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 						A__      :
  
  
  
  
  
  
  
      """simple docstring"""
  
      def __init__( self    ,      __snake_case    ,      __snake_case=2    ,      __snake_case=3_2    ,      __snake_case=1_6    ,      __snake_case=3    ,      __snake_case=True    ,      __snake_case=True    ,      __snake_case=3_2    ,      __snake_case=4    ,      __snake_case=[0, 1, 2, 3]    ,      __snake_case=4    ,      __snake_case=3_7    ,      __snake_case="gelu"    ,      __snake_case=0.1    ,      __snake_case=0.1    ,      __snake_case=0.02    ,      __snake_case=3    ,      __snake_case=False    ,      __snake_case=None    ,      ):
           snake_case					=       parent
           snake_case					=       batch_size
           snake_case					=       image_size
           snake_case					=       patch_size
           snake_case					=       num_channels
           snake_case					=       is_training
           snake_case					=       use_labels
           snake_case					=       hidden_size
           snake_case					=       num_hidden_layers
           snake_case					=       backbone_out_indices
           snake_case					=       num_attention_heads
           snake_case					=       intermediate_size
           snake_case					=       hidden_act
           snake_case					=       hidden_dropout_prob
           snake_case					=       attention_probs_dropout_prob
           snake_case					=       initializer_range
           snake_case					=       num_labels
           snake_case					=       scope
           snake_case					=       is_hybrid
           # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
           snake_case					=       (image_size // patch_size) ** 2
           snake_case					=       num_patches + 1
      def 	a_    ( self					):
           snake_case					=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)
  
           snake_case					=       None
           if self.use_labels:
                snake_case					=       ids_tensor([self.batch_size, self.image_size, self.image_size]    ,      self.num_labels					)
  
           snake_case					=       self.get_config()
  
           return config, pixel_values, labels
      def 	a_    ( self					):
           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 	a_    ( self    ,      __snake_case    ,      __snake_case    ,      __snake_case					):
           snake_case					=       DPTModel(config=__snake_case					)
           model.to(__snake_case					)
           model.eval()
           snake_case					=       model(__snake_case					)
           self.parent.assertEqual(result.last_hidden_state.shape    ,      (self.batch_size, self.seq_length, self.hidden_size)					)
      def 	a_    ( self    ,      __snake_case    ,      __snake_case    ,      __snake_case					):
           snake_case					=       self.num_labels
           snake_case					=       DPTForDepthEstimation(__snake_case					)
           model.to(__snake_case					)
           model.eval()
           snake_case					=       model(__snake_case					)
           self.parent.assertEqual(result.predicted_depth.shape    ,      (self.batch_size, self.image_size, self.image_size)					)
      def 	a_    ( self    ,      __snake_case    ,      __snake_case    ,      __snake_case					):
           snake_case					=       self.num_labels
           snake_case					=       DPTForSemanticSegmentation(__snake_case					)
           model.to(__snake_case					)
           model.eval()
           snake_case					=       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 	a_    ( self					):
           snake_case					=       self.prepare_config_and_inputs()
           snake_case				,						snake_case				,						snake_case					=       config_and_inputs
           snake_case					=       {'''pixel_values''': pixel_values}
           return config, inputs_dict
  
  
  
  
  
  
  @require_torch
  class 						A__      (					snake_case__ ,      snake_case__ ,      unittest.TestCase			):
  
  
  
  
  
  
  
      """simple docstring"""
  
      __magic_name__    						=	(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
      __magic_name__    						=	(
          {
              'depth-estimation': DPTForDepthEstimation,
              'feature-extraction': DPTModel,
              'image-segmentation': DPTForSemanticSegmentation,
          }
          if is_torch_available()
          else {}
      )
  
      __magic_name__    						=	False
      __magic_name__    						=	False
      __magic_name__    						=	False
      def 	a_    ( self					):
           snake_case					=       DPTModelTester(self					)
           snake_case					=       ConfigTester(self    ,      config_class=__snake_case    ,      has_text_modality=__snake_case    ,      hidden_size=3_7					)
      def 	a_    ( self					):
           self.config_tester.run_common_tests()
      @unittest.skip(reason='''DPT does not use inputs_embeds'''					)
      def 	a_    ( self					):
           pass
      def 	a_    ( self					):
           snake_case				,						snake_case					=       self.model_tester.prepare_config_and_inputs_for_common()
  
