






import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


__lowerCamelCase      :    int   		=   version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Dict  ,						snake_case_      :							Any  ,						snake_case_      :							str  ,						snake_case_      :							str  ,						snake_case_      :							Optional[int]  ,						snake_case_      :							int  ,						snake_case_      :							Optional[int]  ,						snake_case_      :							List[str]=False  ,						):
	output_path.parent.mkdir(parents=snake_case_  ,						exist_ok=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(
		    snake_case_  ,						snake_case_  ,						f=output_path.as_posix()  ,						input_names=snake_case_  ,						output_names=snake_case_  ,						dynamic_axes=snake_case_  ,						do_constant_folding=snake_case_  ,						use_external_data_format=snake_case_  ,						enable_onnx_checker=snake_case_  ,						opset_version=snake_case_  ,						)
	else:
		export(
		    snake_case_  ,						snake_case_  ,						f=output_path.as_posix()  ,						input_names=snake_case_  ,						output_names=snake_case_  ,						dynamic_axes=snake_case_  ,						do_constant_folding=snake_case_  ,						opset_version=snake_case_  ,						)







@torch.no_grad()
def   SCREAMING_SNAKE_CASE       (				snake_case_      :							int  ,						snake_case_      :							str  ,						snake_case_      :							List[str]  ,						snake_case_      :							Any = False     ):
	snake_case__				:   List[str]     =						torch.floataa if fpaa else torch.floataa
	if fpaa and torch.cuda.is_available():
		snake_case__				:   List[str]     =						"cuda"
	elif fpaa and not torch.cuda.is_available():
		raise ValueError("`float16` model export is only supported on GPUs with CUDA"     )
	else:
		snake_case__				:   str     =						"cpu"
	snake_case__				:   Any     =						Path(snake_case_     )

	# VAE DECODER
	snake_case__				:   Optional[int]     =						AutoencoderKL.from_pretrained(model_path + "/vae"     )
	snake_case__				:   List[Any]     =						vae_decoder.config.latent_channels
	# forward only through the decoder part
	snake_case__				:   int     =						vae_decoder.decode
	onnx_export(
	    snake_case_  ,						model_args=(
	        torch.randn(1  ,						snake_case_  ,						25  ,						25     ).to(device=snake_case_  ,						dtype=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=snake_case_  ,						)
	del vae_decoder


if __name__ == "__main__":
	__lowerCamelCase      :    Union[str, 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=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""")

	__lowerCamelCase      :    List[Any]   		=   parser.parse_args()
	print(args.output_path)
	convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
	print("""SD: Done: ONNX""")











def   SCREAMING_SNAKE_CASE       (				snake_case_      :							str     ):
	snake_case__				:   Any     =						[0] * len(snake_case_     )

	for i in range(1  ,						len(snake_case_     )     ):
		# use last results for better performance - dynamic programming
		snake_case__				:   Union[str, Any]     =						prefix_result[i - 1]
		while j > 0 and input_string[i] != input_string[j]:
			snake_case__				:   str     =						prefix_result[j - 1]

		if input_string[i] == input_string[j]:
			j += 1
		snake_case__				:   int     =						j

	return prefix_result







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							str     ):
	return max(prefix_function(snake_case_     )     )


if __name__ == "__main__":
	import doctest

	doctest.testmod()



