




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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    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


UpperCamelCase								=					logging.get_logger(__name__)



def 							_A       (						lowerCAmelCase_					: List[str]       ,    lowerCAmelCase_					: Any      ):
     """simple docstring"""

     lowerCAmelCase__          =			b.T
     lowerCAmelCase__          =			np.sum(np.square(lowerCAmelCase_      )       ,    axis=1      )
     lowerCAmelCase__          =			np.sum(np.square(lowerCAmelCase_      )       ,    axis=0      )
     lowerCAmelCase__          =			np.matmul(lowerCAmelCase_       ,    lowerCAmelCase_      )
     lowerCAmelCase__          =			aa[:, None] - 2 * ab + ba[None, :]
     return d



def 							_A       (						lowerCAmelCase_					: List[str]       ,    lowerCAmelCase_					: str      ):
     """simple docstring"""

     lowerCAmelCase__          =			x.reshape(-1       ,    3      )
     lowerCAmelCase__          =			squared_euclidean_distance(lowerCAmelCase_       ,    lowerCAmelCase_      )
     return np.argmin(lowerCAmelCase_       ,    axis=1      )






class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    snake_case__ =				["pixel_values"]


    def __init__(	self					:					Any       ,     SCREAMING_SNAKE_CASE__					:					Optional[Union[List[List[int]], np.ndarray]] = None       ,     SCREAMING_SNAKE_CASE__					:					bool = True       ,     SCREAMING_SNAKE_CASE__					:					Dict[str, int] = None       ,     SCREAMING_SNAKE_CASE__					:					PILImageResampling = PILImageResampling.BILINEAR       ,     SCREAMING_SNAKE_CASE__					:					bool = True       ,     SCREAMING_SNAKE_CASE__					:					bool = True       ,     **SCREAMING_SNAKE_CASE__					:					Any       ,     )				->	None:
         super().__init__(**SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			size if size is not None else {"height": 256, "width": 256}
         lowerCAmelCase__          =			get_size_dict(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			np.array(SCREAMING_SNAKE_CASE__       ) if clusters is not None else None
         lowerCAmelCase__          =			do_resize
         lowerCAmelCase__          =			size
         lowerCAmelCase__          =			resample
         lowerCAmelCase__          =			do_normalize
         lowerCAmelCase__          =			do_color_quantize


    def        a				(	self					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					np.ndarray       ,     SCREAMING_SNAKE_CASE__					:					Dict[str, int]       ,     SCREAMING_SNAKE_CASE__					:					PILImageResampling = PILImageResampling.BILINEAR       ,     SCREAMING_SNAKE_CASE__					:					Optional[Union[str, ChannelDimension]] = None       ,     **SCREAMING_SNAKE_CASE__					:					Tuple       ,     )				->	np.ndarray:
         lowerCAmelCase__          =			get_size_dict(SCREAMING_SNAKE_CASE__       )
         if "height" not in size or "width" not in size:
              raise ValueError(f'Size dictionary must contain both height and width keys. Got {size.keys()}'       )
         return resize(
             SCREAMING_SNAKE_CASE__       ,     size=(size["height"], size["width"])       ,     resample=SCREAMING_SNAKE_CASE__       ,     data_format=SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Any       ,     SCREAMING_SNAKE_CASE__					:					np.ndarray       ,     SCREAMING_SNAKE_CASE__					:					Optional[Union[str, ChannelDimension]] = None       ,     )				->	np.ndarray:
         lowerCAmelCase__          =			rescale(image=SCREAMING_SNAKE_CASE__       ,     scale=1 / 127.5       ,     data_format=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			image - 1
         return image


    def        a				(	self					:					Any       ,     SCREAMING_SNAKE_CASE__					:					ImageInput       ,     SCREAMING_SNAKE_CASE__					:					bool = None       ,     SCREAMING_SNAKE_CASE__					:					Dict[str, int] = None       ,     SCREAMING_SNAKE_CASE__					:					PILImageResampling = None       ,     SCREAMING_SNAKE_CASE__					:					bool = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[bool] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[Union[List[List[int]], np.ndarray]] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[Union[str, TensorType]] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST       ,     **SCREAMING_SNAKE_CASE__					:					List[str]       ,     )				->	PIL.Image.Image:
         lowerCAmelCase__          =			do_resize if do_resize is not None else self.do_resize
         lowerCAmelCase__          =			size if size is not None else self.size
         lowerCAmelCase__          =			get_size_dict(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			resample if resample is not None else self.resample
         lowerCAmelCase__          =			do_normalize if do_normalize is not None else self.do_normalize
         lowerCAmelCase__          =			do_color_quantize if do_color_quantize is not None else self.do_color_quantize
         lowerCAmelCase__          =			clusters if clusters is not None else self.clusters
         lowerCAmelCase__          =			np.array(SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			make_list_of_images(SCREAMING_SNAKE_CASE__       )

         if not valid_images(SCREAMING_SNAKE_CASE__       ):
              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_color_quantize and clusters is None:
              raise ValueError("Clusters must be specified if do_color_quantize is True."       )

         # All transformations expect numpy arrays.
         lowerCAmelCase__          =			[to_numpy_array(SCREAMING_SNAKE_CASE__       ) for image in images]

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

         if do_normalize:
              lowerCAmelCase__          =			[self.normalize(image=SCREAMING_SNAKE_CASE__       ) for image in images]

         if do_color_quantize:
              lowerCAmelCase__          =			[to_channel_dimension_format(SCREAMING_SNAKE_CASE__       ,     ChannelDimension.LAST       ) for image in images]
              # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
              lowerCAmelCase__          =			np.array(SCREAMING_SNAKE_CASE__       )
              lowerCAmelCase__          =			color_quantize(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ).reshape(images.shape[:-1]       )

              # flatten to (batch_size, height*width)
              lowerCAmelCase__          =			images.shape[0]
              lowerCAmelCase__          =			images.reshape(SCREAMING_SNAKE_CASE__       ,     -1       )

              # We need to convert back to a list of images to keep consistent behaviour across processors.
              lowerCAmelCase__          =			list(SCREAMING_SNAKE_CASE__       )
         else:
              lowerCAmelCase__          =			[to_channel_dimension_format(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ) for image in images]

         lowerCAmelCase__          =			{"input_ids": images}
         return BatchFeature(data=SCREAMING_SNAKE_CASE__       ,     tensor_type=SCREAMING_SNAKE_CASE__       )






def 							_A       (						lowerCAmelCase_					: int = 1000      ):
     """simple docstring"""

     return sum(2 * a * ((a - 1) // 2) for a in range(3       ,    n + 1      )      )


if __name__ == "__main__":
     print(solution())
