
def    __snake_case       (  __magic_name__		,						__magic_name__	):







				'''simple docstring'''


				print("\nVertex Distance"	)
				for i in range(__magic_name__	):
								if dist[i] != float("inf"	):
												print(__magic_name__		,						"\t"		,						int(dist[i]	)		,						end="\t"	)
								else:
												print(__magic_name__		,						"\t"		,						"INF"		,						end="\t"	)
								print()

def    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__	):







				'''simple docstring'''


				lowercase						    =			float("inf"	)
				lowercase						    =			-1
				for i in range(__magic_name__	):
								if (not vset[i]) and mdist[i] < min_val:
												lowercase						    =			i
												lowercase						    =			mdist[i]
				return min_ind

def    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__	):







				'''simple docstring'''


				lowercase						    =			[float("inf"	) for _ in range(__magic_name__	)]
				lowercase						    =			[False for _ in range(__magic_name__	)]
				lowercase						    =			0.0

				for _ in range(v - 1	):
								lowercase						    =			min_dist(__magic_name__		,						__magic_name__		,						__magic_name__	)
								lowercase						    =			True

								for i in range(__magic_name__	):
												if (
												    (not vset[i])
												    and graph[u][i] != float("inf"	)
												    and mdist[u] + graph[u][i] < mdist[i]
												):
																lowercase						    =			mdist[u] + graph[u][i]

				print_dist(__magic_name__		,						__magic_name__	)


if __name__ == "__main__":
						_snake_case			:       Any      =						int(input("Enter number of vertices: ").strip())
						_snake_case			:       List[Any]      =						int(input("Enter number of edges: ").strip())

						_snake_case			:       Union[str, Any]      =						[[float("inf") for i in range(V)] for j in range(V)]

						for i in range(V):
												_snake_case			:       List[Any]      =						0.0

						for i in range(E):
												print("\nEdge ", i + 1)
												_snake_case			:       Optional[int]      =						int(input("Enter source:").strip())
												_snake_case			:       str      =						int(input("Enter destination:").strip())
												_snake_case			:       int      =						float(input("Enter weight:").strip())
												_snake_case			:       Any      =						weight

						_snake_case			:       Optional[int]      =						int(input("\nEnter shortest path source:").strip())
						dijkstra(graph, V, gsrc)


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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


_snake_case			:       Tuple      =						logging.get_logger(__name__)


if is_vision_available():
						import PIL





class      UpperCamelCase_			(			__a		):







						'''simple docstring'''



						UpperCamelCase			:     Optional[int]							      =				['''pixel_values''']





						def __init__( self       :List[str]		,						lowerCAmelCase__       :bool = True		,						lowerCAmelCase__       :Dict[str, int] = None		,						lowerCAmelCase__       :PILImageResampling = PILImageResampling.BILINEAR		,						lowerCAmelCase__       :bool = True		,						lowerCAmelCase__       :bool = False		,						lowerCAmelCase__       :bool = True		,						lowerCAmelCase__       :bool = True		,						lowerCAmelCase__       :Union[int, float] = 1 / 255		,						lowerCAmelCase__       :bool = True		,						lowerCAmelCase__       :Optional[Union[float, List[float]]] = None		,						lowerCAmelCase__       :Optional[Union[float, List[float]]] = None		,						**lowerCAmelCase__       :int		,						)     ->None:
										super().__init__(**lowerCAmelCase__    )

										lowercase						    =			size if size is not None else {"height": 2560, "width": 1920}
										if isinstance(lowerCAmelCase__		,						(tuple, list)    ):
														# The previous feature extractor size parameter was in (width, height) format
														lowercase						    =			size[::-1]
										lowercase						    =			get_size_dict(lowerCAmelCase__    )

										lowercase						    =			do_resize
										lowercase						    =			size
										lowercase						    =			resample
										lowercase						    =			do_thumbnail
										lowercase						    =			do_align_long_axis
										lowercase						    =			do_pad
										lowercase						    =			do_rescale
										lowercase						    =			rescale_factor
										lowercase						    =			do_normalize
										lowercase						    =			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										lowercase						    =			image_std if image_std is not None else IMAGENET_STANDARD_STD





						def 					SCREAMING_SNAKE_CASE( self       :Optional[Any]		,						lowerCAmelCase__       :np.ndarray		,						lowerCAmelCase__       :Dict[str, int]		,						lowerCAmelCase__       :Optional[Union[str, ChannelDimension]] = None    )     ->np.ndarray:
										lowercase    ,  lowercase						    =			get_image_size(lowerCAmelCase__    )
										lowercase    ,  lowercase						    =			size["height"], size["width"]

