



"""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  __SCREAMING_SNAKE_CASE			:


					'''simple docstring'''





					def __init__( self							:						int,						lowerCamelCase							:						List[str],						lowerCamelCase							:						Any=2,						lowerCamelCase							:						Optional[int]=32,						lowerCamelCase							:						str=16,						lowerCamelCase							:						int=3,						lowerCamelCase							:						List[str]=True,						lowerCamelCase							:						List[str]=True,						lowerCamelCase							:						str=32,						lowerCamelCase							:						int=4,						lowerCamelCase							:						Optional[int]=[0, 1, 2, 3],						lowerCamelCase							:						str=4,						lowerCamelCase							:						Optional[int]=37,						lowerCamelCase							:						str="gelu",						lowerCamelCase							:						Tuple=0.1,						lowerCamelCase							:						Dict=0.1,						lowerCamelCase							:						Union[str, Any]=0.02,						lowerCamelCase							:						str=3,						lowerCamelCase							:						Dict=[1, 384, 24, 24],						lowerCamelCase							:						List[str]=True,						lowerCamelCase							:						Union[str, Any]=None,						)->      Tuple:
								lowerCamelCase__	:	Optional[Any]				=parent
								lowerCamelCase__	:	Optional[Any]				=batch_size
								lowerCamelCase__	:	Tuple				=image_size
								lowerCamelCase__	:	Optional[int]				=patch_size
								lowerCamelCase__	:	Union[str, Any]				=num_channels
								lowerCamelCase__	:	Any				=is_training
								lowerCamelCase__	:	int				=use_labels
								lowerCamelCase__	:	Dict				=hidden_size
								lowerCamelCase__	:	Optional[Any]				=num_hidden_layers
								lowerCamelCase__	:	Optional[Any]				=backbone_out_indices
								lowerCamelCase__	:	Optional[int]				=num_attention_heads
								lowerCamelCase__	:	int				=intermediate_size
								lowerCamelCase__	:	int				=hidden_act
								lowerCamelCase__	:	str				=hidden_dropout_prob
								lowerCamelCase__	:	Optional[Any]				=attention_probs_dropout_prob
								lowerCamelCase__	:	Union[str, Any]				=initializer_range
								lowerCamelCase__	:	str				=num_labels
								lowerCamelCase__	:	List[str]				=backbone_featmap_shape
								lowerCamelCase__	:	Dict				=scope
								lowerCamelCase__	:	Optional[Any]				=is_hybrid
								# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
								lowerCamelCase__	:	Optional[Any]				=(image_size // patch_size) ** 2
								lowerCamelCase__	:	List[str]				=num_patches + 1



					def 	snake_case ( self							:						int  )->      Optional[int]:
								lowerCamelCase__	:	Any				=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

								lowerCamelCase__	:	List[str]				=self.get_config()

								return config, pixel_values, labels



					def 	snake_case ( self							:						int  )->      Tuple:
								lowerCamelCase__	:	List[Any]				={
								    '''global_padding''': '''same''',
								    '''layer_type''': '''bottleneck''',
								    '''depths''': [3, 4, 9],
								    '''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
								    '''embedding_dynamic_padding''': True,
								    '''hidden_sizes''': [96, 192, 384, 768],
								    '''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=lowerCamelCase,						initializer_range=self.initializer_range,						is_hybrid=self.is_hybrid,						backbone_config=lowerCamelCase,						backbone_featmap_shape=self.backbone_featmap_shape,						)



					def 	snake_case ( self							:						Optional[int],						lowerCamelCase							:						List[Any],						lowerCamelCase							:						List[str],						lowerCamelCase							:						Union[str, Any]  )->      Optional[int]:
								lowerCamelCase__	:	Any				=DPTModel(config=lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowerCamelCase__	:	List[Any]				=model(lowerCamelCase  )
								self.parent.assertEqual(result.last_hidden_state.shape,						(self.batch_size, self.seq_length, self.hidden_size)  )



					def 	snake_case ( self							:						Any,						lowerCamelCase							:						Any,						lowerCamelCase							:						str,						lowerCamelCase							:						Dict  )->      int:
								lowerCamelCase__	:	List[Any]				=self.num_labels
								lowerCamelCase__	:	int				=DPTForDepthEstimation(lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowerCamelCase__	:	List[str]				=model(lowerCamelCase  )
								self.parent.assertEqual(result.predicted_depth.shape,						(self.batch_size, self.image_size, self.image_size)  )



					def 	snake_case ( self							:						str,						lowerCamelCase							:						Any,						lowerCamelCase							:						List[Any],						lowerCamelCase							:						str  )->      int:
								lowerCamelCase__	:	str				=self.num_labels
								lowerCamelCase__	:	Tuple				=DPTForSemanticSegmentation(lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowerCamelCase__	:	Tuple				=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							:						List[Any]  )->      str:
								lowerCamelCase__	:	List[Any]				=self.prepare_config_and_inputs()
								lowerCamelCase__			,     lowerCamelCase__			,     lowerCamelCase__	:	Optional[int]				=config_and_inputs
								lowerCamelCase__	:	Union[str, Any]				={'''pixel_values''': pixel_values}
								return config, inputs_dict





