code: 
  
  
  
  
  
  
  import inspect
  import unittest
  
  from transformers import DPTConfig
  from transformers.file_utils import is_torch_available, is_vision_available
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_torch, require_vision, slow, torch_device
  
  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, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
     from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST
  
  
  if is_vision_available():
     from PIL import Image
  
     from transformers import DPTImageProcessor
  
  class 					snake_case_							:
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     def __init__(    self:	List[Any]   ,       snake_case__:	List[str]   ,       snake_case__:	Any=2   ,       snake_case__:	Tuple=32   ,       snake_case__:	List[Any]=16   ,       snake_case__:	Any=3   ,       snake_case__:	List[Any]=True   ,       snake_case__:	Union[str, Any]=True   ,       snake_case__:	List[Any]=32   ,       snake_case__:	int=4   ,       snake_case__:	Dict=[0, 1, 2, 3]   ,       snake_case__:	Tuple=4   ,       snake_case__:	str=37   ,       snake_case__:	Optional[int]="gelu"   ,       snake_case__:	List[Any]=0.1   ,       snake_case__:	Any=0.1   ,       snake_case__:	List[str]=0.02   ,       snake_case__:	Dict=3   ,       snake_case__:	Optional[Any]=False   ,       snake_case__:	Union[str, Any]=None   ,       )  ->		int:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	parent
         _lowercase     =	batch_size
         _lowercase     =	image_size
         _lowercase     =	patch_size
         _lowercase     =	num_channels
         _lowercase     =	is_training
         _lowercase     =	use_labels
         _lowercase     =	hidden_size
         _lowercase     =	num_hidden_layers
         _lowercase     =	backbone_out_indices
         _lowercase     =	num_attention_heads
         _lowercase     =	intermediate_size
         _lowercase     =	hidden_act
         _lowercase     =	hidden_dropout_prob
         _lowercase     =	attention_probs_dropout_prob
         _lowercase     =	initializer_range
         _lowercase     =	num_labels
         _lowercase     =	scope
         _lowercase     =	is_hybrid
         # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
         _lowercase     =	(image_size // patch_size) ** 2
         _lowercase     =	num_patches + 1
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	List[Any]	)  ->		List[str]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)
  
         _lowercase     =	None
         if self.use_labels:
             _lowercase     =	ids_tensor([self.batch_size, self.image_size, self.image_size]   ,       self.num_labels	)
  
         _lowercase     =	self.get_config()
  
         return config, pixel_values, labels
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	int	)  ->		List[str]:
  
  
         """simple docstring"""
  
  
  
         return DPTConfig(
             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   ,       backbone_out_indices=self.backbone_out_indices   ,       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=snake_case__   ,       initializer_range=self.initializer_range   ,       is_hybrid=self.is_hybrid   ,       )
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	List[str]   ,       snake_case__:	Dict   ,       snake_case__:	Optional[int]   ,       snake_case__:	int	)  ->		Optional[Any]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	DPTModel(config=snake_case__	)
         model.to(snake_case__	)
         model.eval()
         _lowercase     =	model(snake_case__	)
         self.parent.assertEqual(result.last_hidden_state.shape   ,       (self.batch_size, self.seq_length, self.hidden_size)	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Union[str, Any]   ,       snake_case__:	List[str]   ,       snake_case__:	Any   ,       snake_case__:	Any	)  ->		Any:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	self.num_labels
         _lowercase     =	DPTForDepthEstimation(snake_case__	)
         model.to(snake_case__	)
         model.eval()
         _lowercase     =	model(snake_case__	)
         self.parent.assertEqual(result.predicted_depth.shape   ,       (self.batch_size, self.image_size, self.image_size)	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[int]   ,       snake_case__:	Optional[Any]   ,       snake_case__:	Dict   ,       snake_case__:	Optional[Any]	)  ->		Optional[int]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	self.num_labels
         _lowercase     =	DPTForSemanticSegmentation(snake_case__	)
         model.to(snake_case__	)
         model.eval()
         _lowercase     =	model(snake_case__   ,       labels=snake_case__	)
         self.parent.assertEqual(
             result.logits.shape   ,       (self.batch_size, self.num_labels, self.image_size, self.image_size)	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Any	)  ->		Optional[int]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	self.prepare_config_and_inputs()
         _lowercase ,    _lowercase ,    _lowercase     =	config_and_inputs
         _lowercase     =	{'pixel_values': pixel_values}
         return config, inputs_dict
  
  
  
  
  
  
  @require_torch
  class 					snake_case_							(       lowercase_      ,			lowercase_      ,			unittest.TestCase      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     __lowerCAmelCase      =						(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
     __lowerCAmelCase      =						(
         {
             """depth-estimation""": DPTForDepthEstimation,
             """feature-extraction""": DPTModel,
             """image-segmentation""": DPTForSemanticSegmentation,
         }
         if is_torch_available()
         else {}
     )
  
