






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__							  =						logging.get_logger(__name__)






class 						lowercase_   (      UpperCAmelCase_  ):






      '''simple docstring'''




      def __init__( self :		Any					,   __UpperCAmelCase :		Union[List[ControlNetModel], Tuple[ControlNetModel]]    )       ->Union[str, Any]:




       """simple docstring"""


       super().__init__()
       a						    = nn.ModuleList(__lowerCamelCase    )







      def  __lowerCAmelCase   ( self :		List[str]					,   __UpperCAmelCase :		torch.FloatTensor					,   __UpperCAmelCase :		Union[torch.Tensor, float, int]					,   __UpperCAmelCase :		torch.Tensor					,   __UpperCAmelCase :		List[torch.tensor]					,   __UpperCAmelCase :		List[float]					,   __UpperCAmelCase :		Optional[torch.Tensor] = None					,   __UpperCAmelCase :		Optional[torch.Tensor] = None					,   __UpperCAmelCase :		Optional[torch.Tensor] = None					,   __UpperCAmelCase :		Optional[Dict[str, Any]] = None					,   __UpperCAmelCase :		bool = False					,   __UpperCAmelCase :		bool = True					,   )       ->Union[ControlNetOutput, Tuple]:




       """simple docstring"""


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

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

       return down_block_res_samples, mid_block_res_sample







      def  __lowerCAmelCase   ( self :		Optional[int]					,   __UpperCAmelCase :		Union[str, os.PathLike]					,   __UpperCAmelCase :		bool = True					,   __UpperCAmelCase :		Callable = None					,   __UpperCAmelCase :		bool = False					,   __UpperCAmelCase :		Optional[str] = None					,   )       ->List[str]:




       """simple docstring"""


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

        idx += 1
        a						    = model_path_to_save + F"""_{idx}"""







      @classmethod
      def  __lowerCAmelCase   ( cls :		Tuple					,   __UpperCAmelCase :		Optional[Union[str, os.PathLike]]					,   **__UpperCAmelCase :		str    )       ->Dict:




       """simple docstring"""


       a						    = 0
       a						    = []

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

        idx += 1
        a						    = pretrained_model_path + F"""_{idx}"""

       logger.info(F"""{len(__lowerCamelCase    )} controlnets loaded from {pretrained_model_path}."""    )

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

       return cls(__lowerCamelCase    )









def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case						,   snake_case							)		->    int:
		# Return True if there is node that has not iterated.
		__lowercase											=     [False] * len(snake_case							)
		__lowercase											=     []
		queue.append(snake_case							)
		__lowercase											=     True

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

		return visited[t]



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    Dict:
		# This array is filled by BFS and to store path
		__lowercase											=     [-1] * (len(snake_case							))
		__lowercase											=     0
		while bfs(snake_case						,   snake_case						,   snake_case						,   snake_case							):
				__lowercase											=     float('Inf'							)
				__lowercase											=     sink

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

				max_flow += path_flow
				__lowercase											=     sink

				while v != source:
						__lowercase											=     parent[v]
						graph[u][v] -= path_flow
						graph[v][u] += path_flow
						__lowercase											=     parent[v]
		return max_flow


SCREAMING_SNAKE_CASE_   :      Any			=   [
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

SCREAMING_SNAKE_CASE_	,SCREAMING_SNAKE_CASE_   :      Optional[Any]			=   0, 5


print(ford_fulkerson(graph, source, sink))




