





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
#
########################################################################


__lowerCamelCase       :     Optional[int]   =     16
__lowerCamelCase       :     Union[str, Any]   =     32



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase = 16     )						-> Any:
 UpperCamelCase	:					Optional[int]       	=   AutoTokenizer.from_pretrained("bert-base-cased"     )
 UpperCamelCase	:					Dict       	=   DatasetDict(
     {
         "train": dataset["train"].select(_lowerCAmelCase     ),
         "validation": dataset["train"].select(_lowerCAmelCase     ),
         "test": dataset["validation"],
     }     )

 def tokenize_function(_lowerCAmelCase     ):
  # max_length=None => use the model max length (it's actually the default)
  UpperCamelCase	:					str       	=   tokenizer(examples["sentence1"]							,   examples["sentence2"]							,   truncation=_lowerCAmelCase							,   max_length=_lowerCAmelCase     )
  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():
  UpperCamelCase	:					Any       	=   datasets.map(
      _lowerCAmelCase							,   batched=_lowerCAmelCase							,   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
 UpperCamelCase	:					Dict       	=   tokenized_datasets.rename_column("label"							,   "labels"     )

 def collate_fn(_lowerCAmelCase     ):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  UpperCamelCase	:					Dict       	=   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":
   UpperCamelCase	:					Optional[Any]       	=   16
  elif accelerator.mixed_precision != "no":
   UpperCamelCase	:					List[Any]       	=   8
  else:
   UpperCamelCase	:					Tuple       	=   None

  return tokenizer.pad(
      _lowerCAmelCase							,   padding="longest"							,   max_length=_lowerCAmelCase							,   pad_to_multiple_of=_lowerCAmelCase							,   return_tensors="pt"							,   )

 # Instantiate dataloaders.
 UpperCamelCase	:					List[Any]       	=   DataLoader(
     tokenized_datasets["train"]							,   shuffle=_lowerCAmelCase							,   collate_fn=_lowerCAmelCase							,   batch_size=_lowerCAmelCase     )
 UpperCamelCase	:					str       	=   DataLoader(
     tokenized_datasets["validation"]							,   shuffle=_lowerCAmelCase							,   collate_fn=_lowerCAmelCase							,   batch_size=_lowerCAmelCase     )

 UpperCamelCase	:					Dict       	=   DataLoader(
     tokenized_datasets["test"]							,   shuffle=_lowerCAmelCase							,   collate_fn=_lowerCAmelCase							,   batch_size=_lowerCAmelCase     )

 return train_dataloader, eval_dataloader, test_dataloader



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

 UpperCamelCase	:					int       	=   evaluate.load("glue"							,   "mrpc"     )

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

 set_seed(_lowerCAmelCase     )

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

  # 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).
  UpperCamelCase	:					str       	=   model.to(accelerator.device     )

  # Instantiate optimizer
  UpperCamelCase	:					Tuple       	=   AdamW(params=model.parameters()							,   lr=_lowerCAmelCase     )

  # Instantiate scheduler
  UpperCamelCase	:					Union[str, Any]       	=   get_linear_schedule_with_warmup(
      optimizer=_lowerCAmelCase							,   num_warmup_steps=100							,   num_training_steps=(len(_lowerCAmelCase     ) * 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.
  UpperCamelCase	,    UpperCamelCase	,    UpperCamelCase	,    UpperCamelCase	,    UpperCamelCase	:					Any       	=   accelerator.prepare(
      _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     )

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

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

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

  # New Code #
  # We also run predictions on the test set at the very end
  UpperCamelCase	:					Union[str, Any]       	=   []
  for step, batch in enumerate(_lowerCAmelCase     ):
   # We could avoid this line since we set the accelerator with `device_placement=True`.
   batch.to(accelerator.device     )
   with torch.no_grad():
    UpperCamelCase	:					Optional[int]       	=   model(**_lowerCAmelCase     )
   UpperCamelCase	:					Optional[int]       	=   outputs.logits
   UpperCamelCase	,    UpperCamelCase	:					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(_lowerCAmelCase							,   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:
 UpperCamelCase	:					Optional[int]       	=   torch.cat(_lowerCAmelCase							,   dim=0     )
 UpperCamelCase	:					Optional[int]       	=   torch.stack(_lowerCAmelCase							,   dim=0     ).sum(dim=0     ).div(int(args.num_folds     )     ).argmax(dim=-1     )
 UpperCamelCase	:					Optional[Any]       	=   metric.compute(predictions=_lowerCAmelCase							,   references=_lowerCAmelCase     )
 accelerator.print("Average test metrics from all folds:"							,   _lowerCAmelCase     )



def 		A_				(     )						-> int:
 UpperCamelCase	:					Optional[Any]       	=   argparse.ArgumentParser(description="Simple example of training script."     )
 parser.add_argument(
     "--mixed_precision"							,   type=_lowerCAmelCase							,   default=_lowerCAmelCase							,   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=_lowerCAmelCase							,   default=3							,   help="The number of splits to perform across the dataset"     )
 UpperCamelCase	:					int       	=   parser.parse_args()
 UpperCamelCase	:					Union[str, Any]       	=   {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
 training_function(_lowerCAmelCase							,   _lowerCAmelCase     )


if __name__ == "__main__":
    main()






