'''simple docstring'''




from dataclasses import dataclass, field
from typing import Optional

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






@dataclass
class 		__A       :
       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=__lowerCAmelCase ,					metadata={"help": "Pretrained encoder config name or path if not the same as encoder_model_name"}   )
       snake_case       :Optional[str]				    =					field(
           default=__lowerCAmelCase ,					metadata={"help": "Pretrained decoder config name or path if not the same as decoder_model_name"}   )





def _lowercase	(      )					->       Union[str, Any]:





  """simple docstring"""


  __UpperCAmelCase  :				Tuple       				=		HfArgumentParser((ModelArguments,)						)
  (__UpperCAmelCase				)  :				int       				=		parser.parse_args_into_dataclasses()

  # Load pretrained model and tokenizer

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

  # Use explicit specified decoder config
  if model_args.decoder_config_name:
    __UpperCAmelCase  :				Union[str, Any]       				=		AutoConfig.from_pretrained(model_args.decoder_config_name						)
  # Use pretrained decoder model's config
  else:
    __UpperCAmelCase  :				Dict       				=		AutoConfig.from_pretrained(model_args.decoder_model_name_or_path						)

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

  __UpperCAmelCase  :				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=lowerCamelCase__							,    decoder_config=lowerCamelCase__							,    )

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

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

  __UpperCAmelCase  :				int       				=		AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path						)

  __UpperCAmelCase  :				List[Any]       				=		AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path						)
  __UpperCAmelCase  :				Optional[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'''




from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments

def _lowercase	(      )					->       Dict:





       """simple docstring"""


       __UpperCAmelCase  :				str       				=		HfArgumentParser(lowerCamelCase__						)
       __UpperCAmelCase  :				Optional[Any]       				=		parser.parse_args_into_dataclasses()[0]
       __UpperCAmelCase  :				Any       				=		TensorFlowBenchmark(args=lowerCamelCase__						)
       try:
              __UpperCAmelCase  :				List[Any]       				=		parser.parse_args_into_dataclasses()[0]
       except ValueError as e:
              __UpperCAmelCase  :				str       				=		"Arg --no_{0} is no longer used, please use --no-{0} instead."
              __UpperCAmelCase  :				Tuple       				=		" ".join(str(lowerCamelCase__						).split(" "						)[:-1]						)
              __UpperCAmelCase  :				Any       				=		""
              __UpperCAmelCase  :				List[Any]       				=		eval(str(lowerCamelCase__						).split(" "						)[-1]						)
              __UpperCAmelCase  :				Optional[int]       				=		[]
              for arg in depreciated_args:
                     # arg[2:] removes '--'
                     if arg[2:] in TensorFlowBenchmark.deprecated_args:
                            # arg[5:] removes '--no_'
                            full_error_msg += arg_error_msg.format(arg[5:]						)
                     else:
                            wrong_args.append(lowerCamelCase__						)
              if len(lowerCamelCase__						) > 0:
                     __UpperCAmelCase  :				Union[str, Any]       				=		full_error_msg + begin_error_msg + str(lowerCamelCase__						)
              raise ValueError(lowerCamelCase__						)
       benchmark.run()


if __name__ == "__main__":
   main()






