
"""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


UpperCamelCase     :    Dict       =			logging.get_logger(__name__)

class __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ):





     def __init__(     self     ,					__UpperCAmelCase				):

      '''simple docstring'''




      super().__init__()
      __UpperCamelCase      =	nn.ModuleList(__UpperCAmelCase				)





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = False     ,					__UpperCAmelCase = True     ,					):

      '''simple docstring'''




      for i, (image, scale, controlnet) in enumerate(zip(__UpperCAmelCase     ,					__UpperCAmelCase     ,					self.nets				)				):
       __UpperCamelCase  ,     __UpperCamelCase      =	controlnet(
           __UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					)

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

      return down_block_res_samples, mid_block_res_sample





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase = True     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = False     ,					__UpperCAmelCase = None     ,					):

      '''simple docstring'''




      __UpperCamelCase      =	0
      __UpperCamelCase      =	save_directory
      for controlnet in self.nets:
       controlnet.save_pretrained(
           __UpperCAmelCase     ,					is_main_process=__UpperCAmelCase     ,					save_function=__UpperCAmelCase     ,					safe_serialization=__UpperCAmelCase     ,					variant=__UpperCAmelCase     ,					)

       idx += 1
       __UpperCamelCase      =	model_path_to_save + F'_{idx}'







     @classmethod
     def 						UpperCAmelCase (     cls     ,					__UpperCAmelCase     ,					**__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	0
      __UpperCamelCase      =	[]

      # 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`, ...
      __UpperCamelCase      =	pretrained_model_path
      while os.path.isdir(__UpperCAmelCase				):
       __UpperCamelCase      =	ControlNetModel.from_pretrained(__UpperCAmelCase     ,					**__UpperCAmelCase				)
       controlnets.append(__UpperCAmelCase				)

       idx += 1
       __UpperCamelCase      =	pretrained_model_path + F'_{idx}'

      logger.info(F'{len(__UpperCAmelCase				)} controlnets loaded from {pretrained_model_path}.'				)

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

      return cls(__UpperCAmelCase				)





"""simple docstring"""

UpperCamelCase     :    Union[str, Any]       =			[
    [0, 1_6, 1_3, 0, 0, 0],
    [0, 0, 1_0, 1_2, 0, 0],
    [0, 4, 0, 0, 1_4, 0],
    [0, 0, 9, 0, 0, 2_0],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]






def 							A						(    snake_case				:Dict    , snake_case				:Tuple    , snake_case				:str    , snake_case				:Optional[int]		)				->     Union[str, Any]:
 # Return True if there is node that has not iterated.
 __UpperCamelCase      =	[False] * len(snake_case		)
 __UpperCamelCase      =	[s]
 __UpperCamelCase      =	True

 while queue:
  __UpperCamelCase      =	queue.pop(0		)
  for ind in range(len(graph[u]		)		):
   if visited[ind] is False and graph[u][ind] > 0:
    queue.append(snake_case		)
    __UpperCamelCase      =	True
    __UpperCamelCase      =	u

 return visited[t]






def 							A						(    snake_case				:int    , snake_case				:Any    , snake_case				:Union[str, Any]		)				->     Optional[int]:
 __UpperCamelCase      =	[-1] * (len(snake_case		))
 __UpperCamelCase      =	0
 __UpperCamelCase      =	[]
 __UpperCamelCase      =	[i[:] for i in graph]  # Record original cut, copy.
 while bfs(snake_case    , snake_case    , snake_case    , snake_case		):
  __UpperCamelCase      =	float('Inf'		)
  __UpperCamelCase      =	sink

  while s != source:
   # Find the minimum value in select path
   __UpperCamelCase      =	min(snake_case    , graph[parent[s]][s]		)
   __UpperCamelCase      =	parent[s]

  max_flow += path_flow
  __UpperCamelCase      =	sink

  while v != source:
   __UpperCamelCase      =	parent[v]
   graph[u][v] -= path_flow
   graph[v][u] += path_flow
   __UpperCamelCase      =	parent[v]

 for i in range(len(snake_case		)		):
  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))



