
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 	A__				:






				def __init__(		self   : str       ,	a   : Any       ,	a   : int=2       ,	a   : List[str]=32       ,	a   : List[str]=16       ,	a   : List[str]=3       ,	a   : Optional[int]=True       ,	a   : Optional[Any]=True       ,	a   : int=32       ,	a   : List[Any]=4       ,	a   : Tuple=[0, 1, 2, 3]       ,	a   : str=4       ,	a   : str=37       ,	a   : Optional[Any]="gelu"       ,	a   : Union[str, Any]=0.1       ,	a   : Dict=0.1       ,	a   : Dict=0.0_2       ,	a   : Optional[int]=3       ,	a   : Optional[Any]=False       ,	a   : List[str]=None       ,	):






							'''simple docstring'''







							lowerCAmelCase__ : Optional[int]   =						parent
							lowerCAmelCase__ : Optional[int]   =						batch_size
							lowerCAmelCase__ : List[str]   =						image_size
							lowerCAmelCase__ : Optional[int]   =						patch_size
							lowerCAmelCase__ : int   =						num_channels
							lowerCAmelCase__ : int   =						is_training
							lowerCAmelCase__ : Optional[int]   =						use_labels
							lowerCAmelCase__ : Optional[Any]   =						hidden_size
							lowerCAmelCase__ : int   =						num_hidden_layers
							lowerCAmelCase__ : Optional[Any]   =						backbone_out_indices
							lowerCAmelCase__ : Dict   =						num_attention_heads
							lowerCAmelCase__ : Union[str, Any]   =						intermediate_size
							lowerCAmelCase__ : Union[str, Any]   =						hidden_act
							lowerCAmelCase__ : str   =						hidden_dropout_prob
							lowerCAmelCase__ : Tuple   =						attention_probs_dropout_prob
							lowerCAmelCase__ : int   =						initializer_range
							lowerCAmelCase__ : List[Any]   =						num_labels
							lowerCAmelCase__ : int   =						scope
							lowerCAmelCase__ : int   =						is_hybrid
							# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
							lowerCAmelCase__ : Any   =						(image_size // patch_size) ** 2
							lowerCAmelCase__ : Union[str, Any]   =						num_patches + 1






				def 				_lowerCamelCase						(		self   : int  ):






							'''simple docstring'''







							lowerCAmelCase__ : Optional[Any]   =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

							lowerCAmelCase__ : Optional[int]   =						None
							if self.use_labels:
										lowerCAmelCase__ : Dict   =						ids_tensor([self.batch_size, self.image_size, self.image_size]       ,	self.num_labels  )

							lowerCAmelCase__ : Union[str, Any]   =						self.get_config()

							return config, pixel_values, labels






				def 				_lowerCamelCase						(		self   : List[str]  ):






							'''simple docstring'''







							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   : Union[str, Any]       ,	a   : Union[str, Any]       ,	a   : List[str]       ,	a   : Any  ):






							'''simple docstring'''







							lowerCAmelCase__ : Optional[int]   =						DPTModel(config=a  )
							model.to(a  )
							model.eval()
							lowerCAmelCase__ : Optional[Any]   =						model(a  )
							self.parent.assertEqual(result.last_hidden_state.shape       ,	(self.batch_size, self.seq_length, self.hidden_size)  )






				def 				_lowerCamelCase						(		self   : Optional[Any]       ,	a   : int       ,	a   : Dict       ,	a   : str  ):






							'''simple docstring'''







							lowerCAmelCase__ : List[Any]   =						self.num_labels
							lowerCAmelCase__ : Optional[int]   =						DPTForDepthEstimation(a  )
							model.to(a  )
							model.eval()
							lowerCAmelCase__ : Union[str, Any]   =						model(a  )
							self.parent.assertEqual(result.predicted_depth.shape       ,	(self.batch_size, self.image_size, self.image_size)  )






				def 				_lowerCamelCase						(		self   : Tuple       ,	a   : str       ,	a   : Union[str, Any]       ,	a   : Dict  ):






							'''simple docstring'''







							lowerCAmelCase__ : Optional[Any]   =						self.num_labels
							lowerCAmelCase__ : Tuple   =						DPTForSemanticSegmentation(a  )
							model.to(a  )
							model.eval()
							lowerCAmelCase__ : Dict   =						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   : str  ):






							'''simple docstring'''







							lowerCAmelCase__ : str   =						self.prepare_config_and_inputs()
							lowerCAmelCase__ ,							lowerCAmelCase__ ,							lowerCAmelCase__ : List[str]   =						config_and_inputs
							lowerCAmelCase__ : Union[str, Any]   =						{'pixel_values': pixel_values}
							return config, inputs_dict







