






from dataclasses import dataclass, field
from typing import Optional

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



@dataclass
class 				_UpperCamelCase      :
      snake_case_	=				field(
          metadata={"""help""": """The output directory where the model will be written."""}				,		)
      snake_case_	=				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_	=				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_	=				field(
          default=a__				,		metadata={"""help""": """Pretrained encoder config name or path if not the same as encoder_model_name"""}      )
      snake_case_	=				field(
          default=a__				,		metadata={"""help""": """Pretrained decoder config name or path if not the same as decoder_model_name"""}      )





def    __lowerCAmelCase   (    ):
     __lowerCAmelCase             =       HfArgumentParser((ModelArguments,)							)
     (__lowerCAmelCase	)             =       parser.parse_args_into_dataclasses()

     # Load pretrained model and tokenizer

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

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

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

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

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

     __lowerCAmelCase             =       AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path							)

     __lowerCAmelCase             =       AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path							)
     __lowerCAmelCase             =       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()










import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
      snake_case__				:	Optional[int]					       =					argparse.ArgumentParser()

      parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

      parser.add_argument(
          '--txt2img_unclip',
          default='kakaobrain/karlo-v1-alpha',
          type=str,
          required=False,
          help='The pretrained txt2img unclip.',
      )

      snake_case__				:	Tuple					       =					parser.parse_args()

      snake_case__				:	Dict					       =					UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

      snake_case__				:	Optional[int]					       =					CLIPImageProcessor()
      snake_case__				:	Optional[Any]					       =					CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')

      snake_case__				:	Dict					       =					UnCLIPImageVariationPipeline(
          decoder=txtaimg.decoder,
          text_encoder=txtaimg.text_encoder,
          tokenizer=txtaimg.tokenizer,
          text_proj=txtaimg.text_proj,
          feature_extractor=feature_extractor,
          image_encoder=image_encoder,
          super_res_first=txtaimg.super_res_first,
          super_res_last=txtaimg.super_res_last,
          decoder_scheduler=txtaimg.decoder_scheduler,
          super_res_scheduler=txtaimg.super_res_scheduler,
      )

      imgaimg.save_pretrained(args.dump_path)





