





'''simple docstring'''


import logging
import os
import time
from argparse import Namespace
from pathlib import Path

import datasets
import torch
from accelerate import Accelerator, DistributedType
from accelerate.utils import ProjectConfiguration
from arguments import TrainingArguments
from datasets import load_dataset
from huggingface_hub import Repository
from torch.optim import AdamW
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from torch.utils.data.datapipes.iter.combinatorics import ShufflerIterDataPipe

import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, get_scheduler, set_seed

class 				lowerCAmelCase_						(						__magic_name__						):




 def __init__(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase=False     ,       _lowerCAmelCase=1024     ,       _lowerCAmelCase=1024     ,       _lowerCAmelCase=3.6     ,       _lowerCAmelCase=False     ,       )      ->							Any:
     _lowerCAmelCase		     = tokenizer
     _lowerCAmelCase		     = tokenizer.bos_token_id
     _lowerCAmelCase		     = dataset
     _lowerCAmelCase		     = seq_length
     _lowerCAmelCase		     = 0
     _lowerCAmelCase		     = infinite
     _lowerCAmelCase		     = 0
     _lowerCAmelCase		     = tokenized

     if self.tokenized:
         _lowerCAmelCase		     = seq_length * num_of_sequences
         _lowerCAmelCase		     = "input_ids"
     else:
         _lowerCAmelCase		     = seq_length * chars_per_token * num_of_sequences
         _lowerCAmelCase		     = "content"




 def __iter__(		self						)      ->							Tuple:
     _lowerCAmelCase		     = iter(self.dataset						)
     _lowerCAmelCase		     = True
     while more_examples:
         _lowerCAmelCase     ,     _lowerCAmelCase		     = [], 0
         while True:
             if buffer_len >= self.max_buffer_size:
                 break
             try:
                 buffer.append(next(_lowerCAmelCase						)[self.content_field]						)
                 buffer_len += len(buffer[-1]						)
             except StopIteration:
                 if self.infinite:
                     _lowerCAmelCase		     = iter(self.dataset						)
                     self.epoch += 1
                     logger.info(f'''Dataset epoch: {self.epoch}'''						)
                 else:
                     _lowerCAmelCase		     = False
                     break
         if self.tokenized:
             _lowerCAmelCase		     = buffer
         else:
             _lowerCAmelCase		     = self.tokenizer(_lowerCAmelCase     ,       truncation=_lowerCAmelCase						)["input_ids"]
         _lowerCAmelCase		     = []
         for tokenized_input in tokenized_inputs:
             all_token_ids.extend(tokenized_input + [self.concat_token_id]						)
         for i in range(0     ,       len(_lowerCAmelCase						)     ,       self.seq_length						):
             _lowerCAmelCase		     = all_token_ids[i : i + self.seq_length]
             if len(_lowerCAmelCase						) == self.seq_length:
                 self.current_size += 1
                 yield torch.tensor(_lowerCAmelCase						)





 def 							_snake_case				(		self     ,       _lowerCAmelCase=1000						)      ->							str:
     return ShufflerIterDataPipe(self     ,       buffer_size=_lowerCAmelCase						)







def 		__a(SCREAMING_SNAKE_CASE_					:   Dict      ):



    '''simple docstring'''





    _lowerCAmelCase		     = args.model_ckpt.split("/"      )[-1]
    _lowerCAmelCase		     = logging.getLogger(__name__      )
    _lowerCAmelCase		     = Path(args.save_dir      ) / "log/"
    log_dir.mkdir(exist_ok=SCREAMING_SNAKE_CASE_      )
    _lowerCAmelCase		     = F'''debug_{accelerator.process_index}.log'''
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"						,    datefmt="%m/%d/%Y %H:%M:%S"						,    level=logging.INFO						,    handlers=[logging.FileHandler(log_dir / filename      ), logging.StreamHandler()]						,    )
    if accelerator.is_main_process:  # we only want to setup logging once
        accelerator.init_trackers(SCREAMING_SNAKE_CASE_						,    vars(SCREAMING_SNAKE_CASE_      )      )
        _lowerCAmelCase		     = accelerator.trackers[0].run.name
        logger.setLevel(logging.INFO      )
        datasets.utils.logging.set_verbosity_info()
        transformers.utils.logging.set_verbosity_info()
    else:
        _lowerCAmelCase		     = ""
        logger.setLevel(logging.ERROR      )
        datasets.utils.logging.set_verbosity_error()
        transformers.utils.logging.set_verbosity_error()
    return logger, run_name


def 		__a(SCREAMING_SNAKE_CASE_					:   int      ):



