






'''simple docstring'''



import argparse
import logging
import os
from datetime import datetime

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

from transformers import GPTaLMHeadModel


SCREAMING_SNAKE_CASE						=	logging.getLogger(__name__)


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



	"""simple docstring"""




	if os.path.exists(__A			):
		if os.path.exists(os.path.join(__A					,      '''config.json'''			)			) and os.path.isfile(
		    os.path.join(__A					,      '''config.json'''			)			):
			os.remove(os.path.join(__A					,      '''config.json'''			)			)
		if os.path.exists(os.path.join(__A					,      '''pytorch_model.bin'''			)			) and os.path.isfile(
		    os.path.join(__A					,      '''pytorch_model.bin'''			)			):
			os.remove(os.path.join(__A					,      '''pytorch_model.bin'''			)			)
	else:
		os.makedirs(__A			)
	model.save_pretrained(__A			)


def 							lowercase_				(    __A :       int					,      __A :       Any=False			)       ->      Union[str, Any]:



	"""simple docstring"""




	lowercase					:   Union[str, Any]				  =2
	if unlogit:
		lowercase					:   str				  =torch.pow(__A					,      __A			)
	lowercase					:   str				  =p * torch.log(__A			)
	lowercase					:   List[str]				  =0
	return -plogp.sum(dim=-1			)


def 							lowercase_				(    __A :       List[str]			)       ->      int:



	"""simple docstring"""




	logger.info('''lv, h >\t''' + '''\t'''.join(F'{x + 1}' for x in range(len(__A			)			)			)			)
	for row in range(len(__A			)			):
		if tensor.dtype != torch.long:
			logger.info(F'layer {row + 1}:\t' + '''\t'''.join(F'{x:.5f}' for x in tensor[row].cpu().data			)			)
		else:
			logger.info(F'layer {row + 1}:\t' + '''\t'''.join(F'{x:d}' for x in tensor[row].cpu().data			)			)


def 							lowercase_				(    __A :       Tuple					,      __A :       Union[str, Any]					,      __A :       List[str]					,      __A :       Optional[Any]=True					,      __A :       int=True					,      __A :       List[Any]=None					,      __A :       int=False			)       ->      Optional[Any]:



	"""simple docstring"""




	lowercase					:   Dict				  =model.config.num_hidden_layers, model.config.num_attention_heads
	lowercase					:   Optional[int]				  =torch.zeros(__A					,      __A			).to(args.device			)
	lowercase					:   Dict				  =torch.zeros(__A					,      __A			).to(args.device			)

	if head_mask is None:
		lowercase					:   List[Any]				  =torch.ones(__A					,      __A			).to(args.device			)

	head_mask.requires_grad_(requires_grad=__A			)
	# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
	if actually_pruned:
		lowercase					:   int				  =None

	lowercase					:   str				  =0.0
	lowercase					:   Dict				  =0.0
	for step, inputs in enumerate(tqdm(__A					,      desc='''Iteration'''					,      disable=args.local_rank not in [-1, 0]			)			):
		lowercase					:   Dict				  =tuple(t.to(args.device			) for t in inputs			)
		(lowercase		)					:   int				  =inputs

		# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
		lowercase					:   Union[str, Any]				  =model(__A					,      labels=__A					,      head_mask=__A			)
		#  (loss), lm_logits, presents, (all hidden_states), (attentions)
		lowercase					:   int				  =(
		    outputs[0],
		    outputs[1],
		    outputs[-1],
		)  # Loss and logits are the first, attention the last
		loss.backward()  # Backpropagate to populate the gradients in the head mask
		total_loss += loss.detach().cpu().numpy()
		if compute_entropy:
			for layer, attn in enumerate(__A			):
				lowercase					:   Any				  =entropy(attn.detach()					,      __A			)
				attn_entropy[layer] += masked_entropy.sum(-1			).sum(0			).sum(0			).detach()

		if compute_importance:
			head_importance += head_mask.grad.abs().detach()
		tot_tokens += torch.ones_like(__A			).float().detach().sum().data

	# Normalize
	attn_entropy /= tot_tokens
	head_importance /= tot_tokens
	# Layerwise importance normalization
	if not args.dont_normalize_importance_by_layer:
		lowercase					:   Tuple				  =2
		lowercase					:   Any				  =torch.pow(torch.pow(__A					,      __A			).sum(-1			)					,      1 / exponent			)
		head_importance /= norm_by_layer.unsqueeze(-1			) + 1E-20

	if not args.dont_normalize_global_importance:
		lowercase					:   List[str]				  =(head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

