


from dataclasses import dataclass, field
from typing import Optional

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

@dataclass
class 						A__							:
						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=UpperCamelCase__   ,     metadata={"help": "Pretrained encoder config name or path if not the same as encoder_model_name"}  )
						UpperCAmelCase             =						field(
						    default=UpperCamelCase__   ,     metadata={"help": "Pretrained decoder config name or path if not the same as decoder_model_name"}  )







def 						lowerCamelCase(   ):
				_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=a__	,decoder_config=a__	,)

				# 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()



from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig


snake_case_						:			List[Any]													=							logging.get_logger(__name__)

snake_case_						:			Optional[Any]													=							'''T5Config'''

class 						A__							(		UpperCamelCase__  ):
						UpperCAmelCase             =						"mt5"
						UpperCAmelCase             =						MTaConfig

class 						A__							(		UpperCamelCase__  ):
						UpperCAmelCase             =						"mt5"
						UpperCAmelCase             =						MTaConfig

class 						A__							(		UpperCamelCase__  ):
						UpperCAmelCase             =						"mt5"
						UpperCAmelCase             =						MTaConfig