






import argparse
import csv
import logging
import os
import random

import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange

from transformers import (
    CONFIG_NAME,
    WEIGHTS_NAME,
    AdamW,
    OpenAIGPTDoubleHeadsModel,
    OpenAIGPTTokenizer,
    get_linear_schedule_with_warmup,
)


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
SCREAMING_SNAKE_CASE__				     =			logging.getLogger(__name__)

def        UpperCAmelCase__		(						lowerCamelCase_       :       Any   ,  lowerCamelCase_       :       List[Any]				):
			__a						:  Any									=     np.argmax(lowerCamelCase_   ,  axis=1				)
			return np.sum(outputs == labels				)

def        UpperCAmelCase__		(						lowerCamelCase_       :       Tuple				):
			with open(lowerCamelCase_   ,  encoding='utf_8'				) as f:
						__a						:  Optional[Any]									=     csv.reader(lowerCamelCase_				)
						__a						:  Optional[int]									=     []
						next(lowerCamelCase_				)  # skip the first line
						for line in tqdm(lowerCamelCase_				):
									output.append((' '.join(line[1:5]				), line[5], line[6], int(line[-1]				) - 1)				)
			return output

def        UpperCAmelCase__		(						lowerCamelCase_       :       Any   ,  lowerCamelCase_       :       Dict   ,  lowerCamelCase_       :       Dict   ,  lowerCamelCase_       :       List[Any]   ,  lowerCamelCase_       :       str   ,  lowerCamelCase_       :       Optional[Any]				):
			__a						:  int									=     []
			for dataset in encoded_datasets:
						__a						:  List[Any]									=     len(lowerCamelCase_				)
						__a						:  Optional[Any]									=     np.zeros((n_batch, 2, input_len)   ,  dtype=np.intaa				)
						__a						:  Optional[int]									=     np.zeros((n_batch, 2)   ,  dtype=np.intaa				)
						__a						:  str									=     np.full((n_batch, 2, input_len)   ,  fill_value=-1_0_0   ,  dtype=np.intaa				)
						__a						:  List[Any]									=     np.zeros((n_batch,)   ,  dtype=np.intaa				)
						for (
						    i,
						    (story, conta, conta, mc_label),
						) in enumerate(lowerCamelCase_				):
									__a						:  str									=     [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
									__a						:  int									=     [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
									__a						:  List[str]									=     with_conta
									__a						:  Optional[Any]									=     with_conta
									__a						:  Any									=     len(lowerCamelCase_				) - 1
									__a						:  Any									=     len(lowerCamelCase_				) - 1
									__a						:  Optional[int]									=     with_conta
									__a						:  Tuple									=     with_conta
									__a						:  Optional[int]									=     mc_label
						__a						:  Union[str, Any]									=     (input_ids, mc_token_ids, lm_labels, mc_labels)
						tensor_datasets.append(tuple(torch.tensor(lowerCamelCase_				) for t in all_inputs				)				)
			return tensor_datasets

def        UpperCAmelCase__		(						):
			__a						:  List[str]									=     argparse.ArgumentParser()
			parser.add_argument('--model_name'   ,  type=lowerCamelCase_   ,  default='openai-gpt'   ,  help='pretrained model name'				)
			parser.add_argument('--do_train'   ,  action='store_true'   ,  help='Whether to run training.'				)
			parser.add_argument('--do_eval'   ,  action='store_true'   ,  help='Whether to run eval on the dev set.'				)
			parser.add_argument(
			    '--output_dir'   ,  default=lowerCamelCase_   ,  type=lowerCamelCase_   ,  required=lowerCamelCase_   ,  help='The output directory where the model predictions and checkpoints will be written.'   ,  )
			parser.add_argument('--train_dataset'   ,  type=lowerCamelCase_   ,  default=''				)
			parser.add_argument('--eval_dataset'   ,  type=lowerCamelCase_   ,  default=''				)
			parser.add_argument('--seed'   ,  type=lowerCamelCase_   ,  default=4_2				)
			parser.add_argument('--num_train_epochs'   ,  type=lowerCamelCase_   ,  default=3				)
			parser.add_argument('--train_batch_size'   ,  type=lowerCamelCase_   ,  default=8				)
			parser.add_argument('--eval_batch_size'   ,  type=lowerCamelCase_   ,  default=1_6				)
			parser.add_argument('--adam_epsilon'   ,  default=1e-8   ,  type=lowerCamelCase_   ,  help='Epsilon for Adam optimizer.'				)
			parser.add_argument('--max_grad_norm'   ,  type=lowerCamelCase_   ,  default=1				)
			parser.add_argument(
			    '--max_steps'   ,  default=-1   ,  type=lowerCamelCase_   ,  help=(
			        'If > 0: set total number of training                         steps to perform. Override num_train_epochs.'
			    )   ,  )
			parser.add_argument(
			    '--gradient_accumulation_steps'   ,  type=lowerCamelCase_   ,  default=1   ,  help='Number of updates steps to accumulate before                        performing a backward/update pass.'   ,  )
			parser.add_argument('--learning_rate'   ,  type=lowerCamelCase_   ,  default=6.25e-5				)
			parser.add_argument('--warmup_steps'   ,  default=0   ,  type=lowerCamelCase_   ,  help='Linear warmup over warmup_steps.'				)
			parser.add_argument('--lr_schedule'   ,  type=lowerCamelCase_   ,  default='warmup_linear'				)
			parser.add_argument('--weight_decay'   ,  type=lowerCamelCase_   ,  default=0.01				)
			parser.add_argument('--lm_coef'   ,  type=lowerCamelCase_   ,  default=0.9				)
			parser.add_argument('--n_valid'   ,  type=lowerCamelCase_   ,  default=3_7_4				)

