
from manim import *



class    _snake_case (						UpperCAmelCase_				):


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: str     		=   Rectangle(height=0.5       ,	width=0.5)
   lowercase__	: Tuple     		=   Rectangle(height=0.4_6       ,	width=0.4_6).set_stroke(width=0)

   lowercase__	: List[Any]     		=   [mem.copy() for i in range(6)]
   lowercase__	: Tuple     		=   [mem.copy() for i in range(6)]
   lowercase__	: List[str]     		=   VGroup(*SCREAMING_SNAKE_CASE_).arrange(SCREAMING_SNAKE_CASE_       ,	buff=0)
   lowercase__	: Tuple     		=   VGroup(*SCREAMING_SNAKE_CASE_).arrange(SCREAMING_SNAKE_CASE_       ,	buff=0)
   lowercase__	: List[Any]     		=   VGroup(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_).arrange(SCREAMING_SNAKE_CASE_       ,	buff=0)
   lowercase__	: Dict     		=   Text("""CPU"""       ,	font_size=24)
   lowercase__	: List[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_)

   lowercase__	: str     		=   [mem.copy() for i in range(1)]
   lowercase__	: Any     		=   VGroup(*SCREAMING_SNAKE_CASE_).arrange(SCREAMING_SNAKE_CASE_       ,	buff=0)
   lowercase__	: Optional[int]     		=   Text("""GPU"""       ,	font_size=24)
   lowercase__	: Union[str, Any]     		=   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_)

   lowercase__	: Optional[int]     		=   [mem.copy() for i in range(6)]
   lowercase__	: str     		=   VGroup(*SCREAMING_SNAKE_CASE_).arrange(SCREAMING_SNAKE_CASE_       ,	buff=0)

   lowercase__	: Optional[Any]     		=   Text("""Model"""       ,	font_size=24)
   lowercase__	: Tuple     		=   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)       ,	)

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

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

   lowercase__	: Optional[int]     		=   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_)

   lowercase__	: List[Any]     		=   []
   lowercase__	: Union[str, Any]     		=   []
   lowercase__	: str     		=   []
   for i, rect in enumerate(SCREAMING_SNAKE_CASE_):
     lowercase__	: Union[str, Any]     		=   Rectangle(height=0.4_6       ,	width=0.4_6).set_stroke(width=0.0).set_fill(SCREAMING_SNAKE_CASE_       ,	opacity=0.7)
     cpu_target.move_to(SCREAMING_SNAKE_CASE_)
     cpu_target.generate_target()
     lowercase__	: Union[str, Any]     		=   0.4_6 / 4
     lowercase__	: Tuple     		=   0.4_6 / 3

     if i == 0:
       cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT)       ,	buff=0.0_2       ,	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 diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
			a__												=						argparse.ArgumentParser()

			parser.add_argument(
			    '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
			)
			parser.add_argument(
			    '''--original_config_file''',
			    type=str,
			    required=True,
			    help='''The YAML config file corresponding to the original architecture.''',
			)
			parser.add_argument(
			    '''--num_in_channels''',
			    default=None,
			    type=int,
			    help='''The number of input channels. If `None` number of input channels will be automatically inferred.''',
			)
			parser.add_argument(
			    '''--image_size''',
			    default=512,
			    type=int,
			    help=(
			        '''The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'''
			        ''' Base. Use 768 for Stable Diffusion v2.'''
			    ),
			)
			parser.add_argument(
			    '''--extract_ema''',
			    action='''store_true''',
			    help=(
			        '''Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'''
			        ''' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'''
			        ''' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'''
			    ),
			)
			parser.add_argument(
			    '''--upcast_attention''',
			    action='''store_true''',
			    help=(
			        '''Whether the attention computation should always be upcasted. This is necessary when running stable'''
			        ''' diffusion 2.1.'''
			    ),
			)
			parser.add_argument(
			    '''--from_safetensors''',
			    action='''store_true''',
			    help='''If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.''',
			)
			parser.add_argument(
			    '''--to_safetensors''',
			    action='''store_true''',
			    help='''Whether to store pipeline in safetensors format or not.''',
			)
			parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
			parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''')



			def   __UpperCAmelCase					(      __a :     Any      )     ->	List[Any]:

								"""simple docstring"""

								if string == "True":
													return True
								elif string == "False":
													return False
								else:
													raise ValueError(F"""could not parse string as bool {string}"""      )

			parser.add_argument(
			    '''--use_linear_projection''', help='''Override for use linear projection''', required=False, type=parse_bool
			)

			parser.add_argument('''--cross_attention_dim''', help='''Override for cross attention_dim''', required=False, type=int)

			a__												=						parser.parse_args()

			a__												=						download_controlnet_from_original_ckpt(
			    checkpoint_path=args.checkpoint_path,
			    original_config_file=args.original_config_file,
			    image_size=args.image_size,
			    extract_ema=args.extract_ema,
			    num_in_channels=args.num_in_channels,
			    upcast_attention=args.upcast_attention,
			    from_safetensors=args.from_safetensors,
			    device=args.device,
			    use_linear_projection=args.use_linear_projection,
			    cross_attention_dim=args.cross_attention_dim,
			)







			controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)



