import argparse
import gc
import os
import threading

import evaluate
import psutil
import torch
from datasets import load_dataset
from torch.distributed.fsdp.fully_sharded_data_parallel import FullOptimStateDictConfig, FullStateDictConfig
from torch.utils.data import DataLoader
from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    get_linear_schedule_with_warmup,
    set_seed,
)

from accelerate import Accelerator, DistributedType, FullyShardedDataParallelPlugin


########################################################################
# This is a fully working simple example to use Accelerate
#
# 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)
#   - FSDP
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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
#
########################################################################


_SCREAMING_SNAKE_CASE:    Any     						=							16
_SCREAMING_SNAKE_CASE:    Optional[Any]     						=							32






def  UpperCAmelCase__(    lowerCamelCase_	):



			"""simple docstring"""





			return int(x / 2**20	)

class 						_SCREAMING_SNAKE_CASE				:






		"""simple docstring"""







		def __enter__(      self     :					List[str] ):






					"""simple docstring"""
					gc.collect()
					torch.cuda.empty_cache()
					torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
					_snake_case														=   torch.cuda.memory_allocated()
					_snake_case														=   psutil.Process()

					_snake_case														=   self.cpu_mem_used()
					_snake_case														=   True
					_snake_case														=   threading.Thread(target=self.peak_monitor_func )
					_snake_case														=   True
					peak_monitor_thread.start()
					return self



		def 					_a					(      self     :					Optional[Any] ):






					"""simple docstring"""
					return self.process.memory_info().rss



		def 					_a					(      self     :					str ):






					"""simple docstring"""
					_snake_case														=   -1

					while True:
								_snake_case														=   max(self.cpu_mem_used()	,       self.cpu_peak )

								# can't sleep or will not catch the peak right (this comment is here on purpose)
								# time.sleep(0.001) # 1msec

								if not self.peak_monitoring:
											break







		def __exit__(      self     :					Tuple	,       *__SCREAMING_SNAKE_CASE     :					int ):






					"""simple docstring"""
					_snake_case														=   False

					gc.collect()
					torch.cuda.empty_cache()
					_snake_case														=   torch.cuda.memory_allocated()
					_snake_case														=   torch.cuda.max_memory_allocated()
					_snake_case														=   bamb(self.end - self.begin )
					_snake_case														=   bamb(self.peak - self.begin )

					_snake_case														=   self.cpu_mem_used()
					_snake_case														=   bamb(self.cpu_end - self.cpu_begin )
					_snake_case														=   bamb(self.cpu_peak - self.cpu_begin )
					# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")


# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
			from accelerate.test_utils.training import mocked_dataloaders

			_SCREAMING_SNAKE_CASE:    int     						=							mocked_dataloaders  # noqa: F811






def  UpperCAmelCase__(    lowerCamelCase_				,						lowerCamelCase_	):



			"""simple docstring"""





			if os.environ.get("TESTING_MOCKED_DATALOADERS"				,						lowerCamelCase_	) == "1":
						_snake_case														=   2

			# New Code #
			# Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
			_snake_case														=   FullyShardedDataParallelPlugin(
			    state_dict_config=FullStateDictConfig(offload_to_cpu=lowerCamelCase_				,						ranka_only=lowerCamelCase_	)				,						optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=lowerCamelCase_				,						ranka_only=lowerCamelCase_	)				,						)

			# Initialize accelerator
			if args.with_tracking:
						_snake_case														=   Accelerator(
						    cpu=args.cpu				,						mixed_precision=args.mixed_precision				,						log_with="wandb"				,						project_dir=args.logging_dir				,						fsdp_plugin=lowerCamelCase_				,						)
			else:
						_snake_case														=   Accelerator(fsdp_plugin=lowerCamelCase_	)
			accelerator.print(accelerator.distributed_type	)

			if hasattr(args.checkpointing_steps				,						"isdigit"	):
						if args.checkpointing_steps == "epoch":
									_snake_case														=   args.checkpointing_steps
						elif args.checkpointing_steps.isdigit():
									_snake_case														=   int(args.checkpointing_steps	)
						else:
									raise ValueError(
									    f'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'	)
			else:
						_snake_case														=   None
			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			_snake_case														=   config["lr"]
			_snake_case														=   int(config["num_epochs"]	)
			_snake_case														=   int(config["seed"]	)
			_snake_case														=   int(config["batch_size"]	)

