


'''simple docstring'''






import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


__UpperCAmelCase       =							logging.get_logger(__name__)


__UpperCAmelCase       =							{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

# See all LED models at https://huggingface.co/models?filter=LED
__UpperCAmelCase       =							{
    '''vocab_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json''',
    },
}

__UpperCAmelCase       =							{
    '''allenai/led-base-16384''': 16_384,
}


@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def  _snake_case       (					)						->   int:
							lowerCAmelCase__	       =     (
							    list(range(ord('''!'''	)			,   ord('''~'''	) + 1	)	) + list(range(ord('''¡'''	)			,   ord('''¬'''	) + 1	)	) + list(range(ord('''®'''	)			,   ord('''ÿ'''	) + 1	)	)
							)
							lowerCAmelCase__	       =     bs[:]
							lowerCAmelCase__	       =     0
							for b in range(2**8	):
														if b not in bs:
																					bs.append(A	)
																					cs.append(2**8 + n	)
																					n += 1
							lowerCAmelCase__	       =     [chr(A	) for n in cs]
							return dict(zip(A			,   A	)	)


def  _snake_case       (					A	)						->   Optional[int]:
							lowerCAmelCase__	       =     set()
							lowerCAmelCase__	       =     word[0]
							for char in word[1:]:
														pairs.add((prev_char, char)	)
														lowerCAmelCase__	       =     char
							return pairs






class 		a__    (							a__  ):





							'''simple docstring'''






							lowercase__      :       Union[str, Any]     						=				VOCAB_FILES_NAMES
							lowercase__      :       List[Any]     						=				PRETRAINED_VOCAB_FILES_MAP
							lowercase__      :       Optional[int]     						=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase__      :       Optional[int]     						=				["input_ids", "attention_mask"]







							def __init__(		self ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_="replace" ,   lowerCamelCase_="<s>" ,   lowerCamelCase_="</s>" ,   lowerCamelCase_="</s>" ,   lowerCamelCase_="<s>" ,   lowerCamelCase_="<unk>" ,   lowerCamelCase_="<pad>" ,   lowerCamelCase_="<mask>" ,   lowerCamelCase_=False ,   **lowerCamelCase_ ,   )	->							Tuple:
														lowerCAmelCase__	       =     AddedToken(lowerCamelCase_ ,   lstrip=lowerCamelCase_ ,   rstrip=lowerCamelCase_       ) if isinstance(lowerCamelCase_ ,   lowerCamelCase_       ) else bos_token
														lowerCAmelCase__	       =     AddedToken(lowerCamelCase_ ,   lstrip=lowerCamelCase_ ,   rstrip=lowerCamelCase_       ) if isinstance(lowerCamelCase_ ,   lowerCamelCase_       ) else eos_token
														lowerCAmelCase__	       =     AddedToken(lowerCamelCase_ ,   lstrip=lowerCamelCase_ ,   rstrip=lowerCamelCase_       ) if isinstance(lowerCamelCase_ ,   lowerCamelCase_       ) else sep_token
														lowerCAmelCase__	       =     AddedToken(lowerCamelCase_ ,   lstrip=lowerCamelCase_ ,   rstrip=lowerCamelCase_       ) if isinstance(lowerCamelCase_ ,   lowerCamelCase_       ) else cls_token
														lowerCAmelCase__	       =     AddedToken(lowerCamelCase_ ,   lstrip=lowerCamelCase_ ,   rstrip=lowerCamelCase_       ) if isinstance(lowerCamelCase_ ,   lowerCamelCase_       ) else unk_token
														lowerCAmelCase__	       =     AddedToken(lowerCamelCase_ ,   lstrip=lowerCamelCase_ ,   rstrip=lowerCamelCase_       ) if isinstance(lowerCamelCase_ ,   lowerCamelCase_       ) else pad_token

