


"""simple docstring"""


import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


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


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase=False    , ):

			'''simple docstring'''




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


@torch.no_grad()
def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase = False      ):

			'''simple docstring'''




			__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(_UpperCamelCase      )

			# 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(
			    _UpperCamelCase    , model_args=(
			        torch.randn(1    , _UpperCamelCase    , 25    , 25      ).to(device=_UpperCamelCase    , dtype=_UpperCamelCase      ),
			        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=_UpperCamelCase    , )
			del vae_decoder


if __name__ == "__main__":
	A     :   Optional[int]			    =			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     :   Optional[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"""


class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							pass
class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							pass
class   _UpperCamelCase  :
							'''simple docstring'''







							def __init__(     self	):
										__lowerCAmelCase				    =			[
										    [],
										    [],
										    [],
										]

							def 			snake_case				(     self    ,   __a    ,   __a	):
										try:
													if len(self.queues[priority]	) >= 1_00:
																raise OverflowError("Maximum queue size is 100"	)
													self.queues[priority].append(__a	)
										except IndexError:
													raise ValueError("Valid priorities are 0, 1, and 2"	)

							def 			snake_case				(     self	):
										for queue in self.queues:
													if queue:
																return queue.pop(0	)
										raise UnderFlowError("All queues are empty"	)



							def __str__(     self	):
										return "\n".join(f"Priority {i}: {q}" for i, q in enumerate(self.queues	)	)
class   _UpperCamelCase  :
							'''simple docstring'''







							def __init__(     self	):
										__lowerCAmelCase				    =			[]

							def 			snake_case				(     self    ,   __a	):
										if len(self.queue	) == 1_00:
													raise OverFlowError("Maximum queue size is 100"	)
										self.queue.append(__a	)

							def 			snake_case				(     self	):
										if not self.queue:
													raise UnderFlowError("The queue is empty"	)
										else:
													__lowerCAmelCase				    =			min(self.queue	)
													self.queue.remove(__a	)
													return data



							def __str__(     self	):
										return str(self.queue	)


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			FixedPriorityQueue()
			fpq.enqueue(0    , 10      )
			fpq.enqueue(1    , 70      )
			fpq.enqueue(0    , 100      )
			fpq.enqueue(2    , 1      )
			fpq.enqueue(2    , 5      )
			fpq.enqueue(1    , 7      )
			fpq.enqueue(2    , 4      )
			fpq.enqueue(1    , 64      )
			fpq.enqueue(0    , 128      )
			print(_UpperCamelCase      )
			print(fpq.dequeue()      )
			print(fpq.dequeue()      )
			print(fpq.dequeue()      )
			print(fpq.dequeue()      )
			print(fpq.dequeue()      )
			print(_UpperCamelCase      )
			print(fpq.dequeue()      )
			print(fpq.dequeue()      )
			print(fpq.dequeue()      )
			print(fpq.dequeue()      )
			print(fpq.dequeue()      )


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			ElementPriorityQueue()
			epq.enqueue(10      )
			epq.enqueue(70      )
			epq.enqueue(100      )
			epq.enqueue(1      )
			epq.enqueue(5      )
			epq.enqueue(7      )
			epq.enqueue(4      )
			epq.enqueue(64      )
			epq.enqueue(128      )
			print(_UpperCamelCase      )
			print(epq.dequeue()      )
			print(epq.dequeue()      )
			print(epq.dequeue()      )
			print(epq.dequeue()      )
			print(epq.dequeue()      )
			print(_UpperCamelCase      )
			print(epq.dequeue()      )
			print(epq.dequeue()      )
			print(epq.dequeue()      )
			print(epq.dequeue()      )
			print(epq.dequeue()      )


if __name__ == "__main__":
	fixed_priority_queue()
	element_priority_queue()




