"""simple docstring"""




import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


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

    parser.add_argument(
        "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
    )
    # !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
    parser.add_argument(
        "--original_config_file",
        default=None,
        type=str,
        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(
        "--scheduler_type",
        default="pndm",
        type=str,
        help="Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim', 'euler', 'euler-ancestral', 'dpm']",
    )
    parser.add_argument(
        "--pipeline_type",
        default=None,
        type=str,
        help=(
            "The pipeline type. One of 'FrozenOpenCLIPEmbedder', 'FrozenCLIPEmbedder', 'PaintByExample'"
            ". If `None` pipeline will be automatically inferred."
        ),
    )
    parser.add_argument(
        "--image_size",
        default=None,
        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(
        "--prediction_type",
        default=None,
        type=str,
        help=(
            "The prediction type that the model was trained on. Use 'epsilon' for Stable Diffusion v1.X and Stable"
            " Diffusion v2 Base. Use 'v_prediction' 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.)")
    parser.add_argument(
        "--stable_unclip",
        type=str,
        default=None,
        required=False,
        help="Set if this is a stable unCLIP model. One of 'txt2img' or 'img2img'.",
    )
    parser.add_argument(
        "--stable_unclip_prior",
        type=str,
        default=None,
        required=False,
        help="Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.",
    )
    parser.add_argument(
        "--clip_stats_path",
        type=str,
        help="Path to the clip stats file. Only required if the stable unclip model's config specifies `model.params.noise_aug_config.params.clip_stats_path`.",
        required=False,
    )
    parser.add_argument(
        "--controlnet", action="store_true", default=None, help="Set flag if this is a controlnet checkpoint."
    )
    parser.add_argument("--half", action="store_true", help="Save weights in half precision.")
    parser.add_argument(
        "--vae_path",
        type=str,
        default=None,
        required=False,
        help="Set to a path, hub id to an already converted vae to not convert it again.",
    )
    __lowerCamelCase											=  parser.parse_args()

    __lowerCamelCase											=  download_from_original_stable_diffusion_ckpt(
        checkpoint_path=args.checkpoint_path,
        original_config_file=args.original_config_file,
        image_size=args.image_size,
        prediction_type=args.prediction_type,
        model_type=args.pipeline_type,
        extract_ema=args.extract_ema,
        scheduler_type=args.scheduler_type,
        num_in_channels=args.num_in_channels,
        upcast_attention=args.upcast_attention,
        from_safetensors=args.from_safetensors,
        device=args.device,
        stable_unclip=args.stable_unclip,
        stable_unclip_prior=args.stable_unclip_prior,
        clip_stats_path=args.clip_stats_path,
        controlnet=args.controlnet,
        vae_path=args.vae_path,
    )

    if args.half:
        pipe.to(torch_dtype=torch.floataa)

    if args.controlnet:
        # only save the controlnet model
        pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
    else:
        pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)






"""simple docstring"""




from manim import *
class     UpperCamelCase__(			__A ):





						def 				snake_case__	(       self     )					->				List[str]:
													A__	     =							Rectangle(height=0.5		,width=0.5     )
													A__	     =							Rectangle(height=0.4_6		,width=0.4_6     ).set_stroke(width=0     )

													A__	     =							Rectangle(height=0.2_5		,width=0.2_5     )

													A__	     =							[mem.copy() for i in range(6     )]
													A__	     =							[mem.copy() for i in range(6     )]
													A__	     =							VGroup(*__UpperCAmelCase     ).arrange(__UpperCAmelCase		,buff=0     )
													A__	     =							VGroup(*__UpperCAmelCase     ).arrange(__UpperCAmelCase		,buff=0     )
													A__	     =							VGroup(__UpperCAmelCase		,__UpperCAmelCase     ).arrange(__UpperCAmelCase		,buff=0     )
													A__	     =							Text('CPU'		,font_size=24     )
													A__	     =							Group(__UpperCAmelCase		,__UpperCAmelCase     ).arrange(__UpperCAmelCase		,buff=0.5		,aligned_edge=__UpperCAmelCase     )
													cpu.move_to([-2.5, -0.5, 0]     )
													self.add(__UpperCAmelCase     )

