import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
							from ...tokenization_utils_base import TextInput
from ...utils import logging


_lowerCamelCase   :				int     			=						logging.get_logger(__name__)

_lowerCamelCase   :				Dict     			=						{'''vocab_file''': '''spiece.model'''}

_lowerCamelCase   :				Optional[int]     			=						{
    '''vocab_file''': {
        '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/spiece.model''',
        '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/spiece.model''',
        '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/spiece.model''',
        '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/spiece.model''',
        '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/spiece.model''',
    }
}


# TODO(PVP) - this should be removed in Transformers v5
_lowerCamelCase   :				int     			=						{
    '''t5-small''': 5_12,
    '''t5-base''': 5_12,
    '''t5-large''': 5_12,
    '''t5-3b''': 5_12,
    '''t5-11b''': 5_12,
}

_lowerCamelCase   :				Any     			=						'''▁'''


class   lowercase      (       a       ):
							lowercase__  :    Dict									=							VOCAB_FILES_NAMES
							lowercase__  :    Tuple									=							PRETRAINED_VOCAB_FILES_MAP
							lowercase__  :    Tuple									=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase__  :    Optional[Any]									=							["""input_ids""", """attention_mask"""]




							def __init__(  self				:   Dict		,							_UpperCamelCase				:   Optional[Any]		,							_UpperCamelCase				:   Tuple="</s>"		,							_UpperCamelCase				:   List[str]="<unk>"		,							_UpperCamelCase				:   Dict="<pad>"		,							_UpperCamelCase				:   Dict=100		,							_UpperCamelCase				:   List[Any]=None		,							_UpperCamelCase				:   Optional[Dict[str, Any]] = None		,							_UpperCamelCase				:   int=True		,							**_UpperCamelCase				:   str		,							)   ->			None:



												'''simple docstring'''

												if extra_ids > 0 and additional_special_tokens is None:
																	SCREAMING_SNAKE_CASE           =    [F"<extra_id_{i}>" for i in range(_UpperCamelCase )]
												elif extra_ids > 0 and additional_special_tokens is not None:
																	# Check that we have the right number of extra_id special tokens
																	SCREAMING_SNAKE_CASE           =    len(set(filter(lambda _UpperCamelCase : bool("extra_id" in str(_UpperCamelCase ) )		,							_UpperCamelCase ) ) )
																	if extra_tokens != extra_ids:
																						raise ValueError(
																						    F"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"
																						    " provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"
																						    " tokens" )
												if legacy:
																	logger.warning_once(
																	    F"You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to"
																	    " read the related pull request available at https://github.com/huggingface/transformers/pull/24565" )

												SCREAMING_SNAKE_CASE           =    legacy
												SCREAMING_SNAKE_CASE           =    {} if sp_model_kwargs is None else sp_model_kwargs

												super().__init__(
												    eos_token=_UpperCamelCase		,							unk_token=_UpperCamelCase		,							pad_token=_UpperCamelCase		,							extra_ids=_UpperCamelCase		,							additional_special_tokens=_UpperCamelCase		,							sp_model_kwargs=self.sp_model_kwargs		,							legacy=_UpperCamelCase		,							**_UpperCamelCase		,							)

												SCREAMING_SNAKE_CASE           =    vocab_file
												SCREAMING_SNAKE_CASE           =    extra_ids

												SCREAMING_SNAKE_CASE           =    spm.SentencePieceProcessor(**self.sp_model_kwargs )
												self.sp_model.Load(_UpperCamelCase )




							@staticmethod
							def 		__snake_case(  _UpperCamelCase				:   str		,							_UpperCamelCase				:   Union[str, Any]		,							_UpperCamelCase				:   Tuple )   ->			Optional[int]:



												'''simple docstring'''

												if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
																	SCREAMING_SNAKE_CASE           =    TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
																	if init_max_model_length is not None and init_max_model_length != max_model_length:
																						return init_max_model_length
																	elif init_max_model_length is None:
																						warnings.warn(
																						    "This tokenizer was incorrectly instantiated with a model max length of"
																						    F" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"
																						    " behavior is kept to avoid breaking backwards compatibility when padding/encoding with"
																						    " `truncation is True`.\n- Be aware that you SHOULD NOT rely on"
																						    F" {pretrained_model_name_or_path} automatically truncating your input to"
																						    F" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"
																						    F" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"
																						    " `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"
																						    " instantiate this tokenizer with `model_max_length` set to your preferred value."		,							_UpperCamelCase		,							)

												return max_model_length




							@property
							def 		__snake_case(  self				:   str )   ->			Optional[Any]:



												'''simple docstring'''

												return self.sp_model.get_piece_size() + self._extra_ids




							def 		__snake_case(  self				:   Any )   ->			Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    {self.convert_ids_to_tokens(_UpperCamelCase ): i for i in range(self.vocab_size )}
												vocab.update(self.added_tokens_encoder )
												return vocab




							def 		__snake_case(  self				:   List[Any]		,							_UpperCamelCase				:   List[int]		,							_UpperCamelCase				:   Optional[List[int]] = None		,							_UpperCamelCase				:   bool = False )   ->			List[int]:



												'''simple docstring'''

												if already_has_special_tokens:
																	return super().get_special_tokens_mask(
																	    token_ids_a=_UpperCamelCase		,							token_ids_a=_UpperCamelCase		,							already_has_special_tokens=_UpperCamelCase )

												# normal case: some special tokens
												if token_ids_a is None:
																	return ([0] * len(_UpperCamelCase )) + [1]
												return ([0] * len(_UpperCamelCase )) + [1] + ([0] * len(_UpperCamelCase )) + [1]




							def 		__snake_case(  self				:   str )   ->			Any:



												'''simple docstring'''

												return list(
												    set(filter(lambda _UpperCamelCase : bool(re.search(R"<extra_id_\d+>"		,							_UpperCamelCase ) ) is not None		,							self.additional_special_tokens ) ) )




							def 		__snake_case(  self				:   Dict )   ->			List[Any]:



												'''simple docstring'''

												return [self._convert_token_to_id(_UpperCamelCase ) for token in self.get_sentinel_tokens()]




							def 		__snake_case(  self				:   Optional[Any]		,							_UpperCamelCase				:   List[int] )   ->			List[int]:



												'''simple docstring'''

												if len(_UpperCamelCase ) > 0 and token_ids[-1] == self.eos_token_id:
																	warnings.warn(
																	    F"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"
																	    " eos tokens being added." )
																	return token_ids
												else:
																	return token_ids + [self.eos_token_id]




							def 		__snake_case(  self				:   int		,							_UpperCamelCase				:   List[int]		,							_UpperCamelCase				:   Optional[List[int]] = None )   ->			List[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    [self.eos_token_id]

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




							def 		__snake_case(  self				:   int		,							_UpperCamelCase				:   List[int]		,							_UpperCamelCase				:   Optional[List[int]] = None )   ->			List[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self._add_eos_if_not_present(_UpperCamelCase )
												if token_ids_a is None:
																	return token_ids_a
												else:
																	SCREAMING_SNAKE_CASE           =    self._add_eos_if_not_present(_UpperCamelCase )
																	return token_ids_a + token_ids_a




							def __getstate__(  self				:   Any )   ->			List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.__dict__.copy()
												SCREAMING_SNAKE_CASE           =    None
												return state




							def __setstate__(  self				:   Optional[Any]		,							_UpperCamelCase				:   Dict )   ->			Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    d

												# for backward compatibility
												if not hasattr(self		,							"sp_model_kwargs" ):
																	SCREAMING_SNAKE_CASE           =    {}

												SCREAMING_SNAKE_CASE           =    spm.SentencePieceProcessor(**self.sp_model_kwargs )
												self.sp_model.Load(self.vocab_file )




							def 		__snake_case(  self				:   str		,							_UpperCamelCase				:   "TextInput"		,							**_UpperCamelCase				:   List[str] )   ->			List[str]:



												'''simple docstring'''

												if not self.legacy:
																	SCREAMING_SNAKE_CASE           =    SPIECE_UNDERLINE + text.replace(_UpperCamelCase		,							" " )
												return super().tokenize(_UpperCamelCase		,							**_UpperCamelCase )




							def 		__snake_case(  self				:   List[str]		,							_UpperCamelCase				:   List[Any]		,							**_UpperCamelCase				:   Any )   ->			List[str]:



												'''simple docstring'''

												if not self.legacy:
																	SCREAMING_SNAKE_CASE           =    text.startswith(_UpperCamelCase )
																	if is_first:
																						SCREAMING_SNAKE_CASE           =    text[1:]

												SCREAMING_SNAKE_CASE           =    self.sp_model.encode(_UpperCamelCase		,							out_type=_UpperCamelCase )

												if not self.legacy and not is_first and not text.startswith(" " ) and tokens[0].startswith(_UpperCamelCase ):
																	SCREAMING_SNAKE_CASE           =    ([tokens[0][1:]] if len(tokens[0] ) > 1 else []) + tokens[1:]
												return tokens




							def 		__snake_case(  self				:   Tuple		,							_UpperCamelCase				:   Optional[int] )   ->			Tuple:



												'''simple docstring'''

												if token.startswith("<extra_id_" ):
																	SCREAMING_SNAKE_CASE           =    re.match(R"<extra_id_(\d+)>"		,							_UpperCamelCase )
																	SCREAMING_SNAKE_CASE           =    int(match.group(1 ) )
																	return self.vocab_size - num - 1
												return self.sp_model.piece_to_id(_UpperCamelCase )




							def 		__snake_case(  self				:   Optional[Any]		,							_UpperCamelCase				:   List[Any] )   ->			List[str]:



