




import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


_UpperCAmelCase		:  Tuple	=   ["small", "medium", "large"]

_UpperCAmelCase		:  List[str]	=   "lm_head.decoder.weight"
_UpperCAmelCase		:  Dict	=   "lm_head.weight"



def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase      ):
  lowercase			:List[Any]							=  torch.load(lowerCamelCase      )
  lowercase			:Dict							=  d.pop(lowerCamelCase      )
  os.makedirs(lowerCamelCase,	exist_ok=lowerCamelCase      )
  torch.save(lowerCamelCase,	os.path.join(lowerCamelCase,	lowerCamelCase      )      )


if __name__ == "__main__":
     _UpperCAmelCase		:  Tuple	=   argparse.ArgumentParser()
     parser.add_argument("--dialogpt_path", default=".", type=str)
     _UpperCAmelCase		:  Optional[int]	=   parser.parse_args()
     for MODEL in DIALOGPT_MODELS:
          _UpperCAmelCase		:  Tuple	=   os.path.join(args.dialogpt_path, f'''{MODEL}_ft.pkl''')
          _UpperCAmelCase		:  Any	=   f'''./DialoGPT-{MODEL}'''
          convert_dialogpt_checkpoint(
              checkpoint_path,
              pytorch_dump_folder_path,
          )









import os

import pytest
from attr import dataclass


_UpperCAmelCase		:  List[str]	=   "us-east-1"  # defaults region




@dataclass
class       __lowerCAmelCase				:
       _a           =  42
       _a           =  '''arn:aws:iam::558105141721:role/sagemaker_execution_role'''
       _a           =  {
           '''task_name''': '''mnli''',
           '''per_device_train_batch_size''': 16,
           '''per_device_eval_batch_size''': 16,
           '''do_train''': True,
           '''do_eval''': True,
           '''do_predict''': True,
           '''output_dir''': '''/opt/ml/model''',
           '''overwrite_output_dir''': True,
           '''max_steps''': 500,
           '''save_steps''': 5500,
       }
       _a           =  {**hyperparameters, '''max_steps''': 1000}






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    str ):
         if self.framework == "pytorch":
           return [
               {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
               {"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
               {"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
           ]
         else:
           return [
               {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
               {"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
               {"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
           ]






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    Dict ):
         return F"{self.framework}-transfromers-test"






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    Any ):
         return F"./tests/sagemaker/scripts/{self.framework}"






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    Optional[int] ):
         if self.framework == "pytorch":
           return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
         else:
           return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"







@pytest.fixture(scope="class"      )
def 					UpperCAmelCase__     (   lowerCamelCase      ):
  lowercase			:Union[str, Any]							=  SageMakerTestEnvironment(framework=request.cls.framework      )



