



'''simple docstring'''






import os
import socket
from contextlib import contextmanager

import torch

from ..commands.config.default import write_basic_config  # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version


if is_deepspeed_available():
     from deepspeed import DeepSpeedEngine

if is_tpu_available(check_device=False):
     import torch_xla.core.xla_model as xm
def       lowerCamelCase__       (						_A				):





  if is_torch_version('<' ,			'2.0.0'				) or not hasattr(UpperCAmelCase_ ,			'_dynamo'				):
    return False
  return isinstance(UpperCAmelCase_ ,			torch._dynamo.eval_frame.OptimizedModule				)
def       lowerCamelCase__       (						_A ,			_A = True				):
  a						:					List[Any]        =     (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

  a						:					List[str]        =     is_compiled_module(UpperCAmelCase_				)
  if is_compiled:
    a						:					Tuple        =     model
    a						:					Optional[int]        =     model._orig_mod

  if is_deepspeed_available():
    options += (DeepSpeedEngine,)

  while isinstance(UpperCAmelCase_ ,			UpperCAmelCase_				):
    a						:					Any        =     model.module

  if not keep_fpaa_wrapper:
    a						:					Union[str, Any]        =     getattr(UpperCAmelCase_ ,			'forward'				)
    a						:					str        =     model.__dict__.pop('_original_forward' ,			UpperCAmelCase_				)
    if original_forward is not None:
      while hasattr(UpperCAmelCase_ ,			'__wrapped__'				):
        a						:					Tuple        =     forward.__wrapped__
        if forward == original_forward:
          break
      a						:					Union[str, Any]        =     forward
    if getattr(UpperCAmelCase_ ,			'_converted_to_transformer_engine' ,			UpperCAmelCase_				):
      convert_model(UpperCAmelCase_ ,			to_transformer_engine=UpperCAmelCase_				)

  if is_compiled:
    a						:					List[Any]        =     model
    a						:					int        =     compiled_model

  return model
def       lowerCamelCase__       (						):
  PartialState().wait_for_everyone()
def       lowerCamelCase__       (						_A ,			_A				):





  if PartialState().distributed_type == DistributedType.TPU:
    xm.save(UpperCAmelCase_ ,			UpperCAmelCase_				)
  elif PartialState().local_process_index == 0:
    torch.save(UpperCAmelCase_ ,			UpperCAmelCase_				)
@contextmanager
def       lowerCamelCase__       (						**_A				):





  for key, value in kwargs.items():
    a						:					Union[str, Any]        =     str(UpperCAmelCase_				)

  yield

  for key in kwargs:
    if key.upper() in os.environ:
      del os.environ[key.upper()]
def       lowerCamelCase__       (						_A				):





  if not hasattr(UpperCAmelCase_ ,			'__qualname__'				) and not hasattr(UpperCAmelCase_ ,			'__name__'				):
    a						:					List[str]        =     getattr(UpperCAmelCase_ ,			'__class__' ,			UpperCAmelCase_				)
  if hasattr(UpperCAmelCase_ ,			'__qualname__'				):
    return obj.__qualname__
  if hasattr(UpperCAmelCase_ ,			'__name__'				):
    return obj.__name__
  return str(UpperCAmelCase_				)
def       lowerCamelCase__       (						_A ,			_A				):





  for key, value in source.items():
    if isinstance(UpperCAmelCase_ ,			UpperCAmelCase_				):
      a						:					Tuple        =     destination.setdefault(UpperCAmelCase_ ,			{}				)
      merge_dicts(UpperCAmelCase_ ,			UpperCAmelCase_				)
    else:
      a						:					Optional[int]        =     value

  return destination

def       lowerCamelCase__       (						_A = None				):





  if port is None:
    a						:					Any        =     2_9500
  with socket.socket(socket.AF_INET ,			socket.SOCK_STREAM				) as s:
    return s.connect_ex(('localhost', port)				) == 0



def 				__lowerCamelCase						( UpperCAmelCase_						:		list ,  UpperCAmelCase_						:		list ,  UpperCAmelCase_						:		int						):




      """simple docstring"""





      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.
      a						:Optional[int] 							=       [p / w for p, w in zip(UpperCAmelCase_ ,  UpperCAmelCase_						)]

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

      # declaring useful variables
      a						:Dict 							=       len(UpperCAmelCase_						)
      a						:Tuple 							=       0
      a						:List[Any] 							=       0
      a						:str 							=       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						:Optional[Any] 							=       profit_by_weight.index(UpperCAmelCase_						)
            a						:Optional[int] 							=       -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.'''
     )

     snake_case				:    Union[str, Any]       				=     [int(x) for x in input('''Input profits separated by spaces: ''').split()]
     snake_case				:    Tuple       				=     [int(x) for x in input('''Input weights separated by spaces: ''').split()]
     snake_case				:    str       				=     int(input('''Max weight allowed: '''))

     # Function Call
     calc_profit(profit, weight, max_weight)