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     snake_case__							:
				"""simple docstring"""



				def __init__(      self				:     Tuple					,      __lowerCamelCase				:     Optional[int]					,      __lowerCamelCase				:     Dict=2					,      __lowerCamelCase				:     Dict=32					,      __lowerCamelCase				:     Tuple=16					,      __lowerCamelCase				:     Tuple=3					,      __lowerCamelCase				:     int=True					,      __lowerCamelCase				:     int=True					,      __lowerCamelCase				:     Any=32					,      __lowerCamelCase				:     List[Any]=4					,      __lowerCamelCase				:     Tuple=[0, 1, 2, 3]					,      __lowerCamelCase				:     Union[str, Any]=4					,      __lowerCamelCase				:     Union[str, Any]=37					,      __lowerCamelCase				:     Optional[int]="gelu"					,      __lowerCamelCase				:     str=0.1					,      __lowerCamelCase				:     List[str]=0.1					,      __lowerCamelCase				:     Optional[Any]=0.02					,      __lowerCamelCase				:     Optional[int]=3					,      __lowerCamelCase				:     List[Any]=[1, 3_84, 24, 24]					,      __lowerCamelCase				:     List[Any]=True					,      __lowerCamelCase				:     Optional[int]=None					,      )					->     Optional[int]:
							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 							__UpperCAmelCase							(      self				:     Union[str, Any]							)					->     str:
							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 							__UpperCAmelCase							(      self				:     List[str]							)					->     Union[str, Any]:
							a    =			{
							    "global_padding": "same",
							    "layer_type": "bottleneck",
							    "depths": [3, 4, 9],
							    "out_features": ["stage1", "stage2", "stage3"],
							    "embedding_dynamic_padding": True,
							    "hidden_sizes": [96, 1_92, 3_84, 7_68],
							    "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=__lowerCamelCase					,      initializer_range=self.initializer_range					,      is_hybrid=self.is_hybrid					,      backbone_config=__lowerCamelCase					,      backbone_featmap_shape=self.backbone_featmap_shape					,      )





				def 							__UpperCAmelCase							(      self				:     Union[str, Any]					,      __lowerCamelCase				:     Any					,      __lowerCamelCase				:     Optional[int]					,      __lowerCamelCase				:     Optional[Any]							)					->     Optional[Any]:
							a    =			DPTModel(config=__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()
							a    =			model(__lowerCamelCase							)
							self.parent.assertEqual(result.last_hidden_state.shape					,      (self.batch_size, self.seq_length, self.hidden_size)							)





				def 							__UpperCAmelCase							(      self				:     List[str]					,      __lowerCamelCase				:     List[str]					,      __lowerCamelCase				:     List[str]					,      __lowerCamelCase				:     Any							)					->     str:
							a    =			self.num_labels
							a    =			DPTForDepthEstimation(__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()
							a    =			model(__lowerCamelCase							)
							self.parent.assertEqual(result.predicted_depth.shape					,      (self.batch_size, self.image_size, self.image_size)							)





				def 							__UpperCAmelCase							(      self				:     str					,      __lowerCamelCase				:     List[Any]					,      __lowerCamelCase				:     str					,      __lowerCamelCase				:     Tuple							)					->     Any:
							a    =			self.num_labels
							a    =			DPTForSemanticSegmentation(__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()
							a    =			model(__lowerCamelCase					,      labels=__lowerCamelCase							)
							self.parent.assertEqual(
							    result.logits.shape					,      (self.batch_size, self.num_labels, self.image_size, self.image_size)							)





				def 							__UpperCAmelCase							(      self				:     str							)					->     str:
							a    =			self.prepare_config_and_inputs()
							a    ,		a    ,		a    =			config_and_inputs
							a    =			{"pixel_values": pixel_values}
							return config, inputs_dict




