

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
)
__UpperCAmelCase    				=     logging.getLogger(__name__)



def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      Union[str, Any]:

     """simple docstring"""







     _lowerCAmelCase   :					Any        =    np.argmax(SCREAMING_SNAKE_CASE		,							axis=1				)
     return np.sum(outputs == labels				)



def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      List[Any]:

     """simple docstring"""







     with open(SCREAMING_SNAKE_CASE		,							encoding="utf_8"				) as f:
          _lowerCAmelCase   :					Optional[Any]        =    csv.reader(SCREAMING_SNAKE_CASE				)
          _lowerCAmelCase   :					List[str]        =    []
          next(SCREAMING_SNAKE_CASE				)  # skip the first line
          for line in tqdm(SCREAMING_SNAKE_CASE				):
               output.append((" ".join(line[1:5]				), line[5], line[6], int(line[-1]				) - 1)				)
     return output



def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      List[str]:

     """simple docstring"""







     _lowerCAmelCase   :					Any        =    []
     for dataset in encoded_datasets:
          _lowerCAmelCase   :					Optional[int]        =    len(SCREAMING_SNAKE_CASE				)
          _lowerCAmelCase   :					Optional[int]        =    np.zeros((n_batch, 2, input_len)		,							dtype=np.intaa				)
          _lowerCAmelCase   :					int        =    np.zeros((n_batch, 2)		,							dtype=np.intaa				)
          _lowerCAmelCase   :					Any        =    np.full((n_batch, 2, input_len)		,							fill_value=-100		,							dtype=np.intaa				)
          _lowerCAmelCase   :					Optional[Any]        =    np.zeros((n_batch,)		,							dtype=np.intaa				)
          for (
              i,
              (story, conta, conta, mc_label),
          ) in enumerate(SCREAMING_SNAKE_CASE				):
               _lowerCAmelCase   :					Any        =    [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
               _lowerCAmelCase   :					Optional[Any]        =    [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
               _lowerCAmelCase   :					List[str]        =    with_conta
               _lowerCAmelCase   :					Dict        =    with_conta
               _lowerCAmelCase   :					Dict        =    len(SCREAMING_SNAKE_CASE				) - 1
               _lowerCAmelCase   :					Union[str, Any]        =    len(SCREAMING_SNAKE_CASE				) - 1
               _lowerCAmelCase   :					List[Any]        =    with_conta
               _lowerCAmelCase   :					Any        =    with_conta
               _lowerCAmelCase   :					Any        =    mc_label
          _lowerCAmelCase   :					Optional[Any]        =    (input_ids, mc_token_ids, lm_labels, mc_labels)
          tensor_datasets.append(tuple(torch.tensor(SCREAMING_SNAKE_CASE				) for t in all_inputs				)				)
     return tensor_datasets



def 	_snake_case		(    )       ->      Union[str, Any]:

     """simple docstring"""







