
'''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_       :

 '''simple docstring'''







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



   '''simple docstring'''




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







 def        UpperCamelCase     (				self		:     Tuple   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Any												=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

   UpperCAmelCase__			:				Union[str, Any]												=       None
   if self.use_labels:
     UpperCAmelCase__			:				Optional[Any]												=       ids_tensor([self.batch_size, self.image_size, self.image_size] ,							self.num_labels   )

   UpperCAmelCase__			:				Any												=       self.get_config()

   return config, pixel_values, labels







 def        UpperCamelCase     (				self		:     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=lowerCamelCase__ ,							initializer_range=self.initializer_range ,							is_hybrid=self.is_hybrid ,							)







 def        UpperCamelCase     (				self		:     List[str] ,							snake_case__		:     Any ,							snake_case__		:     int ,							snake_case__		:     Dict   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Any												=       DPTModel(config=lowerCamelCase__   )
   model.to(lowerCamelCase__   )
   model.eval()
   UpperCAmelCase__			:				List[str]												=       model(lowerCamelCase__   )
   self.parent.assertEqual(result.last_hidden_state.shape ,							(self.batch_size, self.seq_length, self.hidden_size)   )







 def        UpperCamelCase     (				self		:     Tuple ,							snake_case__		:     Any ,							snake_case__		:     str ,							snake_case__		:     Union[str, Any]   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Tuple												=       self.num_labels
   UpperCAmelCase__			:				Any												=       DPTForDepthEstimation(lowerCamelCase__   )
   model.to(lowerCamelCase__   )
   model.eval()
   UpperCAmelCase__			:				List[str]												=       model(lowerCamelCase__   )
   self.parent.assertEqual(result.predicted_depth.shape ,							(self.batch_size, self.image_size, self.image_size)   )







 def        UpperCamelCase     (				self		:     int ,							snake_case__		:     str ,							snake_case__		:     Optional[Any] ,							snake_case__		:     Dict   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Optional[int]												=       self.num_labels
   UpperCAmelCase__			:				str												=       DPTForSemanticSegmentation(lowerCamelCase__   )
   model.to(lowerCamelCase__   )
   model.eval()
   UpperCAmelCase__			:				Tuple												=       model(lowerCamelCase__ ,							labels=lowerCamelCase__   )
   self.parent.assertEqual(
       result.logits.shape ,							(self.batch_size, self.num_labels, self.image_size, self.image_size)   )







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



   '''simple docstring'''




   UpperCAmelCase__			:				Any												=       self.prepare_config_and_inputs()
   UpperCAmelCase__					, UpperCAmelCase__					, UpperCAmelCase__			:				Any												=       config_and_inputs
   UpperCAmelCase__			:				Optional[Any]												=       {"pixel_values": pixel_values}
   return config, inputs_dict






@require_torch
class        UpperCAmelCase_       (						__UpperCAmelCase				,							__UpperCAmelCase				,							unittest.TestCase							):

 '''simple docstring'''
 lowercase_				:						Union[str, Any]				=       (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
 lowercase_				:						List[Any]				=       (
     {
         """depth-estimation""": DPTForDepthEstimation,
         """feature-extraction""": DPTModel,
         """image-segmentation""": DPTForSemanticSegmentation,
     }
     if is_torch_available()
     else {}
 )

 lowercase_				:						Any				=       False
 lowercase_				:						Any				=       False
 lowercase_				:						Optional[int]				=       False







 def        UpperCamelCase     (				self		:     int   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Dict												=       DPTModelTester(self   )
   UpperCAmelCase__			:				Dict												=       ConfigTester(self ,							config_class=lowerCamelCase__ ,							has_text_modality=lowerCamelCase__ ,							hidden_size=37   )







 def        UpperCamelCase     (				self		:     int   ):



   '''simple docstring'''




   self.config_tester.run_common_tests()







 @unittest.skip(reason="DPT does not use inputs_embeds"   )
 def        UpperCamelCase     (				self		:     Optional[Any]   ):



