code: 
  
  
  """simple docstring"""
  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   lowercase__							:
  
  
  
  
  
     def __init__(       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE=2			,      SCREAMING_SNAKE_CASE=32			,      SCREAMING_SNAKE_CASE=16			,      SCREAMING_SNAKE_CASE=3			,      SCREAMING_SNAKE_CASE=True			,      SCREAMING_SNAKE_CASE=True			,      SCREAMING_SNAKE_CASE=32			,      SCREAMING_SNAKE_CASE=4			,      SCREAMING_SNAKE_CASE=[0, 1, 2, 3]			,      SCREAMING_SNAKE_CASE=4			,      SCREAMING_SNAKE_CASE=37			,      SCREAMING_SNAKE_CASE="gelu"			,      SCREAMING_SNAKE_CASE=0.1			,      SCREAMING_SNAKE_CASE=0.1			,      SCREAMING_SNAKE_CASE=0.02			,      SCREAMING_SNAKE_CASE=3			,      SCREAMING_SNAKE_CASE=False			,      SCREAMING_SNAKE_CASE=None			,      )     ->     Any:
        _lowerCamelCase  :				Optional[Any]      =		parent
        _lowerCamelCase  :				Union[str, Any]      =		batch_size
        _lowerCamelCase  :				List[Any]      =		image_size
        _lowerCamelCase  :				List[Any]      =		patch_size
        _lowerCamelCase  :				int      =		num_channels
        _lowerCamelCase  :				str      =		is_training
        _lowerCamelCase  :				int      =		use_labels
        _lowerCamelCase  :				Optional[Any]      =		hidden_size
        _lowerCamelCase  :				str      =		num_hidden_layers
        _lowerCamelCase  :				Optional[Any]      =		backbone_out_indices
        _lowerCamelCase  :				Any      =		num_attention_heads
        _lowerCamelCase  :				Optional[int]      =		intermediate_size
        _lowerCamelCase  :				str      =		hidden_act
        _lowerCamelCase  :				Tuple      =		hidden_dropout_prob
        _lowerCamelCase  :				Optional[int]      =		attention_probs_dropout_prob
        _lowerCamelCase  :				int      =		initializer_range
        _lowerCamelCase  :				List[Any]      =		num_labels
        _lowerCamelCase  :				List[str]      =		scope
        _lowerCamelCase  :				List[Any]      =		is_hybrid
        # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
        _lowerCamelCase  :				str      =		(image_size // patch_size) ** 2
        _lowerCamelCase  :				List[str]      =		num_patches + 1
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Optional[Any]:
        _lowerCamelCase  :				Optional[int]      =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
  
        _lowerCamelCase  :				Dict      =		None
        if self.use_labels:
           _lowerCamelCase  :				Any      =		ids_tensor([self.batch_size, self.image_size, self.image_size]			,      self.num_labels)
  
        _lowerCamelCase  :				Optional[int]      =		self.get_config()
  
        return config, pixel_values, labels
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     str:
        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=SCREAMING_SNAKE_CASE			,      initializer_range=self.initializer_range			,      is_hybrid=self.is_hybrid			,      )
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)     ->     List[str]:
        _lowerCamelCase  :				Any      =		DPTModel(config=SCREAMING_SNAKE_CASE)
        model.to(SCREAMING_SNAKE_CASE)
        model.eval()
        _lowerCamelCase  :				Optional[int]      =		model(SCREAMING_SNAKE_CASE)
        self.parent.assertEqual(result.last_hidden_state.shape			,      (self.batch_size, self.seq_length, self.hidden_size))
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)     ->     List[Any]:
        _lowerCamelCase  :				Union[str, Any]      =		self.num_labels
        _lowerCamelCase  :				Optional[Any]      =		DPTForDepthEstimation(SCREAMING_SNAKE_CASE)
        model.to(SCREAMING_SNAKE_CASE)
        model.eval()
        _lowerCamelCase  :				Optional[Any]      =		model(SCREAMING_SNAKE_CASE)
        self.parent.assertEqual(result.predicted_depth.shape			,      (self.batch_size, self.image_size, self.image_size))
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)     ->     str:
        _lowerCamelCase  :				List[str]      =		self.num_labels
        _lowerCamelCase  :				Union[str, Any]      =		DPTForSemanticSegmentation(SCREAMING_SNAKE_CASE)
        model.to(SCREAMING_SNAKE_CASE)
        model.eval()
        _lowerCamelCase  :				List[Any]      =		model(SCREAMING_SNAKE_CASE			,      labels=SCREAMING_SNAKE_CASE)
        self.parent.assertEqual(
            result.logits.shape			,      (self.batch_size, self.num_labels, self.image_size, self.image_size))
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     List[Any]:
        _lowerCamelCase  :				List[str]      =		self.prepare_config_and_inputs()
        _lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase  :				Tuple      =		config_and_inputs
        _lowerCamelCase  :				Any      =		{"""pixel_values""": pixel_values}
        return config, inputs_dict
  
