

'''simple docstring'''

def 					__UpperCamelCase				(						lowercase__    :	list,       lowercase__    :	list,       lowercase__    :	int			):






     '''simple docstring'''





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

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

     # declaring useful variables
     __lowercase      		=len(lowercase__			)
     __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(lowercase__			)
          __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.'''
       )

       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)





'''simple docstring'''

import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation


UpperCAmelCase					 =	logging.get_logger(__name__)

UpperCAmelCase					 =	{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase					 =	{
    '''tokenizer_file''': {
        '''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json''',
    },
}

UpperCAmelCase					 =	{
    '''gpt-neox-20b''': 2048,
}


class 	lowerCAmelCase  (   A ):
       lowerCAmelCase_												=			VOCAB_FILES_NAMES
       lowerCAmelCase_												=			PRETRAINED_VOCAB_FILES_MAP
       lowerCAmelCase_												=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCAmelCase_												=			["input_ids", "attention_mask"]







       def __init__(				self      :			Tuple    ,   __lowercase      :			Union[str, Any]=None    ,   __lowercase      :			Union[str, Any]=None    ,   __lowercase      :			str=None    ,   __lowercase      :			Optional[int]="<|endoftext|>"    ,   __lowercase      :			List[Any]="<|endoftext|>"    ,   __lowercase      :			Tuple="<|endoftext|>"    ,   __lowercase      :			Optional[int]=False    ,   **__lowercase      :			List[Any]    ,   ):







            """simple docstring"""







            super().__init__(
                __lowercase    ,   __lowercase    ,   tokenizer_file=__lowercase    ,   unk_token=__lowercase    ,   bos_token=__lowercase    ,   eos_token=__lowercase    ,   add_prefix_space=__lowercase    ,   **__lowercase    ,   )

            __lowercase      		=json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
            if pre_tok_state.get('add_prefix_space'    ,   __lowercase   ) != add_prefix_space:
                 __lowercase      		=getattr(__lowercase    ,   pre_tok_state.pop('type'   )   )
                 __lowercase      		=add_prefix_space
                 __lowercase      		=pre_tok_class(**__lowercase   )

            __lowercase      		=add_prefix_space







       def       snake_case (				self      :			Optional[Any]    ,   __lowercase      :			str    ,   __lowercase      :			Optional[str] = None   ):







            """simple docstring"""







            __lowercase      		=self._tokenizer.model.save(__lowercase    ,   name=__lowercase   )
            return tuple(__lowercase   )







       def       snake_case (				self      :			Optional[Any]    ,   __lowercase      :			"Conversation"   ):







            """simple docstring"""







            __lowercase      		=[]
            for is_user, text in conversation.iter_texts():
                 input_ids.extend(self.encode(__lowercase    ,   add_special_tokens=__lowercase   ) + [self.eos_token_id]   )

            if len(__lowercase   ) > self.model_max_length:
                 __lowercase      		=input_ids[-self.model_max_length :]
            return input_ids


