




from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser


@dataclass
class    SCREAMING_SNAKE_CASE_				:



	"""simple docstring"""


	__magic_name__   :       str      	=		field(
	    metadata={'help': 'The output directory where the model will be written.'}	,  )
	__magic_name__   :       str      	=		field(
	    metadata={
	        'help': (
	            'The encoder model checkpoint for weights initialization.'
	            'Don\'t set if you want to train an encoder model from scratch.'
	        )
	    }	,  )
	__magic_name__   :       str      	=		field(
	    metadata={
	        'help': (
	            'The decoder model checkpoint for weights initialization.'
	            'Don\'t set if you want to train a decoder model from scratch.'
	        )
	    }	,  )
	__magic_name__   :       Optional[str]      	=		field(
	    default=_UpperCamelCase	,  metadata={'help': 'Pretrained encoder config name or path if not the same as encoder_model_name'}   )
	__magic_name__   :       Optional[str]      	=		field(
	    default=_UpperCamelCase	,  metadata={'help': 'Pretrained decoder config name or path if not the same as decoder_model_name'}   )






def 				A__ ()     ->						str:
					__UpperCamelCase					:	Union[str, Any]             =      HfArgumentParser((ModelArguments,)				)
					((__UpperCamelCase)      ,			)					:	Dict             =      parser.parse_args_into_dataclasses()

					# Load pretrained model and tokenizer

					# Use explicit specified encoder config
					if model_args.encoder_config_name:
										__UpperCamelCase					:	int             =      AutoConfig.from_pretrained(model_args.encoder_config_name				)
					# Use pretrained encoder model's config
					else:
										__UpperCamelCase					:	Union[str, Any]             =      AutoConfig.from_pretrained(model_args.encoder_model_name_or_path				)

					# Use explicit specified decoder config
					if model_args.decoder_config_name:
										__UpperCamelCase					:	Optional[Any]             =      AutoConfig.from_pretrained(model_args.decoder_config_name				)
					# Use pretrained decoder model's config
					else:
										__UpperCamelCase					:	Optional[int]             =      AutoConfig.from_pretrained(model_args.decoder_model_name_or_path				)

					# necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
					__UpperCamelCase					:	Dict             =      True
					__UpperCamelCase					:	Optional[int]             =      True

					__UpperCamelCase					:	Optional[int]             =      FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
					    encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path		,	decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path		,	encoder_config=snake_case		,	decoder_config=snake_case		,	)

					# GPT2 only has bos/eos tokens but not decoder_start/pad tokens
					__UpperCamelCase					:	Tuple             =      decoder_config.decoder_start_token_id
					__UpperCamelCase					:	Any             =      decoder_config.pad_token_id
					if decoder_start_token_id is None:
										__UpperCamelCase					:	List[Any]             =      decoder_config.bos_token_id
					if pad_token_id is None:
										__UpperCamelCase					:	List[str]             =      decoder_config.eos_token_id

					# This is necessary to make Flax's generate() work
					__UpperCamelCase					:	Any             =      decoder_config.eos_token_id
					__UpperCamelCase					:	str             =      decoder_start_token_id
					__UpperCamelCase					:	Any             =      pad_token_id

					__UpperCamelCase					:	Union[str, Any]             =      AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path				)

					__UpperCamelCase					:	Optional[int]             =      AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path				)
					__UpperCamelCase					:	List[Any]             =      tokenizer.convert_ids_to_tokens(model.config.pad_token_id				)

					model.save_pretrained(model_args.output_dir				)
					image_processor.save_pretrained(model_args.output_dir				)
					tokenizer.save_pretrained(model_args.output_dir				)


if __name__ == "__main__":
	main()












import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


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

a__		     =     '''lm_head.decoder.weight'''
a__		     =     '''lm_head.weight'''





def 				A__ (snake_case   :		str		,	snake_case   :		str				)     ->						Union[str, Any]:
					__UpperCamelCase					:	Union[str, Any]             =      torch.load(snake_case				)
					__UpperCamelCase					:	int             =      d.pop(snake_case				)
					os.makedirs(snake_case		,	exist_ok=snake_case				)
					torch.save(snake_case		,	os.path.join(snake_case		,	snake_case				)				)


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






