


'''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 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


A				:					Optional[int] 						=			logging.get_logger(__name__)






def 				lowerCAmelCase__	(    lowerCamelCase					:      int  ,lowerCamelCase					:      List[str]       ):
       _A					:     Any     = b.T
       _A					:     List[Any]     = np.sum(np.square(lowerCamelCase_       )  ,axis=1       )
       _A					:     Dict     = np.sum(np.square(lowerCamelCase_       )  ,axis=0       )
       _A					:     Optional[int]     = np.matmul(lowerCamelCase_  ,lowerCamelCase_       )
       _A					:     Union[str, Any]     = aa[:, None] - 2 * ab + ba[None, :]
       return d






def 				lowerCAmelCase__	(    lowerCamelCase					:      Optional[int]  ,lowerCamelCase					:      Any       ):
       _A					:     Tuple     = x.reshape(-1  ,3       )
       _A					:     str     = squared_euclidean_distance(lowerCamelCase_  ,lowerCamelCase_       )
       return np.argmin(lowerCamelCase_  ,axis=1       )


class 			__lowerCamelCase			(	lowerCamelCase__	):
    """simple docstring"""
    a													=   ["pixel_values"]


    def __init__(  self      :							Dict				,							SCREAMING_SNAKE_CASE      :							int = None				,							SCREAMING_SNAKE_CASE      :							Union[str, Any] = True				,							SCREAMING_SNAKE_CASE      :							Union[str, Any] = None				,							SCREAMING_SNAKE_CASE      :							Any = PILImageResampling.BILINEAR				,							SCREAMING_SNAKE_CASE      :							Any = True				,							SCREAMING_SNAKE_CASE      :							Union[str, Any] = True				,							**SCREAMING_SNAKE_CASE      :							Optional[int]				,							):
           super().__init__(**__lowerCamelCase)
           _A					:     Optional[Any]     = size if size is not None else {'''height''': 256, '''width''': 256}
           _A					:     Optional[Any]     = get_size_dict(__lowerCamelCase)
           _A					:     List[str]     = np.array(__lowerCamelCase) if clusters is not None else None
           _A					:     Any     = do_resize
           _A					:     List[str]     = size
           _A					:     str     = resample
           _A					:     List[str]     = do_normalize
           _A					:     Union[str, Any]     = do_color_quantize


    def        A   (  self      :							List[str]				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							Optional[Any] = PILImageResampling.BILINEAR				,							SCREAMING_SNAKE_CASE      :							Optional[Any] = None				,							**SCREAMING_SNAKE_CASE      :							str				,							):
           _A					:     Union[str, Any]     = get_size_dict(__lowerCamelCase)
           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(
               __lowerCamelCase				,							size=(size['height'], size['width'])				,							resample=__lowerCamelCase				,							data_format=__lowerCamelCase				,							**__lowerCamelCase)


    def        A   (  self      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							Optional[Any] = None				,							):
           _A					:     int     = rescale(image=__lowerCamelCase				,							scale=1 / 127.5				,							data_format=__lowerCamelCase)
           _A					:     Union[str, Any]     = image - 1
           return image


    def        A   (  self      :							Any				,							SCREAMING_SNAKE_CASE      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							List[Any] = None				,							SCREAMING_SNAKE_CASE      :							str = None				,							SCREAMING_SNAKE_CASE      :							Union[str, Any] = None				,							SCREAMING_SNAKE_CASE      :							str = None				,							SCREAMING_SNAKE_CASE      :							Any = None				,							SCREAMING_SNAKE_CASE      :							Dict = None				,							SCREAMING_SNAKE_CASE      :							int = None				,							SCREAMING_SNAKE_CASE      :							Optional[int] = ChannelDimension.FIRST				,							**SCREAMING_SNAKE_CASE      :							int				,							):
           _A					:     Any     = do_resize if do_resize is not None else self.do_resize
           _A					:     int     = size if size is not None else self.size
           _A					:     str     = get_size_dict(__lowerCamelCase)
           _A					:     Dict     = resample if resample is not None else self.resample
           _A					:     Dict     = do_normalize if do_normalize is not None else self.do_normalize
           _A					:     str     = do_color_quantize if do_color_quantize is not None else self.do_color_quantize
           _A					:     Any     = clusters if clusters is not None else self.clusters
           _A					:     Tuple     = np.array(__lowerCamelCase)

           _A					:     str     = 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_color_quantize and clusters is None:
                  raise ValueError('Clusters must be specified if do_color_quantize is True.')

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

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

           if do_normalize:
                  _A					:     Optional[Any]     = [self.normalize(image=__lowerCamelCase) for image in images]

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

                  # flatten to (batch_size, height*width)
                  _A					:     Optional[Any]     = images.shape[0]
                  _A					:     List[str]     = images.reshape(__lowerCamelCase				,							-1)

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

           _A					:     Tuple     = {'''input_ids''': images}
           return BatchFeature(data=__lowerCamelCase				,							tensor_type=__lowerCamelCase)










'''simple docstring'''







def 				lowerCAmelCase__	(    lowerCamelCase					:      int  ,lowerCamelCase					:      list[int]  ,lowerCamelCase					:      int       ):

       def count_of_possible_combinations(lowerCamelCase					:      int       ) -> int:
              if target < 0:
                     return 0
              if target == 0:
                     return 1
              return sum(count_of_possible_combinations(target - item       ) for item in array       )

       return count_of_possible_combinations(lowerCamelCase       )






def 				lowerCAmelCase__	(    lowerCamelCase					:      int  ,lowerCamelCase					:      list[int]  ,lowerCamelCase					:      int       ):

       def count_of_possible_combinations_with_dp_array(
           lowerCamelCase					:      int  ,lowerCamelCase					:      list[int]       ) -> int:
              if target < 0:
                     return 0
              if target == 0:
                     return 1
              if dp_array[target] != -1:
                     return dp_array[target]
              _A					:     Optional[Any]     = sum(
                  count_of_possible_combinations_with_dp_array(target - item  ,lowerCamelCase       )
                  for item in array       )
              _A					:     List[str]     = answer
              return answer

       _A					:     Optional[int]     = [-1] * (target + 1)
       return count_of_possible_combinations_with_dp_array(lowerCamelCase  ,lowerCamelCase       )






def 				lowerCAmelCase__	(    lowerCamelCase					:      int  ,lowerCamelCase					:      list[int]  ,lowerCamelCase					:      int       ):
       _A					:     Dict     = [0] * (target + 1)
       _A					:     List[str]     = 1

       for i in range(1  ,target + 1       ):
              for j in range(lowerCamelCase       ):
                     if i - array[j] >= 0:
                            dp_array[i] += dp_array[i - array[j]]

       return dp_array[target]


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   A				:					Dict    						=			3
   A				:					Union[str, Any]    						=			5
   A				:					Union[str, Any]    						=			[1, 2, 5]
   print(combination_sum_iv(n, array, target))






