





import argparse
import os
import shutil
from pathlib import Path

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

from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline


A_												=     version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")





def    __UpperCamelCase      (    a,     a,     a,     a,     a,     a,     a,     a=False,     ) ->Dict:
					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    __UpperCamelCase      (    a,     a,     a,     a = False) ->Optional[int]:
					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__       =       StableDiffusionPipeline.from_pretrained(a,     torch_dtype=a).to(a)
					lowerCamelCase__       =       Path(a)

					# TEXT ENCODER
					lowerCamelCase__       =       pipeline.text_encoder.config.max_position_embeddings
					lowerCamelCase__       =       pipeline.text_encoder.config.hidden_size
					lowerCamelCase__       =       pipeline.tokenizer(
					    "A sample prompt",     padding="max_length",     max_length=pipeline.tokenizer.model_max_length,     truncation=a,     return_tensors="pt",     )
					onnx_export(
					    pipeline.text_encoder,     model_args=(text_input.input_ids.to(device=a,     dtype=torch.intaa)),     output_path=output_path / "text_encoder" / "model.onnx",     ordered_input_names=["input_ids"],     output_names=["last_hidden_state", "pooler_output"],     dynamic_axes={
					        "input_ids": {0: "batch", 1: "sequence"},
					    },     opset=a,     )
					del pipeline.text_encoder

					# UNET
					lowerCamelCase__       =       pipeline.unet.config.in_channels
					lowerCamelCase__       =       pipeline.unet.config.sample_size
					lowerCamelCase__       =       output_path / "unet" / "model.onnx"
					onnx_export(
					    pipeline.unet,     model_args=(
					        torch.randn(2,     a,     a,     a).to(device=a,     dtype=a),
					        torch.randn(2).to(device=a,     dtype=a),
					        torch.randn(2,     a,     a).to(device=a,     dtype=a),
					        False,
					    ),     output_path=a,     ordered_input_names=["sample", "timestep", "encoder_hidden_states", "return_dict"],     output_names=["out_sample"],     dynamic_axes={
					        "sample": {0: "batch", 1: "channels", 2: "height", 3: "width"},
					        "timestep": {0: "batch"},
					        "encoder_hidden_states": {0: "batch", 1: "sequence"},
					    },     opset=a,     use_external_data_format=a,     )
					lowerCamelCase__       =       str(unet_path.absolute().as_posix())
					lowerCamelCase__       =       os.path.dirname(a)
					lowerCamelCase__       =       onnx.load(a)
					# clean up existing tensor files
					shutil.rmtree(a)
					os.mkdir(a)
					# collate external tensor files into one
					onnx.save_model(
					    a,     a,     save_as_external_data=a,     all_tensors_to_one_file=a,     location="weights.pb",     convert_attribute=a,     )
					del pipeline.unet

					# VAE ENCODER
					lowerCamelCase__       =       pipeline.vae
					lowerCamelCase__       =       vae_encoder.config.in_channels
					lowerCamelCase__       =       vae_encoder.config.sample_size
					# need to get the raw tensor output (sample) from the encoder
					lowerCamelCase__       =       lambda a,     a: vae_encoder.encode(a,     a)[0].sample()
					onnx_export(
					    a,     model_args=(
					        torch.randn(1,     a,     a,     a).to(device=a,     dtype=a),
					        False,
					    ),     output_path=output_path / "vae_encoder" / "model.onnx",     ordered_input_names=["sample", "return_dict"],     output_names=["latent_sample"],     dynamic_axes={
					        "sample": {0: "batch", 1: "channels", 2: "height", 3: "width"},
					    },     opset=a,     )

					# VAE DECODER
					lowerCamelCase__       =       pipeline.vae
					lowerCamelCase__       =       vae_decoder.config.latent_channels
					lowerCamelCase__       =       vae_decoder.config.out_channels
					# forward only through the decoder part
					lowerCamelCase__       =       vae_encoder.decode
					onnx_export(
					    a,     model_args=(
					        torch.randn(1,     a,     a,     a).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 pipeline.vae

