"""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     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase		):







  """simple docstring"""
  return [
      int(10_00 * (box[0] / width)		),
      int(10_00 * (box[1] / height)		),
      int(10_00 * (box[2] / width)		),
      int(10_00 * (box[3] / height)		),
  ]






def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      to_pil_image(lowercase		)
  _UpperCAmelCase ,				_UpperCAmelCase						 =      pil_image.size
  _UpperCAmelCase						 =      pytesseract.image_to_data(lowercase   ,lang=lowercase   ,output_type="""dict"""   ,config=lowercase		)
  _UpperCAmelCase ,				_UpperCAmelCase ,				_UpperCAmelCase ,				_UpperCAmelCase ,				_UpperCAmelCase						 =      data["""text"""], data["""left"""], data["""top"""], data["""width"""], data["""height"""]

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

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

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

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

  return words, normalized_boxes


class        a				(		lowerCAmelCase_   ):
    _snake_case     :  str     							=							['pixel_values']


    def __init__(					self	:  List[str]   ,				__lowerCAmelCase	:  bool = True   ,				__lowerCAmelCase	:  Dict[str, int] = None   ,				__lowerCAmelCase	:  PILImageResampling = PILImageResampling.BILINEAR   ,				__lowerCAmelCase	:  bool = True   ,				__lowerCAmelCase	:  float = 1 / 255   ,				__lowerCAmelCase	:  bool = True   ,				__lowerCAmelCase	:  Union[float, Iterable[float]] = None   ,				__lowerCAmelCase	:  Union[float, Iterable[float]] = None   ,				__lowerCAmelCase	:  bool = True   ,				__lowerCAmelCase	:  Optional[str] = None   ,				__lowerCAmelCase	:  Optional[str] = ""   ,				**__lowerCAmelCase	:  List[Any]   ,				):
      super().__init__(**__lowerCAmelCase					)
      _UpperCAmelCase						 =      size if size is not None else {"""height""": 224, """width""": 224}
      _UpperCAmelCase						 =      get_size_dict(__lowerCAmelCase					)

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


    def      lowerCAmelCase_  (					self	:  Tuple   ,				__lowerCAmelCase	:  np.ndarray   ,				__lowerCAmelCase	:  Dict[str, int]   ,				__lowerCAmelCase	:  PILImageResampling = PILImageResampling.BILINEAR   ,				__lowerCAmelCase	:  Optional[Union[str, ChannelDimension]] = None   ,				**__lowerCAmelCase	:  Dict   ,				):
      _UpperCAmelCase						 =      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()}'''					)
      _UpperCAmelCase						 =      (size["""height"""], size["""width"""])
      return resize(__lowerCAmelCase   ,				size=__lowerCAmelCase   ,				resample=__lowerCAmelCase   ,				data_format=__lowerCAmelCase   ,				**__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  Optional[int]   ,				__lowerCAmelCase	:  np.ndarray   ,				__lowerCAmelCase	:  Union[int, float]   ,				__lowerCAmelCase	:  Optional[Union[str, ChannelDimension]] = None   ,				**__lowerCAmelCase	:  Any   ,				):
      return rescale(__lowerCAmelCase   ,				scale=__lowerCAmelCase   ,				data_format=__lowerCAmelCase   ,				**__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  Union[str, Any]   ,				__lowerCAmelCase	:  np.ndarray   ,				__lowerCAmelCase	:  Union[float, Iterable[float]]   ,				__lowerCAmelCase	:  Union[float, Iterable[float]]   ,				__lowerCAmelCase	:  Optional[Union[str, ChannelDimension]] = None   ,				**__lowerCAmelCase	:  Any   ,				):
      return normalize(__lowerCAmelCase   ,				mean=__lowerCAmelCase   ,				std=__lowerCAmelCase   ,				data_format=__lowerCAmelCase   ,				**__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  Optional[int]   ,				__lowerCAmelCase	:  ImageInput   ,				__lowerCAmelCase	:  bool = None   ,				__lowerCAmelCase	:  Dict[str, int] = None   ,				__lowerCAmelCase	:  List[str]=None   ,				__lowerCAmelCase	:  bool = None   ,				__lowerCAmelCase	:  float = None   ,				__lowerCAmelCase	:  bool = None   ,				__lowerCAmelCase	:  Union[float, Iterable[float]] = None   ,				__lowerCAmelCase	:  Union[float, Iterable[float]] = None   ,				__lowerCAmelCase	:  bool = None   ,				__lowerCAmelCase	:  Optional[str] = None   ,				__lowerCAmelCase	:  Optional[str] = None   ,				__lowerCAmelCase	:  Optional[Union[str, TensorType]] = None   ,				__lowerCAmelCase	:  ChannelDimension = ChannelDimension.FIRST   ,				**__lowerCAmelCase	:  str   ,				):
      _UpperCAmelCase						 =      do_resize if do_resize is not None else self.do_resize
      _UpperCAmelCase						 =      size if size is not None else self.size
      _UpperCAmelCase						 =      get_size_dict(__lowerCAmelCase					)
      _UpperCAmelCase						 =      resample if resample is not None else self.resample
      _UpperCAmelCase						 =      do_rescale if do_rescale is not None else self.do_rescale
      _UpperCAmelCase						 =      rescale_factor if rescale_factor is not None else self.rescale_factor
      _UpperCAmelCase						 =      do_normalize if do_normalize is not None else self.do_normalize
      _UpperCAmelCase						 =      image_mean if image_mean is not None else self.image_mean
      _UpperCAmelCase						 =      image_std if image_std is not None else self.image_std
      _UpperCAmelCase						 =      apply_ocr if apply_ocr is not None else self.apply_ocr
      _UpperCAmelCase						 =      ocr_lang if ocr_lang is not None else self.ocr_lang
      _UpperCAmelCase						 =      tesseract_config if tesseract_config is not None else self.tesseract_config

      _UpperCAmelCase						 =      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.
      _UpperCAmelCase						 =      [to_numpy_array(__lowerCAmelCase					) for image in images]

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

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

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

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

      _UpperCAmelCase						 =      [to_channel_dimension_format(__lowerCAmelCase   ,				__lowerCAmelCase					) for image in images]

      _UpperCAmelCase						 =      BatchFeature(data={"""pixel_values""": images}   ,				tensor_type=__lowerCAmelCase					)

      if apply_ocr:
        _UpperCAmelCase						 =      words_batch
        _UpperCAmelCase						 =      boxes_batch
      return data

"""simple docstring"""


def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      abs(lowercase		)
  _UpperCAmelCase						 =      0
  while n > 0:
    res += n % 10
    n //= 10
  return res






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      abs(lowercase		)
  return n if n < 10 else n % 10 + sum_of_digits(n // 10		)






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  return sum(int(lowercase		) for c in str(abs(lowercase		)		)		)






def     __UpperCAmelCase					(			):







  """simple docstring"""
  from collections.abc import Callable
  from timeit import timeit

  def benchmark_a_function(lowercase   ,lowercase		) -> None:
    _UpperCAmelCase						 =      f'''{func.__name__}({value})'''
    _UpperCAmelCase						 =      timeit(f'''__main__.{call}'''   ,setup="""import __main__"""		)
    print(f'''{call:56} = {func(lowercase		)} -- {timing:.4f} seconds'''		)

  for value in (26_21_44, 11_25_89_99_06_84_26_24, 1_26_76_50_60_02_28_22_94_01_49_67_03_20_53_76):
    for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
      benchmark_a_function(lowercase   ,lowercase		)
    print()


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    benchmark()