			parser.add_argument('--server_ip'   ,  type=lowerCamelCase_   ,  default=''   ,  help='Can be used for distant debugging.'				)
			parser.add_argument('--server_port'   ,  type=lowerCamelCase_   ,  default=''   ,  help='Can be used for distant debugging.'				)
			__a						:  int									=     parser.parse_args()
			print(lowerCamelCase_				)

			if args.server_ip and args.server_port:
						# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
						import ptvsd

						print('Waiting for debugger attach'				)
						ptvsd.enable_attach(address=(args.server_ip, args.server_port)   ,  redirect_output=lowerCamelCase_				)
						ptvsd.wait_for_attach()

			random.seed(args.seed				)
			np.random.seed(args.seed				)
			torch.manual_seed(args.seed				)
			torch.cuda.manual_seed_all(args.seed				)

			__a						:  Any									=     torch.device('cuda' if torch.cuda.is_available() else 'cpu'				)
			__a						:  Union[str, Any]									=     torch.cuda.device_count()
			logger.info('device: {}, n_gpu {}'.format(lowerCamelCase_   ,  lowerCamelCase_				)				)

			if not args.do_train and not args.do_eval:
						raise ValueError('At least one of `do_train` or `do_eval` must be True.'				)

			if not os.path.exists(args.output_dir				):
						os.makedirs(args.output_dir				)

			# Load tokenizer and model
			# This loading functions also add new tokens and embeddings called `special tokens`
			# These new embeddings will be fine-tuned on the RocStories dataset
			__a						:  int									=     ['_start_', '_delimiter_', '_classify_']
			__a						:  Optional[int]									=     OpenAIGPTTokenizer.from_pretrained(args.model_name				)
			tokenizer.add_tokens(lowerCamelCase_				)
			__a						:  Optional[int]									=     tokenizer.convert_tokens_to_ids(lowerCamelCase_				)
			__a						:  int									=     OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name				)
			model.resize_token_embeddings(len(lowerCamelCase_				)				)
			model.to(lowerCamelCase_				)

			# Load and encode the datasets
			def tokenize_and_encode(lowerCamelCase_       :       Optional[Any]				):
						if isinstance(lowerCamelCase_   ,  lowerCamelCase_				):
									return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(lowerCamelCase_				)				)
						elif isinstance(lowerCamelCase_   ,  lowerCamelCase_				):
									return obj
						return [tokenize_and_encode(lowerCamelCase_				) for o in obj]

			logger.info('Encoding dataset...'				)
			__a						:  Tuple									=     load_rocstories_dataset(args.train_dataset				)
			__a						:  List[Any]									=     load_rocstories_dataset(args.eval_dataset				)
			__a						:  str									=     (train_dataset, eval_dataset)
			__a						:  str									=     tokenize_and_encode(lowerCamelCase_				)

			# Compute the max input length for the Transformer
			__a						:  Any									=     model.config.n_positions // 2 - 2
			__a						:  Dict									=     max(
			    len(story[:max_length]				) + max(len(conta[:max_length]				)   ,  len(conta[:max_length]				)				) + 3
			    for dataset in encoded_datasets
			    for story, conta, conta, _ in dataset				)
			__a						:  Dict									=     min(lowerCamelCase_   ,  model.config.n_positions				)  # Max size of input for the pre-trained model

