
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCamelCase      =logging.get_logger(__name__)
lowerCamelCase      ={"vocab_file": "spiece.model"}

lowerCamelCase      ={
    "vocab_file": {
        "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/spiece.model",
        "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/spiece.model",
        "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model",
        "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model",
        "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/spiece.model",
        "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/spiece.model",
        "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model",
        "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model",
    }
}

lowerCamelCase      ={
    "albert-base-v1": 5_1_2,
    "albert-large-v1": 5_1_2,
    "albert-xlarge-v1": 5_1_2,
    "albert-xxlarge-v1": 5_1_2,
    "albert-base-v2": 5_1_2,
    "albert-large-v2": 5_1_2,
    "albert-xlarge-v2": 5_1_2,
    "albert-xxlarge-v2": 5_1_2,
}

lowerCamelCase      ="▁"




class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE_		   =  PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE_		   =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES






     def __init__(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE=True						,						__SCREAMING_SNAKE_CASE=True						,						__SCREAMING_SNAKE_CASE=False						,						__SCREAMING_SNAKE_CASE="[CLS]"						,						__SCREAMING_SNAKE_CASE="[SEP]"						,						__SCREAMING_SNAKE_CASE="<unk>"						,						__SCREAMING_SNAKE_CASE="[SEP]"						,						__SCREAMING_SNAKE_CASE="<pad>"						,						__SCREAMING_SNAKE_CASE="[CLS]"						,						__SCREAMING_SNAKE_CASE="[MASK]"						,						__SCREAMING_SNAKE_CASE = None						,						**__SCREAMING_SNAKE_CASE						,						)  ->   None:




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]					=				(
               AddedToken(__SCREAMING_SNAKE_CASE						,						lstrip=__SCREAMING_SNAKE_CASE						,						rstrip=__SCREAMING_SNAKE_CASE						,						normalized=__SCREAMING_SNAKE_CASE  )
               if isinstance(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )
               else mask_token
           )

           UpperCamelCase__    :   List[Any]					=				{} if sp_model_kwargs is None else sp_model_kwargs

           super().__init__(
               do_lower_case=__SCREAMING_SNAKE_CASE						,						remove_space=__SCREAMING_SNAKE_CASE						,						keep_accents=__SCREAMING_SNAKE_CASE						,						bos_token=__SCREAMING_SNAKE_CASE						,						eos_token=__SCREAMING_SNAKE_CASE						,						unk_token=__SCREAMING_SNAKE_CASE						,						sep_token=__SCREAMING_SNAKE_CASE						,						pad_token=__SCREAMING_SNAKE_CASE						,						cls_token=__SCREAMING_SNAKE_CASE						,						mask_token=__SCREAMING_SNAKE_CASE						,						sp_model_kwargs=self.sp_model_kwargs						,						**__SCREAMING_SNAKE_CASE						,						)

           UpperCamelCase__    :   int					=				do_lower_case
           UpperCamelCase__    :   int					=				remove_space
           UpperCamelCase__    :   Tuple					=				keep_accents
           UpperCamelCase__    :   List[Any]					=				vocab_file

           UpperCamelCase__    :   Optional[int]					=				spm.SentencePieceProcessor(**self.sp_model_kwargs  )
           self.sp_model.Load(__SCREAMING_SNAKE_CASE  )






     @property
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




           """simple docstring"""







           return len(self.sp_model  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




           """simple docstring"""







           UpperCamelCase__    :   Optional[Any]					=				{self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE  ): i for i in range(self.vocab_size  )}
           vocab.update(self.added_tokens_encoder  )
           return vocab






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




           """simple docstring"""







           UpperCamelCase__    :   Any					=				self.__dict__.copy()
           UpperCamelCase__    :   Union[str, Any]					=				None
           return state






     def __setstate__(							self						,						__SCREAMING_SNAKE_CASE  )  ->   str:




           """simple docstring"""







           UpperCamelCase__    :   int					=				d

           # for backward compatibility
           if not hasattr(self						,						'''sp_model_kwargs'''  ):
                 UpperCamelCase__    :   List[Any]					=				{}

           UpperCamelCase__    :   Optional[Any]					=				spm.SentencePieceProcessor(**self.sp_model_kwargs  )
           self.sp_model.Load(self.vocab_file  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE  )  ->   List[Any]:




           """simple docstring"""







           if self.remove_space:
                 UpperCamelCase__    :   int					=				''' '''.join(inputs.strip().split()  )
           else:
                 UpperCamelCase__    :   Optional[int]					=				inputs
           UpperCamelCase__    :   int					=				outputs.replace('''``'''						,						'''"'''  ).replace('''\'\''''						,						'''"'''  )

           if not self.keep_accents:
                 UpperCamelCase__    :   str					=				unicodedata.normalize('''NFKD'''						,						__SCREAMING_SNAKE_CASE  )
                 UpperCamelCase__    :   Optional[Any]					=				''''''.join([c for c in outputs if not unicodedata.combining(__SCREAMING_SNAKE_CASE  )]  )
           if self.do_lower_case:
                 UpperCamelCase__    :   Union[str, Any]					=				outputs.lower()

           return outputs






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE  )  ->   List[str]:




