


from dataclasses import dataclass, field
from typing import Optional

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






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





def 	_lowerCAmelCase       (    )						->	Any:


						'''simple docstring'''






						__snake_case				=		HfArgumentParser((ModelArguments,) )
						((__snake_case)		,					)				=		parser.parse_args_into_dataclasses()

						# Load pretrained model and tokenizer

						# Use explicit specified encoder config
						if model_args.encoder_config_name:
												__snake_case				=		AutoConfig.from_pretrained(model_args.encoder_config_name )
						# Use pretrained encoder model's config
						else:
												__snake_case				=		AutoConfig.from_pretrained(model_args.encoder_model_name_or_path )

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

						# necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
						__snake_case				=		True
						__snake_case				=		True

						__snake_case				=		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=_lowerCAmelCase      ,	decoder_config=_lowerCAmelCase      ,	)

						# GPT2 only has bos/eos tokens but not decoder_start/pad tokens
						__snake_case				=		decoder_config.decoder_start_token_id
						__snake_case				=		decoder_config.pad_token_id
						if decoder_start_token_id is None:
												__snake_case				=		decoder_config.bos_token_id
						if pad_token_id is None:
												__snake_case				=		decoder_config.eos_token_id

						# This is necessary to make Flax's generate() work
						__snake_case				=		decoder_config.eos_token_id
						__snake_case				=		decoder_start_token_id
						__snake_case				=		pad_token_id

						__snake_case				=		AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path )

						__snake_case				=		AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path )
						__snake_case				=		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 os
from typing import Dict, List, Tuple, TypeVar, Union


A							:					List[Any]     						=     TypeVar('T')

A							:					Dict     						=     Union[List[T], Tuple[T, ...]]
A							:					Any     						=     Union[T, List[T], Dict[str, T]]
A							:					Optional[int]     						=     Union[str, bytes, os.PathLike]





