
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 			UpperCamelCase_			:




							'''simple docstring'''

							def __init__(				self     ,						UpperCamelCase     ,						UpperCamelCase=2     ,						UpperCamelCase=32     ,						UpperCamelCase=16     ,						UpperCamelCase=3     ,						UpperCamelCase=True     ,						UpperCamelCase=True     ,						UpperCamelCase=32     ,						UpperCamelCase=4     ,						UpperCamelCase=[0, 1, 2, 3]     ,						UpperCamelCase=4     ,						UpperCamelCase=37     ,						UpperCamelCase="gelu"     ,						UpperCamelCase=0.1     ,						UpperCamelCase=0.1     ,						UpperCamelCase=0.02     ,						UpperCamelCase=3     ,						UpperCamelCase=False     ,						UpperCamelCase=None     ,						) ->	Union[str, Any]:
										UpperCamelCase__    :		Any     =				parent
										UpperCamelCase__    :		str     =				batch_size
										UpperCamelCase__    :		Any     =				image_size
										UpperCamelCase__    :		List[Any]     =				patch_size
										UpperCamelCase__    :		Tuple     =				num_channels
										UpperCamelCase__    :		Optional[int]     =				is_training
										UpperCamelCase__    :		List[str]     =				use_labels
										UpperCamelCase__    :		Union[str, Any]     =				hidden_size
										UpperCamelCase__    :		Any     =				num_hidden_layers
										UpperCamelCase__    :		str     =				backbone_out_indices
										UpperCamelCase__    :		Optional[int]     =				num_attention_heads
										UpperCamelCase__    :		List[Any]     =				intermediate_size
										UpperCamelCase__    :		List[Any]     =				hidden_act
										UpperCamelCase__    :		Union[str, Any]     =				hidden_dropout_prob
										UpperCamelCase__    :		Dict     =				attention_probs_dropout_prob
										UpperCamelCase__    :		Any     =				initializer_range
										UpperCamelCase__    :		int     =				num_labels
										UpperCamelCase__    :		Optional[Any]     =				scope
										UpperCamelCase__    :		List[str]     =				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__    :		Union[str, Any]     =				num_patches + 1
							def        lowerCAmelCase__ (				self) ->	List[str]:
										UpperCamelCase__    :		str     =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

										UpperCamelCase__    :		int     =				self.get_config()

										return config, pixel_values, labels
							def        lowerCAmelCase__ (				self) ->	Optional[Any]:
										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=a_     ,						initializer_range=self.initializer_range     ,						is_hybrid=self.is_hybrid     ,						)
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase) ->	List[Any]:
										UpperCamelCase__    :		List[Any]     =				DPTModel(config=a_)
										model.to(a_)
										model.eval()
										UpperCamelCase__    :		Dict     =				model(a_)
										self.parent.assertEqual(result.last_hidden_state.shape     ,						(self.batch_size, self.seq_length, self.hidden_size))
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase) ->	Any:
										UpperCamelCase__    :		Tuple     =				self.num_labels
										UpperCamelCase__    :		Tuple     =				DPTForDepthEstimation(a_)
										model.to(a_)
										model.eval()
										UpperCamelCase__    :		List[str]     =				model(a_)
										self.parent.assertEqual(result.predicted_depth.shape     ,						(self.batch_size, self.image_size, self.image_size))
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase) ->	str:
										UpperCamelCase__    :		int     =				self.num_labels
										UpperCamelCase__    :		List[str]     =				DPTForSemanticSegmentation(a_)
										model.to(a_)
										model.eval()
										UpperCamelCase__    :		int     =				model(a_     ,						labels=a_)
										self.parent.assertEqual(
										    result.logits.shape     ,						(self.batch_size, self.num_labels, self.image_size, self.image_size))






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



@require_torch
class 			UpperCamelCase_			(						__UpperCamelCase	,     __UpperCamelCase	,     unittest.TestCase   ):




