



from dataclasses import dataclass, field
from typing import Optional

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







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





def 							A			(  )       ->				List[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=_UpperCAmelCase							,     decoder_config=_UpperCAmelCase							,     )

			# 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()





def 							A			(  _UpperCAmelCase       :     int							,     _UpperCAmelCase       :     int					)       ->				str:







			'''simple docstring'''
			if a < 0 or b < 0:
						raise ValueError('the value of both inputs must be positive'					)

			_UpperCAmelCase	 =     str(bin(_UpperCAmelCase					)					)[2:]  # remove the leading "0b"
			_UpperCAmelCase	 =     str(bin(_UpperCAmelCase					)					)[2:]  # remove the leading "0b"

			_UpperCAmelCase	 =     max(len(_UpperCAmelCase					)							,     len(_UpperCAmelCase					)					)

			return "0b" + "".join(
			    str(int(char_a != char_b					)					)
			    for char_a, char_b in zip(a_binary.zfill(_UpperCAmelCase					)							,     b_binary.zfill(_UpperCAmelCase					)					)					)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