     _lowerCAmelCase   :					Tuple        =    argparse.ArgumentParser()
     parser.add_argument("--model_name"		,							type=SCREAMING_SNAKE_CASE		,							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=SCREAMING_SNAKE_CASE		,							type=SCREAMING_SNAKE_CASE		,							required=SCREAMING_SNAKE_CASE		,							help="The output directory where the model predictions and checkpoints will be written."		,							)
     parser.add_argument("--train_dataset"		,							type=SCREAMING_SNAKE_CASE		,							default=""				)
     parser.add_argument("--eval_dataset"		,							type=SCREAMING_SNAKE_CASE		,							default=""				)
     parser.add_argument("--seed"		,							type=SCREAMING_SNAKE_CASE		,							default=42				)
     parser.add_argument("--num_train_epochs"		,							type=SCREAMING_SNAKE_CASE		,							default=3				)
     parser.add_argument("--train_batch_size"		,							type=SCREAMING_SNAKE_CASE		,							default=8				)
     parser.add_argument("--eval_batch_size"		,							type=SCREAMING_SNAKE_CASE		,							default=16				)
     parser.add_argument("--adam_epsilon"		,							default=1e-8		,							type=SCREAMING_SNAKE_CASE		,							help="Epsilon for Adam optimizer."				)
     parser.add_argument("--max_grad_norm"		,							type=SCREAMING_SNAKE_CASE		,							default=1				)
     parser.add_argument(
         "--max_steps"		,							default=-1		,							type=SCREAMING_SNAKE_CASE		,							help=(
             "If > 0: set total number of training                         steps to perform. Override num_train_epochs."
         )		,							)
     parser.add_argument(
         "--gradient_accumulation_steps"		,							type=SCREAMING_SNAKE_CASE		,							default=1		,							help="Number of updates steps to accumulate before                        performing a backward/update pass."		,							)
     parser.add_argument("--learning_rate"		,							type=SCREAMING_SNAKE_CASE		,							default=6.25e-5				)
     parser.add_argument("--warmup_steps"		,							default=0		,							type=SCREAMING_SNAKE_CASE		,							help="Linear warmup over warmup_steps."				)
     parser.add_argument("--lr_schedule"		,							type=SCREAMING_SNAKE_CASE		,							default="warmup_linear"				)
     parser.add_argument("--weight_decay"		,							type=SCREAMING_SNAKE_CASE		,							default=0.01				)
     parser.add_argument("--lm_coef"		,							type=SCREAMING_SNAKE_CASE		,							default=0.9				)
     parser.add_argument("--n_valid"		,							type=SCREAMING_SNAKE_CASE		,							default=374				)

     parser.add_argument("--server_ip"		,							type=SCREAMING_SNAKE_CASE		,							default=""		,							help="Can be used for distant debugging."				)
     parser.add_argument("--server_port"		,							type=SCREAMING_SNAKE_CASE		,							default=""		,							help="Can be used for distant debugging."				)
     _lowerCAmelCase   :					Any        =    parser.parse_args()
     print(SCREAMING_SNAKE_CASE				)

     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=SCREAMING_SNAKE_CASE				)
          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				)

     _lowerCAmelCase   :					List[str]        =    torch.device("cuda" if torch.cuda.is_available() else "cpu"				)
     _lowerCAmelCase   :					List[str]        =    torch.cuda.device_count()
     logger.info("device: {}, n_gpu {}".format(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)				)

     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
     _lowerCAmelCase   :					Tuple        =    ["_start_", "_delimiter_", "_classify_"]
     _lowerCAmelCase   :					List[Any]        =    OpenAIGPTTokenizer.from_pretrained(args.model_name				)
     tokenizer.add_tokens(SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase   :					str        =    tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase   :					Tuple        =    OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name				)
     model.resize_token_embeddings(len(SCREAMING_SNAKE_CASE				)				)
     model.to(SCREAMING_SNAKE_CASE				)

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

     logger.info("Encoding dataset..."				)
     _lowerCAmelCase   :					List[str]        =    load_rocstories_dataset(args.train_dataset				)
     _lowerCAmelCase   :					Any        =    load_rocstories_dataset(args.eval_dataset				)
     _lowerCAmelCase   :					Union[str, Any]        =    (train_dataset, eval_dataset)
     _lowerCAmelCase   :					int        =    tokenize_and_encode(SCREAMING_SNAKE_CASE				)

     # Compute the max input length for the Transformer
     _lowerCAmelCase   :					Tuple        =    model.config.n_positions // 2 - 2
     _lowerCAmelCase   :					List[Any]        =    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				)
     _lowerCAmelCase   :					List[Any]        =    min(SCREAMING_SNAKE_CASE		,							model.config.n_positions				)  # Max size of input for the pre-trained model

     # Prepare inputs tensors and dataloaders
     _lowerCAmelCase   :					int        =    pre_process_datasets(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							*SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase      ,			_lowerCAmelCase   :					Any        =    tensor_datasets[0], tensor_datasets[1]