			# We need to initialize the trackers we use, and also store our configuration
			if args.with_tracking:
						_snake_case														=   vars(lowerCamelCase_	)
						accelerator.init_trackers("fsdp_glue_no_trainer"				,						lowerCamelCase_	)

			_snake_case														=   AutoTokenizer.from_pretrained(args.model_name_or_path	)
			_snake_case														=   load_dataset("glue"				,						"mrpc"	)
			_snake_case														=   evaluate.load("glue"				,						"mrpc"	)

			def tokenize_function(lowerCamelCase_	):
						# max_length=None => use the model max length (it's actually the default)
						_snake_case														=   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():
						_snake_case														=   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
			_snake_case														=   tokenized_datasets.rename_column("label"				,						"labels"	)

			# If the batch size is too big we use gradient accumulation
			_snake_case														=   1
			if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
						_snake_case														=   batch_size // MAX_GPU_BATCH_SIZE
						_snake_case														=   MAX_GPU_BATCH_SIZE

			def collate_fn(lowerCamelCase_	):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						_snake_case														=   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":
									_snake_case														=   16
						elif accelerator.mixed_precision != "no":
									_snake_case														=   8
						else:
									_snake_case														=   None

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

			# Instantiate dataloaders.
			_snake_case														=   DataLoader(
			    tokenized_datasets["train"]				,						shuffle=lowerCamelCase_				,						collate_fn=lowerCamelCase_				,						batch_size=lowerCamelCase_	)
			_snake_case														=   DataLoader(
			    tokenized_datasets["validation"]				,						shuffle=lowerCamelCase_				,						collate_fn=lowerCamelCase_				,						batch_size=lowerCamelCase_	)

			set_seed(lowerCamelCase_	)

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			_snake_case														=   AutoModelForSequenceClassification.from_pretrained(
			    args.model_name_or_path				,						return_dict=lowerCamelCase_				,						low_cpu_mem_usage=lowerCamelCase_	)

			# New Code #
			# For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
			_snake_case														=   accelerator.prepare(lowerCamelCase_	)
			accelerator.print(lowerCamelCase_	)

			# Instantiate optimizer
			# New Code #
			# For FSDP feature, at present it doesn't support multiple parameter groups,
			# so we need to create a single parameter group for the whole model
			_snake_case														=   torch.optim.AdamW(params=model.parameters()				,						lr=lowerCamelCase_				,						weight_decay=2E-4	)

			# Instantiate scheduler
			_snake_case														=   get_linear_schedule_with_warmup(
			    optimizer=lowerCamelCase_				,						num_warmup_steps=10				,						num_training_steps=(len(lowerCamelCase_	) * num_epochs) // gradient_accumulation_steps				,						)

			# New Code #
			# For FSDP feature, prepare everything except the model as we have already prepared the model
			# before creating the optimizer
			# 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.
			_snake_case							,			_snake_case							,			_snake_case							,			_snake_case														=   accelerator.prepare(
			    lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_	)

			_snake_case														=   0

			# Potentially load in the weights and states from a previous save
			if args.resume_from_checkpoint:
						if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
									accelerator.print(f'Resumed from checkpoint: {args.resume_from_checkpoint}'	)
									accelerator.load_state(args.resume_from_checkpoint	)
									_snake_case														=   os.path.basename(args.resume_from_checkpoint	)
						else:
									# Get the most recent checkpoint
									_snake_case														=   [f.name for f in os.scandir(os.getcwd()	) if f.is_dir()]
									dirs.sort(key=os.path.getctime	)
									_snake_case														=   dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
						# Extract `epoch_{i}` or `step_{i}`
						_snake_case														=   os.path.splitext(lowerCamelCase_	)[0]

						if "epoch" in training_difference:
									num_epochs -= int(training_difference.replace("epoch_"				,						""	)	)
									_snake_case														=   None
						else:
									_snake_case														=   int(training_difference.replace("step_"				,						""	)	)
									num_epochs -= resume_step // len(lowerCamelCase_	)
									# If resuming by step, we also need to know exactly how far into the DataLoader we went
									_snake_case														=   (num_epochs * len(lowerCamelCase_	)) - resume_step

