






'''simple docstring'''





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    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__):
 # Construct model
 if gpta_config_file == "":
  lowerCamelCase__  =     GPTaConfig()
 else:
  lowerCamelCase__  =     GPTaConfig.from_json_file(lowercase__)
 lowerCamelCase__  =     GPTaModel(lowercase__)

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

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


if __name__ == "__main__":
 __A							:							Any  							=		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."""
     ),
 )
 __A							:							Union[str, Any]  							=		parser.parse_args()
 convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)









'''simple docstring'''





from __future__ import annotations






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__):
 lowerCamelCase__  =     list(range(len(lowercase__)))
 lowerCamelCase__  =     [v / w for v, w in zip(lowercase__				,     lowercase__)]
 index.sort(key=lambda lowercase__: ratio[i]				,     reverse=lowercase__)

 lowerCamelCase__  =     0
 lowerCamelCase__  =     [0] * len(lowercase__)
 for i in index:
  if weight[i] <= capacity:
   lowerCamelCase__  =     1
   max_value += value[i]
   capacity -= weight[i]
  else:
   lowerCamelCase__  =     capacity / weight[i]
   max_value += value[i] * capacity / weight[i]
   break

 return max_value, fractions


if __name__ == "__main__":
 import doctest

 doctest.testmod()