     _lowerCAmelCase   :					List[str]        =    TensorDataset(*SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase   :					List[Any]        =    RandomSampler(SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase   :					int        =    DataLoader(SCREAMING_SNAKE_CASE		,							sampler=SCREAMING_SNAKE_CASE		,							batch_size=args.train_batch_size				)

     _lowerCAmelCase   :					Optional[Any]        =    TensorDataset(*SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase   :					Tuple        =    SequentialSampler(SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase   :					List[str]        =    DataLoader(SCREAMING_SNAKE_CASE		,							sampler=SCREAMING_SNAKE_CASE		,							batch_size=args.eval_batch_size				)

     # Prepare optimizer
     if args.do_train:
          if args.max_steps > 0:
               _lowerCAmelCase   :					Tuple        =    args.max_steps
               _lowerCAmelCase   :					Any        =    args.max_steps // (len(SCREAMING_SNAKE_CASE				) // args.gradient_accumulation_steps) + 1
          else:
               _lowerCAmelCase   :					Tuple        =    len(SCREAMING_SNAKE_CASE				) // args.gradient_accumulation_steps * args.num_train_epochs

          _lowerCAmelCase   :					Optional[int]        =    list(model.named_parameters()				)
          _lowerCAmelCase   :					Optional[int]        =    ["bias", "LayerNorm.bias", "LayerNorm.weight"]
          _lowerCAmelCase   :					Dict        =    [
              {
                  "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},
          ]
          _lowerCAmelCase   :					Any        =    AdamW(SCREAMING_SNAKE_CASE		,							lr=args.learning_rate		,							eps=args.adam_epsilon				)
          _lowerCAmelCase   :					int        =    get_linear_schedule_with_warmup(
              SCREAMING_SNAKE_CASE		,							num_warmup_steps=args.warmup_steps		,							num_training_steps=SCREAMING_SNAKE_CASE				)

     if args.do_train:
          _lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase   :					Optional[int]        =    0, 0, None
          model.train()
          for _ in trange(int(args.num_train_epochs				)		,							desc="Epoch"				):
               _lowerCAmelCase   :					int        =    0
               _lowerCAmelCase   :					Optional[Any]        =    0
               _lowerCAmelCase   :					Any        =    tqdm(SCREAMING_SNAKE_CASE		,							desc="Training"				)
               for step, batch in enumerate(SCREAMING_SNAKE_CASE				):
                    _lowerCAmelCase   :					Optional[Any]        =    tuple(t.to(SCREAMING_SNAKE_CASE				) for t in batch				)
                    _lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase   :					Optional[int]        =    batch
                    _lowerCAmelCase   :					Any        =    model(SCREAMING_SNAKE_CASE		,							mc_token_ids=SCREAMING_SNAKE_CASE		,							lm_labels=SCREAMING_SNAKE_CASE		,							mc_labels=SCREAMING_SNAKE_CASE				)
                    _lowerCAmelCase   :					List[Any]        =    args.lm_coef * losses[0] + losses[1]
                    loss.backward()
                    optimizer.step()
                    scheduler.step()
                    optimizer.zero_grad()
                    tr_loss += loss.item()
                    _lowerCAmelCase   :					int        =    (
                        loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
                    )
                    nb_tr_steps += 1
                    _lowerCAmelCase   :					str        =    "Training loss: {:.2e} lr: {:.2e}".format(SCREAMING_SNAKE_CASE		,							scheduler.get_lr()[0]				)

    # Save a trained model
     if args.do_train:
          # Save a trained model, configuration and tokenizer
          _lowerCAmelCase   :					Optional[int]        =    model.module if hasattr(SCREAMING_SNAKE_CASE		,							"module"				) else model  # Only save the model itself

          # If we save using the predefined names, we can load using `from_pretrained`
          _lowerCAmelCase   :					Optional[Any]        =    os.path.join(args.output_dir		,							SCREAMING_SNAKE_CASE				)
          _lowerCAmelCase   :					int        =    os.path.join(args.output_dir		,							SCREAMING_SNAKE_CASE				)

