


lowerCAmelCase       		=    {
    """a""": """AAAAA""",
    """b""": """AAAAB""",
    """c""": """AAABA""",
    """d""": """AAABB""",
    """e""": """AABAA""",
    """f""": """AABAB""",
    """g""": """AABBA""",
    """h""": """AABBB""",
    """i""": """ABAAA""",
    """j""": """BBBAA""",
    """k""": """ABAAB""",
    """l""": """ABABA""",
    """m""": """ABABB""",
    """n""": """ABBAA""",
    """o""": """ABBAB""",
    """p""": """ABBBA""",
    """q""": """ABBBB""",
    """r""": """BAAAA""",
    """s""": """BAAAB""",
    """t""": """BAABA""",
    """u""": """BAABB""",
    """v""": """BBBAB""",
    """w""": """BABAA""",
    """x""": """BABAB""",
    """y""": """BABBA""",
    """z""": """BABBB""",
    """ """: """ """,
}


lowerCAmelCase       		=    {value: key for key, value in encode_dict.items()}




def     __SCREAMING_SNAKE_CASE							(       lowercase_      )		->     str:




						'''simple docstring'''







						__UpperCAmelCase			:							int	    =	''''''
						for letter in word.lower():
												if letter.isalpha() or letter == " ":
																		encoded += encode_dict[letter]
												else:
																		raise Exception('''encode() accepts only letters of the alphabet and spaces'''      )
						return encoded




def     __SCREAMING_SNAKE_CASE							(       lowercase_      )		->     str:




						'''simple docstring'''







						if set(lowercase_      ) - {"A", "B", " "} != set():
												raise Exception('''decode() accepts only \'A\', \'B\' and spaces'''      )
						__UpperCAmelCase			:							List[Any]	    =	''''''
						for word in coded.split():
												while len(lowercase_      ) != 0:
																		decoded += decode_dict[word[:5]]
																		__UpperCAmelCase			:							List[str]	    =	word[5:]
												decoded += " "
						return decoded.strip()


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

				testmod()





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   :


	def __init__(    self				,     lowercase__				,     lowercase__=2				,     lowercase__=3_2				,     lowercase__=1_6				,     lowercase__=3				,     lowercase__=True				,     lowercase__=True				,     lowercase__=3_2				,     lowercase__=4				,     lowercase__=[0, 1, 2, 3]				,     lowercase__=4				,     lowercase__=3_7				,     lowercase__="gelu"				,     lowercase__=0.1				,     lowercase__=0.1				,     lowercase__=0.0_2				,     lowercase__=3				,     lowercase__=[1, 3_8_4, 2_4, 2_4]				,     lowercase__=True				,     lowercase__=None				,     ):
							__UpperCAmelCase			:							Any	    =	parent
							__UpperCAmelCase			:							Tuple	    =	batch_size
							__UpperCAmelCase			:							Optional[int]	    =	image_size
							__UpperCAmelCase			:							Tuple	    =	patch_size
							__UpperCAmelCase			:							int	    =	num_channels
							__UpperCAmelCase			:							Optional[Any]	    =	is_training
							__UpperCAmelCase			:							Union[str, Any]	    =	use_labels
							__UpperCAmelCase			:							List[Any]	    =	hidden_size
							__UpperCAmelCase			:							Optional[Any]	    =	num_hidden_layers
							__UpperCAmelCase			:							Any	    =	backbone_out_indices
							__UpperCAmelCase			:							Union[str, Any]	    =	num_attention_heads
							__UpperCAmelCase			:							Dict	    =	intermediate_size
							__UpperCAmelCase			:							Dict	    =	hidden_act
							__UpperCAmelCase			:							Dict	    =	hidden_dropout_prob
							__UpperCAmelCase			:							List[Any]	    =	attention_probs_dropout_prob
							__UpperCAmelCase			:							List[Any]	    =	initializer_range
							__UpperCAmelCase			:							Union[str, Any]	    =	num_labels
							__UpperCAmelCase			:							List[Any]	    =	backbone_featmap_shape
							__UpperCAmelCase			:							Any	    =	scope
							__UpperCAmelCase			:							Optional[int]	    =	is_hybrid
							# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
							__UpperCAmelCase			:							Optional[Any]	    =	(image_size // patch_size) ** 2
							__UpperCAmelCase			:							Any	    =	num_patches + 1


	def  A(    self):
							__UpperCAmelCase			:							Tuple	    =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