					# SAFETY CHECKER
					if pipeline.safety_checker is not None:
										lowerCamelCase__       =       pipeline.safety_checker
										lowerCamelCase__       =       safety_checker.config.vision_config.num_channels
										lowerCamelCase__       =       safety_checker.config.vision_config.image_size
										lowerCamelCase__       =       safety_checker.forward_onnx
										onnx_export(
										    pipeline.safety_checker,     model_args=(
										        torch.randn(
										            1,     a,     a,     a,     ).to(device=a,     dtype=a),
										        torch.randn(1,     a,     a,     a).to(device=a,     dtype=a),
										    ),     output_path=output_path / "safety_checker" / "model.onnx",     ordered_input_names=["clip_input", "images"],     output_names=["out_images", "has_nsfw_concepts"],     dynamic_axes={
										        "clip_input": {0: "batch", 1: "channels", 2: "height", 3: "width"},
										        "images": {0: "batch", 1: "height", 2: "width", 3: "channels"},
										    },     opset=a,     )
										del pipeline.safety_checker
										lowerCamelCase__       =       OnnxRuntimeModel.from_pretrained(output_path / "safety_checker")
										lowerCamelCase__       =       pipeline.feature_extractor
					else:
										lowerCamelCase__       =       None
										lowerCamelCase__       =       None

					lowerCamelCase__       =       OnnxStableDiffusionPipeline(
					    vae_encoder=OnnxRuntimeModel.from_pretrained(output_path / "vae_encoder"),     vae_decoder=OnnxRuntimeModel.from_pretrained(output_path / "vae_decoder"),     text_encoder=OnnxRuntimeModel.from_pretrained(output_path / "text_encoder"),     tokenizer=pipeline.tokenizer,     unet=OnnxRuntimeModel.from_pretrained(output_path / "unet"),     scheduler=pipeline.scheduler,     safety_checker=a,     feature_extractor=a,     requires_safety_checker=safety_checker is not None,     )

					onnx_pipeline.save_pretrained(a)
					print("ONNX pipeline saved to",     a)

					del pipeline
					del onnx_pipeline
					lowerCamelCase__       =       OnnxStableDiffusionPipeline.from_pretrained(a,     provider="CPUExecutionProvider")
					print("ONNX pipeline is loadable")


if __name__ == "__main__":
						A_												=     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_												=     parser.parse_args()

						convert_models(args.model_path, args.output_path, args.opset, args.fpaa)








def    __UpperCamelCase      (    a,     a) ->Dict:
					print("\nThe shortest path matrix using Floyd Warshall algorithm\n")
					for i in range(a):
										for j in range(a):
															if dist[i][j] != float("inf"):
																				print(int(dist[i][j]),     end="\t")
															else:
																				print("INF",     end="\t")
										print()





def    __UpperCamelCase      (    a,     a) ->Optional[int]:
					lowerCamelCase__       =       [[float("inf") for _ in range(a)] for _ in range(a)]

					for i in range(a):
										for j in range(a):
															lowerCamelCase__       =       graph[i][j]

															# check vertex k against all other vertices (i, j)
					for k in range(a):
										# looping through rows of graph array
										for i in range(a):
															# looping through columns of graph array
															for j in range(a):
																				if (
																				    dist[i][k] != float("inf")
																				    and dist[k][j] != float("inf")
																				    and dist[i][k] + dist[k][j] < dist[i][j]
																				):
																									lowerCamelCase__       =       dist[i][k] + dist[k][j]

					_print_dist(a,     a)
					return dist, v


if __name__ == "__main__":
						A_												=     int(input("Enter number of vertices: "))
						A_												=     int(input("Enter number of edges: "))

						A_												=     [[float("inf") for i in range(v)] for j in range(v)]

						for i in range(v):
												A_												=     0.0

												# src and dst are indices that must be within the array size graph[e][v]
												# failure to follow this will result in an error
						for i in range(e):
												print("\nEdge ", i + 1)
												A_												=     int(input("Enter source:"))
												A_												=     int(input("Enter destination:"))
												A_												=     float(input("Enter weight:"))
												A_												=     weight

						floyd_warshall(graph, v)

						# Example Input
						# Enter number of vertices: 3
						# Enter number of edges: 2

						# # generated graph from vertex and edge inputs
						# [[inf, inf, inf], [inf, inf, inf], [inf, inf, inf]]
						# [[0.0, inf, inf], [inf, 0.0, inf], [inf, inf, 0.0]]

						# specify source, destination and weight for edge #1
						# Edge  1
						# Enter source:1
						# Enter destination:2
						# Enter weight:2

						# specify source, destination and weight for edge #2
						# Edge  2
						# Enter source:2
						# Enter destination:1
						# Enter weight:1

						# # Expected Output from the vertice, edge and src, dst, weight inputs!!
						# 0		INF	INF
						# INF	0	2
						# INF	1	0

