




"""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       UpperCAmelCase_    :




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




     '''simple docstring'''

     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__			=							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 				__magic_name__				(       self		:			int ) ->				int:




     '''simple docstring'''

     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 				__magic_name__				(       self		:			Tuple ) ->				List[Any]:




     '''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=snake_case_      ,					initializer_range=self.initializer_range      ,					is_hybrid=self.is_hybrid      ,					)




    def 				__magic_name__				(       self		:			str      ,					snake_case_		:			Dict      ,					snake_case_		:			Any      ,					snake_case_		:			str ) ->				Any:




     '''simple docstring'''

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




    def 				__magic_name__				(       self		:			Optional[Any]      ,					snake_case_		:			Optional[int]      ,					snake_case_		:			List[Any]      ,					snake_case_		:			Tuple ) ->				Optional[int]:




     '''simple docstring'''

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




    def 				__magic_name__				(       self		:			str      ,					snake_case_		:			Tuple      ,					snake_case_		:			str      ,					snake_case_		:			Union[str, Any] ) ->				Optional[Any]:




     '''simple docstring'''

     A__			=							self.num_labels
     A__			=							DPTForSemanticSegmentation(snake_case_ )
     model.to(snake_case_ )
     model.eval()
     A__			=							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 				__magic_name__				(       self		:			Union[str, Any] ) ->				Tuple:




     '''simple docstring'''

     A__			=							self.prepare_config_and_inputs()
     A__, A__, A__			=							config_and_inputs
     A__			=							{"pixel_values": pixel_values}
     return config, inputs_dict






@require_torch
class       UpperCAmelCase_    (		A_,  A_,  unittest.TestCase				):
    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 				__magic_name__				(       self		:			int ) ->				List[Any]:




     '''simple docstring'''

     A__			=							DPTModelTester(self )
     A__			=							ConfigTester(self      ,					config_class=snake_case_      ,					has_text_modality=snake_case_      ,					hidden_size=37 )




    def 				__magic_name__				(       self		:			Optional[Any] ) ->				Dict:




     '''simple docstring'''

     self.config_tester.run_common_tests()




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




     '''simple docstring'''

     pass




    def 				__magic_name__				(       self		:			Optional[Any] ) ->				List[Any]:




     '''simple docstring'''

     A__, A__			=							self.model_tester.prepare_config_and_inputs_for_common()

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




    def 				__magic_name__				(       self		:			Dict ) ->				Any:




     '''simple docstring'''

     A__, A__			=							self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
      A__			=							model_class(snake_case_ )
      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]      ,					snake_case_ )




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




     '''simple docstring'''

     A__			=							self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*snake_case_ )




    def 				__magic_name__				(       self		:			Optional[Any] ) ->				Dict:




     '''simple docstring'''

     A__			=							self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_depth_estimation(*snake_case_ )




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




     '''simple docstring'''

     A__			=							self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_semantic_segmentation(*snake_case_ )




    def 				__magic_name__				(       self		:			Any ) ->				Tuple:




     '''simple docstring'''

     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(snake_case_ ):
       continue

      A__			=							model_class(snake_case_ )
      model.to(snake_case_ )
      model.train()
      A__			=							self._prepare_for_class(snake_case_      ,					snake_case_      ,					return_labels=snake_case_ )
      A__			=							model(**snake_case_ ).loss
      loss.backward()




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




     '''simple docstring'''

     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(snake_case_ ) or not model_class.supports_gradient_checkpointing:
       continue
      A__			=							model_class(snake_case_ )
      model.to(snake_case_ )
      model.gradient_checkpointing_enable()
      model.train()
      A__			=							self._prepare_for_class(snake_case_      ,					snake_case_      ,					return_labels=snake_case_ )
      A__			=							model(**snake_case_ ).loss
      loss.backward()




    def 				__magic_name__				(       self		:			Dict ) ->				Tuple:




     '''simple docstring'''

     A__, A__			=							self.model_tester.prepare_config_and_inputs_for_common()

     A__			=							_config_zero_init(snake_case_ )
     for model_class in self.all_model_classes:
      A__			=							model_class(config=snake_case_ )
      # 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 				__magic_name__				(       self		:			Dict ) ->				Tuple:




     '''simple docstring'''

     pass




    @slow
    def 				__magic_name__				(       self		:			int ) ->				List[Any]:




     '''simple docstring'''

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







def     _SCREAMING_SNAKE_CASE  (				)		->						Optional[Any]:
 A__			=							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
 return image






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




    def 				__magic_name__				(       self		:			Tuple ) ->				Optional[int]:




     '''simple docstring'''

     A__			=							DPTImageProcessor.from_pretrained("Intel/dpt-large" )
     A__			=							DPTForDepthEstimation.from_pretrained("Intel/dpt-large" ).to(snake_case_ )

     A__			=							prepare_img()
     A__			=							image_processor(images=snake_case_      ,					return_tensors="pt" ).to(snake_case_ )

     # forward pass
     with torch.no_grad():
      A__			=							model(**snake_case_ )
      A__			=							outputs.predicted_depth

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

     A__			=							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 				__magic_name__				(       self		:			List[str] ) ->				Tuple:




     '''simple docstring'''

     A__			=							DPTImageProcessor.from_pretrained("Intel/dpt-large-ade" )
     A__			=							DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade" ).to(snake_case_ )

     A__			=							prepare_img()
     A__			=							image_processor(images=snake_case_      ,					return_tensors="pt" ).to(snake_case_ )

     # forward pass
     with torch.no_grad():
      A__			=							model(**snake_case_ )

     # verify the logits
     A__			=							torch.Size((1, 150, 480, 480) )
     self.assertEqual(outputs.logits.shape      ,					snake_case_ )

     A__			=							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 				__magic_name__				(       self		:			str ) ->				List[str]:




     '''simple docstring'''

     A__			=							DPTImageProcessor.from_pretrained("Intel/dpt-large-ade" )
     A__			=							DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade" ).to(snake_case_ )

     A__			=							prepare_img()
     A__			=							image_processor(images=snake_case_      ,					return_tensors="pt" ).to(snake_case_ )

     # forward pass
     with torch.no_grad():
      A__			=							model(**snake_case_ )

     A__			=							outputs.logits.detach().cpu()

     A__			=							image_processor.post_process_semantic_segmentation(outputs=snake_case_      ,					target_sizes=[(500, 300)] )
     A__			=							torch.Size((500, 300) )
     self.assertEqual(segmentation[0].shape      ,					snake_case_ )

     A__			=							image_processor.post_process_semantic_segmentation(outputs=snake_case_ )
     A__			=							torch.Size((480, 480) )
     self.assertEqual(segmentation[0].shape      ,					snake_case_ )











"""simple docstring"""



def     _SCREAMING_SNAKE_CASE  (				lowercase_ = 10_00		)		->						int:
 A__			=							0
 A__			=							0
 while 1:
  num += 3
  if num >= n:
   break
  total += num
  num += 2
  if num >= n:
   break
  total += num
  num += 1
  if num >= n:
   break
  total += num
  num += 3
  if num >= n:
   break
  total += num
  num += 1
  if num >= n:
   break
  total += num
  num += 2
  if num >= n:
   break
  total += num
  num += 3
  if num >= n:
   break
  total += num
 return total


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





