


from dataclasses import dataclass, field
from typing import Optional

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






@dataclass
class    UpperCAmelCase__	:



    """simple docstring"""






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

def 						_UpperCAmelCase    (      ):
     __UpperCamelCase   							=HfArgumentParser((ModelArguments,)    )
     ((__UpperCamelCase)							,				)   							=parser.parse_args_into_dataclasses()

     # Load pretrained model and tokenizer

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

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

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

     __UpperCamelCase   							=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=SCREAMING_SNAKE_CASE__      ,					decoder_config=SCREAMING_SNAKE_CASE__      ,					)

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

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

     __UpperCamelCase   							=AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path    )

     __UpperCamelCase   							=AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path    )
     __UpperCamelCase   							=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 sys

from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

_A			=						'python tqdm regex requests packaging filelock numpy tokenizers'.split()
if sys.version_info < (3, 7):
   pkgs_to_check_at_runtime.append('dataclasses')
if sys.version_info < (3, 8):
   pkgs_to_check_at_runtime.append('importlib_metadata')

for pkg in pkgs_to_check_at_runtime:
   if pkg in deps:
      if pkg == "tokenizers":
         # must be loaded here, or else tqdm check may fail
         from .utils import is_tokenizers_available

         if not is_tokenizers_available():
            continue  # not required, check version only if installed

      require_version_core(deps[pkg])
   else:
      raise ValueError(f"""can't find {pkg} in {deps.keys()}, check dependency_versions_table.py""")

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Union[str, Any]      ,					SCREAMING_SNAKE_CASE__      : Optional[int]=None    ):
     require_version(deps[pkg]      ,					SCREAMING_SNAKE_CASE__    )