  
  
  
  
  
  
  @require_torch
  class   lowercase__							(				A_   ,A_   ,unittest.TestCase							):
     __UpperCAmelCase       					=				(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
     __UpperCAmelCase       					=				(
         {
             '''depth-estimation''': DPTForDepthEstimation,
             '''feature-extraction''': DPTModel,
             '''image-segmentation''': DPTForSemanticSegmentation,
         }
         if is_torch_available()
         else {}
     )
  
     __UpperCAmelCase       					=				False
     __UpperCAmelCase       					=				False
     __UpperCAmelCase       					=				False
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Any:
        _lowerCamelCase  :				Union[str, Any]      =		DPTModelTester(self)
        _lowerCamelCase  :				int      =		ConfigTester(self			,      config_class=SCREAMING_SNAKE_CASE			,      has_text_modality=SCREAMING_SNAKE_CASE			,      hidden_size=37)
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     int:
        self.config_tester.run_common_tests()
  
  
  
  
  
     @unittest.skip(reason="""DPT does not use inputs_embeds""")
     def 		UpperCamelCase_     (       self)     ->     Union[str, Any]:
        pass
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Dict:
        _lowerCamelCase      ,						_lowerCamelCase  :				Optional[int]      =		self.model_tester.prepare_config_and_inputs_for_common()
  
        for model_class in self.all_model_classes:
           _lowerCamelCase  :				List[str]      =		model_class(SCREAMING_SNAKE_CASE)
           self.assertIsInstance(model.get_input_embeddings()			,      (nn.Module))
           _lowerCamelCase  :				Any      =		model.get_output_embeddings()
           self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE			,      nn.Linear))
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Optional[Any]:
        _lowerCamelCase      ,						_lowerCamelCase  :				Union[str, Any]      =		self.model_tester.prepare_config_and_inputs_for_common()
  
        for model_class in self.all_model_classes:
           _lowerCamelCase  :				List[Any]      =		model_class(SCREAMING_SNAKE_CASE)
           _lowerCamelCase  :				str      =		inspect.signature(model.forward)
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           _lowerCamelCase  :				Optional[Any]      =		[*signature.parameters.keys()]
  
           _lowerCamelCase  :				Tuple      =		["""pixel_values"""]
           self.assertListEqual(arg_names[:1]			,      SCREAMING_SNAKE_CASE)
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     List[Any]:
        _lowerCamelCase  :				Tuple      =		self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE)
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Union[str, Any]:
        _lowerCamelCase  :				List[str]      =		self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_depth_estimation(*SCREAMING_SNAKE_CASE)
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Tuple:
        _lowerCamelCase  :				List[str]      =		self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_semantic_segmentation(*SCREAMING_SNAKE_CASE)
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Optional[Any]:
        for model_class in self.all_model_classes:
           if model_class.__name__ == "DPTForDepthEstimation":
              continue
  
           _lowerCamelCase      ,						_lowerCamelCase  :				int      =		self.model_tester.prepare_config_and_inputs_for_common()
           _lowerCamelCase  :				List[str]      =		True
  
           if model_class in get_values(SCREAMING_SNAKE_CASE):
              continue
  
           _lowerCamelCase  :				List[str]      =		model_class(SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)
           model.train()
           _lowerCamelCase  :				Union[str, Any]      =		self._prepare_for_class(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      return_labels=SCREAMING_SNAKE_CASE)
           _lowerCamelCase  :				int      =		model(**SCREAMING_SNAKE_CASE).loss
           loss.backward()
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Dict:
        for model_class in self.all_model_classes:
           if model_class.__name__ == "DPTForDepthEstimation":
              continue
  
           _lowerCamelCase      ,						_lowerCamelCase  :				List[Any]      =		self.model_tester.prepare_config_and_inputs_for_common()
           _lowerCamelCase  :				Tuple      =		False
           _lowerCamelCase  :				Any      =		True
  
           if model_class in get_values(SCREAMING_SNAKE_CASE) or not model_class.supports_gradient_checkpointing:
              continue
           _lowerCamelCase  :				List[str]      =		model_class(SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)
           model.gradient_checkpointing_enable()
           model.train()
           _lowerCamelCase  :				Any      =		self._prepare_for_class(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      return_labels=SCREAMING_SNAKE_CASE)
           _lowerCamelCase  :				Tuple      =		model(**SCREAMING_SNAKE_CASE).loss
           loss.backward()
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Dict:
        _lowerCamelCase      ,						_lowerCamelCase  :				Optional[Any]      =		self.model_tester.prepare_config_and_inputs_for_common()
  
