






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


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


if is_vision_available():
       import PIL
class 					a					(				lowercase__     ):



      """simple docstring"""


      a						:				str							 =			['pixel_values']
      def __init__(   self      :    Tuple	,   __lowercase      :    bool = True	,   __lowercase      :    Dict[str, int] = None	,   __lowercase      :    PILImageResampling = PILImageResampling.BICUBIC	,   __lowercase      :    bool = True	,   __lowercase      :    Dict[str, int] = None	,   __lowercase      :    bool = True	,   __lowercase      :    Union[int, float] = 1 / 255	,   __lowercase      :    bool = True	,   __lowercase      :    Optional[Union[float, List[float]]] = None	,   __lowercase      :    Optional[Union[float, List[float]]] = None	,   __lowercase      :    bool = True	,   **__lowercase      :    List[Any]	,   )							->   None:
        super().__init__(**__lowercase      )
        __UpperCAmelCase							:			Optional[int]     =						size if size is not None else {"""shortest_edge""": 224}
        __UpperCAmelCase							:			int     =						get_size_dict(__lowercase	,   default_to_square=__lowercase      )
        __UpperCAmelCase							:			Optional[Any]     =						crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
        __UpperCAmelCase							:			List[str]     =						get_size_dict(__lowercase      )

        __UpperCAmelCase							:			Tuple     =						do_resize
        __UpperCAmelCase							:			int     =						size
        __UpperCAmelCase							:			List[str]     =						resample
        __UpperCAmelCase							:			Union[str, Any]     =						do_center_crop
        __UpperCAmelCase							:			Any     =						crop_size
        __UpperCAmelCase							:			Optional[int]     =						do_rescale
        __UpperCAmelCase							:			Optional[Any]     =						rescale_factor
        __UpperCAmelCase							:			str     =						do_normalize
        __UpperCAmelCase							:			Union[str, Any]     =						image_mean if image_mean is not None else OPENAI_CLIP_MEAN
        __UpperCAmelCase							:			Union[str, Any]     =						image_std if image_std is not None else OPENAI_CLIP_STD
        __UpperCAmelCase							:			List[str]     =						do_convert_rgb
      def 			UpperCAmelCase   (   self      :    str	,   __lowercase      :    np.ndarray	,   __lowercase      :    Dict[str, int]	,   __lowercase      :    PILImageResampling = PILImageResampling.BICUBIC	,   __lowercase      :    Optional[Union[str, ChannelDimension]] = None	,   **__lowercase      :    Optional[int]	,   )							->   np.ndarray:
        __UpperCAmelCase							:			Optional[Any]     =						get_size_dict(__lowercase	,   default_to_square=__lowercase      )
        __UpperCAmelCase							:			List[Any]     =						get_resize_output_image_size(
            __lowercase	,   size=(size["""height"""], size["""width"""])	,   default_to_square=__lowercase      )
        return resize(__lowercase	,   size=__lowercase	,   resample=__lowercase	,   data_format=__lowercase	,   **__lowercase      )
      def 			UpperCAmelCase   (   self      :    List[str]	,   __lowercase      :    np.ndarray	,   __lowercase      :    Dict[str, int]	,   __lowercase      :    Optional[Union[str, ChannelDimension]] = None	,   **__lowercase      :    int	,   )							->   np.ndarray:
        __UpperCAmelCase							:			Union[str, Any]     =						get_size_dict(__lowercase      )
        return center_crop(__lowercase	,   size=(size["""height"""], size["""width"""])	,   data_format=__lowercase	,   **__lowercase      )
      def 			UpperCAmelCase   (   self      :    Any	,   __lowercase      :    np.ndarray	,   __lowercase      :    Union[int, float]	,   __lowercase      :    Optional[Union[str, ChannelDimension]] = None	,   **__lowercase      :    str	,   )							->   str:
        return rescale(__lowercase	,   scale=__lowercase	,   data_format=__lowercase	,   **__lowercase      )
      def 			UpperCAmelCase   (   self      :    Tuple	,   __lowercase      :    np.ndarray	,   __lowercase      :    Union[float, List[float]]	,   __lowercase      :    Union[float, List[float]]	,   __lowercase      :    Optional[Union[str, ChannelDimension]] = None	,   **__lowercase      :    Optional[Any]	,   )							->   np.ndarray:
        return normalize(__lowercase	,   mean=__lowercase	,   std=__lowercase	,   data_format=__lowercase	,   **__lowercase      )