	# Print matrices
	if compute_entropy:
		logger.info('''Attention entropies'''			)
		print_ad_tensor(__A			)
	if compute_importance:
		logger.info('''Head importance scores'''			)
		print_ad_tensor(__A			)
	logger.info('''Head ranked by importance scores'''			)
	lowercase					:   Any				  =torch.zeros(head_importance.numel()					,      dtype=torch.long					,      device=args.device			)
	lowercase					:   Union[str, Any]				  =torch.arange(
	    head_importance.numel()					,      device=args.device			)
	lowercase					:   int				  =head_ranks.view_as(__A			)
	print_ad_tensor(__A			)
	return attn_entropy, head_importance, total_loss


def 							lowercase_				(    __A :       Any					,      __A :       Dict					,      __A :       Optional[Any]			)       ->      Tuple:



	"""simple docstring"""




	lowercase					:   List[Any]				  =compute_heads_importance(__A					,      __A					,      __A					,      compute_entropy=__A			)
	lowercase					:   Tuple				  =1 / loss  # instead of downsteam score use the LM loss
	logger.info('''Pruning: original score: %f, threshold: %f'''					,      __A					,      original_score * args.masking_threshold			)

	lowercase					:   Tuple				  =torch.ones_like(__A			)
	lowercase					:   str				  =max(1					,      int(new_head_mask.numel() * args.masking_amount			)			)

	lowercase					:   Any				  =original_score
	while current_score >= original_score * args.masking_threshold:
		lowercase					:   Optional[int]				  =new_head_mask.clone().detach()  # save current head mask
		# heads from least important to most - keep only not-masked heads
		lowercase					:   Dict				  =float('''Inf'''			)
		lowercase					:   List[Any]				  =head_importance.view(-1			).sort()[1]

		if len(__A			) <= num_to_mask:
			print('''BREAK BY num_to_mask'''			)
			break

		# mask heads
		lowercase					:   List[Any]				  =current_heads_to_mask[:num_to_mask]
		logger.info('''Heads to mask: %s'''					,      str(current_heads_to_mask.tolist()			)			)
		lowercase					:   Optional[int]				  =new_head_mask.view(-1			)
		lowercase					:   Optional[Any]				  =0.0
		lowercase					:   Dict				  =new_head_mask.view_as(__A			)
		lowercase					:   Any				  =new_head_mask.clone().detach()
		print_ad_tensor(__A			)

		# Compute metric and head importance again
		lowercase					:   List[Any]				  =compute_heads_importance(
		    __A					,      __A					,      __A					,      compute_entropy=__A					,      head_mask=__A			)
		lowercase					:   Optional[int]				  =1 / loss
		logger.info(
		    '''Masking: current score: %f, remaining heads %d (%.1f percents)'''					,      __A					,      new_head_mask.sum()					,      new_head_mask.sum() / new_head_mask.numel() * 1_0_0					,      )

	logger.info('''Final head mask'''			)
	print_ad_tensor(__A			)
	np.save(os.path.join(args.output_dir					,      '''head_mask.npy'''			)					,      head_mask.detach().cpu().numpy()			)

	return head_mask


def 							lowercase_				(    __A :       Dict					,      __A :       int					,      __A :       Dict					,      __A :       int			)       ->      Optional[int]:



	"""simple docstring"""




	lowercase					:   Optional[Any]				  =datetime.now()
	lowercase					:   int				  =compute_heads_importance(
	    __A					,      __A					,      __A					,      compute_entropy=__A					,      compute_importance=__A					,      head_mask=__A			)
	lowercase					:   Tuple				  =1 / loss
	lowercase					:   Union[str, Any]				  =datetime.now() - before_time

	lowercase					:   Dict				  =sum(p.numel() for p in model.parameters()			)
	lowercase					:   Tuple				  ={
	    layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(__A			)			)
	}

	for k, v in heads_to_prune.items():
		if isinstance(__A					,      __A			):
			lowercase					:   Optional[int]				  =[
			    v,
			]

	assert sum(len(__A			) for h in heads_to_prune.values()			) == (1 - head_mask.long()).sum().item()
	model.prune_heads(__A			)
	lowercase					:   str				  =sum(p.numel() for p in model.parameters()			)

	lowercase					:   Optional[Any]				  =datetime.now()
	lowercase					:   List[str]				  =compute_heads_importance(
	    __A					,      __A					,      __A					,      compute_entropy=__A					,      compute_importance=__A					,      head_mask=__A					,      actually_pruned=__A					,      )

	lowercase					:   Any				  =1 / loss
	lowercase					:   List[str]				  =datetime.now() - before_time

	logger.info(
	    '''Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)'''					,      __A					,      __A					,      pruned_num_params / original_num_params * 1_0_0					,      )
	logger.info('''Pruning: score with masking: %f score with pruning: %f'''					,      __A					,      __A			)
	logger.info('''Pruning: speed ratio (original timing / new timing): %f percents'''					,      original_time / new_time * 1_0_0			)
	save_model(__A					,      args.output_dir			)


def 							lowercase_				(    )       ->      List[str]:



