


'''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


SCREAMING_SNAKE_CASE_      =			logging.get_logger(__name__)


class  lowerCAmelCase_						(       __a      ):





  """simple docstring"""



  def __init__(					self     :							int				,							SCREAMING_SNAKE_CASE__     :							Union[List[ControlNetModel], Tuple[ControlNetModel]]						):





       '''simple docstring'''

       super().__init__()
       __a       =   nn.ModuleList(a_						)

  def __a						(					self     :							Dict				,							SCREAMING_SNAKE_CASE__     :							torch.FloatTensor				,							SCREAMING_SNAKE_CASE__     :							Union[torch.Tensor, float, int]				,							SCREAMING_SNAKE_CASE__     :							torch.Tensor				,							SCREAMING_SNAKE_CASE__     :							List[torch.tensor]				,							SCREAMING_SNAKE_CASE__     :							List[float]				,							SCREAMING_SNAKE_CASE__     :							Optional[torch.Tensor] = None				,							SCREAMING_SNAKE_CASE__     :							Optional[torch.Tensor] = None				,							SCREAMING_SNAKE_CASE__     :							Optional[torch.Tensor] = None				,							SCREAMING_SNAKE_CASE__     :							Optional[Dict[str, Any]] = None				,							SCREAMING_SNAKE_CASE__     :							bool = False				,							SCREAMING_SNAKE_CASE__     :							bool = True				,							):





       '''simple docstring'''

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

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

       return down_block_res_samples, mid_block_res_sample

  def __a						(					self     :							Union[str, Any]				,							SCREAMING_SNAKE_CASE__     :							Union[str, os.PathLike]				,							SCREAMING_SNAKE_CASE__     :							bool = True				,							SCREAMING_SNAKE_CASE__     :							Callable = None				,							SCREAMING_SNAKE_CASE__     :							bool = False				,							SCREAMING_SNAKE_CASE__     :							Optional[str] = None				,							):





       '''simple docstring'''

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

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


  @classmethod
  def __a						(					cls     :							Any				,							SCREAMING_SNAKE_CASE__     :							Optional[Union[str, os.PathLike]]				,							**SCREAMING_SNAKE_CASE__     :							str						):





       '''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(a_						):
            __a       =   ControlNetModel.from_pretrained(a_				,							**a_						)
            controlnets.append(a_						)

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

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

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

       return cls(a_						)










'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE_      =			{
    'configuration_rag': ['RagConfig'],
    'retrieval_rag': ['RagRetriever'],
    'tokenization_rag': ['RagTokenizer'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_      =			[
     'RagModel',
     'RagPreTrainedModel',
     'RagSequenceForGeneration',
     'RagTokenForGeneration',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_      =			[
     'TFRagModel',
     'TFRagPreTrainedModel',
     'TFRagSequenceForGeneration',
     'TFRagTokenForGeneration',
 ]


if TYPE_CHECKING:
 from .configuration_rag import RagConfig
 from .retrieval_rag import RagRetriever
 from .tokenization_rag import RagTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_rag import (
      TFRagModel,
      TFRagPreTrainedModel,
      TFRagSequenceForGeneration,
      TFRagTokenForGeneration,
  )

else:
 import sys

 SCREAMING_SNAKE_CASE_      =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