@require_torch
class 	A__				(	__magic_name__		,  __magic_name__		,  unittest.TestCase      ):
				lowercase            =    (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
				lowercase            =    (
				    {
				        'depth-estimation': DPTForDepthEstimation,
				        'feature-extraction': DPTModel,
				        'image-segmentation': DPTForSemanticSegmentation,
				    }
				    if is_torch_available()
				    else {}
				)

				lowercase            =    False
				lowercase            =    False
				lowercase            =    False






				def 				_lowerCamelCase						(		self   : Any  ):






							'''simple docstring'''







							lowerCAmelCase__ : str   =						DPTModelTester(self  )
							lowerCAmelCase__ : Optional[int]   =						ConfigTester(self       ,	config_class=a       ,	has_text_modality=a       ,	hidden_size=37  )






				def 				_lowerCamelCase						(		self   : Tuple  ):






							'''simple docstring'''







							self.config_tester.run_common_tests()






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






							'''simple docstring'''







							pass






				def 				_lowerCamelCase						(		self   : Optional[Any]  ):






							'''simple docstring'''







							lowerCAmelCase__ ,							lowerCAmelCase__ : Union[str, Any]   =						self.model_tester.prepare_config_and_inputs_for_common()

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






				def 				_lowerCamelCase						(		self   : List[str]  ):






							'''simple docstring'''







							lowerCAmelCase__ ,							lowerCAmelCase__ : Optional[int]   =						self.model_tester.prepare_config_and_inputs_for_common()

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

										lowerCAmelCase__ : Union[str, Any]   =						['pixel_values']
										self.assertListEqual(arg_names[:1]       ,	a  )






				def 				_lowerCamelCase						(		self   : int  ):






							'''simple docstring'''







							lowerCAmelCase__ : Any   =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*a  )






				def 				_lowerCamelCase						(		self   : Optional[int]  ):






							'''simple docstring'''







							lowerCAmelCase__ : Any   =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_depth_estimation(*a  )






				def 				_lowerCamelCase						(		self   : Tuple  ):






							'''simple docstring'''







							lowerCAmelCase__ : Optional[Any]   =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_semantic_segmentation(*a  )






				def 				_lowerCamelCase						(		self   : Tuple  ):






							'''simple docstring'''







							for model_class in self.all_model_classes:
										if model_class.__name__ == "DPTForDepthEstimation":
													continue

										lowerCAmelCase__ ,							lowerCAmelCase__ : Optional[int]   =						self.model_tester.prepare_config_and_inputs_for_common()
										lowerCAmelCase__ : Union[str, Any]   =						True

										if model_class in get_values(a  ):
													continue

										lowerCAmelCase__ : Optional[int]   =						model_class(a  )
										model.to(a  )
										model.train()
										lowerCAmelCase__ : List[Any]   =						self._prepare_for_class(a       ,	a       ,	return_labels=a  )
										lowerCAmelCase__ : int   =						model(**a  ).loss
										loss.backward()






				def 				_lowerCamelCase						(		self   : Tuple  ):






							'''simple docstring'''







							for model_class in self.all_model_classes:
										if model_class.__name__ == "DPTForDepthEstimation":
													continue

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

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






				def 				_lowerCamelCase						(		self   : List[str]  ):






							'''simple docstring'''







							lowerCAmelCase__ ,							lowerCAmelCase__ : Any   =						self.model_tester.prepare_config_and_inputs_for_common()

							lowerCAmelCase__ : Union[str, Any]   =						_config_zero_init(a  )
							for model_class in self.all_model_classes:
										lowerCAmelCase__ : Dict   =						model_class(config=a  )
										# Skip the check for the backbone
										lowerCAmelCase__ : str   =						[]
										for name, module in model.named_modules():
													if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																lowerCAmelCase__ : 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 				_lowerCamelCase						(		self   : int  ):






							'''simple docstring'''







							pass






				@slow
				def 				_lowerCamelCase						(		self   : str  ):






							'''simple docstring'''







							for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowerCAmelCase__ : Optional[Any]   =						DPTModel.from_pretrained(a  )
										self.assertIsNotNone(a  )






def 					lowerCAmelCase__     (    )							->						List[str]:
			lowerCAmelCase__ : Union[str, Any]   =						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
			return image







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






				def 				_lowerCamelCase						(		self   : List[str]  ):






							'''simple docstring'''







							lowerCAmelCase__ : Tuple   =						DPTImageProcessor.from_pretrained('Intel/dpt-large'  )
							lowerCAmelCase__ : List[Any]   =						DPTForDepthEstimation.from_pretrained('Intel/dpt-large'  ).to(a  )

							lowerCAmelCase__ : Optional[int]   =						prepare_img()
							lowerCAmelCase__ : List[Any]   =						image_processor(images=a       ,	return_tensors='pt'  ).to(a  )

							# forward pass
							with torch.no_grad():
										lowerCAmelCase__ : Optional[int]   =						model(**a  )
										lowerCAmelCase__ : Optional[Any]   =						outputs.predicted_depth