							__UpperCAmelCase			:							List[str]	    =	None
							if self.use_labels:
													__UpperCAmelCase			:							Dict	    =	ids_tensor([self.batch_size, self.image_size, self.image_size]				,     self.num_labels)

							__UpperCAmelCase			:							Dict	    =	self.get_config()

							return config, pixel_values, labels


	def  A(    self):
							__UpperCAmelCase			:							Dict	    =	{
							    '''global_padding''': '''same''',
							    '''layer_type''': '''bottleneck''',
							    '''depths''': [3, 4, 9],
							    '''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
							    '''embedding_dynamic_padding''': True,
							    '''hidden_sizes''': [9_6, 1_9_2, 3_8_4, 7_6_8],
							    '''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				,     lowercase__				,     lowercase__				,     lowercase__):
							__UpperCAmelCase			:							List[str]	    =	DPTModel(config=lowercase__)
							model.to(lowercase__)
							model.eval()
							__UpperCAmelCase			:							Dict	    =	model(lowercase__)
							self.parent.assertEqual(result.last_hidden_state.shape				,     (self.batch_size, self.seq_length, self.hidden_size))


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__):
							__UpperCAmelCase			:							List[str]	    =	self.num_labels
							__UpperCAmelCase			:							Optional[Any]	    =	DPTForDepthEstimation(lowercase__)
							model.to(lowercase__)
							model.eval()
							__UpperCAmelCase			:							str	    =	model(lowercase__)
							self.parent.assertEqual(result.predicted_depth.shape				,     (self.batch_size, self.image_size, self.image_size))


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__):
							__UpperCAmelCase			:							Tuple	    =	self.num_labels
							__UpperCAmelCase			:							Optional[int]	    =	DPTForSemanticSegmentation(lowercase__)
							model.to(lowercase__)
							model.eval()
							__UpperCAmelCase			:							str	    =	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):
							__UpperCAmelCase			:							int	    =	self.prepare_config_and_inputs()
							__UpperCAmelCase    ,      __UpperCAmelCase    ,      __UpperCAmelCase			:							Optional[int]	    =	config_and_inputs
							__UpperCAmelCase			:							Union[str, Any]	    =	{'''pixel_values''': pixel_values}
							return config, inputs_dict






@require_torch
class 							lowerCamelCase   (  _UpperCamelCase							,						_UpperCamelCase							,						unittest.TestCase ):
	_lowerCAmelCase     :		Union[str, Any]										=						(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
	_lowerCAmelCase     :		Optional[int]										=						(
	    {
	        '''depth-estimation''': DPTForDepthEstimation,
	        '''feature-extraction''': DPTModel,
	        '''image-segmentation''': DPTForSemanticSegmentation,
	    }
	    if is_torch_available()
	    else {}
	)

	_lowerCAmelCase     :		Any										=						False
	_lowerCAmelCase     :		str										=						False
	_lowerCAmelCase     :		List[Any]										=						False


	def  A(    self):
							__UpperCAmelCase			:							Any	    =	DPTModelTester(self)
							__UpperCAmelCase			:							str	    =	ConfigTester(self				,     config_class=lowercase__				,     has_text_modality=lowercase__				,     hidden_size=3_7)


	def  A(    self):
							self.config_tester.run_common_tests()


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


	def  A(    self):
							__UpperCAmelCase    ,      __UpperCAmelCase			:							Optional[Any]	    =	self.model_tester.prepare_config_and_inputs_for_common()

