from dataclasses import dataclass, field
from typing import Optional

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



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




def  snake_case						()	->	Optional[Any]:

						'''simple docstring'''



						_snake_case						:					List[str]    =   HfArgumentParser((ModelArguments,)						)
						((_snake_case)    ,)						:					List[Any]    =   parser.parse_args_into_dataclasses()

						# Load pretrained model and tokenizer

						# Use explicit specified encoder config
						if model_args.encoder_config_name:
												_snake_case						:					List[str]    =   AutoConfig.from_pretrained(model_args.encoder_config_name						)
						# Use pretrained encoder model's config
						else:
												_snake_case						:					int    =   AutoConfig.from_pretrained(model_args.encoder_model_name_or_path						)

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

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

						_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=__lowercase ,      decoder_config=__lowercase ,      )

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

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

						_snake_case						:					List[Any]    =   AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path						)

						_snake_case						:					Dict    =   AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path						)
						_snake_case						:					Union[str, 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()
def  snake_case						(__lowercase						)	->	list:

						'''simple docstring'''



						return [
						    txt[:a] + txt[a].upper() + txt[a + 1 :]
						    for a in range(len(__lowercase						)						)
						    if txt[a].isalpha()
						]


if __name__ == "__main__":
				__import__('doctest').testmod()