





'''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__							:							List[str] =logging.get_logger(__name__)
def 				lowercase__   (   __lowercase						:  str				,  __lowercase						:  List[Any]     ) ->      Dict:



       """simple docstring"""






       __UpperCamelCase       =  b.T
       __UpperCamelCase       =  np.sum(np.square(__lowercase     )				,  axis=1     )
       __UpperCamelCase       =  np.sum(np.square(__lowercase     )				,  axis=0     )
       __UpperCamelCase       =  np.matmul(__lowercase				,  __lowercase     )
       __UpperCamelCase       =  aa[:, None] - 2 * ab + ba[None, :]
       return d
def 				lowercase__   (   __lowercase						:  Tuple				,  __lowercase						:  Dict     ) ->      List[Any]:



       """simple docstring"""






       __UpperCamelCase       =  x.reshape(-1				,  3     )
       __UpperCamelCase       =  squared_euclidean_distance(__lowercase				,  __lowercase     )
       return np.argmin(__lowercase				,  axis=1     )


class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					List[Any]					=["pixel_values"]






 def __init__(       self       :							Union[str, Any]							,	__A       :							Optional[Union[List[List[int]], np.ndarray]] = None							,	__A       :							bool = True							,	__A       :							Dict[str, int] = None							,	__A       :							PILImageResampling = PILImageResampling.BILINEAR							,	__A       :							bool = True							,	__A       :							bool = True							,	**__A       :							List[str]							,	):
        super().__init__(**__A	)
        __UpperCamelCase       =  size if size is not None else {'height': 2_5_6, 'width': 2_5_6}
        __UpperCamelCase       =  get_size_dict(__A	)
        __UpperCamelCase       =  np.array(__A	) if clusters is not None else None
        __UpperCamelCase       =  do_resize
        __UpperCamelCase       =  size
        __UpperCamelCase       =  resample
        __UpperCamelCase       =  do_normalize
        __UpperCamelCase       =  do_color_quantize






 def        _lowerCamelCase					(       self       :							Optional[int]							,	__A       :							np.ndarray							,	__A       :							Dict[str, int]							,	__A       :							PILImageResampling = PILImageResampling.BILINEAR							,	__A       :							Optional[Union[str, ChannelDimension]] = None							,	**__A       :							Union[str, Any]							,	):
        __UpperCamelCase       =  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        _lowerCamelCase					(       self       :							Dict							,	__A       :							np.ndarray							,	__A       :							Optional[Union[str, ChannelDimension]] = None							,	):
        __UpperCamelCase       =  rescale(image=__A							,	scale=1 / 127.5							,	data_format=__A	)
        __UpperCamelCase       =  image - 1
        return image






 def        _lowerCamelCase					(       self       :							Optional[Any]							,	__A       :							ImageInput							,	__A       :							bool = None							,	__A       :							Dict[str, int] = None							,	__A       :							PILImageResampling = None							,	__A       :							bool = None							,	__A       :							Optional[bool] = None							,	__A       :							Optional[Union[List[List[int]], np.ndarray]] = None							,	__A       :							Optional[Union[str, TensorType]] = None							,	__A       :							Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST							,	**__A       :							Optional[int]							,	):
        __UpperCamelCase       =  do_resize if do_resize is not None else self.do_resize
        __UpperCamelCase       =  size if size is not None else self.size
        __UpperCamelCase       =  get_size_dict(__A	)
        __UpperCamelCase       =  resample if resample is not None else self.resample
        __UpperCamelCase       =  do_normalize if do_normalize is not None else self.do_normalize
        __UpperCamelCase       =  do_color_quantize if do_color_quantize is not None else self.do_color_quantize
        __UpperCamelCase       =  clusters if clusters is not None else self.clusters
        __UpperCamelCase       =  np.array(__A	)

        __UpperCamelCase       =  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       =  [to_numpy_array(__A	) for image in images]

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

        if do_normalize:
               __UpperCamelCase       =  [self.normalize(image=__A	) for image in images]

        if do_color_quantize:
               __UpperCamelCase       =  [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       =  np.array(__A	)
               __UpperCamelCase       =  color_quantize(__A							,	__A	).reshape(images.shape[:-1]	)

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

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

        __UpperCamelCase       =  {'input_ids': images}
        return BatchFeature(data=__A							,	tensor_type=__A	)




_A		       =						{0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}

_A		       =						{0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					dict[int, list[int]]			,     SCREAMING_SNAKE_CASE__			:					int			,     SCREAMING_SNAKE_CASE__			:					list[bool]				):
     __UpperCamelCase    							=True
     __UpperCamelCase    							=[]

     for neighbour in graph[vert]:
          if not visited[neighbour]:
               order += topology_sort(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)

     order.append(SCREAMING_SNAKE_CASE__				)

     return order

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					dict[int, list[int]]			,     SCREAMING_SNAKE_CASE__			:					int			,     SCREAMING_SNAKE_CASE__			:					list[bool]				):
     __UpperCamelCase    							=True
     __UpperCamelCase    							=[vert]

     for neighbour in reversed_graph[vert]:
          if not visited[neighbour]:
               component += find_components(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)

     return component





def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					dict[int, list[int]]				):
     __UpperCamelCase    							=len(SCREAMING_SNAKE_CASE__				) * [False]
     __UpperCamelCase    							={vert: [] for vert in range(len(SCREAMING_SNAKE_CASE__				)				)}

     for vert, neighbours in graph.items():
          for neighbour in neighbours:
               reversed_graph[neighbour].append(SCREAMING_SNAKE_CASE__				)

     __UpperCamelCase    							=[]
     for i, was_visited in enumerate(SCREAMING_SNAKE_CASE__				):
          if not was_visited:
               order += topology_sort(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)

     __UpperCamelCase    							=[]
     __UpperCamelCase    							=len(SCREAMING_SNAKE_CASE__				) * [False]

     for i in range(len(SCREAMING_SNAKE_CASE__				)				):
          __UpperCamelCase    							=order[len(SCREAMING_SNAKE_CASE__				) - i - 1]
          if not visited[vert]:
               __UpperCamelCase    							=find_components(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)
               components_list.append(SCREAMING_SNAKE_CASE__				)

     return components_list





