
'''simple docstring'''


from dataclasses import dataclass, field
from typing import Optional

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

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




def lowercase_						( )  ->				Optional[int]:
					'''simple docstring'''

					lowerCamelCase_       :   List[str]  =		HfArgumentParser((ModelArguments,)					)
					(lowerCamelCase_    )       :   int  =		parser.parse_args_into_dataclasses()

					# Load pretrained model and tokenizer

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

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

					# necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
					lowerCamelCase_       :   List[Any]  =		True
					lowerCamelCase_       :   Tuple  =		True

					lowerCamelCase_       :   Optional[int]  =		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=_lowercase      ,							decoder_config=_lowercase      ,							)

					# GPT2 only has bos/eos tokens but not decoder_start/pad tokens
					lowerCamelCase_       :   Optional[int]  =		decoder_config.decoder_start_token_id
					lowerCamelCase_       :   Optional[Any]  =		decoder_config.pad_token_id
					if decoder_start_token_id is None:
										lowerCamelCase_       :   Any  =		decoder_config.bos_token_id
					if pad_token_id is None:
										lowerCamelCase_       :   Tuple  =		decoder_config.eos_token_id

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

					lowerCamelCase_       :   Dict  =		AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path					)

					lowerCamelCase_       :   Optional[Any]  =		AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path					)
					lowerCamelCase_       :   str  =		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"""





from packaging import version

from .import_utils import is_accelerate_available


if is_accelerate_available():
							import accelerate



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				str ):


							'''simple docstring'''







							if not is_accelerate_available():
														return method
							__lowerCamelCase   :   Dict       =version.parse(accelerate.__version__ ).base_version
							if version.parse(SCREAMING_SNAKE_CASE ) < version.parse('''0.17.0''' ):
														return method

							def wrapper(self							:				List[Any]     ,    *SCREAMING_SNAKE_CASE							:				Any     ,    **SCREAMING_SNAKE_CASE							:				List[str] ):
														if hasattr(self     ,    '''_hf_hook''' ) and hasattr(self._hf_hook     ,    '''pre_forward''' ):
																					self._hf_hook.pre_forward(self )
														return method(self     ,    *SCREAMING_SNAKE_CASE     ,    **SCREAMING_SNAKE_CASE )

							return wrapper