    '''simple docstring'''





    _lowerCAmelCase		     = {"streaming": True}
    _lowerCAmelCase		     = load_dataset(args.dataset_name_train						,    split="train"						,    **SCREAMING_SNAKE_CASE_      )
    _lowerCAmelCase		     = train_data.shuffle(buffer_size=args.shuffle_buffer						,    seed=args.seed      )
    _lowerCAmelCase		     = load_dataset(args.dataset_name_valid						,    split="train"						,    **SCREAMING_SNAKE_CASE_      )
    _lowerCAmelCase		     = ConstantLengthDataset(
        SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_						,    infinite=SCREAMING_SNAKE_CASE_						,    seq_length=args.seq_length						,    tokenized=args.tokenized      )
    _lowerCAmelCase		     = ConstantLengthDataset(
        SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_						,    infinite=SCREAMING_SNAKE_CASE_						,    seq_length=args.seq_length						,    tokenized=args.tokenized      )
    _lowerCAmelCase		     = train_dataset.shuffle(buffer_size=args.shuffle_buffer      )
    _lowerCAmelCase		     = DataLoader(SCREAMING_SNAKE_CASE_						,    batch_size=args.train_batch_size						,    shuffle=SCREAMING_SNAKE_CASE_      )
    _lowerCAmelCase		     = DataLoader(SCREAMING_SNAKE_CASE_						,    batch_size=args.valid_batch_size      )
    return train_dataloader, eval_dataloader


def 		__a(SCREAMING_SNAKE_CASE_					:   List[Any]						,    SCREAMING_SNAKE_CASE_					:   str						,    SCREAMING_SNAKE_CASE_					:   int=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]      ):



    '''simple docstring'''





    _lowerCAmelCase     ,     _lowerCAmelCase		     = [], []
    for n, p in model.named_parameters():
        if any(nd in n for nd in no_decay      ):
            params_without_wd.append(SCREAMING_SNAKE_CASE_      )
        else:
            params_with_wd.append(SCREAMING_SNAKE_CASE_      )
    return [
        {"params": params_with_wd, "weight_decay": args.weight_decay},
        {"params": params_without_wd, "weight_decay": 0.0},
    ]


def 		__a(SCREAMING_SNAKE_CASE_					:   Optional[int]						,    SCREAMING_SNAKE_CASE_					:   List[Any]      ):



    '''simple docstring'''





    logger.info(F'''Step {step}: {metrics}'''      )
    if accelerator.is_main_process:
        accelerator.log(SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_      )


def 		__a(SCREAMING_SNAKE_CASE_					:   Optional[int]						,    SCREAMING_SNAKE_CASE_					:   Any						,    SCREAMING_SNAKE_CASE_					:   Tuple      ):



    '''simple docstring'''





    _lowerCAmelCase		     = accelerator.unwrap_model(SCREAMING_SNAKE_CASE_      ).config
    _lowerCAmelCase		     = 4 if args.gradient_checkpointing else 3
    _lowerCAmelCase		     = args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
    _lowerCAmelCase		     = 24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
    _lowerCAmelCase		     = factor * (
        1.0
        + (args.seq_length / (6.0 * config_model.n_embd))
        + (tokenizer.vocab_size / (16.0 * config_model.n_layer * config_model.n_embd))
    )
    _lowerCAmelCase		     = flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
    return tflops


def 		__a(SCREAMING_SNAKE_CASE_					:   List[Any]      ):



    '''simple docstring'''





    model.eval()
    _lowerCAmelCase		     = []
    for step, batch in enumerate(SCREAMING_SNAKE_CASE_      ):
        with torch.no_grad():
            _lowerCAmelCase		     = model(SCREAMING_SNAKE_CASE_						,    labels=SCREAMING_SNAKE_CASE_      )
        _lowerCAmelCase		     = outputs.loss.repeat(args.valid_batch_size      )
        losses.append(accelerator.gather(SCREAMING_SNAKE_CASE_      )      )
        if args.max_eval_steps > 0 and step >= args.max_eval_steps:
            break
    _lowerCAmelCase		     = torch.cat(SCREAMING_SNAKE_CASE_      )
    _lowerCAmelCase		     = losses[: eval_dataloader.dataset.current_size].mean()
    try:
        _lowerCAmelCase		     = torch.exp(SCREAMING_SNAKE_CASE_      )
    except OverflowError:
        _lowerCAmelCase		     = float("inf"      )
    return loss.item(), perplexity.item()


