




'''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     SCREAMING_SNAKE_CASE						:






      def __init__(   self      :     str	,	lowercase__      :     Optional[Any]	,	lowercase__      :     Optional[int]=2	,	lowercase__      :     Tuple=32	,	lowercase__      :     Any=16	,	lowercase__      :     Optional[int]=3	,	lowercase__      :     List[str]=True	,	lowercase__      :     List[str]=True	,	lowercase__      :     str=32	,	lowercase__      :     Dict=4	,	lowercase__      :     Optional[Any]=[0, 1, 2, 3]	,	lowercase__      :     Optional[Any]=4	,	lowercase__      :     int=37	,	lowercase__      :     Optional[Any]="gelu"	,	lowercase__      :     Optional[int]=0.1	,	lowercase__      :     Dict=0.1	,	lowercase__      :     Optional[int]=0.02	,	lowercase__      :     str=3	,	lowercase__      :     List[str]=False	,	lowercase__      :     int=None	,	):


           '''simple docstring'''

           a_    :       Tuple    	=     parent
           a_    :       Optional[int]    	=     batch_size
           a_    :       Tuple    	=     image_size
           a_    :       List[Any]    	=     patch_size
           a_    :       Union[str, Any]    	=     num_channels
           a_    :       int    	=     is_training
           a_    :       Dict    	=     use_labels
           a_    :       Union[str, Any]    	=     hidden_size
           a_    :       List[str]    	=     num_hidden_layers
           a_    :       List[Any]    	=     backbone_out_indices
           a_    :       List[str]    	=     num_attention_heads
           a_    :       Union[str, Any]    	=     intermediate_size
           a_    :       Union[str, Any]    	=     hidden_act
           a_    :       Any    	=     hidden_dropout_prob
           a_    :       Optional[Any]    	=     attention_probs_dropout_prob
           a_    :       Union[str, Any]    	=     initializer_range
           a_    :       int    	=     num_labels
           a_    :       str    	=     scope
           a_    :       Tuple    	=     is_hybrid
           # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
           a_    :       Tuple    	=     (image_size // patch_size) ** 2
           a_    :       Dict    	=     num_patches + 1






      def        lowercase_	(   self      :     Any					):


           '''simple docstring'''

           a_    :       Optional[int]    	=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

           a_    :       Any    	=     None
           if self.use_labels:
                a_    :       List[str]    	=     ids_tensor([self.batch_size, self.image_size, self.image_size]	,	self.num_labels					)

           a_    :       List[str]    	=     self.get_config()

           return config, pixel_values, labels






      def        lowercase_	(   self      :     Tuple					):


           '''simple docstring'''

           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=lowercase__	,	initializer_range=self.initializer_range	,	is_hybrid=self.is_hybrid	,	)






      def        lowercase_	(   self      :     List[Any]	,	lowercase__      :     List[Any]	,	lowercase__      :     str	,	lowercase__      :     Optional[Any]					):


           '''simple docstring'''

           a_    :       Optional[int]    	=     DPTModel(config=lowercase__					)
           model.to(lowercase__					)
           model.eval()
           a_    :       Optional[Any]    	=     model(lowercase__					)
           self.parent.assertEqual(result.last_hidden_state.shape	,	(self.batch_size, self.seq_length, self.hidden_size)					)






      def        lowercase_	(   self      :     List[str]	,	lowercase__      :     Optional[Any]	,	lowercase__      :     int	,	lowercase__      :     Dict					):


           '''simple docstring'''

           a_    :       List[str]    	=     self.num_labels
           a_    :       Optional[Any]    	=     DPTForDepthEstimation(lowercase__					)
           model.to(lowercase__					)
           model.eval()
           a_    :       Optional[int]    	=     model(lowercase__					)
           self.parent.assertEqual(result.predicted_depth.shape	,	(self.batch_size, self.image_size, self.image_size)					)






      def        lowercase_	(   self      :     Tuple	,	lowercase__      :     Optional[Any]	,	lowercase__      :     str	,	lowercase__      :     Optional[Any]					):


           '''simple docstring'''

           a_    :       Dict    	=     self.num_labels
           a_    :       List[Any]    	=     DPTForSemanticSegmentation(lowercase__					)
           model.to(lowercase__					)
           model.eval()
           a_    :       int    	=     model(lowercase__	,	labels=lowercase__					)
           self.parent.assertEqual(
               result.logits.shape	,	(self.batch_size, self.num_labels, self.image_size, self.image_size)					)






      def        lowercase_	(   self      :     Union[str, Any]					):


