


'''simple docstring'''

import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


_UpperCAmelCase				: Tuple							    =				version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")

def 							__magic_name__(						lowerCamelCase						,				lowerCamelCase						,				lowerCamelCase						,				lowerCamelCase						,				lowerCamelCase						,				lowerCamelCase						,				lowerCamelCase						,				lowerCamelCase=False						,				):
						output_path.parent.mkdir(parents=lowerCamelCase						,				exist_ok=lowerCamelCase)
						# 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(
												    lowerCamelCase						,				lowerCamelCase						,				f=output_path.as_posix()						,				input_names=lowerCamelCase						,				output_names=lowerCamelCase						,				dynamic_axes=lowerCamelCase						,				do_constant_folding=lowerCamelCase						,				use_external_data_format=lowerCamelCase						,				enable_onnx_checker=lowerCamelCase						,				opset_version=lowerCamelCase						,				)
						else:
												export(
												    lowerCamelCase						,				lowerCamelCase						,				f=output_path.as_posix()						,				input_names=lowerCamelCase						,				output_names=lowerCamelCase						,				dynamic_axes=lowerCamelCase						,				do_constant_folding=lowerCamelCase						,				opset_version=lowerCamelCase						,				)

@torch.no_grad()
def 							__magic_name__(						lowerCamelCase						,				lowerCamelCase						,				lowerCamelCase						,				lowerCamelCase = False):
						__lowerCAmelCase							       =				torch.floataa if fpaa else torch.floataa
						if fpaa and torch.cuda.is_available():
												__lowerCAmelCase							       =				'''cuda'''
						elif fpaa and not torch.cuda.is_available():
												raise ValueError('''`float16` model export is only supported on GPUs with CUDA''')
						else:
												__lowerCAmelCase							       =				'''cpu'''
						__lowerCAmelCase							       =				Path(lowerCamelCase)

						# VAE DECODER
						__lowerCAmelCase							       =				AutoencoderKL.from_pretrained(model_path + '''/vae''')
						__lowerCAmelCase							       =				vae_decoder.config.latent_channels
						# forward only through the decoder part
						__lowerCAmelCase							       =				vae_decoder.decode
						onnx_export(
						    lowerCamelCase						,				model_args=(
						        torch.randn(1						,				lowerCamelCase						,				2_5						,				2_5).to(device=lowerCamelCase						,				dtype=lowerCamelCase),
						        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=lowerCamelCase						,				)
						del vae_decoder


if __name__ == "__main__":
	_UpperCAmelCase				: Dict							    =				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=1_4,
	    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""")

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





'''simple docstring'''

from manim import *


class       a__				( __A      ):







				"""simple docstring"""



				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				Rectangle(height=0.5       ,			width=0.5		)
										__lowerCAmelCase							       =				Rectangle(height=0.4_6       ,			width=0.4_6		).set_stroke(width=0		)

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

										__lowerCAmelCase							       =				[mem.copy() for i in range(1		)]
										__lowerCAmelCase							       =				VGroup(*__lowercase		).arrange(__lowercase       ,			buff=0		)
										__lowerCAmelCase							       =				Text('''GPU'''       ,			font_size=24		)
										__lowerCAmelCase							       =				Group(__lowercase       ,			__lowercase		).arrange(__lowercase       ,			buff=0.5       ,			aligned_edge=__lowercase		)
										gpu.align_to(__lowercase       ,			__lowercase		)
										gpu.set_x(gpu.get_x() - 1		)

										self.add(__lowercase		)

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

										__lowerCAmelCase							       =				Text('''Model'''       ,			font_size=24		)
										__lowerCAmelCase							       =				Group(__lowercase       ,			__lowercase		).arrange(__lowercase       ,			buff=0.5       ,			aligned_edge=__lowercase		)
										model.move_to([3, -1.0, 0]		)

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

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

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

										__lowerCAmelCase							       =				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(__lowercase       ,			run_time=2.5		)       ,			Write(__lowercase		)       ,			Write(__lowercase		)		)

										self.add(__lowercase		)

										__lowerCAmelCase							       =				[]
										__lowerCAmelCase							       =				[]
										__lowerCAmelCase							       =				[]
										for i, rect in enumerate(__lowercase		):
																__lowerCAmelCase							       =				Rectangle(height=0.4_6       ,			width=0.4_6		).set_stroke(width=0.0		).set_fill(__lowercase       ,			opacity=0.7		)
																cpu_target.move_to(__lowercase		)
																cpu_target.generate_target()
																__lowerCAmelCase							       =				0.4_6 / 4
																__lowerCAmelCase							       =				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=__lowercase		)
																						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=__lowercase       ,			buff=0.0		)
																else:
																						cpu_target.target.next_to(cpu_targs[i - 1].target       ,			direction=__lowercase       ,			buff=0.0		)
																cpu_targs.append(__lowercase		)

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

										self.play(*__lowercase		)
										self.play(*__lowercase		)

										self.wait()

