






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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


SCREAMING_SNAKE_CASE 							=						logging.get_logger(__name__)


if is_vision_available():
  import PIL
class 							A_    (						UpperCAmelCase__			):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:			List[Any] =					['pixel_values']


 def __init__(	self							,  _A = True							,  _A = None							,  _A = PILImageResampling.BICUBIC							,  _A = True							,  _A = None							,  _A = True							,  _A = 1 / 255							,  _A = True							,  _A = None							,  _A = None							,  _A = True							,  **_A							,  ) ->		None:

     """simple docstring"""



     super().__init__(**_A)
     _UpperCAmelCase				:      Optional[int]			  =				size if size is not None else {"""shortest_edge""": 224}
     _UpperCAmelCase				:      Tuple			  =				get_size_dict(_A							,  default_to_square=_A)
     _UpperCAmelCase				:      Dict			  =				crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
     _UpperCAmelCase				:      List[Any]			  =				get_size_dict(_A							,  default_to_square=_A							,  param_name='''crop_size''')

     _UpperCAmelCase				:      int			  =				do_resize
     _UpperCAmelCase				:      int			  =				size
     _UpperCAmelCase				:      Any			  =				resample
     _UpperCAmelCase				:      Optional[Any]			  =				do_center_crop
     _UpperCAmelCase				:      Optional[Any]			  =				crop_size
     _UpperCAmelCase				:      Dict			  =				do_rescale
     _UpperCAmelCase				:      int			  =				rescale_factor
     _UpperCAmelCase				:      Union[str, Any]			  =				do_normalize
     _UpperCAmelCase				:      List[Any]			  =				image_mean if image_mean is not None else OPENAI_CLIP_MEAN
     _UpperCAmelCase				:      str			  =				image_std if image_std is not None else OPENAI_CLIP_STD
     _UpperCAmelCase				:      Optional[Any]			  =				do_convert_rgb


 def  snake_case__				(	self							,  _A							,  _A							,  _A = PILImageResampling.BICUBIC							,  _A = None							,  **_A							,  ) ->		np.ndarray:

     """simple docstring"""



     _UpperCAmelCase				:      List[Any]			  =				get_size_dict(_A							,  default_to_square=_A)
     if "shortest_edge" not in size:
         raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}''')
     _UpperCAmelCase				:      List[str]			  =				get_resize_output_image_size(_A							,  size=size['''shortest_edge''']							,  default_to_square=_A)
     return resize(_A							,  size=_A							,  resample=_A							,  data_format=_A							,  **_A)


 def  snake_case__				(	self							,  _A							,  _A							,  _A = None							,  **_A							,  ) ->		np.ndarray:

     """simple docstring"""



     _UpperCAmelCase				:      int			  =				get_size_dict(_A)
     if "height" not in size or "width" not in size:
         raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}''')
     return center_crop(_A							,  size=(size['''height'''], size['''width'''])							,  data_format=_A							,  **_A)


 def  snake_case__				(	self							,  _A							,  _A							,  _A = None							,  **_A							,  ) ->		Optional[Any]:

     """simple docstring"""



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


 def  snake_case__				(	self							,  _A							,  _A							,  _A							,  _A = None							,  **_A							,  ) ->		np.ndarray:

     """simple docstring"""



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


 def  snake_case__				(	self							,  _A							,  _A = None							,  _A = None							,  _A = None							,  _A = None							,  _A = None							,  _A = None							,  _A = None							,  _A = None							,  _A = None							,  _A = None							,  _A = None							,  _A = None							,  _A = ChannelDimension.FIRST							,  **_A							,  ) ->		PIL.Image.Image:

     """simple docstring"""