	"""simple docstring"""




	lowercase					:   str				  =argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--data_dir'''					,      default=__A					,      type=__A					,      required=__A					,      help='''The input data dir. Should contain the .tsv files (or other data files) for the task.'''					,      )
	parser.add_argument(
	    '''--model_name_or_path'''					,      default=__A					,      type=__A					,      required=__A					,      help='''Path to pretrained model or model identifier from huggingface.co/models'''					,      )
	parser.add_argument(
	    '''--output_dir'''					,      default=__A					,      type=__A					,      required=__A					,      help='''The output directory where the model predictions and checkpoints will be written.'''					,      )

	# Other parameters
	parser.add_argument(
	    '''--config_name'''					,      default=''''''					,      type=__A					,      help='''Pretrained config name or path if not the same as model_name_or_path'''					,      )
	parser.add_argument(
	    '''--tokenizer_name'''					,      default=''''''					,      type=__A					,      help='''Pretrained tokenizer name or path if not the same as model_name_or_path'''					,      )
	parser.add_argument(
	    '''--cache_dir'''					,      default=__A					,      type=__A					,      help='''Where do you want to store the pre-trained models downloaded from s3'''					,      )
	parser.add_argument(
	    '''--data_subset'''					,      type=__A					,      default=-1					,      help='''If > 0: limit the data to a subset of data_subset instances.'''			)
	parser.add_argument(
	    '''--overwrite_output_dir'''					,      action='''store_true'''					,      help='''Whether to overwrite data in output directory'''			)
	parser.add_argument(
	    '''--overwrite_cache'''					,      action='''store_true'''					,      help='''Overwrite the cached training and evaluation sets'''			)

	parser.add_argument(
	    '''--dont_normalize_importance_by_layer'''					,      action='''store_true'''					,      help='''Don\'t normalize importance score by layers'''			)
	parser.add_argument(
	    '''--dont_normalize_global_importance'''					,      action='''store_true'''					,      help='''Don\'t normalize all importance scores between 0 and 1'''					,      )

	parser.add_argument(
	    '''--try_masking'''					,      action='''store_true'''					,      help='''Whether to try to mask head until a threshold of accuracy.'''			)
	parser.add_argument(
	    '''--masking_threshold'''					,      default=0.9					,      type=__A					,      help='''masking threshold in term of metrics (stop masking when metric < threshold * original metric value).'''					,      )
	parser.add_argument(
	    '''--masking_amount'''					,      default=0.1					,      type=__A					,      help='''Amount to heads to masking at each masking step.'''			)
	parser.add_argument('''--metric_name'''					,      default='''acc'''					,      type=__A					,      help='''Metric to use for head masking.'''			)

	parser.add_argument(
	    '''--max_seq_length'''					,      default=1_2_8					,      type=__A					,      help=(
	        '''The maximum total input sequence length after WordPiece tokenization. \n'''
	        '''Sequences longer than this will be truncated, sequences shorter padded.'''
	    )					,      )
	parser.add_argument('''--batch_size'''					,      default=1					,      type=__A					,      help='''Batch size.'''			)

	parser.add_argument('''--seed'''					,      type=__A					,      default=4_2			)
	parser.add_argument('''--local_rank'''					,      type=__A					,      default=-1					,      help='''local_rank for distributed training on gpus'''			)
	parser.add_argument('''--no_cuda'''					,      action='''store_true'''					,      help='''Whether not to use CUDA when available'''			)
	parser.add_argument('''--server_ip'''					,      type=__A					,      default=''''''					,      help='''Can be used for distant debugging.'''			)
	parser.add_argument('''--server_port'''					,      type=__A					,      default=''''''					,      help='''Can be used for distant debugging.'''			)
	lowercase					:   str				  =parser.parse_args()

	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=__A			)
		ptvsd.wait_for_attach()

	# Setup devices and distributed training
	if args.local_rank == -1 or args.no_cuda:
		lowercase					:   Optional[Any]				  =torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''			)
		lowercase					:   Dict				  =0 if args.no_cuda else torch.cuda.device_count()
	else:
		torch.cuda.set_device(args.local_rank			)
		lowercase					:   Dict				  =torch.device('''cuda'''					,      args.local_rank			)
		lowercase					:   Dict				  =1
		torch.distributed.init_process_group(backend='''nccl'''			)  # Initializes the distributed backend