      def 			UpperCAmelCase   (   self      :    List[Any]	,   __lowercase      :    ImageInput	,   __lowercase      :    bool = None	,   __lowercase      :    Dict[str, int] = None	,   __lowercase      :    PILImageResampling = None	,   __lowercase      :    bool = None	,   __lowercase      :    int = None	,   __lowercase      :    bool = None	,   __lowercase      :    float = None	,   __lowercase      :    bool = None	,   __lowercase      :    Optional[Union[float, List[float]]] = None	,   __lowercase      :    Optional[Union[float, List[float]]] = None	,   __lowercase      :    bool = None	,   __lowercase      :    Optional[Union[str, TensorType]] = None	,   __lowercase      :    Optional[ChannelDimension] = ChannelDimension.FIRST	,   **__lowercase      :    Union[str, Any]	,   )							->   PIL.Image.Image:
        __UpperCAmelCase							:			Dict     =						do_resize if do_resize is not None else self.do_resize
        __UpperCAmelCase							:			List[str]     =						size if size is not None else self.size
        __UpperCAmelCase							:			List[Any]     =						get_size_dict(__lowercase	,   default_to_square=__lowercase      )
        __UpperCAmelCase							:			Tuple     =						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							:			List[str]     =						crop_size if crop_size is not None else self.crop_size
        __UpperCAmelCase							:			Optional[Any]     =						get_size_dict(__lowercase      )
        __UpperCAmelCase							:			List[str]     =						do_rescale if do_rescale is not None else self.do_rescale
        __UpperCAmelCase							:			int     =						rescale_factor if rescale_factor is not None else self.rescale_factor
        __UpperCAmelCase							:			Dict     =						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							:			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							:			Union[str, Any]     =						make_list_of_images(__lowercase      )

        if not valid_images(__lowercase      ):
          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							:			Optional[Any]     =						[convert_to_rgb(__lowercase      ) for image in images]

        # All transformations expect numpy arrays.
        __UpperCAmelCase							:			Tuple     =						[to_numpy_array(__lowercase      ) for image in images]

        if do_resize:
          __UpperCAmelCase							:			Optional[int]     =						[self.resize(image=__lowercase	,   size=__lowercase	,   resample=__lowercase      ) for image in images]

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

        if do_rescale:
          __UpperCAmelCase							:			List[str]     =						[self.rescale(image=__lowercase	,   scale=__lowercase      ) for image in images]

        if do_normalize:
          __UpperCAmelCase							:			int     =						[self.normalize(image=__lowercase	,   mean=__lowercase	,   std=__lowercase      ) for image in images]

        __UpperCAmelCase							:			str     =						[to_channel_dimension_format(__lowercase	,   __lowercase      ) for image in images]

        __UpperCAmelCase							:			Optional[int]     =						{"""pixel_values""": images}
        return BatchFeature(data=__lowercase	,   tensor_type=__lowercase      )










import decimal






def        lowerCamelCase__   (					):
  __UpperCAmelCase							:			str     =						0
  __UpperCAmelCase							:			Tuple     =						decimal.Context(prec=105	)
  for i in range(2						,	100	):
    __UpperCAmelCase							:			Optional[int]     =						decimal.Decimal(__lowerCamelCase	)
    __UpperCAmelCase							:			Any     =						number.sqrt(__lowerCamelCase	)
    if len(str(__lowerCamelCase	)	) > 1:
      answer += int(str(__lowerCamelCase	)[0]	)
      __UpperCAmelCase							:			Optional[Any]     =						str(__lowerCamelCase	)[2:101]
      answer += sum(int(__lowerCamelCase	) for x in sqrt_number_str	)
  return answer


if __name__ == "__main__":
       import doctest

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


