




from dataclasses import dataclass, field
from typing import Optional

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




@dataclass
class 						UpperCamelCase__      :

    '''simple docstring'''



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




def 	A ( )      ->					Optional[int]:
       lowerCamelCase  : List[Any]     					=   HfArgumentParser((ModelArguments,)       )
       ((lowerCamelCase							)					, 							)  : Union[str, Any]     					=   parser.parse_args_into_dataclasses()

       # Load pretrained model and tokenizer

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

       # Use explicit specified decoder config
       if model_args.decoder_config_name:
              lowerCamelCase  : Tuple     					=   AutoConfig.from_pretrained(model_args.decoder_config_name       )
       # Use pretrained decoder model's config
       else:
              lowerCamelCase  : List[str]     					=   AutoConfig.from_pretrained(model_args.decoder_model_name_or_path       )

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

       lowerCamelCase  : Union[str, 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=UpperCAmelCase_					,decoder_config=UpperCAmelCase_					,)

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

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

       lowerCamelCase  : str     					=   AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path       )

       lowerCamelCase  : Union[str, Any]     					=   AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path       )
       lowerCamelCase  : int     					=   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()











"""simple docstring"""







def       _lowerCamelCase				(							UpperCAmelCase_ :       bytes     )	-> str:






	"""simple docstring"""





	return "".join([hex(UpperCAmelCase_     )[2:].zfill(2     ).upper() for byte in list(UpperCAmelCase_     )]     )

def       _lowerCamelCase				(							UpperCAmelCase_ :       str     )	-> bytes:






	"""simple docstring"""





	if (len(UpperCAmelCase_     ) % 2) != 0:
		raise ValueError(
		    "Base16 encoded data is invalid:\nData does not have an even number of hex digits."     )
	# Check the character set - the standard base16 alphabet
	# is uppercase according to RFC3548 section 6
	if not set(UpperCAmelCase_     ) <= set("0123456789ABCDEF"     ):
		raise ValueError(
		    "Base16 encoded data is invalid:\nData is not uppercase hex or it contains invalid characters."     )
	# For every two hexadecimal digits (= a byte), turn it into an integer.
	# Then, string the result together into bytes, and return it.
	return bytes(int(data[i] + data[i + 1],	16     ) for i in range(0,	len(UpperCAmelCase_     ),	2     )     )


if __name__ == "__main__":
			import doctest

			doctest.testmod()




