

'''simple docstring'''





import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel

from ...utils import logging


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

class 				a  (	UpperCAmelCase				):
						_lowercase    						=			CLIPConfig

						_lowercase    						=			["CLIPEncoderLayer"]


						def __init__(       self						,					A_	):







										'''simple docstring'''


										super().__init__(A_	)

										_UpperCAmelCase       :		Tuple 		=    CLIPVisionModelWithProjection(config.vision_config	)

										_UpperCAmelCase       :		str 		=    nn.Linear(config.vision_config.projection_dim						,					1	)
										_UpperCAmelCase       :		List[Any] 		=    nn.Linear(config.vision_config.projection_dim						,					1	)





						@torch.no_grad()
						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_=0.5						,					A_=0.5	):







										'''simple docstring'''


										_UpperCAmelCase       :		str 		=    self.vision_model(A_	)[0]

										_UpperCAmelCase       :		Optional[int] 		=    self.p_head(A_	)
										_UpperCAmelCase       :		Tuple 		=    nsfw_detected.flatten()
										_UpperCAmelCase       :		List[Any] 		=    nsfw_detected > p_threshold
										_UpperCAmelCase       :		Optional[Any] 		=    nsfw_detected.tolist()

										if any(A_	):
														logger.warning(
														    "Potential NSFW content was detected in one or more images. A black image will be returned instead."
														    " Try again with a different prompt and/or seed."	)

										for idx, nsfw_detected_ in enumerate(A_	):
														if nsfw_detected_:
																		_UpperCAmelCase       :		Tuple 		=    np.zeros(images[idx].shape	)

										_UpperCAmelCase       :		Tuple 		=    self.w_head(A_	)
										_UpperCAmelCase       :		Optional[Any] 		=    watermark_detected.flatten()
										_UpperCAmelCase       :		str 		=    watermark_detected > w_threshold
										_UpperCAmelCase       :		Optional[int] 		=    watermark_detected.tolist()

										if any(A_	):
														logger.warning(
														    "Potential watermarked content was detected in one or more images. A black image will be returned instead."
														    " Try again with a different prompt and/or seed."	)

										for idx, watermark_detected_ in enumerate(A_	):
														if watermark_detected_:
																		_UpperCAmelCase       :		Dict 		=    np.zeros(images[idx].shape	)

										return images, nsfw_detected, watermark_detected





import fire

from utils import calculate_rouge, save_json

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  int  ,      lowerCAmelCase:  Tuple  ,      lowerCAmelCase:  Any=None  ,      **lowerCAmelCase:  Any )   ->       Dict:
				_UpperCAmelCase       :		Optional[Any] 		=    [x.strip() for x in open(lowerCAmelCase ).readlines()]
				_UpperCAmelCase       :		str 		=    [x.strip() for x in open(lowerCAmelCase ).readlines()][: len(lowerCAmelCase )]
				_UpperCAmelCase       :		str 		=    calculate_rouge(lowerCAmelCase  ,      lowerCAmelCase  ,      **lowerCAmelCase )
				if save_path is not None:
								save_json(lowerCAmelCase  ,      lowerCAmelCase  ,      indent=lowerCAmelCase )
				return metrics  # these print nicely


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


