




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

from ...utils import logging


UpperCAmelCase_						=logging.get_logger(__name__)


class 							__UpperCamelCase			( snake_case__  ):





							'''simple docstring'''



							__a							: Optional[int]										=CLIPConfig

							__a							: List[Any]										=["""CLIPEncoderLayer"""]



							def __init__( self       , UpperCAmelCase_						):
														super().__init__(lowercase_						)

														lowerCAmelCase   =		CLIPVisionModelWithProjection(config.vision_config						)

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






							@torch.no_grad()
							def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_=0.5       , UpperCAmelCase_=0.5						):
														lowerCAmelCase   =		self.vision_model(lowercase_						)[0]

														lowerCAmelCase   =		self.p_head(lowercase_						)
														lowerCAmelCase   =		nsfw_detected.flatten()
														lowerCAmelCase   =		nsfw_detected > p_threshold
														lowerCAmelCase   =		nsfw_detected.tolist()

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

														lowerCAmelCase   =		self.w_head(lowercase_						)
														lowerCAmelCase   =		watermark_detected.flatten()
														lowerCAmelCase   =		watermark_detected > w_threshold
														lowerCAmelCase   =		watermark_detected.tolist()

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

														return images, nsfw_detected, watermark_detected









def       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case							):

							def count_of_possible_combinations(_snake_case							) -> int:
														if target < 0:
																					return 0
														if target == 0:
																					return 1
														return sum(count_of_possible_combinations(target - item							) for item in array							)

							return count_of_possible_combinations(_snake_case							)



def       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case							):

							def count_of_possible_combinations_with_dp_array(
							    _snake_case						, _snake_case							) -> int:
														if target < 0:
																					return 0
														if target == 0:
																					return 1
														if dp_array[target] != -1:
																					return dp_array[target]
														lowerCAmelCase   =		sum(
														    count_of_possible_combinations_with_dp_array(target - item						, _snake_case							)
														    for item in array							)
														lowerCAmelCase   =		answer
														return answer

							lowerCAmelCase   =		[-1] * (target + 1)
							return count_of_possible_combinations_with_dp_array(_snake_case						, _snake_case							)



def       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case							):
							lowerCAmelCase   =		[0] * (target + 1)
							lowerCAmelCase   =		1

							for i in range(1						, target + 1							):
														for j in range(_snake_case							):
																					if i - array[j] >= 0:
																												dp_array[i] += dp_array[i - array[j]]

							return dp_array[target]


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		UpperCAmelCase_						=3
		UpperCAmelCase_						=5
		UpperCAmelCase_						=[1, 2, 5]
		print(combination_sum_iv(n, array, target))



