




'''simple docstring'''







from dataclasses import dataclass, field
from typing import Optional

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


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






def 		snake_case_							():
    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=_a					,     decoder_config=_a					,     )

    # 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()










'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A									={
    'configuration_table_transformer': [
        'TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TableTransformerConfig',
        'TableTransformerOnnxConfig',
    ]
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A									=[
       'TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TableTransformerForObjectDetection',
       'TableTransformerModel',
       'TableTransformerPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_table_transformer import (
       TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       TableTransformerConfig,
       TableTransformerOnnxConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_table_transformer import (
          TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TableTransformerForObjectDetection,
          TableTransformerModel,
          TableTransformerPreTrainedModel,
      )

else:
   import sys

   A									=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




