


from datetime import datetime

import matplotlib.pyplot as plt
import torch



def     __lowerCAmelCase						(							snake_case	:    Dict )       ->							Any:
   for param in module.parameters():
      __lowerCamelCase:				Optional[int]     =	False



def     __lowerCAmelCase						(							)       ->							List[str]:
   __lowerCamelCase:				str     =	"""cuda""" if torch.cuda.is_available() else """cpu"""
   if torch.backends.mps.is_available() and torch.backends.mps.is_built():
      __lowerCamelCase:				Any     =	"""mps"""
   if device == "mps":
      print(
          """WARNING: MPS currently doesn't seem to work, and messes up backpropagation without any visible torch"""
          """ errors. I recommend using CUDA on a colab notebook or CPU instead if you're facing inexplicable issues"""
          """ with generations.""" )
   return device



def     __lowerCAmelCase						(							snake_case	:    Dict )       ->							Union[str, Any]:
   __lowerCamelCase:				Tuple     =	plt.imshow(snake_case )
   fig.axes.get_xaxis().set_visible(snake_case )
   fig.axes.get_yaxis().set_visible(snake_case )
   plt.show()







def     __lowerCAmelCase						(							)       ->							str:
   __lowerCamelCase:				Dict     =	datetime.now()
   __lowerCamelCase:				Union[str, Any]     =	current_time.strftime("""%H:%M:%S""" )
   return timestamp






import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
   _A		:				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.''',
   )

   _A		:				Optional[Any]									=       parser.parse_args()

   _A		:				str									=       UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

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

   _A		:				str									=       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)


