

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


_lowerCamelCase       :     Dict           =							logging.get_logger(__name__)


if is_vision_available():
     import PIL





class       __UpperCAmelCase  ( lowerCamelCase__		):
   UpperCamelCase       =  ["""pixel_values"""]



   def __init__(							self							:  Tuple,	__A							:  bool = True,	__A							:  Dict[str, int] = None,	__A							:  PILImageResampling = PILImageResampling.BICUBIC,	__A							:  bool = True,	__A							:  Dict[str, int] = None,	__A							:  bool = True,	__A							:  Union[int, float] = 1 / 2_5_5,	__A							:  bool = True,	__A							:  Optional[Union[float, List[float]]] = None,	__A							:  Optional[Union[float, List[float]]] = None,	__A							:  bool = True,	**__A							:  Union[str, Any],	):
     super().__init__(**__A		)
     UpperCAmelCase :							Optional[int]								=				size if size is not None else {'''shortest_edge''': 2_2_4}
     UpperCAmelCase :							str								=				get_size_dict(__A,	default_to_square=__A		)
     UpperCAmelCase :							List[Any]								=				crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
     UpperCAmelCase :							Any								=				get_size_dict(__A		)

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



   def  __magic_name__					(							self							:  Dict,	__A							:  np.ndarray,	__A							:  Dict[str, int],	__A							:  PILImageResampling = PILImageResampling.BICUBIC,	__A							:  Optional[Union[str, ChannelDimension]] = None,	**__A							:  Union[str, Any],	):
     UpperCAmelCase :							Any								=				get_size_dict(__A,	default_to_square=__A		)
     UpperCAmelCase :							int								=				get_resize_output_image_size(
         __A,	size=(size['''height'''], size['''width''']),	default_to_square=__A		)
     return resize(__A,	size=__A,	resample=__A,	data_format=__A,	**__A		)



   def  __magic_name__					(							self							:  Optional[int],	__A							:  np.ndarray,	__A							:  Dict[str, int],	__A							:  Optional[Union[str, ChannelDimension]] = None,	**__A							:  Union[str, Any],	):
     UpperCAmelCase :							str								=				get_size_dict(__A		)
     return center_crop(__A,	size=(size['''height'''], size['''width''']),	data_format=__A,	**__A		)



   def  __magic_name__					(							self							:  int,	__A							:  np.ndarray,	__A							:  Union[int, float],	__A							:  Optional[Union[str, ChannelDimension]] = None,	**__A							:  str,	):
     return rescale(__A,	scale=__A,	data_format=__A,	**__A		)



   def  __magic_name__					(							self							:  Tuple,	__A							:  np.ndarray,	__A							:  Union[float, List[float]],	__A							:  Union[float, List[float]],	__A							:  Optional[Union[str, ChannelDimension]] = None,	**__A							:  Union[str, Any],	):
     return normalize(__A,	mean=__A,	std=__A,	data_format=__A,	**__A		)





   def  __magic_name__					(							self							:  List[Any],	__A							:  ImageInput,	__A							:  bool = None,	__A							:  Dict[str, int] = None,	__A							:  PILImageResampling = None,	__A							:  bool = None,	__A							:  int = None,	__A							:  bool = None,	__A							:  float = None,	__A							:  bool = None,	__A							:  Optional[Union[float, List[float]]] = None,	__A							:  Optional[Union[float, List[float]]] = None,	__A							:  bool = None,	__A							:  Optional[Union[str, TensorType]] = None,	__A							:  Optional[ChannelDimension] = ChannelDimension.FIRST,	**__A							:  Optional[int],	):
     UpperCAmelCase :							Union[str, Any]								=				do_resize if do_resize is not None else self.do_resize
     UpperCAmelCase :							Optional[Any]								=				size if size is not None else self.size
     UpperCAmelCase :							Dict								=				get_size_dict(__A,	default_to_square=__A		)
     UpperCAmelCase :							List[Any]								=				resample if resample is not None else self.resample
     UpperCAmelCase :							Any								=				do_center_crop if do_center_crop is not None else self.do_center_crop
     UpperCAmelCase :							int								=				crop_size if crop_size is not None else self.crop_size
     UpperCAmelCase :							int								=				get_size_dict(__A		)
     UpperCAmelCase :							List[Any]								=				do_rescale if do_rescale is not None else self.do_rescale
     UpperCAmelCase :							Tuple								=				rescale_factor if rescale_factor is not None else self.rescale_factor
     UpperCAmelCase :							Optional[int]								=				do_normalize if do_normalize is not None else self.do_normalize
     UpperCAmelCase :							int								=				image_mean if image_mean is not None else self.image_mean
     UpperCAmelCase :							List[Any]								=				image_std if image_std is not None else self.image_std
     UpperCAmelCase :							Any								=				do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

     UpperCAmelCase :							List[str]								=				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 :							Tuple								=				[convert_to_rgb(__A		) for image in images]

     # All transformations expect numpy arrays.
     UpperCAmelCase :							int								=				[to_numpy_array(__A		) for image in images]

     if do_resize:
       UpperCAmelCase :							Optional[Any]								=				[self.resize(image=__A,	size=__A,	resample=__A		) for image in images]

     if do_center_crop:
       UpperCAmelCase :							List[str]								=				[self.center_crop(image=__A,	size=__A		) for image in images]

     if do_rescale:
       UpperCAmelCase :							List[Any]								=				[self.rescale(image=__A,	scale=__A		) for image in images]

     if do_normalize:
       UpperCAmelCase :							Union[str, Any]								=				[self.normalize(image=__A,	mean=__A,	std=__A		) for image in images]

     UpperCAmelCase :							Dict								=				[to_channel_dimension_format(__A,	__A		) for image in images]

     UpperCAmelCase :							Dict								=				{'''pixel_values''': images}
     return BatchFeature(data=__A,	tensor_type=__A		)








import decimal

def     a__  (	)						->		int:
  UpperCAmelCase :							List[Any]								=				0
  UpperCAmelCase :							Dict								=				decimal.Context(prec=105    )
  for i in range(2       ,				100    ):
    UpperCAmelCase :							List[str]								=				decimal.Decimal(UpperCAmelCase    )
    UpperCAmelCase :							List[str]								=				number.sqrt(UpperCAmelCase    )
    if len(str(UpperCAmelCase    )    ) > 1:
      answer += int(str(UpperCAmelCase    )[0]    )
      UpperCAmelCase :							str								=				str(UpperCAmelCase    )[2:101]
      answer += sum(int(UpperCAmelCase    ) for x in sqrt_number_str    )
  return answer


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(f"""{solution() = }""")