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


	def  A(    self):
							__UpperCAmelCase    ,      __UpperCAmelCase			:							Any	    =	self.model_tester.prepare_config_and_inputs_for_common()

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

													__UpperCAmelCase			:							str	    =	['''pixel_values''']
													self.assertListEqual(arg_names[:1]				,     lowercase__)


	def  A(    self):
							__UpperCAmelCase			:							Tuple	    =	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowercase__)


	def  A(    self):
							__UpperCAmelCase			:							List[str]	    =	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_depth_estimation(*lowercase__)


	def  A(    self):
							__UpperCAmelCase			:							Dict	    =	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_semantic_segmentation(*lowercase__)


	def  A(    self):
							for model_class in self.all_model_classes:
													if model_class.__name__ == "DPTForDepthEstimation":
																			continue

													__UpperCAmelCase    ,      __UpperCAmelCase			:							str	    =	self.model_tester.prepare_config_and_inputs_for_common()
													__UpperCAmelCase			:							Any	    =	True

													if model_class in get_values(lowercase__):
																			continue

													__UpperCAmelCase			:							List[Any]	    =	model_class(lowercase__)
													model.to(lowercase__)
													model.train()
													__UpperCAmelCase			:							Any	    =	self._prepare_for_class(lowercase__				,     lowercase__				,     return_labels=lowercase__)
													__UpperCAmelCase			:							Any	    =	model(**lowercase__).loss
													loss.backward()


	def  A(    self):
							for model_class in self.all_model_classes:
													if model_class.__name__ == "DPTForDepthEstimation":
																			continue

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

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


	def  A(    self):
							__UpperCAmelCase    ,      __UpperCAmelCase			:							Optional[int]	    =	self.model_tester.prepare_config_and_inputs_for_common()

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


	@slow
	def  A(    self):
							for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
													__UpperCAmelCase			:							Optional[int]	    =	DPTModel.from_pretrained(lowercase__)
													self.assertIsNotNone(lowercase__)


	def  A(    self):
							# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
							__UpperCAmelCase    ,      __UpperCAmelCase			:							Tuple	    =	self.model_tester.prepare_config_and_inputs_for_common()
							__UpperCAmelCase			:							List[str]	    =	'''add'''
							with self.assertRaises(lowercase__):
													__UpperCAmelCase			:							Optional[Any]	    =	DPTForDepthEstimation(lowercase__)






def     __SCREAMING_SNAKE_CASE							(       )		->     Dict:




						'''simple docstring'''







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





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


	def  A(    self):
							__UpperCAmelCase			:							str	    =	DPTImageProcessor.from_pretrained('''Intel/dpt-hybrid-midas''')
							__UpperCAmelCase			:							str	    =	DPTForDepthEstimation.from_pretrained('''Intel/dpt-hybrid-midas''').to(lowercase__)

							__UpperCAmelCase			:							List[str]	    =	prepare_img()
							__UpperCAmelCase			:							Tuple	    =	image_processor(images=lowercase__				,     return_tensors='''pt''').to(lowercase__)

							# forward pass
							with torch.no_grad():
													__UpperCAmelCase			:							List[Any]	    =	model(**lowercase__)
													__UpperCAmelCase			:							str	    =	outputs.predicted_depth

							# verify the predicted depth
							__UpperCAmelCase			:							Union[str, Any]	    =	torch.Size((1, 3_8_4, 3_8_4))
							self.assertEqual(predicted_depth.shape				,     lowercase__)

							__UpperCAmelCase			:							List[str]	    =	torch.tensor(
							    [[[5.6_4_3_7, 5.6_1_4_6, 5.6_5_1_1], [5.4_3_7_1, 5.5_6_4_9, 5.5_9_5_8], [5.5_2_1_5, 5.5_1_8_4, 5.5_2_9_3]]]).to(lowercase__)

							self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 1_0_0				,     lowercase__				,     atol=1e-4))