# Settings
_SCREAMING_SNAKE_CASE			=							HfArgumentParser(TrainingArguments)
_SCREAMING_SNAKE_CASE			=							parser.parse_args()

# Accelerator
_SCREAMING_SNAKE_CASE			=							ProjectConfiguration(project_dir=args.save_dir, logging_dir="log")
_SCREAMING_SNAKE_CASE			=							Accelerator(log_with=["wandb", "tensorboard"], project_config=config)
_SCREAMING_SNAKE_CASE			=							{str(k): str(v) for k, v in accelerator.state.__dict__.items()}

_SCREAMING_SNAKE_CASE			=							Namespace(**vars(args), **acc_state)
_SCREAMING_SNAKE_CASE			=							accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

# Clone model repository
if accelerator.is_main_process:
       _SCREAMING_SNAKE_CASE			=							Repository(args.save_dir, clone_from=args.model_ckpt)

# Logging
_SCREAMING_SNAKE_CASE	,					_SCREAMING_SNAKE_CASE			=							setup_logging(args)
logger.info(accelerator.state)

# Checkout new branch on repo
if accelerator.is_main_process:
       hf_repo.git_checkout(run_name, create_branch_ok=True)

# Load model and tokenizer
_SCREAMING_SNAKE_CASE			=							AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
       model.gradient_checkpointing_enable()
_SCREAMING_SNAKE_CASE			=							AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
_SCREAMING_SNAKE_CASE	,					_SCREAMING_SNAKE_CASE			=							create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
_SCREAMING_SNAKE_CASE			=							AdamW(get_grouped_params(model, args), lr=args.learning_rate)
_SCREAMING_SNAKE_CASE			=							get_scheduler(
    name=args.lr_scheduler_type,
    optimizer=optimizer,
    num_warmup_steps=args.num_warmup_steps,
    num_training_steps=args.max_train_steps,
)
accelerator.register_for_checkpointing(lr_scheduler)


def 		__a():



    '''simple docstring'''





    return optimizer.param_groups[0]["lr"]


# Prepare everything with our `accelerator`.
_SCREAMING_SNAKE_CASE	,					_SCREAMING_SNAKE_CASE	,					_SCREAMING_SNAKE_CASE	,					_SCREAMING_SNAKE_CASE			=							accelerator.prepare(
    model, optimizer, train_dataloader, eval_dataloader
)

# 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)
              _SCREAMING_SNAKE_CASE			=							os.path.basename(args.resume_from_checkpoint)
       else:
              # Get the most recent checkpoint
              _SCREAMING_SNAKE_CASE			=							[f.name for f in os.scandir(args.save_dir) if f.is_dir() and "step" in str(f)]
              dirs.sort(key=os.path.getctime)
              _SCREAMING_SNAKE_CASE			=							dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
       # Extract the step of the checkpoint to continue from there
       _SCREAMING_SNAKE_CASE			=							os.path.splitext(path)[0]
       _SCREAMING_SNAKE_CASE			=							int(training_difference.replace("step_", ""))