    # Now we train the model
			for epoch in range(lowerCamelCase_	):
						# New Code #
						# context manager to track the peak memory usage during the training epoch
						with TorchTracemalloc() as tracemalloc:
									model.train()
									if args.with_tracking:
												_snake_case														=   0
									for step, batch in enumerate(lowerCamelCase_	):
												# We need to skip steps until we reach the resumed step
												if args.resume_from_checkpoint and epoch == 0:
															if resume_step is not None and step < resume_step:
																		pass
                # We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device	)
												_snake_case														=   model(**lowerCamelCase_	)
												_snake_case														=   outputs.loss
												_snake_case														=   loss / gradient_accumulation_steps
												# We keep track of the loss at each epoch
												if args.with_tracking:
															total_loss += loss.detach().float()
												accelerator.backward(lowerCamelCase_	)
												if step % gradient_accumulation_steps == 0:
															optimizer.step()
															lr_scheduler.step()
															optimizer.zero_grad()
															# accelerator.print(lr_scheduler.get_lr())

												overall_step += 1

												if isinstance(lowerCamelCase_				,						lowerCamelCase_	):
															_snake_case														=   f'step_{overall_step}'
															if overall_step % checkpointing_steps == 0:
																		if args.output_dir is not None:
																					_snake_case														=   os.path.join(args.output_dir				,						lowerCamelCase_	)
																		accelerator.save_state(lowerCamelCase_	)
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
						accelerator.print("Memory before entering the train : {}".format(bamb(tracemalloc.begin	)	)	)
						accelerator.print("Memory consumed at the end of the train (end-begin): {}".format(tracemalloc.used	)	)
						accelerator.print("Peak Memory consumed during the train (max-begin): {}".format(tracemalloc.peaked	)	)
						accelerator.print(
						    "Total Peak Memory consumed during the train (max): {}".format(
						        tracemalloc.peaked + bamb(tracemalloc.begin	)	)	)
						# Logging the peak memory usage of the GPU to the tracker
						if args.with_tracking:
									accelerator.log(
									    {
									        "train_total_peak_memory": tracemalloc.peaked + bamb(tracemalloc.begin	),
									    }				,						step=lowerCamelCase_				,						)

						# New Code #
						# context manager to track the peak memory usage during the evaluation
						with TorchTracemalloc() as tracemalloc:
									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():
															_snake_case														=   model(**lowerCamelCase_	)
												_snake_case														=   outputs.logits.argmax(dim=-1	)
												_snake_case							,			_snake_case														=   accelerator.gather_for_metrics((predictions, batch["labels"])	)
												metric.add_batch(
												    predictions=lowerCamelCase_				,						references=lowerCamelCase_				,						)

									_snake_case														=   metric.compute()
									# Use accelerator.print to print only on the main process.
									accelerator.print(f'epoch {epoch}:'				,						lowerCamelCase_	)
									if args.with_tracking:
												accelerator.log(
												    {
												        "accuracy": eval_metric["accuracy"],
												        "f1": eval_metric["f1"],
												        "train_loss": total_loss.item() / len(lowerCamelCase_	),
												    }				,						step=lowerCamelCase_				,						)

									if checkpointing_steps == "epoch":
												_snake_case														=   f'epoch_{epoch}'
												if args.output_dir is not None:
															_snake_case														=   os.path.join(args.output_dir				,						lowerCamelCase_	)
												accelerator.save_state(lowerCamelCase_	)
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
						accelerator.print("Memory before entering the eval : {}".format(bamb(tracemalloc.begin	)	)	)
						accelerator.print("Memory consumed at the end of the eval (end-begin): {}".format(tracemalloc.used	)	)
						accelerator.print("Peak Memory consumed during the eval (max-begin): {}".format(tracemalloc.peaked	)	)
						accelerator.print(
						    "Total Peak Memory consumed during the eval (max): {}".format(tracemalloc.peaked + bamb(tracemalloc.begin	)	)	)
						# Logging the peak memory usage of the GPU to the tracker
						if args.with_tracking:
									accelerator.log(
									    {
									        "eval_total_peak_memory": tracemalloc.peaked + bamb(tracemalloc.begin	),
									    }				,						step=lowerCamelCase_				,						)

			if args.with_tracking:
						accelerator.end_training()






def  UpperCAmelCase__(    ):