           '''simple docstring'''

           a_    :       List[Any]    	=     self.prepare_config_and_inputs()
           a_	,    a_	,    a_    :       List[Any]    	=     config_and_inputs
           a_    :       Union[str, Any]    	=     {"""pixel_values""": pixel_values}
           return config, inputs_dict







@require_torch
class     SCREAMING_SNAKE_CASE						(	snake_case_       ,			snake_case_       ,			unittest.TestCase							):
      __magic_name__     :					int             =				(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
      __magic_name__     :					str             =				(
          {
              '''depth-estimation''': DPTForDepthEstimation,
              '''feature-extraction''': DPTModel,
              '''image-segmentation''': DPTForSemanticSegmentation,
          }
          if is_torch_available()
          else {}
      )

      __magic_name__     :					Optional[Any]             =				False
      __magic_name__     :					Tuple             =				False
      __magic_name__     :					str             =				False






      def        lowercase_	(   self      :     Optional[Any]					):


           '''simple docstring'''

           a_    :       List[Any]    	=     DPTModelTester(self					)
           a_    :       Any    	=     ConfigTester(self	,	config_class=lowercase__	,	has_text_modality=lowercase__	,	hidden_size=37					)






      def        lowercase_	(   self      :     str					):


           '''simple docstring'''

           self.config_tester.run_common_tests()






      @unittest.skip(reason="""DPT does not use inputs_embeds"""					)
      def        lowercase_	(   self      :     Union[str, Any]					):


           '''simple docstring'''

           pass






      def        lowercase_	(   self      :     int					):


           '''simple docstring'''

           a_	,    a_    :       Dict    	=     self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                a_    :       str    	=     model_class(lowercase__					)
                self.assertIsInstance(model.get_input_embeddings()	,	(nn.Module)					)
                a_    :       List[str]    	=     model.get_output_embeddings()
                self.assertTrue(x is None or isinstance(lowercase__	,	nn.Linear					)					)






      def        lowercase_	(   self      :     Tuple					):


           '''simple docstring'''

           a_	,    a_    :       Dict    	=     self.model_tester.prepare_config_and_inputs_for_common()

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

                a_    :       Optional[Any]    	=     ["""pixel_values"""]
                self.assertListEqual(arg_names[:1]	,	lowercase__					)






      def        lowercase_	(   self      :     str					):


           '''simple docstring'''

           a_    :       Optional[Any]    	=     self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowercase__					)






      def        lowercase_	(   self      :     int					):


           '''simple docstring'''

           a_    :       Union[str, Any]    	=     self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_depth_estimation(*lowercase__					)






      def        lowercase_	(   self      :     str					):


           '''simple docstring'''

           a_    :       Optional[int]    	=     self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_semantic_segmentation(*lowercase__					)






      def        lowercase_	(   self      :     List[str]					):


           '''simple docstring'''

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

                a_	,    a_    :       Dict    	=     self.model_tester.prepare_config_and_inputs_for_common()
                a_    :       str    	=     True

                if model_class in get_values(lowercase__					):
                     continue

                a_    :       str    	=     model_class(lowercase__					)
                model.to(lowercase__					)
                model.train()
                a_    :       Tuple    	=     self._prepare_for_class(lowercase__	,	lowercase__	,	return_labels=lowercase__					)
                a_    :       Optional[int]    	=     model(**lowercase__					).loss
                loss.backward()






      def        lowercase_	(   self      :     Dict					):


           '''simple docstring'''

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

                a_	,    a_    :       List[Any]    	=     self.model_tester.prepare_config_and_inputs_for_common()
                a_    :       str    	=     False
                a_    :       List[str]    	=     True

                if model_class in get_values(lowercase__					) or not model_class.supports_gradient_checkpointing:
                     continue
                a_    :       List[str]    	=     model_class(lowercase__					)
                model.to(lowercase__					)
                model.gradient_checkpointing_enable()
                model.train()
                a_    :       Optional[int]    	=     self._prepare_for_class(lowercase__	,	lowercase__	,	return_labels=lowercase__					)
                a_    :       int    	=     model(**lowercase__					).loss
                loss.backward()






      def        lowercase_	(   self      :     List[Any]					):