           for model_class in self.all_model_classes:
                snake_case					=       model_class(__snake_case					)
                self.assertIsInstance(model.get_input_embeddings()    ,      (nn.Module)					)
                snake_case					=       model.get_output_embeddings()
                self.assertTrue(x is None or isinstance(__snake_case    ,      nn.Linear					)					)
      def 	a_    ( self					):
           snake_case				,						snake_case					=       self.model_tester.prepare_config_and_inputs_for_common()
  
           for model_class in self.all_model_classes:
                snake_case					=       model_class(__snake_case					)
                snake_case					=       inspect.signature(model.forward					)
                # signature.parameters is an OrderedDict => so arg_names order is deterministic
                snake_case					=       [*signature.parameters.keys()]
  
                snake_case					=       ['''pixel_values''']
                self.assertListEqual(arg_names[:1]    ,      __snake_case					)
      def 	a_    ( self					):
           snake_case					=       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__snake_case					)
      def 	a_    ( self					):
           snake_case					=       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_depth_estimation(*__snake_case					)
      def 	a_    ( self					):
           snake_case					=       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_semantic_segmentation(*__snake_case					)
      def 	a_    ( self					):
           for model_class in self.all_model_classes:
                if model_class.__name__ == "DPTForDepthEstimation":
                     continue
  
                snake_case				,						snake_case					=       self.model_tester.prepare_config_and_inputs_for_common()
                snake_case					=       True
  
                if model_class in get_values(__snake_case					):
                     continue
  
                snake_case					=       model_class(__snake_case					)
                model.to(__snake_case					)
                model.train()
                snake_case					=       self._prepare_for_class(__snake_case    ,      __snake_case    ,      return_labels=__snake_case					)
                snake_case					=       model(**__snake_case					).loss
                loss.backward()
      def 	a_    ( self					):
           for model_class in self.all_model_classes:
                if model_class.__name__ == "DPTForDepthEstimation":
                     continue
  
                snake_case				,						snake_case					=       self.model_tester.prepare_config_and_inputs_for_common()
                snake_case					=       False
                snake_case					=       True
  
                if model_class in get_values(__snake_case					) or not model_class.supports_gradient_checkpointing:
                     continue
                snake_case					=       model_class(__snake_case					)
                model.to(__snake_case					)
                model.gradient_checkpointing_enable()
                model.train()
                snake_case					=       self._prepare_for_class(__snake_case    ,      __snake_case    ,      return_labels=__snake_case					)
                snake_case					=       model(**__snake_case					).loss
                loss.backward()
      def 	a_    ( self					):
           snake_case				,						snake_case					=       self.model_tester.prepare_config_and_inputs_for_common()
  
           snake_case					=       _config_zero_init(__snake_case					)
           for model_class in self.all_model_classes:
                snake_case					=       model_class(config=__snake_case					)
                # Skip the check for the backbone
                snake_case					=       []
                for name, module in model.named_modules():
                     if module.__class__.__name__ == "DPTViTHybridEmbeddings":
                          snake_case					=       [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 	a_    ( self					):
           pass
  
  
  
  
      @slow
      def 	a_    ( self					):
           for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                snake_case					=       DPTModel.from_pretrained(__snake_case					)
                self.assertIsNotNone(__snake_case					)
  
  
  
  
  
  
  def     UpperCAmelCase__		():
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
       return image
  
  @require_torch
  @require_vision
  @slow
  class 						A__      (					unittest.TestCase			):
  
  
  
  
  
  
  
      """simple docstring"""
  
      def 	a_    ( self					):
           snake_case					=       DPTImageProcessor.from_pretrained('''Intel/dpt-large'''					)
           snake_case					=       DPTForDepthEstimation.from_pretrained('''Intel/dpt-large'''					).to(__snake_case					)
  
           snake_case					=       prepare_img()
           snake_case					=       image_processor(images=__snake_case    ,      return_tensors='''pt'''					).to(__snake_case					)
  
           # forward pass
           with torch.no_grad():
                snake_case					=       model(**__snake_case					)
                snake_case					=       outputs.predicted_depth
  