										if (output_width < output_height and input_width > input_height) or (
										    output_width > output_height and input_width < input_height
										):
														lowercase						    =			np.rotaa(lowerCAmelCase__		,						3    )

										if data_format is not None:
														lowercase						    =			to_channel_dimension_format(lowerCAmelCase__		,						lowerCAmelCase__    )

										return image





						def 					SCREAMING_SNAKE_CASE( self       :Tuple		,						lowerCAmelCase__       :np.ndarray		,						lowerCAmelCase__       :Dict[str, int]		,						lowerCAmelCase__       :bool = False		,						lowerCAmelCase__       :Optional[Union[str, ChannelDimension]] = None		,						)     ->np.ndarray:
										lowercase    ,  lowercase						    =			size["height"], size["width"]
										lowercase    ,  lowercase						    =			get_image_size(lowerCAmelCase__    )

										lowercase						    =			output_width - input_width
										lowercase						    =			output_height - input_height

										if random_padding:
														lowercase						    =			np.random.randint(low=0		,						high=delta_height + 1    )
														lowercase						    =			np.random.randint(low=0		,						high=delta_width + 1    )
										else:
														lowercase						    =			delta_height // 2
														lowercase						    =			delta_width // 2

										lowercase						    =			delta_height - pad_top
										lowercase						    =			delta_width - pad_left

										lowercase						    =			((pad_top, pad_bottom), (pad_left, pad_right))
										return pad(lowerCAmelCase__		,						lowerCAmelCase__		,						data_format=lowerCAmelCase__    )





						def 					SCREAMING_SNAKE_CASE( self       :Dict		,						*lowerCAmelCase__       :Union[str, Any]		,						**lowerCAmelCase__       :str    )     ->List[str]:
										logger.info("pad is deprecated and will be removed in version 4.27. Please use pad_image instead."    )
										return self.pad_image(*lowerCAmelCase__		,						**lowerCAmelCase__    )





						def 					SCREAMING_SNAKE_CASE( self       :Tuple		,						lowerCAmelCase__       :np.ndarray		,						lowerCAmelCase__       :Dict[str, int]		,						lowerCAmelCase__       :PILImageResampling = PILImageResampling.BICUBIC		,						lowerCAmelCase__       :Optional[Union[str, ChannelDimension]] = None		,						**lowerCAmelCase__       :Dict		,						)     ->np.ndarray:
										lowercase    ,  lowercase						    =			get_image_size(lowerCAmelCase__    )
										lowercase    ,  lowercase						    =			size["height"], size["width"]

										# We always resize to the smallest of either the input or output size.
										lowercase						    =			min(lowerCAmelCase__		,						lowerCAmelCase__    )
										lowercase						    =			min(lowerCAmelCase__		,						lowerCAmelCase__    )

										if height == input_height and width == input_width:
														return image

										if input_height > input_width:
														lowercase						    =			int(input_width * height / input_height    )
										elif input_width > input_height:
														lowercase						    =			int(input_height * width / input_width    )

										return resize(
										    lowerCAmelCase__		,						size=(height, width)		,						resample=lowerCAmelCase__		,						reducing_gap=2.0		,						data_format=lowerCAmelCase__		,						**lowerCAmelCase__    )





						def 					SCREAMING_SNAKE_CASE( self       :List[str]		,						lowerCAmelCase__       :np.ndarray		,						lowerCAmelCase__       :Dict[str, int]		,						lowerCAmelCase__       :PILImageResampling = PILImageResampling.BICUBIC		,						lowerCAmelCase__       :Optional[Union[str, ChannelDimension]] = None		,						**lowerCAmelCase__       :List[str]		,						)     ->np.ndarray:
										lowercase						    =			get_size_dict(lowerCAmelCase__    )
										lowercase						    =			min(size["height"]		,						size["width"]    )
										lowercase						    =			get_resize_output_image_size(lowerCAmelCase__		,						size=lowerCAmelCase__		,						default_to_square=lowerCAmelCase__    )
										lowercase						    =			resize(lowerCAmelCase__		,						size=lowerCAmelCase__		,						resample=lowerCAmelCase__		,						data_format=lowerCAmelCase__		,						**lowerCAmelCase__    )
										return resized_image





						def 					SCREAMING_SNAKE_CASE( self       :Any		,						lowerCAmelCase__       :np.ndarray		,						lowerCAmelCase__       :Union[int, float]		,						lowerCAmelCase__       :Optional[Union[str, ChannelDimension]] = None		,						**lowerCAmelCase__       :List[Any]		,						)     ->List[Any]:
										return rescale(lowerCAmelCase__		,						scale=lowerCAmelCase__		,						data_format=lowerCAmelCase__		,						**lowerCAmelCase__    )