														# Mask token behave like a normal word, i.e. include the space before it
														lowerCAmelCase__	       =     AddedToken(lowerCamelCase_ ,   lstrip=lowerCamelCase_ ,   rstrip=lowerCamelCase_       ) if isinstance(lowerCamelCase_ ,   lowerCamelCase_       ) else mask_token

														super().__init__(
														    errors=lowerCamelCase_ ,   bos_token=lowerCamelCase_ ,   eos_token=lowerCamelCase_ ,   unk_token=lowerCamelCase_ ,   sep_token=lowerCamelCase_ ,   cls_token=lowerCamelCase_ ,   pad_token=lowerCamelCase_ ,   mask_token=lowerCamelCase_ ,   add_prefix_space=lowerCamelCase_ ,   **lowerCamelCase_ ,   )

														with open(lowerCamelCase_ ,   encoding='''utf-8'''       ) as vocab_handle:
																					lowerCAmelCase__	       =     json.load(lowerCamelCase_       )
														lowerCAmelCase__	       =     {v: k for k, v in self.encoder.items()}
														lowerCAmelCase__	       =     errors  # how to handle errors in decoding
														lowerCAmelCase__	       =     bytes_to_unicode()
														lowerCAmelCase__	       =     {v: k for k, v in self.byte_encoder.items()}
														with open(lowerCamelCase_ ,   encoding='''utf-8'''       ) as merges_handle:
																					lowerCAmelCase__	       =     merges_handle.read().split('''\n'''       )[1:-1]
														lowerCAmelCase__	       =     [tuple(merge.split()       ) for merge in bpe_merges]
														lowerCAmelCase__	       =     dict(zip(lowerCamelCase_ ,   range(len(lowerCamelCase_       )       )       )       )
														lowerCAmelCase__	       =     {}
														lowerCAmelCase__	       =     add_prefix_space

														# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
														lowerCAmelCase__	       =     re.compile(r'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''       )







							@property
							# Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Dict:
														return len(self.encoder       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							str:
														return dict(self.encoder ,   **self.added_tokens_encoder       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							Dict:
														if token in self.cache:
																					return self.cache[token]
														lowerCAmelCase__	       =     tuple(lowerCamelCase_       )
														lowerCAmelCase__	       =     get_pairs(lowerCamelCase_       )

														if not pairs:
																					return token

														while True:
																					lowerCAmelCase__	       =     min(lowerCamelCase_ ,   key=lambda lowerCamelCase_       : self.bpe_ranks.get(lowerCamelCase_ ,   float('''inf'''       )       )       )
																					if bigram not in self.bpe_ranks:
																												break
																					lowerCAmelCase__       , lowerCAmelCase__	       =     bigram
																					lowerCAmelCase__	       =     []
																					lowerCAmelCase__	       =     0
																					while i < len(lowerCamelCase_       ):
																												try:
																																			lowerCAmelCase__	       =     word.index(lowerCamelCase_ ,   lowerCamelCase_       )
																												except ValueError:
																																			new_word.extend(word[i:]       )
																																			break
																												else:
																																			new_word.extend(word[i:j]       )
																																			lowerCAmelCase__	       =     j

																												if word[i] == first and i < len(lowerCamelCase_       ) - 1 and word[i + 1] == second:
																																			new_word.append(first + second       )
																																			i += 2
																												else:
																																			new_word.append(word[i]       )
																																			i += 1
																					lowerCAmelCase__	       =     tuple(lowerCamelCase_       )
																					lowerCAmelCase__	       =     new_word
																					if len(lowerCamelCase_       ) == 1:
																												break
																					else:
																												lowerCAmelCase__	       =     get_pairs(lowerCamelCase_       )
														lowerCAmelCase__	       =     ''' '''.join(lowerCamelCase_       )
														lowerCAmelCase__	       =     word
														return word







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							Optional[Any]:
														lowerCAmelCase__	       =     []
														for token in re.findall(self.pat ,   lowerCamelCase_       ):
																					lowerCAmelCase__	       =     ''''''.join(
																					    self.byte_encoder[b] for b in token.encode('''utf-8'''       )       )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
																					bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCamelCase_       ).split(''' '''       )       )
														return bpe_tokens







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							Tuple:
														return self.encoder.get(lowerCamelCase_ ,   self.encoder.get(self.unk_token       )       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							List[str]:
														return self.decoder.get(lowerCamelCase_       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							Optional[int]:
														lowerCAmelCase__	       =     ''''''.join(lowerCamelCase_       )
														lowerCAmelCase__	       =     bytearray([self.byte_decoder[c] for c in text]       ).decode('''utf-8''' ,   errors=self.errors       )
														return text







