import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


__lowerCamelCase			: Optional[Any]							   =		['''small''', '''medium''', '''large''']

__lowerCamelCase			: Tuple							   =		'''lm_head.decoder.weight'''
__lowerCamelCase			: Union[str, Any]							   =		'''lm_head.weight'''



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       str ,     __UpperCamelCase    :       str	)							->					Tuple:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      torch.load(__UpperCamelCase	)
  SCREAMING_SNAKE_CASE__										=      d.pop(__UpperCamelCase	)
  os.makedirs(__UpperCamelCase ,     exist_ok=__UpperCamelCase	)
  torch.save(__UpperCamelCase ,     os.path.join(__UpperCamelCase ,     __UpperCamelCase	)	)


if __name__ == "__main__":
   __lowerCamelCase			: Tuple							   =		argparse.ArgumentParser()
   parser.add_argument('''--dialogpt_path''', default='''.''', type=str)
   __lowerCamelCase			: Any							   =		parser.parse_args()
   for MODEL in DIALOGPT_MODELS:
      __lowerCamelCase			: Tuple							   =		os.path.join(args.dialogpt_path, F"""{MODEL}_ft.pkl""")
      __lowerCamelCase			: str							   =		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 typing import TYPE_CHECKING

from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
   from PIL import Image







class __snake_case				(						lowerCamelCase_		):
   lowerCAmelCase_														=							"Salesforce/blip-image-captioning-base"
   lowerCAmelCase_														=							(
       "This is a tool that generates a description of an image. It takes an input named `image` which should be the "
       "image to caption, and returns a text that contains the description in English."
   )
   lowerCAmelCase_														=							"image_captioner"
   lowerCAmelCase_														=							AutoModelForVisionaSeq

   lowerCAmelCase_														=							["image"]
   lowerCAmelCase_														=							["text"]




   def __init__(    self			:				List[Any]       ,	*_lowercase			:				Optional[int]       ,	**_lowercase			:				Union[str, Any]      ):



     """simple docstring"""






     requires_backends(self       ,	["""vision"""]      )
     super().__init__(*_lowercase       ,	**_lowercase      )




   def 	__a						(    self			:				Tuple       ,	_lowercase			:				"Image"      ):



     """simple docstring"""






     return self.pre_processor(images=_lowercase       ,	return_tensors="""pt"""      )




   def 	__a						(    self			:				Union[str, Any]       ,	_lowercase			:				Optional[int]      ):



     """simple docstring"""






     return self.model.generate(**_lowercase      )




   def 	__a						(    self			:				int       ,	_lowercase			:				Any      ):



     """simple docstring"""






     return self.pre_processor.batch_decode(_lowercase       ,	skip_special_tokens=_lowercase      )[0].strip()
