
from dataclasses import dataclass, field
from typing import Optional

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




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





def 	SCREAMING_SNAKE_CASE__    (      ):
    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=UpperCamelCase__    ,		decoder_config=UpperCamelCase__    ,		)

    # 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()





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):
    return price * (1 + tax_rate)


if __name__ == "__main__":
     print(F'''{price_plus_tax(100, 0.2_5) = }''')
     print(F'''{price_plus_tax(1_2_5.5_0, 0.0_5) = }''')



