'''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_								=							logging.get_logger(__name__)





class      lowerCAmelCase						(					_snake_case			):



     def __init__(					self				,      lowerCAmelCase__       ):
            super().__init__()
            _A=	nn.ModuleList(lowerCAmelCase__       )



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__ = None				,      lowerCAmelCase__ = None				,      lowerCAmelCase__ = None				,      lowerCAmelCase__ = None				,      lowerCAmelCase__ = False				,      lowerCAmelCase__ = True				,      ):

            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 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__ = True				,      lowerCAmelCase__ = None				,      lowerCAmelCase__ = False				,      lowerCAmelCase__ = None				,      ):
            _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 			a__  (					cls				,      lowerCAmelCase__				,      **lowerCAmelCase__       ):
            _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__       )
from ..utils import DummyObject, requires_backends





class      lowerCAmelCase						(					metaclass=_a			):
     _SCREAMING_SNAKE_CASE     :     Optional[int]		=["""transformers""", """torch""", """note_seq"""]



     def __init__(					self				,      *lowerCAmelCase__				,      **lowerCAmelCase__       ):
            requires_backends(self				,      ['transformers', 'torch', 'note_seq']       )



     @classmethod
     def 			a__  (					cls				,      *lowerCAmelCase__				,      **lowerCAmelCase__       ):
            requires_backends(cls				,      ['transformers', 'torch', 'note_seq']       )





     @classmethod
     def 			a__  (					cls				,      *lowerCAmelCase__				,      **lowerCAmelCase__       ):
            requires_backends(cls				,      ['transformers', 'torch', 'note_seq']       )