


import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


A			:						str        =  version.parse(version.parse(torch.__version__).base_version) < version.parse('1.11')






def 					UpperCamelCase   (      __magic_name__	:				Dict       ,       __magic_name__	:				tuple       ,       __magic_name__	:				Path       ,       __magic_name__	:				Tuple       ,       __magic_name__	:				Tuple       ,       __magic_name__	:				Any       ,       __magic_name__	:				Tuple       ,       __magic_name__	:				Tuple=False       ,       )  ->    str:
		"""simple docstring"""




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






@torch.no_grad()
def 					UpperCamelCase   (      __magic_name__	:				str       ,       __magic_name__	:				str       ,       __magic_name__	:				int       ,       __magic_name__	:				bool = False	)  ->    int:
		"""simple docstring"""




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

		# VAE DECODER
		lowercase__	     =						AutoencoderKL.from_pretrained(model_path + """/vae"""	)
		lowercase__	     =						vae_decoder.config.latent_channels
		# forward only through the decoder part
		lowercase__	     =						vae_decoder.decode
		onnx_export(
		    __magic_name__       ,       model_args=(
		        torch.randn(1       ,       __magic_name__       ,       25       ,       25	).to(device=__magic_name__       ,       dtype=__magic_name__	),
		        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=__magic_name__       ,       )
		del vae_decoder


if __name__ == "__main__":
	A			:						Any        =  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')

	A			:						Dict        =  parser.parse_args()
	print(args.output_path)
	convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
	print('SD: Done: ONNX')





from __future__ import annotations






def 					UpperCamelCase   (      __magic_name__	:				list[int]	)  ->    list[int]:  # This function is recursive
		"""simple docstring"""




		lowercase__	     =						len(__magic_name__	)
		# If the array contains only one element, we return it (it's the stop condition of
		# recursion)
		if array_length <= 1:
				return array
				# Else
		lowercase__	     =						array[0]
		lowercase__	     =						False
		lowercase__	     =						1
		lowercase__	     =						[]
		while not is_found and i < array_length:
				if array[i] < pivot:
						lowercase__	     =						True
						lowercase__	     =						[element for element in array[i:] if element >= array[i]]
						lowercase__	     =						longest_subsequence(__magic_name__	)
						if len(__magic_name__	) > len(__magic_name__	):
								lowercase__	     =						temp_array
				else:
						i += 1

		lowercase__	     =						[element for element in array[1:] if element >= pivot]
		lowercase__	     =						[pivot, *longest_subsequence(__magic_name__	)]
		if len(__magic_name__	) > len(__magic_name__	):
				return temp_array
		else:
				return longest_subseq


if __name__ == "__main__":
	import doctest

	doctest.testmod()

