


"""simple docstring"""







import functools
from typing import Any




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					if not isinstance(lowerCAmelCase_  ,						lowerCAmelCase_       ) or len(lowerCAmelCase_       ) == 0:
										raise ValueError("the string should be not empty string"       )

					if not isinstance(lowerCAmelCase_  ,						lowerCAmelCase_       ) or not all(
					    isinstance(lowerCAmelCase_  ,						lowerCAmelCase_       ) and len(lowerCAmelCase_       ) > 0 for item in words       ):
										raise ValueError("the words should be a list of non-empty strings"       )

					# Build trie
					__SCREAMING_SNAKE_CASE						       =    {}
					__SCREAMING_SNAKE_CASE						       =    "WORD_KEEPER"

					for word in words:
										__SCREAMING_SNAKE_CASE						       =    trie
										for c in word:
															if c not in trie_node:
																				__SCREAMING_SNAKE_CASE						       =    {}

															__SCREAMING_SNAKE_CASE						       =    trie_node[c]

										__SCREAMING_SNAKE_CASE						       =    True

					__SCREAMING_SNAKE_CASE						       =    len(lowerCAmelCase_       )

					# Dynamic programming method
					@functools.cache
					def is_breakable(lowerCAmelCase_       ) -> bool:
										if index == len_string:
															return True

										__SCREAMING_SNAKE_CASE						       =    trie
										for i in range(lowerCAmelCase_  ,						lowerCAmelCase_       ):
															__SCREAMING_SNAKE_CASE						       =    trie_node.get(string[i]  ,						lowerCAmelCase_       )

															if trie_node is None:
																				return False

															if trie_node.get(lowerCAmelCase_  ,						lowerCAmelCase_       ) and is_breakable(i + 1       ):
																				return True

										return False

					return is_breakable(0       )


if __name__ == "__main__":
		import doctest

		doctest.testmod()




"""simple docstring"""







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

from ...utils import logging


a__    :							Optional[Any]            =					logging.get_logger(__name__)




class 			UpperCamelCase_     (			UpperCamelCase):

	"""simple docstring"""





	snake_case__    :  int    =      CLIPConfig

	snake_case__    :  str    =      ["CLIPEncoderLayer"]




	def __init__(			self      :    Optional[int]      ,      UpperCAmelCase__      :    CLIPConfig       )	->     Dict:
						super().__init__(UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE						       =    CLIPVisionModelWithProjection(config.vision_config       )

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




	@torch.no_grad()
	def   UpperCAmelCase_				(			self      :    str      ,      UpperCAmelCase__      :    List[str]      ,      UpperCAmelCase__      :    str      ,      UpperCAmelCase__      :    int=0.5      ,      UpperCAmelCase__      :    Optional[int]=0.5       )	->     Union[str, Any]:
						__SCREAMING_SNAKE_CASE						       =    self.vision_model(UpperCAmelCase__       )[0]

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

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

						return images, nsfw_detected, watermark_detected
