





from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class SCREAMING_SNAKE_CASE_						:
						__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=snake_case_       ,				metadata={"help": "Pretrained encoder config name or path if not the same as encoder_model_name"}					)
						__magic_name__:			Optional[str]       =       field(
						    default=snake_case_       ,				metadata={"help": "Pretrained decoder config name or path if not the same as decoder_model_name"}					)


def 							SCREAMING_SNAKE_CASE__						(    ):
	snake_case_		:			str											=	HfArgumentParser((ModelArguments,)   )
	((snake_case_)						,)		:			Union[str, Any]											=	parser.parse_args_into_dataclasses()

	# Load pretrained model and tokenizer

	# Use explicit specified encoder config
	if model_args.encoder_config_name:
		snake_case_		:			Optional[Any]											=	AutoConfig.from_pretrained(model_args.encoder_config_name   )
	# Use pretrained encoder model's config
	else:
		snake_case_		:			Optional[Any]											=	AutoConfig.from_pretrained(model_args.encoder_model_name_or_path   )

	# Use explicit specified decoder config
	if model_args.decoder_config_name:
		snake_case_		:			int											=	AutoConfig.from_pretrained(model_args.decoder_config_name   )
	# Use pretrained decoder model's config
	else:
		snake_case_		:			int											=	AutoConfig.from_pretrained(model_args.decoder_model_name_or_path   )

	# necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
	snake_case_		:			List[str]											=	True
	snake_case_		:			Dict											=	True

	snake_case_		:			List[Any]											=	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=__a   , decoder_config=__a   , )

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

	# This is necessary to make Flax's generate() work
	snake_case_		:			str											=	decoder_config.eos_token_id
	snake_case_		:			Optional[int]											=	decoder_start_token_id
	snake_case_		:			List[str]											=	pad_token_id

	snake_case_		:			List[Any]											=	AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path   )

	snake_case_		:			Optional[int]											=	AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path   )
	snake_case_		:			Tuple											=	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()








def 							SCREAMING_SNAKE_CASE__						(    __a   ):
	if not isinstance(__a   , __a   ):
		raise ValueError('Input must be an integer'   )
	if input_num <= 0:
		raise ValueError('Input must be positive'   )
	return sum(
	    divisor for divisor in range(1   , input_num // 2 + 1   ) if input_num % divisor == 0   )


if __name__ == "__main__":
						import doctest

						doctest.testmod()