           '''simple docstring'''

           a_	,    a_    :       List[Any]    	=     self.model_tester.prepare_config_and_inputs_for_common()

           a_    :       Union[str, Any]    	=     _config_zero_init(lowercase__					)
           for model_class in self.all_model_classes:
                a_    :       List[Any]    	=     model_class(config=lowercase__					)
                # Skip the check for the backbone
                a_    :       Optional[int]    	=     []
                for name, module in model.named_modules():
                     if module.__class__.__name__ == "DPTViTHybridEmbeddings":
                          a_    :       Optional[int]    	=     [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        lowercase_	(   self      :     int					):


           '''simple docstring'''

           pass






      @slow
      def        lowercase_	(   self      :     List[str]					):


           '''simple docstring'''

           for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                a_    :       int    	=     DPTModel.from_pretrained(lowercase__					)
                self.assertIsNotNone(lowercase__					)



def    _SCREAMING_SNAKE_CASE    (			):




     """simple docstring"""


     a_    :       Union[str, Any]    	=     Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
     return image







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






      def        lowercase_	(   self      :     Optional[Any]					):


           '''simple docstring'''

           a_    :       List[Any]    	=     DPTImageProcessor.from_pretrained("""Intel/dpt-large"""					)
           a_    :       Dict    	=     DPTForDepthEstimation.from_pretrained("""Intel/dpt-large"""					).to(lowercase__					)

           a_    :       Any    	=     prepare_img()
           a_    :       Any    	=     image_processor(images=lowercase__	,	return_tensors="""pt"""					).to(lowercase__					)

           # forward pass
           with torch.no_grad():
                a_    :       Any    	=     model(**lowercase__					)
                a_    :       int    	=     outputs.predicted_depth

           # verify the predicted depth
           a_    :       List[Any]    	=     torch.Size((1, 384, 384)					)
           self.assertEqual(predicted_depth.shape	,	lowercase__					)

           a_    :       Tuple    	=     torch.tensor(
               [[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]					).to(lowercase__					)

           self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]	,	lowercase__	,	atol=1e-4					)					)






      def        lowercase_	(   self      :     Optional[Any]					):


           '''simple docstring'''

           a_    :       Union[str, Any]    	=     DPTImageProcessor.from_pretrained("""Intel/dpt-large-ade"""					)
           a_    :       int    	=     DPTForSemanticSegmentation.from_pretrained("""Intel/dpt-large-ade"""					).to(lowercase__					)

           a_    :       int    	=     prepare_img()
           a_    :       Union[str, Any]    	=     image_processor(images=lowercase__	,	return_tensors="""pt"""					).to(lowercase__					)

           # forward pass
           with torch.no_grad():
                a_    :       str    	=     model(**lowercase__					)

           # verify the logits
           a_    :       Union[str, Any]    	=     torch.Size((1, 150, 480, 480)					)
           self.assertEqual(outputs.logits.shape	,	lowercase__					)

           a_    :       Union[str, Any]    	=     torch.tensor(
               [[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]					).to(lowercase__					)

           self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]	,	lowercase__	,	atol=1e-4					)					)






      def        lowercase_	(   self      :     Optional[int]					):


           '''simple docstring'''

           a_    :       Any    	=     DPTImageProcessor.from_pretrained("""Intel/dpt-large-ade"""					)
           a_    :       int    	=     DPTForSemanticSegmentation.from_pretrained("""Intel/dpt-large-ade"""					).to(lowercase__					)

           a_    :       List[str]    	=     prepare_img()
           a_    :       int    	=     image_processor(images=lowercase__	,	return_tensors="""pt"""					).to(lowercase__					)

           # forward pass
           with torch.no_grad():
                a_    :       Dict    	=     model(**lowercase__					)

           a_    :       Union[str, Any]    	=     outputs.logits.detach().cpu()

           a_    :       List[str]    	=     image_processor.post_process_semantic_segmentation(outputs=lowercase__	,	target_sizes=[(500, 300)]					)
           a_    :       Union[str, Any]    	=     torch.Size((500, 300)					)
           self.assertEqual(segmentation[0].shape	,	lowercase__					)

           a_    :       Optional[int]    	=     image_processor.post_process_semantic_segmentation(outputs=lowercase__					)
           a_    :       int    	=     torch.Size((480, 480)					)
           self.assertEqual(segmentation[0].shape	,	lowercase__					)






'''simple docstring'''


def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				int = 1000							):




     """simple docstring"""


     return sum(i for i in range(UpperCamelCase__							) if i % 3 == 0 or i % 5 == 0							)


if __name__ == "__main__":
 print(f"{solution() = }")