        _lowerCamelCase  :				Any      =		_config_zero_init(SCREAMING_SNAKE_CASE)
        for model_class in self.all_model_classes:
           _lowerCamelCase  :				Tuple      =		model_class(config=SCREAMING_SNAKE_CASE)
           # Skip the check for the backbone
           _lowerCamelCase  :				Union[str, Any]      =		[]
           for name, module in model.named_modules():
              if module.__class__.__name__ == "DPTViTHybridEmbeddings":
                 _lowerCamelCase  :				Optional[Any]      =		[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 		UpperCamelCase_     (       self)     ->     Optional[Any]:
        pass
  
  
  
  
  
     @slow
     def 		UpperCamelCase_     (       self)     ->     Dict:
        for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _lowerCamelCase  :				List[Any]      =		DPTModel.from_pretrained(SCREAMING_SNAKE_CASE)
           self.assertIsNotNone(SCREAMING_SNAKE_CASE)
  
  
  
  
  
  
  
  def 							_snake_case			(      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase  :				List[Any]      =		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
     return image
  
  
  
  
  
  @require_torch
  @require_vision
  @slow
  class   lowercase__							(				unittest.TestCase							):
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Union[str, Any]:
        _lowerCamelCase  :				Optional[int]      =		DPTImageProcessor.from_pretrained("""Intel/dpt-large""")
        _lowerCamelCase  :				str      =		DPTForDepthEstimation.from_pretrained("""Intel/dpt-large""").to(SCREAMING_SNAKE_CASE)
  
        _lowerCamelCase  :				Dict      =		prepare_img()
        _lowerCamelCase  :				Union[str, Any]      =		image_processor(images=SCREAMING_SNAKE_CASE			,      return_tensors="""pt""").to(SCREAMING_SNAKE_CASE)
  
        # forward pass
        with torch.no_grad():
           _lowerCamelCase  :				Tuple      =		model(**SCREAMING_SNAKE_CASE)
           _lowerCamelCase  :				Dict      =		outputs.predicted_depth
  
        # verify the predicted depth
        _lowerCamelCase  :				Tuple      =		torch.Size((1, 384, 384))
        self.assertEqual(predicted_depth.shape			,      SCREAMING_SNAKE_CASE)
  
        _lowerCamelCase  :				List[str]      =		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(SCREAMING_SNAKE_CASE)
  
        self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]			,      SCREAMING_SNAKE_CASE			,      atol=1e-4))
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     int:
        _lowerCamelCase  :				int      =		DPTImageProcessor.from_pretrained("""Intel/dpt-large-ade""")
        _lowerCamelCase  :				int      =		DPTForSemanticSegmentation.from_pretrained("""Intel/dpt-large-ade""").to(SCREAMING_SNAKE_CASE)
  
        _lowerCamelCase  :				Dict      =		prepare_img()
        _lowerCamelCase  :				Union[str, Any]      =		image_processor(images=SCREAMING_SNAKE_CASE			,      return_tensors="""pt""").to(SCREAMING_SNAKE_CASE)
  
        # forward pass
        with torch.no_grad():
           _lowerCamelCase  :				int      =		model(**SCREAMING_SNAKE_CASE)
  
        # verify the logits
        _lowerCamelCase  :				Optional[int]      =		torch.Size((1, 150, 480, 480))
        self.assertEqual(outputs.logits.shape			,      SCREAMING_SNAKE_CASE)
  
        _lowerCamelCase  :				str      =		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(SCREAMING_SNAKE_CASE)
  
        self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]			,      SCREAMING_SNAKE_CASE			,      atol=1e-4))
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     List[str]:
        _lowerCamelCase  :				List[str]      =		DPTImageProcessor.from_pretrained("""Intel/dpt-large-ade""")
        _lowerCamelCase  :				List[str]      =		DPTForSemanticSegmentation.from_pretrained("""Intel/dpt-large-ade""").to(SCREAMING_SNAKE_CASE)
  
        _lowerCamelCase  :				Optional[Any]      =		prepare_img()
        _lowerCamelCase  :				Any      =		image_processor(images=SCREAMING_SNAKE_CASE			,      return_tensors="""pt""").to(SCREAMING_SNAKE_CASE)
  
        # forward pass
        with torch.no_grad():
           _lowerCamelCase  :				Tuple      =		model(**SCREAMING_SNAKE_CASE)
  
