




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCAmelCase_						=			{'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_						=			[
     'MRA_PRETRAINED_MODEL_ARCHIVE_LIST',
     'MraForMaskedLM',
     'MraForMultipleChoice',
     'MraForQuestionAnswering',
     'MraForSequenceClassification',
     'MraForTokenClassification',
     'MraLayer',
     'MraModel',
     'MraPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mra import (
      MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
      MraForMaskedLM,
      MraForMultipleChoice,
      MraForQuestionAnswering,
      MraForSequenceClassification,
      MraForTokenClassification,
      MraLayer,
      MraModel,
      MraPreTrainedModel,
  )
else:
 import sys

 lowerCAmelCase_						=			_LazyModule(__name__, globals()['__file__'], _import_structure)





def  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__   )			->							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 :       str       =			[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 :       str       =			sorted(__magic_name__   )

     # declaring useful variables
     lowercase :       Union[str, Any]       =			len(__magic_name__   )
     lowercase :       Optional[int]       =			0
     lowercase :       Optional[int]       =			0
     lowercase :       int       =			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 :       Optional[int]       =			sorted_profit_by_weight[length - i - 1]
          lowercase :       Union[str, Any]       =			profit_by_weight.index(__magic_name__   )
          lowercase :       Any       =			-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.'
 )

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

 # Function Call
 calc_profit(profit, weight, max_weight)