




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 :  Optional[int]						,							A_ :  Union[str, Any]						,							A_ :  Tuple=2						,							A_ :  int=32						,							A_ :  Union[str, Any]=16						,							A_ :  Optional[Any]=3						,							A_ :  Any=True						,							A_ :  Optional[Any]=True						,							A_ :  List[Any]=32						,							A_ :  Dict=4						,							A_ :  Union[str, Any]=[0, 1, 2, 3]						,							A_ :  Dict=4						,							A_ :  List[Any]=37						,							A_ :  int="gelu"						,							A_ :  List[str]=0.1						,							A_ :  str=0.1						,							A_ :  Any=0.02						,							A_ :  List[Any]=3						,							A_ :  Any=[1, 384, 24, 24]						,							A_ :  Optional[Any]=True						,							A_ :  Dict=None						,							)       ->     List[str]:

       """simple docstring"""






       lowerCamelCase_							=   parent
       lowerCamelCase_							=   batch_size
       lowerCamelCase_							=   image_size
       lowerCamelCase_							=   patch_size
       lowerCamelCase_							=   num_channels
       lowerCamelCase_							=   is_training
       lowerCamelCase_							=   use_labels
       lowerCamelCase_							=   hidden_size
       lowerCamelCase_							=   num_hidden_layers
       lowerCamelCase_							=   backbone_out_indices
       lowerCamelCase_							=   num_attention_heads
       lowerCamelCase_							=   intermediate_size
       lowerCamelCase_							=   hidden_act
       lowerCamelCase_							=   hidden_dropout_prob
       lowerCamelCase_							=   attention_probs_dropout_prob
       lowerCamelCase_							=   initializer_range
       lowerCamelCase_							=   num_labels
       lowerCamelCase_							=   backbone_featmap_shape
       lowerCamelCase_							=   scope
       lowerCamelCase_							=   is_hybrid
       # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
       lowerCamelCase_							=   (image_size // patch_size) ** 2
       lowerCamelCase_							=   num_patches + 1

 def     a__			(    self :  str  )       ->     Any:

       """simple docstring"""






       lowerCamelCase_							=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

       lowerCamelCase_							=   self.get_config()

       return config, pixel_values, labels

 def     a__			(    self :  List[str]  )       ->     Any:

       """simple docstring"""






       lowerCamelCase_							=   {
           'global_padding': 'same',
           'layer_type': 'bottleneck',
           'depths': [3, 4, 9],
           'out_features': ['stage1', 'stage2', 'stage3'],
           'embedding_dynamic_padding': True,
           'hidden_sizes': [96, 192, 384, 768],
           '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=A_						,							initializer_range=self.initializer_range						,							is_hybrid=self.is_hybrid						,							backbone_config=A_						,							backbone_featmap_shape=self.backbone_featmap_shape						,							)

 def     a__			(    self :  List[Any]						,							A_ :  List[Any]						,							A_ :  int						,							A_ :  Dict  )       ->     str:

       """simple docstring"""






       lowerCamelCase_							=   DPTModel(config=A_  )
       model.to(A_  )
       model.eval()
       lowerCamelCase_							=   model(A_  )
       self.parent.assertEqual(result.last_hidden_state.shape						,							(self.batch_size, self.seq_length, self.hidden_size)  )

 def     a__			(    self :  Any						,							A_ :  List[Any]						,							A_ :  List[str]						,							A_ :  Dict  )       ->     str:

       """simple docstring"""






       lowerCamelCase_							=   self.num_labels
       lowerCamelCase_							=   DPTForDepthEstimation(A_  )
       model.to(A_  )
       model.eval()
       lowerCamelCase_							=   model(A_  )
       self.parent.assertEqual(result.predicted_depth.shape						,							(self.batch_size, self.image_size, self.image_size)  )

 def     a__			(    self :  Optional[int]						,							A_ :  List[Any]						,							A_ :  Any						,							A_ :  Any  )       ->     Dict:

