


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




class 					UpperCamelCase_      (					A__):

	"""simple docstring"""





	snake_case__							:    Tuple          =     ["pixel_values"]




	def __init__(					self     :							Optional[Any]     ,     UpperCAmelCase__     :							bool = True     ,     UpperCAmelCase__     :							Dict[str, int] = None     ,     UpperCAmelCase__     :							PILImageResampling = PILImageResampling.BICUBIC     ,     UpperCAmelCase__     :							bool = True     ,     UpperCAmelCase__     :							Dict[str, int] = None     ,     UpperCAmelCase__     :							bool = True     ,     UpperCAmelCase__     :							Union[int, float] = 1 / 2_5_5     ,     UpperCAmelCase__     :							bool = True     ,     UpperCAmelCase__     :							Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN     ,     UpperCAmelCase__     :							Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD     ,     **UpperCAmelCase__     :							Tuple     ,     )			->      None:
						super().__init__(**__snake_case  )
						__SCREAMING_SNAKE_CASE		  =							size if size is not None else {"shortest_edge": 2_2_4}
						__SCREAMING_SNAKE_CASE		  =							get_size_dict(__snake_case     ,     default_to_square=__snake_case  )
						__SCREAMING_SNAKE_CASE		  =							crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
						__SCREAMING_SNAKE_CASE		  =							get_size_dict(__snake_case     ,     param_name="crop_size"  )

						__SCREAMING_SNAKE_CASE		  =							do_resize
						__SCREAMING_SNAKE_CASE		  =							size
						__SCREAMING_SNAKE_CASE		  =							resample
						__SCREAMING_SNAKE_CASE		  =							do_center_crop
						__SCREAMING_SNAKE_CASE		  =							crop_size
						__SCREAMING_SNAKE_CASE		  =							do_rescale
						__SCREAMING_SNAKE_CASE		  =							rescale_factor
						__SCREAMING_SNAKE_CASE		  =							do_normalize
						__SCREAMING_SNAKE_CASE		  =							image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
						__SCREAMING_SNAKE_CASE		  =							image_std if image_std is not None else IMAGENET_DEFAULT_STD




	def     UpperCAmelCase_ (					self     :							str     ,     UpperCAmelCase__     :							np.ndarray     ,     UpperCAmelCase__     :							Dict[str, int]     ,     UpperCAmelCase__     :							PILImageResampling = PILImageResampling.BICUBIC     ,     UpperCAmelCase__     :							Optional[Union[str, ChannelDimension]] = None     ,     **UpperCAmelCase__     :							str     ,     )			->      np.ndarray:
						__SCREAMING_SNAKE_CASE		  =							get_size_dict(__snake_case     ,     default_to_square=__snake_case  )
						# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
						if "shortest_edge" in size:
											__SCREAMING_SNAKE_CASE		  =							int((2_5_6 / 2_2_4) * size["shortest_edge"]  )
											__SCREAMING_SNAKE_CASE		  =							get_resize_output_image_size(__snake_case     ,     size=__snake_case     ,     default_to_square=__snake_case  )
											__SCREAMING_SNAKE_CASE		  =							{"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(
						    __snake_case     ,     size=(size_dict["height"], size_dict["width"])     ,     resample=__snake_case     ,     data_format=__snake_case     ,     **__snake_case  )




	def     UpperCAmelCase_ (					self     :							Union[str, Any]     ,     UpperCAmelCase__     :							np.ndarray     ,     UpperCAmelCase__     :							Dict[str, int]     ,     UpperCAmelCase__     :							Optional[Union[str, ChannelDimension]] = None     ,     **UpperCAmelCase__     :							Any     ,     )			->      np.ndarray:
						__SCREAMING_SNAKE_CASE		  =							get_size_dict(__snake_case  )
						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(__snake_case     ,     size=(size["height"], size["width"])     ,     data_format=__snake_case     ,     **__snake_case  )




	def     UpperCAmelCase_ (					self     :							Optional[int]     ,     UpperCAmelCase__     :							np.ndarray     ,     UpperCAmelCase__     :							Union[int, float]     ,     UpperCAmelCase__     :							Optional[Union[str, ChannelDimension]] = None     ,     **UpperCAmelCase__     :							List[str]     ,     )			->      np.ndarray:
						return rescale(__snake_case     ,     scale=__snake_case     ,     data_format=__snake_case     ,     **__snake_case  )




