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 			__magic_name__	(		__a     ):





   """simple docstring"""




   def __init__(						self       :	Dict		,	_lowercase       :	Union[List[ControlNetModel], Tuple[ControlNetModel]]				):



    """simple docstring"""



    super().__init__()
    _UpperCamelCase:  Dict    						=			nn.ModuleList(_lowercase				)



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



    """simple docstring"""



    for i, (image, scale, controlnet) in enumerate(zip(_lowercase		,	_lowercase		,	self.nets				)				):
     _UpperCamelCase						,				_UpperCamelCase:  Union[str, Any]    						=			controlnet(
         _lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	)

     # merge samples
     if i == 0:
      _UpperCamelCase						,				_UpperCamelCase:  int    						=			down_samples, mid_sample
     else:
      _UpperCamelCase:  List[str]    						=			[
          samples_prev + samples_curr
          for samples_prev, samples_curr in zip(_lowercase		,	_lowercase				)
      ]
      mid_block_res_sample += mid_sample

    return down_block_res_samples, mid_block_res_sample



   def 	lowerCAmelCase    (						self       :	Tuple		,	_lowercase       :	Union[str, os.PathLike]		,	_lowercase       :	bool = True		,	_lowercase       :	Callable = None		,	_lowercase       :	bool = False		,	_lowercase       :	Optional[str] = None		,	):



    """simple docstring"""



    _UpperCamelCase:  Optional[int]    						=			0
    _UpperCamelCase:  Dict    						=			save_directory
    for controlnet in self.nets:
     controlnet.save_pretrained(
         _lowercase		,	is_main_process=_lowercase		,	save_function=_lowercase		,	safe_serialization=_lowercase		,	variant=_lowercase		,	)

     idx += 1
     _UpperCamelCase:  Optional[int]    						=			model_path_to_save + f"""_{idx}"""



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



    """simple docstring"""



    _UpperCamelCase:  Dict    						=			0
    _UpperCamelCase:  Union[str, Any]    						=			[]

    # 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:  Union[str, Any]    						=			pretrained_model_path
    while os.path.isdir(_lowercase				):
     _UpperCamelCase:  List[Any]    						=			ControlNetModel.from_pretrained(_lowercase		,	**_lowercase				)
     controlnets.append(_lowercase				)

     idx += 1
     _UpperCamelCase:  Tuple    						=			pretrained_model_path + f"""_{idx}"""

    logger.info(f"""{len(_lowercase				)} controlnets loaded from {pretrained_model_path}."""				)

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

    return cls(_lowercase				)
UpperCAmelCase_           =				'''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

UpperCAmelCase_           =				[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
UpperCAmelCase_           =				{
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}