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ = None       )	->							Tuple[str]:
														if not os.path.isdir(lowerCamelCase_       ):
																					logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""       )
																					return
														lowerCAmelCase__	       =     os.path.join(
														    lowerCamelCase_ ,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']       )
														lowerCAmelCase__	       =     os.path.join(
														    lowerCamelCase_ ,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']       )

														with open(lowerCamelCase_ ,   '''w''' ,   encoding='''utf-8'''       ) as f:
																					f.write(json.dumps(self.encoder ,   indent=2 ,   sort_keys=lowerCamelCase_ ,   ensure_ascii=lowerCamelCase_       ) + '''\n'''       )

														lowerCAmelCase__	       =     0
														with open(lowerCamelCase_ ,   '''w''' ,   encoding='''utf-8'''       ) as writer:
																					writer.write('''#version: 0.2\n'''       )
																					for bpe_tokens, token_index in sorted(self.bpe_ranks.items() ,   key=lambda lowerCamelCase_       : kv[1]       ):
																												if index != token_index:
																																			logger.warning(
																																			    F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
																																			    ''' Please check that the tokenizer is not corrupted!'''       )
																																			lowerCAmelCase__	       =     token_index
																												writer.write(''' '''.join(lowerCamelCase_       ) + '''\n'''       )
																												index += 1

														return vocab_file, merge_file







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ = None       )	->							List[int]:
														if token_ids_a is None:
																					return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
														lowerCAmelCase__	       =     [self.cls_token_id]
														lowerCAmelCase__	       =     [self.sep_token_id]
														return cls + token_ids_a + sep + sep + token_ids_a + sep







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ = None ,   lowerCamelCase_ = False       )	->							List[int]:
														if already_has_special_tokens:
																					return super().get_special_tokens_mask(
																					    token_ids_a=lowerCamelCase_ ,   token_ids_a=lowerCamelCase_ ,   already_has_special_tokens=lowerCamelCase_       )

														if token_ids_a is None:
																					return [1] + ([0] * len(lowerCamelCase_       )) + [1]
														return [1] + ([0] * len(lowerCamelCase_       )) + [1, 1] + ([0] * len(lowerCamelCase_       )) + [1]







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ = None       )	->							List[int]:
														lowerCAmelCase__	       =     [self.sep_token_id]
														lowerCAmelCase__	       =     [self.cls_token_id]

														if token_ids_a is None:
																					return len(cls + token_ids_a + sep       ) * [0]
														return len(cls + token_ids_a + sep + sep + token_ids_a + sep       ) * [0]







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_=False ,   **lowerCamelCase_       )	->							Optional[int]:
														lowerCAmelCase__	       =     kwargs.pop('''add_prefix_space''' ,   self.add_prefix_space       )
														if (is_split_into_words or add_prefix_space) and (len(lowerCamelCase_       ) > 0 and not text[0].isspace()):
																					lowerCAmelCase__	       =     ''' ''' + text
														return (text, kwargs)







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ = None ,   lowerCamelCase_ = PaddingStrategy.DO_NOT_PAD ,   lowerCamelCase_ = None ,   lowerCamelCase_ = None ,   )	->							dict:
														lowerCAmelCase__	       =     super()._pad(
														    encoded_inputs=lowerCamelCase_ ,   max_length=lowerCamelCase_ ,   padding_strategy=lowerCamelCase_ ,   pad_to_multiple_of=lowerCamelCase_ ,   return_attention_mask=lowerCamelCase_ ,   )

														# Load from model defaults
														if return_attention_mask is None:
																					lowerCAmelCase__	       =     '''attention_mask''' in self.model_input_names

														if return_attention_mask and "global_attention_mask" in encoded_inputs:
																					lowerCAmelCase__	       =     encoded_inputs[self.model_input_names[0]]
																					# `global_attention_mask` need to have the same length as other (sequential) inputs.
																					lowerCAmelCase__	       =     len(encoded_inputs['''global_attention_mask''']       ) != len(lowerCamelCase_       )

																					if needs_to_be_padded:
																												lowerCAmelCase__	       =     len(lowerCamelCase_       ) - len(encoded_inputs['''global_attention_mask''']       )