# Train model
model.train()
_SCREAMING_SNAKE_CASE			=							0
_SCREAMING_SNAKE_CASE			=							time.time()
_SCREAMING_SNAKE_CASE			=							0
for step, batch in enumerate(train_dataloader, start=1):
       if args.resume_from_checkpoint and step < resume_step:
              continue  # we need to skip steps until we reach the resumed step
       _SCREAMING_SNAKE_CASE			=							model(batch, labels=batch, use_cache=False).loss
       _SCREAMING_SNAKE_CASE			=							accelerator.gather(loss.repeat(args.train_batch_size)).mean()
       loss_tracking += avg_loss.item() / args.gradient_accumulation_steps
       log_metrics(step, {"samples": step * samples_per_step, "loss_per_step/train": loss.item()})
       _SCREAMING_SNAKE_CASE			=							loss / args.gradient_accumulation_steps
       if step % args.gradient_accumulation_steps != 0:
              # Prevent backward from doing gradient all_reduce in every step
              if accelerator.distributed_type == DistributedType.MULTI_GPU:
                     with model.no_sync():
                            accelerator.backward(loss)
              else:
                     accelerator.backward(loss)
       else:
              _SCREAMING_SNAKE_CASE			=							get_lr()
              accelerator.backward(loss)
              accelerator.clip_grad_norm_(model.parameters(), 1.0)
              optimizer.step()
              lr_scheduler.step()
              optimizer.zero_grad()
              _SCREAMING_SNAKE_CASE			=							time.time() - t_start
              _SCREAMING_SNAKE_CASE			=							compute_tflops(elapsed_time, accelerator, args)
              log_metrics(
                  step,
                  {
                      "steps": completed_steps,
                      "loss/train": loss_tracking,
                      "lr": lr,
                      "tflops": tflops,
                      "time_per_iteration": elapsed_time,
                  },
              )
              _SCREAMING_SNAKE_CASE			=							time.time()
              _SCREAMING_SNAKE_CASE			=							0
              completed_steps += 1
       if step % args.save_checkpoint_steps == 0:
              logger.info("Evaluating and saving model checkpoint")
              _SCREAMING_SNAKE_CASE	,					_SCREAMING_SNAKE_CASE			=							evaluate(args)
              log_metrics(step, {"loss/eval": eval_loss, "perplexity": perplexity})
              accelerator.wait_for_everyone()
              _SCREAMING_SNAKE_CASE			=							os.path.join(args.save_dir, f'''step_{step}''')
              accelerator.save_state(save_dir)
              if accelerator.is_main_process:
                     hf_repo.push_to_hub(commit_message=f'''step {step}''')
              model.train()
       if completed_steps >= args.max_train_steps:
              break

# Evaluate and save the last checkpoint
logger.info("Evaluating and saving model after training")
_SCREAMING_SNAKE_CASE	,					_SCREAMING_SNAKE_CASE			=							evaluate(args)
log_metrics(step, {"loss/eval": eval_loss, "perplexity": perplexity})
accelerator.wait_for_everyone()
_SCREAMING_SNAKE_CASE			=							accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
_SCREAMING_SNAKE_CASE			=							os.path.join(args.save_dir, f'''step_{step}''')
accelerator.save_state(save_dir)
if accelerator.is_main_process:
       hf_repo.push_to_hub(commit_message="final model")









'''simple docstring'''


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 center_crop, 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, is_vision_available, logging


if is_vision_available():
       import PIL


_SCREAMING_SNAKE_CASE			=							logging.get_logger(__name__)

class 				lowerCAmelCase_						(						__magic_name__						):
 __lowerCamelCase  :							Optional[int]    					=   ["pixel_values"]




 def __init__(		self     ,       _lowerCAmelCase = True     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = PIL.Image.NEAREST     ,       _lowerCAmelCase = False     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = 1 / 255     ,       _lowerCAmelCase = False     ,       _lowerCAmelCase = True     ,       _lowerCAmelCase = True     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = True     ,       **_lowerCAmelCase     ,       )      ->							None:
     super().__init__(**_lowerCAmelCase						)
     _lowerCAmelCase		     = size if size is not None else {"height": 346, "width": 346}
     _lowerCAmelCase		     = get_size_dict(_lowerCAmelCase						)
     _lowerCAmelCase		     = crop_size if crop_size is not None else {"height": 289, "width": 289}
     _lowerCAmelCase		     = get_size_dict(_lowerCAmelCase     ,       param_name="crop_size"						)

     _lowerCAmelCase		     = do_resize
     _lowerCAmelCase		     = size
     _lowerCAmelCase		     = resample
     _lowerCAmelCase		     = do_center_crop
     _lowerCAmelCase		     = crop_size
     _lowerCAmelCase		     = do_rescale
     _lowerCAmelCase		     = rescale_factor
     _lowerCAmelCase		     = rescale_offset
     _lowerCAmelCase		     = do_normalize
     _lowerCAmelCase		     = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     _lowerCAmelCase		     = image_std if image_std is not None else IMAGENET_STANDARD_STD
     _lowerCAmelCase		     = include_top