       """simple docstring"""






       lowerCamelCase_							=   self.num_labels
       lowerCamelCase_							=   DPTForSemanticSegmentation(A_  )
       model.to(A_  )
       model.eval()
       lowerCamelCase_							=   model(A_						,							labels=A_  )
       self.parent.assertEqual(
           result.logits.shape						,							(self.batch_size, self.num_labels, self.image_size, self.image_size)  )





 def     a__			(    self :  Dict  )       ->     Dict:

       """simple docstring"""






       lowerCamelCase_							=   self.prepare_config_and_inputs()
       lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_							=   config_and_inputs
       lowerCamelCase_							=   {'pixel_values': pixel_values}
       return config, inputs_dict







@require_torch
class   A(						UpperCamelCase							,				UpperCamelCase							,				unittest.TestCase       ):



 '''simple docstring'''


 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     a__			(    self :  Tuple  )       ->     Any:

       """simple docstring"""






       lowerCamelCase_							=   DPTModelTester(self  )
       lowerCamelCase_							=   ConfigTester(self						,							config_class=A_						,							has_text_modality=A_						,							hidden_size=37  )

 def     a__			(    self :  Any  )       ->     Tuple:

       """simple docstring"""






       self.config_tester.run_common_tests()

 @unittest.skip(reason='DPT does not use inputs_embeds'  )
 def     a__			(    self :  Optional[Any]  )       ->     Tuple:

       """simple docstring"""






       pass

 def     a__			(    self :  Dict  )       ->     List[Any]:

       """simple docstring"""






       lowerCamelCase_					,    lowerCamelCase_							=   self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             lowerCamelCase_							=   model_class(A_  )
             self.assertIsInstance(model.get_input_embeddings()						,							(nn.Module)  )
             lowerCamelCase_							=   model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(A_						,							nn.Linear  )  )

 def     a__			(    self :  Optional[int]  )       ->     List[str]:

       """simple docstring"""






       lowerCamelCase_					,    lowerCamelCase_							=   self.model_tester.prepare_config_and_inputs_for_common()

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

             lowerCamelCase_							=   ['pixel_values']
             self.assertListEqual(arg_names[:1]						,							A_  )

 def     a__			(    self :  List[Any]  )       ->     int:

       """simple docstring"""






       lowerCamelCase_							=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*A_  )

 def     a__			(    self :  Union[str, Any]  )       ->     Tuple:

       """simple docstring"""






       lowerCamelCase_							=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_depth_estimation(*A_  )

 def     a__			(    self :  int  )       ->     Optional[int]:

       """simple docstring"""






       lowerCamelCase_							=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_semantic_segmentation(*A_  )

 def     a__			(    self :  Optional[Any]  )       ->     Optional[int]:

       """simple docstring"""






       for model_class in self.all_model_classes:
             if model_class.__name__ == "DPTForDepthEstimation":
                   continue

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

             if model_class in get_values(A_  ):
                   continue

             lowerCamelCase_							=   model_class(A_  )
             model.to(A_  )
             model.train()
             lowerCamelCase_							=   self._prepare_for_class(A_						,							A_						,							return_labels=A_  )
             lowerCamelCase_							=   model(**A_  ).loss
             loss.backward()

 def     a__			(    self :  str  )       ->     Tuple:

       """simple docstring"""






       for model_class in self.all_model_classes:
             if model_class.__name__ == "DPTForDepthEstimation":
                   continue

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

             if model_class in get_values(A_  ) or not model_class.supports_gradient_checkpointing:
                   continue
             lowerCamelCase_							=   model_class(A_  )
             model.to(A_  )
             model.gradient_checkpointing_enable()
             model.train()
             lowerCamelCase_							=   self._prepare_for_class(A_						,							A_						,							return_labels=A_  )
             lowerCamelCase_							=   model(**A_  ).loss
             loss.backward()

 def     a__			(    self :  Any  )       ->     Tuple:

       """simple docstring"""






       lowerCamelCase_					,    lowerCamelCase_							=   self.model_tester.prepare_config_and_inputs_for_common()

       lowerCamelCase_							=   _config_zero_init(A_  )
       for model_class in self.all_model_classes:
             lowerCamelCase_							=   model_class(config=A_  )
             # Skip the check for the backbone
             lowerCamelCase_							=   []
             for name, module in model.named_modules():
                   if module.__class__.__name__ == "DPTViTHybridEmbeddings":
                         lowerCamelCase_							=   [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 :  List[Any]  )       ->     Tuple:

       """simple docstring"""






       pass

 @slow
 def     a__			(    self :  Optional[Any]  )       ->     Optional[Any]:

       """simple docstring"""






       for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
             lowerCamelCase_							=   DPTModel.from_pretrained(A_  )
             self.assertIsNotNone(A_  )





 def     a__			(    self :  Tuple  )       ->     Optional[Any]:

       """simple docstring"""






       lowerCamelCase_					,    lowerCamelCase_							=   self.model_tester.prepare_config_and_inputs_for_common()
       lowerCamelCase_							=   'add'
       with self.assertRaises(A_  ):
             lowerCamelCase_							=   DPTForDepthEstimation(A_  )

def       _SCREAMING_SNAKE_CASE      (							):


      '''simple docstring'''



      lowerCamelCase_							=   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 :  List[str]  )       ->     Tuple:

       """simple docstring"""






       lowerCamelCase_							=   DPTImageProcessor.from_pretrained('Intel/dpt-hybrid-midas'  )
       lowerCamelCase_							=   DPTForDepthEstimation.from_pretrained('Intel/dpt-hybrid-midas'  ).to(A_  )

       lowerCamelCase_							=   prepare_img()
       lowerCamelCase_							=   image_processor(images=A_						,							return_tensors='pt'  ).to(A_  )

       # forward pass
       with torch.no_grad():
             lowerCamelCase_							=   model(**A_  )
             lowerCamelCase_							=   outputs.predicted_depth

       # verify the predicted depth
       lowerCamelCase_							=   torch.Size((1, 384, 384)  )
       self.assertEqual(predicted_depth.shape						,							A_  )

       lowerCamelCase_							=   torch.tensor(
           [[[5.6437, 5.6146, 5.6511], [5.4371, 5.5649, 5.5958], [5.5215, 5.5184, 5.5293]]]  ).to(A_  )

       self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 100						,							A_						,							atol=1E-4  )  )









def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Tuple     ,							lowercase     :	Dict     ,							lowercase     :	List[str]     ,							lowercase     :	Dict     ,							lowercase     :	Dict     ,							lowercase     :	List[str]					):


      '''simple docstring'''



      if index == r:
            for j in range(lowercase					):
                  print(data[j]     ,							end=' '					)
            print(' '					)
            return
      #  When no more elements are there to put in data[]
      if i >= n:
            return
      # current is included, put next at next location
      lowerCamelCase_							=   arr[i]
      combination_util(lowercase     ,							lowercase     ,							lowercase     ,							index + 1     ,							lowercase     ,							i + 1					)
      # current is excluded, replace it with
      # next (Note that i+1 is passed, but
      # index is not changed)
      combination_util(lowercase     ,							lowercase     ,							lowercase     ,							lowercase     ,							lowercase     ,							i + 1					)
      # The main function that prints all combinations
      # of size r in arr[] of size n. This function
      # mainly uses combinationUtil()

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	int     ,							lowercase     :	Any     ,							lowercase     :	Tuple					):


      '''simple docstring'''



      lowerCamelCase_							=   [0] * r
      # Print all combination using temporary array 'data[]'
      combination_util(lowercase     ,							lowercase     ,							lowercase     ,							0     ,							lowercase     ,							0					)


if __name__ == "__main__":
 # Driver code to check the function above
 lowerCamelCase    :		int			   =       [10, 20, 30, 40, 50]
 print_combination(arr, len(arr), 3)
 # This code is contributed by Ambuj sahu



