'''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_      	=	16
UpperCamelCase_      	=	32


def    lowerCamelCase       (			UpperCAmelCase__				:  Accelerator				,						UpperCAmelCase__				:  DatasetDict				,						UpperCAmelCase__				:  List[int]				,						UpperCAmelCase__				:  List[int]				,						UpperCAmelCase__				:  int = 1_6		)			->	Any:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :List[Any]						    = AutoTokenizer.from_pretrained('bert-base-cased'		)
							SCREAMING_SNAKE_CASE__     :int						    = DatasetDict(
							    {
							        'train': dataset['train'].select(UpperCAmelCase__		),
							        'validation': dataset['train'].select(UpperCAmelCase__		),
							        'test': dataset['validation'],
							    }		)

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

							def collate_fn(UpperCAmelCase__				:  List[str]		):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														SCREAMING_SNAKE_CASE__     :Tuple						    = 1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
														# When using mixed precision we want round multiples of 8/16
														if accelerator.mixed_precision == "fp8":
																					SCREAMING_SNAKE_CASE__     :Tuple						    = 1_6
														elif accelerator.mixed_precision != "no":
																					SCREAMING_SNAKE_CASE__     :Dict						    = 8
														else:
																					SCREAMING_SNAKE_CASE__     :Optional[Any]						    = None

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

							# Instantiate dataloaders.
							SCREAMING_SNAKE_CASE__     :Optional[int]						    = DataLoader(
							    tokenized_datasets['train']				,						shuffle=UpperCAmelCase__				,						collate_fn=UpperCAmelCase__				,						batch_size=UpperCAmelCase__		)
							SCREAMING_SNAKE_CASE__     :List[Any]						    = DataLoader(
							    tokenized_datasets['validation']				,						shuffle=UpperCAmelCase__				,						collate_fn=UpperCAmelCase__				,						batch_size=UpperCAmelCase__		)

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

							return train_dataloader, eval_dataloader, test_dataloader


def    lowerCamelCase       (			UpperCAmelCase__				:  int				,						UpperCAmelCase__				:  Union[str, Any]		)			->	str:


							'''simple docstring'''






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

							SCREAMING_SNAKE_CASE__     :List[str]						    = evaluate.load('glue'				,						'mrpc'		)

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

							set_seed(UpperCAmelCase__		)

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

														# 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).
														SCREAMING_SNAKE_CASE__     :Optional[Any]						    = model.to(accelerator.device		)

														# Instantiate optimizer
														SCREAMING_SNAKE_CASE__     :Optional[int]						    = AdamW(params=model.parameters()				,						lr=UpperCAmelCase__		)

														# Instantiate scheduler
														SCREAMING_SNAKE_CASE__     :str						    = get_linear_schedule_with_warmup(
														    optimizer=UpperCAmelCase__				,						num_warmup_steps=1_0_0				,						num_training_steps=(len(UpperCAmelCase__		) * 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.
														SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Any						    = accelerator.prepare(
														    UpperCAmelCase__				,						UpperCAmelCase__				,						UpperCAmelCase__				,						UpperCAmelCase__				,						UpperCAmelCase__		)

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

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

																					SCREAMING_SNAKE_CASE__     :str						    = metric.compute()
																					# Use accelerator.print to print only on the main process.
																					accelerator.print(F'''epoch {epoch}:'''				,						UpperCAmelCase__		)

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


def    lowerCamelCase       (			)			->	List[Any]:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :int						    = argparse.ArgumentParser(description='Simple example of training script.'		)
							parser.add_argument(
							    '--mixed_precision'				,						type=UpperCAmelCase__				,						default=UpperCAmelCase__				,						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=UpperCAmelCase__				,						default=3				,						help='The number of splits to perform across the dataset'		)
							SCREAMING_SNAKE_CASE__     :Any						    = parser.parse_args()
							SCREAMING_SNAKE_CASE__     :Tuple						    = {'lr': 2e-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
							training_function(UpperCAmelCase__				,						UpperCAmelCase__		)


if __name__ == "__main__":
		main()







