import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE :	List[str]							     =		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.""",
  )

  SCREAMING_SNAKE_CASE :	int							     =		parser.parse_args()

  SCREAMING_SNAKE_CASE :	List[Any]							     =		UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

  SCREAMING_SNAKE_CASE :	Tuple							     =		CLIPImageProcessor()
  SCREAMING_SNAKE_CASE :	List[str]							     =		CLIPVisionModelWithProjection.from_pretrained("""openai/clip-vit-large-patch14""")

  SCREAMING_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)



from ... import PretrainedConfig


SCREAMING_SNAKE_CASE :	Any							     =		{
    """sijunhe/nezha-cn-base""": """https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json""",
}
class    A_ (       a_ ):
  _SCREAMING_SNAKE_CASE    				=			NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
  _SCREAMING_SNAKE_CASE    				=			"""nezha"""

  def __init__(		self :      Any     ,							__SCREAMING_SNAKE_CASE :      List[str]=2_11_28     ,							__SCREAMING_SNAKE_CASE :      Dict=7_68     ,							__SCREAMING_SNAKE_CASE :      List[Any]=12     ,							__SCREAMING_SNAKE_CASE :      str=12     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=30_72     ,							__SCREAMING_SNAKE_CASE :      Any="gelu"     ,							__SCREAMING_SNAKE_CASE :      int=0.1     ,							__SCREAMING_SNAKE_CASE :      List[str]=0.1     ,							__SCREAMING_SNAKE_CASE :      int=5_12     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]=64     ,							__SCREAMING_SNAKE_CASE :      List[Any]=2     ,							__SCREAMING_SNAKE_CASE :      int=0.02     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=1E-12     ,							__SCREAMING_SNAKE_CASE :      int=0.1     ,							__SCREAMING_SNAKE_CASE :      Any=0     ,							__SCREAMING_SNAKE_CASE :      Any=2     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=3     ,							__SCREAMING_SNAKE_CASE :      List[Any]=True     ,							**__SCREAMING_SNAKE_CASE :      List[str]     ,							):
   super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE     ,							bos_token_id=__SCREAMING_SNAKE_CASE     ,							eos_token_id=__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE       )

   __a       =  vocab_size
   __a       =  hidden_size
   __a       =  num_hidden_layers
   __a       =  num_attention_heads
   __a       =  hidden_act
   __a       =  intermediate_size
   __a       =  hidden_dropout_prob
   __a       =  attention_probs_dropout_prob
   __a       =  max_position_embeddings
   __a       =  max_relative_position
   __a       =  type_vocab_size
   __a       =  initializer_range
   __a       =  layer_norm_eps
   __a       =  classifier_dropout
   __a       =  use_cache


