
"""simple docstring"""



import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
    from .tokenization_albert import AlbertTokenizer
else:
    UpperCamelCase__          =	None

UpperCamelCase__          =	logging.get_logger(__name__)
UpperCamelCase__          =	{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

UpperCamelCase__          =	{
    'vocab_file': {
        'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/spiece.model',
        'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/spiece.model',
        'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model',
        'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model',
        'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/spiece.model',
        'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/spiece.model',
        'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model',
        'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json',
        'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json',
        'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json',
        'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json',
        'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json',
        'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json',
        'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json',
        'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json',
    },
}

UpperCamelCase__          =	{
    'albert-base-v1': 5_12,
    'albert-large-v1': 5_12,
    'albert-xlarge-v1': 5_12,
    'albert-xxlarge-v1': 5_12,
    'albert-base-v2': 5_12,
    'albert-large-v2': 5_12,
    'albert-xlarge-v2': 5_12,
    'albert-xxlarge-v2': 5_12,
}

UpperCamelCase__          =	'▁'





class 				a			(					lowercase      ):
      UpperCamelCase							:   Optional[Any]           =     VOCAB_FILES_NAMES
      UpperCamelCase							:   Optional[Any]           =     PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase							:   Tuple           =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase							:   Optional[Any]           =     AlbertTokenizer



      def __init__(   self     ,	UpperCamelCase_=None     ,	UpperCamelCase_=None     ,	UpperCamelCase_=True     ,	UpperCamelCase_=True     ,	UpperCamelCase_=False     ,	UpperCamelCase_="[CLS]"     ,	UpperCamelCase_="[SEP]"     ,	UpperCamelCase_="<unk>"     ,	UpperCamelCase_="[SEP]"     ,	UpperCamelCase_="<pad>"     ,	UpperCamelCase_="[CLS]"     ,	UpperCamelCase_="[MASK]"     ,	**UpperCamelCase_     ,	):
          # Mask token behave like a normal word, i.e. include the space before it and
          # is included in the raw text, there should be a match in a non-normalized sentence.
          UpperCAmelCase__	:      Dict     							=					(
              AddedToken(UpperCamelCase_     ,	lstrip=UpperCamelCase_     ,	rstrip=UpperCamelCase_     ,	normalized=UpperCamelCase_      )
              if isinstance(UpperCamelCase_     ,	UpperCamelCase_      )
              else mask_token
          )

          super().__init__(
              UpperCamelCase_     ,	tokenizer_file=UpperCamelCase_     ,	do_lower_case=UpperCamelCase_     ,	remove_space=UpperCamelCase_     ,	keep_accents=UpperCamelCase_     ,	bos_token=UpperCamelCase_     ,	eos_token=UpperCamelCase_     ,	unk_token=UpperCamelCase_     ,	sep_token=UpperCamelCase_     ,	pad_token=UpperCamelCase_     ,	cls_token=UpperCamelCase_     ,	mask_token=UpperCamelCase_     ,	**UpperCamelCase_     ,	)

          UpperCAmelCase__	:      Any     							=					do_lower_case
          UpperCAmelCase__	:      Dict     							=					remove_space
          UpperCAmelCase__	:      Optional[int]     							=					keep_accents
          UpperCAmelCase__	:      Optional[int]     							=					vocab_file
          UpperCAmelCase__	:      Dict     							=					False if not self.vocab_file else True



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_ = None      ):
          UpperCAmelCase__	:      Optional[Any]     							=					[self.sep_token_id]
          UpperCAmelCase__	:      Any     							=					[self.cls_token_id]
          if token_ids_a is None:
              return cls + token_ids_a + sep
          return cls + token_ids_a + sep + token_ids_a + sep



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_ = None      ):
          UpperCAmelCase__	:      Union[str, Any]     							=					[self.sep_token_id]
          UpperCAmelCase__	:      Dict     							=					[self.cls_token_id]

          if token_ids_a is None:
              return len(cls + token_ids_a + sep      ) * [0]
          return len(cls + token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1]



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_ = None      ):
          if not self.can_save_slow_tokenizer:
              raise ValueError(
                  'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
                  'tokenizer.'      )

          if not os.path.isdir(UpperCamelCase_      ):
              logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''      )
              return
          UpperCAmelCase__	:      Optional[Any]     							=					os.path.join(
              UpperCamelCase_     ,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']      )

          if os.path.abspath(self.vocab_file      ) != os.path.abspath(UpperCamelCase_      ):
              copyfile(self.vocab_file     ,	UpperCamelCase_      )

          return (out_vocab_file,)



"""simple docstring"""



def 					lowerCamelCase   (     _snake_case    ,_snake_case    ,_snake_case ):
    if len(_snake_case ) != len(_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.
    UpperCAmelCase__	:      Any     							=					[p / w for p, w in zip(_snake_case    ,_snake_case )]

    # Creating a copy of the list and sorting profit/weight in ascending order
    UpperCAmelCase__	:      Union[str, Any]     							=					sorted(_snake_case )

    # declaring useful variables
    UpperCAmelCase__	:      Tuple     							=					len(_snake_case )
    UpperCAmelCase__	:      List[Any]     							=					0
    UpperCAmelCase__	:      Union[str, Any]     							=					0
    UpperCAmelCase__	:      Any     							=					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
        UpperCAmelCase__	:      Tuple     							=					sorted_profit_by_weight[length - i - 1]
        UpperCAmelCase__	:      int     							=					profit_by_weight.index(_snake_case )
        UpperCAmelCase__	:      str     							=					-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.'
    )

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

    # Function Call
    calc_profit(profit, weight, max_weight)