   '''simple docstring'''




   pass







 def        UpperCamelCase     (				self		:     List[str]   ):



   '''simple docstring'''




   UpperCAmelCase__					, UpperCAmelCase__			:				Tuple												=       self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     UpperCAmelCase__			:				Optional[Any]												=       model_class(lowerCamelCase__   )
     self.assertIsInstance(model.get_input_embeddings() ,							(nn.Module)   )
     UpperCAmelCase__			:				Union[str, Any]												=       model.get_output_embeddings()
     self.assertTrue(x is None or isinstance(lowerCamelCase__ ,							nn.Linear   )   )







 def        UpperCamelCase     (				self		:     List[Any]   ):



   '''simple docstring'''




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

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

     UpperCAmelCase__			:				int												=       ["pixel_values"]
     self.assertListEqual(arg_names[:1] ,							lowerCamelCase__   )







 def        UpperCamelCase     (				self		:     str   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Optional[Any]												=       self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*lowerCamelCase__   )







 def        UpperCamelCase     (				self		:     int   ):



   '''simple docstring'''




   UpperCAmelCase__			:				int												=       self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_depth_estimation(*lowerCamelCase__   )







 def        UpperCamelCase     (				self		:     Tuple   ):



   '''simple docstring'''




   UpperCAmelCase__			:				List[str]												=       self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_semantic_segmentation(*lowerCamelCase__   )







 def        UpperCamelCase     (				self		:     Any   ):



   '''simple docstring'''




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

     UpperCAmelCase__					, UpperCAmelCase__			:				Optional[Any]												=       self.model_tester.prepare_config_and_inputs_for_common()
     UpperCAmelCase__			:				Any												=       True

     if model_class in get_values(lowerCamelCase__   ):
       continue

     UpperCAmelCase__			:				Dict												=       model_class(lowerCamelCase__   )
     model.to(lowerCamelCase__   )
     model.train()
     UpperCAmelCase__			:				List[Any]												=       self._prepare_for_class(lowerCamelCase__ ,							lowerCamelCase__ ,							return_labels=lowerCamelCase__   )
     UpperCAmelCase__			:				Tuple												=       model(**lowerCamelCase__   ).loss
     loss.backward()







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



   '''simple docstring'''




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

     UpperCAmelCase__					, UpperCAmelCase__			:				Optional[int]												=       self.model_tester.prepare_config_and_inputs_for_common()
     UpperCAmelCase__			:				Optional[int]												=       False
     UpperCAmelCase__			:				List[str]												=       True

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







 def        UpperCamelCase     (				self		:     Optional[Any]   ):



   '''simple docstring'''




   UpperCAmelCase__					, UpperCAmelCase__			:				List[str]												=       self.model_tester.prepare_config_and_inputs_for_common()

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



   '''simple docstring'''




   pass







 @slow
 def        UpperCamelCase     (				self		:     Union[str, Any]   ):



   '''simple docstring'''




   for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     UpperCAmelCase__			:				Tuple												=       DPTModel.from_pretrained(lowerCamelCase__   )
     self.assertIsNotNone(lowerCamelCase__   )







def snake_case_    (				):
  UpperCAmelCase__			:				int												=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
  return image






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

 '''simple docstring'''







 def        UpperCamelCase     (				self		:     Any   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Optional[Any]												=       DPTImageProcessor.from_pretrained("Intel/dpt-large"   )
   UpperCAmelCase__			:				Optional[int]												=       DPTForDepthEstimation.from_pretrained("Intel/dpt-large"   ).to(lowerCamelCase__   )

   UpperCAmelCase__			:				List[Any]												=       prepare_img()
   UpperCAmelCase__			:				Union[str, Any]												=       image_processor(images=lowerCamelCase__ ,							return_tensors="pt"   ).to(lowerCamelCase__   )

   # forward pass
   with torch.no_grad():
     UpperCAmelCase__			:				Optional[int]												=       model(**lowerCamelCase__   )
     UpperCAmelCase__			:				Tuple												=       outputs.predicted_depth

