





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


A_												=     logging.get_logger(__name__)

class SCREAMING_SNAKE_CASE_ (					lowercase__   ):





     """simple docstring"""


     A__				 =    ["pixel_values"]





     def __init__( self	,  _lowerCAmelCase = True	,  _lowerCAmelCase = 1 / 255	,  _lowerCAmelCase = True	,  _lowerCAmelCase = 8	,  **_lowerCAmelCase	,  ):
          super().__init__(**UpperCAmelCase__	)

          lowerCamelCase__       =       do_rescale
          lowerCamelCase__       =       rescale_factor
          lowerCamelCase__       =       do_pad
          lowerCamelCase__       =       pad_size





     def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase = None	,  **_lowerCAmelCase	):
          return rescale(UpperCAmelCase__	,  scale=UpperCAmelCase__	,  data_format=UpperCAmelCase__	,  **UpperCAmelCase__	)





     def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase = None	):
          lowerCamelCase__       =       get_image_size(UpperCAmelCase__	)
          lowerCamelCase__       =       (old_height // size + 1) * size - old_height
          lowerCamelCase__       =       (old_width // size + 1) * size - old_width

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






     def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = ChannelDimension.FIRST	,  **_lowerCAmelCase	,  ):
          lowerCamelCase__       =       do_rescale if do_rescale is not None else self.do_rescale
          lowerCamelCase__       =       rescale_factor if rescale_factor is not None else self.rescale_factor
          lowerCamelCase__       =       do_pad if do_pad is not None else self.do_pad
          lowerCamelCase__       =       pad_size if pad_size is not None else self.pad_size

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

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

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

          if do_pad:
               lowerCamelCase__       =       [self.pad(UpperCAmelCase__	,  size=UpperCAmelCase__	) for image in images]

          lowerCamelCase__       =       [to_channel_dimension_format(UpperCAmelCase__	,  UpperCAmelCase__	) for image in images]

          lowerCamelCase__       =       {'''pixel_values''': images}
          return BatchFeature(data=UpperCAmelCase__	,  tensor_type=UpperCAmelCase__	)








def    __UpperCamelCase      (    a,     a) ->Dict:
					print("\nThe shortest path matrix using Floyd Warshall algorithm\n")
					for i in range(a):
										for j in range(a):
															if dist[i][j] != float("inf"):
																				print(int(dist[i][j]),     end="\t")
															else:
																				print("INF",     end="\t")
										print()





def    __UpperCamelCase      (    a,     a) ->Optional[int]:
					lowerCamelCase__       =       [[float("inf") for _ in range(a)] for _ in range(a)]

					for i in range(a):
										for j in range(a):
															lowerCamelCase__       =       graph[i][j]

															# check vertex k against all other vertices (i, j)
					for k in range(a):
										# looping through rows of graph array
										for i in range(a):
															# looping through columns of graph array
															for j in range(a):
																				if (
																				    dist[i][k] != float("inf")
																				    and dist[k][j] != float("inf")
																				    and dist[i][k] + dist[k][j] < dist[i][j]
																				):
																									lowerCamelCase__       =       dist[i][k] + dist[k][j]

					_print_dist(a,     a)
					return dist, v


if __name__ == "__main__":
						A_												=     int(input("Enter number of vertices: "))
						A_												=     int(input("Enter number of edges: "))

						A_												=     [[float("inf") for i in range(v)] for j in range(v)]

						for i in range(v):
												A_												=     0.0

												# src and dst are indices that must be within the array size graph[e][v]
												# failure to follow this will result in an error
						for i in range(e):
												print("\nEdge ", i + 1)
												A_												=     int(input("Enter source:"))
												A_												=     int(input("Enter destination:"))
												A_												=     float(input("Enter weight:"))
												A_												=     weight

						floyd_warshall(graph, v)

						# Example Input
						# Enter number of vertices: 3
						# Enter number of edges: 2

						# # generated graph from vertex and edge inputs
						# [[inf, inf, inf], [inf, inf, inf], [inf, inf, inf]]
						# [[0.0, inf, inf], [inf, 0.0, inf], [inf, inf, 0.0]]

						# specify source, destination and weight for edge #1
						# Edge  1
						# Enter source:1
						# Enter destination:2
						# Enter weight:2

						# specify source, destination and weight for edge #2
						# Edge  2
						# Enter source:2
						# Enter destination:1
						# Enter weight:1

						# # Expected Output from the vertice, edge and src, dst, weight inputs!!
						# 0		INF	INF
						# INF	0	2
						# INF	1	0

