'''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_       :







   """simple docstring"""

   def __init__(      self      :			Union[str, Any], UpperCamelCase__      :			Any, UpperCamelCase__      :			Optional[Any]=2, UpperCamelCase__      :			Union[str, Any]=32, UpperCamelCase__      :			Optional[Any]=16, UpperCamelCase__      :			Union[str, Any]=3, UpperCamelCase__      :			Optional[int]=True, UpperCamelCase__      :			Union[str, Any]=True, UpperCamelCase__      :			Union[str, Any]=32, UpperCamelCase__      :			Union[str, Any]=4, UpperCamelCase__      :			List[str]=[0, 1, 2, 3], UpperCamelCase__      :			List[Any]=4, UpperCamelCase__      :			Any=37, UpperCamelCase__      :			List[Any]="gelu", UpperCamelCase__      :			Any=0.1, UpperCamelCase__      :			List[str]=0.1, UpperCamelCase__      :			Tuple=0.02, UpperCamelCase__      :			Optional[int]=3, UpperCamelCase__      :			int=[1, 3_84, 24, 24], UpperCamelCase__      :			int=True, UpperCamelCase__      :			Optional[Any]=None, )  ->			str:
        _A    		=  parent
        _A    		=  batch_size
        _A    		=  image_size
        _A    		=  patch_size
        _A    		=  num_channels
        _A    		=  is_training
        _A    		=  use_labels
        _A    		=  hidden_size
        _A    		=  num_hidden_layers
        _A    		=  backbone_out_indices
        _A    		=  num_attention_heads
        _A    		=  intermediate_size
        _A    		=  hidden_act
        _A    		=  hidden_dropout_prob
        _A    		=  attention_probs_dropout_prob
        _A    		=  initializer_range
        _A    		=  num_labels
        _A    		=  backbone_featmap_shape
        _A    		=  scope
        _A    		=  is_hybrid
        # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
        _A    		=  (image_size // patch_size) ** 2
        _A    		=  num_patches + 1
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			Union[str, Any]:
        _A    		=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

        _A    		=  None
        if self.use_labels:
             _A    		=  ids_tensor([self.batch_size, self.image_size, self.image_size], self.num_labels     )

        _A    		=  self.get_config()

        return config, pixel_values, labels
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			int:
        _A    		=  {
            'global_padding': 'same',
            'layer_type': 'bottleneck',
            'depths': [3, 4, 9],
            'out_features': ['stage1', 'stage2', 'stage3'],
            'embedding_dynamic_padding': True,
            'hidden_sizes': [96, 1_92, 3_84, 7_68],
            'num_groups': 2,
        }

        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=UpperCamelCase__, initializer_range=self.initializer_range, is_hybrid=self.is_hybrid, backbone_config=UpperCamelCase__, backbone_featmap_shape=self.backbone_featmap_shape, )
   def 	__UpperCAmelCase   (      self      :			int, UpperCamelCase__      :			str, UpperCamelCase__      :			int, UpperCamelCase__      :			List[str]     )  ->			str:
        _A    		=  DPTModel(config=UpperCamelCase__     )
        model.to(UpperCamelCase__     )
        model.eval()
        _A    		=  model(UpperCamelCase__     )
        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)     )
   def 	__UpperCAmelCase   (      self      :			Tuple, UpperCamelCase__      :			List[str], UpperCamelCase__      :			List[Any], UpperCamelCase__      :			int     )  ->			Union[str, Any]:
        _A    		=  self.num_labels
        _A    		=  DPTForDepthEstimation(UpperCamelCase__     )
        model.to(UpperCamelCase__     )
        model.eval()
        _A    		=  model(UpperCamelCase__     )
        self.parent.assertEqual(result.predicted_depth.shape, (self.batch_size, self.image_size, self.image_size)     )
   def 	__UpperCAmelCase   (      self      :			Tuple, UpperCamelCase__      :			Optional[Any], UpperCamelCase__      :			Union[str, Any], UpperCamelCase__      :			int     )  ->			Any:
        _A    		=  self.num_labels
        _A    		=  DPTForSemanticSegmentation(UpperCamelCase__     )
        model.to(UpperCamelCase__     )
        model.eval()
        _A    		=  model(UpperCamelCase__, labels=UpperCamelCase__     )
        self.parent.assertEqual(
            result.logits.shape, (self.batch_size, self.num_labels, self.image_size, self.image_size)     )