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

          # Load a trained model and vocabulary that you have fine-tuned
          _lowerCAmelCase   :					List[str]        =    OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir				)
          _lowerCAmelCase   :					Any        =    OpenAIGPTTokenizer.from_pretrained(args.output_dir				)
          model.to(SCREAMING_SNAKE_CASE				)

     if args.do_eval:
          model.eval()
          _lowerCAmelCase      ,			_lowerCAmelCase   :					int        =    0, 0
          _lowerCAmelCase      ,			_lowerCAmelCase   :					Optional[Any]        =    0, 0
          for batch in tqdm(SCREAMING_SNAKE_CASE		,							desc="Evaluating"				):
               _lowerCAmelCase   :					Union[str, Any]        =    tuple(t.to(SCREAMING_SNAKE_CASE				) for t in batch				)
               _lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase   :					Any        =    batch
               with torch.no_grad():
                    _lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase   :					str        =    model(
                        SCREAMING_SNAKE_CASE		,							mc_token_ids=SCREAMING_SNAKE_CASE		,							lm_labels=SCREAMING_SNAKE_CASE		,							mc_labels=SCREAMING_SNAKE_CASE				)

               _lowerCAmelCase   :					Optional[int]        =    mc_logits.detach().cpu().numpy()
               _lowerCAmelCase   :					Union[str, Any]        =    mc_labels.to("cpu"				).numpy()
               _lowerCAmelCase   :					Any        =    accuracy(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)

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

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

          _lowerCAmelCase   :					int        =    eval_loss / nb_eval_steps
          _lowerCAmelCase   :					Tuple        =    eval_accuracy / nb_eval_examples
          _lowerCAmelCase   :					Optional[int]        =    tr_loss / nb_tr_steps if args.do_train else None
          _lowerCAmelCase   :					int        =    {"eval_loss": eval_loss, "eval_accuracy": eval_accuracy, "train_loss": train_loss}

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


if __name__ == "__main__":
  main()









from typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    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 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      Union[str, Any]:

     """simple docstring"""







     return [
         int(1000 * (box[0] / width)				),
         int(1000 * (box[1] / height)				),
         int(1000 * (box[2] / width)				),
         int(1000 * (box[3] / height)				),
     ]







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE = None				)       ->      str:

     """simple docstring"""







     _lowerCAmelCase   :					str        =    tesseract_config if tesseract_config is not None else ""

     # apply OCR
     _lowerCAmelCase   :					List[str]        =    to_pil_image(SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase      ,			_lowerCAmelCase   :					List[str]        =    pil_image.size
     _lowerCAmelCase   :					Union[str, Any]        =    pytesseract.image_to_data(SCREAMING_SNAKE_CASE		,							lang=SCREAMING_SNAKE_CASE		,							output_type="dict"		,							config=SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase   :					Optional[Any]        =    data["text"], data["left"], data["top"], data["width"], data["height"]

     # filter empty words and corresponding coordinates
     _lowerCAmelCase   :					Dict        =    [idx for idx, word in enumerate(SCREAMING_SNAKE_CASE				) if not word.strip()]
     _lowerCAmelCase   :					Any        =    [word for idx, word in enumerate(SCREAMING_SNAKE_CASE				) if idx not in irrelevant_indices]
     _lowerCAmelCase   :					Dict        =    [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE				) if idx not in irrelevant_indices]
     _lowerCAmelCase   :					Optional[Any]        =    [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE				) if idx not in irrelevant_indices]
     _lowerCAmelCase   :					Optional[Any]        =    [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE				) if idx not in irrelevant_indices]
     _lowerCAmelCase   :					Optional[int]        =    [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE				) if idx not in irrelevant_indices]

     # turn coordinates into (left, top, left+width, top+height) format
     _lowerCAmelCase   :					Union[str, Any]        =    []
     for x, y, w, h in zip(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				):
          _lowerCAmelCase   :					Tuple        =    [x, y, x + w, y + h]
          actual_boxes.append(SCREAMING_SNAKE_CASE				)