			# Prepare inputs tensors and dataloaders
			__a						:  Dict									=     pre_process_datasets(lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  *lowerCamelCase_				)
			__a						,					__a						:  str									=     tensor_datasets[0], tensor_datasets[1]

			__a						:  str									=     TensorDataset(*lowerCamelCase_				)
			__a						:  int									=     RandomSampler(lowerCamelCase_				)
			__a						:  str									=     DataLoader(lowerCamelCase_   ,  sampler=lowerCamelCase_   ,  batch_size=args.train_batch_size				)

			__a						:  List[Any]									=     TensorDataset(*lowerCamelCase_				)
			__a						:  str									=     SequentialSampler(lowerCamelCase_				)
			__a						:  Dict									=     DataLoader(lowerCamelCase_   ,  sampler=lowerCamelCase_   ,  batch_size=args.eval_batch_size				)

			# Prepare optimizer
			if args.do_train:
						if args.max_steps > 0:
									__a						:  Optional[Any]									=     args.max_steps
									__a						:  int									=     args.max_steps // (len(lowerCamelCase_				) // args.gradient_accumulation_steps) + 1
						else:
									__a						:  List[Any]									=     len(lowerCamelCase_				) // args.gradient_accumulation_steps * args.num_train_epochs

						__a						:  Any									=     list(model.named_parameters()				)
						__a						:  Optional[int]									=     ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
						__a						:  int									=     [
						    {
						        'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay				)],
						        'weight_decay': args.weight_decay,
						    },
						    {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay				)], 'weight_decay': 0.0},
						]
						__a						:  List[str]									=     AdamW(lowerCamelCase_   ,  lr=args.learning_rate   ,  eps=args.adam_epsilon				)
						__a						:  Optional[Any]									=     get_linear_schedule_with_warmup(
						    lowerCamelCase_   ,  num_warmup_steps=args.warmup_steps   ,  num_training_steps=lowerCamelCase_				)

			if args.do_train:
						__a						,					__a						,					__a						:  List[Any]									=     0, 0, None
						model.train()
						for _ in trange(int(args.num_train_epochs				)   ,  desc='Epoch'				):
									__a						:  Union[str, Any]									=     0
									__a						:  int									=     0
									__a						:  str									=     tqdm(lowerCamelCase_   ,  desc='Training'				)
									for step, batch in enumerate(lowerCamelCase_				):
												__a						:  int									=     tuple(t.to(lowerCamelCase_				) for t in batch				)
												__a						,					__a						,					__a						,					__a						:  Any									=     batch
												__a						:  List[Any]									=     model(lowerCamelCase_   ,  mc_token_ids=lowerCamelCase_   ,  lm_labels=lowerCamelCase_   ,  mc_labels=lowerCamelCase_				)
												__a						:  Union[str, Any]									=     args.lm_coef * losses[0] + losses[1]
												loss.backward()
												optimizer.step()
												scheduler.step()
												optimizer.zero_grad()
												tr_loss += loss.item()
												__a						:  Optional[int]									=     (
												    loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
												)
												nb_tr_steps += 1
												__a						:  Optional[Any]									=     'Training loss: {:.2e} lr: {:.2e}'.format(lowerCamelCase_   ,  scheduler.get_lr()[0]				)

    # Save a trained model
			if args.do_train:
						# Save a trained model, configuration and tokenizer
						__a						:  List[str]									=     model.module if hasattr(lowerCamelCase_   ,  'module'				) else model  # Only save the model itself

						# If we save using the predefined names, we can load using `from_pretrained`
						__a						:  Union[str, Any]									=     os.path.join(args.output_dir   ,  lowerCamelCase_				)
						__a						:  List[Any]									=     os.path.join(args.output_dir   ,  lowerCamelCase_				)

						torch.save(model_to_save.state_dict()   ,  lowerCamelCase_				)
						model_to_save.config.to_json_file(lowerCamelCase_				)
						tokenizer.save_vocabulary(args.output_dir				)

