



import os
from collections import deque

import torch
from torch.utils.data import Dataset

class   _A   ( _lowercase	):


						'''simple docstring'''





						def __init__(	self	:							List[Any]							,      lowerCamelCase	:							Dict=""							,      lowerCamelCase	:							int="train"	):






							'''simple docstring'''



							assert os.path.isdir(lowerCamelCase	)

							__lowercase		       =			[]
							__lowercase		       =			os.listdir(lowerCamelCase	)
							for story_filename in story_filenames_list:
								if "summary" in story_filename:
									continue
								__lowercase		       =			os.path.join(lowerCamelCase							,      lowerCamelCase	)
								if not os.path.isfile(lowerCamelCase	):
									continue
								self.documents.append(lowerCamelCase	)



						def __len__(	self	:							Dict	):






							'''simple docstring'''



							return len(self.documents	)



						def __getitem__(	self	:							int							,      lowerCamelCase	:							Tuple	):






							'''simple docstring'''



							__lowercase		       =			self.documents[idx]
							__lowercase		       =			document_path.split("/"	)[-1]
							with open(lowerCamelCase							,      encoding="utf-8"	) as source:
								__lowercase		       =			source.read()
								__lowercase       ,    __lowercase		       =			process_story(lowerCamelCase	)
							return document_name, story_lines, summary_lines



def    snake_case_				(    _SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			list(filter(lambda _SCREAMING_SNAKE_CASE   : len(_SCREAMING_SNAKE_CASE   ) != 0  ,	[line.strip() for line in raw_story.split("\n"   )]   )   )

	# for some unknown reason some lines miss a period, add it
	__lowercase		       =			[_add_missing_period(_SCREAMING_SNAKE_CASE   ) for line in nonempty_lines]

	# gather article lines
	__lowercase		       =			[]
	__lowercase		       =			deque(_SCREAMING_SNAKE_CASE   )
	while True:
		try:
			__lowercase		       =			lines.popleft()
			if element.startswith("@highlight"   ):
				break
			story_lines.append(_SCREAMING_SNAKE_CASE   )
		except IndexError:
			# if "@highlight" is absent from the file we pop
			# all elements until there is None, raising an exception.
			return story_lines, []

    # gather summary lines
	__lowercase		       =			list(filter(lambda _SCREAMING_SNAKE_CASE   : not t.startswith("@highlight"   )  ,	_SCREAMING_SNAKE_CASE   )   )

	return story_lines, summary_lines



def    snake_case_				(    _SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			[".", "!", "?", "...", "'", "`", "\"", "\u2019", "\u2019", ")"]
	if line.startswith("@highlight"   ):
		return line
	if line[-1] in END_TOKENS:
		return line
	return line + "."



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	if len(_SCREAMING_SNAKE_CASE   ) > block_size:
		return sequence[:block_size]
	else:
		sequence.extend([pad_token_id] * (block_size - len(_SCREAMING_SNAKE_CASE   ))   )
		return sequence



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			torch.ones_like(_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			sequence == pad_token_id
	__lowercase		       =			0
	return mask



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			[tokenizer.encode(_SCREAMING_SNAKE_CASE   ) for line in story_lines]
	__lowercase		       =			[token for sentence in story_lines_token_ids for token in sentence]
	__lowercase		       =			[tokenizer.encode(_SCREAMING_SNAKE_CASE   ) for line in summary_lines]
	__lowercase		       =			[token for sentence in summary_lines_token_ids for token in sentence]

	return story_token_ids, summary_token_ids



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			[]
	for sequence in batch:
		__lowercase		       =			-1
		__lowercase		       =			[]
		for s in sequence:
			if s == separator_token_id:
				sentence_num += 1
			embeddings.append(sentence_num % 2   )
		batch_embeddings.append(_SCREAMING_SNAKE_CASE   )
	return torch.tensor(_SCREAMING_SNAKE_CASE   )








def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	if len(_SCREAMING_SNAKE_CASE   ) != len(_SCREAMING_SNAKE_CASE   ):
		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(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )]

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

	# declaring useful variables
	__lowercase		       =			len(_SCREAMING_SNAKE_CASE   )
	__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(_SCREAMING_SNAKE_CASE   )
		__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."""
	)

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

	# Function Call
	calc_profit(profit, weight, max_weight)