@require_torch
class     snake_case__							(_UpperCamelCase    ,       _UpperCamelCase    ,       unittest.TestCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							Union[str, Any]							 =					(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
				SCREAMING_SNAKE_CASE_       :							Union[str, Any]							 =					(
				    {
				        """depth-estimation""": DPTForDepthEstimation,
				        """feature-extraction""": DPTModel,
				        """image-segmentation""": DPTForSemanticSegmentation,
				    }
				    if is_torch_available()
				    else {}
				)

				SCREAMING_SNAKE_CASE_       :							Optional[int]							 =					False
				SCREAMING_SNAKE_CASE_       :							str							 =					False
				SCREAMING_SNAKE_CASE_       :							str							 =					False





				def 							__UpperCAmelCase							(      self				:     int							)					->     List[str]:
							a    =			DPTModelTester(self							)
							a    =			ConfigTester(self					,      config_class=__lowerCamelCase					,      has_text_modality=__lowerCamelCase					,      hidden_size=37							)





				def 							__UpperCAmelCase							(      self				:     int							)					->     List[str]:
							self.config_tester.run_common_tests()





				@unittest.skip(reason="DPT does not use inputs_embeds"							)
				def 							__UpperCAmelCase							(      self				:     Optional[Any]							)					->     List[str]:
							pass





				def 							__UpperCAmelCase							(      self				:     List[str]							)					->     Optional[Any]:
							a    ,		a    =			self.model_tester.prepare_config_and_inputs_for_common()

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





				def 							__UpperCAmelCase							(      self				:     Tuple							)					->     List[str]:
							a    ,		a    =			self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
										a    =			model_class(__lowerCamelCase							)
										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]					,      __lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Any							)					->     Union[str, Any]:
							a    =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Any							)					->     Dict:
							a    =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_depth_estimation(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     List[str]							)					->     Optional[int]:
							a    =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_semantic_segmentation(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     List[str]							)					->     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    =			True

										if model_class in get_values(__lowerCamelCase							):
													continue

										a    =			model_class(__lowerCamelCase							)
										model.to(__lowerCamelCase							)
										model.train()
										a    =			self._prepare_for_class(__lowerCamelCase					,      __lowerCamelCase					,      return_labels=__lowerCamelCase							)
										a    =			model(**__lowerCamelCase							).loss
										loss.backward()





				def 							__UpperCAmelCase							(      self				:     Dict							)					->     List[str]:
							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(__lowerCamelCase							) or not model_class.supports_gradient_checkpointing:
													continue
										a    =			model_class(__lowerCamelCase							)
										model.to(__lowerCamelCase							)
										model.gradient_checkpointing_enable()
										model.train()
										a    =			self._prepare_for_class(__lowerCamelCase					,      __lowerCamelCase					,      return_labels=__lowerCamelCase							)
										a    =			model(**__lowerCamelCase							).loss
										loss.backward()





				def 							__UpperCAmelCase							(      self				:     Tuple							)					->     Union[str, Any]:
							a    ,		a    =			self.model_tester.prepare_config_and_inputs_for_common()

							a    =			_config_zero_init(__lowerCamelCase							)
							for model_class in self.all_model_classes:
										a    =			model_class(config=__lowerCamelCase							)
										# 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 							__UpperCAmelCase							(      self				:     int							)					->     Any:
							pass





				@slow
				def 							__UpperCAmelCase							(      self				:     str							)					->     Optional[int]:
							for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
										a    =			DPTModel.from_pretrained(__lowerCamelCase							)
										self.assertIsNotNone(__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Tuple							)					->     Dict:
							# 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(__lowerCamelCase							):
										a    =			DPTForDepthEstimation(__lowerCamelCase							)




def 			__magic_name__				( ):






			'''simple docstring'''







			a    =			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
			return image






@require_torch
@require_vision
@slow
class     snake_case__							(unittest.TestCase   ):
				"""simple docstring"""



				def 							__UpperCAmelCase							(      self				:     List[str]							)					->     Optional[Any]:
							a    =			DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas"							)
							a    =			DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas"							).to(__lowerCamelCase							)

							a    =			prepare_img()
							a    =			image_processor(images=__lowerCamelCase					,      return_tensors="pt"							).to(__lowerCamelCase							)

							# forward pass
							with torch.no_grad():
										a    =			model(**__lowerCamelCase							)
										a    =			outputs.predicted_depth

							# verify the predicted depth
							a    =			torch.Size((1, 3_84, 3_84)							)
							self.assertEqual(predicted_depth.shape					,      __lowerCamelCase							)

							a    =			torch.tensor(
							    [[[5.6_437, 5.6_146, 5.6_511], [5.4_371, 5.5_649, 5.5_958], [5.5_215, 5.5_184, 5.5_293]]]							).to(__lowerCamelCase							)

							self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 1_00					,      __lowerCamelCase					,      atol=1e-4							)							)






