



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


a__   :					Optional[Any] 						=      logging.get_logger(__name__)



class    UpperCAmelCase__(				__lowercase						):

   '''simple docstring'''




   A     :	Dict												=     ["pixel_values"]


   def __init__(    self	:      Optional[int]      ,					lowerCAmelCase	:      Any = True      ,					lowerCAmelCase	:      int = None      ,					lowerCAmelCase	:      int = None      ,					lowerCAmelCase	:      List[Any] = PILImageResampling.BILINEAR      ,					lowerCAmelCase	:      List[str] = True      ,					lowerCAmelCase	:      int = 1 / 2_55      ,					lowerCAmelCase	:      List[str] = True      ,					lowerCAmelCase	:      int = None      ,					lowerCAmelCase	:      List[Any] = None      ,					**lowerCAmelCase	:      Union[str, Any]      ,					)						->					None:




         """simple docstring"""

         super().__init__(**lowerCAmelCase)
         lowercase__  =	size if size is not None else {'shortest_edge': 3_84}
         lowercase__  =	get_size_dict(lowerCAmelCase      ,					default_to_square=lowerCAmelCase)

         lowercase__  =	do_resize
         lowercase__  =	size
         # Default value set here for backwards compatibility where the value in config is None
         lowercase__  =	crop_pct if crop_pct is not None else 2_24 / 2_56
         lowercase__  =	resample
         lowercase__  =	do_rescale
         lowercase__  =	rescale_factor
         lowercase__  =	do_normalize
         lowercase__  =	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         lowercase__  =	image_std if image_std is not None else IMAGENET_STANDARD_STD


   def       UpperCAmelCase		(    self	:      str      ,					lowerCAmelCase	:      Optional[int]      ,					lowerCAmelCase	:      Union[str, Any]      ,					lowerCAmelCase	:      Optional[Any]      ,					lowerCAmelCase	:      List[str] = PILImageResampling.BICUBIC      ,					lowerCAmelCase	:      int = None      ,					**lowerCAmelCase	:      int      ,					)						->					np.ndarray:




         """simple docstring"""

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

         if shortest_edge < 3_84:
               # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
               lowercase__  =	int(shortest_edge / crop_pct)
               lowercase__  =	get_resize_output_image_size(lowerCAmelCase      ,					size=lowerCAmelCase      ,					default_to_square=lowerCAmelCase)
               lowercase__  =	resize(image=lowerCAmelCase      ,					size=lowerCAmelCase      ,					resample=lowerCAmelCase      ,					data_format=lowerCAmelCase      ,					**lowerCAmelCase)
               # then crop to (shortest_edge, shortest_edge)
               return center_crop(image=lowerCAmelCase      ,					size=(shortest_edge, shortest_edge)      ,					data_format=lowerCAmelCase      ,					**lowerCAmelCase)
         else:
               # warping (no cropping) when evaluated at 384 or larger
               return resize(
                   lowerCAmelCase      ,					size=(shortest_edge, shortest_edge)      ,					resample=lowerCAmelCase      ,					data_format=lowerCAmelCase      ,					**lowerCAmelCase)


   def       UpperCAmelCase		(    self	:      Dict      ,					lowerCAmelCase	:      Optional[int]      ,					lowerCAmelCase	:      Any      ,					lowerCAmelCase	:      str = None      ,					**lowerCAmelCase	:      List[Any]      ,					)						->					Optional[int]:




         """simple docstring"""

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


   def       UpperCAmelCase		(    self	:      Union[str, Any]      ,					lowerCAmelCase	:      Any      ,					lowerCAmelCase	:      Optional[Any]      ,					lowerCAmelCase	:      Tuple      ,					lowerCAmelCase	:      Optional[Any] = None      ,					**lowerCAmelCase	:      Any      ,					)						->					np.ndarray:




         """simple docstring"""

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



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




         """simple docstring"""

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

         lowercase__  =	size if size is not None else self.size
         lowercase__  =	get_size_dict(lowerCAmelCase      ,					default_to_square=lowerCAmelCase)

         lowercase__  =	make_list_of_images(lowerCAmelCase)

         if not valid_images(lowerCAmelCase):
               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"] < 3_84 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.
         lowercase__  =	[to_numpy_array(lowerCAmelCase) for image in images]

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

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

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

         lowercase__  =	[to_channel_dimension_format(lowerCAmelCase      ,					lowerCAmelCase) for image in images]

         lowercase__  =	{'pixel_values': images}
         return BatchFeature(data=lowerCAmelCase      ,					tensor_type=lowerCAmelCase)


def 				__A(lowerCAmelCase			)		->					bool:

		"""simple docstring"""






		if not isinstance(lowerCAmelCase    ,	lowerCAmelCase			):
				raise ValueError("""check_bouncy() accepts only integer arguments"""			)
		_UpperCamelCase         =						str(lowerCAmelCase			)
		_UpperCamelCase         =						"""""".join(sorted(lowerCAmelCase			)			)
		return sorted_str_n != str_n and sorted_str_n[::-1] != str_n


def 				__A(lowerCAmelCase = 9_9			)		->					int:

		"""simple docstring"""






		if not 0 < percent < 1_0_0:
				raise ValueError("""solution() only accepts values from 0 to 100"""			)
		_UpperCamelCase         =						0
		_UpperCamelCase         =						1

		while True:
				if check_bouncy(lowerCAmelCase			):
						bouncy_num += 1
				if (bouncy_num / num) * 1_0_0 >= percent:
						return num
				num += 1


if __name__ == "__main__":
		from doctest import testmod

		testmod()
		print(F"""{solution(99)}""")



