





'''simple docstring'''
from dataclasses import dataclass, field
from typing import Optional

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

@dataclass
class      snake_case     :




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					str					=field(
     metadata={"help": "The output directory where the model will be written."}			,       )
 SCREAMING_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."
         )
     }			,       )
 SCREAMING_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."
         )
     }			,       )
 SCREAMING_SNAKE_CASE_	:					Optional[str]					=field(
     default=__lowerCamelCase			,       metadata={"help": "Pretrained encoder config name or path if not the same as encoder_model_name"}       )
 SCREAMING_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__   (   ) ->      Any:



       """simple docstring"""






       __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=__lowercase				,  decoder_config=__lowercase				,  )

       # 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()







from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCamelCase       :     Optional[int]   =     {"""configuration_mmbt""": ["""MMBTConfig"""]}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase       :     List[Any]   =     ["""MMBTForClassification""", """MMBTModel""", """ModalEmbeddings"""]


if TYPE_CHECKING:
    from .configuration_mmbt import MMBTConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings

else:
    import sys

    __lowerCamelCase       :     int   =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
