



'''simple docstring'''






from typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    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:      Dict		 =		logging.get_logger(__name__)
def    lowerCamelCase__ (		_A				,    _A				,    _A       ):
  return [
      int(1000 * (box[0] / width)       ),
      int(1000 * (box[1] / height)       ),
      int(1000 * (box[2] / width)       ),
      int(1000 * (box[3] / height)       ),
  ]
def    lowerCamelCase__ (		_A				,    _A				,    _A = None       ):
  a		:      Dict       =  tesseract_config if tesseract_config is not None else ''

  # apply OCR
  a		:      Union[str, Any]       =  to_pil_image(_A       )
  a     ,			a		:      Tuple       =  pil_image.size
  a		:      Any       =  pytesseract.image_to_data(_A				,    lang=_A				,    output_type='dict'				,    config=_A       )
  a     ,			a     ,			a     ,			a     ,			a		:      List[Any]       =  data['text'], data['left'], data['top'], data['width'], data['height']

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

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

  # finally, normalize the bounding boxes
  a		:      List[Any]       =  []
  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__(      lowerCamelCase__			):
    lowercase__				=					["""pixel_values"""]







    def __init__(   self				:						Optional[Any]		,    __snake_case				:						bool = True		,    __snake_case				:						Dict[str, int] = None		,    __snake_case				:						PILImageResampling = PILImageResampling.BILINEAR		,    __snake_case				:						bool = True		,    __snake_case				:						Optional[str] = None		,    __snake_case				:						Optional[str] = ""		,    **__snake_case				:						List[Any]		,    ):
      super().__init__(**__snake_case			)
      a		:      int       =  size if size is not None else {'height': 2_24, 'width': 2_24}
      a		:      Optional[Any]       =  get_size_dict(__snake_case			)

      a		:      List[Any]       =  do_resize
      a		:      Dict       =  size
      a		:      List[Any]       =  resample
      a		:      Union[str, Any]       =  apply_ocr
      a		:      Optional[int]       =  ocr_lang
      a		:      Any       =  tesseract_config







    def     lowercase_ (   self				:						str		,    __snake_case				:						np.ndarray		,    __snake_case				:						Dict[str, int]		,    __snake_case				:						PILImageResampling = PILImageResampling.BILINEAR		,    __snake_case				:						Optional[Union[str, ChannelDimension]] = None		,    **__snake_case				:						Any		,    ):
      a		:      Tuple       =  get_size_dict(__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()}"""			)
      a		:      Optional[int]       =  (size['height'], size['width'])
      return resize(__snake_case		,    size=__snake_case		,    resample=__snake_case		,    data_format=__snake_case		,    **__snake_case			)







    def     lowercase_ (   self				:						str		,    __snake_case				:						ImageInput		,    __snake_case				:						bool = None		,    __snake_case				:						Dict[str, int] = None		,    __snake_case				:						PILImageResampling = None		,    __snake_case				:						bool = None		,    __snake_case				:						Optional[str] = None		,    __snake_case				:						Optional[str] = None		,    __snake_case				:						Optional[Union[str, TensorType]] = None		,    __snake_case				:						ChannelDimension = ChannelDimension.FIRST		,    **__snake_case				:						Dict		,    ):
      a		:      str       =  do_resize if do_resize is not None else self.do_resize
      a		:      List[Any]       =  size if size is not None else self.size
      a		:      Dict       =  get_size_dict(__snake_case			)
      a		:      Optional[int]       =  resample if resample is not None else self.resample
      a		:      Any       =  apply_ocr if apply_ocr is not None else self.apply_ocr
      a		:      Union[str, Any]       =  ocr_lang if ocr_lang is not None else self.ocr_lang
      a		:      Optional[Any]       =  tesseract_config if tesseract_config is not None else self.tesseract_config

      a		:      Union[str, Any]       =  make_list_of_images(__snake_case			)

      if not valid_images(__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.'			)

      # All transformations expect numpy arrays.
      a		:      int       =  [to_numpy_array(__snake_case			) for image in images]

      if apply_ocr:
        requires_backends(self		,    'pytesseract'			)
        a		:      Optional[Any]       =  []
        a		:      Union[str, Any]       =  []
        for image in images:
          a     ,			a		:      Union[str, Any]       =  apply_tesseract(__snake_case		,    __snake_case		,    __snake_case			)
          words_batch.append(__snake_case			)
          boxes_batch.append(__snake_case			)

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

      # flip color channels from RGB to BGR (as Detectron2 requires this)
      a		:      Any       =  [flip_channel_order(__snake_case			) for image in images]
      a		:      str       =  [to_channel_dimension_format(__snake_case		,    __snake_case			) for image in images]

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

      if apply_ocr:
        a		:      Tuple       =  words_batch
        a		:      Optional[Any]       =  boxes_batch
      return data




'''simple docstring'''






def    lowerCamelCase__ (		_A = 6008_5147_5143       ):

  try:
    a		:      Optional[int]       =  int(_A       )
  except (TypeError, ValueError):
    raise TypeError('Parameter n must be int or castable to int.'       )
  if n <= 0:
    raise ValueError('Parameter n must be greater than or equal to one.'       )
  a		:      Any       =  1
  a		:      Union[str, Any]       =  2
  while i * i <= n:
    while n % i == 0:
      a		:      str       =  i
      n //= i
    i += 1
  if n > 1:
    a		:      Any       =  n
  return int(_A       )


if __name__ == "__main__":
     print(F"{solution() = }")