




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 	lowerCamelCase_							:


 '''simple docstring'''







 def __init__(							self  :		Optional[Any]    ,    _lowerCAmelCase  :		Dict    ,    _lowerCAmelCase  :		Optional[Any]=2    ,    _lowerCAmelCase  :		Tuple=32    ,    _lowerCAmelCase  :		int=16    ,    _lowerCAmelCase  :		Dict=3    ,    _lowerCAmelCase  :		str=True    ,    _lowerCAmelCase  :		List[Any]=True    ,    _lowerCAmelCase  :		int=32    ,    _lowerCAmelCase  :		Optional[int]=4    ,    _lowerCAmelCase  :		int=[0, 1, 2, 3]    ,    _lowerCAmelCase  :		Optional[int]=4    ,    _lowerCAmelCase  :		Optional[int]=37    ,    _lowerCAmelCase  :		Tuple="gelu"    ,    _lowerCAmelCase  :		str=0.1    ,    _lowerCAmelCase  :		Union[str, Any]=0.1    ,    _lowerCAmelCase  :		Union[str, Any]=0.02    ,    _lowerCAmelCase  :		Union[str, Any]=3    ,    _lowerCAmelCase  :		Tuple=[1, 384, 24, 24]    ,    _lowerCAmelCase  :		List[str]=True    ,    _lowerCAmelCase  :		Optional[int]=None    ,    ):
       SCREAMING_SNAKE_CASE_     						=      parent
       SCREAMING_SNAKE_CASE_     						=      batch_size
       SCREAMING_SNAKE_CASE_     						=      image_size
       SCREAMING_SNAKE_CASE_     						=      patch_size
       SCREAMING_SNAKE_CASE_     						=      num_channels
       SCREAMING_SNAKE_CASE_     						=      is_training
       SCREAMING_SNAKE_CASE_     						=      use_labels
       SCREAMING_SNAKE_CASE_     						=      hidden_size
       SCREAMING_SNAKE_CASE_     						=      num_hidden_layers
       SCREAMING_SNAKE_CASE_     						=      backbone_out_indices
       SCREAMING_SNAKE_CASE_     						=      num_attention_heads
       SCREAMING_SNAKE_CASE_     						=      intermediate_size
       SCREAMING_SNAKE_CASE_     						=      hidden_act
       SCREAMING_SNAKE_CASE_     						=      hidden_dropout_prob
       SCREAMING_SNAKE_CASE_     						=      attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE_     						=      initializer_range
       SCREAMING_SNAKE_CASE_     						=      num_labels
       SCREAMING_SNAKE_CASE_     						=      backbone_featmap_shape
       SCREAMING_SNAKE_CASE_     						=      scope
       SCREAMING_SNAKE_CASE_     						=      is_hybrid
       # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
       SCREAMING_SNAKE_CASE_     						=      (image_size // patch_size) ** 2
       SCREAMING_SNAKE_CASE_     						=      num_patches + 1



 def 				lowerCAmelCase_  (							self  :		List[Any]						):
       SCREAMING_SNAKE_CASE_     						=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

       SCREAMING_SNAKE_CASE_     						=      self.get_config()

       return config, pixel_values, labels



 def 				lowerCAmelCase_  (							self  :		Dict						):
       SCREAMING_SNAKE_CASE_     						=      {
           '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=_lowerCAmelCase    ,    initializer_range=self.initializer_range    ,    is_hybrid=self.is_hybrid    ,    backbone_config=_lowerCAmelCase    ,    backbone_featmap_shape=self.backbone_featmap_shape    ,    )



 def 				lowerCAmelCase_  (							self  :		Optional[Any]    ,    _lowerCAmelCase  :		str    ,    _lowerCAmelCase  :		int    ,    _lowerCAmelCase  :		Union[str, Any]						):
       SCREAMING_SNAKE_CASE_     						=      DPTModel(config=_lowerCAmelCase						)
       model.to(_lowerCAmelCase						)
       model.eval()
       SCREAMING_SNAKE_CASE_     						=      model(_lowerCAmelCase						)
       self.parent.assertEqual(result.last_hidden_state.shape    ,    (self.batch_size, self.seq_length, self.hidden_size)						)



