





"""simple docstring"""




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=SCREAMING_SNAKE_CASE__     ,    metadata={'''help''': '''Pretrained encoder config name or path if not the same as encoder_model_name'''}    )
				UpperCamelCase								=   field(
				    default=SCREAMING_SNAKE_CASE__     ,    metadata={'''help''': '''Pretrained decoder config name or path if not the same as decoder_model_name'''}    )






def 	a_    (							):
						_UpperCamelCase      : Dict         =   HfArgumentParser((ModelArguments,)       )
						(_UpperCamelCase      )      : List[str]         =   parser.parse_args_into_dataclasses()

						# Load pretrained model and tokenizer

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

						# Use explicit specified decoder config
						if model_args.decoder_config_name:
												_UpperCamelCase      : List[str]         =   AutoConfig.from_pretrained(model_args.decoder_config_name       )
						# Use pretrained decoder model's config
						else:
												_UpperCamelCase      : Tuple         =   AutoConfig.from_pretrained(model_args.decoder_model_name_or_path       )

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

						_UpperCamelCase      : 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=_UpperCAmelCase	,						decoder_config=_UpperCAmelCase	,						)

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

						# This is necessary to make Flax's generate() work
						_UpperCamelCase      : int         =   decoder_config.eos_token_id
						_UpperCamelCase      : Tuple         =   decoder_start_token_id
						_UpperCamelCase      : int         =   pad_token_id

						_UpperCamelCase      : Optional[Any]         =   AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path       )

						_UpperCamelCase      : Any         =   AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path       )
						_UpperCamelCase      : Tuple         =   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 ..utils import is_flax_available, is_torch_available


if is_torch_available():
						from .autoencoder_kl import AutoencoderKL
						from .controlnet import ControlNetModel
						from .dual_transformer_ad import DualTransformeraDModel
						from .modeling_utils import ModelMixin
						from .prior_transformer import PriorTransformer
						from .ta_film_transformer import TaFilmDecoder
						from .transformer_ad import TransformeraDModel
						from .unet_ad import UNetaDModel
						from .unet_ad import UNetaDModel
						from .unet_ad_condition import UNetaDConditionModel
						from .unet_ad_condition import UNetaDConditionModel
						from .vq_model import VQModel

if is_flax_available():
						from .controlnet_flax import FlaxControlNetModel
						from .unet_ad_condition_flax import FlaxUNetaDConditionModel
						from .vae_flax import FlaxAutoencoderKL