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]					=				self.preprocess_text(__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Union[str, Any]					=				self.sp_model.encode(__SCREAMING_SNAKE_CASE						,						out_type=__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   List[str]					=				[]
           for piece in pieces:
                 if len(__SCREAMING_SNAKE_CASE  ) > 1 and piece[-1] == str(''','''  ) and piece[-2].isdigit():
                       UpperCamelCase__    :   Tuple					=				self.sp_model.EncodeAsPieces(piece[:-1].replace(__SCREAMING_SNAKE_CASE						,						''''''  )  )
                       if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                             if len(cur_pieces[0]  ) == 1:
                                   UpperCamelCase__    :   Tuple					=				cur_pieces[1:]
                             else:
                                   UpperCamelCase__    :   List[Any]					=				cur_pieces[0][1:]
                       cur_pieces.append(piece[-1]  )
                       new_pieces.extend(__SCREAMING_SNAKE_CASE  )
                 else:
                       new_pieces.append(__SCREAMING_SNAKE_CASE  )

           return new_pieces






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE  )  ->   Tuple:




           """simple docstring"""







           return self.sp_model.PieceToId(__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE  )  ->   Optional[Any]:




           """simple docstring"""







           return self.sp_model.IdToPiece(__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE  )  ->   Any:




           """simple docstring"""







           UpperCamelCase__    :   Tuple					=				[]
           UpperCamelCase__    :   List[Any]					=				''''''
           UpperCamelCase__    :   str					=				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(__SCREAMING_SNAKE_CASE  ) + token
                       UpperCamelCase__    :   str					=				True
                       UpperCamelCase__    :   str					=				[]
                 else:
                       current_sub_tokens.append(__SCREAMING_SNAKE_CASE  )
                       UpperCamelCase__    :   Optional[Any]					=				False
           out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE  )
           return out_string.strip()






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = None  )  ->   List[int]:




           """simple docstring"""







           UpperCamelCase__    :   Optional[Any]					=				[self.sep_token_id]
           UpperCamelCase__    :   Tuple					=				[self.cls_token_id]
           if token_ids_a is None:
                 return cls + token_ids_a + sep
           return cls + token_ids_a + sep + token_ids_a + sep






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = False  )  ->   List[int]:




           """simple docstring"""







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

           if token_ids_a is not None:
                 return [1] + ([0] * len(__SCREAMING_SNAKE_CASE  )) + [1] + ([0] * len(__SCREAMING_SNAKE_CASE  )) + [1]
           return [1] + ([0] * len(__SCREAMING_SNAKE_CASE  )) + [1]






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = None  )  ->   List[int]:




           """simple docstring"""







           UpperCamelCase__    :   List[str]					=				[self.sep_token_id]
           UpperCamelCase__    :   int					=				[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  ) * [0] + len(token_ids_a + sep  ) * [1]






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = None  )  ->   Tuple[str]:




           """simple docstring"""







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

           if os.path.abspath(self.vocab_file  ) != os.path.abspath(__SCREAMING_SNAKE_CASE  ) and os.path.isfile(self.vocab_file  ):
                 copyfile(self.vocab_file						,						__SCREAMING_SNAKE_CASE  )
           elif not os.path.isfile(self.vocab_file  ):
                 with open(__SCREAMING_SNAKE_CASE						,						'''wb'''  ) as fi:
                       UpperCamelCase__    :   Dict					=				self.sp_model.serialized_model_proto()
                       fi.write(__SCREAMING_SNAKE_CASE  )

           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      =logging.get_logger(__name__)


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

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

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

      # finally, normalize the bounding boxes
      UpperCamelCase__    :   str					=				[]
      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 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  ['''pixel_values''']






     def __init__(							self						,						__SCREAMING_SNAKE_CASE = True						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR						,						__SCREAMING_SNAKE_CASE = True						,						__SCREAMING_SNAKE_CASE = 1 / 2_5_5						,						__SCREAMING_SNAKE_CASE = True						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = True						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = ""						,						**__SCREAMING_SNAKE_CASE						,						)  ->   None:




           """simple docstring"""







           super().__init__(**__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Optional[Any]					=				size if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
           UpperCamelCase__    :   Optional[int]					=				get_size_dict(__SCREAMING_SNAKE_CASE  )

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






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




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]					=				get_size_dict(__SCREAMING_SNAKE_CASE  )
           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()}'''  )
           UpperCamelCase__    :   int					=				(size['''height'''], size['''width'''])
           return resize(__SCREAMING_SNAKE_CASE						,						size=__SCREAMING_SNAKE_CASE						,						resample=__SCREAMING_SNAKE_CASE						,						data_format=__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )






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




           """simple docstring"""







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






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




           """simple docstring"""







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






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




           """simple docstring"""







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

           UpperCamelCase__    :   List[Any]					=				make_list_of_images(__SCREAMING_SNAKE_CASE  )

           if not valid_images(__SCREAMING_SNAKE_CASE  ):
                 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.
           UpperCamelCase__    :   List[Any]					=				[to_numpy_array(__SCREAMING_SNAKE_CASE  ) for image in images]

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

           if do_resize:
                 UpperCamelCase__    :   str					=				[self.resize(image=__SCREAMING_SNAKE_CASE						,						size=__SCREAMING_SNAKE_CASE						,						resample=__SCREAMING_SNAKE_CASE  ) for image in images]

           if do_rescale:
                 UpperCamelCase__    :   List[Any]					=				[self.rescale(image=__SCREAMING_SNAKE_CASE						,						scale=__SCREAMING_SNAKE_CASE  ) for image in images]

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

           UpperCamelCase__    :   int					=				[to_channel_dimension_format(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  ) for image in images]

           UpperCamelCase__    :   List[Any]					=				BatchFeature(data={'''pixel_values''': images}						,						tensor_type=__SCREAMING_SNAKE_CASE  )

           if apply_ocr:
                 UpperCamelCase__    :   str					=				words_batch
                 UpperCamelCase__    :   Union[str, Any]					=				boxes_batch
           return data


