






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__			:    Dict         =				logging.get_logger(__name__)






def 						a    (		lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    b.T
				lowercase__     				=    np.sum(np.square(lowerCamelCase_       )	,     axis=1       )
				lowercase__     				=    np.sum(np.square(lowerCamelCase_       )	,     axis=0       )
				lowercase__     				=    np.matmul(lowerCamelCase_	,     lowerCamelCase_       )
				lowercase__     				=    aa[:, None] - 2 * ab + ba[None, :]
				return d






def 						a    (		lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    x.reshape(-1	,     3       )
				lowercase__     				=    squared_euclidean_distance(lowerCamelCase_	,     lowerCamelCase_       )
				return np.argmin(lowerCamelCase_	,     axis=1       )




class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				lowercase__       							= ["""pixel_values"""]




				def __init__(  self		:					Union[str, Any],						lowerCamelCase		:					Optional[Union[List[List[int]], np.ndarray]] = None,						lowerCamelCase		:					bool = True,						lowerCamelCase		:					Dict[str, int] = None,						lowerCamelCase		:					PILImageResampling = PILImageResampling.BILINEAR,						lowerCamelCase		:					bool = True,						lowerCamelCase		:					bool = True,						**lowerCamelCase		:					str,						):

								'''simple docstring'''
								super().__init__(**lowerCamelCase						)
								lowercase__     				=    size if size is not None else {'''height''': 256, '''width''': 256}
								lowercase__     				=    get_size_dict(lowerCamelCase						)
								lowercase__     				=    np.array(lowerCamelCase						) if clusters is not None else None
								lowercase__     				=    do_resize
								lowercase__     				=    size
								lowercase__     				=    resample
								lowercase__     				=    do_normalize
								lowercase__     				=    do_color_quantize




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

								'''simple docstring'''
								lowercase__     				=    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        lowercase__     (  self		:					Any,						lowerCamelCase		:					np.ndarray,						lowerCamelCase		:					Optional[Union[str, ChannelDimension]] = None,						):

								'''simple docstring'''
								lowercase__     				=    rescale(image=lowerCamelCase,						scale=1 / 127.5,						data_format=lowerCamelCase						)
								lowercase__     				=    image - 1
								return image




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

								'''simple docstring'''
								lowercase__     				=    do_resize if do_resize is not None else self.do_resize
								lowercase__     				=    size if size is not None else self.size
								lowercase__     				=    get_size_dict(lowerCamelCase						)
								lowercase__     				=    resample if resample is not None else self.resample
								lowercase__     				=    do_normalize if do_normalize is not None else self.do_normalize
								lowercase__     				=    do_color_quantize if do_color_quantize is not None else self.do_color_quantize
								lowercase__     				=    clusters if clusters is not None else self.clusters
								lowercase__     				=    np.array(lowerCamelCase						)

								lowercase__     				=    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.
								lowercase__     				=    [to_numpy_array(lowerCamelCase						) for image in images]

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

								if do_normalize:
												lowercase__     				=    [self.normalize(image=lowerCamelCase						) for image in images]

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

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

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

								lowercase__     				=    {'''input_ids''': images}
								return BatchFeature(data=lowerCamelCase,						tensor_type=lowerCamelCase						)












from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class    _UpperCAmelCase      :

				"""simple docstring"""




				lowercase__       							= 42
				lowercase__       							= 42



class    _UpperCAmelCase      :

				"""simple docstring"""




				def __init__(  self		:					str,						lowerCamelCase		:					int						):

								'''simple docstring'''
								lowercase__     				=    [[] for _ in range(lowerCamelCase						)]
								lowercase__     				=    size




				def __getitem__(  self		:					Optional[Any],						lowerCamelCase		:					int						):

								'''simple docstring'''
								return iter(self._graph[vertex]						)




				@property
				def        lowercase__     (  self		:					str						):

								'''simple docstring'''
								return self._size




				def        lowercase__     (  self		:					Union[str, Any],						lowerCamelCase		:					int,						lowerCamelCase		:					int,						lowerCamelCase		:					int						):

								'''simple docstring'''
								if weight not in (0, 1):
												raise ValueError('''Edge weight must be either 0 or 1.'''						)

								if to_vertex < 0 or to_vertex >= self.size:
												raise ValueError('''Vertex indexes must be in [0; size).'''						)

								self._graph[from_vertex].append(Edge(lowerCamelCase,						lowerCamelCase						)						)




				def        lowercase__     (  self		:					Optional[int],						lowerCamelCase		:					int,						lowerCamelCase		:					int						):

								'''simple docstring'''
								lowercase__     				=    deque([start_vertex]						)
								lowercase__     				=    [None] * self.size
								lowercase__     				=    0

								while queue:
												lowercase__     				=    queue.popleft()
												lowercase__     				=    distances[current_vertex]
												if current_distance is None:
																continue

												for edge in self[current_vertex]:
																lowercase__     				=    current_distance + edge.weight
																lowercase__     				=    distances[edge.destination_vertex]
																if (
																    isinstance(lowerCamelCase,						lowerCamelCase						)
																    and new_distance >= dest_vertex_distance
																):
																				continue
																lowercase__     				=    new_distance
																if edge.weight == 0:
																				queue.appendleft(edge.destination_vertex						)
																else:
																				queue.append(edge.destination_vertex						)

								if distances[finish_vertex] is None:
												raise ValueError('''No path from start_vertex to finish_vertex.'''						)

								return distances[finish_vertex]


if __name__ == "__main__":
							import doctest

							doctest.testmod()




