






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_		:		str		     =			logging.get_logger(__name__)




class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       a    :		List[str]									=['''pixel_values''']





       def __init__( self  ,      _lowerCamelCase = True  ,      _lowerCamelCase = None  ,      _lowerCamelCase = PILImageResampling.BICUBIC  ,      _lowerCamelCase = True  ,      _lowerCamelCase = None  ,      _lowerCamelCase = True  ,      _lowerCamelCase = 1 / 2_5_5  ,      _lowerCamelCase = True  ,      _lowerCamelCase = IMAGENET_DEFAULT_MEAN  ,      _lowerCamelCase = IMAGENET_DEFAULT_STD  ,      **_lowerCamelCase  ,      ):
         super().__init__(**_lowerCamelCase							)
         UpperCamelCase_: Tuple   				=    size if size is not None else {'shortest_edge': 2_2_4}
         UpperCamelCase_: Optional[int]   				=    get_size_dict(_lowerCamelCase  ,      default_to_square=_lowerCamelCase							)
         UpperCamelCase_: str   				=    crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
         UpperCamelCase_: List[str]   				=    get_size_dict(_lowerCamelCase  ,      param_name='crop_size'							)

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





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase = PILImageResampling.BICUBIC  ,      _lowerCamelCase = None  ,      **_lowerCamelCase  ,      ):
         UpperCamelCase_: Optional[Any]   				=    get_size_dict(_lowerCamelCase  ,      default_to_square=_lowerCamelCase							)
         # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
         if "shortest_edge" in size:
           UpperCamelCase_: str   				=    int((2_5_6 / 2_2_4) * size['shortest_edge']							)
           UpperCamelCase_: List[Any]   				=    get_resize_output_image_size(_lowerCamelCase  ,      size=_lowerCamelCase  ,      default_to_square=_lowerCamelCase							)
           UpperCamelCase_: 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(
             _lowerCamelCase  ,      size=(size_dict['height'], size_dict['width'])  ,      resample=_lowerCamelCase  ,      data_format=_lowerCamelCase  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase = None  ,      **_lowerCamelCase  ,      ):
         UpperCamelCase_: Tuple   				=    get_size_dict(_lowerCamelCase							)
         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(_lowerCamelCase  ,      size=(size['height'], size['width'])  ,      data_format=_lowerCamelCase  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase = None  ,      **_lowerCamelCase  ,      ):
         return rescale(_lowerCamelCase  ,      scale=_lowerCamelCase  ,      data_format=_lowerCamelCase  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase = None  ,      **_lowerCamelCase  ,      ):
         return normalize(_lowerCamelCase  ,      mean=_lowerCamelCase  ,      std=_lowerCamelCase  ,      data_format=_lowerCamelCase  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = ChannelDimension.FIRST  ,      **_lowerCamelCase  ,      ):
         UpperCamelCase_: int   				=    do_resize if do_resize is not None else self.do_resize
         UpperCamelCase_: Union[str, Any]   				=    resample if resample is not None else self.resample
         UpperCamelCase_: int   				=    do_center_crop if do_center_crop is not None else self.do_center_crop
         UpperCamelCase_: Any   				=    do_rescale if do_rescale is not None else self.do_rescale
         UpperCamelCase_: List[str]   				=    rescale_factor if rescale_factor is not None else self.rescale_factor
         UpperCamelCase_: Any   				=    do_normalize if do_normalize is not None else self.do_normalize
         UpperCamelCase_: Any   				=    image_mean if image_mean is not None else self.image_mean
         UpperCamelCase_: str   				=    image_std if image_std is not None else self.image_std

         UpperCamelCase_: int   				=    size if size is not None else self.size
         UpperCamelCase_: Optional[Any]   				=    get_size_dict(_lowerCamelCase  ,      default_to_square=_lowerCamelCase							)
         UpperCamelCase_: str   				=    crop_size if crop_size is not None else self.crop_size
         UpperCamelCase_: List[str]   				=    get_size_dict(_lowerCamelCase  ,      param_name='crop_size'							)

         UpperCamelCase_: List[Any]   				=    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:
           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.
         UpperCamelCase_: Tuple   				=    [to_numpy_array(_lowerCamelCase							) for image in images]

         if do_resize:
           UpperCamelCase_: str   				=    [self.resize(_lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase							) for image in images]

         if do_center_crop:
           UpperCamelCase_: Dict   				=    [self.center_crop(_lowerCamelCase  ,      _lowerCamelCase							) for image in images]

         if do_rescale:
           UpperCamelCase_: Optional[Any]   				=    [self.rescale(_lowerCamelCase  ,      _lowerCamelCase							) for image in images]

         if do_normalize:
           UpperCamelCase_: Dict   				=    [self.normalize(_lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase							) for image in images]

         UpperCamelCase_: str   				=    [to_channel_dimension_format(_lowerCamelCase  ,      _lowerCamelCase							) for image in images]

         UpperCamelCase_: List[str]   				=    {'pixel_values': images}
         return BatchFeature(data=_lowerCamelCase  ,      tensor_type=_lowerCamelCase							)







def   snake_case (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:
    UpperCamelCase_: List[Any]   				=    F'''The input value of [n={number}] has to be > 0'''
    raise ValueError(UpperCAmelCase__  )
  else:
    UpperCamelCase_: str   				=    sylvester(number - 1  )
    UpperCamelCase_: str   				=    num - 1
    UpperCamelCase_: Any   				=    num
    return lower * upper + 1


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