 def 				lowerCAmelCase_  (							self  :		Union[str, Any]    ,    _lowerCAmelCase  :		Union[str, Any]    ,    _lowerCAmelCase  :		List[str]    ,    _lowerCAmelCase  :		str						):
       SCREAMING_SNAKE_CASE_     						=      self.num_labels
       SCREAMING_SNAKE_CASE_     						=      DPTForDepthEstimation(_lowerCAmelCase						)
       model.to(_lowerCAmelCase						)
       model.eval()
       SCREAMING_SNAKE_CASE_     						=      model(_lowerCAmelCase						)
       self.parent.assertEqual(result.predicted_depth.shape    ,    (self.batch_size, self.image_size, self.image_size)						)



 def 				lowerCAmelCase_  (							self  :		List[str]    ,    _lowerCAmelCase  :		List[Any]    ,    _lowerCAmelCase  :		Union[str, Any]    ,    _lowerCAmelCase  :		Tuple						):
       SCREAMING_SNAKE_CASE_     						=      self.num_labels
       SCREAMING_SNAKE_CASE_     						=      DPTForSemanticSegmentation(_lowerCAmelCase						)
       model.to(_lowerCAmelCase						)
       model.eval()
       SCREAMING_SNAKE_CASE_     						=      model(_lowerCAmelCase    ,    labels=_lowerCAmelCase						)
       self.parent.assertEqual(
           result.logits.shape    ,    (self.batch_size, self.num_labels, self.image_size, self.image_size)						)




 def 				lowerCAmelCase_  (							self  :		Optional[Any]						):
       SCREAMING_SNAKE_CASE_     						=      self.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      config_and_inputs
       SCREAMING_SNAKE_CASE_     						=      {'pixel_values': pixel_values}
       return config, inputs_dict





@require_torch
class 	lowerCamelCase_							(				_SCREAMING_SNAKE_CASE	,       _SCREAMING_SNAKE_CASE	,       unittest.TestCase ):


 '''simple docstring'''







 lowercase_       =   (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
 lowercase_       =   (
     {
         "depth-estimation": DPTForDepthEstimation,
         "feature-extraction": DPTModel,
         "image-segmentation": DPTForSemanticSegmentation,
     }
     if is_torch_available()
     else {}
 )

 lowercase_       =   False
 lowercase_       =   False
 lowercase_       =   False



 def 				lowerCAmelCase_  (							self  :		Any						):
       SCREAMING_SNAKE_CASE_     						=      DPTModelTester(self						)
       SCREAMING_SNAKE_CASE_     						=      ConfigTester(self    ,    config_class=_lowerCAmelCase    ,    has_text_modality=_lowerCAmelCase    ,    hidden_size=37						)



 def 				lowerCAmelCase_  (							self  :		Optional[Any]						):
       self.config_tester.run_common_tests()



 @unittest.skip(reason='DPT does not use inputs_embeds'						)
 def 				lowerCAmelCase_  (							self  :		List[Any]						):
       pass



 def 				lowerCAmelCase_  (							self  :		Tuple						):
       SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      self.model_tester.prepare_config_and_inputs_for_common()

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



 def 				lowerCAmelCase_  (							self  :		int						):
       SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      self.model_tester.prepare_config_and_inputs_for_common()

