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_							=      logging.get_logger(__name__)


class 			UpperCAmelCase__    (		snake_case					):



  """simple docstring"""






  def __init__(				self:			Optional[Any]				,				__lowerCAmelCase:			Union[List[ControlNetModel], Tuple[ControlNetModel]]   ) -> List[str]:




    '''simple docstring'''




    super().__init__()
    __UpperCAmelCase						    =       nn.ModuleList(__lowerCAmelCase   )






  def 	_UpperCAmelCase				(				self:			Any				,				__lowerCAmelCase:			torch.FloatTensor				,				__lowerCAmelCase:			Union[torch.Tensor, float, int]				,				__lowerCAmelCase:			torch.Tensor				,				__lowerCAmelCase:			List[torch.tensor]				,				__lowerCAmelCase:			List[float]				,				__lowerCAmelCase:			Optional[torch.Tensor] = None				,				__lowerCAmelCase:			Optional[torch.Tensor] = None				,				__lowerCAmelCase:			Optional[torch.Tensor] = None				,				__lowerCAmelCase:			Optional[Dict[str, Any]] = None				,				__lowerCAmelCase:			bool = False				,				__lowerCAmelCase:			bool = True				,				) -> Union[ControlNetOutput, Tuple]:




    '''simple docstring'''




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

      # merge samples
      if i == 0:
        __UpperCAmelCase    ,		__UpperCAmelCase						    =       down_samples, mid_sample
      else:
        __UpperCAmelCase						    =       [
            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 	_UpperCAmelCase				(				self:			List[Any]				,				__lowerCAmelCase:			Union[str, os.PathLike]				,				__lowerCAmelCase:			bool = True				,				__lowerCAmelCase:			Callable = None				,				__lowerCAmelCase:			bool = False				,				__lowerCAmelCase:			Optional[str] = None				,				) -> List[str]:




    '''simple docstring'''




    __UpperCAmelCase						    =       0
    __UpperCAmelCase						    =       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
      __UpperCAmelCase						    =       model_path_to_save + F'''_{idx}'''






  @classmethod
  def 	_UpperCAmelCase				(				cls:			Any				,				__lowerCAmelCase:			Optional[Union[str, os.PathLike]]				,				**__lowerCAmelCase:			Dict   ) -> List[str]:




    '''simple docstring'''




    __UpperCAmelCase						    =       0
    __UpperCAmelCase						    =       []

    # 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`, ...
    __UpperCAmelCase						    =       pretrained_model_path
    while os.path.isdir(__lowerCAmelCase   ):
      __UpperCAmelCase						    =       ControlNetModel.from_pretrained(__lowerCAmelCase				,				**__lowerCAmelCase   )
      controlnets.append(__lowerCAmelCase   )

      idx += 1
      __UpperCAmelCase						    =       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 			UpperCAmelCase__    (		metaclass=snake_case					):



  """simple docstring"""






  lowerCAmelCase__		:	List[str]    		= ['transformers', 'torch', 'note_seq']






  def __init__(				self:			List[str]				,				*__lowerCAmelCase:			Optional[int]				,				**__lowerCAmelCase:			List[Any]   ) -> Optional[int]:




    '''simple docstring'''




    requires_backends(self				,				["transformers", "torch", "note_seq"]   )






  @classmethod
  def 	_UpperCAmelCase				(				cls:			Optional[int]				,				*__lowerCAmelCase:			Any				,				**__lowerCAmelCase:			List[str]   ) -> Dict:




    '''simple docstring'''




    requires_backends(cls				,				["transformers", "torch", "note_seq"]   )






  @classmethod
  def 	_UpperCAmelCase				(				cls:			Union[str, Any]				,				*__lowerCAmelCase:			Optional[Any]				,				**__lowerCAmelCase:			Optional[Any]   ) -> Any:




    '''simple docstring'''




    requires_backends(cls				,				["transformers", "torch", "note_seq"]   )