																												if self.padding_side == "right":
																																			# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
																																			lowerCAmelCase__	       =     (
																																			    encoded_inputs['''global_attention_mask'''] + [-1] * difference
																																			)
																												elif self.padding_side == "left":
																																			lowerCAmelCase__	       =     [-1] * difference + encoded_inputs[
																																			    '''global_attention_mask'''
																																			]
																												else:
																																			raise ValueError('''Invalid padding strategy:''' + str(self.padding_side       )       )

														return encoded_inputs



'''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

__UpperCAmelCase       =							logging.get_logger(__name__)


def  _snake_case       (					A			,   A			,   A	)						->   Optional[Any]:
							return [
							    int(1000 * (box[0] / width)	),
							    int(1000 * (box[1] / height)	),
							    int(1000 * (box[2] / width)	),
							    int(1000 * (box[3] / height)	),
							]


def  _snake_case       (					A			,   A			,   A	)						->   Union[str, Any]:
							lowerCAmelCase__	       =     to_pil_image(A	)
							lowerCAmelCase__       , lowerCAmelCase__	       =     pil_image.size
							lowerCAmelCase__	       =     pytesseract.image_to_data(A			,   lang=A			,   output_type='''dict'''			,   config=A	)
							lowerCAmelCase__       , lowerCAmelCase__       , lowerCAmelCase__       , lowerCAmelCase__       , lowerCAmelCase__	       =     data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

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

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

							# finally, normalize the bounding boxes
							lowerCAmelCase__	       =     []
							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 		a__    (							a__  ):





							'''simple docstring'''






							lowercase__      :       Any     						=				["pixel_values"]







							def __init__(		self ,   lowerCamelCase_ = True ,   lowerCamelCase_ = None ,   lowerCamelCase_ = PILImageResampling.BILINEAR ,   lowerCamelCase_ = True ,   lowerCamelCase_ = 1 / 2_55 ,   lowerCamelCase_ = True ,   lowerCamelCase_ = None ,   lowerCamelCase_ = None ,   lowerCamelCase_ = True ,   lowerCamelCase_ = None ,   lowerCamelCase_ = "" ,   **lowerCamelCase_ ,   )	->							None:
														super().__init__(**lowerCamelCase_       )
														lowerCAmelCase__	       =     size if size is not None else {'''height''': 2_24, '''width''': 2_24}
														lowerCAmelCase__	       =     get_size_dict(lowerCamelCase_       )

														lowerCAmelCase__	       =     do_resize
														lowerCAmelCase__	       =     size
														lowerCAmelCase__	       =     resample
														lowerCAmelCase__	       =     do_rescale
														lowerCAmelCase__	       =     rescale_value
														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__	       =     apply_ocr
														lowerCAmelCase__	       =     ocr_lang
														lowerCAmelCase__	       =     tesseract_config







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ = PILImageResampling.BILINEAR ,   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 contain the keys 'height' and 'width'. Got {size.keys()}"""       )
														lowerCAmelCase__	       =     (size['''height'''], size['''width'''])
														return resize(lowerCamelCase_ ,   size=lowerCamelCase_ ,   resample=lowerCamelCase_ ,   data_format=lowerCamelCase_ ,   **lowerCamelCase_       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ = None ,   **lowerCamelCase_ ,   )	->							np.ndarray:
														return rescale(lowerCamelCase_ ,   scale=lowerCamelCase_ ,   data_format=lowerCamelCase_ ,   **lowerCamelCase_       )







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







							def   __SCREAMING_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_ = ChannelDimension.FIRST ,   **lowerCamelCase_ ,   )	->							PIL.Image.Image:
														lowerCAmelCase__	       =     do_resize if do_resize is not None else self.do_resize
														lowerCAmelCase__	       =     size if size is not None else self.size
														lowerCAmelCase__	       =     get_size_dict(lowerCamelCase_       )
														lowerCAmelCase__	       =     resample if resample is not None else self.resample
														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__	       =     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__	       =     apply_ocr if apply_ocr is not None else self.apply_ocr
														lowerCAmelCase__	       =     ocr_lang if ocr_lang is not None else self.ocr_lang
														lowerCAmelCase__	       =     tesseract_config if tesseract_config is not None else self.tesseract_config

														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:
																					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.
														lowerCAmelCase__	       =     [to_numpy_array(lowerCamelCase_       ) for image in images]

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

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

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

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

														lowerCAmelCase__	       =     [to_channel_dimension_format(lowerCamelCase_ ,   lowerCamelCase_       ) for image in images]

														lowerCAmelCase__	       =     BatchFeature(data={'''pixel_values''': images} ,   tensor_type=lowerCamelCase_       )

														if apply_ocr:
																					lowerCAmelCase__	       =     words_batch
																					lowerCAmelCase__	       =     boxes_batch
														return data