




"""simple docstring"""






import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to perform Cross Validation,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


UpperCamelCase__						=			1_6
UpperCamelCase__						=			3_2





def 							UpperCAmelCase		(		snake_case			:					Accelerator      ,       snake_case			:					DatasetDict      ,       snake_case			:					List[int]      ,       snake_case			:					List[int]      ,       snake_case			:					int = 16    ):
					_lowerCAmelCase:Dict									=  AutoTokenizer.from_pretrained('''bert-base-cased'''    )
					_lowerCAmelCase:Dict									=  DatasetDict(
					    {
					        '''train''': dataset['''train'''].select(snake_case    ),
					        '''validation''': dataset['''train'''].select(snake_case    ),
					        '''test''': dataset['''validation'''],
					    }    )

					def tokenize_function(snake_case			:					str    ):
										# max_length=None => use the model max length (it's actually the default)
										_lowerCAmelCase:List[str]									=  tokenizer(examples['''sentence1''']      ,       examples['''sentence2''']      ,       truncation=snake_case      ,       max_length=snake_case    )
										return outputs

					# Apply the method we just defined to all the examples in all the splits of the dataset
					# starting with the main process first:
					with accelerator.main_process_first():
										_lowerCAmelCase:str									=  datasets.map(
										    snake_case      ,       batched=snake_case      ,       remove_columns=['''idx''', '''sentence1''', '''sentence2''']      ,       )

					# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
					# transformers library
					_lowerCAmelCase:Tuple									=  tokenized_datasets.rename_column('''label'''      ,       '''labels'''    )

					def collate_fn(snake_case			:					str    ):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										_lowerCAmelCase:List[str]									=  128 if accelerator.distributed_type == DistributedType.TPU else None
										# When using mixed precision we want round multiples of 8/16
										if accelerator.mixed_precision == "fp8":
															_lowerCAmelCase:List[Any]									=  16
										elif accelerator.mixed_precision != "no":
															_lowerCAmelCase:Tuple									=  8
										else:
															_lowerCAmelCase:int									=  None

										return tokenizer.pad(
										    snake_case      ,       padding='''longest'''      ,       max_length=snake_case      ,       pad_to_multiple_of=snake_case      ,       return_tensors='''pt'''      ,       )

					# Instantiate dataloaders.
					_lowerCAmelCase:str									=  DataLoader(
					    tokenized_datasets['''train''']      ,       shuffle=snake_case      ,       collate_fn=snake_case      ,       batch_size=snake_case    )
					_lowerCAmelCase:Tuple									=  DataLoader(
					    tokenized_datasets['''validation''']      ,       shuffle=snake_case      ,       collate_fn=snake_case      ,       batch_size=snake_case    )

					_lowerCAmelCase:List[Any]									=  DataLoader(
					    tokenized_datasets['''test''']      ,       shuffle=snake_case      ,       collate_fn=snake_case      ,       batch_size=snake_case    )

					return train_dataloader, eval_dataloader, test_dataloader





def 							UpperCAmelCase		(		snake_case			:					Union[str, Any]      ,       snake_case			:					Optional[int]    ):
					# New Code #
					_lowerCAmelCase:Dict									=  []
					# Download the dataset
					_lowerCAmelCase:Dict									=  load_dataset('''glue'''      ,       '''mrpc'''    )
					# Create our splits
					_lowerCAmelCase:Optional[Any]									=  StratifiedKFold(n_splits=int(args.num_folds    )    )
					# Initialize accelerator
					_lowerCAmelCase:str									=  Accelerator(cpu=args.cpu      ,       mixed_precision=args.mixed_precision    )
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					_lowerCAmelCase:Optional[int]									=  config['''lr''']
					_lowerCAmelCase:List[str]									=  int(config['''num_epochs''']    )
					_lowerCAmelCase:Optional[int]									=  int(config['''seed''']    )
					_lowerCAmelCase:List[Any]									=  int(config['''batch_size''']    )

					_lowerCAmelCase:Dict									=  evaluate.load('''glue'''      ,       '''mrpc'''    )

					# If the batch size is too big we use gradient accumulation
					_lowerCAmelCase:Union[str, Any]									=  1
					if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
										_lowerCAmelCase:Union[str, Any]									=  batch_size // MAX_GPU_BATCH_SIZE
										_lowerCAmelCase:List[str]									=  MAX_GPU_BATCH_SIZE

					set_seed(snake_case    )

