


"""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

__lowercase    					=      logging.get_logger(__name__)


def      lowercase			(				A_  ,   A_  ,   A_       )->      Any:
  '''simple docstring'''







  return [
      int(1_000 * (box[0] / width)       ),
      int(1_000 * (box[1] / height)       ),
      int(1_000 * (box[2] / width)       ),
      int(1_000 * (box[3] / height)       ),
  ]


def      lowercase			(				A_  ,   A_  ,   A_ = None       )->      Union[str, Any]:
  '''simple docstring'''







  a						:		List[str]      							=			tesseract_config if tesseract_config is not None else ""

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

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

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

  # finally, normalize the bounding boxes
  a						:		Dict      							=			[]
  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  (  _a  ):







    """simple docstring"""




    UpperCAmelCase       :							List[str]	=       ["""pixel_values"""]






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

      a						:		List[Any]      							=			do_resize
      a						:		List[str]      							=			size
      a						:		List[str]      							=			resample
      a						:		str      							=			apply_ocr
      a						:		Tuple      							=			ocr_lang
      a						:		str      							=			tesseract_config






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






    def 	__snake_case							(					self				:    Dict				, __UpperCAmelCase				:    ImageInput				, __UpperCAmelCase				:    bool = None				, __UpperCAmelCase				:    Dict[str, int] = None				, __UpperCAmelCase				:    PILImageResampling = 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]				, ):
      a						:		str      							=			do_resize if do_resize is not None else self.do_resize
      a						:		int      							=			size if size is not None else self.size
      a						:		Tuple      							=			get_size_dict(__UpperCAmelCase)
      a						:		Tuple      							=			resample if resample is not None else self.resample
      a						:		Dict      							=			apply_ocr if apply_ocr is not None else self.apply_ocr
      a						:		Tuple      							=			ocr_lang if ocr_lang is not None else self.ocr_lang
      a						:		str      							=			tesseract_config if tesseract_config is not None else self.tesseract_config

      a						:		List[Any]      							=			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.")

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

      if apply_ocr:
        requires_backends(self				, "pytesseract")
        a						:		Tuple      							=			[]
        a						:		Union[str, Any]      							=			[]
        for image in images:
          a							,		a						:		List[str]      							=			apply_tesseract(__UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase)
          words_batch.append(__UpperCAmelCase)
          boxes_batch.append(__UpperCAmelCase)

      if do_resize:
        a						:		Optional[int]      							=			[self.resize(image=__UpperCAmelCase				, size=__UpperCAmelCase				, resample=__UpperCAmelCase) for image in images]

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

      a						:		Tuple      							=			BatchFeature(data={"pixel_values": images}				, tensor_type=__UpperCAmelCase)

      if apply_ocr:
        a						:		Any      							=			words_batch
        a						:		Dict      							=			boxes_batch
      return data










"""simple docstring"""







from itertools import permutations


def      lowercase			(				A_       )->      bool:
  '''simple docstring'''







  if num[3] % 2 != 0:
    return False

  if (num[2] + num[3] + num[4]) % 3 != 0:
    return False

  if num[5] % 5 != 0:
    return False

  a						:		Optional[int]      							=			[7, 11, 13, 17]
  for i, test in enumerate(A_       ):
    if (num[i + 4] * 100 + num[i + 5] * 10 + num[i + 6]) % test != 0:
      return False
  return True


def      lowercase			(				A_ = 10       )->      int:
  '''simple docstring'''







  return sum(
      int("".join(map(A_  ,   A_       )       )       )
      for num in permutations(range(A_       )       )
      if is_substring_divisible(A_       )       )


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