 def 							_snake_case				(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase = PIL.Image.NEAREST     ,       _lowerCAmelCase = None     ,       **_lowerCAmelCase     ,       )      ->							np.ndarray:
     _lowerCAmelCase		     = get_size_dict(_lowerCAmelCase						)
     if "height" not in size or "width" not in size:
         raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''						)
     return resize(
         _lowerCAmelCase     ,       size=(size["height"], size["width"])     ,       resample=_lowerCAmelCase     ,       data_format=_lowerCAmelCase     ,       **_lowerCAmelCase						)




 def 							_snake_case				(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase = None     ,       **_lowerCAmelCase     ,       )      ->							np.ndarray:
     _lowerCAmelCase		     = get_size_dict(_lowerCAmelCase						)
     if "height" not in size or "width" not in size:
         raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''						)
     return center_crop(_lowerCAmelCase     ,       size=(size["height"], size["width"])     ,       data_format=_lowerCAmelCase     ,       **_lowerCAmelCase						)




 def 							_snake_case				(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase = True     ,       _lowerCAmelCase = None     ,       **_lowerCAmelCase     ,       )      ->							List[str]:
     if offset:
         _lowerCAmelCase		     = (image - 127.5) * scale
         if data_format is not None:
             _lowerCAmelCase		     = to_channel_dimension_format(_lowerCAmelCase     ,       _lowerCAmelCase						)
         _lowerCAmelCase		     = rescaled_image.astype(np.floataa						)
     else:
         _lowerCAmelCase		     = rescale(_lowerCAmelCase     ,       scale=_lowerCAmelCase     ,       data_format=_lowerCAmelCase     ,       **_lowerCAmelCase						)
     return rescaled_image




 def 							_snake_case				(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase = None     ,       **_lowerCAmelCase     ,       )      ->							np.ndarray:
     return normalize(_lowerCAmelCase     ,       mean=_lowerCAmelCase     ,       std=_lowerCAmelCase     ,       data_format=_lowerCAmelCase     ,       **_lowerCAmelCase						)





 def 							_snake_case				(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase=None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = ChannelDimension.FIRST     ,       **_lowerCAmelCase     ,       )      ->							PIL.Image.Image:
     _lowerCAmelCase		     = do_resize if do_resize is not None else self.do_resize
     _lowerCAmelCase		     = resample if resample is not None else self.resample
     _lowerCAmelCase		     = do_center_crop if do_center_crop is not None else self.do_center_crop
     _lowerCAmelCase		     = do_rescale if do_rescale is not None else self.do_rescale
     _lowerCAmelCase		     = rescale_factor if rescale_factor is not None else self.rescale_factor
     _lowerCAmelCase		     = rescale_offset if rescale_offset is not None else self.rescale_offset
     _lowerCAmelCase		     = do_normalize if do_normalize is not None else self.do_normalize
     _lowerCAmelCase		     = image_mean if image_mean is not None else self.image_mean
     _lowerCAmelCase		     = image_std if image_std is not None else self.image_std
     _lowerCAmelCase		     = include_top if include_top is not None else self.include_top

     _lowerCAmelCase		     = size if size is not None else self.size
     _lowerCAmelCase		     = get_size_dict(_lowerCAmelCase						)
     _lowerCAmelCase		     = crop_size if crop_size is not None else self.crop_size
     _lowerCAmelCase		     = get_size_dict(_lowerCAmelCase     ,       param_name="crop_size"						)

     _lowerCAmelCase		     = make_list_of_images(_lowerCAmelCase						)

     if not valid_images(_lowerCAmelCase						):
         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 or resample is None:
         raise ValueError("Size and resample must be specified if do_resize is True."						)

     if do_center_crop and crop_size is None:
         raise ValueError("Crop size must be specified if do_center_crop is True."						)

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

     if do_normalize and (image_mean is None or image_std is None):
         raise ValueError("Image mean and std must be specified if do_normalize is True."						)

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

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

     if do_center_crop:
         _lowerCAmelCase		     = [self.center_crop(image=_lowerCAmelCase     ,       size=_lowerCAmelCase						) for image in images]

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

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

     if include_top:
         _lowerCAmelCase		     = [self.normalize(image=_lowerCAmelCase     ,       mean=[0, 0, 0]     ,       std=_lowerCAmelCase						) for image in images]

     _lowerCAmelCase		     = [to_channel_dimension_format(_lowerCAmelCase     ,       _lowerCAmelCase						) for image in images]

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


