


from manim import *



class        a					(			UpperCAmelCase__   ):





					def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]							):
								__lowerCamelCase:				Union[str, Any]     =	Rectangle(height=0.5    ,	width=0.5							)
								__lowerCamelCase:				int     =	Rectangle(height=0.46    ,	width=0.46							).set_stroke(width=0							)

								__lowerCamelCase:				Optional[Any]     =	[mem.copy() for i in range(6							)]
								__lowerCamelCase:				List[Any]     =	[mem.copy() for i in range(6							)]
								__lowerCamelCase:				Dict     =	VGroup(*SCREAMING_SNAKE_CASE_							).arrange(SCREAMING_SNAKE_CASE_    ,	buff=0							)
								__lowerCamelCase:				Dict     =	VGroup(*SCREAMING_SNAKE_CASE_							).arrange(SCREAMING_SNAKE_CASE_    ,	buff=0							)
								__lowerCamelCase:				Optional[int]     =	VGroup(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							).arrange(SCREAMING_SNAKE_CASE_    ,	buff=0							)
								__lowerCamelCase:				Union[str, Any]     =	Text("""CPU"""    ,	font_size=24							)
								__lowerCamelCase:				str     =	Group(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							).arrange(SCREAMING_SNAKE_CASE_    ,	buff=0.5    ,	aligned_edge=SCREAMING_SNAKE_CASE_							)
								cpu.move_to([-2.5, -0.5, 0]							)
								self.add(SCREAMING_SNAKE_CASE_							)

								__lowerCamelCase:				Any     =	[mem.copy() for i in range(1							)]
								__lowerCamelCase:				Dict     =	VGroup(*SCREAMING_SNAKE_CASE_							).arrange(SCREAMING_SNAKE_CASE_    ,	buff=0							)
								__lowerCamelCase:				Dict     =	Text("""GPU"""    ,	font_size=24							)
								__lowerCamelCase:				str     =	Group(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							).arrange(SCREAMING_SNAKE_CASE_    ,	buff=0.5    ,	aligned_edge=SCREAMING_SNAKE_CASE_							)
								gpu.align_to(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)
								gpu.set_x(gpu.get_x() - 1							)

								self.add(SCREAMING_SNAKE_CASE_							)

								__lowerCamelCase:				Tuple     =	[mem.copy() for i in range(6							)]
								__lowerCamelCase:				Tuple     =	VGroup(*SCREAMING_SNAKE_CASE_							).arrange(SCREAMING_SNAKE_CASE_    ,	buff=0							)

								__lowerCamelCase:				Union[str, Any]     =	Text("""Model"""    ,	font_size=24							)
								__lowerCamelCase:				List[Any]     =	Group(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							).arrange(SCREAMING_SNAKE_CASE_    ,	buff=0.5    ,	aligned_edge=SCREAMING_SNAKE_CASE_							)
								model.move_to([3, -1.0, 0]							)

								self.play(
								    Create(SCREAMING_SNAKE_CASE_    ,	run_time=1							)    ,	Create(SCREAMING_SNAKE_CASE_    ,	run_time=1							)    ,	Create(SCREAMING_SNAKE_CASE_    ,	run_time=1							)    ,	)

								__lowerCamelCase:				List[Any]     =	MarkupText(
								    F'First, an empty model skeleton is loaded\ninto <span fgcolor=\'{YELLOW}\'>memory</span> without using much RAM.'    ,	font_size=24    ,	)

								__lowerCamelCase:				Union[str, Any]     =	Square(side_length=2.2							)
								key.move_to([-5, 2, 0]							)

								__lowerCamelCase:				Any     =	MarkupText(
								    F'<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model'    ,	font_size=18    ,	)

								key_text.move_to([-5, 2.4, 0]							)

								step_a.move_to([2, 2, 0]							)
								self.play(Write(SCREAMING_SNAKE_CASE_    ,	run_time=2.5							)    ,	Write(SCREAMING_SNAKE_CASE_							)    ,	Write(SCREAMING_SNAKE_CASE_							)							)

								self.add(SCREAMING_SNAKE_CASE_							)

								__lowerCamelCase:				int     =	[]
								__lowerCamelCase:				Union[str, Any]     =	[]
								__lowerCamelCase:				List[Any]     =	[]
								for i, rect in enumerate(SCREAMING_SNAKE_CASE_							):
											__lowerCamelCase:				Dict     =	Rectangle(height=0.46    ,	width=0.46							).set_stroke(width=0.0							).set_fill(SCREAMING_SNAKE_CASE_    ,	opacity=0.7							)
											cpu_target.move_to(SCREAMING_SNAKE_CASE_							)
											cpu_target.generate_target()
											__lowerCamelCase:				str     =	0.46 / 4
											__lowerCamelCase:				Union[str, Any]     =	0.46 / 3

											if i == 0:
														cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT							)    ,	buff=0.02    ,	direction=SCREAMING_SNAKE_CASE_							)
														cpu_target.target.set_x(cpu_target.target.get_x() + 0.1							)
											elif i == 3:
														cpu_target.target.next_to(cpu_targs[0].target    ,	direction=SCREAMING_SNAKE_CASE_    ,	buff=0.0							)
											else:
														cpu_target.target.next_to(cpu_targs[i - 1].target    ,	direction=SCREAMING_SNAKE_CASE_    ,	buff=0.0							)
											cpu_targs.append(SCREAMING_SNAKE_CASE_							)

											first_animations.append(rect.animate(run_time=0.5							).set_stroke(SCREAMING_SNAKE_CASE_							)							)
											second_animations.append(MoveToTarget(SCREAMING_SNAKE_CASE_    ,	run_time=1.5							)							)

								self.play(*SCREAMING_SNAKE_CASE_							)
								self.play(*SCREAMING_SNAKE_CASE_							)

								self.wait()






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)