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

             SCREAMING_SNAKE_CASE_     						=      ['pixel_values']
             self.assertListEqual(arg_names[:1]    ,    _lowerCAmelCase						)



 def 				lowerCAmelCase_  (							self  :		str						):
       SCREAMING_SNAKE_CASE_     						=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_lowerCAmelCase						)



 def 				lowerCAmelCase_  (							self  :		Tuple						):
       SCREAMING_SNAKE_CASE_     						=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_depth_estimation(*_lowerCAmelCase						)



 def 				lowerCAmelCase_  (							self  :		List[str]						):
       SCREAMING_SNAKE_CASE_     						=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_semantic_segmentation(*_lowerCAmelCase						)



 def 				lowerCAmelCase_  (							self  :		Union[str, Any]						):
       for model_class in self.all_model_classes:
             if model_class.__name__ == "DPTForDepthEstimation":
                   continue

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

             if model_class in get_values(_lowerCAmelCase						):
                   continue

             SCREAMING_SNAKE_CASE_     						=      model_class(_lowerCAmelCase						)
             model.to(_lowerCAmelCase						)
             model.train()
             SCREAMING_SNAKE_CASE_     						=      self._prepare_for_class(_lowerCAmelCase    ,    _lowerCAmelCase    ,    return_labels=_lowerCAmelCase						)
             SCREAMING_SNAKE_CASE_     						=      model(**_lowerCAmelCase						).loss
             loss.backward()



 def 				lowerCAmelCase_  (							self  :		Optional[int]						):
       for model_class in self.all_model_classes:
             if model_class.__name__ == "DPTForDepthEstimation":
                   continue

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

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



 def 				lowerCAmelCase_  (							self  :		Tuple						):
       SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      self.model_tester.prepare_config_and_inputs_for_common()

       SCREAMING_SNAKE_CASE_     						=      _config_zero_init(_lowerCAmelCase						)
       for model_class in self.all_model_classes:
             SCREAMING_SNAKE_CASE_     						=      model_class(config=_lowerCAmelCase						)
             # Skip the check for the backbone
             SCREAMING_SNAKE_CASE_     						=      []
             for name, module in model.named_modules():
                   if module.__class__.__name__ == "DPTViTHybridEmbeddings":
                         SCREAMING_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 				lowerCAmelCase_  (							self  :		int						):
       pass



 @slow
 def 				lowerCAmelCase_  (							self  :		Tuple						):
       for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
             SCREAMING_SNAKE_CASE_     						=      DPTModel.from_pretrained(_lowerCAmelCase						)
             self.assertIsNotNone(_lowerCAmelCase						)




 def 				lowerCAmelCase_  (							self  :		Optional[int]						):
       # We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
       SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      self.model_tester.prepare_config_and_inputs_for_common()
       SCREAMING_SNAKE_CASE_     						=      'add'
       with self.assertRaises(_lowerCAmelCase						):
             SCREAMING_SNAKE_CASE_     						=      DPTForDepthEstimation(_lowerCAmelCase						)


def  UpperCAmelCase_							(				) ->			Union[str, Any]:
      SCREAMING_SNAKE_CASE_     						=      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
      return image





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


 '''simple docstring'''







 def 				lowerCAmelCase_  (							self  :		Tuple						):
       SCREAMING_SNAKE_CASE_     						=      DPTImageProcessor.from_pretrained('Intel/dpt-hybrid-midas'						)
       SCREAMING_SNAKE_CASE_     						=      DPTForDepthEstimation.from_pretrained('Intel/dpt-hybrid-midas'						).to(_lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_     						=      prepare_img()
       SCREAMING_SNAKE_CASE_     						=      image_processor(images=_lowerCAmelCase    ,    return_tensors='pt'						).to(_lowerCAmelCase						)

       # forward pass
       with torch.no_grad():
             SCREAMING_SNAKE_CASE_     						=      model(**_lowerCAmelCase						)
             SCREAMING_SNAKE_CASE_     						=      outputs.predicted_depth

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

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

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





def  UpperCAmelCase_							(				__UpperCAmelCase						:     str							) ->			list:
      return [
          txt[:a] + txt[a].upper() + txt[a + 1 :]
          for a in range(len(__UpperCAmelCase							)							)
          if txt[a].isalpha()
      ]


if __name__ == "__main__":
   __import__('doctest').testmod()