


'''simple docstring'''

from transformers import PretrainedConfig







class 			UpperCAmelCase			(							SCREAMING_SNAKE_CASE__):
 _lowerCamelCase			:       Dict            =       'custom'

 def __init__(			self			:						Tuple,       a_			:						Optional[int]=1,       **a_			:						str       ):





    """simple docstring"""



    UpperCamelCase__			    =    attribute
    super().__init__(**a_       )







class 			UpperCAmelCase			(							SCREAMING_SNAKE_CASE__):
 _lowerCamelCase			:       List[Any]            =       'custom'

 def __init__(			self			:						Any,       a_			:						Dict=1,       **a_			:						Any       ):





    """simple docstring"""



    UpperCamelCase__			    =    attribute
"""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 	lowerCAmelCase_     :



				"""simple docstring"""

				def __init__(					self		,     lowerCAmelCase		,     lowerCAmelCase=2		,     lowerCAmelCase=32		,     lowerCAmelCase=16		,     lowerCAmelCase=3		,     lowerCAmelCase=True		,     lowerCAmelCase=True		,     lowerCAmelCase=32		,     lowerCAmelCase=4		,     lowerCAmelCase=[0, 1, 2, 3]		,     lowerCAmelCase=4		,     lowerCAmelCase=37		,     lowerCAmelCase="gelu"		,     lowerCAmelCase=0.1		,     lowerCAmelCase=0.1		,     lowerCAmelCase=0.02		,     lowerCAmelCase=3		,     lowerCAmelCase=False		,     lowerCAmelCase=None		,     ):






										"""simple docstring"""

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

				def 		snake_case			(					self     ):






										"""simple docstring"""

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

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

										snake_case             =      self.get_config()

										return config, pixel_values, labels

				def 		snake_case			(					self     ):






										"""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 		snake_case			(					self		,     lowerCAmelCase		,     lowerCAmelCase		,     lowerCAmelCase     ):






										"""simple docstring"""

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

				def 		snake_case			(					self		,     lowerCAmelCase		,     lowerCAmelCase		,     lowerCAmelCase     ):






										"""simple docstring"""

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

				def 		snake_case			(					self		,     lowerCAmelCase		,     lowerCAmelCase		,     lowerCAmelCase     ):






										"""simple docstring"""

										snake_case             =      self.num_labels
										snake_case             =      DPTForSemanticSegmentation(lowerCAmelCase     )
										model.to(lowerCAmelCase     )
										model.eval()
										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 		snake_case			(					self     ):






										"""simple docstring"""

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





@require_torch
class 	lowerCAmelCase_     (      lowerCAmelCase  ,     lowerCAmelCase  ,     unittest.TestCase       ):



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

				_lowerCAmelCase  :     str      	=    False
				_lowerCAmelCase  :     Any      	=    False
				_lowerCAmelCase  :     Dict      	=    False

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      DPTModelTester(self     )
										snake_case             =      ConfigTester(self		,     config_class=lowerCAmelCase		,     has_text_modality=lowerCAmelCase		,     hidden_size=37     )

				def 		snake_case			(					self     ):






										"""simple docstring"""

										self.config_tester.run_common_tests()

				@unittest.skip(reason='DPT does not use inputs_embeds'     )
				def 		snake_case			(					self     ):






										"""simple docstring"""

										pass

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case       ,snake_case             =      self.model_tester.prepare_config_and_inputs_for_common()

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

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case       ,snake_case             =      self.model_tester.prepare_config_and_inputs_for_common()

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

																snake_case             =      ['pixel_values']
																self.assertListEqual(arg_names[:1]		,     lowerCAmelCase     )

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase     )

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_depth_estimation(*lowerCAmelCase     )

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_semantic_segmentation(*lowerCAmelCase     )

				def 		snake_case			(					self     ):






										"""simple docstring"""

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

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

																if model_class in get_values(lowerCAmelCase     ):
																						continue

																snake_case             =      model_class(lowerCAmelCase     )
																model.to(lowerCAmelCase     )
																model.train()
																snake_case             =      self._prepare_for_class(lowerCAmelCase		,     lowerCAmelCase		,     return_labels=lowerCAmelCase     )
																snake_case             =      model(**lowerCAmelCase     ).loss
																loss.backward()

				def 		snake_case			(					self     ):






										"""simple docstring"""

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

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

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

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case       ,snake_case             =      self.model_tester.prepare_config_and_inputs_for_common()

										snake_case             =      _config_zero_init(lowerCAmelCase     )
										for model_class in self.all_model_classes:
																snake_case             =      model_class(config=lowerCAmelCase     )
																# Skip the check for the backbone
																snake_case             =      []
																for name, module in model.named_modules():
																						if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																												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 		snake_case			(					self     ):






										"""simple docstring"""

										pass

				@slow
				def 		snake_case			(					self     ):






										"""simple docstring"""

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





def  lowerCAmelCase__     (      )       ->					Union[str, Any]:





						"""simple docstring"""






						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 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      DPTImageProcessor.from_pretrained('Intel/dpt-large'     )
										snake_case             =      DPTForDepthEstimation.from_pretrained('Intel/dpt-large'     ).to(lowerCAmelCase     )

										snake_case             =      prepare_img()
										snake_case             =      image_processor(images=lowerCAmelCase		,     return_tensors='pt'     ).to(lowerCAmelCase     )

										# forward pass
										with torch.no_grad():
																snake_case             =      model(**lowerCAmelCase     )
																snake_case             =      outputs.predicted_depth

										# verify the predicted depth
										snake_case             =      torch.Size((1, 3_84, 3_84)     )
										self.assertEqual(predicted_depth.shape		,     lowerCAmelCase     )

										snake_case             =      torch.tensor(
										    [[6.31_99, 6.36_29, 6.41_48], [6.38_50, 6.36_15, 6.41_66], [6.35_19, 6.31_76, 6.35_75]]     ).to(lowerCAmelCase     )

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

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'     )
										snake_case             =      DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'     ).to(lowerCAmelCase     )

										snake_case             =      prepare_img()
										snake_case             =      image_processor(images=lowerCAmelCase		,     return_tensors='pt'     ).to(lowerCAmelCase     )

										# forward pass
										with torch.no_grad():
																snake_case             =      model(**lowerCAmelCase     )

										# verify the logits
										snake_case             =      torch.Size((1, 1_50, 4_80, 4_80)     )
										self.assertEqual(outputs.logits.shape		,     lowerCAmelCase     )

										snake_case             =      torch.tensor(
										    [[4.04_80, 4.24_20, 4.43_60], [4.31_24, 4.56_93, 4.82_61], [4.57_68, 4.89_65, 5.21_63]]     ).to(lowerCAmelCase     )

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

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'     )
										snake_case             =      DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'     ).to(lowerCAmelCase     )

										snake_case             =      prepare_img()
										snake_case             =      image_processor(images=lowerCAmelCase		,     return_tensors='pt'     ).to(lowerCAmelCase     )

										# forward pass
										with torch.no_grad():
																snake_case             =      model(**lowerCAmelCase     )

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

										snake_case             =      image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase		,     target_sizes=[(5_00, 3_00)]     )
										snake_case             =      torch.Size((5_00, 3_00)     )
										self.assertEqual(segmentation[0].shape		,     lowerCAmelCase     )

										snake_case             =      image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase     )
										snake_case             =      torch.Size((4_80, 4_80)     )
										self.assertEqual(segmentation[0].shape		,     lowerCAmelCase     )


