"""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__ 	=     logging.get_logger(__name__)







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->Optional[int]:
  return [
      int(1000 * (box[0] / width)       ),
      int(1000 * (box[1] / height)       ),
      int(1000 * (box[2] / width)       ),
      int(1000 * (box[3] / height)       ),
  ]







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->List[Any]:
  a__:					Optional[Any]      							=     to_pil_image(_SCREAMING_SNAKE_CASE       )
  a__							,   a__:					List[Any]      							=     pil_image.size
  a__:					List[Any]      							=     pytesseract.image_to_data(_SCREAMING_SNAKE_CASE		,					lang=_SCREAMING_SNAKE_CASE		,					output_type='dict'		,					config=_SCREAMING_SNAKE_CASE       )
  a__							,   a__							,   a__							,   a__							,   a__:					List[Any]      							=     data['text'], data['left'], data['top'], data['width'], data['height']

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

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

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

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

  return words, normalized_boxes




class        __snake_case	(					__lowerCAmelCase    ):
      a__				=		["""pixel_values"""]



      def __init__(						self   ,      lowercase = True   ,      lowercase = None   ,      lowercase = PILImageResampling.BILINEAR   ,      lowercase = True   ,      lowercase = 1 / 2_55   ,      lowercase = True   ,      lowercase = None   ,      lowercase = None   ,      lowercase = True   ,      lowercase = None   ,      lowercase = ""   ,      **lowercase   ,      )     ->      None:





        '''simple docstring'''





        super().__init__(**lowercase)
        a__:					int      							=     size if size is not None else {'height': 2_24, 'width': 2_24}
        a__:					Optional[Any]      							=     get_size_dict(lowercase)

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



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





        '''simple docstring'''





        a__:					List[str]      							=     get_size_dict(lowercase)
        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__:					List[Any]      							=     (size['height'], size['width'])
        return resize(lowercase   ,      size=lowercase   ,      resample=lowercase   ,      data_format=lowercase   ,      **lowercase)



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





        '''simple docstring'''





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



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





        '''simple docstring'''





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



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





        '''simple docstring'''





        a__:					Union[str, Any]      							=     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__:					Optional[int]      							=     get_size_dict(lowercase)
        a__:					Optional[int]      							=     resample if resample is not None else self.resample
        a__:					Union[str, Any]      							=     do_rescale if do_rescale is not None else self.do_rescale
        a__:					List[str]      							=     rescale_factor if rescale_factor is not None else self.rescale_factor
        a__:					Union[str, Any]      							=     do_normalize if do_normalize is not None else self.do_normalize
        a__:					List[Any]      							=     image_mean if image_mean is not None else self.image_mean
        a__:					int      							=     image_std if image_std is not None else self.image_std
        a__:					Dict      							=     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__:					int      							=     make_list_of_images(lowercase)

        if not valid_images(lowercase):
          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.
        a__:					Optional[int]      							=     [to_numpy_array(lowercase) for image in images]

        # Tesseract OCR to get words + normalized bounding boxes
        if apply_ocr:
          requires_backends(self   ,      'pytesseract')
          a__:					str      							=     []
          a__:					Union[str, Any]      							=     []
          for image in images:
            a__							,   a__:					Tuple      							=     apply_tesseract(lowercase   ,      lowercase   ,      lowercase)
            words_batch.append(lowercase)
            boxes_batch.append(lowercase)

        if do_resize:
          a__:					Union[str, Any]      							=     [self.resize(image=lowercase   ,      size=lowercase   ,      resample=lowercase) for image in images]

        if do_rescale:
          a__:					int      							=     [self.rescale(image=lowercase   ,      scale=lowercase) for image in images]

        if do_normalize:
          a__:					List[Any]      							=     [self.normalize(image=lowercase   ,      mean=lowercase   ,      std=lowercase) for image in images]

        a__:					Optional[Any]      							=     [to_channel_dimension_format(lowercase   ,      lowercase) for image in images]

        a__:					Optional[Any]      							=     BatchFeature(data={'pixel_values': images}   ,      tensor_type=lowercase)

        if apply_ocr:
          a__:					int      							=     words_batch
          a__:					Optional[int]      							=     boxes_batch
        return data



"""simple docstring"""
def 						__a       (     _SCREAMING_SNAKE_CASE = 1000000       )     ->int:
  a__:					Any      							=     limit + 1
  a__:					List[str]      							=     [0] * limit
  for first_term in range(1		,					_SCREAMING_SNAKE_CASE       ):
    for n in range(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       ):
      a__:					Any      							=     first_term + n / first_term
      if common_difference % 4:  # d must be divisble by 4
        continue
      else:
        common_difference /= 4
        if (
            first_term > common_difference
            and first_term < 4 * common_difference
        ):  # since x,y,z are positive integers
          frequency[n] += 1  # so z>0 and a>d ,also 4d<a

  a__:					Optional[int]      							=     sum(1 for x in frequency[1:limit] if x == 10       )

  return count


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


