




import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__   )			->							Union[str, Any]:





     '''simple docstring'''


     if gpta_config_file == "":
          lowercase :       Tuple       =			GPTaConfig()
     else:
          lowercase :       List[str]       =			GPTaConfig.from_json_file(__magic_name__   )
     lowercase :       int       =			GPTaModel(__magic_name__   )

     # Load weights from numpy
     load_tf_weights_in_gpta(__magic_name__			,		__magic_name__			,		__magic_name__   )

     # Save pytorch-model
     lowercase :       int       =			pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
     lowercase :       Any       =			pytorch_dump_folder_path + '''/''' + CONFIG_NAME
     print(F"""Save PyTorch model to {pytorch_weights_dump_path}"""   )
     torch.save(model.state_dict()			,		__magic_name__   )
     print(F"""Save configuration file to {pytorch_config_dump_path}"""   )
     with open(__magic_name__			,		'''w'''			,		encoding='''utf-8'''   ) as f:
          f.write(config.to_json_string()   )


if __name__ == "__main__":
 lowerCAmelCase_						=			argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--gpt2_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
 )
 parser.add_argument(
     '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
 )
 parser.add_argument(
     '--gpt2_config_file',
     default='',
     type=str,
     help=(
         'An optional config json file corresponding to the pre-trained OpenAI model. \n'
         'This specifies the model architecture.'
     ),
 )
 lowerCAmelCase_						=			parser.parse_args()
 convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)





def  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__   )			->							int:





     '''simple docstring'''


     if len(__magic_name__   ) != len(__magic_name__   ):
          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.
     lowercase :       str       =			[p / w for p, w in zip(__magic_name__			,		__magic_name__   )]

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

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

 # Function Call
 calc_profit(profit, weight, max_weight)