



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


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






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





							'''simple docstring'''




							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 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase = False  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Optional[Any]    =    torch.floataa if fpaa else torch.floataa
							if fpaa and torch.cuda.is_available():
														_lowerCAmelCase     :							str    =    "cuda"
							elif fpaa and not torch.cuda.is_available():
														raise ValueError("`float16` model export is only supported on GPUs with CUDA"  )
							else:
														_lowerCAmelCase     :							int    =    "cpu"
							_lowerCAmelCase     :							Tuple    =    StableDiffusionPipeline.from_pretrained(A__				, torch_dtype=A__  ).to(A__  )
							_lowerCAmelCase     :							List[str]    =    Path(A__  )

							# TEXT ENCODER
							_lowerCAmelCase     :							List[Any]    =    pipeline.text_encoder.config.max_position_embeddings
							_lowerCAmelCase     :							List[Any]    =    pipeline.text_encoder.config.hidden_size
							_lowerCAmelCase     :							str    =    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     :							List[str]    =    pipeline.unet.config.in_channels
							_lowerCAmelCase     :							List[str]    =    pipeline.unet.config.sample_size
							_lowerCAmelCase     :							List[Any]    =    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     :							Dict    =    str(unet_path.absolute().as_posix()  )
							_lowerCAmelCase     :							Optional[Any]    =    os.path.dirname(A__  )
							_lowerCAmelCase     :							Union[str, Any]    =    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     :							Dict    =    pipeline.vae
							_lowerCAmelCase     :							Any    =    vae_encoder.config.in_channels
							_lowerCAmelCase     :							List[Any]    =    vae_encoder.config.sample_size
							# need to get the raw tensor output (sample) from the encoder
							_lowerCAmelCase     :							List[str]    =    lambda _lowerCamelCase				, _lowerCamelCase  : 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     :							List[str]    =    pipeline.vae
							_lowerCAmelCase     :							str    =    vae_decoder.config.latent_channels
							_lowerCAmelCase     :							Optional[int]    =    vae_decoder.config.out_channels
							# forward only through the decoder part
							_lowerCAmelCase     :							Dict    =    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     :							Optional[int]    =    pipeline.safety_checker
														_lowerCAmelCase     :							Any    =    safety_checker.config.vision_config.num_channels
														_lowerCAmelCase     :							str    =    safety_checker.config.vision_config.image_size
														_lowerCAmelCase     :							List[Any]    =    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     :							Union[str, Any]    =    OnnxRuntimeModel.from_pretrained(output_path / "safety_checker"  )
														_lowerCAmelCase     :							List[str]    =    pipeline.feature_extractor
							else:
														_lowerCAmelCase     :							Any    =    None
														_lowerCAmelCase     :							Union[str, Any]    =    None

							_lowerCAmelCase     :							str    =    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     :							Tuple    =    OnnxStableDiffusionPipeline.from_pretrained(A__				, provider="CPUExecutionProvider"  )
							print("ONNX pipeline is loadable"  )


if __name__ == "__main__":
						_snake_case		=  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")

						_snake_case		=  parser.parse_args()

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





'''simple docstring'''


import math

class  SCREAMING_SNAKE_CASE :






    """simple docstring"""





    def __init__(  self      :					Union[str, Any]  ,						UpperCamelCase__      :					Optional[Any]=0 ):  # a graph with Node 0,1,...,N-1


       """simple docstring"""







       UpperCamelCase					=  n
       UpperCamelCase					=  [
           [math.inf for j in range(0  ,						UpperCamelCase__ )] for i in range(0  ,						UpperCamelCase__ )
       ]  # adjacency matrix for weight
       UpperCamelCase					=  [
           [math.inf for j in range(0  ,						UpperCamelCase__ )] for i in range(0  ,						UpperCamelCase__ )
       ]  # dp[i][j] stores minimum distance from i to j






    def  A       (  self      :					Optional[Any]  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					Tuple ):


       """simple docstring"""







       UpperCamelCase					=  w






    def  A       (  self      :					str ):


       """simple docstring"""







       for k in range(0  ,						self.n ):
          for i in range(0  ,						self.n ):
             for j in range(0  ,						self.n ):
                UpperCamelCase					=  min(self.dp[i][j]  ,						self.dp[i][k] + self.dp[k][j] )






    def  A       (  self      :					Optional[Any]  ,						UpperCamelCase__      :					str  ,						UpperCamelCase__      :					List[Any] ):


       """simple docstring"""







       return self.dp[u][v]


if __name__ == "__main__":
   _lowerCamelCase	:       List[str]      	=	Graph(5)
   graph.add_edge(0, 2, 9)
   graph.add_edge(0, 4, 10)
   graph.add_edge(1, 3, 5)
   graph.add_edge(2, 3, 7)
   graph.add_edge(3, 0, 10)
   graph.add_edge(3, 1, 2)
   graph.add_edge(3, 2, 1)
   graph.add_edge(3, 4, 6)
   graph.add_edge(4, 1, 3)
   graph.add_edge(4, 2, 4)
   graph.add_edge(4, 3, 9)
   graph.floyd_warshall()
   graph.show_min(1, 4)
   graph.show_min(0, 3)