           # verify the predicted depth
           snake_case					=       torch.Size((1, 3_8_4, 3_8_4)					)
           self.assertEqual(predicted_depth.shape    ,      __snake_case					)
  
           snake_case					=       torch.tensor(
               [[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]					).to(__snake_case					)
  
           self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]    ,      __snake_case    ,      atol=1E-4					)					)
      def 	a_    ( self					):
           snake_case					=       DPTImageProcessor.from_pretrained('''Intel/dpt-large-ade'''					)
           snake_case					=       DPTForSemanticSegmentation.from_pretrained('''Intel/dpt-large-ade'''					).to(__snake_case					)
  
           snake_case					=       prepare_img()
           snake_case					=       image_processor(images=__snake_case    ,      return_tensors='''pt'''					).to(__snake_case					)
  
           # forward pass
           with torch.no_grad():
                snake_case					=       model(**__snake_case					)
  
           # verify the logits
           snake_case					=       torch.Size((1, 1_5_0, 4_8_0, 4_8_0)					)
           self.assertEqual(outputs.logits.shape    ,      __snake_case					)
  
           snake_case					=       torch.tensor(
               [[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]					).to(__snake_case					)
  
           self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]    ,      __snake_case    ,      atol=1E-4					)					)
  
  
  
  
      def 	a_    ( self					):
           snake_case					=       DPTImageProcessor.from_pretrained('''Intel/dpt-large-ade'''					)
           snake_case					=       DPTForSemanticSegmentation.from_pretrained('''Intel/dpt-large-ade'''					).to(__snake_case					)
  
           snake_case					=       prepare_img()
           snake_case					=       image_processor(images=__snake_case    ,      return_tensors='''pt'''					).to(__snake_case					)
  
           # forward pass
           with torch.no_grad():
                snake_case					=       model(**__snake_case					)
  
           snake_case					=       outputs.logits.detach().cpu()
  
           snake_case					=       image_processor.post_process_semantic_segmentation(outputs=__snake_case    ,      target_sizes=[(5_0_0, 3_0_0)]					)
           snake_case					=       torch.Size((5_0_0, 3_0_0)					)
           self.assertEqual(segmentation[0].shape    ,      __snake_case					)
  
           snake_case					=       image_processor.post_process_semantic_segmentation(outputs=__snake_case					)
           snake_case					=       torch.Size((4_8_0, 4_8_0)					)
           self.assertEqual(segmentation[0].shape    ,      __snake_case					)
  
  
  
  
  
style_context: 
  
  
  from __future__ import annotations
  
  import random
  from collections.abc import Iterable
  
  class 						A__      :
  
  
  
  
  
  
  
      """simple docstring"""
  
      def __init__( self    ,      __snake_case					):
           snake_case					=       {literal: None for literal in literals}
      def __str__( self					):
           return "{" + " , ".join(self.literals					) + "}"
      def __len__( self					):
           return len(self.literals					)
      def 	a_    ( self    ,      __snake_case					):
           for literal in self.literals:
                snake_case					=       literal[:2]
                if symbol in model:
                     snake_case					=       model[symbol]
                else:
                     continue
                if value is not None:
                     # Complement assignment if literal is in complemented form
                     if literal.endswith('''\''''					):
                          snake_case					=       not value
                snake_case					=       value
  
  
  
  
      def 	a_    ( self    ,      __snake_case					):
           for literal in self.literals:
                snake_case					=       literal.rstrip('''\''''					) if literal.endswith('''\''''					) else literal + '''\''''
                if symbol in self.literals:
                     return True
  
           self.assign(__snake_case					)
           for value in self.literals.values():
                if value in (True, None):
                     return value
           return any(self.literals.values()					)
  
  
  
  
  
  
  class 						A__      :
  
  
  
  
  
  
  
      """simple docstring"""
  
      def __init__( self    ,      __snake_case					):
           snake_case					=       list(__snake_case					)
  
  
  
  
      def __str__( self					):
           return "{" + " , ".join(str(__snake_case					) for clause in self.clauses					) + "}"
  
  
  