							# verify the predicted depth
							lowerCAmelCase__ : Optional[Any]   =						torch.Size((1, 384, 384)  )
							self.assertEqual(predicted_depth.shape       ,	a  )

							lowerCAmelCase__ : str   =						torch.tensor(
							    [[6.3_1_9_9, 6.3_6_2_9, 6.4_1_4_8], [6.3_8_5_0, 6.3_6_1_5, 6.4_1_6_6], [6.3_5_1_9, 6.3_1_7_6, 6.3_5_7_5]]  ).to(a  )

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






				def 				_lowerCamelCase						(		self   : Any  ):






							'''simple docstring'''







							lowerCAmelCase__ : List[Any]   =						DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'  )
							lowerCAmelCase__ : Union[str, Any]   =						DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'  ).to(a  )

							lowerCAmelCase__ : Union[str, Any]   =						prepare_img()
							lowerCAmelCase__ : Optional[int]   =						image_processor(images=a       ,	return_tensors='pt'  ).to(a  )

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

							# verify the logits
							lowerCAmelCase__ : Dict   =						torch.Size((1, 150, 480, 480)  )
							self.assertEqual(outputs.logits.shape       ,	a  )

							lowerCAmelCase__ : List[str]   =						torch.tensor(
							    [[4.0_4_8_0, 4.2_4_2_0, 4.4_3_6_0], [4.3_1_2_4, 4.5_6_9_3, 4.8_2_6_1], [4.5_7_6_8, 4.8_9_6_5, 5.2_1_6_3]]  ).to(a  )

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






				def 				_lowerCamelCase						(		self   : Any  ):






							'''simple docstring'''







							lowerCAmelCase__ : int   =						DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'  )
							lowerCAmelCase__ : Any   =						DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'  ).to(a  )

							lowerCAmelCase__ : Optional[int]   =						prepare_img()
							lowerCAmelCase__ : Union[str, Any]   =						image_processor(images=a       ,	return_tensors='pt'  ).to(a  )

							# forward pass
							with torch.no_grad():
										lowerCAmelCase__ : Union[str, Any]   =						model(**a  )

							lowerCAmelCase__ : List[str]   =						outputs.logits.detach().cpu()

							lowerCAmelCase__ : str   =						image_processor.post_process_semantic_segmentation(outputs=a       ,	target_sizes=[(500, 300)]  )
							lowerCAmelCase__ : Any   =						torch.Size((500, 300)  )
							self.assertEqual(segmentation[0].shape       ,	a  )

							lowerCAmelCase__ : Optional[int]   =						image_processor.post_process_semantic_segmentation(outputs=a  )
							lowerCAmelCase__ : List[Any]   =						torch.Size((480, 480)  )
							self.assertEqual(segmentation[0].shape       ,	a  )

import torch

from accelerate import Accelerator, DistributedType







class 	A__				:






				def __enter__(		self   : Optional[Any]  ):






							'''simple docstring'''







							if self.enabled:
										lowerCAmelCase__ : Any   =						self.model.no_sync()
										self.model_sync_obj.__enter__()

							return self






				def __exit__(		self   : int       ,	a   : int       ,	a   : Optional[Any]       ,	a   : str  ):






							'''simple docstring'''







							if self.enabled:
										# Average all models on exit
										self._sync_and_avg_model_params()
										self.model_sync_obj.__exit__(a       ,	a       ,	a  )






				def __init__(		self   : List[str]       ,	a   : Accelerator       ,	a   : torch.nn.Module       ,	a   : int       ,	a   : bool = True  ):






							'''simple docstring'''







							if accelerator.distributed_type not in [
							    DistributedType.NO,
							    DistributedType.MULTI_CPU,
							    DistributedType.MULTI_GPU,
							]:
										raise NotImplementedError('LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'  )
							lowerCAmelCase__ : Union[str, Any]   =						enabled and accelerator.distributed_type != DistributedType.NO
							lowerCAmelCase__ : str   =						0
							if self.enabled:
										lowerCAmelCase__ : Dict   =						accelerator
										lowerCAmelCase__ : Dict   =						model
										lowerCAmelCase__ : Optional[int]   =						local_sgd_steps






				def 				_lowerCamelCase						(		self   : Optional[Any]  ):






							'''simple docstring'''







							self.num_steps += 1
							if not self.enabled:
										return

							if self.num_steps % self.local_sgd_steps == 0:
										self._sync_and_avg_model_params()






				def 				_lowerCamelCase						(		self   : int  ):






							'''simple docstring'''







							self.accelerator.wait_for_everyone()
							with self.accelerator.autocast():
										for param in self.model.parameters():
													lowerCAmelCase__ : Dict   =						self.accelerator.reduce(param.data       ,	reduction='mean'  )