






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					:





							"""simple docstring"""

							def __init__(	self: Any		,  __A: Union[str, Any]		,  __A: Dict=2		,  __A: Optional[int]=32		,  __A: Tuple=16		,  __A: List[Any]=3		,  __A: Any=True		,  __A: Optional[int]=True		,  __A: Tuple=32		,  __A: Any=4		,  __A: List[str]=[0, 1, 2, 3]		,  __A: int=4		,  __A: Any=37		,  __A: Optional[Any]="gelu"		,  __A: Tuple=0.1		,  __A: Tuple=0.1		,  __A: Tuple=0.02		,  __A: List[Any]=3		,  __A: int=[1, 3_84, 24, 24]		,  __A: List[str]=True		,  __A: List[str]=None		,  )     ->					int:
												_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										=			backbone_featmap_shape
												_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    __A			(	self: Any )     ->					List[Any]:
												_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    __A			(	self: List[Any] )     ->					List[str]:
												_A										=			{
												    '''global_padding''': '''same''',
												    '''layer_type''': '''bottleneck''',
												    '''depths''': [3, 4, 9],
												    '''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
												    '''embedding_dynamic_padding''': True,
												    '''hidden_sizes''': [96, 1_92, 3_84, 7_68],
												    '''num_groups''': 2,
												}

												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		,  backbone_config=lowercase_		,  backbone_featmap_shape=self.backbone_featmap_shape		,  )

							def    __A			(	self: Union[str, Any]		,  __A: Dict		,  __A: List[Any]		,  __A: str )     ->					Tuple:
												_A										=			DPTModel(config=lowercase_ )
												model.to(lowercase_ )
												model.eval()
												_A										=			model(lowercase_ )
												self.parent.assertEqual(result.last_hidden_state.shape		,  (self.batch_size, self.seq_length, self.hidden_size) )

							def    __A			(	self: str		,  __A: Optional[Any]		,  __A: int		,  __A: List[str] )     ->					str:
												_A										=			self.num_labels
												_A										=			DPTForDepthEstimation(lowercase_ )
												model.to(lowercase_ )
												model.eval()
												_A										=			model(lowercase_ )
												self.parent.assertEqual(result.predicted_depth.shape		,  (self.batch_size, self.image_size, self.image_size) )

							def    __A			(	self: List[str]		,  __A: List[Any]		,  __A: Union[str, Any]		,  __A: Tuple )     ->					Any:
												_A										=			self.num_labels
												_A										=			DPTForSemanticSegmentation(lowercase_ )
												model.to(lowercase_ )
												model.eval()
												_A										=			model(lowercase_		,  labels=lowercase_ )
												self.parent.assertEqual(
												    result.logits.shape		,  (self.batch_size, self.num_labels, self.image_size, self.image_size) )



							def    __A			(	self: List[str] )     ->					Optional[Any]:
												_A										=			self.prepare_config_and_inputs()
												_A										=			config_and_inputs
												_A										=			{'''pixel_values''': pixel_values}
												return config, inputs_dict





@require_torch
class     SCREAMING_SNAKE_CASE					(			a_	,						a_	,						unittest.TestCase       ):





							"""simple docstring"""

							A_    	=       (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
							A_    	=       (
							    {
							        "depth-estimation": DPTForDepthEstimation,
							        "feature-extraction": DPTModel,
							        "image-segmentation": DPTForSemanticSegmentation,
							    }
							    if is_torch_available()
							    else {}
							)

							A_    	=       False
							A_    	=       False
							A_    	=       False

							def    __A			(	self: List[str] )     ->					str:
												_A										=			DPTModelTester(self )
												_A										=			ConfigTester(self		,  config_class=lowercase_		,  has_text_modality=lowercase_		,  hidden_size=37 )

							def    __A			(	self: Union[str, Any] )     ->					Union[str, Any]:
												self.config_tester.run_common_tests()

							@unittest.skip(reason='''DPT does not use inputs_embeds''' )
							def    __A			(	self: List[str] )     ->					str:
												pass

							def    __A			(	self: Optional[Any] )     ->					Optional[Any]:
												_A										=			self.model_tester.prepare_config_and_inputs_for_common()