	def     UpperCAmelCase_ (					self     :							int     ,     UpperCAmelCase__     :							np.ndarray     ,     UpperCAmelCase__     :							Union[float, List[float]]     ,     UpperCAmelCase__     :							Union[float, List[float]]     ,     UpperCAmelCase__     :							Optional[Union[str, ChannelDimension]] = None     ,     **UpperCAmelCase__     :							Dict     ,     )			->      np.ndarray:
						return normalize(__snake_case     ,     mean=__snake_case     ,     std=__snake_case     ,     data_format=__snake_case     ,     **__snake_case  )




	def     UpperCAmelCase_ (					self     :							Optional[int]     ,     UpperCAmelCase__     :							ImageInput     ,     UpperCAmelCase__     :							Optional[bool] = None     ,     UpperCAmelCase__     :							Optional[Dict[str, int]] = None     ,     UpperCAmelCase__     :							PILImageResampling = None     ,     UpperCAmelCase__     :							Optional[bool] = None     ,     UpperCAmelCase__     :							Optional[Dict[str, int]] = None     ,     UpperCAmelCase__     :							Optional[bool] = None     ,     UpperCAmelCase__     :							Optional[float] = None     ,     UpperCAmelCase__     :							Optional[bool] = None     ,     UpperCAmelCase__     :							Optional[Union[float, Iterable[float]]] = None     ,     UpperCAmelCase__     :							Optional[Union[float, Iterable[float]]] = None     ,     UpperCAmelCase__     :							Optional[TensorType] = None     ,     UpperCAmelCase__     :							ChannelDimension = ChannelDimension.FIRST     ,     **UpperCAmelCase__     :							int     ,     )			->      BatchFeature:
						__SCREAMING_SNAKE_CASE		  =							do_resize if do_resize is not None else self.do_resize
						__SCREAMING_SNAKE_CASE		  =							resample if resample is not None else self.resample
						__SCREAMING_SNAKE_CASE		  =							do_center_crop if do_center_crop is not None else self.do_center_crop
						__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_normalize if do_normalize is not None else self.do_normalize
						__SCREAMING_SNAKE_CASE		  =							image_mean if image_mean is not None else self.image_mean
						__SCREAMING_SNAKE_CASE		  =							image_std if image_std is not None else self.image_std

						__SCREAMING_SNAKE_CASE		  =							size if size is not None else self.size
						__SCREAMING_SNAKE_CASE		  =							get_size_dict(__snake_case     ,     default_to_square=__snake_case  )
						__SCREAMING_SNAKE_CASE		  =							crop_size if crop_size is not None else self.crop_size
						__SCREAMING_SNAKE_CASE		  =							get_size_dict(__snake_case     ,     param_name="crop_size"  )

						__SCREAMING_SNAKE_CASE		  =							make_list_of_images(__snake_case  )

						if not valid_images(__snake_case  ):
											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.
						__SCREAMING_SNAKE_CASE		  =							[to_numpy_array(__snake_case  ) for image in images]

						if do_resize:
											__SCREAMING_SNAKE_CASE		  =							[self.resize(__snake_case     ,     __snake_case     ,     __snake_case  ) for image in images]

						if do_center_crop:
											__SCREAMING_SNAKE_CASE		  =							[self.center_crop(__snake_case     ,     __snake_case  ) for image in images]

						if do_rescale:
											__SCREAMING_SNAKE_CASE		  =							[self.rescale(__snake_case     ,     __snake_case  ) for image in images]

						if do_normalize:
											__SCREAMING_SNAKE_CASE		  =							[self.normalize(__snake_case     ,     __snake_case     ,     __snake_case  ) for image in images]

						__SCREAMING_SNAKE_CASE		  =							[to_channel_dimension_format(__snake_case     ,     __snake_case  ) for image in images]

						__SCREAMING_SNAKE_CASE		  =							{"pixel_values": images}
						return BatchFeature(data=__snake_case     ,     tensor_type=__snake_case  )







'''simple docstring'''




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

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


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

