


def 					UpperCamelCase   (      __magic_name__	:				list       ,       __magic_name__	:				list       ,       __magic_name__	:				int	)  ->    int:
		"""simple docstring"""




		if len(__magic_name__	) != len(__magic_name__	):
				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(__magic_name__       ,       __magic_name__	)]

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

		# declaring useful variables
		lowercase__	     =						len(__magic_name__	)
		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(__magic_name__	)
				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.'
	)

	A			:						Tuple        =  [int(x) for x in input('Input profits separated by spaces: ').split()]
	A			:						Optional[int]        =  [int(x) for x in input('Input weights separated by spaces: ').split()]
	A			:						Optional[int]        =  int(input('Max weight allowed: '))

	# Function Call
	calc_profit(profit, weight, max_weight)





import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow



@require_torch
class 				A       (			unittest.TestCase				):
					'''simple docstring'''






					def     lowerCamelCase__	(self       :		List[Any]						)		->  List[str]:




							"""simple docstring"""


							lowercase__	     =						"""ylacombe/bark-small"""
							lowercase__	     =						tempfile.mkdtemp()
							lowercase__	     =						"""en_speaker_1"""
							lowercase__	     =						"""This is a test string"""
							lowercase__	     =						"""speaker_embeddings_path.json"""
							lowercase__	     =						"""speaker_embeddings"""

					def     lowerCamelCase__	(self       :		str ,   **_UpperCAmelCase       :		Optional[int]						)		->  str:




							"""simple docstring"""


							return AutoTokenizer.from_pretrained(self.checkpoint ,   **_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		str						)		->  List[str]:




							"""simple docstring"""


							shutil.rmtree(self.tmpdirname						)

					def     lowerCamelCase__	(self       :		Optional[int]						)		->  List[str]:




							"""simple docstring"""


							lowercase__	     =						self.get_tokenizer()

							lowercase__	     =						BarkProcessor(tokenizer=_UpperCAmelCase						)

							processor.save_pretrained(self.tmpdirname						)
							lowercase__	     =						BarkProcessor.from_pretrained(self.tmpdirname						)

							self.assertEqual(processor.tokenizer.get_vocab() ,   tokenizer.get_vocab()						)

					@slow
					def     lowerCamelCase__	(self       :		str						)		->  Tuple:




							"""simple docstring"""


							lowercase__	     =						BarkProcessor.from_pretrained(
							    pretrained_processor_name_or_path=self.checkpoint ,   speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,   )
							processor.save_pretrained(
							    self.tmpdirname ,   speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,   speaker_embeddings_directory=self.speaker_embeddings_directory ,   )

							lowercase__	     =						self.get_tokenizer(bos_token="""(BOS)""" ,   eos_token="""(EOS)"""						)

							lowercase__	     =						BarkProcessor.from_pretrained(
							    self.tmpdirname ,   self.speaker_embeddings_dict_path ,   bos_token="""(BOS)""" ,   eos_token="""(EOS)""" ,   )

							self.assertEqual(processor.tokenizer.get_vocab() ,   tokenizer_add_kwargs.get_vocab()						)

					def     lowerCamelCase__	(self       :		List[str]						)		->  List[Any]:




							"""simple docstring"""


							lowercase__	     =						BarkProcessor.from_pretrained(
							    pretrained_processor_name_or_path=self.checkpoint ,   speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,   )

							lowercase__	     =						35
							lowercase__	     =						2
							lowercase__	     =						8

							lowercase__	     =						{
							    """semantic_prompt""": np.ones(_UpperCAmelCase						),
							    """coarse_prompt""": np.ones((nb_codebooks_coarse, seq_len)						),
							    """fine_prompt""": np.ones((nb_codebooks_total, seq_len)						),
							}

							# test providing already loaded voice_preset
							lowercase__	     =						processor(text=self.input_string ,   voice_preset=_UpperCAmelCase						)

							lowercase__	     =						inputs["""history_prompt"""]
							for key in voice_preset:
									self.assertListEqual(voice_preset[key].tolist() ,   processed_voice_preset.get(_UpperCAmelCase ,   np.array([]						)						).tolist()						)

							# test loading voice preset from npz file
							lowercase__	     =						os.path.join(self.tmpdirname ,   """file.npz"""						)
							np.savez(_UpperCAmelCase ,   **_UpperCAmelCase						)
							lowercase__	     =						processor(text=self.input_string ,   voice_preset=_UpperCAmelCase						)
							lowercase__	     =						inputs["""history_prompt"""]

							for key in voice_preset:
									self.assertListEqual(voice_preset[key].tolist() ,   processed_voice_preset.get(_UpperCAmelCase ,   np.array([]						)						).tolist()						)

							# test loading voice preset from the hub
							lowercase__	     =						processor(text=self.input_string ,   voice_preset=self.voice_preset						)


					def     lowerCamelCase__	(self       :		int						)		->  Tuple:




							"""simple docstring"""


							lowercase__	     =						self.get_tokenizer()

							lowercase__	     =						BarkProcessor(tokenizer=_UpperCAmelCase						)

							lowercase__	     =						processor(text=self.input_string						)

							lowercase__	     =						tokenizer(
							    self.input_string ,   padding="""max_length""" ,   max_length=256 ,   add_special_tokens=_UpperCAmelCase ,   return_attention_mask=_UpperCAmelCase ,   return_token_type_ids=_UpperCAmelCase ,   )

							for key in encoded_tok.keys():
									self.assertListEqual(encoded_tok[key] ,   encoded_processor[key].squeeze().tolist()						)