					# New Code #
					# Create our folds:
					_lowerCAmelCase:int									=  kfold.split(np.zeros(datasets['''train'''].num_rows    )      ,       datasets['''train''']['''label''']    )
					_lowerCAmelCase:List[Any]									=  []
					# Iterate over them
					for i, (train_idxs, valid_idxs) in enumerate(snake_case    ):
										_lowerCAmelCase:Union[str, Any]									=  get_fold_dataloaders(
										    snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       )
										# Instantiate the model (we build the model here so that the seed also control new weights initialization)
										_lowerCAmelCase:Any									=  AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''      ,       return_dict=snake_case    )

										# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
										# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
										# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
										_lowerCAmelCase:Optional[Any]									=  model.to(accelerator.device    )

										# Instantiate optimizer
										_lowerCAmelCase:Union[str, Any]									=  AdamW(params=model.parameters()      ,       lr=snake_case    )

										# Instantiate scheduler
										_lowerCAmelCase:List[str]									=  get_linear_schedule_with_warmup(
										    optimizer=snake_case      ,       num_warmup_steps=100      ,       num_training_steps=(len(snake_case    ) * num_epochs) // gradient_accumulation_steps      ,       )

										# Prepare everything
										# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
										# prepare method.
										_lowerCAmelCase:List[str]									=  accelerator.prepare(
										    snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       snake_case    )

										# Now we train the model
										for epoch in range(snake_case    ):
															model.train()
															for step, batch in enumerate(snake_case    ):
																				# We could avoid this line since we set the accelerator with `device_placement=True`.
																				batch.to(accelerator.device    )
																				_lowerCAmelCase:Any									=  model(**snake_case    )
																				_lowerCAmelCase:str									=  outputs.loss
																				_lowerCAmelCase:Tuple									=  loss / gradient_accumulation_steps
																				accelerator.backward(snake_case    )
																				if step % gradient_accumulation_steps == 0:
																									optimizer.step()
																									lr_scheduler.step()
																									optimizer.zero_grad()

															model.eval()
															for step, batch in enumerate(snake_case    ):
																				# We could avoid this line since we set the accelerator with `device_placement=True`.
																				batch.to(accelerator.device    )
																				with torch.no_grad():
																									_lowerCAmelCase:str									=  model(**snake_case    )
																				_lowerCAmelCase:List[str]									=  outputs.logits.argmax(dim=-1    )
																				_lowerCAmelCase:str									=  accelerator.gather_for_metrics((predictions, batch['''labels'''])    )
																				metric.add_batch(
																				    predictions=snake_case      ,       references=snake_case      ,       )

															_lowerCAmelCase:Optional[Any]									=  metric.compute()
															# Use accelerator.print to print only on the main process.
															accelerator.print(F'epoch {epoch}:'      ,       snake_case    )

										# New Code #
										# We also run predictions on the test set at the very end
										_lowerCAmelCase:Any									=  []
										for step, batch in enumerate(snake_case    ):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															batch.to(accelerator.device    )
															with torch.no_grad():
																				_lowerCAmelCase:Any									=  model(**snake_case    )
															_lowerCAmelCase:Optional[Any]									=  outputs.logits
															_lowerCAmelCase:Optional[Any]									=  accelerator.gather_for_metrics((predictions, batch['''labels'''])    )
															fold_predictions.append(predictions.cpu()    )
															if i == 0:
																				# We need all of the test predictions
																				test_references.append(references.cpu()    )
        # Use accelerator.print to print only on the main process.
										test_predictions.append(torch.cat(snake_case      ,       dim=0    )    )
										# We now need to release all our memory and get rid of the current model, optimizer, etc
										accelerator.free_memory()
					# New Code #
					# Finally we check the accuracy of our folded results:
					_lowerCAmelCase:str									=  torch.cat(snake_case      ,       dim=0    )
					_lowerCAmelCase:Dict									=  torch.stack(snake_case      ,       dim=0    ).sum(dim=0    ).div(int(args.num_folds    )    ).argmax(dim=-1    )
					_lowerCAmelCase:Tuple									=  metric.compute(predictions=snake_case      ,       references=snake_case    )
					accelerator.print('''Average test metrics from all folds:'''      ,       snake_case    )