     _UpperCAmelCase				:      Optional[int]			  =				do_resize if do_resize is not None else self.do_resize
     _UpperCAmelCase				:      Tuple			  =				size if size is not None else self.size
     _UpperCAmelCase				:      Optional[int]			  =				get_size_dict(_A							,  param_name='''size'''							,  default_to_square=_A)
     _UpperCAmelCase				:      Union[str, Any]			  =				resample if resample is not None else self.resample
     _UpperCAmelCase				:      Dict			  =				do_center_crop if do_center_crop is not None else self.do_center_crop
     _UpperCAmelCase				:      Any			  =				crop_size if crop_size is not None else self.crop_size
     _UpperCAmelCase				:      List[Any]			  =				get_size_dict(_A							,  param_name='''crop_size'''							,  default_to_square=_A)
     _UpperCAmelCase				:      List[Any]			  =				do_rescale if do_rescale is not None else self.do_rescale
     _UpperCAmelCase				:      str			  =				rescale_factor if rescale_factor is not None else self.rescale_factor
     _UpperCAmelCase				:      Union[str, Any]			  =				do_normalize if do_normalize is not None else self.do_normalize
     _UpperCAmelCase				:      Tuple			  =				image_mean if image_mean is not None else self.image_mean
     _UpperCAmelCase				:      Optional[Any]			  =				image_std if image_std is not None else self.image_std
     _UpperCAmelCase				:      Dict			  =				do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

     _UpperCAmelCase				:      Optional[int]			  =				make_list_of_images(_A)

     if not valid_images(_A):
         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.''')

     # PIL RGBA images are converted to RGB
     if do_convert_rgb:
         _UpperCAmelCase				:      List[Any]			  =				[convert_to_rgb(_A) for image in images]

     # All transformations expect numpy arrays.
     _UpperCAmelCase				:      Union[str, Any]			  =				[to_numpy_array(_A) for image in images]

     if do_resize:
         _UpperCAmelCase				:      List[Any]			  =				[self.resize(image=_A							,  size=_A							,  resample=_A) for image in images]

     if do_center_crop:
         _UpperCAmelCase				:      Union[str, Any]			  =				[self.center_crop(image=_A							,  size=_A) for image in images]

     if do_rescale:
         _UpperCAmelCase				:      Tuple			  =				[self.rescale(image=_A							,  scale=_A) for image in images]

     if do_normalize:
         _UpperCAmelCase				:      Optional[Any]			  =				[self.normalize(image=_A							,  mean=_A							,  std=_A) for image in images]

     _UpperCAmelCase				:      Tuple			  =				[to_channel_dimension_format(_A							,  _A) for image in images]

     _UpperCAmelCase				:      Tuple			  =				{"""pixel_values""": images}
     return BatchFeature(data=_A							,  tensor_type=_A)












import math

def      _lowerCamelCase	(						__A    :							int )      ->					int:

    if not isinstance(__A		,		__A ):
        _UpperCAmelCase				:      List[Any]			  =				f'''Input value of [number={number}] must be an integer'''
        raise TypeError(__A )

    if number < 1:
        _UpperCAmelCase				:      Optional[Any]			  =				f'''Input value of [number={number}] must be > 0'''
        raise ValueError(__A )
    elif number == 1:
        return 3
    elif number == 2:
        return 5
    else:
        _UpperCAmelCase				:      Any			  =				int(math.log(number // 3		,		2 ) ) + 2

        _UpperCAmelCase				:      Dict			  =				[3, 5]
        _UpperCAmelCase				:      Tuple			  =				2
        _UpperCAmelCase				:      Dict			  =				3
        for block in range(1		,		__A ):
            for _ in range(__A ):
                proth_list.append(2 ** (block + 1) + proth_list[proth_index - 1] )
                proth_index += 1
            increment *= 2

    return proth_list[number - 1]


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  for number in range(11):
    SCREAMING_SNAKE_CASE 							=						0
    try:
      SCREAMING_SNAKE_CASE 							=						proth(number)
    except ValueError:
      print(F'ValueError: there is no {number}th Proth number')
      continue

    print(F'The {number}th Proth number: {value}')




