
"""simple docstring"""




import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin







class   _lowercase						(       unittest.TestCase	,   lowerCAmelCase_				):


     '''simple docstring'''





     def  lowerCAmelCase__       (      self  )->					Dict:
          UpperCAmelCase__							: Optional[Any] 							=						load_tool("text-classification"  )
          self.tool.setup()
          UpperCAmelCase__							: List[str] 							=						load_tool("text-classification" ,			remote=__UpperCamelCase  )



     def  lowerCAmelCase__       (      self  )->					Union[str, Any]:
          UpperCAmelCase__							: Dict 							=						self.tool("That's quite cool" ,			["positive", "negative"]  )
          self.assertEqual(__UpperCamelCase ,			"positive"  )



     def  lowerCAmelCase__       (      self  )->					str:
          UpperCAmelCase__							: List[Any] 							=						self.remote_tool("That's quite cool" ,			["positive", "negative"]  )
          self.assertEqual(__UpperCamelCase ,			"positive"  )



     def  lowerCAmelCase__       (      self  )->					Optional[int]:
          UpperCAmelCase__							: Any 							=						self.tool(text="That's quite cool" ,			labels=["positive", "negative"]  )
          self.assertEqual(__UpperCamelCase ,			"positive"  )



     def  lowerCAmelCase__       (      self  )->					str:
          UpperCAmelCase__							: str 							=						self.remote_tool(text="That's quite cool" ,			labels=["positive", "negative"]  )
          self.assertEqual(__UpperCamelCase ,			"positive"  )





"""simple docstring"""




import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


A__      :	Optional[int]          =    ["""small""", """medium""", """large"""]

A__      :	Optional[int]          =    """lm_head.decoder.weight"""
A__      :	Dict          =    """lm_head.weight"""



def 							a__ (				lowerCAmelCase	:       str       ,    lowerCAmelCase	:       str		):






     '''simple docstring'''
     UpperCAmelCase__							: Dict 							=						torch.load(lowerCAmelCase		)
     UpperCAmelCase__							: Optional[int] 							=						d.pop(lowerCAmelCase		)
     os.makedirs(lowerCAmelCase       ,    exist_ok=lowerCAmelCase		)
     torch.save(lowerCAmelCase       ,    os.path.join(lowerCAmelCase       ,    lowerCAmelCase		)		)


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