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

							def    __A			(	self: Optional[int] )     ->					str:
												_A										=			self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	_A										=			model_class(lowercase_ )
																	_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]		,  lowercase_ )

							def    __A			(	self: Tuple )     ->					List[Any]:
												_A										=			self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*lowercase_ )

							def    __A			(	self: List[str] )     ->					List[str]:
												_A										=			self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_depth_estimation(*lowercase_ )

							def    __A			(	self: int )     ->					List[str]:
												_A										=			self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_semantic_segmentation(*lowercase_ )

							def    __A			(	self: str )     ->					List[str]:
												for model_class in self.all_model_classes:
																	if model_class.__name__ == "DPTForDepthEstimation":
																						continue

																	_A										=			self.model_tester.prepare_config_and_inputs_for_common()
																	_A										=			True

																	if model_class in get_values(lowercase_ ):
																						continue

																	_A										=			model_class(lowercase_ )
																	model.to(lowercase_ )
																	model.train()
																	_A										=			self._prepare_for_class(lowercase_		,  lowercase_		,  return_labels=lowercase_ )
																	_A										=			model(**lowercase_ ).loss
																	loss.backward()

							def    __A			(	self: List[str] )     ->					Tuple:
												for model_class in self.all_model_classes:
																	if model_class.__name__ == "DPTForDepthEstimation":
																						continue

																	_A										=			self.model_tester.prepare_config_and_inputs_for_common()
																	_A										=			False
																	_A										=			True

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

							def    __A			(	self: Optional[Any] )     ->					Union[str, Any]:
												_A										=			self.model_tester.prepare_config_and_inputs_for_common()

												_A										=			_config_zero_init(lowercase_ )
												for model_class in self.all_model_classes:
																	_A										=			model_class(config=lowercase_ )
																	# 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    __A			(	self: Tuple )     ->					Dict:
												pass

							@slow
							def    __A			(	self: str )     ->					Any:
												for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
																	_A										=			DPTModel.from_pretrained(lowercase_ )
																	self.assertIsNotNone(lowercase_ )



							def    __A			(	self: Tuple )     ->					List[Any]:
												# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
												_A										=			self.model_tester.prepare_config_and_inputs_for_common()
												_A										=			'''add'''
												with self.assertRaises(lowercase_ ):
																	_A										=			DPTForDepthEstimation(lowercase_ )




def   __A       (  ):

					'''simple docstring'''







					_A										=			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
					return image





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





							"""simple docstring"""

							def    __A			(	self: Tuple )     ->					List[Any]:
												_A										=			DPTImageProcessor.from_pretrained('''Intel/dpt-hybrid-midas''' )
												_A										=			DPTForDepthEstimation.from_pretrained('''Intel/dpt-hybrid-midas''' ).to(lowercase_ )

												_A										=			prepare_img()
												_A										=			image_processor(images=lowercase_		,  return_tensors='''pt''' ).to(lowercase_ )

												# forward pass
												with torch.no_grad():
																	_A										=			model(**lowercase_ )
																	_A										=			outputs.predicted_depth

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

												_A										=			torch.tensor(
												    [[[5.6_437, 5.6_146, 5.6_511], [5.4_371, 5.5_649, 5.5_958], [5.5_215, 5.5_184, 5.5_293]]] ).to(lowercase_ )

												self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 1_00		,  lowercase_		,  atol=1e-4 ) )






"""simple docstring"""





def     __SCREAMING_SNAKE_CASE    ( A_					):
	lowerCAmelCase__      :		Any    							=      int(A_					)
	if decimal in (0, 1):  # Exit cases for the recursion
		return str(A_					)
	lowerCAmelCase__							,lowerCAmelCase__      :		Any    							=      divmod(A_		,	2					)
	return binary_recursive(A_					) + str(A_					)

def     __SCREAMING_SNAKE_CASE    ( A_					):
	lowerCAmelCase__      :		Union[str, Any]    							=      str(A_					).strip()
	if not number:
		raise ValueError('''No input value was provided'''					)
	lowerCAmelCase__      :		Any    							=      '''-''' if number.startswith('''-'''					) else ''''''
	lowerCAmelCase__      :		Dict    							=      number.lstrip('''-'''					)
	if not number.isnumeric():
		raise ValueError('''Input value is not an integer'''					)
	return f'{negative}0b{binary_recursive(int(A_					)					)}'


if __name__ == "__main__":
							from doctest import testmod

							testmod()