  
  
  
  def     UpperCAmelCase__		():
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       []
       snake_case					=       random.randint(1							,5       )
       snake_case					=       '''A'''
       snake_case					=       0
       while i < no_of_literals:
            snake_case					=       random.randint(1							,5       )
            snake_case					=       base_var + str(UpperCamelCase_       )
            snake_case					=       random.randint(0							,1       )
            if var_complement == 1:
                 var_name += "'"
            if var_name in literals:
                 i -= 1
            else:
                 literals.append(UpperCamelCase_       )
            i += 1
       return Clause(UpperCamelCase_       )
  def     UpperCAmelCase__		():
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       set()
       snake_case					=       random.randint(1							,10       )
       while len(UpperCamelCase_       ) < no_of_clauses:
            clauses.add(generate_clause()       )
       return Formula(UpperCamelCase_       )
  def     UpperCAmelCase__		(UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       formula.clauses
       snake_case					=       []
       for clause in formula.clauses:
            for literal in clause.literals:
                 snake_case					=       literal[:2]
                 if symbol not in symbols_set:
                      symbols_set.append(UpperCamelCase_       )
       return clauses, symbols_set
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       []
       snake_case					=       {}
       snake_case					=       []
  
       for clause in clauses:
            if clause.evaluate(UpperCamelCase_       ):
                 continue
            for literal in clause.literals:
                 literals.append(UpperCamelCase_       )
  
       for s in symbols:
            snake_case					=       s + '''\''''
            if (s in literals and sym not in literals) or (
                s not in literals and sym in literals
            ):
                 pure_symbols.append(UpperCamelCase_       )
       for p in pure_symbols:
            snake_case					=       None
       for s in pure_symbols:
            snake_case					=       s + '''\''''
            if s in literals:
                 snake_case					=       True
            elif sym in literals:
                 snake_case					=       False
       return pure_symbols, assignment
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       []
       for clause in clauses:
            if len(UpperCamelCase_       ) == 1:
                 unit_symbols.append(list(clause.literals.keys()       )[0]       )
            else:
                 snake_case				,						snake_case					=       0, 0
                 for literal, value in clause.literals.items():
                      if value is False:
                           f_count += 1
                      elif value is None:
                           snake_case					=       literal
                           n_count += 1
                 if f_count == len(UpperCamelCase_       ) - 1 and n_count == 1:
                      unit_symbols.append(UpperCamelCase_       )
       snake_case					=       {}
       for i in unit_symbols:
            snake_case					=       i[:2]
            snake_case					=       len(UpperCamelCase_       ) == 2
       snake_case					=       [i[:2] for i in unit_symbols]
  
       return unit_symbols, assignment
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       True
       for clause in clauses:
            snake_case					=       clause.evaluate(UpperCamelCase_       )
            if clause_check is False:
                 return False, None
            elif clause_check is None:
                 snake_case					=       False
                 continue
  
       if check_clause_all_true:
            return True, model
  
       try:
            snake_case				,						snake_case					=       find_pure_symbols(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       )
       except RecursionError:
            print('''raises a RecursionError and is'''       )
            return None, {}
       snake_case					=       None
       if len(UpperCamelCase_       ) > 0:
            snake_case				,						snake_case					=       pure_symbols[0], assignment[pure_symbols[0]]
  
       if p:
            snake_case					=       model
            snake_case					=       value
            snake_case					=       list(UpperCamelCase_       )
            if p in tmp_symbols:
                 tmp_symbols.remove(UpperCamelCase_       )
            return dpll_algorithm(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       )
  
       snake_case				,						snake_case					=       find_unit_clauses(UpperCamelCase_							,UpperCamelCase_       )
       snake_case					=       None
       if len(UpperCamelCase_       ) > 0:
            snake_case				,						snake_case					=       unit_symbols[0], assignment[unit_symbols[0]]
       if p:
            snake_case					=       model
            snake_case					=       value
            snake_case					=       list(UpperCamelCase_       )
            if p in tmp_symbols:
                 tmp_symbols.remove(UpperCamelCase_       )
            return dpll_algorithm(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       )
       snake_case					=       symbols[0]
       snake_case					=       symbols[1:]
       snake_case				,						snake_case					=       model, model
       snake_case				,						snake_case					=       True, False
  
       return dpll_algorithm(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       ) or dpll_algorithm(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       )
  
  
  if __name__ == "__main__":
   import doctest
  
   doctest.testmod()
  
   _SCREAMING_SNAKE_CASE    :				Dict       =					generate_formula()
   print(f'''The formula {formula} is''', end=" ")
  
   _SCREAMING_SNAKE_CASE   ,     _SCREAMING_SNAKE_CASE    :				Any       =					generate_parameters(formula)
   _SCREAMING_SNAKE_CASE   ,     _SCREAMING_SNAKE_CASE    :				Optional[Any]       =					dpll_algorithm(clauses, symbols, {})
  
   if solution:
    print(f'''satisfiable with the assignment {model}.''')
   else:
    print("not satisfiable.")
  
  
  
  
  