



'''simple docstring'''




from dataclasses import dataclass, field
from typing import Optional

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






@dataclass
class 		_UpperCamelCase						:


			'''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=__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        lowerCAmelCase_     (			):
		__SCREAMING_SNAKE_CASE :	int					 =	HfArgumentParser((ModelArguments,)      )
		(__SCREAMING_SNAKE_CASE       ) :	Optional[Any]					 =	parser.parse_args_into_dataclasses()

		# Load pretrained model and tokenizer

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

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

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

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

		# This is necessary to make Flax's generate() work
		__SCREAMING_SNAKE_CASE :	int					 =	decoder_config.eos_token_id
		__SCREAMING_SNAKE_CASE :	str					 =	decoder_start_token_id
		__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	pad_token_id

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

		__SCREAMING_SNAKE_CASE :	List[str]					 =	AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path      )
		__SCREAMING_SNAKE_CASE :	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_deit import DeiTImageProcessor


UpperCamelCase__ :			List[str]												=				logging.get_logger(__name__)






class 		_UpperCamelCase						(							lowerCamelCase__					):


			'''simple docstring'''

			def __init__(							self  :				Union[str, Any]							,  *lowerCAmelCase__  :				str							,  **lowerCAmelCase__  :				Any		):





					"""simple docstring"""






					warnings.warn(
					    """The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
					    """ use DeiTImageProcessor instead."""							,  lowerCAmelCase__							,  )
					super().__init__(*lowerCAmelCase__							,  **lowerCAmelCase__		)