





from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


_lowerCamelCase					:   List[Any]							=		logging.get_logger(__name__)


class 						lowercase    (     a     ):
							lowercase__						:       Tuple								=							["""pixel_values"""]




							def __init__(						self   :					int	,							_UpperCamelCase   :					bool = True	,							_UpperCamelCase   :					Union[int, float] = 1 / 255	,							_UpperCamelCase   :					bool = True	,							_UpperCamelCase   :					int = 8	,							**_UpperCamelCase   :					Optional[Any]	,							)					->				List[str]:



												'''simple docstring'''

												super().__init__(**_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       do_rescale
												SCREAMING_SNAKE_CASE    			=       rescale_factor
												SCREAMING_SNAKE_CASE    			=       do_pad
												SCREAMING_SNAKE_CASE    			=       pad_size




							def 	__snake_case(						self   :					Optional[Any]	,							_UpperCamelCase   :					np.ndarray	,							_UpperCamelCase   :					float	,							_UpperCamelCase   :					Optional[Union[str, ChannelDimension]] = None	,							**_UpperCamelCase   :					Any      )					->				List[Any]:



												'''simple docstring'''

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




							def 	__snake_case(						self   :					Tuple	,							_UpperCamelCase   :					np.ndarray	,							_UpperCamelCase   :					int	,							_UpperCamelCase   :					Optional[Union[str, ChannelDimension]] = None      )					->				Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       get_image_size(_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       (old_height // size + 1) * size - old_height
												SCREAMING_SNAKE_CASE    			=       (old_width // size + 1) * size - old_width

												return pad(_UpperCamelCase	,							((0, pad_height), (0, pad_width))	,							mode="symmetric"	,							data_format=_UpperCamelCase      )





							def 	__snake_case(						self   :					List[Any]	,							_UpperCamelCase   :					ImageInput	,							_UpperCamelCase   :					Optional[bool] = None	,							_UpperCamelCase   :					Optional[float] = None	,							_UpperCamelCase   :					Optional[bool] = None	,							_UpperCamelCase   :					Optional[int] = None	,							_UpperCamelCase   :					Optional[Union[str, TensorType]] = None	,							_UpperCamelCase   :					Union[str, ChannelDimension] = ChannelDimension.FIRST	,							**_UpperCamelCase   :					Optional[Any]	,							)					->				Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       do_rescale if do_rescale is not None else self.do_rescale
												SCREAMING_SNAKE_CASE    			=       rescale_factor if rescale_factor is not None else self.rescale_factor
												SCREAMING_SNAKE_CASE    			=       do_pad if do_pad is not None else self.do_pad
												SCREAMING_SNAKE_CASE    			=       pad_size if pad_size is not None else self.pad_size

												SCREAMING_SNAKE_CASE    			=       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_rescale and rescale_factor is None:
																	raise ValueError("Rescale factor must be specified if do_rescale is True."      )

												# All transformations expect numpy arrays.
												SCREAMING_SNAKE_CASE    			=       [to_numpy_array(_UpperCamelCase      ) for image in images]

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

												if do_pad:
																	SCREAMING_SNAKE_CASE    			=       [self.pad(_UpperCamelCase	,							size=_UpperCamelCase      ) for image in images]

												SCREAMING_SNAKE_CASE    			=       [to_channel_dimension_format(_UpperCamelCase	,							_UpperCamelCase      ) for image in images]

												SCREAMING_SNAKE_CASE    			=       {'pixel_values': images}
												return BatchFeature(data=_UpperCamelCase	,							tensor_type=_UpperCamelCase      )





def 	__lowerCamelCase							(UpperCAmelCase__     :							int  ):
					assert isinstance(UpperCAmelCase__      , UpperCAmelCase__  ), F"The input value of [n={number}] is not an integer"

					if number == 1:
										return 2
					elif number < 1:
										SCREAMING_SNAKE_CASE    			=       F"The input value of [n={number}] has to be > 0"
										raise ValueError(UpperCAmelCase__  )
					else:
										SCREAMING_SNAKE_CASE    			=       sylvester(number - 1  )
										SCREAMING_SNAKE_CASE    			=       num - 1
										SCREAMING_SNAKE_CASE    			=       num
										return lower * upper + 1


if __name__ == "__main__":
							print(f"""The 8th number in Sylvester's sequence: {sylvester(8)}""")




