




"""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      A__   :




	def __init__( self	:						int   ,     lowercase	:						Union[str, Any]   ,     lowercase	:						Dict=2   ,     lowercase	:						List[str]=32   ,     lowercase	:						Optional[Any]=16   ,     lowercase	:						Optional[Any]=3   ,     lowercase	:						str=True   ,     lowercase	:						Any=True   ,     lowercase	:						Optional[Any]=32   ,     lowercase	:						Any=4   ,     lowercase	:						str=[0, 1, 2, 3]   ,     lowercase	:						Any=4   ,     lowercase	:						Dict=37   ,     lowercase	:						Any="gelu"   ,     lowercase	:						List[Any]=0.1   ,     lowercase	:						Optional[int]=0.1   ,     lowercase	:						Union[str, Any]=0.02   ,     lowercase	:						str=3   ,     lowercase	:						Tuple=False   ,     lowercase	:						int=None   ,     )       ->			List[Any]:
						__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 				a_		( self	:						Dict						)       ->			Union[str, Any]:
						__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 				a_		( self	:						List[Any]						)       ->			Tuple:
						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 				a_		( self	:						Optional[Any]   ,     lowercase	:						Dict   ,     lowercase	:						List[str]   ,     lowercase	:						Any						)       ->			List[str]:
						__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 				a_		( self	:						Dict   ,     lowercase	:						int   ,     lowercase	:						int   ,     lowercase	:						List[str]						)       ->			Optional[int]:
						__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 				a_		( self	:						Tuple   ,     lowercase	:						Optional[Any]   ,     lowercase	:						Any   ,     lowercase	:						List[Any]						)       ->			Optional[int]:
						__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 				a_		( self	:						int						)       ->			List[Any]:
						__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      A__   (   _lowerCAmelCase  ,						_lowerCAmelCase  ,						unittest.TestCase  ):
	A_				:					Tuple      =   (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
	A_				:					str      =   (
	    {
	        "depth-estimation": DPTForDepthEstimation,
	        "feature-extraction": DPTModel,
	        "image-segmentation": DPTForSemanticSegmentation,
	    }
	    if is_torch_available()
	    else {}
	)

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




	def 				a_		( self	:						str						)       ->			Any:
						__snake_case        =						DPTModelTester(self						)
						__snake_case        =						ConfigTester(self   ,     config_class=_lowerCamelCase   ,     has_text_modality=_lowerCamelCase   ,     hidden_size=37						)




	def 				a_		( self	:						List[str]						)       ->			Dict:
						self.config_tester.run_common_tests()




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




	def 				a_		( self	:						List[str]						)       ->			List[str]:
						__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 				a_		( self	:						Optional[Any]						)       ->			str:
						__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 				a_		( self	:						Optional[Any]						)       ->			List[Any]:
						__snake_case        =						self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*_lowerCamelCase						)




	def 				a_		( self	:						Optional[int]						)       ->			Tuple:
						__snake_case        =						self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_depth_estimation(*_lowerCamelCase						)




	def 				a_		( self	:						str						)       ->			Optional[Any]:
						__snake_case        =						self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_semantic_segmentation(*_lowerCamelCase						)




	def 				a_		( self	:						Tuple						)       ->			Optional[Any]:
						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 				a_		( self	:						Optional[Any]						)       ->			Optional[int]:
						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 				a_		( self	:						str						)       ->			Tuple:
						__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 				a_		( self	:						Any						)       ->			Any:
						pass




	@slow
	def 				a_		( self	:						int						)       ->			int:
						for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__snake_case        =						DPTModel.from_pretrained(_lowerCamelCase						)
											self.assertIsNotNone(_lowerCamelCase						)




def    snake_case__						(							):
					__snake_case        =						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
					return image

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




	def 				a_		( self	:						Union[str, Any]						)       ->			Tuple:
						__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, 384, 384)						)
						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 				a_		( self	:						Dict						)       ->			Dict:
						__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, 150, 480, 480)						)
						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 				a_		( self	:						List[str]						)       ->			Optional[int]:
						__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=[(500, 300)]						)
						__snake_case        =						torch.Size((500, 300)						)
						self.assertEqual(segmentation[0].shape   ,     _lowerCamelCase						)

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










"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_vilt import ViltImageProcessor


lowercase_								=	logging.get_logger(__name__)


class    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   def __init__(      self					:	Optional[int],			*_lowerCamelCase					:	int,			**_lowerCamelCase					:	str    ):

     '''simple docstring'''
     warnings.warn(
         '''The class ViltFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
         ''' use ViltImageProcessor instead.''',			_lowerCamelCase,			)
     super().__init__(*_lowerCamelCase,			**_lowerCamelCase    )