def 							UpperCAmelCase		(		):
					_lowerCAmelCase:Tuple									=  argparse.ArgumentParser(description='''Simple example of training script.'''    )
					parser.add_argument(
					    '''--mixed_precision'''      ,       type=snake_case      ,       default=snake_case      ,       choices=['''no''', '''fp16''', '''bf16''', '''fp8''']      ,       help='''Whether to use mixed precision. Choose'''
					    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
					    '''and an Nvidia Ampere GPU.'''      ,       )
					parser.add_argument('''--cpu'''      ,       action='''store_true'''      ,       help='''If passed, will train on the CPU.'''    )
					# New Code #
					parser.add_argument('''--num_folds'''      ,       type=snake_case      ,       default=3      ,       help='''The number of splits to perform across the dataset'''    )
					_lowerCAmelCase:Optional[Any]									=  parser.parse_args()
					_lowerCAmelCase:Dict									=  {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
					training_function(snake_case      ,       snake_case    )


if __name__ == "__main__":
				main()









"""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   a__			:





						def __init__(							self      :	List[str]     ,a__      :	List[Any]     ,a__      :	List[str]=2     ,a__      :	Optional[Any]=32     ,a__      :	int=16     ,a__      :	Dict=3     ,a__      :	Optional[int]=True     ,a__      :	int=True     ,a__      :	Optional[Any]=32     ,a__      :	str=4     ,a__      :	Tuple=[0, 1, 2, 3]     ,a__      :	Any=4     ,a__      :	int=37     ,a__      :	int="gelu"     ,a__      :	Optional[int]=0.1     ,a__      :	List[Any]=0.1     ,a__      :	Optional[Any]=0.02     ,a__      :	str=3     ,a__      :	str=[1, 384, 24, 24]     ,a__      :	Optional[Any]=True     ,a__      :	Tuple=None     ,)						->       Optional[Any]:




											"""simple docstring"""







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





						def       __UpperCamelCase     (							self      :	Tuple)						->       Dict:




											"""simple docstring"""







											_lowerCAmelCase:List[str]									=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

											_lowerCAmelCase:Optional[Any]									=  self.get_config()

											return config, pixel_values, labels





						def       __UpperCamelCase     (							self      :	Union[str, Any])						->       Dict:




											"""simple docstring"""







											_lowerCAmelCase:Optional[int]									=  {
											    '''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=a__     ,initializer_range=self.initializer_range     ,is_hybrid=self.is_hybrid     ,backbone_config=a__     ,backbone_featmap_shape=self.backbone_featmap_shape     ,)





						def       __UpperCamelCase     (							self      :	int     ,a__      :	Tuple     ,a__      :	Tuple     ,a__      :	Tuple)						->       Dict:




											"""simple docstring"""







											_lowerCAmelCase:Dict									=  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       __UpperCamelCase     (							self      :	str     ,a__      :	Optional[int]     ,a__      :	Union[str, Any]     ,a__      :	int)						->       Optional[int]:




											"""simple docstring"""







											_lowerCAmelCase:Tuple									=  self.num_labels
											_lowerCAmelCase:List[Any]									=  DPTForDepthEstimation(a__)
											model.to(a__)
											model.eval()
											_lowerCAmelCase:int									=  model(a__)
											self.parent.assertEqual(result.predicted_depth.shape     ,(self.batch_size, self.image_size, self.image_size))





						def       __UpperCamelCase     (							self      :	Union[str, Any]     ,a__      :	Dict     ,a__      :	List[Any]     ,a__      :	str)						->       Optional[int]:




											"""simple docstring"""







											_lowerCAmelCase:Dict									=  self.num_labels
											_lowerCAmelCase:str									=  DPTForSemanticSegmentation(a__)
											model.to(a__)
											model.eval()
											_lowerCAmelCase:int									=  model(a__     ,labels=a__)
											self.parent.assertEqual(
											    result.logits.shape     ,(self.batch_size, self.num_labels, self.image_size, self.image_size))







						def       __UpperCamelCase     (							self      :	Optional[int])						->       List[str]:




											"""simple docstring"""







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




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

						snake_case__             = False
						snake_case__             = False
						snake_case__             = False





						def       __UpperCamelCase     (							self      :	Optional[Any])						->       List[str]:




											"""simple docstring"""







											_lowerCAmelCase:List[str]									=  DPTModelTester(self)
											_lowerCAmelCase:List[str]									=  ConfigTester(self     ,config_class=a__     ,has_text_modality=a__     ,hidden_size=37)





						def       __UpperCamelCase     (							self      :	str)						->       Optional[int]:




											"""simple docstring"""







											self.config_tester.run_common_tests()





						@unittest.skip(reason='''DPT does not use inputs_embeds''')
						def       __UpperCamelCase     (							self      :	Optional[int])						->       Dict:




