

import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


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



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



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

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


if __name__ == "__main__":
				_a:    str 							=			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""")

				_a:    Union[str, Any] 							=			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       __lowerCAmelCase      (						A       ,     A    ):
	print(F"Vertex\tShortest Distance from vertex {src}"    )
	for i, d in enumerate(A    ):
		print(F"{i}\t\t{d}"    )



def       __lowerCAmelCase      (						A       ,     A       ,     A    ):
	for j in range(A    ):
		UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_       				=			(graph[j][k] for k in ["src", "dst", "weight"])
		if distance[u] != float("inf"    ) and distance[u] + w < distance[v]:
			return True
	return False



def       __lowerCAmelCase      (						A       ,     A       ,     A       ,     A    ):
	UpperCAmelCase_       				=			[float("inf"    )] * vertex_count
	UpperCAmelCase_       				=			0.0

	for _ in range(vertex_count - 1    ):
		for j in range(A    ):
			UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_       				=			(graph[j][k] for k in ["src", "dst", "weight"])

			if distance[u] != float("inf"    ) and distance[u] + w < distance[v]:
				UpperCAmelCase_       				=			distance[u] + w

	UpperCAmelCase_       				=			check_negative_cycle(A       ,     A       ,     A    )
	if negative_cycle_exists:
		raise Exception("Negative cycle found"    )

	return distance


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				_a:    List[str] 							=			int(input("""Enter number of vertices: """).strip())
				_a:    str 							=			int(input("""Enter number of edges: """).strip())

				_a:    list[dict[str, int]] 							=			[{} for _ in range(E)]

				for i in range(E):
								print("""Edge """, i + 1)
								_a							,			_a							,			_a:    Any 							=			(
								    int(x)
								    for x in input("""Enter source, destination, weight: """).strip().split(""" """)
								)
								_a:    str 							=			{"""src""": src, """dst""": dest, """weight""": weight}

				_a:    List[Any] 							=			int(input("""\nEnter shortest path source:""").strip())
				_a:    Optional[int] 							=			bellman_ford(graph, V, E, source)
				print_distance(shortest_distance, 0)