






from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


lowerCamelCase       :       Any        =				logging.get_logger(__name__)

class 						lowerCAmelCase   (       __a	):

					'''simple docstring'''

					_A  :	List[str]     =					['''pixel_values''']



					def __init__(     self :		Dict						,   __a :		bool = True						,   __a :		Union[int, float] = 1 / 255						,   __a :		bool = True						,   __a :		int = 8						,   **__a :		int						,   )	->     None:


												"""simple docstring"""

												super().__init__(**__a							)

												__lowercase					:							Union[str, Any]			=   do_rescale
												__lowercase					:							Optional[int]			=   rescale_factor
												__lowercase					:							Union[str, Any]			=   do_pad
												__lowercase					:							List[str]			=   pad_size



					def 					lowerCAmelCase (     self :		Dict						,   __a :		np.ndarray						,   __a :		float						,   __a :		Optional[Union[str, ChannelDimension]] = None						,   **__a :		List[str]							)	->     np.ndarray:


												"""simple docstring"""

												return rescale(__a						,   scale=__a						,   data_format=__a						,   **__a							)



					def 					lowerCAmelCase (     self :		List[Any]						,   __a :		np.ndarray						,   __a :		int						,   __a :		Optional[Union[str, ChannelDimension]] = None							)	->     Dict:


												"""simple docstring"""

												__lowercase	,	__lowercase					:							Union[str, Any]			=   get_image_size(__a							)
												__lowercase					:							str			=   (old_height // size + 1) * size - old_height
												__lowercase					:							Tuple			=   (old_width // size + 1) * size - old_width

												return pad(__a						,   ((0, pad_height), (0, pad_width))						,   mode="""symmetric"""						,   data_format=__a							)



					def 					lowerCAmelCase (     self :		List[str]						,   __a :		ImageInput						,   __a :		Optional[bool] = None						,   __a :		Optional[float] = None						,   __a :		Optional[bool] = None						,   __a :		Optional[int] = None						,   __a :		Optional[Union[str, TensorType]] = None						,   __a :		Union[str, ChannelDimension] = ChannelDimension.FIRST						,   **__a :		Tuple						,   )	->     Union[str, Any]:


												"""simple docstring"""

												__lowercase					:							int			=   do_rescale if do_rescale is not None else self.do_rescale
												__lowercase					:							List[Any]			=   rescale_factor if rescale_factor is not None else self.rescale_factor
												__lowercase					:							List[Any]			=   do_pad if do_pad is not None else self.do_pad
												__lowercase					:							Optional[int]			=   pad_size if pad_size is not None else self.pad_size

												__lowercase					:							Tuple			=   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_rescale and rescale_factor is None:
																			raise ValueError("""Rescale factor must be specified if do_rescale is True."""							)

												# All transformations expect numpy arrays.
												__lowercase					:							List[str]			=   [to_numpy_array(__a							) for image in images]

												if do_rescale:
																			__lowercase					:							int			=   [self.rescale(image=__a						,   scale=__a							) for image in images]

												if do_pad:
																			__lowercase					:							Optional[Any]			=   [self.pad(__a						,   size=__a							) for image in images]

												__lowercase					:							Any			=   [to_channel_dimension_format(__a						,   __a							) for image in images]

												__lowercase					:							List[str]			=   {"""pixel_values""": images}
												return BatchFeature(data=__a						,   tensor_type=__a							)







import math

class 						lowerCAmelCase   :

					'''simple docstring'''

					def __init__(     self :		Tuple						,   __a :		int=0							)	->     Optional[Any]:  # a graph with Node 0,1,...,N-1


												"""simple docstring"""

												__lowercase					:							Any			=   n
												__lowercase					:							Optional[int]			=   [
												    [math.inf for j in range(0						,   __a							)] for i in range(0						,   __a							)
												]  # adjacency matrix for weight
												__lowercase					:							Dict			=   [
												    [math.inf for j in range(0						,   __a							)] for i in range(0						,   __a							)
												]  # dp[i][j] stores minimum distance from i to j



					def 					lowerCAmelCase (     self :		int						,   __a :		Optional[int]						,   __a :		Tuple						,   __a :		Dict							)	->     Dict:


												"""simple docstring"""

												__lowercase					:							Any			=   w



					def 					lowerCAmelCase (     self :		List[str]							)	->     Tuple:


												"""simple docstring"""

												for k in range(0						,   self.n							):
																			for i in range(0						,   self.n							):
																										for j in range(0						,   self.n							):
																																	__lowercase					:							List[str]			=   min(self.dp[i][j]						,   self.dp[i][k] + self.dp[k][j]							)



					def 					lowerCAmelCase (     self :		Any						,   __a :		Any						,   __a :		str							)	->     int:


												"""simple docstring"""

												return self.dp[u][v]


if __name__ == "__main__":
	lowerCamelCase       :       int        =				Graph(5)
	graph.add_edge(0, 2, 9)
	graph.add_edge(0, 4, 10)
	graph.add_edge(1, 3, 5)
	graph.add_edge(2, 3, 7)
	graph.add_edge(3, 0, 10)
	graph.add_edge(3, 1, 2)
	graph.add_edge(3, 2, 1)
	graph.add_edge(3, 4, 6)
	graph.add_edge(4, 1, 3)
	graph.add_edge(4, 2, 4)
	graph.add_edge(4, 3, 9)
	graph.floyd_warshall()
	graph.show_min(1, 4)
	graph.show_min(0, 3)