





'''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


__lowerCAmelCase							:	Tuple			=logging.get_logger(__name__)






class    UpperCAmelCase	(       UpperCamelCase__				):
 def __init__(				self      :Union[str, Any]						,       lowercase_      :Union[List[ControlNetModel], Tuple[ControlNetModel]]							)->							str:
     super().__init__()
     A__				=			nn.ModuleList(lowercase_							)
 def      UpperCAmelCase_							(				self      :Dict						,       lowercase_      :torch.FloatTensor						,       lowercase_      :Union[torch.Tensor, float, int]						,       lowercase_      :torch.Tensor						,       lowercase_      :List[torch.tensor]						,       lowercase_      :List[float]						,       lowercase_      :Optional[torch.Tensor] = None						,       lowercase_      :Optional[torch.Tensor] = None						,       lowercase_      :Optional[torch.Tensor] = None						,       lowercase_      :Optional[Dict[str, Any]] = None						,       lowercase_      :bool = False						,       lowercase_      :bool = True						,       )->							Union[ControlNetOutput, Tuple]:
     for i, (image, scale, controlnet) in enumerate(zip(lowercase_						,       lowercase_						,       self.nets							)							):
         A__,	A__				=			controlnet(
             lowercase_						,       lowercase_						,       lowercase_						,       lowercase_						,       lowercase_						,       lowercase_						,       lowercase_						,       lowercase_						,       lowercase_						,       lowercase_						,       lowercase_						,       )

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

     return down_block_res_samples, mid_block_res_sample
 def      UpperCAmelCase_							(				self      :Any						,       lowercase_      :Union[str, os.PathLike]						,       lowercase_      :bool = True						,       lowercase_      :Callable = None						,       lowercase_      :bool = False						,       lowercase_      :Optional[str] = None						,       )->							List[Any]:
     A__				=			0
     A__				=			save_directory
     for controlnet in self.nets:
         controlnet.save_pretrained(
             lowercase_						,       is_main_process=lowercase_						,       save_function=lowercase_						,       safe_serialization=lowercase_						,       variant=lowercase_						,       )

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



 @classmethod
 def      UpperCAmelCase_							(				cls      :Any						,       lowercase_      :Optional[Union[str, os.PathLike]]						,       **lowercase_      :Dict							)->							Union[str, Any]:
     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(lowercase_							):
         A__				=			ControlNetModel.from_pretrained(lowercase_						,       **lowercase_							)
         controlnets.append(lowercase_							)

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

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

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

     return cls(lowercase_							)










'''simple docstring'''

import warnings


warnings.warn(
    "memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: "
    "`from accelerate import find_executable_batch_size` to avoid this warning.",
    FutureWarning,
)



