






from math import ceil


def 				_a     (   UpperCamelCase_    : Any			,      UpperCamelCase_    : Any      )       ->      Tuple:
						"""simple docstring"""







						lowerCAmelCase__       					=  list(range(0			,      A__      )      )

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

						# Duplicate check
						lowerCAmelCase__       					=  []
						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
						lowerCAmelCase__       					=  [i for i in blocks if i not in device_map_blocks]
						lowerCAmelCase__       					=  [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     (   UpperCamelCase_    : List[Any]			,      UpperCamelCase_    : Optional[Any]      )       ->      Optional[int]:
						"""simple docstring"""







						lowerCAmelCase__       					=  list(range(A__      )      )
						lowerCAmelCase__       					=  int(ceil(n_layers / len(A__      )      )      )
						lowerCAmelCase__       					=  [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)

