





'''simple docstring'''

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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
    import PIL


a   	=    logging.get_logger(__name__)







class __a			( _snake_case      ):
   __UpperCamelCase :			Optional[Any]						   =						['pixel_values']





   def __init__(       self					:      Optional[Any]					,lowerCamelCase					:      bool = True					,lowerCamelCase					:      Dict[str, int] = None					,lowerCamelCase					:      PILImageResampling = PIL.Image.BICUBIC					,lowerCamelCase					:      bool = True					,lowerCamelCase					:      Dict[str, int] = None					,lowerCamelCase					:      Union[int, float] = 1 / 255					,lowerCamelCase					:      bool = True					,lowerCamelCase					:      bool = True					,lowerCamelCase					:      Optional[Union[float, List[float]]] = None					,lowerCamelCase					:      Optional[Union[float, List[float]]] = None					,**lowerCamelCase					:      Union[str, Any]					,):

      '''simple docstring'''

      super().__init__(**lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							size if size is not None else {"""height""": 256, """width""": 256}
      __SCREAMING_SNAKE_CASE   				=							get_size_dict(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
      __SCREAMING_SNAKE_CASE   				=							get_size_dict(lowerCamelCase					,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_STANDARD_MEAN
      __SCREAMING_SNAKE_CASE   				=							image_std if image_std is not None else IMAGENET_STANDARD_STD





   def   UpperCAmelCase__  (       self					:      Optional[int]					,lowerCamelCase					:      np.ndarray					,lowerCamelCase					:      Dict[str, int]					,lowerCamelCase					:      PILImageResampling = PIL.Image.BICUBIC					,lowerCamelCase					:      Optional[Union[str, ChannelDimension]] = None					,**lowerCamelCase					:      List[Any]					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							get_size_dict(lowerCamelCase     )
      if "height" not in size or "width" not in size:
         raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""     )
      return resize(
          lowerCamelCase					,size=(size["""height"""], size["""width"""])					,resample=lowerCamelCase					,data_format=lowerCamelCase					,**lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Dict					,lowerCamelCase					:      np.ndarray					,lowerCamelCase					:      Dict[str, int]					,lowerCamelCase					:      Optional[Union[str, ChannelDimension]] = None					,**lowerCamelCase					:      str					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							get_size_dict(lowerCamelCase     )
      if "height" not in size or "width" not in size:
         raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""     )
      return center_crop(lowerCamelCase					,size=(size["""height"""], size["""width"""])					,data_format=lowerCamelCase					,**lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Optional[int]					,lowerCamelCase					:      np.ndarray					,lowerCamelCase					:      Union[int, float]					,lowerCamelCase					:      Optional[Union[str, ChannelDimension]] = None					,**lowerCamelCase					:      Optional[Any]					,):

      '''simple docstring'''

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





   def   UpperCAmelCase__  (       self					:      Dict					,lowerCamelCase					:      np.ndarray					,lowerCamelCase					:      Union[float, List[float]]					,lowerCamelCase					:      Union[float, List[float]]					,lowerCamelCase					:      Optional[Union[str, ChannelDimension]] = None					,**lowerCamelCase					:      Any					,):

      '''simple docstring'''

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





   def   UpperCAmelCase__  (       self					:      List[Any]					,lowerCamelCase					:      ImageInput					,lowerCamelCase					:      bool = None					,lowerCamelCase					:      Dict[str, int] = None					,lowerCamelCase					:      Any=None					,lowerCamelCase					:      bool = None					,lowerCamelCase					:      Dict[str, int] = None					,lowerCamelCase					:      bool = None					,lowerCamelCase					:      float = None					,lowerCamelCase					:      bool = None					,lowerCamelCase					:      Optional[Union[float, List[float]]] = None					,lowerCamelCase					:      Optional[Union[float, List[float]]] = None					,lowerCamelCase					:      Optional[Union[str, TensorType]] = None					,lowerCamelCase					:      ChannelDimension = ChannelDimension.FIRST					,**lowerCamelCase					:      Optional[int]					,):

      '''simple docstring'''

      __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(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							crop_size if crop_size is not None else self.crop_size
      __SCREAMING_SNAKE_CASE   				=							get_size_dict(lowerCamelCase					,param_name="""crop_size"""     )

      __SCREAMING_SNAKE_CASE   				=							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 or resample is None:
         raise ValueError("""Size and resample 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(lowerCamelCase     ) for image in images]

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

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

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

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

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

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








def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE = 100   ):
      '''simple docstring'''







      A_							       =		0
      A_							       =		0
      for i in range(1		,				n + 1   ):
            sum_of_squares += i**2
            sum_of_ints += i
      return sum_of_ints**2 - sum_of_squares


if __name__ == "__main__":
      print(f'{solution() = }')





