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,
    )
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool




class  __magic_name__  (		__a    ):





   """simple docstring"""




   lowerCAmelCase      :    Optional[Any]  			=   '''philschmid/bart-large-cnn-samsum'''
   lowerCAmelCase      :    Any  			=   (
       '''This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, '''
       '''and returns a summary of the text.'''
   )
   lowerCAmelCase      :    Any  			=   '''summarizer'''
   lowerCAmelCase      :    Tuple  			=   AutoTokenizer
   lowerCAmelCase      :    Optional[Any]  			=   AutoModelForSeqaSeqLM

   lowerCAmelCase      :    Union[str, Any]  			=   ['''text''']
   lowerCAmelCase      :    Dict  			=   ['''text''']



   def   lowerCAmelCase						(	self					:  str		,  _lowercase					:  Union[str, Any]							):



    """simple docstring"""



    return self.pre_processor(_lowercase		,  return_tensors='''pt'''		,  truncation=_lowercase							)



   def   lowerCAmelCase						(	self					:  List[Any]		,  _lowercase					:  Optional[Any]							):



    """simple docstring"""



    return self.model.generate(**_lowercase							)[0]



   def   lowerCAmelCase						(	self					:  int		,  _lowercase					:  List[str]							):



    """simple docstring"""



    return self.pre_processor.decode(_lowercase		,  skip_special_tokens=_lowercase		,  clean_up_tokenization_spaces=_lowercase							)