


from ..utils import DummyObject, requires_backends






class      UpperCamelCase					(		metaclass=lowercase__     ):


   '''simple docstring'''







   lowercase					:	List[str]									=["""note_seq"""]
   def __init__(     self   ,							*UpperCamelCase_   ,							**UpperCamelCase_		):
        requires_backends(self   ,							['''note_seq''']		)
   @classmethod
   def 		UpperCamelCase  (     cls   ,							*UpperCamelCase_   ,							**UpperCamelCase_		):
        requires_backends(cls   ,							['''note_seq''']		)




   @classmethod
   def 		UpperCamelCase  (     cls   ,							*UpperCamelCase_   ,							**UpperCamelCase_		):
        requires_backends(cls   ,							['''note_seq''']		)





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

from ...utils import logging


SCREAMING_SNAKE_CASE       :		int         =				logging.get_logger(__name__)






class      UpperCamelCase					(		lowercase__     ):


   '''simple docstring'''







   lowercase					:	List[str]									=CLIPConfig

   lowercase					:	Tuple									=["""CLIPEncoderLayer"""]
   def __init__(     self   ,							UpperCamelCase_		):
        super().__init__(UpperCamelCase_		)

        lowercase_       :Union[str, Any]     =       CLIPVisionModelWithProjection(config.vision_config		)

        lowercase_       :str     =       nn.Linear(config.vision_config.projection_dim   ,							1		)
        lowercase_       :Optional[int]     =       nn.Linear(config.vision_config.projection_dim   ,							1		)




   @torch.no_grad()
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_=0.5   ,							UpperCamelCase_=0.5		):
        lowercase_       :Optional[int]     =       self.vision_model(UpperCamelCase_		)[0]

        lowercase_       :Optional[int]     =       self.p_head(UpperCamelCase_		)
        lowercase_       :List[Any]     =       nsfw_detected.flatten()
        lowercase_       :List[str]     =       nsfw_detected > p_threshold
        lowercase_       :Optional[int]     =       nsfw_detected.tolist()

        if any(UpperCamelCase_		):
             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(UpperCamelCase_		):
             if nsfw_detected_:
                  lowercase_       :str     =       np.zeros(images[idx].shape		)

        lowercase_       :int     =       self.w_head(UpperCamelCase_		)
        lowercase_       :Union[str, Any]     =       watermark_detected.flatten()
        lowercase_       :int     =       watermark_detected > w_threshold
        lowercase_       :int     =       watermark_detected.tolist()

        if any(UpperCamelCase_		):
             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(UpperCamelCase_		):
             if watermark_detected_:
                  lowercase_       :Dict     =       np.zeros(images[idx].shape		)

        return images, nsfw_detected, watermark_detected

