

from manim import *


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    def 						snake_case__     (      self :       Any		)->    Optional[int]:





        '''simple docstring'''







        A__          =   Rectangle(height=0.5,width=0.5		)
        A__          =   Rectangle(height=0.25,width=0.25		)
        A__          =   Rectangle(height=0.46,width=0.46		).set_stroke(width=0		)

        A__          =   [mem.copy() for i in range(6		)]
        A__          =   [mem.copy() for i in range(6		)]
        A__          =   VGroup(*lowercase_		).arrange(lowercase_,buff=0		)
        A__          =   VGroup(*lowercase_		).arrange(lowercase_,buff=0		)
        A__          =   VGroup(lowercase_,lowercase_		).arrange(lowercase_,buff=0		)
        A__          =   Text('CPU',font_size=2_4		)
        A__          =   Group(lowercase_,lowercase_		).arrange(lowercase_,buff=0.5,aligned_edge=lowercase_		)
        cpu.move_to([-2.5, -0.5, 0]		)
        self.add(lowercase_		)

        A__          =   [mem.copy() for i in range(4		)]
        A__          =   VGroup(*lowercase_		).arrange(lowercase_,buff=0		)
        A__          =   Text('GPU',font_size=2_4		)
        A__          =   Group(lowercase_,lowercase_		).arrange(lowercase_,buff=0.5,aligned_edge=lowercase_		)
        gpu.move_to([-1, -1, 0]		)
        self.add(lowercase_		)

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

        A__          =   Text('Model',font_size=2_4		)
        A__          =   Group(lowercase_,lowercase_		).arrange(lowercase_,buff=0.5,aligned_edge=lowercase_		)
        model.move_to([3, -1.0, 0]		)
        self.add(lowercase_		)

        A__          =   []
        A__          =   []
        A__          =   []

        for i, rect in enumerate(lowercase_		):
            rect.set_stroke(lowercase_		)

            A__          =   Rectangle(height=0.46 / 4,width=0.46 / 3		).set_stroke(width=0.0		).set_fill(lowercase_,opacity=0.7		)

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

        self.add(*lowercase_,*lowercase_,*lowercase_		)

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

        A__          =   Text('Loaded Checkpoint',font_size=2_4		)
        A__          =   Group(lowercase_,lowercase_		).arrange(lowercase_,buff=0.5,aligned_edge=lowercase_		)
        checkpoint.move_to([3, 0.5, 0]		)

        self.add(lowercase_		)

        A__          =   []
        A__          =   []

        for i, rect in enumerate(lowercase_		):
            A__          =   fill.copy().set_fill(lowercase_,opacity=0.7		)
            target.move_to(lowercase_		)
            ckpt_arr.append(lowercase_		)

            A__          =   target.copy()
            if i < 5:
                cpu_target.move_to(cpu_left_col_base[i + 1]		)
            else:
                cpu_target.move_to(cpu_right_col_base[i - 5]		)
            ckpt_cpu_arr.append(lowercase_		)
        self.add(*lowercase_,*lowercase_		)

        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=1_8,)

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

        self.add(lowercase_,lowercase_		)

        A__          =   MarkupText(
            F'<span fgcolor=\'{BLUE}\'>●</span> Checkpoint',font_size=1_8,)

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

        A__          =   MarkupText(
            F'Based on the passed in configuration, weights are stored in\na variety of np.memmaps on disk or to a particular device.',font_size=2_4,)
        step_a.move_to([2, 2, 0]		)

        A__          =   [meta_mem.copy() for i in range(6		)]
        A__          =   [meta_mem.copy() for i in range(6		)]
        A__          =   VGroup(*lowercase_		).arrange(lowercase_,buff=0		)
        A__          =   VGroup(*lowercase_		).arrange(lowercase_,buff=0		)
        A__          =   VGroup(lowercase_,lowercase_		).arrange(lowercase_,buff=0		)
        A__          =   Text('Disk',font_size=2_4		)
        A__          =   Group(lowercase_,lowercase_		).arrange(lowercase_,buff=0.5,aligned_edge=lowercase_		)
        disk.move_to([-4.0, -1.25, 0]		)
        self.play(Write(lowercase_,run_time=3		),Write(lowercase_,run_time=1		),Create(lowercase_,run_time=1		)		)

        A__          =   []
        for i, rect in enumerate(lowercase_		):
            A__          =   rect.copy()
            target.generate_target()
            target.target.move_to(disk_left_col_base[i]		).scale(0.5		)
            animations.append(MoveToTarget(lowercase_,run_time=1.5		)		)
        self.play(*lowercase_		)

        self.play(FadeOut(lowercase_		)		)