						def 					SCREAMING_SNAKE_CASE( self       :List[str]		,						lowerCAmelCase__       :np.ndarray		,						lowerCAmelCase__       :Union[float, List[float]]		,						lowerCAmelCase__       :Union[float, List[float]]		,						lowerCAmelCase__       :Optional[Union[str, ChannelDimension]] = None		,						**lowerCAmelCase__       :str		,						)     ->np.ndarray:
										return normalize(lowerCAmelCase__		,						mean=lowerCAmelCase__		,						std=lowerCAmelCase__		,						data_format=lowerCAmelCase__		,						**lowerCAmelCase__    )





						def 					SCREAMING_SNAKE_CASE( self       :Optional[Any]		,						lowerCAmelCase__       :ImageInput		,						lowerCAmelCase__       :bool = None		,						lowerCAmelCase__       :Dict[str, int] = None		,						lowerCAmelCase__       :PILImageResampling = None		,						lowerCAmelCase__       :bool = None		,						lowerCAmelCase__       :bool = None		,						lowerCAmelCase__       :bool = None		,						lowerCAmelCase__       :bool = False		,						lowerCAmelCase__       :bool = None		,						lowerCAmelCase__       :float = None		,						lowerCAmelCase__       :bool = None		,						lowerCAmelCase__       :Optional[Union[float, List[float]]] = None		,						lowerCAmelCase__       :Optional[Union[float, List[float]]] = None		,						lowerCAmelCase__       :Optional[Union[str, TensorType]] = None		,						lowerCAmelCase__       :Optional[ChannelDimension] = ChannelDimension.FIRST		,						**lowerCAmelCase__       :Tuple		,						)     ->PIL.Image.Image:
										lowercase						    =			do_resize if do_resize is not None else self.do_resize
										lowercase						    =			size if size is not None else self.size
										if isinstance(lowerCAmelCase__		,						(tuple, list)    ):
														# Previous feature extractor had size in (width, height) format
														lowercase						    =			size[::-1]
										lowercase						    =			get_size_dict(lowerCAmelCase__    )
										lowercase						    =			resample if resample is not None else self.resample
										lowercase						    =			do_thumbnail if do_thumbnail is not None else self.do_thumbnail
										lowercase						    =			do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
										lowercase						    =			do_pad if do_pad is not None else self.do_pad
										lowercase						    =			do_rescale if do_rescale is not None else self.do_rescale
										lowercase						    =			rescale_factor if rescale_factor is not None else self.rescale_factor
										lowercase						    =			do_normalize if do_normalize is not None else self.do_normalize
										lowercase						    =			image_mean if image_mean is not None else self.image_mean
										lowercase						    =			image_std if image_std is not None else self.image_std

										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:
														raise ValueError("Size must be specified if do_resize is True."    )

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

										if do_pad and size is None:
														raise ValueError("Size must be specified if do_pad is True."    )

										if do_normalize and (image_mean is None or image_std is None):
														raise ValueError("Image mean and std must be specified if do_normalize is True."    )

										# All transformations expect numpy arrays.
										lowercase						    =			[to_numpy_array(lowerCAmelCase__    ) for image in images]

										if do_align_long_axis:
														lowercase						    =			[self.align_long_axis(lowerCAmelCase__		,						size=lowerCAmelCase__    ) for image in images]

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

										if do_thumbnail:
														lowercase						    =			[self.thumbnail(image=lowerCAmelCase__		,						size=lowerCAmelCase__    ) for image in images]

										if do_pad:
														lowercase						    =			[self.pad_image(image=lowerCAmelCase__		,						size=lowerCAmelCase__		,						random_padding=lowerCAmelCase__    ) for image in images]

										if do_rescale:
														lowercase						    =			[self.rescale(image=lowerCAmelCase__		,						scale=lowerCAmelCase__    ) for image in images]

										if do_normalize:
														lowercase						    =			[self.normalize(image=lowerCAmelCase__		,						mean=lowerCAmelCase__		,						std=lowerCAmelCase__    ) for image in images]

										lowercase						    =			[to_channel_dimension_format(lowerCAmelCase__		,						lowerCAmelCase__    ) for image in images]

										lowercase						    =			{"pixel_values": images}
										return BatchFeature(data=lowerCAmelCase__		,						tensor_type=lowerCAmelCase__    )
