


"""simple docstring"""






UpperCAmelCase    :     List[Any]   						=		[
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]
def      lowerCamelCase       (     _UpperCamelCase		:      int       ,			_UpperCamelCase		:      Optional[Any]       ,			_UpperCamelCase		:      Any       ,			_UpperCamelCase		:      List[Any]							)		->       Any:

							'''simple docstring'''



							__UpperCAmelCase   :						Union[str, Any]      							=					[False] * len(_UpperCamelCase							)
							__UpperCAmelCase   :						str      							=					[s]
							__UpperCAmelCase   :						Tuple      							=					True

							while queue:
														__UpperCAmelCase   :						List[Any]      							=					queue.pop(0							)
														for ind in range(len(graph[u]							)							):
																					if visited[ind] is False and graph[u][ind] > 0:
																												queue.append(_UpperCamelCase							)
																												__UpperCAmelCase   :						Optional[Any]      							=					True
																												__UpperCAmelCase   :						Tuple      							=					u

							return visited[t]
def      lowerCamelCase       (     _UpperCamelCase		:      Tuple       ,			_UpperCamelCase		:      Tuple       ,			_UpperCamelCase		:      List[Any]							)		->       Dict:

							'''simple docstring'''



							__UpperCAmelCase   :						Any      							=					[-1] * (len(_UpperCamelCase							))
							__UpperCAmelCase   :						Tuple      							=					0
							__UpperCAmelCase   :						Dict      							=					[]
							__UpperCAmelCase   :						Union[str, Any]      							=					[i[:] for i in graph]  # Record original cut, copy.
							while bfs(_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase							):
														__UpperCAmelCase   :						int      							=					float("""Inf"""							)
														__UpperCAmelCase   :						Optional[int]      							=					sink

														while s != source:
																					# Find the minimum value in select path
																					__UpperCAmelCase   :						Optional[Any]      							=					min(_UpperCamelCase       ,			graph[parent[s]][s]							)
																					__UpperCAmelCase   :						List[str]      							=					parent[s]

														max_flow += path_flow
														__UpperCAmelCase   :						str      							=					sink

														while v != source:
																					__UpperCAmelCase   :						int      							=					parent[v]
																					graph[u][v] -= path_flow
																					graph[v][u] += path_flow
																					__UpperCAmelCase   :						Tuple      							=					parent[v]

							for i in range(len(_UpperCamelCase							)							):
														for j in range(len(graph[0]							)							):
																					if graph[i][j] == 0 and temp[i][j] > 0:
																												res.append((i, j)							)

							return res


if __name__ == "__main__":
							print(mincut(test_graph, source=0, sink=5))




"""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


UpperCAmelCase    :     Optional[Any]   						=		logging.get_logger(__name__)
def      lowerCamelCase       (     _UpperCamelCase		:      Any       ,			_UpperCamelCase		:      Optional[Any]							)		->       int:

							'''simple docstring'''



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

							'''simple docstring'''



							__UpperCAmelCase   :						Tuple      							=					x.reshape(-1       ,			3							)
							__UpperCAmelCase   :						Optional[Any]      							=					squared_euclidean_distance(_UpperCamelCase       ,			_UpperCamelCase							)
							return np.argmin(_UpperCamelCase       ,			axis=1							)





class      lowerCamelCase__   (     A				):







					"""simple docstring"""


					__a	       =							["""pixel_values"""]






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






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






					def lowerCamelCase__							(       self    : List[str] ,       UpperCamelCase    : np.ndarray ,       UpperCamelCase    : Dict[str, int] ,       UpperCamelCase    : PILImageResampling = PILImageResampling.BILINEAR ,       UpperCamelCase    : Optional[Union[str, ChannelDimension]] = None ,       **UpperCamelCase    : str ,       ):
												'''simple docstring'''






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






					def lowerCamelCase__							(       self    : Dict ,       UpperCamelCase    : np.ndarray ,       UpperCamelCase    : Optional[Union[str, ChannelDimension]] = None ,       ):
												'''simple docstring'''






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






					def lowerCamelCase__							(       self    : Dict ,       UpperCamelCase    : ImageInput ,       UpperCamelCase    : bool = None ,       UpperCamelCase    : Dict[str, int] = None ,       UpperCamelCase    : PILImageResampling = None ,       UpperCamelCase    : bool = None ,       UpperCamelCase    : Optional[bool] = None ,       UpperCamelCase    : Optional[Union[List[List[int]], np.ndarray]] = None ,       UpperCamelCase    : Optional[Union[str, TensorType]] = None ,       UpperCamelCase    : Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST ,       **UpperCamelCase    : Optional[int] ,       ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[str]      							=					do_resize if do_resize is not None else self.do_resize
												__UpperCAmelCase   :						Any      							=					size if size is not None else self.size
												__UpperCAmelCase   :						Any      							=					get_size_dict(UpperCamelCase  )
												__UpperCAmelCase   :						Optional[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   :						Any      							=					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   :						Tuple      							=					np.array(UpperCamelCase  )

												__UpperCAmelCase   :						int      							=					make_list_of_images(UpperCamelCase  )

												if not valid_images(UpperCamelCase  ):
																			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   :						Tuple      							=					[to_numpy_array(UpperCamelCase  ) for image in images]

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

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

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

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

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

												__UpperCAmelCase   :						List[str]      							=					{"""input_ids""": images}
												return BatchFeature(data=UpperCamelCase ,       tensor_type=UpperCamelCase  )
