
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       a		(   __UpperCAmelCase					):







							def __init__(						self  :  Dict      ,				snake_case__  :  Union[List[ControlNetModel], Tuple[ControlNetModel]] ):
								"""simple docstring"""







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







							def       UpperCAmelCase__					(						self  :  Optional[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      ,				):
								"""simple docstring"""







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

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

								return down_block_res_samples, mid_block_res_sample







							def       UpperCAmelCase__					(						self  :  Union[str, Any]      ,				snake_case__  :  Union[str, os.PathLike]      ,				snake_case__  :  bool = True      ,				snake_case__  :  Callable = None      ,				snake_case__  :  bool = False      ,				snake_case__  :  Optional[str] = None      ,				):
								"""simple docstring"""







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

									idx += 1
									__lowerCAmelCase         =							model_path_to_save + F"_{idx}"







							@classmethod
							def       UpperCAmelCase__					(						cls  :  List[Any]      ,				snake_case__  :  Optional[Union[str, os.PathLike]]      ,				**snake_case__  :  Optional[Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							0
								__lowerCAmelCase         =							[]

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

									idx += 1
									__lowerCAmelCase         =							pretrained_model_path + F"_{idx}"

								logger.info(F"{len(snake_case__ )} controlnets loaded from {pretrained_model_path}." )

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

								return cls(snake_case__ )








import fire

from utils import calculate_rouge, save_json






def 		_UpperCAmelCase   (	UpperCamelCase:     Any			,							UpperCamelCase:     Union[str, Any]			,							UpperCamelCase:     List[Any]=None			,							**UpperCamelCase:     Optional[int]  ):






	"""simple docstring"""


	__lowerCAmelCase         =							[x.strip() for x in open(UpperCamelCase  ).readlines()]
	__lowerCAmelCase         =							[x.strip() for x in open(UpperCamelCase  ).readlines()][: len(UpperCamelCase  )]
	__lowerCAmelCase         =							calculate_rouge(UpperCamelCase			,							UpperCamelCase			,							**UpperCamelCase  )
	if save_path is not None:
		save_json(UpperCamelCase			,							UpperCamelCase			,							indent=UpperCamelCase  )
	return metrics  # these print nicely


if __name__ == "__main__":
					fire.Fire(calculate_rouge_path)