						# Load a trained model and vocabulary that you have fine-tuned
						__a						:  str									=     OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir				)
						__a						:  Tuple									=     OpenAIGPTTokenizer.from_pretrained(args.output_dir				)
						model.to(lowerCamelCase_				)

			if args.do_eval:
						model.eval()
						__a						,					__a						:  Union[str, Any]									=     0, 0
						__a						,					__a						:  List[Any]									=     0, 0
						for batch in tqdm(lowerCamelCase_   ,  desc='Evaluating'				):
									__a						:  int									=     tuple(t.to(lowerCamelCase_				) for t in batch				)
									__a						,					__a						,					__a						,					__a						:  Dict									=     batch
									with torch.no_grad():
												__a						,					__a						,					__a						,					__a						:  Dict									=     model(
												    lowerCamelCase_   ,  mc_token_ids=lowerCamelCase_   ,  lm_labels=lowerCamelCase_   ,  mc_labels=lowerCamelCase_				)

									__a						:  int									=     mc_logits.detach().cpu().numpy()
									__a						:  List[str]									=     mc_labels.to('cpu'				).numpy()
									__a						:  str									=     accuracy(lowerCamelCase_   ,  lowerCamelCase_				)

									eval_loss += mc_loss.mean().item()
									eval_accuracy += tmp_eval_accuracy

									nb_eval_examples += input_ids.size(0				)
									nb_eval_steps += 1

						__a						:  Optional[int]									=     eval_loss / nb_eval_steps
						__a						:  Dict									=     eval_accuracy / nb_eval_examples
						__a						:  Dict									=     tr_loss / nb_tr_steps if args.do_train else None
						__a						:  Dict									=     {'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy, 'train_loss': train_loss}

						__a						:  str									=     os.path.join(args.output_dir   ,  'eval_results.txt'				)
						with open(lowerCamelCase_   ,  'w'				) as writer:
									logger.info('***** Eval results *****'				)
									for key in sorted(result.keys()				):
												logger.info('  %s = %s'   ,  lowerCamelCase_   ,  str(result[key]				)				)
												writer.write('%s = %s\n' % (key, str(result[key]				))				)


if __name__ == "__main__":
							main()








from typing import Dict, Iterable, 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, to_pil_image
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_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
    import PIL

# soft dependency
if is_pytesseract_available():
    import pytesseract

UpperCamelCase__		 =     logging.get_logger(__name__)







def 						lowerCamelCase__      ( __A    :Optional[int]	,__A    :Any	,__A    :str		):


       """simple docstring"""




       return [
           int(1_0_0_0 * (box[0] / width)		),
           int(1_0_0_0 * (box[1] / height)		),
           int(1_0_0_0 * (box[2] / width)		),
           int(1_0_0_0 * (box[3] / height)		),
       ]







def 						lowerCamelCase__      ( __A    :np.ndarray	,__A    :Optional[str]	,__A    :Optional[str]		):


       """simple docstring"""




       __snake_case       	=							to_pil_image(__A		)
       __snake_case	,    __snake_case       	=							pil_image.size
       __snake_case       	=							pytesseract.image_to_data(__A	,lang=__A	,output_type="""dict"""	,config=__A		)
       __snake_case	,    __snake_case	,    __snake_case	,    __snake_case	,    __snake_case       	=							data["""text"""], data["""left"""], data["""top"""], data["""width"""], data["""height"""]

       # filter empty words and corresponding coordinates
       __snake_case       	=							[idx for idx, word in enumerate(__A		) if not word.strip()]
       __snake_case       	=							[word for idx, word in enumerate(__A		) if idx not in irrelevant_indices]
       __snake_case       	=							[coord for idx, coord in enumerate(__A		) if idx not in irrelevant_indices]
       __snake_case       	=							[coord for idx, coord in enumerate(__A		) if idx not in irrelevant_indices]
       __snake_case       	=							[coord for idx, coord in enumerate(__A		) if idx not in irrelevant_indices]
       __snake_case       	=							[coord for idx, coord in enumerate(__A		) if idx not in irrelevant_indices]

       # turn coordinates into (left, top, left+width, top+height) format
       __snake_case       	=							[]
       for x, y, w, h in zip(__A	,__A	,__A	,__A		):
              __snake_case       	=							[x, y, x + w, y + h]
              actual_boxes.append(__A		)

       # finally, normalize the bounding boxes
       __snake_case       	=							[]
       for box in actual_boxes:
              normalized_boxes.append(normalize_box(__A	,__A	,__A		)		)

       assert len(__A		) == len(__A		), "Not as many words as there are bounding boxes"

       return words, normalized_boxes




class     __snake_case    (				snake_case__  ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE				    =							["pixel_values"]


       def __init__(		self      , _UpperCamelCase = True      , _UpperCamelCase = None      , _UpperCamelCase = PILImageResampling.BILINEAR      , _UpperCamelCase = True      , _UpperCamelCase = 1 / 2_55      , _UpperCamelCase = True      , _UpperCamelCase = None      , _UpperCamelCase = None      , _UpperCamelCase = True      , _UpperCamelCase = None      , _UpperCamelCase = ""      , **_UpperCamelCase      , )      ->							None:






              """simple docstring"""


              super().__init__(**_UpperCamelCase   )
              __snake_case       	=							size if size is not None else {"""height""": 2_24, """width""": 2_24}
              __snake_case       	=							get_size_dict(_UpperCamelCase   )

              __snake_case       	=							do_resize
              __snake_case       	=							size
              __snake_case       	=							resample
              __snake_case       	=							do_rescale
              __snake_case       	=							rescale_value
              __snake_case       	=							do_normalize
              __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
              __snake_case       	=							apply_ocr
              __snake_case       	=							ocr_lang
              __snake_case       	=							tesseract_config


       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase = PILImageResampling.BILINEAR      , _UpperCamelCase = None      , **_UpperCamelCase      , )      ->							np.ndarray:






              """simple docstring"""


              __snake_case       	=							get_size_dict(_UpperCamelCase   )
              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()}'   )
              __snake_case       	=							(size["""height"""], size["""width"""])
              return resize(_UpperCamelCase      , size=_UpperCamelCase      , resample=_UpperCamelCase      , data_format=_UpperCamelCase      , **_UpperCamelCase   )


       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase = None      , **_UpperCamelCase      , )      ->							np.ndarray:






              """simple docstring"""


              return rescale(_UpperCamelCase      , scale=_UpperCamelCase      , data_format=_UpperCamelCase      , **_UpperCamelCase   )


       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase = None      , **_UpperCamelCase      , )      ->							np.ndarray:






              """simple docstring"""


              return normalize(_UpperCamelCase      , mean=_UpperCamelCase      , std=_UpperCamelCase      , data_format=_UpperCamelCase      , **_UpperCamelCase   )






       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase = None      , _UpperCamelCase = None      , _UpperCamelCase=None      , _UpperCamelCase = None      , _UpperCamelCase = None      , _UpperCamelCase = None      , _UpperCamelCase = None      , _UpperCamelCase = None      , _UpperCamelCase = None      , _UpperCamelCase = None      , _UpperCamelCase = None      , _UpperCamelCase = None      , _UpperCamelCase = ChannelDimension.FIRST      , **_UpperCamelCase      , )      ->							PIL.Image.Image:






              """simple docstring"""


              __snake_case       	=							do_resize if do_resize is not None else self.do_resize
              __snake_case       	=							size if size is not None else self.size
              __snake_case       	=							get_size_dict(_UpperCamelCase   )
              __snake_case       	=							resample if resample is not None else self.resample
              __snake_case       	=							do_rescale if do_rescale is not None else self.do_rescale
              __snake_case       	=							rescale_factor if rescale_factor is not None else self.rescale_factor
              __snake_case       	=							do_normalize if do_normalize is not None else self.do_normalize
              __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       	=							apply_ocr if apply_ocr is not None else self.apply_ocr
              __snake_case       	=							ocr_lang if ocr_lang is not None else self.ocr_lang
              __snake_case       	=							tesseract_config if tesseract_config is not None else self.tesseract_config

              __snake_case       	=							make_list_of_images(_UpperCamelCase   )

              if not valid_images(_UpperCamelCase   ):
                     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."""   )

              if do_normalize and (image_mean is None or image_std is None):
                     raise ValueError("""If do_normalize is True, image_mean and image_std must be specified."""   )

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

              # Tesseract OCR to get words + normalized bounding boxes
              if apply_ocr:
                     requires_backends(self      , """pytesseract"""   )
                     __snake_case       	=							[]
                     __snake_case       	=							[]
                     for image in images:
                            __snake_case	,    __snake_case       	=							apply_tesseract(_UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase   )
                            words_batch.append(_UpperCamelCase   )
                            boxes_batch.append(_UpperCamelCase   )

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

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

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

              __snake_case       	=							[to_channel_dimension_format(_UpperCamelCase      , _UpperCamelCase   ) for image in images]

              __snake_case       	=							BatchFeature(data={"""pixel_values""": images}      , tensor_type=_UpperCamelCase   )

              if apply_ocr:
                     __snake_case       	=							words_batch
                     __snake_case       	=							boxes_batch
              return data

