


"""simple docstring"""


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




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


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




			__lowerCAmelCase							       =     [[float("inf" ) for _ in range(_UpperCamelCase )] for _ in range(_UpperCamelCase )]

			for i in range(_UpperCamelCase ):
						for j in range(_UpperCamelCase ):
									__lowerCAmelCase							       =     graph[i][j]

									# check vertex k against all other vertices (i, j)
			for k in range(_UpperCamelCase ):
						# looping through rows of graph array
						for i in range(_UpperCamelCase ):
									# looping through columns of graph array
									for j in range(_UpperCamelCase ):
												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(_UpperCamelCase       ,					_UpperCamelCase )
			return dist, v


if __name__ == "__main__":
	A	:  Union[str, Any]            =     int(input("Enter number of vertices: "))
	A	:  Tuple            =     int(input("Enter number of edges: "))

	A	:  Optional[Any]            =     [[float("inf") for i in range(v)] for j in range(v)]

	for i in range(v):
		A	:  int            =     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            =     int(input("Enter source:"))
		A	:  Tuple            =     int(input("Enter destination:"))
		A	:  Dict            =     float(input("Enter weight:"))
		A	:  str            =     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








"""simple docstring"""


import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn

from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging


A	:  List[Any]            =     logging.get_logger(__name__)
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a      ):
										super().__init__()
										__lowerCAmelCase							       =     nn.ModuleList(__a      )

							def      snake_case							(	self       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a = None       ,  __a = None       ,  __a = None       ,  __a = None       ,  __a = False       ,  __a = True       ,  ):
										for i, (image, scale, controlnet) in enumerate(zip(__a       ,  __a       ,  self.nets      )      ):
													__lowerCAmelCase       ,      __lowerCAmelCase							       =     controlnet(
													    __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  )

													# merge samples
													if i == 0:
																__lowerCAmelCase       ,      __lowerCAmelCase							       =     down_samples, mid_sample
													else:
																__lowerCAmelCase							       =     [
																    samples_prev + samples_curr
																    for samples_prev, samples_curr in zip(__a       ,  __a      )
																]
																mid_block_res_sample += mid_sample

										return down_block_res_samples, mid_block_res_sample

							def      snake_case							(	self       ,  __a       ,  __a = True       ,  __a = None       ,  __a = False       ,  __a = None       ,  ):
										__lowerCAmelCase							       =     0
										__lowerCAmelCase							       =     save_directory
										for controlnet in self.nets:
													controlnet.save_pretrained(
													    __a       ,  is_main_process=__a       ,  save_function=__a       ,  safe_serialization=__a       ,  variant=__a       ,  )

													idx += 1
													__lowerCAmelCase							       =     model_path_to_save + f"_{idx}"



							@classmethod
							def      snake_case							(	cls       ,  __a       ,  **__a      ):
										__lowerCAmelCase							       =     0
										__lowerCAmelCase							       =     []

										# load controlnet and append to list until no controlnet directory exists anymore
										# first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
										# second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
										__lowerCAmelCase							       =     pretrained_model_path
										while os.path.isdir(__a      ):
													__lowerCAmelCase							       =     ControlNetModel.from_pretrained(__a       ,  **__a      )
													controlnets.append(__a      )

													idx += 1
													__lowerCAmelCase							       =     pretrained_model_path + f"_{idx}"

										logger.info(f"{len(__a      )} controlnets loaded from {pretrained_model_path}."      )

										if len(__a      ) == 0:
													raise ValueError(
													    f"No ControlNets found under {os.path.dirname(__a      )}. Expected at least {pretrained_model_path + '_0'}."      )

										return cls(__a      )