@require_torch
class  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_	,     lowerCAmelCase_	,     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 	snake_case ( self							:						str  )->      Tuple:
								lowerCamelCase__	:	Any				=DPTModelTester(self  )
								lowerCamelCase__	:	Optional[Any]				=ConfigTester(self,						config_class=lowerCamelCase,						has_text_modality=lowerCamelCase,						hidden_size=37  )



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



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



					def 	snake_case ( self							:						int  )->      Union[str, Any]:
								lowerCamelCase__			,     lowerCamelCase__	:	str				=self.model_tester.prepare_config_and_inputs_for_common()

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



					def 	snake_case ( self							:						str  )->      List[str]:
								lowerCamelCase__			,     lowerCamelCase__	:	Tuple				=self.model_tester.prepare_config_and_inputs_for_common()

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

											lowerCamelCase__	:	Optional[Any]				=['''pixel_values''']
											self.assertListEqual(arg_names[:1],						lowerCamelCase  )



					def 	snake_case ( self							:						List[str]  )->      Optional[Any]:
								lowerCamelCase__	:	Union[str, Any]				=self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowerCamelCase  )



					def 	snake_case ( self							:						Optional[Any]  )->      List[Any]:
								lowerCamelCase__	:	Dict				=self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_depth_estimation(*lowerCamelCase  )



					def 	snake_case ( self							:						Any  )->      Optional[Any]:
								lowerCamelCase__	:	Tuple				=self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_semantic_segmentation(*lowerCamelCase  )



					def 	snake_case ( self							:						int  )->      Any:
								for model_class in self.all_model_classes:
											if model_class.__name__ == "DPTForDepthEstimation":
														continue

											lowerCamelCase__			,     lowerCamelCase__	:	Union[str, Any]				=self.model_tester.prepare_config_and_inputs_for_common()
											lowerCamelCase__	:	Dict				=True

											if model_class in get_values(lowerCamelCase  ):
														continue

											lowerCamelCase__	:	List[Any]				=model_class(lowerCamelCase  )
											model.to(lowerCamelCase  )
											model.train()
											lowerCamelCase__	:	Union[str, Any]				=self._prepare_for_class(lowerCamelCase,						lowerCamelCase,						return_labels=lowerCamelCase  )
											lowerCamelCase__	:	List[str]				=model(**lowerCamelCase  ).loss
											loss.backward()



					def 	snake_case ( self							:						Any  )->      Optional[Any]:
								for model_class in self.all_model_classes:
											if model_class.__name__ == "DPTForDepthEstimation":
														continue

											lowerCamelCase__			,     lowerCamelCase__	:	int				=self.model_tester.prepare_config_and_inputs_for_common()
											lowerCamelCase__	:	Optional[int]				=False
											lowerCamelCase__	:	Optional[int]				=True

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



					def 	snake_case ( self							:						Any  )->      Optional[Any]:
								lowerCamelCase__			,     lowerCamelCase__	:	int				=self.model_tester.prepare_config_and_inputs_for_common()

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



					@slow
					def 	snake_case ( self							:						Tuple  )->      Any:
								for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
											lowerCamelCase__	:	Optional[int]				=DPTModel.from_pretrained(lowerCamelCase  )
											self.assertIsNotNone(lowerCamelCase  )




					def 	snake_case ( self							:						Any  )->      List[str]:
								# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
								lowerCamelCase__			,     lowerCamelCase__	:	int				=self.model_tester.prepare_config_and_inputs_for_common()
								lowerCamelCase__	:	Optional[int]				='''add'''
								with self.assertRaises(lowerCamelCase  ):
											lowerCamelCase__	:	Optional[int]				=DPTForDepthEstimation(lowerCamelCase  )






def   snake_case__     (	):
			"""simple docstring"""
			lowerCamelCase__	:	str				=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 	snake_case ( self							:						Optional[Any]  )->      Optional[int]:
								lowerCamelCase__	:	Any				=DPTImageProcessor.from_pretrained('''Intel/dpt-hybrid-midas'''  )
								lowerCamelCase__	:	List[str]				=DPTForDepthEstimation.from_pretrained('''Intel/dpt-hybrid-midas'''  ).to(lowerCamelCase  )

								lowerCamelCase__	:	Any				=prepare_img()
								lowerCamelCase__	:	Optional[Any]				=image_processor(images=lowerCamelCase,						return_tensors='''pt'''  ).to(lowerCamelCase  )

								# forward pass
								with torch.no_grad():
											lowerCamelCase__	:	List[str]				=model(**lowerCamelCase  )
											lowerCamelCase__	:	Dict				=outputs.predicted_depth

								# verify the predicted depth
								lowerCamelCase__	:	Tuple				=torch.Size((1, 384, 384)  )
								self.assertEqual(predicted_depth.shape,						lowerCamelCase  )

								lowerCamelCase__	:	Optional[int]				=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(lowerCamelCase  )

								self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 100,						lowerCamelCase,						atol=1E-4  )  )









"""simple docstring"""






def   snake_case__     (	__lowerCamelCase       :					str			):
			"""simple docstring"""
			return " ".join(
			    ''''''.join(word[::-1]			) if len(__lowerCamelCase			) > 4 else word for word in sentence.split()			)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		print(reverse_long_words("Hey wollef sroirraw"))