        A__          =   MarkupText(F'Then, the checkpoint is removed from memory\nthrough garbage collection.',font_size=2_4		)
        step_a.move_to([2, 2, 0]		)

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

        self.play(
            FadeOut(lowercase_,lowercase_,*lowercase_,*lowercase_		),)

        self.wait()




import argparse
from pathlib import Path

import torch
from packaging import version
from torch.onnx import export

from diffusers import AutoencoderKL


lowercase_       		=  version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Dict    ,			SCREAMING_SNAKE_CASE__			:	tuple    ,			SCREAMING_SNAKE_CASE__			:	Path    ,			SCREAMING_SNAKE_CASE__			:	List[Any]    ,			SCREAMING_SNAKE_CASE__			:	Any    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]    ,			SCREAMING_SNAKE_CASE__			:	Tuple    ,			SCREAMING_SNAKE_CASE__			:	Union[str, Any]=False    ,			)							->      Union[str, Any]:





    '''simple docstring'''






    output_path.parent.mkdir(parents=SCREAMING_SNAKE_CASE__    ,			exist_ok=SCREAMING_SNAKE_CASE__      )
    # PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
    # so we check the torch version for backwards compatibility
    if is_torch_less_than_1_11:
        export(
            SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			f=output_path.as_posix()    ,			input_names=SCREAMING_SNAKE_CASE__    ,			output_names=SCREAMING_SNAKE_CASE__    ,			dynamic_axes=SCREAMING_SNAKE_CASE__    ,			do_constant_folding=SCREAMING_SNAKE_CASE__    ,			use_external_data_format=SCREAMING_SNAKE_CASE__    ,			enable_onnx_checker=SCREAMING_SNAKE_CASE__    ,			opset_version=SCREAMING_SNAKE_CASE__    ,			)
    else:
        export(
            SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			f=output_path.as_posix()    ,			input_names=SCREAMING_SNAKE_CASE__    ,			output_names=SCREAMING_SNAKE_CASE__    ,			dynamic_axes=SCREAMING_SNAKE_CASE__    ,			do_constant_folding=SCREAMING_SNAKE_CASE__    ,			opset_version=SCREAMING_SNAKE_CASE__    ,			)
@torch.no_grad()
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	str    ,			SCREAMING_SNAKE_CASE__			:	str    ,			SCREAMING_SNAKE_CASE__			:	int    ,			SCREAMING_SNAKE_CASE__			:	bool = False      )							->      Tuple:





    '''simple docstring'''






    A__          =   torch.floataa if fpaa else torch.floataa
    if fpaa and torch.cuda.is_available():
        A__          =   'cuda'
    elif fpaa and not torch.cuda.is_available():
        raise ValueError('`float16` model export is only supported on GPUs with CUDA'      )
    else:
        A__          =   'cpu'
    A__          =   Path(SCREAMING_SNAKE_CASE__      )

    # VAE DECODER
    A__          =   AutoencoderKL.from_pretrained(model_path + '/vae'      )
    A__          =   vae_decoder.config.latent_channels
    # forward only through the decoder part
    A__          =   vae_decoder.decode
    onnx_export(
        SCREAMING_SNAKE_CASE__    ,			model_args=(
            torch.randn(1    ,			SCREAMING_SNAKE_CASE__    ,			25    ,			25      ).to(device=SCREAMING_SNAKE_CASE__    ,			dtype=SCREAMING_SNAKE_CASE__      ),
            False,
        )    ,			output_path=output_path / 'vae_decoder' / 'model.onnx'    ,			ordered_input_names=['latent_sample', 'return_dict']    ,			output_names=['sample']    ,			dynamic_axes={
            'latent_sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
        }    ,			opset=SCREAMING_SNAKE_CASE__    ,			)
    del vae_decoder


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

     parser.add_argument(
         "--model_path",
         type=str,
         required=True,
         help="Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).",
     )

     parser.add_argument("--output_path", type=str, required=True, help="Path to the output model.")
     parser.add_argument(
         "--opset",
         default=14,
         type=int,
         help="The version of the ONNX operator set to use.",
     )
     parser.add_argument("--fp16", action="store_true", default=False, help="Export the models in `float16` mode")

     lowercase_       		=  parser.parse_args()
     print(args.output_path)
     convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
     print("SD: Done: ONNX")