'''simple docstring'''







import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import BridgeTowerImageProcessor




class _SCREAMING_SNAKE_CASE(							unittest.TestCase					):




						def __init__(    self :       Any							,							UpperCamelCase_ :       List[Any]							,							UpperCamelCase_ :       bool = True							,							UpperCamelCase_ :       Dict[str, int] = None							,							UpperCamelCase_ :       int = 32							,							UpperCamelCase_ :       bool = True							,							UpperCamelCase_ :       Union[int, float] = 1 / 2_55							,							UpperCamelCase_ :       bool = True							,							UpperCamelCase_ :       bool = True							,							UpperCamelCase_ :       Optional[Union[float, List[float]]] = [0.4814_5466, 0.457_8275, 0.4082_1073]							,							UpperCamelCase_ :       Optional[Union[float, List[float]]] = [0.2686_2954, 0.2613_0258, 0.2757_7711]							,							UpperCamelCase_ :       bool = True							,							UpperCamelCase_ :       int=7							,							UpperCamelCase_ :       Dict=30							,							UpperCamelCase_ :       Tuple=4_00							,							UpperCamelCase_ :       List[Any]=3							,							)     ->				List[Any]:
													SCREAMING_SNAKE_CASE__     :List[str]						    = parent
													SCREAMING_SNAKE_CASE__     :Tuple						    = do_resize
													SCREAMING_SNAKE_CASE__     :List[Any]						    = size if size is not None else {'shortest_edge': 2_88}
													SCREAMING_SNAKE_CASE__     :str						    = size_divisor
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = do_rescale
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = rescale_factor
													SCREAMING_SNAKE_CASE__     :str						    = do_normalize
													SCREAMING_SNAKE_CASE__     :int						    = do_center_crop
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = image_mean
													SCREAMING_SNAKE_CASE__     :str						    = image_std
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = do_pad
													SCREAMING_SNAKE_CASE__     :Tuple						    = batch_size
													SCREAMING_SNAKE_CASE__     :List[str]						    = num_channels
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = min_resolution
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = max_resolution




						def      __lowerCamelCase       (    self :       Tuple      )     ->				Optional[int]:
													return {
													    "image_mean": self.image_mean,
													    "image_std": self.image_std,
													    "do_normalize": self.do_normalize,
													    "do_resize": self.do_resize,
													    "size": self.size,
													    "size_divisor": self.size_divisor,
													}




						def      __lowerCamelCase       (    self :       int							,							UpperCamelCase_ :       str							,							UpperCamelCase_ :       str=False      )     ->				Optional[int]:
													if not batched:
																				SCREAMING_SNAKE_CASE__     :Dict						    = self.size['shortest_edge']
																				SCREAMING_SNAKE_CASE__     :List[Any]						    = image_inputs[0]
																				if isinstance(UpperCamelCase_							,							Image.Image      ):
																											SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Optional[int]						    = image.size
																				else:
																											SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :str						    = image.shape[1], image.shape[2]
																				SCREAMING_SNAKE_CASE__     :Optional[int]						    = size / min(UpperCamelCase_							,							UpperCamelCase_      )
																				if h < w:
																											SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :List[Any]						    = size, scale * w
																				else:
																											SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :str						    = scale * h, size

																				SCREAMING_SNAKE_CASE__     :Any						    = int((13_33 / 8_00) * size      )
																				if max(UpperCamelCase_							,							UpperCamelCase_      ) > max_size:
																											SCREAMING_SNAKE_CASE__     :Tuple						    = max_size / max(UpperCamelCase_							,							UpperCamelCase_      )
																											SCREAMING_SNAKE_CASE__     :str						    = newh * scale
																											SCREAMING_SNAKE_CASE__     :Any						    = neww * scale

																				SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :int						    = int(newh + 0.5      ), int(neww + 0.5      )
																				SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Optional[Any]						    = (
																				    newh // self.size_divisor * self.size_divisor,
																				    neww // self.size_divisor * self.size_divisor,
																				)

													else:
																				SCREAMING_SNAKE_CASE__     :Any						    = []
																				for image in image_inputs:
																											SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :str						    = self.get_expected_values([image]      )
																											expected_values.append((expected_height, expected_width)      )
																				SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = max(UpperCamelCase_							,							key=lambda UpperCamelCase_      : item[0]      )[0]
																				SCREAMING_SNAKE_CASE__     :Optional[int]						    = max(UpperCamelCase_							,							key=lambda UpperCamelCase_      : item[1]      )[1]

													return expected_height, expected_width




@require_torch
@require_vision
class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE						,						unittest.TestCase					):
						A_			:							List[Any]     		= BridgeTowerImageProcessor if is_vision_available() else None




						def      __lowerCamelCase       (    self :       Optional[Any]      )     ->				str:
													SCREAMING_SNAKE_CASE__     :List[str]						    = BridgeTowerImageProcessingTester(self      )




