


'''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


a_   :      Union[str, Any]				    =      logging.get_logger(__name__)




class 	a       (       __UpperCamelCase    ):
	def __init__(	self ,			__magic_name__  )   ->							Optional[int]:
						super().__init__()
						_a      =							nn.ModuleList(__magic_name__  )
	def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ = None ,			__magic_name__ = None ,			__magic_name__ = None ,			__magic_name__ = None ,			__magic_name__ = False ,			__magic_name__ = True ,			)   ->							Union[ControlNetOutput, Tuple]:
						for i, (image, scale, controlnet) in enumerate(zip(__magic_name__ ,			__magic_name__ ,			self.nets  )  ):
											_a      =							controlnet(
											    __magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			)

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

						return down_block_res_samples, mid_block_res_sample
	def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ = True ,			__magic_name__ = None ,			__magic_name__ = False ,			__magic_name__ = None ,			)   ->							List[str]:
						_a      =							0
						_a      =							save_directory
						for controlnet in self.nets:
											controlnet.save_pretrained(
											    __magic_name__ ,			is_main_process=__magic_name__ ,			save_function=__magic_name__ ,			safe_serialization=__magic_name__ ,			variant=__magic_name__ ,			)

											idx += 1
											_a      =							model_path_to_save + f'_{idx}'




	@classmethod
	def 							__UpperCAmelCase						(	cls ,			__magic_name__ ,			**__magic_name__  )   ->							Optional[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(__magic_name__  ):
											_a      =							ControlNetModel.from_pretrained(__magic_name__ ,			**__magic_name__  )
											controlnets.append(__magic_name__  )

											idx += 1
											_a      =							pretrained_model_path + f'_{idx}'

						logger.info(f'{len(__magic_name__  )} controlnets loaded from {pretrained_model_path}.'  )

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

						return cls(__magic_name__  )





'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_   :      str				    =      {
    "configuration_nllb_moe": [
        "NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "NllbMoeConfig",
    ]
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      Tuple				    =      [
     "NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST",
     "NllbMoeForConditionalGeneration",
     "NllbMoeModel",
     "NllbMoePreTrainedModel",
     "NllbMoeTop2Router",
     "NllbMoeSparseMLP",
 ]


if TYPE_CHECKING:
 from .configuration_nllb_moe import (
     NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
     NllbMoeConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_nllb_moe import (
      NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
      NllbMoeForConditionalGeneration,
      NllbMoeModel,
      NllbMoePreTrainedModel,
      NllbMoeSparseMLP,
      NllbMoeTopaRouter,
  )


else:
 import sys

 a_   :      str				    =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