   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			Optional[int]:
        _A    		=  self.prepare_config_and_inputs()
        _A		,       _A		,       _A    		=  config_and_inputs
        _A    		=  {'pixel_values': pixel_values}
        return config, inputs_dict







@require_torch
class lowercase_       (		_UpperCamelCase    ,    _UpperCamelCase    ,    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 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			Optional[Any]:
        _A    		=  DPTModelTester(self     )
        _A    		=  ConfigTester(self, config_class=UpperCamelCase__, has_text_modality=UpperCamelCase__, hidden_size=37     )
   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			Union[str, Any]:
        self.config_tester.run_common_tests()
   @unittest.skip(reason='DPT does not use inputs_embeds'     )
   def 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			str:
        pass
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			List[Any]:
        _A		,       _A    		=  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             _A    		=  model_class(UpperCamelCase__     )
             self.assertIsInstance(model.get_input_embeddings(), (nn.Module)     )
             _A    		=  model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(UpperCamelCase__, nn.Linear     )     )
   def 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			Tuple:
        _A		,       _A    		=  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             _A    		=  model_class(UpperCamelCase__     )
             _A    		=  inspect.signature(model.forward     )
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             _A    		=  [*signature.parameters.keys()]

             _A    		=  ['pixel_values']
             self.assertListEqual(arg_names[:1], UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			str     )  ->			Optional[int]:
        _A    		=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			int     )  ->			Optional[int]:
        _A    		=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_depth_estimation(*UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			Any     )  ->			List[Any]:
        _A    		=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_semantic_segmentation(*UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			Optional[Any]:
        for model_class in self.all_model_classes:
             if model_class.__name__ == "DPTForDepthEstimation":
                  continue

             _A		,       _A    		=  self.model_tester.prepare_config_and_inputs_for_common()
             _A    		=  True

             if model_class in get_values(UpperCamelCase__     ):
                  continue

             _A    		=  model_class(UpperCamelCase__     )
             model.to(UpperCamelCase__     )
             model.train()
             _A    		=  self._prepare_for_class(UpperCamelCase__, UpperCamelCase__, return_labels=UpperCamelCase__     )
             _A    		=  model(**UpperCamelCase__     ).loss
             loss.backward()
   def 	__UpperCAmelCase   (      self      :			Any     )  ->			List[Any]:
        for model_class in self.all_model_classes:
             if model_class.__name__ == "DPTForDepthEstimation":
                  continue

             _A		,       _A    		=  self.model_tester.prepare_config_and_inputs_for_common()
             _A    		=  False
             _A    		=  True

             if model_class in get_values(UpperCamelCase__     ) or not model_class.supports_gradient_checkpointing:
                  continue
             _A    		=  model_class(UpperCamelCase__     )
             model.to(UpperCamelCase__     )
             model.gradient_checkpointing_enable()
             model.train()
             _A    		=  self._prepare_for_class(UpperCamelCase__, UpperCamelCase__, return_labels=UpperCamelCase__     )
             _A    		=  model(**UpperCamelCase__     ).loss
             loss.backward()
   def 	__UpperCAmelCase   (      self      :			int     )  ->			List[Any]:
        _A		,       _A    		=  self.model_tester.prepare_config_and_inputs_for_common()