	# Setup logging
	logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN			)
	logger.info('''device: {} n_gpu: {}, distributed: {}'''.format(args.device					,      args.n_gpu					,      bool(args.local_rank != -1			)			)			)

	lowercase					:   int				  =GPTaLMHeadModel.from_pretrained(args.model_name_or_path			)

	# Distributed and parallel training
	model.to(args.device			)
	if args.local_rank != -1:
		lowercase					:   Union[str, Any]				  =nn.parallel.DistributedDataParallel(
		    __A					,      device_ids=[args.local_rank]					,      output_device=args.local_rank					,      find_unused_parameters=__A			)
	elif args.n_gpu > 1:
		lowercase					:   Any				  =nn.DataParallel(__A			)

	# Print/save training arguments
	os.makedirs(args.output_dir					,      exist_ok=__A			)
	torch.save(__A					,      os.path.join(args.output_dir					,      '''run_args.bin'''			)			)
	logger.info('''Training/evaluation parameters %s'''					,      __A			)

	# Prepare dataset
	lowercase					:   Union[str, Any]				  =np.concatenate(
	    [
	        np.loadtxt(args.data_dir					,      dtype=np.intaa			),
	    ]			)
	lowercase					:   Dict				  =(torch.from_numpy(__A			),)
	lowercase					:   Dict				  =TensorDataset(*__A			)
	lowercase					:   Optional[Any]				  =RandomSampler(__A			)
	lowercase					:   List[Any]				  =DataLoader(__A					,      sampler=__A					,      batch_size=args.batch_size			)

	# Compute head entropy and importance score
	compute_heads_importance(__A					,      __A					,      __A			)

	# Try head masking (set heads to zero until the score goes under a threshole)
	# and head pruning (remove masked heads and see the effect on the network)
	if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
		lowercase					:   Tuple				  =mask_heads(__A					,      __A					,      __A			)
		prune_heads(__A					,      __A					,      __A					,      __A			)


if __name__ == "__main__":
						main()











'''simple docstring'''



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

SCREAMING_SNAKE_CASE						=	logging.get_logger(__name__)


def 							lowercase_				(    __A :       List[Any]					,      __A :       int					,      __A :       int			)       ->      Optional[int]:



 """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 							lowercase_				(    __A :       np.ndarray					,      __A :       Optional[str]					,      __A :       Optional[str]			)       ->      Optional[Any]:



 """simple docstring"""




 lowercase					:   int				  =to_pil_image(__A			)
 lowercase  ,		lowercase					:   Tuple				  =pil_image.size
 lowercase					:   Optional[Any]				  =pytesseract.image_to_data(__A					,      lang=__A					,      output_type='''dict'''					,      config=__A			)
 lowercase  ,		lowercase  ,		lowercase  ,		lowercase  ,		lowercase					:   Optional[Any]				  =data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

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

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

 # finally, normalize the bounding boxes
 lowercase					:   List[str]				  =[]
 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 UpperCAmelCase_					(    __A				):




      """simple docstring"""




      UpperCamelCase_								=			['''pixel_values''']






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


       '''simple docstring'''

       super().__init__(**UpperCAmelCase		)
       lowercase					:   Tuple				  =size if size is not None else {'''height''': 224, '''width''': 224}
       lowercase					:   Optional[Any]				  =get_size_dict(UpperCAmelCase		)

       lowercase					:   Optional[Any]				  =do_resize
       lowercase					:   List[Any]				  =size
       lowercase					:   List[str]				  =resample
       lowercase					:   Dict				  =do_rescale
       lowercase					:   str				  =rescale_value
       lowercase					:   Optional[int]				  =do_normalize
       lowercase					:   Any				  =image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       lowercase					:   Union[str, Any]				  =image_std if image_std is not None else IMAGENET_STANDARD_STD
       lowercase					:   List[Any]				  =apply_ocr
       lowercase					:   Union[str, Any]				  =ocr_lang
       lowercase					:   str				  =tesseract_config






      def 				A__      ( self			:		Dict     ,      UpperCAmelCase			:		np.ndarray     ,      UpperCAmelCase			:		Dict[str, int]     ,      UpperCAmelCase			:		PILImageResampling = PILImageResampling.BILINEAR     ,      UpperCAmelCase			:		Optional[Union[str, ChannelDimension]] = None     ,      **UpperCAmelCase			:		Optional[Any]     ,      )  ->  np.ndarray:


       '''simple docstring'''

       lowercase					:   Tuple				  =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()}'		)
       lowercase					:   Optional[Any]				  =(size['''height'''], size['''width'''])
       return resize(UpperCAmelCase     ,      size=UpperCAmelCase     ,      resample=UpperCAmelCase     ,      data_format=UpperCAmelCase     ,      **UpperCAmelCase		)






      def 				A__      ( self			:		Union[str, Any]     ,      UpperCAmelCase			:		np.ndarray     ,      UpperCAmelCase			:		Union[int, float]     ,      UpperCAmelCase			:		Optional[Union[str, ChannelDimension]] = None     ,      **UpperCAmelCase			:		Optional[Any]     ,      )  ->  np.ndarray:


       '''simple docstring'''

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






      def 				A__      ( self			:		Union[str, Any]     ,      UpperCAmelCase			:		np.ndarray     ,      UpperCAmelCase			:		Union[float, Iterable[float]]     ,      UpperCAmelCase			:		Union[float, Iterable[float]]     ,      UpperCAmelCase			:		Optional[Union[str, ChannelDimension]] = None     ,      **UpperCAmelCase			:		Optional[int]     ,      )  ->  np.ndarray:


       '''simple docstring'''

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






      def 				A__      ( self			:		Any     ,      UpperCAmelCase			:		ImageInput     ,      UpperCAmelCase			:		bool = None     ,      UpperCAmelCase			:		Dict[str, int] = None     ,      UpperCAmelCase			:		Union[str, Any]=None     ,      UpperCAmelCase			:		bool = None     ,      UpperCAmelCase			:		float = None     ,      UpperCAmelCase			:		bool = None     ,      UpperCAmelCase			:		Union[float, Iterable[float]] = None     ,      UpperCAmelCase			:		Union[float, Iterable[float]] = None     ,      UpperCAmelCase			:		bool = None     ,      UpperCAmelCase			:		Optional[str] = None     ,      UpperCAmelCase			:		Optional[str] = None     ,      UpperCAmelCase			:		Optional[Union[str, TensorType]] = None     ,      UpperCAmelCase			:		ChannelDimension = ChannelDimension.FIRST     ,      **UpperCAmelCase			:		List[str]     ,      )  ->  PIL.Image.Image:


       '''simple docstring'''

       lowercase					:   Optional[int]				  =do_resize if do_resize is not None else self.do_resize
       lowercase					:   Tuple				  =size if size is not None else self.size
       lowercase					:   Optional[int]				  =get_size_dict(UpperCAmelCase		)
       lowercase					:   List[str]				  =resample if resample is not None else self.resample
       lowercase					:   List[Any]				  =do_rescale if do_rescale is not None else self.do_rescale
       lowercase					:   List[Any]				  =rescale_factor if rescale_factor is not None else self.rescale_factor
       lowercase					:   Optional[int]				  =do_normalize if do_normalize is not None else self.do_normalize
       lowercase					:   List[Any]				  =image_mean if image_mean is not None else self.image_mean
       lowercase					:   Optional[int]				  =image_std if image_std is not None else self.image_std
       lowercase					:   Any				  =apply_ocr if apply_ocr is not None else self.apply_ocr
       lowercase					:   Any				  =ocr_lang if ocr_lang is not None else self.ocr_lang
       lowercase					:   Dict				  =tesseract_config if tesseract_config is not None else self.tesseract_config

       lowercase					:   str				  =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.
       lowercase					:   Tuple				  =[to_numpy_array(UpperCAmelCase		) for image in images]

       # Tesseract OCR to get words + normalized bounding boxes
       if apply_ocr:
        requires_backends(self     ,      '''pytesseract'''		)
        lowercase					:   int				  =[]
        lowercase					:   Tuple				  =[]
        for image in images:
         lowercase  ,		lowercase					:   Dict				  =apply_tesseract(UpperCAmelCase     ,      UpperCAmelCase     ,      UpperCAmelCase		)
         words_batch.append(UpperCAmelCase		)
         boxes_batch.append(UpperCAmelCase		)

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

       if do_rescale:
        lowercase					:   Tuple				  =[self.rescale(image=UpperCAmelCase     ,      scale=UpperCAmelCase		) for image in images]

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

       lowercase					:   Optional[Any]				  =[to_channel_dimension_format(UpperCAmelCase     ,      UpperCAmelCase		) for image in images]

       lowercase					:   Dict				  =BatchFeature(data={'''pixel_values''': images}     ,      tensor_type=UpperCAmelCase		)

       if apply_ocr:
        lowercase					:   int				  =words_batch
        lowercase					:   List[str]				  =boxes_batch
       return data



