


'''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  lowerCAmelCase_						(       snake_case__      ):





  """simple docstring"""



  a_       :Optional[Any]												=CLIPConfig

  a_       :str												=["""CLIPEncoderLayer"""]

  def __init__(					self     :							Any				,							SCREAMING_SNAKE_CASE__     :							CLIPConfig						):





       '''simple docstring'''

       super().__init__(SCREAMING_SNAKE_CASE__						)

       __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 __a						(					self     :							Dict				,							SCREAMING_SNAKE_CASE__     :							List[str]				,							SCREAMING_SNAKE_CASE__     :							List[Any]				,							SCREAMING_SNAKE_CASE__     :							int=0.5				,							SCREAMING_SNAKE_CASE__     :							str=0.5						):





       '''simple docstring'''

       __a       =   self.vision_model(SCREAMING_SNAKE_CASE__						)[0]

       __a       =   self.p_head(SCREAMING_SNAKE_CASE__						)
       __a       =   nsfw_detected.flatten()
       __a       =   nsfw_detected > p_threshold
       __a       =   nsfw_detected.tolist()

       if any(SCREAMING_SNAKE_CASE__						):
            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(SCREAMING_SNAKE_CASE__						):
            if nsfw_detected_:
                 __a       =   np.zeros(images[idx].shape						)

       __a       =   self.w_head(SCREAMING_SNAKE_CASE__						)
       __a       =   watermark_detected.flatten()
       __a       =   watermark_detected > w_threshold
       __a       =   watermark_detected.tolist()

       if any(SCREAMING_SNAKE_CASE__						):
            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(SCREAMING_SNAKE_CASE__						):
            if watermark_detected_:
                 __a       =   np.zeros(images[idx].shape						)

       return images, nsfw_detected, watermark_detected










'''simple docstring'''







def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					Dict:
     """simple docstring"""




     __a       =   []
     __a       =   set({"""(""", """[""", """{"""}		)
     __a       =   set({""")""", """]""", """}"""}		)
     __a       =   {"""{""": """}""", """[""": """]""", """(""": """)"""}

     for i in range(len(__SCREAMING_SNAKE_CASE		)		):
          if s[i] in open_brackets:
               stack.append(s[i]		)

          elif s[i] in closed_brackets and (
              len(__SCREAMING_SNAKE_CASE		) == 0 or (len(__SCREAMING_SNAKE_CASE		) > 0 and open_to_closed[stack.pop()] != s[i])
          ):
               return False

     return len(__SCREAMING_SNAKE_CASE		) == 0




def        __lowercase						(      )				->					List[str]:
     """simple docstring"""




     __a       =   input("""Enter sequence of brackets: """		)
     if is_balanced(__SCREAMING_SNAKE_CASE		):
          print(__SCREAMING_SNAKE_CASE				,  """is balanced"""		)
     else:
          print(__SCREAMING_SNAKE_CASE				,  """is not balanced"""		)


if __name__ == "__main__":
 main()






