


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 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			if is_torch_version('''<'''							,      '''2.0.0'''	) or not hasattr(_snake_case							,      '''_dynamo'''	):
						return False
			return isinstance(_snake_case							,      torch._dynamo.eval_frame.OptimizedModule	)


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase = True	):


			'''simple docstring'''







			__lowercase			       = (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

			__lowercase			       = is_compiled_module(_snake_case	)
			if is_compiled:
						__lowercase			       = model
						__lowercase			       = model._orig_mod

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

			while isinstance(_snake_case							,      _snake_case	):
						__lowercase			       = model.module

			if not keep_fpaa_wrapper:
						__lowercase			       = getattr(_snake_case							,      '''forward'''	)
						__lowercase			       = model.__dict__.pop('''_original_forward'''							,      _snake_case	)
						if original_forward is not None:
									while hasattr(_snake_case							,      '''__wrapped__'''	):
												__lowercase			       = forward.__wrapped__
												if forward == original_forward:
															break
									__lowercase			       = forward
						if getattr(_snake_case							,      '''_converted_to_transformer_engine'''							,      _snake_case	):
									convert_model(_snake_case							,      to_transformer_engine=_snake_case	)

			if is_compiled:
						__lowercase			       = model
						__lowercase			       = compiled_model

			return model


def 		lowercase_	(  ):


			'''simple docstring'''







			PartialState().wait_for_everyone()


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase	):


			'''simple docstring'''







			if PartialState().distributed_type == DistributedType.TPU:
						xm.save(_snake_case							,      _snake_case	)
			elif PartialState().local_process_index == 0:
						torch.save(_snake_case							,      _snake_case	)


@contextmanager
def 		lowercase_	(  **_UpperCamelCase	):


			'''simple docstring'''







			for key, value in kwargs.items():
						__lowercase			       = str(_snake_case	)

			yield

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


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			if not hasattr(_snake_case							,      '''__qualname__'''	) and not hasattr(_snake_case							,      '''__name__'''	):
						__lowercase			       = getattr(_snake_case							,      '''__class__'''							,      _snake_case	)
			if hasattr(_snake_case							,      '''__qualname__'''	):
						return obj.__qualname__
			if hasattr(_snake_case							,      '''__name__'''	):
						return obj.__name__
			return str(_snake_case	)


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase	):


			'''simple docstring'''







			for key, value in source.items():
						if isinstance(_snake_case							,      _snake_case	):
									__lowercase			       = destination.setdefault(_snake_case							,      {}	)
									merge_dicts(_snake_case							,      _snake_case	)
						else:
									__lowercase			       = value

			return destination





def 		lowercase_	(  _UpperCamelCase = None	):


			'''simple docstring'''







			if port is None:
						__lowercase			       = 2_95_00
			with socket.socket(socket.AF_INET							,      socket.SOCK_STREAM	) as s:
						return s.connect_ex(('''localhost''', port)	) == 0




"""simple docstring"""



def 					lowerCamelCase   (     _snake_case    ,_snake_case    ,_snake_case ):
    if len(_snake_case ) != len(_snake_case ):
        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__	:      Any     							=					[p / w for p, w in zip(_snake_case    ,_snake_case )]

    # Creating a copy of the list and sorting profit/weight in ascending order
    UpperCAmelCase__	:      Union[str, Any]     							=					sorted(_snake_case )

    # declaring useful variables
    UpperCAmelCase__	:      Tuple     							=					len(_snake_case )
    UpperCAmelCase__	:      List[Any]     							=					0
    UpperCAmelCase__	:      Union[str, 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__	:      Tuple     							=					sorted_profit_by_weight[length - i - 1]
        UpperCAmelCase__	:      int     							=					profit_by_weight.index(_snake_case )
        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.'
    )

    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)

