
def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					if len(lowerCAmelCase__	) != len(lowerCAmelCase__	):
										raise ValueError('''The length of profit and weight must be same.'''	)
					if max_weight <= 0:
										raise ValueError('''max_weight must greater than zero.'''	)
					if any(p < 0 for p in profit	):
										raise ValueError('''Profit can not be negative.'''	)
					if any(w < 0 for w in weight	):
										raise ValueError('''Weight can not be negative.'''	)

					# List created to store profit gained for the 1kg in case of each weight
					# respectively.  Calculate and append profit/weight for each element.
					lowercase					=		[p / w for p, w in zip(lowerCAmelCase__				,    lowerCAmelCase__	)]

					# Creating a copy of the list and sorting profit/weight in ascending order
					lowercase					=		sorted(lowerCAmelCase__	)

					# declaring useful variables
					lowercase					=		len(lowerCAmelCase__	)
					lowercase					=		0
					lowercase					=		0
					lowercase					=		0

					# loop till the total weight do not reach max limit e.g. 15 kg and till i<length
					while limit <= max_weight and i < length:
										# flag value for encountered greatest element in sorted_profit_by_weight
										lowercase					=		sorted_profit_by_weight[length - i - 1]
										lowercase					=		profit_by_weight.index(lowerCAmelCase__	)
										lowercase					=		-1

										# check if the weight encountered is less than the total weight
										# encountered before.
										if max_weight - limit >= weight[index]:
															limit += weight[index]
															# Adding profit gained for the given weight 1 ===
															# weight[index]/weight[index]
															gain += 1 * profit[index]
										else:
															# Since the weight encountered is greater than limit, therefore take the
															# required number of remaining kgs and calculate profit for it.
															# weight remaining / weight[index]
															gain += (max_weight - limit) / weight[index] * profit[index]
															break
										i += 1
					return gain


if __name__ == "__main__":
							print(
							    "Input profits, weights, and then max_weight (all positive ints) separated by "
							    "spaces."
							)

							lowercase__	:int										=					[int(x) for x in input("Input profits separated by spaces: ").split()]
							lowercase__	:Any										=					[int(x) for x in input("Input weights separated by spaces: ").split()]
							lowercase__	:Optional[int]										=					int(input("Max weight allowed: "))

							# Function Call
							calc_profit(profit, weight, max_weight)






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

from ...utils import logging


lowercase__	:Union[str, Any]										=					logging.get_logger(__name__)







class        lowercase	(      SCREAMING_SNAKE_CASE__	):
							lowercase_ : Union[str, Any]       =CLIPConfig

							lowercase_ : str       =['''CLIPEncoderLayer''']




							def __init__(       self     ,A__):
												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 				A__   (       self     ,A__     ,A__     ,A__=0.5     ,A__=0.5):
												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