						@property
						def      __lowerCamelCase       (    self :       int      )     ->				Any:
													return self.image_processor_tester.prepare_image_processor_dict()




						def      __lowerCamelCase       (    self :       int      )     ->				Union[str, Any]:
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = self.image_processing_class(**self.image_processor_dict      )
													self.assertTrue(hasattr(UpperCamelCase_							,							'image_mean'      )      )
													self.assertTrue(hasattr(UpperCamelCase_							,							'image_std'      )      )
													self.assertTrue(hasattr(UpperCamelCase_							,							'do_normalize'      )      )
													self.assertTrue(hasattr(UpperCamelCase_							,							'do_resize'      )      )
													self.assertTrue(hasattr(UpperCamelCase_							,							'size'      )      )
													self.assertTrue(hasattr(UpperCamelCase_							,							'size_divisor'      )      )




						def      __lowerCamelCase       (    self :       Union[str, Any]      )     ->				List[str]:
													pass




						def      __lowerCamelCase       (    self :       int      )     ->				Dict:
													# Initialize image processor
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = self.image_processing_class(**self.image_processor_dict      )
													# create random PIL images
													SCREAMING_SNAKE_CASE__     :Any						    = prepare_image_inputs(self.image_processor_tester							,							equal_resolution=UpperCamelCase_      )
													for image in image_inputs:
																				self.assertIsInstance(UpperCamelCase_							,							Image.Image      )

													# Test not batched input
													SCREAMING_SNAKE_CASE__     :str						    = image_processing(image_inputs[0]							,							return_tensors='pt'      ).pixel_values

													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Dict						    = self.image_processor_tester.get_expected_values(UpperCamelCase_      )
													self.assertEqual(
													    encoded_images.shape							,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,							)

													# Test batched
													SCREAMING_SNAKE_CASE__     :Any						    = image_processing(UpperCamelCase_							,							return_tensors='pt'      ).pixel_values

													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :int						    = self.image_processor_tester.get_expected_values(UpperCamelCase_							,							batched=UpperCamelCase_      )
													self.assertEqual(
													    encoded_images.shape							,							(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        expected_height,
													        expected_width,
													    )							,							)




						def      __lowerCamelCase       (    self :       List[Any]      )     ->				Tuple:
													# Initialize image processor
													SCREAMING_SNAKE_CASE__     :Dict						    = self.image_processing_class(**self.image_processor_dict      )
													# create random numpy tensors
													SCREAMING_SNAKE_CASE__     :Tuple						    = prepare_image_inputs(self.image_processor_tester							,							equal_resolution=UpperCamelCase_							,							numpify=UpperCamelCase_      )
													for image in image_inputs:
																				self.assertIsInstance(UpperCamelCase_							,							np.ndarray      )

													# Test not batched input
													SCREAMING_SNAKE_CASE__     :List[str]						    = image_processing(image_inputs[0]							,							return_tensors='pt'      ).pixel_values

													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :int						    = self.image_processor_tester.get_expected_values(UpperCamelCase_      )
													self.assertEqual(
													    encoded_images.shape							,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,							)

													# Test batched
													SCREAMING_SNAKE_CASE__     :str						    = image_processing(UpperCamelCase_							,							return_tensors='pt'      ).pixel_values

													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Any						    = self.image_processor_tester.get_expected_values(UpperCamelCase_							,							batched=UpperCamelCase_      )
													self.assertEqual(
													    encoded_images.shape							,							(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        expected_height,
													        expected_width,
													    )							,							)




						def      __lowerCamelCase       (    self :       List[str]      )     ->				List[Any]:
													# Initialize image processor
													SCREAMING_SNAKE_CASE__     :List[str]						    = self.image_processing_class(**self.image_processor_dict      )
													# create random PyTorch tensors
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = prepare_image_inputs(self.image_processor_tester							,							equal_resolution=UpperCamelCase_							,							torchify=UpperCamelCase_      )
													for image in image_inputs:
																				self.assertIsInstance(UpperCamelCase_							,							torch.Tensor      )

													# Test not batched input
													SCREAMING_SNAKE_CASE__     :List[str]						    = image_processing(image_inputs[0]							,							return_tensors='pt'      ).pixel_values

													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Optional[int]						    = self.image_processor_tester.get_expected_values(UpperCamelCase_      )
													self.assertEqual(
													    encoded_images.shape							,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,							)

													# Test batched
													SCREAMING_SNAKE_CASE__     :List[Any]						    = image_processing(UpperCamelCase_							,							return_tensors='pt'      ).pixel_values

													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :List[Any]						    = self.image_processor_tester.get_expected_values(UpperCamelCase_							,							batched=UpperCamelCase_      )
													self.assertEqual(
													    encoded_images.shape							,							(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        expected_height,
													        expected_width,
													    )							,							)






