


"""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__						:







						def __init__(							self   :    List[str]			,			_lowercase   :    int			,			_lowercase   :    Tuple=2			,			_lowercase   :    List[Any]=32			,			_lowercase   :    str=16			,			_lowercase   :    Optional[Any]=3			,			_lowercase   :    Tuple=True			,			_lowercase   :    Dict=True			,			_lowercase   :    Optional[int]=32			,			_lowercase   :    Optional[int]=4			,			_lowercase   :    Optional[int]=[0, 1, 2, 3]			,			_lowercase   :    int=4			,			_lowercase   :    Optional[int]=37			,			_lowercase   :    List[Any]="gelu"			,			_lowercase   :    List[Any]=0.1			,			_lowercase   :    Dict=0.1			,			_lowercase   :    int=0.0_2			,			_lowercase   :    Optional[Any]=3			,			_lowercase   :    Dict=[1, 384, 24, 24]			,			_lowercase   :    Tuple=True			,			_lowercase   :    List[str]=None			,			):
								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   :    Tuple		):
								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]		):
								A					       = {
								    '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=_lowercase			,			initializer_range=self.initializer_range			,			is_hybrid=self.is_hybrid			,			backbone_config=_lowercase			,			backbone_featmap_shape=self.backbone_featmap_shape			,			)







						def __a						(							self   :    int			,			_lowercase   :    List[Any]			,			_lowercase   :    str			,			_lowercase   :    Any		):
								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   :    List[str]			,			_lowercase   :    List[Any]			,			_lowercase   :    Union[str, Any]			,			_lowercase   :    List[Any]		):
								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   :    Dict			,			_lowercase   :    Union[str, Any]			,			_lowercase   :    Tuple			,			_lowercase   :    List[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   :    Union[str, Any]		):
								A					       = self.prepare_config_and_inputs()
								A       , A       , A					       = config_and_inputs
								A					       = {'pixel_values': pixel_values}
								return config, inputs_dict



@require_torch
class 			lowerCamelCase__						(   UpperCAmelCase_							,    UpperCAmelCase_							,    unittest.TestCase			):
						lowerCAmelCase       =     (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
						lowerCAmelCase       =     (
						    {
						        """depth-estimation""": DPTForDepthEstimation,
						        """feature-extraction""": DPTModel,
						        """image-segmentation""": DPTForSemanticSegmentation,
						    }
						    if is_torch_available()
						    else {}
						)

						lowerCAmelCase       =     False
						lowerCAmelCase       =     False
						lowerCAmelCase       =     False







						def __a						(							self   :    Tuple		):
								A					       = DPTModelTester(self		)
								A					       = ConfigTester(self			,			config_class=_lowercase			,			has_text_modality=_lowercase			,			hidden_size=37		)







						def __a						(							self   :    Union[str, Any]		):
								self.config_tester.run_common_tests()







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







						def __a						(							self   :    Optional[int]		):
								A       , 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   :    Dict		):
								A       , 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   :    List[str]		):
								A					       = self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_lowercase		)







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







						def __a						(							self   :    List[Any]		):
								A					       = self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_semantic_segmentation(*_lowercase		)







						def __a						(							self   :    Union[str, Any]		):
								for model_class in self.all_model_classes:
										if model_class.__name__ == "DPTForDepthEstimation":
												continue

										A       , 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   :    Optional[Any]		):
								for model_class in self.all_model_classes:
										if model_class.__name__ == "DPTForDepthEstimation":
												continue

										A       , 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[int]		):
								A       , 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   :    Any		):
								pass







						@slow
						def __a						(							self   :    Tuple		):
								for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
										A					       = DPTModel.from_pretrained(_lowercase		)
										self.assertIsNotNone(_lowercase		)







						def __a						(							self   :    str		):
								# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
								A       , A					       = self.model_tester.prepare_config_and_inputs_for_common()
								A					       = 'add'
								with self.assertRaises(_lowercase		):
										A					       = DPTForDepthEstimation(_lowercase		)







def  __snake_case       (   )      ->		List[str]:







		"""simple docstring"""





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



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







						def __a						(							self   :    Union[str, 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, 384, 384)		)
								self.assertEqual(predicted_depth.shape			,			_lowercase		)

								A					       = 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] / 100			,			_lowercase			,			atol=1e-4		)		)









"""simple docstring"""


from statistics import mean, stdev







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ = 3	)      ->		list:







		"""simple docstring"""





		A					       = min(UpperCamelCase__	)
		A					       = max(UpperCamelCase__	)
		# normalize data
		return [round((x - x_min) / (x_max - x_min) ,			UpperCamelCase__	) for x in data]







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ = 3	)      ->		list:







		"""simple docstring"""





		A					       = mean(UpperCamelCase__	)
		A					       = stdev(UpperCamelCase__	)
		# standardize data
		return [round((x - mu) / (sigma) ,			UpperCamelCase__	) for x in data]





