


"""simple docstring"""







from dataclasses import dataclass, field
from typing import Optional

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




@dataclass
class 			UpperCamelCase_     :

	"""simple docstring"""





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




def        UpperCAmelCase__	():




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    HfArgumentParser((ModelArguments,)       )
					((__SCREAMING_SNAKE_CASE)       ,  )						       =    parser.parse_args_into_dataclasses()

					# Load pretrained model and tokenizer

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

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

					# necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
					__SCREAMING_SNAKE_CASE						       =    True
					__SCREAMING_SNAKE_CASE						       =    True

					__SCREAMING_SNAKE_CASE						       =    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						       =    decoder_config.decoder_start_token_id
					__SCREAMING_SNAKE_CASE						       =    decoder_config.pad_token_id
					if decoder_start_token_id is None:
										__SCREAMING_SNAKE_CASE						       =    decoder_config.bos_token_id
					if pad_token_id is None:
										__SCREAMING_SNAKE_CASE						       =    decoder_config.eos_token_id

					# This is necessary to make Flax's generate() work
					__SCREAMING_SNAKE_CASE						       =    decoder_config.eos_token_id
					__SCREAMING_SNAKE_CASE						       =    decoder_start_token_id
					__SCREAMING_SNAKE_CASE						       =    pad_token_id

					__SCREAMING_SNAKE_CASE						       =    AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path       )

					__SCREAMING_SNAKE_CASE						       =    AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path       )
					__SCREAMING_SNAKE_CASE						       =    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"""







def        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''simple docstring'''


					return " ".join(
					    "".join(word[::-1]       ) if len(lowerCAmelCase_       ) > 4 else word for word in sentence.split()       )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		print(reverse_long_words('''Hey wollef sroirraw'''))
