'''simple docstring'''





import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


UpperCAmelCase_			    =							['''small''', '''medium''', '''large''']

UpperCAmelCase_			    =							'''lm_head.decoder.weight'''
UpperCAmelCase_			    =							'''lm_head.weight'''




def       lowerCAmelCase_     (		lowercase:							str  ,					lowercase:							str		)    ->				Optional[Any]:







 '''simple docstring'''
 _UpperCamelCase:     int							= torch.load(lowercase		)
 _UpperCamelCase:     Any							= d.pop(lowercase		)
 os.makedirs(lowercase  ,					exist_ok=lowercase		)
 torch.save(lowercase  ,					os.path.join(lowercase  ,					lowercase		)		)


if __name__ == "__main__":
  UpperCAmelCase_			    =							argparse.ArgumentParser()
  parser.add_argument('''--dialogpt_path''', default='''.''', type=str)
  UpperCAmelCase_			    =							parser.parse_args()
  for MODEL in DIALOGPT_MODELS:
    UpperCAmelCase_			    =							os.path.join(args.dialogpt_path, f"""{MODEL}_ft.pkl""")
    UpperCAmelCase_			    =							f"""./DialoGPT-{MODEL}"""
    convert_dialogpt_checkpoint(
        checkpoint_path,
        pytorch_dump_folder_path,
    )
import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict




@pytest.mark.parametrize(
    '''split_dict'''  ,					[
        SplitDict(),
        SplitDict({'''train''': SplitInfo(name='''train'''  ,					num_bytes=1_337  ,					num_examples=42  ,					dataset_name='''my_dataset'''		)}		),
        SplitDict({'''train''': SplitInfo(name='''train'''  ,					num_bytes=1_337  ,					num_examples=42		)}		),
        SplitDict({'''train''': SplitInfo()}		),
    ]  ,					)
def       lowerCAmelCase_     (		lowercase:							SplitDict		)    ->				List[str]:







 '''simple docstring'''
 _UpperCamelCase:     List[str]							= split_dict._to_yaml_list()
 assert len(lowercase		) == len(lowercase		)
 _UpperCamelCase:     Tuple							= SplitDict._from_yaml_list(lowercase		)
 for split_name, split_info in split_dict.items():
  # dataset_name field is deprecated, and is therefore not part of the YAML dump
  _UpperCamelCase:     Optional[Any]							= None
  # the split name of split_dict takes over the name of the split info object
  _UpperCamelCase:     Any							= split_name
 assert split_dict == reloaded





@pytest.mark.parametrize(
    '''split_info'''  ,					[SplitInfo(), SplitInfo(dataset_name=lowercase		), SplitInfo(dataset_name='''my_dataset'''		)]		)
def       lowerCAmelCase_     (		lowercase:							str		)    ->				Tuple:







 '''simple docstring'''
 # For backward compatibility, we need asdict(split_dict) to return split info dictrionaries with the "dataset_name"
 # field even if it's deprecated. This way old versionso of `datasets` can still reload dataset_infos.json files
 _UpperCamelCase:     Optional[Any]							= asdict(SplitDict({'''train''': split_info}		)		)
 assert "dataset_name" in split_dict_asdict["train"]
 assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name