



"""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

lowercase__		:				Tuple          =	logging.get_logger(__name__)

def 				UpperCamelCase_    (    lowerCAmelCase__      :				List[str]    ,   lowerCAmelCase__      :				List[Any]    ,   lowerCAmelCase__      :				str						)						->   int:

  """simple docstring"""




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

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Any    ,   lowerCAmelCase__      :				Tuple    ,   lowerCAmelCase__      :				Optional[Any]						)						->   Any:

  """simple docstring"""




  lowerCAmelCase_   :				List[Any]								= to_pil_image(lowerCamelCase__						)
  lowerCAmelCase_   :				int								= pil_image.size
  lowerCAmelCase_   :				Optional[Any]								= pytesseract.image_to_data(lowerCamelCase__    ,   lang=lowerCamelCase__    ,   output_type='dict'    ,   config=lowerCamelCase__						)
  lowerCAmelCase_   :				Any								= data["""text"""], data["""left"""], data["""top"""], data["""width"""], data["""height"""]

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

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

  # finally, normalize the bounding boxes
  lowerCAmelCase_   :				List[str]								= []
  for box in actual_boxes:
    normalized_boxes.append(normalize_box(lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__						)						)

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

  return words, normalized_boxes







class       UpperCamelCase__   (	__UpperCAmelCase  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     ['pixel_values']



      def __init__(      self				:      Optional[int]    ,			SCREAMING_SNAKE_CASE_				:      int = True    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any] = None    ,			SCREAMING_SNAKE_CASE_				:      str = PILImageResampling.BILINEAR    ,			SCREAMING_SNAKE_CASE_				:      List[str] = True    ,			SCREAMING_SNAKE_CASE_				:      Tuple = 1 / 2_5_5    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any] = True    ,			SCREAMING_SNAKE_CASE_				:      Any = None    ,			SCREAMING_SNAKE_CASE_				:      Any = None    ,			SCREAMING_SNAKE_CASE_				:      List[Any] = True    ,			SCREAMING_SNAKE_CASE_				:      List[str] = None    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any] = ""    ,			**SCREAMING_SNAKE_CASE_				:      Dict    ,			):
        super().__init__(**SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				Optional[Any]								= size if size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
        lowerCAmelCase_   :				int								= get_size_dict(SCREAMING_SNAKE_CASE_       )

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



      def      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, Any]    ,			SCREAMING_SNAKE_CASE_				:      Tuple    ,			SCREAMING_SNAKE_CASE_				:      int    ,			SCREAMING_SNAKE_CASE_				:      Tuple = PILImageResampling.BILINEAR    ,			SCREAMING_SNAKE_CASE_				:      Dict = None    ,			**SCREAMING_SNAKE_CASE_				:      Any    ,			):
        lowerCAmelCase_   :				int								= 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()}"       )
        lowerCAmelCase_   :				Union[str, Any]								= (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				:      List[Any]    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]    ,			SCREAMING_SNAKE_CASE_				:      str = None    ,			**SCREAMING_SNAKE_CASE_				:      Any    ,			):
        return rescale(SCREAMING_SNAKE_CASE_    ,			scale=SCREAMING_SNAKE_CASE_    ,			data_format=SCREAMING_SNAKE_CASE_    ,			**SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]    ,			SCREAMING_SNAKE_CASE_				:      str    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]    ,			SCREAMING_SNAKE_CASE_				:      List[str]    ,			SCREAMING_SNAKE_CASE_				:      str = None    ,			**SCREAMING_SNAKE_CASE_				:      Any    ,			):
        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				:      Optional[Any]    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any] = None    ,			SCREAMING_SNAKE_CASE_				:      Tuple = None    ,			SCREAMING_SNAKE_CASE_				:      int=None    ,			SCREAMING_SNAKE_CASE_				:      int = None    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any] = None    ,			SCREAMING_SNAKE_CASE_				:      Optional[int] = None    ,			SCREAMING_SNAKE_CASE_				:      List[str] = None    ,			SCREAMING_SNAKE_CASE_				:      List[str] = None    ,			SCREAMING_SNAKE_CASE_				:      Optional[int] = None    ,			SCREAMING_SNAKE_CASE_				:      int = None    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any] = None    ,			SCREAMING_SNAKE_CASE_				:      int = None    ,			SCREAMING_SNAKE_CASE_				:      Any = ChannelDimension.FIRST    ,			**SCREAMING_SNAKE_CASE_				:      str    ,			):
        lowerCAmelCase_   :				Dict								= do_resize if do_resize is not None else self.do_resize
        lowerCAmelCase_   :				Dict								= size if size is not None else self.size
        lowerCAmelCase_   :				Union[str, Any]								= get_size_dict(SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				List[Any]								= resample if resample is not None else self.resample
        lowerCAmelCase_   :				List[Any]								= do_rescale if do_rescale is not None else self.do_rescale
        lowerCAmelCase_   :				Union[str, Any]								= rescale_factor if rescale_factor is not None else self.rescale_factor
        lowerCAmelCase_   :				str								= do_normalize if do_normalize is not None else self.do_normalize
        lowerCAmelCase_   :				Optional[int]								= image_mean if image_mean is not None else self.image_mean
        lowerCAmelCase_   :				Any								= image_std if image_std is not None else self.image_std
        lowerCAmelCase_   :				Optional[int]								= apply_ocr if apply_ocr is not None else self.apply_ocr
        lowerCAmelCase_   :				Dict								= ocr_lang if ocr_lang is not None else self.ocr_lang
        lowerCAmelCase_   :				Optional[int]								= tesseract_config if tesseract_config is not None else self.tesseract_config

