





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

def   __snake_case    (       _lowerCAmelCase			:			List[Any]=None						)			->    int:
   if subparsers is not None:
      A_       :					Optional[Any]        =      subparsers.add_parser("config"					,					description=_lowerCAmelCase						)
   else:
      A_       :					Union[str, Any]        =      argparse.ArgumentParser("Accelerate config command"					,					description=_lowerCAmelCase						)

   parser.add_argument(
       "--config_file"					,					default=_lowerCAmelCase					,					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=_lowerCAmelCase						)
   return parser

def   __snake_case    (       _lowerCAmelCase			:			Tuple						)			->    str:
   A_       :					Tuple        =      get_user_input()
   if args.config_file is not None:
      A_       :					Optional[int]        =      args.config_file
   else:
      if not os.path.isdir(_lowerCAmelCase						):
         os.makedirs(_lowerCAmelCase						)
      A_       :					int        =      default_yaml_config_file

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

def   __snake_case    (       )			->    Optional[Any]:
   A_       :					Dict        =      config_command_parser()
   A_       :					Union[str, Any]        =      parser.parse_args()
   config_command(_lowerCAmelCase						)


if __name__ == "__main__":
 main()







def   __snake_case    (       _lowerCAmelCase			:			list					,					_lowerCAmelCase			:			list					,					_lowerCAmelCase			:			int						)			->    int:
   if len(_lowerCAmelCase						) != len(_lowerCAmelCase						):
      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.
   A_       :					Dict        =      [p / w for p, w in zip(_lowerCAmelCase					,					_lowerCAmelCase						)]

   # Creating a copy of the list and sorting profit/weight in ascending order
   A_       :					Optional[Any]        =      sorted(_lowerCAmelCase						)

   # declaring useful variables
   A_       :					List[Any]        =      len(_lowerCAmelCase						)
   A_       :					List[Any]        =      0
   A_       :					Any        =      0
   A_       :					List[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
      A_       :					List[Any]        =      sorted_profit_by_weight[length - i - 1]
      A_       :					List[Any]        =      profit_by_weight.index(_lowerCAmelCase						)
      A_       :					List[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						       =						[int(x) for x in input('''Input profits separated by spaces: ''').split()]
 _lowerCAmelCase				:   Dict						       =						[int(x) for x in input('''Input weights separated by spaces: ''').split()]
 _lowerCAmelCase				:   Union[str, Any]						       =						int(input('''Max weight allowed: '''))

 # Function Call
 calc_profit(profit, weight, max_weight)