													A__	     =							[mem.copy() for i in range(4     )]
													A__	     =							VGroup(*__UpperCAmelCase     ).arrange(__UpperCAmelCase		,buff=0     )
													A__	     =							Text('GPU'		,font_size=24     )
													A__	     =							Group(__UpperCAmelCase		,__UpperCAmelCase     ).arrange(__UpperCAmelCase		,buff=0.5		,aligned_edge=__UpperCAmelCase     )
													gpu.move_to([-1, -1, 0]     )
													self.add(__UpperCAmelCase     )

													A__	     =							[mem.copy() for i in range(6     )]
													A__	     =							VGroup(*__UpperCAmelCase     ).arrange(__UpperCAmelCase		,buff=0     )

													A__	     =							Text('Model'		,font_size=24     )
													A__	     =							Group(__UpperCAmelCase		,__UpperCAmelCase     ).arrange(__UpperCAmelCase		,buff=0.5		,aligned_edge=__UpperCAmelCase     )
													model.move_to([3, -1.0, 0]     )
													self.add(__UpperCAmelCase     )

													A__	     =							[]
													A__	     =							[]

													for i, rect in enumerate(__UpperCAmelCase     ):
																				A__	     =							fill.copy().set_fill(__UpperCAmelCase		,opacity=0.8     )
																				target.move_to(__UpperCAmelCase     )
																				model_arr.append(__UpperCAmelCase     )

																				A__	     =							Rectangle(height=0.4_6		,width=0.4_6     ).set_stroke(width=0.0     ).set_fill(__UpperCAmelCase		,opacity=0.8     )
																				cpu_target.move_to(cpu_left_col_base[i]     )
																				model_cpu_arr.append(__UpperCAmelCase     )

													self.add(*__UpperCAmelCase		,*__UpperCAmelCase     )

													A__	     =							[meta_mem.copy() for i in range(6     )]
													A__	     =							[meta_mem.copy() for i in range(6     )]
													A__	     =							VGroup(*__UpperCAmelCase     ).arrange(__UpperCAmelCase		,buff=0     )
													A__	     =							VGroup(*__UpperCAmelCase     ).arrange(__UpperCAmelCase		,buff=0     )
													A__	     =							VGroup(__UpperCAmelCase		,__UpperCAmelCase     ).arrange(__UpperCAmelCase		,buff=0     )
													A__	     =							Text('Disk'		,font_size=24     )
													A__	     =							Group(__UpperCAmelCase		,__UpperCAmelCase     ).arrange(__UpperCAmelCase		,buff=0.5		,aligned_edge=__UpperCAmelCase     )
													disk.move_to([-4, -1.2_5, 0]     )
													self.add(__UpperCAmelCase		,__UpperCAmelCase     )

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

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

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

													self.add(__UpperCAmelCase		,__UpperCAmelCase     )

													A__	     =							MarkupText(
													    f'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint'''		,font_size=18		,)

													blue_text.next_to(__UpperCAmelCase		,DOWN * 2.4		,aligned_edge=key_text.get_left()     )
													self.add(__UpperCAmelCase     )

													A__	     =							MarkupText(
													    f'''Now watch as an input is passed through the model\nand how the memory is utilized and handled.'''		,font_size=24		,)
													step_a.move_to([2, 2, 0]     )

													self.play(Write(__UpperCAmelCase     )     )

													A__	     =							Square(0.3     )
													input.set_fill(__UpperCAmelCase		,opacity=1.0     )
													input.set_stroke(width=0.0     )
													input.next_to(model_base[0]		,__UpperCAmelCase		,buff=0.5     )

													self.play(Write(__UpperCAmelCase     )     )

													input.generate_target()
													input.target.next_to(model_arr[0]		,direction=__UpperCAmelCase		,buff=0.0_2     )
													self.play(MoveToTarget(__UpperCAmelCase     )     )

													self.play(FadeOut(__UpperCAmelCase     )     )

													A__	     =							Arrow(start=__UpperCAmelCase		,end=__UpperCAmelCase		,color=__UpperCAmelCase		,buff=0.5     )
													a.next_to(model_arr[0].get_left()		,__UpperCAmelCase		,buff=0.2     )

													model_cpu_arr[0].generate_target()
													model_cpu_arr[0].target.move_to(gpu_rect[0]     )

