

def       a__					(		UpperCAmelCase							:      list    ,			UpperCAmelCase							:      list    ,			UpperCAmelCase							:      int      )  ->						int:
  if len(UpperCAmelCase      ) != len(UpperCAmelCase      ):
    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	:							Tuple						=			[p / w for p, w in zip(UpperCAmelCase    ,			UpperCAmelCase      )]

  # Creating a copy of the list and sorting profit/weight in ascending order
  UpperCAmelCase	:							str						=			sorted(UpperCAmelCase      )

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

     _lowerCamelCase    :       Tuple        =							[int(x) for x in input("Input profits separated by spaces: ").split()]
     _lowerCamelCase    :       Tuple        =							[int(x) for x in input("Input weights separated by spaces: ").split()]
     _lowerCamelCase    :       Dict        =							int(input("Max weight allowed: "))

     # Function Call
     calc_profit(profit, weight, max_weight)








# Copyright 2021 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.

import argparse
import os

from accelerate.utils import ComputeEnvironment

from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file  # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment  # noqa: F401
from .sagemaker import get_sagemaker_input


_lowerCamelCase    :       Optional[Any]        =							"Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine"

def       a__					(		)  ->						List[Any]:
  UpperCAmelCase	:							Dict						=			_ask_options(
      '''In which compute environment are you running?'''    ,			['''This machine''', '''AWS (Amazon SageMaker)''']    ,			_convert_compute_environment    ,			)
  if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
    UpperCAmelCase	:							List[Any]						=			get_sagemaker_input()
  else:
    UpperCAmelCase	:							Optional[Any]						=			get_cluster_input()
  return config

def       a__					(		UpperCAmelCase							:      Union[str, Any]=None      )  ->						List[Any]:
  if subparsers is not None:
    UpperCAmelCase	:							Optional[Any]						=			subparsers.add_parser('''config'''    ,			description=UpperCAmelCase      )
  else:
    UpperCAmelCase	:							List[str]						=			argparse.ArgumentParser('''Accelerate config command'''    ,			description=UpperCAmelCase      )

  parser.add_argument(
      '''--config_file'''    ,			default=UpperCAmelCase    ,			help=(
          '''The path to use to store the config file. Will default to a file named default_config.yaml in the cache '''
          '''location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '''
          '''such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '''
          '''with \'huggingface\'.'''
      )    ,			)

  if subparsers is not None:
    parser.set_defaults(func=UpperCAmelCase      )
  return parser

def       a__					(		UpperCAmelCase							:      List[Any]      )  ->						Optional[int]:
  UpperCAmelCase	:							str						=			get_user_input()
  if args.config_file is not None:
    UpperCAmelCase	:							Any						=			args.config_file
  else:
    if not os.path.isdir(UpperCAmelCase      ):
      os.makedirs(UpperCAmelCase      )
    UpperCAmelCase	:							List[str]						=			default_yaml_config_file

  if config_file.endswith('''.json'''      ):
    config.to_json_file(UpperCAmelCase      )
  else:
    config.to_yaml_file(UpperCAmelCase      )
  print(f'''accelerate configuration saved at {config_file}'''      )

def       a__					(		)  ->						Dict:
  UpperCAmelCase	:							str						=			config_command_parser()
  UpperCAmelCase	:							str						=			parser.parse_args()
  config_command(UpperCAmelCase      )


if __name__ == "__main__":
     main()