											"""simple docstring"""







											pass





						def       __UpperCamelCase     (							self      :	Optional[Any])						->       List[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:Union[str, Any]									=  model_class(a__)
																self.assertIsInstance(model.get_input_embeddings()     ,(nn.Module))
																_lowerCAmelCase:Optional[int]									=  model.get_output_embeddings()
																self.assertTrue(x is None or isinstance(a__     ,nn.Linear))





						def       __UpperCamelCase     (							self      :	Dict)						->       int:




											"""simple docstring"""







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

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

																_lowerCAmelCase:Any									=  ['''pixel_values''']
																self.assertListEqual(arg_names[:1]     ,a__)





						def       __UpperCamelCase     (							self      :	Tuple)						->       int:




											"""simple docstring"""







											_lowerCAmelCase:Optional[Any]									=  self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*a__)





						def       __UpperCamelCase     (							self      :	Dict)						->       int:




											"""simple docstring"""







											_lowerCAmelCase:Dict									=  self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_depth_estimation(*a__)





						def       __UpperCamelCase     (							self      :	Optional[Any])						->       Union[str, Any]:




											"""simple docstring"""







											_lowerCAmelCase:Union[str, Any]									=  self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_semantic_segmentation(*a__)





						def       __UpperCamelCase     (							self      :	List[Any])						->       List[str]:




											"""simple docstring"""







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

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

																if model_class in get_values(a__):
																					continue

																_lowerCAmelCase:Union[str, Any]									=  model_class(a__)
																model.to(a__)
																model.train()
																_lowerCAmelCase:Optional[int]									=  self._prepare_for_class(a__     ,a__     ,return_labels=a__)
																_lowerCAmelCase:Union[str, Any]									=  model(**a__).loss
																loss.backward()





						def       __UpperCamelCase     (							self      :	Optional[Any])						->       List[str]:




											"""simple docstring"""







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

																_lowerCAmelCase							,   _lowerCAmelCase:Optional[Any]									=  self.model_tester.prepare_config_and_inputs_for_common()
																_lowerCAmelCase:str									=  False
																_lowerCAmelCase:str									=  True

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





						def       __UpperCamelCase     (							self      :	Any)						->       int:




											"""simple docstring"""







											_lowerCAmelCase							,   _lowerCAmelCase:Optional[Any]									=  self.model_tester.prepare_config_and_inputs_for_common()

											_lowerCAmelCase:Dict									=  _config_zero_init(a__)
											for model_class in self.all_model_classes:
																_lowerCAmelCase:Tuple									=  model_class(config=a__)
																# Skip the check for the backbone
																_lowerCAmelCase:Tuple									=  []
																for name, module in model.named_modules():
																					if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																										_lowerCAmelCase: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       __UpperCamelCase     (							self      :	Dict)						->       Any:




											"""simple docstring"""







											pass





						@slow
						def       __UpperCamelCase     (							self      :	List[Any])						->       List[str]:




											"""simple docstring"""







											for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
																_lowerCAmelCase:str									=  DPTModel.from_pretrained(a__)
																self.assertIsNotNone(a__)







						def       __UpperCamelCase     (							self      :	int)						->       Optional[int]:




											"""simple docstring"""







											_lowerCAmelCase							,   _lowerCAmelCase:List[str]									=  self.model_tester.prepare_config_and_inputs_for_common()
											_lowerCAmelCase:Any									=  '''add'''
											with self.assertRaises(a__):
																_lowerCAmelCase:Any									=  DPTForDepthEstimation(a__)





def 							UpperCAmelCase		(		):
					_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       __UpperCamelCase     (							self      :	Optional[int])						->       Union[str, Any]:




											"""simple docstring"""







											_lowerCAmelCase:List[str]									=  DPTImageProcessor.from_pretrained('''Intel/dpt-hybrid-midas''')
											_lowerCAmelCase:Optional[int]									=  DPTForDepthEstimation.from_pretrained('''Intel/dpt-hybrid-midas''').to(a__)

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

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

											# verify the predicted depth
											_lowerCAmelCase:str									=  torch.Size((1, 384, 384))
											self.assertEqual(predicted_depth.shape     ,a__)

											_lowerCAmelCase:Any									=  torch.tensor(
											    [[[5.6437, 5.6146, 5.6511], [5.4371, 5.5649, 5.5958], [5.5215, 5.5184, 5.5293]]]).to(a__)

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