							'''simple docstring'''

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

							UpperCamelCase_					   =							False
							UpperCamelCase_					   =							False
							UpperCamelCase_					   =							False
							def        lowerCAmelCase__ (				self) ->	int:
										UpperCamelCase__    :		str     =				DPTModelTester(self)
										UpperCamelCase__    :		str     =				ConfigTester(self     ,						config_class=a_     ,						has_text_modality=a_     ,						hidden_size=37)
							def        lowerCAmelCase__ (				self) ->	Union[str, Any]:
										self.config_tester.run_common_tests()
							@unittest.skip(reason='DPT does not use inputs_embeds')
							def        lowerCAmelCase__ (				self) ->	Dict:
										pass
							def        lowerCAmelCase__ (				self) ->	int:
										UpperCamelCase__   ,			UpperCamelCase__    :		Optional[int]     =				self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													UpperCamelCase__    :		Optional[int]     =				model_class(a_)
													self.assertIsInstance(model.get_input_embeddings()     ,						(nn.Module))
													UpperCamelCase__    :		List[Any]     =				model.get_output_embeddings()
													self.assertTrue(x is None or isinstance(a_     ,						nn.Linear))
							def        lowerCAmelCase__ (				self) ->	Any:
										UpperCamelCase__   ,			UpperCamelCase__    :		str     =				self.model_tester.prepare_config_and_inputs_for_common()

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

													UpperCamelCase__    :		Optional[Any]     =				['pixel_values']
													self.assertListEqual(arg_names[:1]     ,						a_)
							def        lowerCAmelCase__ (				self) ->	str:
										UpperCamelCase__    :		Any     =				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*a_)
							def        lowerCAmelCase__ (				self) ->	Optional[Any]:
										UpperCamelCase__    :		Optional[int]     =				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_depth_estimation(*a_)
							def        lowerCAmelCase__ (				self) ->	Optional[Any]:
										UpperCamelCase__    :		Tuple     =				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_semantic_segmentation(*a_)
							def        lowerCAmelCase__ (				self) ->	str:
										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__    :		Optional[int]     =				True

													if model_class in get_values(a_):
																continue

													UpperCamelCase__    :		Optional[int]     =				model_class(a_)
													model.to(a_)
													model.train()
													UpperCamelCase__    :		Union[str, Any]     =				self._prepare_for_class(a_     ,						a_     ,						return_labels=a_)
													UpperCamelCase__    :		Any     =				model(**a_).loss
													loss.backward()
							def        lowerCAmelCase__ (				self) ->	List[Any]:
										for model_class in self.all_model_classes:
													if model_class.__name__ == "DPTForDepthEstimation":
																continue

													UpperCamelCase__   ,			UpperCamelCase__    :		List[Any]     =				self.model_tester.prepare_config_and_inputs_for_common()
													UpperCamelCase__    :		Union[str, Any]     =				False
													UpperCamelCase__    :		Union[str, Any]     =				True

													if model_class in get_values(a_) or not model_class.supports_gradient_checkpointing:
																continue
													UpperCamelCase__    :		Optional[Any]     =				model_class(a_)
													model.to(a_)
													model.gradient_checkpointing_enable()
													model.train()
													UpperCamelCase__    :		Optional[Any]     =				self._prepare_for_class(a_     ,						a_     ,						return_labels=a_)
													UpperCamelCase__    :		List[Any]     =				model(**a_).loss
													loss.backward()
							def        lowerCAmelCase__ (				self) ->	Optional[Any]:
										UpperCamelCase__   ,			UpperCamelCase__    :		Union[str, Any]     =				self.model_tester.prepare_config_and_inputs_for_common()

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






							@slow
							def        lowerCAmelCase__ (				self) ->	Dict:
										for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													UpperCamelCase__    :		Tuple     =				DPTModel.from_pretrained(a_)
													self.assertIsNotNone(a_)



def       _lowercase					(      )	->     Optional[Any]:
			UpperCamelCase__    :		str     =				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
			return image



