


'''simple docstring'''

from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class 					_lowerCamelCase	:



		'''simple docstring'''







		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=lowercase__		,					metadata={"""help""": """Pretrained encoder config name or path if not the same as encoder_model_name"""}							)
		A_     :      Optional[str]				   =    field(
		    default=lowercase__		,					metadata={"""help""": """Pretrained decoder config name or path if not the same as decoder_model_name"""}							)







def lowerCamelCase      (      ):





	"""simple docstring"""







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

	# Load pretrained model and tokenizer

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

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

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

	__magic_name__	:   Dict					     =					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
	__magic_name__	:   str					     =					decoder_config.decoder_start_token_id
	__magic_name__	:   Union[str, Any]					     =					decoder_config.pad_token_id
	if decoder_start_token_id is None:
		__magic_name__	:   Optional[Any]					     =					decoder_config.bos_token_id
	if pad_token_id is None:
		__magic_name__	:   int					     =					decoder_config.eos_token_id

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

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

	__magic_name__	:   int					     =					AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path		)
	__magic_name__	:   Any					     =					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'''

import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


lowerCAmelCase				:Tuple        =      logging.get_logger(__name__)
class _lowerCamelCase	(						lowercase__							):



		'''simple docstring'''







		def __init__( self		:						Any  ,					*_A		:						List[Any]  ,					**_A		:						str      )  ->			None:
			warnings.warn(
			    'The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
			    ' use BeitImageProcessor instead.'  ,					_A  ,					)
			super().__init__(*_A  ,					**_A      )