			"""simple docstring"""





			_snake_case														=   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."	)
			parser.add_argument(
			    "--checkpointing_steps"				,						type=lowerCamelCase_				,						default=lowerCamelCase_				,						help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."				,						)
			parser.add_argument(
			    "--resume_from_checkpoint"				,						type=lowerCamelCase_				,						default=lowerCamelCase_				,						help="If the training should continue from a checkpoint folder."				,						)
			parser.add_argument(
			    "--with_tracking"				,						action="store_true"				,						help="Whether to load in all available experiment trackers from the environment and use them for logging."				,						)
			parser.add_argument(
			    "--output_dir"				,						type=lowerCamelCase_				,						default="."				,						help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."				,						)
			parser.add_argument(
			    "--logging_dir"				,						type=lowerCamelCase_				,						default="logs"				,						help="Location on where to store experiment tracking logs`"				,						)
			parser.add_argument(
			    "--model_name_or_path"				,						type=lowerCamelCase_				,						help="Path to pretrained model or model identifier from huggingface.co/models."				,						required=lowerCamelCase_				,						)
			_snake_case														=   parser.parse_args()
			_snake_case														=   {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
			training_function(lowerCamelCase_				,						lowerCamelCase_	)


if __name__ == "__main__":
			main()


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_SCREAMING_SNAKE_CASE:    List[Any]     						=							logging.get_logger(__name__)

class 						_SCREAMING_SNAKE_CASE				(  _lowercase				):






		"""simple docstring"""







		A_    			=   ['''pixel_values''']



		def __init__(      self     :					Tuple	,       __SCREAMING_SNAKE_CASE     :					bool = True	,       __SCREAMING_SNAKE_CASE     :					Optional[Dict[str, int]] = None	,       __SCREAMING_SNAKE_CASE     :					PILImageResampling = PILImageResampling.BILINEAR	,       __SCREAMING_SNAKE_CASE     :					bool = True	,       __SCREAMING_SNAKE_CASE     :					Union[int, float] = 1 / 255	,       __SCREAMING_SNAKE_CASE     :					bool = True	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[float, List[float]]] = None	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[float, List[float]]] = None	,       **__SCREAMING_SNAKE_CASE     :					str	,       ):






					"""simple docstring"""
					super().__init__(**__SCREAMING_SNAKE_CASE )
					_snake_case														=   size if size is not None else {"height": 224, "width": 224}
					_snake_case														=   get_size_dict(__SCREAMING_SNAKE_CASE )
					_snake_case														=   do_resize
					_snake_case														=   do_rescale
					_snake_case														=   do_normalize
					_snake_case														=   size
					_snake_case														=   resample
					_snake_case														=   rescale_factor
					_snake_case														=   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
					_snake_case														=   image_std if image_std is not None else IMAGENET_STANDARD_STD



		def 					_a					(      self     :					int	,       __SCREAMING_SNAKE_CASE     :					np.ndarray	,       __SCREAMING_SNAKE_CASE     :					Dict[str, int]	,       __SCREAMING_SNAKE_CASE     :					PILImageResampling = PILImageResampling.BILINEAR	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[str, ChannelDimension]] = None	,       **__SCREAMING_SNAKE_CASE     :					Tuple	,       ):






					"""simple docstring"""
					_snake_case														=   get_size_dict(__SCREAMING_SNAKE_CASE )
					if "height" not in size or "width" not in size:
								raise ValueError(F'The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}' )
					return resize(
					    __SCREAMING_SNAKE_CASE	,       size=(size["height"], size["width"])	,       resample=__SCREAMING_SNAKE_CASE	,       data_format=__SCREAMING_SNAKE_CASE	,       **__SCREAMING_SNAKE_CASE )



		def 					_a					(      self     :					Tuple	,       __SCREAMING_SNAKE_CASE     :					np.ndarray	,       __SCREAMING_SNAKE_CASE     :					float	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[str, ChannelDimension]] = None	,       **__SCREAMING_SNAKE_CASE     :					List[str] ):