													A__	     =							MarkupText(
													    f'''As the input reaches a layer, the hook triggers\nand weights are moved from the CPU\nto the GPU and back.'''		,font_size=24		,)
													step_a.move_to([2, 2, 0]     )

													self.play(Write(__UpperCAmelCase		,run_time=3     )     )

													A__	     =							{'run_time': 1, 'fade_in': True, 'fade_out': True, 'buff': 0.0_2}

													self.play(
													    Write(__UpperCAmelCase     )		,Circumscribe(model_arr[0]		,color=__UpperCAmelCase		,**__UpperCAmelCase     )		,Circumscribe(model_cpu_arr[0]		,color=__UpperCAmelCase		,**__UpperCAmelCase     )		,Circumscribe(gpu_rect[0]		,color=__UpperCAmelCase		,**__UpperCAmelCase     )		,)
													self.play(MoveToTarget(model_cpu_arr[0]     )     )

													A__	     =							a.copy()
													for i in range(6     ):
																				a_c.next_to(model_arr[i].get_right() + 0.0_2		,__UpperCAmelCase		,buff=0.2     )

																				input.generate_target()
																				input.target.move_to(model_arr[i].get_right() + 0.0_2     )

																				A__	     =							AnimationGroup(
																				    FadeOut(__UpperCAmelCase		,run_time=0.5     )		,MoveToTarget(__UpperCAmelCase		,run_time=0.5     )		,FadeIn(__UpperCAmelCase		,run_time=0.5     )		,lag_ratio=0.2     )

																				self.play(__UpperCAmelCase     )

																				model_cpu_arr[i].generate_target()
																				model_cpu_arr[i].target.move_to(cpu_left_col_base[i]     )

																				if i < 5:
																											model_cpu_arr[i + 1].generate_target()
																											model_cpu_arr[i + 1].target.move_to(gpu_rect[0]     )
																											if i >= 1:
																																		A__	     =							0.7

																											self.play(
																											    Circumscribe(model_arr[i]		,**__UpperCAmelCase     )		,Circumscribe(cpu_left_col_base[i]		,**__UpperCAmelCase     )		,Circumscribe(cpu_left_col_base[i + 1]		,color=__UpperCAmelCase		,**__UpperCAmelCase     )		,Circumscribe(gpu_rect[0]		,color=__UpperCAmelCase		,**__UpperCAmelCase     )		,Circumscribe(model_arr[i + 1]		,color=__UpperCAmelCase		,**__UpperCAmelCase     )		,)
																											if i < 1:
																																		self.play(
																																		    MoveToTarget(model_cpu_arr[i]     )		,MoveToTarget(model_cpu_arr[i + 1]     )		,)
																											else:
																																		self.play(
																																		    MoveToTarget(model_cpu_arr[i]		,run_time=0.7     )		,MoveToTarget(model_cpu_arr[i + 1]		,run_time=0.7     )		,)
																				else:
																											model_cpu_arr[i].generate_target()
																											model_cpu_arr[i].target.move_to(cpu_left_col_base[-1]     )
																											input.generate_target()
																											input.target.next_to(model_arr[-1].get_right()		,RIGHT + 0.0_2		,buff=0.2     )

																											self.play(
																											    Circumscribe(model_arr[-1]		,color=__UpperCAmelCase		,**__UpperCAmelCase     )		,Circumscribe(cpu_left_col_base[-1]		,color=__UpperCAmelCase		,**__UpperCAmelCase     )		,Circumscribe(gpu_rect[0]		,color=__UpperCAmelCase		,**__UpperCAmelCase     )		,)

																											self.play(MoveToTarget(model_cpu_arr[i]     )     )

																				A__	     =							a_c
																				A__	     =							a_c.copy()

													input.generate_target()
													input.target.next_to(model_base[-1]		,RIGHT + 0.0_2		,buff=0.5     )
													self.play(
													    FadeOut(__UpperCAmelCase     )		,FadeOut(__UpperCAmelCase		,run_time=0.5     )		,)

													A__	     =							MarkupText(f'''Inference on a model too large for GPU memory\nis successfully completed.'''		,font_size=24     )
													step_a.move_to([2, 2, 0]     )

													self.play(Write(__UpperCAmelCase		,run_time=3     )		,MoveToTarget(__UpperCAmelCase     )     )

													self.wait()





