"""simple docstring"""






from typing import Dict, Iterable, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__A			   = logging.get_logger(__name__)


class     UpperCAmelCase						(_UpperCAmelCase  ):



    """simple docstring"""

    _UpperCAmelCase	:str						    = ["pixel_values"]






    def __init__(   self    ,			_UpperCAmelCase = True    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = PILImageResampling.BICUBIC    ,			_UpperCAmelCase = True    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = True    ,			_UpperCAmelCase = 1 / 255    ,			_UpperCAmelCase = True    ,			_UpperCAmelCase = IMAGENET_DEFAULT_MEAN    ,			_UpperCAmelCase = IMAGENET_DEFAULT_STD    ,			**_UpperCAmelCase    ,			):
       super().__init__(**_UpperCAmelCase							)
       lowercase__:   List[str]					=      size if size is not None else {'''shortest_edge''': 224}
       lowercase__:   Optional[int]					=      get_size_dict(_UpperCAmelCase    ,			default_to_square=_UpperCAmelCase							)
       lowercase__:   Dict					=      crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
       lowercase__:   Any					=      get_size_dict(_UpperCAmelCase    ,			param_name='''crop_size'''							)

       lowercase__:   Optional[int]					=      do_resize
       lowercase__:   List[str]					=      size
       lowercase__:   Optional[int]					=      resample
       lowercase__:   Tuple					=      do_center_crop
       lowercase__:   Dict					=      crop_size
       lowercase__:   Optional[Any]					=      do_rescale
       lowercase__:   List[str]					=      rescale_factor
       lowercase__:   str					=      do_normalize
       lowercase__:   List[Any]					=      image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
       lowercase__:   List[str]					=      image_std if image_std is not None else IMAGENET_DEFAULT_STD






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase = PILImageResampling.BICUBIC    ,			_UpperCAmelCase = None    ,			**_UpperCAmelCase    ,			):
       lowercase__:   int					=      get_size_dict(_UpperCAmelCase    ,			default_to_square=_UpperCAmelCase							)
       # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
       if "shortest_edge" in size:
          lowercase__:   str					=      int((256 / 224) * size['''shortest_edge''']							)
          lowercase__:   Union[str, Any]					=      get_resize_output_image_size(_UpperCAmelCase    ,			size=_UpperCAmelCase    ,			default_to_square=_UpperCAmelCase							)
          lowercase__:   Any					=      {'''height''': output_size[0], '''width''': output_size[1]}
       if "height" not in size_dict or "width" not in size_dict:
          raise ValueError(
              F"""Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}"""							)
       return resize(
           _UpperCAmelCase    ,			size=(size_dict['''height'''], size_dict['''width'''])    ,			resample=_UpperCAmelCase    ,			data_format=_UpperCAmelCase    ,			**_UpperCAmelCase							)






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase = None    ,			**_UpperCAmelCase    ,			):
       lowercase__:   Optional[Any]					=      get_size_dict(_UpperCAmelCase							)
       if "height" not in size or "width" not in size:
          raise ValueError(F"""Size dict must have keys 'height' and 'width'. Got {size.keys()}"""							)
       return center_crop(_UpperCAmelCase    ,			size=(size['''height'''], size['''width'''])    ,			data_format=_UpperCAmelCase    ,			**_UpperCAmelCase							)






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase = None    ,			**_UpperCAmelCase    ,			):
       return rescale(_UpperCAmelCase    ,			scale=_UpperCAmelCase    ,			data_format=_UpperCAmelCase    ,			**_UpperCAmelCase							)






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase = None    ,			**_UpperCAmelCase    ,			):
       return normalize(_UpperCAmelCase    ,			mean=_UpperCAmelCase    ,			std=_UpperCAmelCase    ,			data_format=_UpperCAmelCase    ,			**_UpperCAmelCase							)






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = ChannelDimension.FIRST    ,			**_UpperCAmelCase    ,			):
       lowercase__:   int					=      do_resize if do_resize is not None else self.do_resize
       lowercase__:   Optional[int]					=      resample if resample is not None else self.resample
       lowercase__:   List[Any]					=      do_center_crop if do_center_crop is not None else self.do_center_crop
       lowercase__:   Any					=      do_rescale if do_rescale is not None else self.do_rescale
       lowercase__:   Optional[Any]					=      rescale_factor if rescale_factor is not None else self.rescale_factor
       lowercase__:   Any					=      do_normalize if do_normalize is not None else self.do_normalize
       lowercase__:   int					=      image_mean if image_mean is not None else self.image_mean
       lowercase__:   List[Any]					=      image_std if image_std is not None else self.image_std

       lowercase__:   int					=      size if size is not None else self.size
       lowercase__:   str					=      get_size_dict(_UpperCAmelCase    ,			default_to_square=_UpperCAmelCase							)
       lowercase__:   Any					=      crop_size if crop_size is not None else self.crop_size
       lowercase__:   Tuple					=      get_size_dict(_UpperCAmelCase    ,			param_name='''crop_size'''							)

       lowercase__:   Dict					=      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.'''							)

       if do_center_crop and crop_size is None:
          raise ValueError('''Crop size must be specified if do_center_crop 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('''Image mean and std must be specified if do_normalize is True.'''							)

       # All transformations expect numpy arrays.
       lowercase__:   List[str]					=      [to_numpy_array(_UpperCAmelCase							) for image in images]

       if do_resize:
          lowercase__:   List[Any]					=      [self.resize(_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase							) for image in images]

       if do_center_crop:
          lowercase__:   List[Any]					=      [self.center_crop(_UpperCAmelCase    ,			_UpperCAmelCase							) for image in images]

       if do_rescale:
          lowercase__:   Optional[int]					=      [self.rescale(_UpperCAmelCase    ,			_UpperCAmelCase							) for image in images]

       if do_normalize:
          lowercase__:   Optional[Any]					=      [self.normalize(_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase							) for image in images]

       lowercase__:   List[str]					=      [to_channel_dimension_format(_UpperCAmelCase    ,			_UpperCAmelCase							) for image in images]

       lowercase__:   List[Any]					=      {'''pixel_values''': images}
       return BatchFeature(data=_UpperCAmelCase    ,			tensor_type=_UpperCAmelCase							)

"""simple docstring"""






from __future__ import annotations

from fractions import Fraction



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase )  ->  bool:
   return (
       num != den and num % 1_0 == den // 1_0 and (num // 1_0) / (den % 1_0) == num / den
   )



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  list[str]:
   lowercase__:   str					=      []
   lowercase__:   str					=      1_1
   lowercase__:   str					=      int('''1''' + '''0''' * digit_len )
   for num in range(__UpperCAmelCase       ,    __UpperCAmelCase ):
      while den <= 9_9:
         if (num != den) and (num % 1_0 == den // 1_0) and (den % 1_0 != 0):
            if is_digit_cancelling(__UpperCAmelCase       ,    __UpperCAmelCase ):
               solutions.append(F"""{num}/{den}""" )
         den += 1
      num += 1
      lowercase__:   Dict					=      1_0
   return solutions



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase = 2 )  ->  int:
   lowercase__:   List[str]					=      1.0
   for fraction in fraction_list(__UpperCAmelCase ):
      lowercase__:   List[str]					=      Fraction(__UpperCAmelCase )
      result *= frac.denominator / frac.numerator
   return int(__UpperCAmelCase )


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