

"""simple docstring"""





import argparse

import torch

from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()




def   _A  (  UpperCamelCase_     :       Union[str, Any],			UpperCamelCase_     :       str,			UpperCamelCase_     :       Optional[Any])	->						List[Any]:
   '''simple docstring'''


   if openai_config_file == "":
      __lowercase								=						OpenAIGPTConfig()
   else:
      __lowercase								=						OpenAIGPTConfig.from_json_file(UpperCamelCase_)
   __lowercase								=						OpenAIGPTModel(UpperCamelCase_)

   # Load weights from numpy
   load_tf_weights_in_openai_gpt(UpperCamelCase_,			UpperCamelCase_,			UpperCamelCase_)

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


if __name__ == "__main__":
   _a       =							argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--openai_checkpoint_folder_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(
       '--openai_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       =							parser.parse_args()
   convert_openai_checkpoint_to_pytorch(
       args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
   )







"""simple docstring"""





def   _A  (  UpperCamelCase_     :       Any)	->						List[str]:
   '''simple docstring'''


   __lowercase		,__lowercase								=						[], []
   while len(UpperCamelCase_) > 1:
      __lowercase		,__lowercase								=						min(UpperCamelCase_), max(UpperCamelCase_)
      start.append(UpperCamelCase_)
      end.append(UpperCamelCase_)
      collection.remove(UpperCamelCase_)
      collection.remove(UpperCamelCase_)
   end.reverse()
   return start + collection + end


if __name__ == "__main__":
   _a       =							input('Enter numbers separated by a comma:\n').strip()
   _a       =							[int(item) for item in user_input.split(',')]
   print(*merge_sort(unsorted), sep=',')