												'''simple docstring'''

												if index < self.sp_model.get_piece_size():
																	SCREAMING_SNAKE_CASE           =    self.sp_model.IdToPiece(_UpperCamelCase )
												else:
																	SCREAMING_SNAKE_CASE           =    F"<extra_id_{self.vocab_size - 1 - index}>"
												return token




							def 		__snake_case(  self				:   Optional[Any]		,							_UpperCamelCase				:   Any )   ->			str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    []
												SCREAMING_SNAKE_CASE           =    ""
												SCREAMING_SNAKE_CASE           =    False
												for token in tokens:
																	# make sure that special tokens are not decoded using sentencepiece model
																	if token in self.all_special_tokens:
																						if not prev_is_special:
																											out_string += " "
																						out_string += self.sp_model.decode(_UpperCamelCase ) + token
																						SCREAMING_SNAKE_CASE           =    True
																						SCREAMING_SNAKE_CASE           =    []
																	else:
																						current_sub_tokens.append(_UpperCamelCase )
																						SCREAMING_SNAKE_CASE           =    False
												out_string += self.sp_model.decode(_UpperCamelCase )
												return out_string.strip()





							def 		__snake_case(  self				:   int		,							_UpperCamelCase				:   str		,							_UpperCamelCase				:   Optional[str] = None )   ->			Tuple[str]:



												'''simple docstring'''

												if not os.path.isdir(_UpperCamelCase ):
																	logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
																	return
												SCREAMING_SNAKE_CASE           =    os.path.join(
												    _UpperCamelCase		,							(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

												if os.path.abspath(self.vocab_file ) != os.path.abspath(_UpperCamelCase ) and os.path.isfile(self.vocab_file ):
																	copyfile(self.vocab_file		,							_UpperCamelCase )
												elif not os.path.isfile(self.vocab_file ):
																	with open(_UpperCamelCase		,							"wb" ) as fi:
																						SCREAMING_SNAKE_CASE           =    self.sp_model.serialized_model_proto()
																						fi.write(_UpperCamelCase )

												return (out_vocab_file,)





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

_lowerCamelCase   :				str     			=						logging.get_logger(__name__)





def 		__lowerCamelCase							(UpperCAmelCase__       :							Any ,	UpperCAmelCase__       :							Optional[int] ,	UpperCAmelCase__       :							List[str]					):
					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							(UpperCAmelCase__       :							np.ndarray ,	UpperCAmelCase__       :							Optional[str] ,	UpperCAmelCase__       :							Optional[str]					):
					SCREAMING_SNAKE_CASE           =    to_pil_image(UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    pil_image.size
					SCREAMING_SNAKE_CASE           =    pytesseract.image_to_data(UpperCAmelCase__ ,	lang=UpperCAmelCase__ ,	output_type="dict" ,	config=UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    data["text"], data["left"], data["top"], data["width"], data["height"]

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

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

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

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

					return words, normalized_boxes


class   lowercase      (       a       ):
							lowercase__  :    Optional[int]									=							["""pixel_values"""]




							def __init__(  self				:   int		,							_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				:   Union[str, Any]		,							)   ->			None:



												'''simple docstring'''

												super().__init__(**_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    size if size is not None else {"height": 224, "width": 224}
												SCREAMING_SNAKE_CASE           =    get_size_dict(_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    do_resize
												SCREAMING_SNAKE_CASE           =    size
												SCREAMING_SNAKE_CASE           =    resample
												SCREAMING_SNAKE_CASE           =    do_rescale
												SCREAMING_SNAKE_CASE           =    rescale_value
												SCREAMING_SNAKE_CASE           =    do_normalize
												SCREAMING_SNAKE_CASE           =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
												SCREAMING_SNAKE_CASE           =    image_std if image_std is not None else IMAGENET_STANDARD_STD
												SCREAMING_SNAKE_CASE           =    apply_ocr
												SCREAMING_SNAKE_CASE           =    ocr_lang
												SCREAMING_SNAKE_CASE           =    tesseract_config




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



												'''simple docstring'''

												SCREAMING_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()}" )
												SCREAMING_SNAKE_CASE           =    (size["height"], size["width"])
												return resize(_UpperCamelCase		,							size=_UpperCamelCase		,							resample=_UpperCamelCase		,							data_format=_UpperCamelCase		,							**_UpperCamelCase )




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



												'''simple docstring'''

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




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



												'''simple docstring'''

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





							def 		__snake_case(  self				:   Tuple		,							_UpperCamelCase				:   ImageInput		,							_UpperCamelCase				:   bool = None		,							_UpperCamelCase				:   Dict[str, int] = None		,							_UpperCamelCase				:   Optional[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[Any]		,							)   ->			PIL.Image.Image:



												'''simple docstring'''

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

												SCREAMING_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.
												SCREAMING_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" )
																	SCREAMING_SNAKE_CASE           =    []
																	SCREAMING_SNAKE_CASE           =    []
																	for image in images:
																						SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    apply_tesseract(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase )
																						words_batch.append(_UpperCamelCase )
																						boxes_batch.append(_UpperCamelCase )

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

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

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

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

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

												if apply_ocr:
																	SCREAMING_SNAKE_CASE           =    words_batch
																	SCREAMING_SNAKE_CASE           =    boxes_batch
												return data




