


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					)








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.")