   # verify the predicted depth
   UpperCAmelCase__			:				Optional[int]												=       torch.Size((1, 3_84, 3_84)   )
   self.assertEqual(predicted_depth.shape ,							lowerCamelCase__   )

   UpperCAmelCase__			:				Dict												=       torch.tensor(
       [[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]   ).to(lowerCamelCase__   )

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







 def        UpperCamelCase     (				self		:     Tuple   ):



   '''simple docstring'''




   UpperCAmelCase__			:				int												=       DPTImageProcessor.from_pretrained("Intel/dpt-large-ade"   )
   UpperCAmelCase__			:				Tuple												=       DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade"   ).to(lowerCamelCase__   )

   UpperCAmelCase__			:				Optional[Any]												=       prepare_img()
   UpperCAmelCase__			:				int												=       image_processor(images=lowerCamelCase__ ,							return_tensors="pt"   ).to(lowerCamelCase__   )

   # forward pass
   with torch.no_grad():
     UpperCAmelCase__			:				Dict												=       model(**lowerCamelCase__   )

   # verify the logits
   UpperCAmelCase__			:				Dict												=       torch.Size((1, 1_50, 4_80, 4_80)   )
   self.assertEqual(outputs.logits.shape ,							lowerCamelCase__   )

   UpperCAmelCase__			:				int												=       torch.tensor(
       [[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]   ).to(lowerCamelCase__   )

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







 def        UpperCamelCase     (				self		:     Tuple   ):



   '''simple docstring'''




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

   UpperCAmelCase__			:				Any												=       prepare_img()
   UpperCAmelCase__			:				Optional[int]												=       image_processor(images=lowerCamelCase__ ,							return_tensors="pt"   ).to(lowerCamelCase__   )

   # forward pass
   with torch.no_grad():
     UpperCAmelCase__			:				Union[str, Any]												=       model(**lowerCamelCase__   )

   UpperCAmelCase__			:				List[str]												=       outputs.logits.detach().cpu()

   UpperCAmelCase__			:				str												=       image_processor.post_process_semantic_segmentation(outputs=lowerCamelCase__ ,							target_sizes=[(5_00, 3_00)]   )
   UpperCAmelCase__			:				Optional[int]												=       torch.Size((5_00, 3_00)   )
   self.assertEqual(segmentation[0].shape ,							lowerCamelCase__   )

   UpperCAmelCase__			:				List[str]												=       image_processor.post_process_semantic_segmentation(outputs=lowerCamelCase__   )
   UpperCAmelCase__			:				str												=       torch.Size((4_80, 4_80)   )
   self.assertEqual(segmentation[0].shape ,							lowerCamelCase__   )





'''simple docstring'''





from math import cos, sin







def snake_case_    (				lowercase__		):
  UpperCAmelCase__			:				List[Any]												=       float(lowercase__		)
  return [[scaling_factor * int(x == y		) for x in range(2		)] for y in range(2		)]







def snake_case_    (				lowercase__		):
  UpperCAmelCase__					, UpperCAmelCase__			:				List[Any]												=       cos(lowercase__		), sin(lowercase__		)
  return [[c, -s], [s, c]]







def snake_case_    (				lowercase__		):
  UpperCAmelCase__					, UpperCAmelCase__			:				Dict												=       cos(lowercase__		), sin(lowercase__		)
  UpperCAmelCase__			:				Tuple												=       c * s
  return [[c * c, cs], [cs, s * s]]







def snake_case_    (				lowercase__		):
  UpperCAmelCase__					, UpperCAmelCase__			:				Dict												=       cos(lowercase__		), sin(lowercase__		)
  UpperCAmelCase__			:				Any												=       c * s
  return [[2 * c - 1, 2 * cs], [2 * cs, 2 * s - 1]]


print(F'    {scaling(5) = }')
print(F'  {rotation(4_5) = }')
print(F'{projection(4_5) = }')






print(F'{reflection(4_5) = }')



