



def        lowercase_							(A : Any		,					A : Any		,					A : Any      ):


       if len(__A      ) != len(__A      ):
              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.
       snake_case__							:	int   	=	[p / w for p, w in zip(__A		,					__A      )]

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

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

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

 # Function Call
 calc_profit(profit, weight, max_weight)







from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

UpperCamelCase__											=					[
    'python',
    'tqdm',
    'regex',
    'requests',
    'packaging',
    'filelock',
    'numpy',
    'tokenizers',
    'huggingface-hub',
    'safetensors',
    'accelerate',
    'pyyaml',
]

for pkg in pkgs_to_check_at_runtime:
      if pkg in deps:
            if pkg == "tokenizers":
                  # must be loaded here, or else tqdm check may fail
                  from .utils import is_tokenizers_available

                  if not is_tokenizers_available():
                        continue  # not required, check version only if installed
            elif pkg == "accelerate":
                  # must be loaded here, or else tqdm check may fail
                  from .utils import is_accelerate_available

                  # Maybe switch to is_torch_available in the future here so that Accelerate is hard dep of
                  # Transformers with PyTorch
                  if not is_accelerate_available():
                        continue  # not required, check version only if installed

            require_version_core(deps[pkg])
      else:
            raise ValueError(f'''can\'t find {pkg} in {deps.keys()}, check dependency_versions_table.py''')




def    lowerCAmelCase_ (     __A,  __A=None							)     ->				Dict:







      '''simple docstring'''


      require_version(deps[pkg],  __A							)