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

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

     return words, normalized_boxes





class A__    ( A ):



 """simple docstring"""







 _lowercase     :					Union[str, Any]      =						['''pixel_values''']

 def __init__(		self :	Tuple     ,						A_ :	bool = True     ,						A_ :	Dict[str, int] = None     ,						A_ :	PILImageResampling = PILImageResampling.BILINEAR     ,						A_ :	bool = True     ,						A_ :	Optional[str] = None     ,						A_ :	Optional[str] = ""     ,						**A_ :	Dict     ,						):






      '''simple docstring'''




      super().__init__(**A_       )
      _lowerCAmelCase   :					int        =    size if size is not None else {"height": 2_2_4, "width": 2_2_4}
      _lowerCAmelCase   :					Any        =    get_size_dict(A_       )

      _lowerCAmelCase   :					Any        =    do_resize
      _lowerCAmelCase   :					Any        =    size
      _lowerCAmelCase   :					Dict        =    resample
      _lowerCAmelCase   :					Optional[int]        =    apply_ocr
      _lowerCAmelCase   :					Dict        =    ocr_lang
      _lowerCAmelCase   :					Optional[int]        =    tesseract_config

 def    __magic_name__       (		self :	Dict     ,						A_ :	np.ndarray     ,						A_ :	Dict[str, int]     ,						A_ :	PILImageResampling = PILImageResampling.BILINEAR     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	List[str]     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    get_size_dict(A_       )
      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()}'''       )
      _lowerCAmelCase   :					Dict        =    (size["height"], size["width"])
      return resize(A_     ,						size=A_     ,						resample=A_     ,						data_format=A_     ,						**A_       )


 def    __magic_name__       (		self :	Dict     ,						A_ :	ImageInput     ,						A_ :	bool = None     ,						A_ :	Dict[str, int] = None     ,						A_ :	PILImageResampling = None     ,						A_ :	bool = None     ,						A_ :	Optional[str] = None     ,						A_ :	Optional[str] = None     ,						A_ :	Optional[Union[str, TensorType]] = None     ,						A_ :	ChannelDimension = ChannelDimension.FIRST     ,						**A_ :	Dict     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					List[Any]        =    do_resize if do_resize is not None else self.do_resize
      _lowerCAmelCase   :					str        =    size if size is not None else self.size
      _lowerCAmelCase   :					List[Any]        =    get_size_dict(A_       )
      _lowerCAmelCase   :					Tuple        =    resample if resample is not None else self.resample
      _lowerCAmelCase   :					Union[str, Any]        =    apply_ocr if apply_ocr is not None else self.apply_ocr
      _lowerCAmelCase   :					Tuple        =    ocr_lang if ocr_lang is not None else self.ocr_lang
      _lowerCAmelCase   :					str        =    tesseract_config if tesseract_config is not None else self.tesseract_config

      _lowerCAmelCase   :					Union[str, Any]        =    make_list_of_images(A_       )

      if not valid_images(A_       ):
           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."       )

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

      if apply_ocr:
           requires_backends(self     ,						"pytesseract"       )
           _lowerCAmelCase   :					Any        =    []
           _lowerCAmelCase   :					List[Any]        =    []
           for image in images:
                _lowerCAmelCase      ,			_lowerCAmelCase   :					Optional[int]        =    apply_tesseract(A_     ,						A_     ,						A_       )
                words_batch.append(A_       )
                boxes_batch.append(A_       )

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

      # flip color channels from RGB to BGR (as Detectron2 requires this)
      _lowerCAmelCase   :					Optional[int]        =    [flip_channel_order(A_       ) for image in images]
      _lowerCAmelCase   :					List[Any]        =    [to_channel_dimension_format(A_     ,						A_       ) for image in images]

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

      if apply_ocr:
           _lowerCAmelCase   :					Tuple        =    words_batch
           _lowerCAmelCase   :					Any        =    boxes_batch
      return data