        _lowerCamelCase  :				Any      =		outputs.logits.detach().cpu()
  
        _lowerCamelCase  :				List[str]      =		image_processor.post_process_semantic_segmentation(outputs=SCREAMING_SNAKE_CASE			,      target_sizes=[(500, 300)])
        _lowerCamelCase  :				Tuple      =		torch.Size((500, 300))
        self.assertEqual(segmentation[0].shape			,      SCREAMING_SNAKE_CASE)
  
        _lowerCamelCase  :				Optional[Any]      =		image_processor.post_process_semantic_segmentation(outputs=SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				Any      =		torch.Size((480, 480))
        self.assertEqual(segmentation[0].shape			,      SCREAMING_SNAKE_CASE)
  
  
style_context: 
  
  
  """simple docstring"""
  from dataclasses import dataclass
  
  UpperCAmelCase   =	"""0.1"""
  UpperCAmelCase   =	"""Lucia Harcekova"""
  
  
  
  
  @dataclass
  class   lowercase__							:
     __UpperCAmelCase       					=				42
     __UpperCAmelCase       					=				42
     __UpperCAmelCase       					=				42
  
  
  
  
  
     def __repr__(       self)     ->     str:
        return F'({self.offset}, {self.length}, {self.indicator})'
  
  
  
  
  
  
  
  class   lowercase__							:
  
  
  
  
  
     def __init__(       self			,      SCREAMING_SNAKE_CASE = 13			,      SCREAMING_SNAKE_CASE = 6)     ->     None:
        _lowerCamelCase  :				Optional[Any]      =		window_size
        _lowerCamelCase  :				List[str]      =		lookahead_buffer_size
        _lowerCamelCase  :				List[Any]      =		self.window_size - self.lookahead_buffer_size
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE)     ->     list[Token]:
        _lowerCamelCase  :				Union[str, Any]      =		[]
        _lowerCamelCase  :				Tuple      =		""""""
  
        # while there are still characters in text to compress
        while text:
           # find the next encoding phrase
           # - triplet with offset, length, indicator (the next encoding character)
           _lowerCamelCase  :				List[Any]      =		self._find_encoding_token(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)
  
           # update the search buffer:
           # - add new characters from text into it
           # - check if size exceed the max search buffer size, if so, drop the
           #   oldest elements
           search_buffer += text[: token.length + 1]
           if len(SCREAMING_SNAKE_CASE) > self.search_buffer_size:
              _lowerCamelCase  :				int      =		search_buffer[-self.search_buffer_size :]
  
           # update the text
           _lowerCamelCase  :				Tuple      =		text[token.length + 1 :]
  
           # append the token to output
           output.append(SCREAMING_SNAKE_CASE)
  
        return output
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE)     ->     str:
        _lowerCamelCase  :				str      =		""""""
  
        for token in tokens:
           for _ in range(token.length):
              output += output[-token.offset]
           output += token.indicator
  
        return output
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)     ->     Token:
  
        if not text:
           raise ValueError("""We need some text to work with.""")
  
        # Initialise result parameters to default values
        _lowerCamelCase      ,						_lowerCamelCase  :				List[str]      =		0, 0
  
        if not search_buffer:
           return Token(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      text[length])
  
        for i, character in enumerate(SCREAMING_SNAKE_CASE):
           _lowerCamelCase  :				Tuple      =		len(SCREAMING_SNAKE_CASE) - i
           if character == text[0]:
              _lowerCamelCase  :				Any      =		self._match_length_from_index(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      0			,      SCREAMING_SNAKE_CASE)
              # if the found length is bigger than the current or if it's equal,
              # which means it's offset is smaller: update offset and length
              if found_length >= length:
                 _lowerCamelCase      ,						_lowerCamelCase  :				Optional[int]      =		found_offset, found_length
  
        return Token(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      text[length])
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)     ->     int:
        if not text or text[text_index] != window[window_index]:
           return 0
        return 1 + self._match_length_from_index(
            SCREAMING_SNAKE_CASE			,      window + text[text_index]			,      text_index + 1			,      window_index + 1)
  
  
  if __name__ == "__main__":
   from doctest import testmod
  
   testmod()
   # Initialize compressor class
   UpperCAmelCase   =	LZaaCompressor(window_size=13, lookahead_buffer_size=6)
  
   # Example
   UpperCAmelCase   =	"""cabracadabrarrarrad"""
   UpperCAmelCase   =	lzaa_compressor.compress(TEXT)
   print(lzaa_compressor.compress("""ababcbababaa"""))
   UpperCAmelCase   =	lzaa_compressor.decompress(compressed_text)
   assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."
  
  