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




							'''simple docstring'''

							def        lowerCAmelCase__ (				self) ->	Optional[int]:
										UpperCamelCase__    :		Union[str, Any]     =				DPTImageProcessor.from_pretrained('Intel/dpt-large')
										UpperCamelCase__    :		Union[str, Any]     =				DPTForDepthEstimation.from_pretrained('Intel/dpt-large').to(a_)

										UpperCamelCase__    :		Any     =				prepare_img()
										UpperCamelCase__    :		int     =				image_processor(images=a_     ,						return_tensors='pt').to(a_)

										# forward pass
										with torch.no_grad():
													UpperCamelCase__    :		Union[str, Any]     =				model(**a_)
													UpperCamelCase__    :		Union[str, Any]     =				outputs.predicted_depth

										# verify the predicted depth
										UpperCamelCase__    :		Tuple     =				torch.Size((1, 3_84, 3_84))
										self.assertEqual(predicted_depth.shape     ,						a_)

										UpperCamelCase__    :		Union[str, Any]     =				torch.tensor(
										    [[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]).to(a_)

										self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]     ,						a_     ,						atol=1E-4))
							def        lowerCAmelCase__ (				self) ->	int:
										UpperCamelCase__    :		str     =				DPTImageProcessor.from_pretrained('Intel/dpt-large-ade')
										UpperCamelCase__    :		Any     =				DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade').to(a_)

										UpperCamelCase__    :		int     =				prepare_img()
										UpperCamelCase__    :		Optional[int]     =				image_processor(images=a_     ,						return_tensors='pt').to(a_)

										# forward pass
										with torch.no_grad():
													UpperCamelCase__    :		Tuple     =				model(**a_)

										# verify the logits
										UpperCamelCase__    :		Optional[int]     =				torch.Size((1, 1_50, 4_80, 4_80))
										self.assertEqual(outputs.logits.shape     ,						a_)

										UpperCamelCase__    :		Union[str, Any]     =				torch.tensor(
										    [[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]).to(a_)

										self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]     ,						a_     ,						atol=1E-4))






							def        lowerCAmelCase__ (				self) ->	List[str]:
										UpperCamelCase__    :		Optional[int]     =				DPTImageProcessor.from_pretrained('Intel/dpt-large-ade')
										UpperCamelCase__    :		str     =				DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade').to(a_)

										UpperCamelCase__    :		Tuple     =				prepare_img()
										UpperCamelCase__    :		str     =				image_processor(images=a_     ,						return_tensors='pt').to(a_)

										# forward pass
										with torch.no_grad():
													UpperCamelCase__    :		List[Any]     =				model(**a_)

										UpperCamelCase__    :		int     =				outputs.logits.detach().cpu()

										UpperCamelCase__    :		Dict     =				image_processor.post_process_semantic_segmentation(outputs=a_     ,						target_sizes=[(5_00, 3_00)])
										UpperCamelCase__    :		Dict     =				torch.Size((5_00, 3_00))
										self.assertEqual(segmentation[0].shape     ,						a_)

										UpperCamelCase__    :		str     =				image_processor.post_process_semantic_segmentation(outputs=a_)
										UpperCamelCase__    :		Optional[Any]     =				torch.Size((4_80, 4_80))
										self.assertEqual(segmentation[0].shape     ,						a_)





from __future__ import annotations

import re



def       _lowercase					(      __SCREAMING_SNAKE_CASE )	->     list[str]:

			def alphanum_key(__SCREAMING_SNAKE_CASE ):
						return [int(__SCREAMING_SNAKE_CASE ) if s.isdigit() else s.lower() for s in re.split('([0-9]+)'				,    __SCREAMING_SNAKE_CASE )]

			return sorted(__SCREAMING_SNAKE_CASE				,    key=__SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
							import doctest

							doctest.testmod()



