

'''simple docstring'''






from math import ceil


def 		_A     (    A__     ,				A__      ):






 """simple docstring"""



 __lowercase							  = list(range(0     ,				A__      )      )

 __lowercase							  = [item for sublist in list(device_map.values()      ) for item in sublist]

 # Duplicate check
 __lowercase							  = []
 for i in device_map_blocks:
  if device_map_blocks.count(A__      ) > 1 and i not in duplicate_blocks:
   duplicate_blocks.append(A__      )
    # Missing blocks
 __lowercase							  = [i for i in blocks if i not in device_map_blocks]
 __lowercase							  = [i for i in device_map_blocks if i not in blocks]

 if len(A__      ) != 0:
  raise ValueError(
      '''Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device.'''
      ''' These attention blocks were specified more than once: ''' + str(A__      )      )
 if len(A__      ) != 0:
  raise ValueError(
      '''There are attention blocks for this model that are not specified in the device_map. Add these attention '''
      '''blocks to a device on the device_map: ''' + str(A__      )      )
 if len(A__      ) != 0:
  raise ValueError(
      '''The device_map contains more attention blocks than this model has. Remove these from the device_map:'''
      + str(A__      )      )






def 		_A     (    A__     ,				A__      ):






 """simple docstring"""



 __lowercase							  = list(range(A__      )      )
 __lowercase							  = int(ceil(n_layers / len(A__      )      )      )
 __lowercase							  = [layers[i : i + n_blocks] for i in range(0     ,				A__     ,				A__      )]

 return dict(zip(A__     ,				A__      )      )




'''simple docstring'''






import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


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

 lowerCAmelCase__											=	parser.parse_args()

 lowerCAmelCase__											=	UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

 lowerCAmelCase__											=	CLIPImageProcessor()
 lowerCAmelCase__											=	CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

 lowerCAmelCase__											=	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)

