






from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class 						lowerCamelCase_				:
				a__					       =  field(
				    metadata={'''help''': '''The output directory where the model will be written.'''}			,				)
				a__					       =  field(
				    metadata={
				        '''help''': (
				            '''The encoder model checkpoint for weights initialization.'''
				            '''Don\'t set if you want to train an encoder model from scratch.'''
				        )
				    }			,				)
				a__					       =  field(
				    metadata={
				        '''help''': (
				            '''The decoder model checkpoint for weights initialization.'''
				            '''Don\'t set if you want to train a decoder model from scratch.'''
				        )
				    }			,				)
				a__					       =  field(
				    default=lowerCamelCase			,				metadata={'''help''': '''Pretrained encoder config name or path if not the same as encoder_model_name'''} )
				a__					       =  field(
				    default=lowerCamelCase			,				metadata={'''help''': '''Pretrained decoder config name or path if not the same as decoder_model_name'''} )







def      __lowercase					( ):







				"""simple docstring"""






				__magic_name__		:Any        =      HfArgumentParser((ModelArguments,) )
				((__magic_name__)     ,				)		:Optional[Any]        =      parser.parse_args_into_dataclasses()

				# Load pretrained model and tokenizer

				# Use explicit specified encoder config
				if model_args.encoder_config_name:
								__magic_name__		:Any        =      AutoConfig.from_pretrained(model_args.encoder_config_name )
				# Use pretrained encoder model's config
				else:
								__magic_name__		:Optional[int]        =      AutoConfig.from_pretrained(model_args.encoder_model_name_or_path )

				# Use explicit specified decoder config
				if model_args.decoder_config_name:
								__magic_name__		:Optional[Any]        =      AutoConfig.from_pretrained(model_args.decoder_config_name )
				# Use pretrained decoder model's config
				else:
								__magic_name__		:Any        =      AutoConfig.from_pretrained(model_args.decoder_model_name_or_path )

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

				__magic_name__		:Union[str, 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=snake_case,  decoder_config=snake_case,  )

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

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

				__magic_name__		:Any        =      AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path )

				__magic_name__		:Optional[Any]        =      AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path )
				__magic_name__		:Dict        =      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


SCREAMING_SNAKE_CASE__      :			Tuple							=		{
    """configuration_table_transformer""": [
        """TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """TableTransformerConfig""",
        """TableTransformerOnnxConfig""",
    ]
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__      :			List[str]							=		[
					    """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

					SCREAMING_SNAKE_CASE__      :			Union[str, Any]							=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






