from typing import Dict, 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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
 import PIL


lowerCamelCase       :	str									=     logging.get_logger(__name__)







class 						A(		SCREAMING_SNAKE_CASE__     ):



 '''simple docstring'''


 UpperCamelCase		     =		['''pixel_values''']

 def __init__(       self      :						str		,							A_      :						Union[str, Any] = True		,							A_      :						Dict = None		,							A_      :						List[str] = None		,							A_      :						int = PILImageResampling.BILINEAR		,							A_      :						List[str] = True		,							A_      :						List[Any] = 1 / 255		,							A_      :						Any = True		,							A_      :						Dict = None		,							A_      :						Optional[Any] = None		,							**A_      :						str		,							)     ->			None:

       """simple docstring"""






       super().__init__(**_lowercase						)
       lowerCamelCase_	  =					size if size is not None else {"""shortest_edge""": 384}
       lowerCamelCase_	  =					get_size_dict(_lowercase		,							default_to_square=_lowercase						)

       lowerCamelCase_	  =					do_resize
       lowerCamelCase_	  =					size
       # Default value set here for backwards compatibility where the value in config is None
       lowerCamelCase_	  =					crop_pct if crop_pct is not None else 224 / 256
       lowerCamelCase_	  =					resample
       lowerCamelCase_	  =					do_rescale
       lowerCamelCase_	  =					rescale_factor
       lowerCamelCase_	  =					do_normalize
       lowerCamelCase_	  =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       lowerCamelCase_	  =					image_std if image_std is not None else IMAGENET_STANDARD_STD

 def        a__				(       self      :						str		,							A_      :						Tuple		,							A_      :						List[Any]		,							A_      :						str		,							A_      :						Optional[Any] = PILImageResampling.BICUBIC		,							A_      :						Tuple = None		,							**A_      :						int		,							)     ->			np.ndarray:

       """simple docstring"""






       lowerCamelCase_	  =					get_size_dict(_lowercase		,							default_to_square=_lowercase						)
       if "shortest_edge" not in size:
             raise ValueError(f"""Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}"""						)
       lowerCamelCase_	  =					size["""shortest_edge"""]

       if shortest_edge < 384:
             # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
             lowerCamelCase_	  =					int(shortest_edge / crop_pct						)
             lowerCamelCase_	  =					get_resize_output_image_size(_lowercase		,							size=_lowercase		,							default_to_square=_lowercase						)
             lowerCamelCase_	  =					resize(image=_lowercase		,							size=_lowercase		,							resample=_lowercase		,							data_format=_lowercase		,							**_lowercase						)
             # then crop to (shortest_edge, shortest_edge)
             return center_crop(image=_lowercase		,							size=(shortest_edge, shortest_edge)		,							data_format=_lowercase		,							**_lowercase						)
       else:
             # warping (no cropping) when evaluated at 384 or larger
             return resize(
                 _lowercase		,							size=(shortest_edge, shortest_edge)		,							resample=_lowercase		,							data_format=_lowercase		,							**_lowercase						)

 def        a__				(       self      :						Union[str, Any]		,							A_      :						str		,							A_      :						str		,							A_      :						Dict = None		,							**A_      :						Tuple		,							)     ->			Union[str, Any]:

       """simple docstring"""






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

 def        a__				(       self      :						Dict		,							A_      :						Any		,							A_      :						Dict		,							A_      :						Optional[Any]		,							A_      :						List[str] = None		,							**A_      :						str		,							)     ->			np.ndarray:

       """simple docstring"""






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





 def        a__				(       self      :						List[Any]		,							A_      :						List[str]		,							A_      :						Optional[Any] = None		,							A_      :						Dict = None		,							A_      :						Dict = None		,							A_      :						Optional[Any] = None		,							A_      :						int = None		,							A_      :						List[str] = None		,							A_      :						Optional[int] = None		,							A_      :						Optional[Any] = None		,							A_      :						str = None		,							A_      :						List[Any] = None		,							A_      :						int = ChannelDimension.FIRST		,							**A_      :						Tuple		,							)     ->			PIL.Image.Image:

       """simple docstring"""






       lowerCamelCase_	  =					do_resize if do_resize is not None else self.do_resize
       lowerCamelCase_	  =					crop_pct if crop_pct is not None else self.crop_pct
       lowerCamelCase_	  =					resample if resample is not None else self.resample
       lowerCamelCase_	  =					do_rescale if do_rescale is not None else self.do_rescale
       lowerCamelCase_	  =					rescale_factor if rescale_factor is not None else self.rescale_factor
       lowerCamelCase_	  =					do_normalize if do_normalize is not None else self.do_normalize
       lowerCamelCase_	  =					image_mean if image_mean is not None else self.image_mean
       lowerCamelCase_	  =					image_std if image_std is not None else self.image_std

       lowerCamelCase_	  =					size if size is not None else self.size
       lowerCamelCase_	  =					get_size_dict(_lowercase		,							default_to_square=_lowercase						)

       lowerCamelCase_	  =					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 or resample is None:
             raise ValueError('Size and resample must be specified if do_resize is True.'						)

       if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
             raise ValueError('crop_pct must be specified if size < 384.'						)

       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.
       lowerCamelCase_	  =					[to_numpy_array(_lowercase						) for image in images]

       if do_resize:
             lowerCamelCase_	  =					[self.resize(image=_lowercase		,							size=_lowercase		,							crop_pct=_lowercase		,							resample=_lowercase						) for image in images]

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

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

       lowerCamelCase_	  =					[to_channel_dimension_format(_lowercase		,							_lowercase						) for image in images]

       lowerCamelCase_	  =					{"""pixel_values""": images}
       return BatchFeature(data=_lowercase		,							tensor_type=_lowercase						)









from __future__ import annotations

from fractions import Fraction

def     _SCREAMING_SNAKE_CASE    (							lowercase			: int			,							lowercase			: int  ):


      '''simple docstring'''



      return (
          num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
      )

def     _SCREAMING_SNAKE_CASE    (							lowercase			: int  ):


      '''simple docstring'''



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

def     _SCREAMING_SNAKE_CASE    (							lowercase			: int = 2  ):


      '''simple docstring'''



      lowerCamelCase_	  =					1.0
      for fraction in fraction_list(lowercase  ):
            lowerCamelCase_	  =					Fraction(lowercase  )
            result *= frac.denominator / frac.numerator
      return int(lowercase  )


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



