snake_case_       (   self):
           lowercase__   ,				lowercase__					:     List[Any]   = self.model_tester.prepare_config_and_inputs_for_common()
  
           lowercase__					:     List[Any]   = _config_zero_init(a)
           for model_class in self.all_model_classes:
                lowercase__					:     str   = model_class(config=a)
                for name, param in model.named_parameters():
                     # we skip lambda parameters as these require special initial values
                     # determined by config.layer_scale_init_value
                     if "lambda" in name:
                          continue
                     if param.requires_grad:
                          self.assertIn(
                              ((param.data.mean() * 1e9).round() / 1e9).item()		,     [0.0, 1.0]		,     msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""		,     )
  
  
  
  
  
      def    snake_case_       (   self		,     a		,     a		,     a		,     a=2e-4		,     a="outputs"		,     a=None):
           # We override with a slightly higher tol value, as semseg models tend to diverge a bit more
           super().check_pt_tf_outputs(a		,     a		,     a		,     a		,     a		,     a)
  
  
  
  
  
      def    snake_case_       (   self):
           lowercase__					:     Union[str, Any]   = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*a)
  
  
  
  
  
      @slow
      def    snake_case_       (   self):
           for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                lowercase__					:     List[Any]   = DataaVecVisionModel.from_pretrained(a)
                self.assertIsNotNone(a)
  
  
  
  def 			snake_case__    (					):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     Optional[int]   = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'    )
       return image
  
  
  @require_torch
  @require_vision
  class    SCREAMING_SNAKE_CASE__      (unittest.TestCase  ):
  
  
  
  
  
      @cached_property
      def    snake_case_       (   self):
           return (
               BeitImageProcessor.from_pretrained('facebook/data2vec-vision-base-ft1k') if is_vision_available() else None
           )
  
  
  
  
  
      @slow
      def    snake_case_       (   self):
           lowercase__					:     Dict   = DataaVecVisionForImageClassification.from_pretrained('facebook/data2vec-vision-base-ft1k').to(
               a)
  
           lowercase__					:     Optional[Any]   = self.default_image_processor
           lowercase__					:     Optional[int]   = prepare_img()
           lowercase__					:     List[Any]   = image_processor(images=a		,     return_tensors='pt').to(a)
  
           # forward pass
           with torch.no_grad():
                lowercase__					:     Dict   = model(**a)
           lowercase__					:     List[Any]   = outputs.logits
  
           # verify the logits
           lowercase__					:     Any   = torch.Size((1, 1000))
           self.assertEqual(logits.shape		,     a)
  
           lowercase__					:     Union[str, Any]   = torch.tensor([0.3_277, -0.1_395, 0.0_911]).to(a)
  
           self.assertTrue(torch.allclose(logits[0, :3]		,     a		,     atol=1e-4))
  
           lowercase__					:     Tuple   = [model.config.labelaid[i] for i in ['remote control, remote', 'tabby, tabby cat']]
           self.assertEqual(logits[0].topk(2).indices.cpu().tolist()		,     a)
  
  
style_context: 
  
  # as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.
  
  import os
  import sys
  
  from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow
  
  from .utils import load_json
  
  
  class    SCREAMING_SNAKE_CASE__      (__snake_case  ):
  
  
  
  
  
      @classmethod
      def    snake_case_       (   cls):
           return cls
  
  
  
  
  
      @slow
      @require_torch_gpu
      def    snake_case_       (   self):
           lowercase__					:     List[Any]   = self.get_auto_remove_tmp_dir()
           lowercase__					:     List[str]   = f"""
              --model_name Helsinki-NLP/opus-mt-en-ro
              --save_dir {output_dir}
              --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro
              --num_beams 2
              --task translation
          """.split()
  
           # we want this test to run even if there is only one GPU, but if there are more we use them all
           lowercase__					:     int   = get_gpu_count()
           lowercase__					:     Tuple   = f"""
              -m torch.distributed.launch
              --nproc_per_node={n_gpu}
              {self.test_file_dir}/run_distributed_eval.py
          """.split()
           lowercase__					:     Optional[Any]   = [sys.executable] + distributed_args + args
           execute_subprocess_async(a		,     env=self.get_env())
  
           lowercase__					:     str   = os.path.join(a		,     'test_bleu.json')
           lowercase__					:     List[str]   = load_json(a)
           # print(metrics)
           self.assertGreaterEqual(metrics['bleu']		,     25)
  
  