        _A    		=  _config_zero_init(UpperCamelCase__     )
        for model_class in self.all_model_classes:
             _A    		=  model_class(config=UpperCamelCase__     )
             # Skip the check for the backbone
             _A    		=  []
             for name, module in model.named_modules():
                  if module.__class__.__name__ == "DPTViTHybridEmbeddings":
                       _A    		=  [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[int]     )  ->			int:
        pass
   @slow
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			Dict:
        for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
             _A    		=  DPTModel.from_pretrained(UpperCamelCase__     )
             self.assertIsNotNone(UpperCamelCase__     )

   def 	__UpperCAmelCase   (      self      :			Any     )  ->			List[Any]:
        # We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
        _A		,       _A    		=  self.model_tester.prepare_config_and_inputs_for_common()
        _A    		=  'add'
        with self.assertRaises(UpperCamelCase__     ):
             _A    		=  DPTForDepthEstimation(UpperCamelCase__     )






def  _SCREAMING_SNAKE_CASE		(      ):
     _A    		=  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
     return image







@require_torch
@require_vision
@slow
class lowercase_       (		unittest.TestCase			):







   """simple docstring"""

   def 	__UpperCAmelCase   (      self      :			int     )  ->			List[str]:
        _A    		=  DPTImageProcessor.from_pretrained('Intel/dpt-hybrid-midas'     )
        _A    		=  DPTForDepthEstimation.from_pretrained('Intel/dpt-hybrid-midas'     ).to(UpperCamelCase__     )

        _A    		=  prepare_img()
        _A    		=  image_processor(images=UpperCamelCase__, return_tensors='pt'     ).to(UpperCamelCase__     )

        # forward pass
        with torch.no_grad():
             _A    		=  model(**UpperCamelCase__     )
             _A    		=  outputs.predicted_depth

        # verify the predicted depth
        _A    		=  torch.Size((1, 3_84, 3_84)     )
        self.assertEqual(predicted_depth.shape, UpperCamelCase__     )

        _A    		=  torch.tensor(
            [[[5.6_437, 5.6_146, 5.6_511], [5.4_371, 5.5_649, 5.5_958], [5.5_215, 5.5_184, 5.5_293]]]     ).to(UpperCamelCase__     )

        self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 1_00, UpperCamelCase__, atol=1e-4     )     )







"""simple docstring"""
def 				_snake_case      (   __snake_case							:      list[list[int]]						, __snake_case							:      int						, __snake_case							:      int						, __snake_case							:      list[int]   ):




   """simple docstring"""







   if graph[path[curr_ind - 1]][next_ver] == 0:
      return False

   # 2. Validate that next vertex is not already in path
   return not any(vertex == next_ver for vertex in path   )

def 				_snake_case      (   __snake_case							:      list[list[int]]						, __snake_case							:      list[int]						, __snake_case							:      int   ):




   """simple docstring"""







   if curr_ind == len(__snake_case   ):
      # return whether path exists between current and starting vertices
      return graph[path[curr_ind - 1]][path[0]] == 1

   # Recursive Step
   for next_ver in range(0						, len(__snake_case   )   ):
      if valid_connection(__snake_case						, __snake_case						, __snake_case						, __snake_case   ):
         # Insert current vertex  into path as next transition
         _lowerCamelCase	:					List[str]   =		next_ver
         # Validate created path
         if util_hamilton_cycle(__snake_case						, __snake_case						, curr_ind + 1   ):
            return True
         # Backtrack
         _lowerCamelCase	:					Tuple   =		-1
   return False





def 				_snake_case      (   __snake_case							:      list[list[int]]						, __snake_case							:      int = 0   ):




   """simple docstring"""







   _lowerCamelCase	:					Any   =		[-1] * (len(__snake_case   ) + 1)
   # initialize start and end of path with starting index
   _lowerCamelCase	:					Optional[int]   =		start_index
   # evaluate and if we find answer return path either return empty array
   return path if util_hamilton_cycle(__snake_case						, __snake_case						, 1   ) else []