        lowerCAmelCase_   :				int								= 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.
        lowerCAmelCase_   :				int								= [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'       )
          lowerCAmelCase_   :				Optional[int]								= []
          lowerCAmelCase_   :				Dict								= []
          for image in images:
            lowerCAmelCase_   :				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:
          lowerCAmelCase_   :				int								= [self.resize(image=SCREAMING_SNAKE_CASE_    ,			size=SCREAMING_SNAKE_CASE_    ,			resample=SCREAMING_SNAKE_CASE_       ) for image in images]

        if do_rescale:
          lowerCAmelCase_   :				Optional[int]								= [self.rescale(image=SCREAMING_SNAKE_CASE_    ,			scale=SCREAMING_SNAKE_CASE_       ) for image in images]

        if do_normalize:
          lowerCAmelCase_   :				Optional[Any]								= [self.normalize(image=SCREAMING_SNAKE_CASE_    ,			mean=SCREAMING_SNAKE_CASE_    ,			std=SCREAMING_SNAKE_CASE_       ) for image in images]

        lowerCAmelCase_   :				int								= [to_channel_dimension_format(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       ) for image in images]

        lowerCAmelCase_   :				str								= BatchFeature(data={'pixel_values': images}    ,			tensor_type=SCREAMING_SNAKE_CASE_       )

        if apply_ocr:
          lowerCAmelCase_   :				Tuple								= words_batch
          lowerCAmelCase_   :				int								= boxes_batch
        return data










'''simple docstring'''




import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save



def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__=10_24						,      lowerCamelCase__=10_24						,      lowerCamelCase__=False						,      **lowerCamelCase__    ):
			'''simple docstring'''





			A_	:						Any	=     AutoTokenizer.from_pretrained(lowerCamelCase__    )
			A_	:						Union[str, Any]	=     SeqaSeqDataset(lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      type_path="""train"""						,      **lowerCamelCase__    )
			A_	:						Optional[Any]	=     tok.pad_token_id

			def get_lens(lowerCamelCase__    ):
						A_	:						int	=     tqdm(
						    DataLoader(lowerCamelCase__						,      batch_size=5_12						,      num_workers=8						,      shuffle=lowerCamelCase__						,      collate_fn=ds.collate_fn    )						,      desc=str(ds.len_file    )						,      )
						A_	:						int	=     []
						for batch in dl:
									A_	:						str	=     batch["""input_ids"""].ne(lowerCamelCase__    ).sum(1    ).tolist()
									A_	:						Tuple	=     batch["""labels"""].ne(lowerCamelCase__    ).sum(1    ).tolist()
									if consider_target:
												for src, tgt in zip(lowerCamelCase__						,      lowerCamelCase__    ):
															max_lens.append(max(lowerCamelCase__						,      lowerCamelCase__    )    )
									else:
												max_lens.extend(lowerCamelCase__    )
						return max_lens

			A_	:						str	=     get_lens(lowerCamelCase__    )
			A_	:						Dict	=     SeqaSeqDataset(lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      type_path="""val"""						,      **lowerCamelCase__    )
			A_	:						List[Any]	=     get_lens(lowerCamelCase__    )
			pickle_save(lowerCamelCase__						,      train_ds.len_file    )
			pickle_save(lowerCamelCase__						,      val_ds.len_file    )


if __name__ == "__main__":
						fire.Fire(save_len_file)