					"""simple docstring"""
					return rescale(__SCREAMING_SNAKE_CASE	,       scale=__SCREAMING_SNAKE_CASE	,       data_format=__SCREAMING_SNAKE_CASE	,       **__SCREAMING_SNAKE_CASE )



		def 					_a					(      self     :					Optional[Any]	,       __SCREAMING_SNAKE_CASE     :					np.ndarray	,       __SCREAMING_SNAKE_CASE     :					Union[float, List[float]]	,       __SCREAMING_SNAKE_CASE     :					Union[float, List[float]]	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[str, ChannelDimension]] = None	,       **__SCREAMING_SNAKE_CASE     :					Optional[Any]	,       ):






					"""simple docstring"""
					return normalize(__SCREAMING_SNAKE_CASE	,       mean=__SCREAMING_SNAKE_CASE	,       std=__SCREAMING_SNAKE_CASE	,       data_format=__SCREAMING_SNAKE_CASE	,       **__SCREAMING_SNAKE_CASE )







		def 					_a					(      self     :					int	,       __SCREAMING_SNAKE_CASE     :					ImageInput	,       __SCREAMING_SNAKE_CASE     :					Optional[bool] = None	,       __SCREAMING_SNAKE_CASE     :					Dict[str, int] = None	,       __SCREAMING_SNAKE_CASE     :					PILImageResampling = None	,       __SCREAMING_SNAKE_CASE     :					Optional[bool] = None	,       __SCREAMING_SNAKE_CASE     :					Optional[float] = None	,       __SCREAMING_SNAKE_CASE     :					Optional[bool] = None	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[float, List[float]]] = None	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[float, List[float]]] = None	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[str, TensorType]] = None	,       __SCREAMING_SNAKE_CASE     :					Union[str, ChannelDimension] = ChannelDimension.FIRST	,       **__SCREAMING_SNAKE_CASE     :					List[Any]	,       ):






					"""simple docstring"""
					_snake_case														=   do_resize if do_resize is not None else self.do_resize
					_snake_case														=   do_rescale if do_rescale is not None else self.do_rescale
					_snake_case														=   do_normalize if do_normalize is not None else self.do_normalize
					_snake_case														=   resample if resample is not None else self.resample
					_snake_case														=   rescale_factor if rescale_factor is not None else self.rescale_factor
					_snake_case														=   image_mean if image_mean is not None else self.image_mean
					_snake_case														=   image_std if image_std is not None else self.image_std

					_snake_case														=   size if size is not None else self.size
					_snake_case														=   get_size_dict(__SCREAMING_SNAKE_CASE )

					_snake_case														=   make_list_of_images(__SCREAMING_SNAKE_CASE )

					if not valid_images(__SCREAMING_SNAKE_CASE ):
								raise ValueError(
								    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
								    "torch.Tensor, tf.Tensor or jax.ndarray." )

					if do_resize and size is None:
								raise ValueError("Size must be specified if do_resize is True." )

					if do_rescale and rescale_factor is None:
								raise ValueError("Rescale factor must be specified if do_rescale is True." )

					# All transformations expect numpy arrays.
					_snake_case														=   [to_numpy_array(__SCREAMING_SNAKE_CASE ) for image in images]

					if do_resize:
								_snake_case														=   [self.resize(image=__SCREAMING_SNAKE_CASE	,       size=__SCREAMING_SNAKE_CASE	,       resample=__SCREAMING_SNAKE_CASE ) for image in images]

					if do_rescale:
								_snake_case														=   [self.rescale(image=__SCREAMING_SNAKE_CASE	,       scale=__SCREAMING_SNAKE_CASE ) for image in images]

					if do_normalize:
								_snake_case														=   [self.normalize(image=__SCREAMING_SNAKE_CASE	,       mean=__SCREAMING_SNAKE_CASE	,       std=__SCREAMING_SNAKE_CASE ) for image in images]

					_snake_case														=   [to_channel_dimension_format(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE ) for image in images]

					_snake_case														=   {"pixel_values": images}
					return BatchFeature(data=__SCREAMING_SNAKE_CASE	,       tensor_type=__SCREAMING_SNAKE_CASE )