     __lowerCAmelCase      =						False
     __lowerCAmelCase      =						False
     __lowerCAmelCase      =						False
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Dict	)  ->		List[Any]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	DPTModelTester(self	)
         _lowercase     =	ConfigTester(self   ,       config_class=snake_case__   ,       has_text_modality=snake_case__   ,       hidden_size=37	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	str	)  ->		Dict:
  
  
         """simple docstring"""
  
  
  
         self.config_tester.run_common_tests()
  
     @unittest.skip(reason='DPT does not use inputs_embeds'	)
     def 			SCREAMING_SNAKE_CASE__				(    self:	Union[str, Any]	)  ->		str:
  
  
         """simple docstring"""
  
  
  
         pass
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Dict	)  ->		str:
  
  
         """simple docstring"""
  
  
  
         _lowercase ,    _lowercase     =	self.model_tester.prepare_config_and_inputs_for_common()
  
         for model_class in self.all_model_classes:
             _lowercase     =	model_class(snake_case__	)
             self.assertIsInstance(model.get_input_embeddings()   ,       (nn.Module)	)
             _lowercase     =	model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(snake_case__   ,       nn.Linear	)	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	List[str]	)  ->		Dict:
  
  
         """simple docstring"""
  
  
  
         _lowercase ,    _lowercase     =	self.model_tester.prepare_config_and_inputs_for_common()
  
         for model_class in self.all_model_classes:
             _lowercase     =	model_class(snake_case__	)
             _lowercase     =	inspect.signature(model.forward	)
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             _lowercase     =	[*signature.parameters.keys()]
  
             _lowercase     =	['pixel_values']
             self.assertListEqual(arg_names[:1]   ,       snake_case__	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Dict	)  ->		Tuple:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*snake_case__	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	str	)  ->		Any:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_depth_estimation(*snake_case__	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	str	)  ->		List[Any]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_semantic_segmentation(*snake_case__	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	int	)  ->		Union[str, Any]:
  
  
         """simple docstring"""
  
  
  
         for model_class in self.all_model_classes:
             if model_class.__name__ == "DPTForDepthEstimation":
                 continue
  
             _lowercase ,    _lowercase     =	self.model_tester.prepare_config_and_inputs_for_common()
             _lowercase     =	True
  
             if model_class in get_values(snake_case__	):
                 continue
  
             _lowercase     =	model_class(snake_case__	)
             model.to(snake_case__	)
             model.train()
             _lowercase     =	self._prepare_for_class(snake_case__   ,       snake_case__   ,       return_labels=snake_case__	)
             _lowercase     =	model(**snake_case__	).loss
             loss.backward()
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Union[str, Any]	)  ->		str:
  
  
         """simple docstring"""
  
  
  
         for model_class in self.all_model_classes:
             if model_class.__name__ == "DPTForDepthEstimation":
                 continue
  
             _lowercase ,    _lowercase     =	self.model_tester.prepare_config_and_inputs_for_common()
             _lowercase     =	False
             _lowercase     =	True
  
             if model_class in get_values(snake_case__	) or not model_class.supports_gradient_checkpointing:
                 continue
             _lowercase     =	model_class(snake_case__	)
             model.to(snake_case__	)
             model.gradient_checkpointing_enable()
             model.train()
             _lowercase     =	self._prepare_for_class(snake_case__   ,       snake_case__   ,       return_labels=snake_case__	)
             _lowercase     =	model(**snake_case__	).loss
             loss.backward()
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	str	)  ->		Optional[int]:
  
  
         """simple docstring"""
  
  
  
         _lowercase ,    _lowercase     =	self.model_tester.prepare_config_and_inputs_for_common()
  
         _lowercase     =	_config_zero_init(snake_case__	)
         for model_class in self.all_model_classes:
             _lowercase     =	model_class(config=snake_case__	)
             # Skip the check for the backbone
             _lowercase     =	[]
             for name, module in model.named_modules():
                 if module.__class__.__name__ == "DPTViTHybridEmbeddings":
                     _lowercase     =	[f'''{name}.{key}''' for key in module.state_dict().keys()]
                     break
  
             for name, param in model.named_parameters():
                 if param.requires_grad:
                     if name in backbone_params:
                         continue
                     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'''   ,       )
  
     @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'	)
     def 			SCREAMING_SNAKE_CASE__				(    self:	Tuple	)  ->		Any:
  
  
         """simple docstring"""
  
  
  
         pass
  
     @slow
     def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[int]	)  ->		List[Any]:
  
  
         """simple docstring"""
  
  
  
         for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             _lowercase     =	DPTModel.from_pretrained(snake_case__	)
             self.assertIsNotNone(snake_case__	)
  
  
  
  
  
  
  def        UpperCAmelCase      (   ):
  
  
      '''simple docstring'''
  
  
  
      _lowercase     =	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
      return image
  
  @require_torch
  @require_vision
  @slow
  class 					snake_case_							(       unittest.TestCase      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[Any]	)  ->		Dict:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	DPTImageProcessor.from_pretrained('Intel/dpt-large'	)
         _lowercase     =	DPTForDepthEstimation.from_pretrained('Intel/dpt-large'	).to(snake_case__	)
  
