





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

from ...utils import logging


a__: str							   =						logging.get_logger(__name__)




class        SCREAMING_SNAKE_CASE__					(		UpperCamelCase__ ):
 __SCREAMING_SNAKE_CASE							=      CLIPConfig

 __SCREAMING_SNAKE_CASE							=      ['''CLIPEncoderLayer''']



 def __init__(				self,__lowerCamelCase					):
        super().__init__(__lowerCamelCase					)

        A__		   =		CLIPVisionModelWithProjection(config.vision_config					)

        A__		   =		nn.Linear(config.vision_config.projection_dim,1					)
        A__		   =		nn.Linear(config.vision_config.projection_dim,1					)




 @torch.no_grad()
 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase=0.5,__lowerCamelCase=0.5					):
        A__		   =		self.vision_model(__lowerCamelCase					)[0]

        A__		   =		self.p_head(__lowerCamelCase					)
        A__		   =		nsfw_detected.flatten()
        A__		   =		nsfw_detected > p_threshold
        A__		   =		nsfw_detected.tolist()

        if any(__lowerCamelCase					):
               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(__lowerCamelCase					):
               if nsfw_detected_:
                      A__		   =		np.zeros(images[idx].shape					)

        A__		   =		self.w_head(__lowerCamelCase					)
        A__		   =		watermark_detected.flatten()
        A__		   =		watermark_detected > w_threshold
        A__		   =		watermark_detected.tolist()

        if any(__lowerCamelCase					):
               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(__lowerCamelCase					):
               if watermark_detected_:
                      A__		   =		np.zeros(images[idx].shape					)

        return images, nsfw_detected, watermark_detected












import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    'The `inpainting.py` script is outdated. Please use directly `from diffusers import'
    ' StableDiffusionInpaintPipeline` instead.'
)





