"""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   					=    logging.get_logger(__name__)

class lowerCamelCase		(_UpperCAmelCase      ):






					'''simple docstring'''


					def __init__(						self			:				List[str]   , _snake_case			:				Union[List[ControlNetModel], Tuple[ControlNetModel]]		)							->       Union[str, Any]:
									super().__init__()
									SCREAMING_SNAKE_CASE__		  =				nn.ModuleList(lowerCamelCase_		)



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




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

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



					@classmethod
					def 			lowerCAmelCase_					(						cls			:				Dict   , _snake_case			:				Optional[Union[str, os.PathLike]]   , **_snake_case			:				Tuple		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				0
									SCREAMING_SNAKE_CASE__		  =				[]

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

													idx += 1
													SCREAMING_SNAKE_CASE__		  =				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_		)





"""simple docstring"""






import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


_A   					=    ['small', 'medium', 'large']

_A   					=    'lm_head.decoder.weight'
_A   					=    'lm_head.weight'




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   List[str]:
				SCREAMING_SNAKE_CASE__		  =				torch.load(__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				d.pop(__UpperCAmelCase       )
				os.makedirs(__UpperCAmelCase  ,					exist_ok=__UpperCAmelCase       )
				torch.save(__UpperCAmelCase  ,					os.path.join(__UpperCAmelCase  ,					__UpperCAmelCase       )       )


if __name__ == "__main__":
						_A   					=    argparse.ArgumentParser()
						parser.add_argument('--dialogpt_path', default='.', type=str)
						_A   					=    parser.parse_args()
						for MODEL in DIALOGPT_MODELS:
												_A   					=    os.path.join(args.dialogpt_path, F'{MODEL}_ft.pkl')
												_A   					=    F'./DialoGPT-{MODEL}'
												convert_dialogpt_checkpoint(
												    checkpoint_path,
												    pytorch_dump_folder_path,
												)




