'''simple docstring'''






# 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    a (       __lowerCAmelCase				):



  """simple docstring"""
  __lowerCAmelCase				      =					"""philschmid/bart-large-cnn-samsum"""
  __lowerCAmelCase				      =					(
      """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				      =					"""summarizer"""
  __lowerCAmelCase				      =					AutoTokenizer
  __lowerCAmelCase				      =					AutoModelForSeqaSeqLM

  __lowerCAmelCase				      =					["""text"""]
  __lowerCAmelCase				      =					["""text"""]







  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







    return self.pre_processor(snake_case_   ,    return_tensors="""pt"""   ,    truncation=snake_case_					)







  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







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







  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







    return self.pre_processor.decode(snake_case_   ,    skip_special_tokens=snake_case_   ,    clean_up_tokenization_spaces=snake_case_					)
'''simple docstring'''






import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


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

SCREAMING_SNAKE_CASE_	   =			'lm_head.decoder.weight'
SCREAMING_SNAKE_CASE_	   =			'lm_head.weight'






def  UpperCamelCase__					(					_lowercase    :				str   ,    _lowercase    :				str     )							->	List[str]:
  __UpperCAmelCase:					Tuple		= torch.load(_lowercase     )
  __UpperCAmelCase:					Tuple		= d.pop(_lowercase     )
  os.makedirs(_lowercase   ,    exist_ok=_lowercase     )
  torch.save(_lowercase   ,    os.path.join(_lowercase   ,    _lowercase     )     )


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