





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


__lowerCamelCase :  str										=  logging.get_logger(__name__)



def 	A_       (  _lowerCAmelCase							, _lowerCAmelCase  )		->				int:
 UpperCamelCase			:      List[Any] 			=     b.T
 UpperCamelCase			:      Union[str, Any] 			=     np.sum(np.square(_lowerCAmelCase  )							, axis=1  )
 UpperCamelCase			:      List[str] 			=     np.sum(np.square(_lowerCAmelCase  )							, axis=0  )
 UpperCamelCase			:      Dict 			=     np.matmul(_lowerCAmelCase							, _lowerCAmelCase  )
 UpperCamelCase			:      List[str] 			=     aa[:, None] - 2 * ab + ba[None, :]
 return d



def 	A_       (  _lowerCAmelCase							, _lowerCAmelCase  )		->				List[str]:
 UpperCamelCase			:      Dict 			=     x.reshape(-1							, 3  )
 UpperCamelCase			:      List[Any] 			=     squared_euclidean_distance(_lowerCAmelCase							, _lowerCAmelCase  )
 return np.argmin(_lowerCAmelCase							, axis=1  )



class       A__     ( __snake_case    ):
      _UpperCAmelCase			:Optional[int]							=     ['pixel_values']





      def __init__(				self ,							A_ = None ,							A_ = True ,							A_ = None ,							A_ = PILImageResampling.BILINEAR ,							A_ = True ,							A_ = True ,							**A_ ,							):
       '''simple docstring'''




       super().__init__(**A_     )
       UpperCamelCase			:      List[str] 			=     size if size is not None else {"height": 256, "width": 256}
       UpperCamelCase			:      Optional[int] 			=     get_size_dict(A_     )
       UpperCamelCase			:      int 			=     np.array(A_     ) if clusters is not None else None
       UpperCamelCase			:      Optional[Any] 			=     do_resize
       UpperCamelCase			:      List[str] 			=     size
       UpperCamelCase			:      int 			=     resample
       UpperCamelCase			:      Dict 			=     do_normalize
       UpperCamelCase			:      Any 			=     do_color_quantize





      def  __UpperCamelCase(				self ,							A_ ,							A_ ,							A_ = PILImageResampling.BILINEAR ,							A_ = None ,							**A_ ,							):
       '''simple docstring'''




       UpperCamelCase			:      Dict 			=     get_size_dict(A_     )
       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(
           A_ ,							size=(size["height"], size["width"]) ,							resample=A_ ,							data_format=A_ ,							**A_     )





      def  __UpperCamelCase(				self ,							A_ ,							A_ = None ,							):
       '''simple docstring'''




       UpperCamelCase			:      Optional[Any] 			=     rescale(image=A_ ,							scale=1 / 127.5 ,							data_format=A_     )
       UpperCamelCase			:      Tuple 			=     image - 1
       return image





      def  __UpperCamelCase(				self ,							A_ ,							A_ = None ,							A_ = None ,							A_ = None ,							A_ = None ,							A_ = None ,							A_ = None ,							A_ = None ,							A_ = ChannelDimension.FIRST ,							**A_ ,							):
       '''simple docstring'''




       UpperCamelCase			:      Optional[int] 			=     do_resize if do_resize is not None else self.do_resize
       UpperCamelCase			:      str 			=     size if size is not None else self.size
       UpperCamelCase			:      List[str] 			=     get_size_dict(A_     )
       UpperCamelCase			:      Any 			=     resample if resample is not None else self.resample
       UpperCamelCase			:      Any 			=     do_normalize if do_normalize is not None else self.do_normalize
       UpperCamelCase			:      Optional[int] 			=     do_color_quantize if do_color_quantize is not None else self.do_color_quantize
       UpperCamelCase			:      Tuple 			=     clusters if clusters is not None else self.clusters
       UpperCamelCase			:      Dict 			=     np.array(A_     )

       UpperCamelCase			:      Optional[Any] 			=     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 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.
       UpperCamelCase			:      int 			=     [to_numpy_array(A_     ) for image in images]

       if do_resize:
        UpperCamelCase			:      str 			=     [self.resize(image=A_ ,							size=A_ ,							resample=A_     ) for image in images]

       if do_normalize:
        UpperCamelCase			:      Any 			=     [self.normalize(image=A_     ) for image in images]

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

        # flatten to (batch_size, height*width)
        UpperCamelCase			:      int 			=     images.shape[0]
        UpperCamelCase			:      Optional[int] 			=     images.reshape(A_ ,							-1     )

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

       UpperCamelCase			:      Dict 			=     {"input_ids": images}
       return BatchFeature(data=A_ ,							tensor_type=A_     )







from __future__ import annotations



def 	A_       (  _lowerCAmelCase  )		->				list[int]:
 UpperCamelCase			:      Optional[Any] 			=     [True] * limit
 UpperCamelCase			:      Optional[Any] 			=     False
 UpperCamelCase			:      List[str] 			=     False
 UpperCamelCase			:      Tuple 			=     True

 for i in range(3							,     int(limit**0.5 + 1  )							,     2  ):
  UpperCamelCase			:      Optional[Any] 			=     i * 2
  while index < limit:
   UpperCamelCase			:      int 			=     False
   UpperCamelCase			:      Optional[int] 			=     index + i

 UpperCamelCase			:      Any 			=     [2]

 for i in range(3							,     _lowerCAmelCase							,     2  ):
  if is_prime[i]:
   primes.append(_lowerCAmelCase  )

 return primes



def 	A_       (  _lowerCAmelCase = 100_0000  )		->				int:
 UpperCamelCase			:      Union[str, Any] 			=     prime_sieve(_lowerCAmelCase  )
 UpperCamelCase			:      List[str] 			=     0
 UpperCamelCase			:      Union[str, Any] 			=     0

 for i in range(len(_lowerCAmelCase  )  ):
  for j in range(i + length							,     len(_lowerCAmelCase  )  ):
   UpperCamelCase			:      Dict 			=     sum(primes[i:j]  )
   if sol >= ceiling:
    break

   if sol in primes:
    UpperCamelCase			:      int 			=     j - i
    UpperCamelCase			:      Dict 			=     sol

 return largest


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