





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

from ...utils import logging


lowercase_          =		logging.get_logger(__name__)
class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):
   _UpperCamelCase   :		Tuple			 =	CLIPConfig

   _UpperCamelCase   :		List[str]			 =	['CLIPEncoderLayer']


   def __init__(   self      :    Dict      ,					a      :    CLIPConfig	)->       Union[str, Any]:





     """simple docstring"""







     super().__init__(a	)

     lowercase__           =  CLIPVisionModelWithProjection(config.vision_config	)

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


   @torch.no_grad()
   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[Any]      ,					a      :    Tuple      ,					a      :    int      ,					a      :    Dict=0.5      ,					a      :    Tuple=0.5	)->       List[Any]:





     """simple docstring"""







     lowercase__           =  self.vision_model(a	)[0]

     lowercase__           =  self.p_head(a	)
     lowercase__           =  nsfw_detected.flatten()
     lowercase__           =  nsfw_detected > p_threshold
     lowercase__           =  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_:
         lowercase__           =  np.zeros(images[idx].shape	)

     lowercase__           =  self.w_head(a	)
     lowercase__           =  watermark_detected.flatten()
     lowercase__           =  watermark_detected > w_threshold
     lowercase__           =  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_:
         lowercase__           =  np.zeros(images[idx].shape	)

     return images, nsfw_detected, watermark_detected









import sys

lowercase_          =		(
    """73167176531330624919225119674426574742355349194934"""
    """96983520312774506326239578318016984801869478851843"""
    """85861560789112949495459501737958331952853208805511"""
    """12540698747158523863050715693290963295227443043557"""
    """66896648950445244523161731856403098711121722383113"""
    """62229893423380308135336276614282806444486645238749"""
    """30358907296290491560440772390713810515859307960866"""
    """70172427121883998797908792274921901699720888093776"""
    """65727333001053367881220235421809751254540594752243"""
    """52584907711670556013604839586446706324415722155397"""
    """53697817977846174064955149290862569321978468622482"""
    """83972241375657056057490261407972968652414535100474"""
    """82166370484403199890008895243450658541227588666881"""
    """16427171479924442928230863465674813919123162824586"""
    """17866458359124566529476545682848912883142607690042"""
    """24219022671055626321111109370544217506941658960408"""
    """07198403850962455444362981230987879927244284909188"""
    """84580156166097919133875499200524063689912560717606"""
    """05886116467109405077541002256983155200055935729725"""
    """71636269561882670428252483600823257530420752963450"""
)




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE = N		)    ->       int:
  lowercase__           =  -sys.maxsize - 1
  for i in range(len(_SCREAMING_SNAKE_CASE		) - 12		):
    lowercase__           =  1
    for j in range(13		):
      product *= int(n[i + j]		)
    if product > largest_product:
      lowercase__           =  product
  return largest_product


if __name__ == "__main__":
      print(f'''{solution() = }''')


