

'''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

_lowerCAmelCase         =						logging.get_logger(__name__)




def 						UpperCamelCase	(			a		,						a		,						a			)				->     List[Any]:






     '''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	(			a		,						a		,						a			)				->     Tuple:






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

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

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

     # finally, normalize the bounding boxes
     __magic_name__						       =    []
     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   _SCREAMING_SNAKE_CASE   (   __a     ):
 __SCREAMING_SNAKE_CASE					:int      	=					["""pixel_values"""]



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

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



 def 			snake_case__  (   self		:   Union[str, Any]			,				a__		:   np.ndarray			,				a__		:   Dict[str, int]			,				a__		:   PILImageResampling = PILImageResampling.BILINEAR			,				a__		:   Optional[Union[str, ChannelDimension]] = None			,				**a__		:   List[str]			,				):
      __magic_name__						       =    get_size_dict(a__      )
      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()}'''      )
      __magic_name__						       =    (size['''height'''], size['''width'''])
      return resize(a__			,				size=a__			,				resample=a__			,				data_format=a__			,				**a__      )



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



 def 			snake_case__  (   self		:   Optional[int]			,				a__		:   np.ndarray			,				a__		:   Union[float, Iterable[float]]			,				a__		:   Union[float, Iterable[float]]			,				a__		:   Optional[Union[str, ChannelDimension]] = None			,				**a__		:   List[str]			,				):
      return normalize(a__			,				mean=a__			,				std=a__			,				data_format=a__			,				**a__      )



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

      __magic_name__						       =    make_list_of_images(a__      )

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

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

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

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

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

      __magic_name__						       =    [to_channel_dimension_format(a__			,				a__      ) for image in images]

      __magic_name__						       =    BatchFeature(data={'''pixel_values''': images}			,				tensor_type=a__      )

      if apply_ocr:
           __magic_name__						       =    words_batch
           __magic_name__						       =    boxes_batch
      return data









'''simple docstring'''




from __future__ import annotations

from fractions import Fraction




def 						UpperCamelCase	(			a		,						a			)				->     bool:






     '''simple docstring'''
     return (
         num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
     )




def 						UpperCamelCase	(			a			)				->     list[str]:






     '''simple docstring'''
     __magic_name__						       =    []
     __magic_name__						       =    11
     __magic_name__						       =    int('''1''' + '''0''' * digit_len			)
     for num in range(a		,						a			):
          while den <= 99:
               if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
                    if is_digit_cancelling(a		,						a			):
                         solutions.append(F'''{num}/{den}'''			)
               den += 1
          num += 1
          __magic_name__						       =    10
     return solutions




def 						UpperCamelCase	(			a = 2			)				->     int:






     '''simple docstring'''
     __magic_name__						       =    1.0
     for fraction in fraction_list(a			):
          __magic_name__						       =    Fraction(a			)
          result *= frac.denominator / frac.numerator
     return int(a			)


if __name__ == "__main__":
      print(solution())