         _lowercase     =	prepare_img()
         _lowercase     =	image_processor(images=snake_case__   ,       return_tensors='pt'	).to(snake_case__	)
  
         # forward pass
         with torch.no_grad():
             _lowercase     =	model(**snake_case__	)
             _lowercase     =	outputs.predicted_depth
  
         # verify the predicted depth
         _lowercase     =	torch.Size((1, 384, 384)	)
         self.assertEqual(predicted_depth.shape   ,       snake_case__	)
  
         _lowercase     =	torch.tensor(
             [[6.31_99, 6.36_29, 6.41_48], [6.38_50, 6.36_15, 6.41_66], [6.35_19, 6.31_76, 6.35_75]]	).to(snake_case__	)
  
         self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]   ,       snake_case__   ,       atol=1E-4	)	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Dict	)  ->		Optional[int]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'	)
         _lowercase     =	DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'	).to(snake_case__	)
  
         _lowercase     =	prepare_img()
         _lowercase     =	image_processor(images=snake_case__   ,       return_tensors='pt'	).to(snake_case__	)
  
         # forward pass
         with torch.no_grad():
             _lowercase     =	model(**snake_case__	)
  
         # verify the logits
         _lowercase     =	torch.Size((1, 150, 480, 480)	)
         self.assertEqual(outputs.logits.shape   ,       snake_case__	)
  
         _lowercase     =	torch.tensor(
             [[4.04_80, 4.24_20, 4.43_60], [4.31_24, 4.56_93, 4.82_61], [4.57_68, 4.89_65, 5.21_63]]	).to(snake_case__	)
  
         self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]   ,       snake_case__   ,       atol=1E-4	)	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Union[str, Any]	)  ->		Tuple:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'	)
         _lowercase     =	DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'	).to(snake_case__	)
  
         _lowercase     =	prepare_img()
         _lowercase     =	image_processor(images=snake_case__   ,       return_tensors='pt'	).to(snake_case__	)
  
         # forward pass
         with torch.no_grad():
             _lowercase     =	model(**snake_case__	)
  
         _lowercase     =	outputs.logits.detach().cpu()
  
         _lowercase     =	image_processor.post_process_semantic_segmentation(outputs=snake_case__   ,       target_sizes=[(500, 300)]	)
         _lowercase     =	torch.Size((500, 300)	)
         self.assertEqual(segmentation[0].shape   ,       snake_case__	)
  
         _lowercase     =	image_processor.post_process_semantic_segmentation(outputs=snake_case__	)
         _lowercase     =	torch.Size((480, 480)	)
         self.assertEqual(segmentation[0].shape   ,       snake_case__	)
  
style_context: 
  
  
  
  
  
  
  import argparse
  
  import torch
  
  from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging
  
  
  logging.set_verbosity_info()
  lowerCamelCase										=  logging.get_logger(__name__)
  
  lowerCamelCase										=  ['''UtteranceLevel''']
  @torch.no_grad()
  def        UpperCAmelCase      (   _lowercase,      _lowercase,      _lowercase,      _lowercase      ):
  
  
      '''simple docstring'''
  
  
  
      _lowercase     =	torch.load(_lowercase,      map_location='cpu'      )
      if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
          raise NotImplementedError(f'''The supported s3prl models are {SUPPORTED_MODELS}'''      )
  
      _lowercase     =	checkpoint['Downstream']
  
      _lowercase     =	HubertConfig.from_pretrained(_lowercase      )
      _lowercase     =	HubertForSequenceClassification.from_pretrained(_lowercase,      config=_lowercase      )
      _lowercase     =	WavaVecaFeatureExtractor.from_pretrained(
          _lowercase,      return_attention_mask=_lowercase,      do_normalize=_lowercase      )
  
      if hf_congfig.use_weighted_layer_sum:
          _lowercase     =	checkpoint['Featurizer']['weights']
  
      _lowercase     =	downstream_dict['projector.weight']
      _lowercase     =	downstream_dict['projector.bias']
      _lowercase     =	downstream_dict['model.post_net.linear.weight']
      _lowercase     =	downstream_dict['model.post_net.linear.bias']
  
      hf_feature_extractor.save_pretrained(_lowercase      )
      hf_model.save_pretrained(_lowercase      )
  
  
  if __name__ == "__main__":
     lowerCamelCase										=  argparse.ArgumentParser()
     parser.add_argument(
         '''--base_model_name''', default=None, type=str, help='''Name of the huggingface pretrained base model.'''
     )
     parser.add_argument('''--config_path''', default=None, type=str, help='''Path to the huggingface classifier config.''')
     parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to the s3prl checkpoint.''')
     parser.add_argument('''--model_dump_path''', default=None, type=str, help='''Path to the final converted model.''')
     lowerCamelCase										=  parser.parse_args()
     convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
  