



'''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									=   logging.get_logger(__name__)

class 	__magic_name__						(    _UpperCamelCase   ):







							def __init__(							self					:       Dict	,_UpperCAmelCase					:       Union[List[ControlNetModel], Tuple[ControlNetModel]]		):
									super().__init__()
									_a     :      int        =							nn.ModuleList(_UpperCAmelCase		)







							def 							__lowercase			(							self					:       int	,_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	,):
									for i, (image, scale, controlnet) in enumerate(zip(_UpperCAmelCase	,_UpperCAmelCase	,self.nets		)		):
											_a       ,					_a     :      Optional[Any]        =							controlnet(
											    _UpperCAmelCase	,_UpperCAmelCase	,_UpperCAmelCase	,_UpperCAmelCase	,_UpperCAmelCase	,_UpperCAmelCase	,_UpperCAmelCase	,_UpperCAmelCase	,_UpperCAmelCase	,_UpperCAmelCase	,_UpperCAmelCase	,)

											# merge samples
											if i == 0:
													_a       ,					_a     :      int        =							down_samples, mid_sample
											else:
													_a     :      Union[str, Any]        =							[
													    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 							__lowercase			(							self					:       Optional[int]	,_UpperCAmelCase					:       Union[str, os.PathLike]	,_UpperCAmelCase					:       bool = True	,_UpperCAmelCase					:       Callable = None	,_UpperCAmelCase					:       bool = False	,_UpperCAmelCase					:       Optional[str] = None	,):
									_a     :      Union[str, Any]        =							0
									_a     :      Union[str, Any]        =							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
											_a     :      str        =							model_path_to_save + F"""_{idx}"""







							@classmethod
							def 							__lowercase			(							cls					:       str	,_UpperCAmelCase					:       Optional[Union[str, os.PathLike]]	,**_UpperCAmelCase					:       Optional[Any]		):
									_a     :      Any        =							0
									_a     :      int        =							[]

									# 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     :      str        =							pretrained_model_path
									while os.path.isdir(_UpperCAmelCase		):
											_a     :      int        =							ControlNetModel.from_pretrained(_UpperCAmelCase	,**_UpperCAmelCase		)
											controlnets.append(_UpperCAmelCase		)

											idx += 1
											_a     :      int        =							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		)







from .imports import is_tqdm_available


if is_tqdm_available():
  from tqdm.auto import tqdm as _tqdm

from ..state import PartialState
def      a      (			A__	:   bool = True      ,   *A__	:   int      ,   **A__	:   Union[str, Any]     )    ->      List[str]:





      """simple docstring"""



      if not is_tqdm_available():
            raise ImportError('Accelerate\'s `tqdm` module requires `tqdm` to be installed. Please run `pip install tqdm`.'     )
      _lowercase	       =False
      if main_process_only:
            _lowercase	       =PartialState().local_process_index == 0
      return _tqdm(*A__      ,   **A__      ,   disable=A__     )


