






"""simple docstring"""



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__	:    Optional[int]			 = version.parse(version.parse(torch.__version__).base_version) < version.parse('1.11')

def 			_snake_case    (					lowerCamelCase__     :   int    ,						lowerCamelCase__     :   tuple    ,						lowerCamelCase__     :   Path    ,						lowerCamelCase__     :   Optional[Any]    ,						lowerCamelCase__     :   int    ,						lowerCamelCase__     :   str    ,						lowerCamelCase__     :   str    ,						lowerCamelCase__     :   Optional[int]=False    ,						)    ->     Optional[int]:
							output_path.parent.mkdir(parents=lowercase__    ,						exist_ok=lowercase__  )
							# 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(
														    lowercase__    ,						lowercase__    ,						f=output_path.as_posix()    ,						input_names=lowercase__    ,						output_names=lowercase__    ,						dynamic_axes=lowercase__    ,						do_constant_folding=lowercase__    ,						use_external_data_format=lowercase__    ,						enable_onnx_checker=lowercase__    ,						opset_version=lowercase__    ,						)
							else:
														export(
														    lowercase__    ,						lowercase__    ,						f=output_path.as_posix()    ,						input_names=lowercase__    ,						output_names=lowercase__    ,						dynamic_axes=lowercase__    ,						do_constant_folding=lowercase__    ,						opset_version=lowercase__    ,						)

@torch.no_grad()
def 			_snake_case    (					lowerCamelCase__     :   str    ,						lowerCamelCase__     :   str    ,						lowerCamelCase__     :   int    ,						lowerCamelCase__     :   bool = False  )    ->     Any:
							lowerCamelCase_	:							Any									=torch.floataa if fpaa else torch.floataa
							if fpaa and torch.cuda.is_available():
														lowerCamelCase_	:							Union[str, Any]									="""cuda"""
							elif fpaa and not torch.cuda.is_available():
														raise ValueError("`float16` model export is only supported on GPUs with CUDA"  )
							else:
														lowerCamelCase_	:							Any									="""cpu"""
							lowerCamelCase_	:							Any									=StableDiffusionPipeline.from_pretrained(lowercase__    ,						torch_dtype=lowercase__  ).to(lowercase__  )
							lowerCamelCase_	:							int									=Path(lowercase__  )

							# TEXT ENCODER
							lowerCamelCase_	:							int									=pipeline.text_encoder.config.max_position_embeddings
							lowerCamelCase_	:							int									=pipeline.text_encoder.config.hidden_size
							lowerCamelCase_	:							List[Any]									=pipeline.tokenizer(
							    "A sample prompt"    ,						padding="max_length"    ,						max_length=pipeline.tokenizer.model_max_length    ,						truncation=lowercase__    ,						return_tensors="pt"    ,						)
							onnx_export(
							    pipeline.text_encoder    ,						model_args=(text_input.input_ids.to(device=lowercase__    ,						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=lowercase__    ,						)
							del pipeline.text_encoder

							# UNET
							lowerCamelCase_	:							Optional[Any]									=pipeline.unet.config.in_channels
							lowerCamelCase_	:							Union[str, Any]									=pipeline.unet.config.sample_size
							lowerCamelCase_	:							List[Any]									=output_path / """unet""" / """model.onnx"""
							onnx_export(
							    pipeline.unet    ,						model_args=(
							        torch.randn(2    ,						lowercase__    ,						lowercase__    ,						lowercase__  ).to(device=lowercase__    ,						dtype=lowercase__  ),
							        torch.randn(2  ).to(device=lowercase__    ,						dtype=lowercase__  ),
							        torch.randn(2    ,						lowercase__    ,						lowercase__  ).to(device=lowercase__    ,						dtype=lowercase__  ),
							        False,
							    )    ,						output_path=lowercase__    ,						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=lowercase__    ,						use_external_data_format=lowercase__    ,						)
							lowerCamelCase_	:							Union[str, Any]									=str(unet_path.absolute().as_posix()  )
							lowerCamelCase_	:							int									=os.path.dirname(lowercase__  )
							lowerCamelCase_	:							Tuple									=onnx.load(lowercase__  )
							# clean up existing tensor files
							shutil.rmtree(lowercase__  )
							os.mkdir(lowercase__  )
							# collate external tensor files into one
							onnx.save_model(
							    lowercase__    ,						lowercase__    ,						save_as_external_data=lowercase__    ,						all_tensors_to_one_file=lowercase__    ,						location="weights.pb"    ,						convert_attribute=lowercase__    ,						)
							del pipeline.unet

							# VAE ENCODER
							lowerCamelCase_	:							int									=pipeline.vae
							lowerCamelCase_	:							Tuple									=vae_encoder.config.in_channels
							lowerCamelCase_	:							List[str]									=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(lowercase__    ,						lowercase__  )[0].sample()
							onnx_export(
							    lowercase__    ,						model_args=(
							        torch.randn(1    ,						lowercase__    ,						lowercase__    ,						lowercase__  ).to(device=lowercase__    ,						dtype=lowercase__  ),
							        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=lowercase__    ,						)

							# VAE DECODER
							lowerCamelCase_	:							int									=pipeline.vae
							lowerCamelCase_	:							int									=vae_decoder.config.latent_channels
							lowerCamelCase_	:							Dict									=vae_decoder.config.out_channels
							# forward only through the decoder part
							lowerCamelCase_	:							Union[str, Any]									=vae_encoder.decode
							onnx_export(
							    lowercase__    ,						model_args=(
							        torch.randn(1    ,						lowercase__    ,						lowercase__    ,						lowercase__  ).to(device=lowercase__    ,						dtype=lowercase__  ),
							        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=lowercase__    ,						)
							del pipeline.vae

							# SAFETY CHECKER
							if pipeline.safety_checker is not None:
														lowerCamelCase_	:							Union[str, Any]									=pipeline.safety_checker
														lowerCamelCase_	:							Tuple									=safety_checker.config.vision_config.num_channels
														lowerCamelCase_	:							Union[str, Any]									=safety_checker.config.vision_config.image_size
														lowerCamelCase_	:							List[Any]									=safety_checker.forward_onnx
														onnx_export(
														    pipeline.safety_checker    ,						model_args=(
														        torch.randn(
														            1    ,						lowercase__    ,						lowercase__    ,						lowercase__    ,						).to(device=lowercase__    ,						dtype=lowercase__  ),
														        torch.randn(1    ,						lowercase__    ,						lowercase__    ,						lowercase__  ).to(device=lowercase__    ,						dtype=lowercase__  ),
														    )    ,						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=lowercase__    ,						)
														del pipeline.safety_checker
														lowerCamelCase_	:							Dict									=OnnxRuntimeModel.from_pretrained(output_path / "safety_checker"  )
														lowerCamelCase_	:							Optional[int]									=pipeline.feature_extractor
							else:
														lowerCamelCase_	:							Tuple									=None
														lowerCamelCase_	:							Tuple									=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=lowercase__    ,						feature_extractor=lowercase__    ,						requires_safety_checker=safety_checker is not None    ,						)

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

							del pipeline
							del onnx_pipeline
							lowerCamelCase_	:							int									=OnnxStableDiffusionPipeline.from_pretrained(lowercase__    ,						provider="CPUExecutionProvider"  )
							print("ONNX pipeline is loadable"  )


if __name__ == "__main__":
						A__	:    Optional[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')

						A__	:    int			 = parser.parse_args()

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











"""simple docstring"""



def 			_snake_case    (					lowerCamelCase__     :   int  )    ->     int:

							if not isinstance(lowerCamelCase__    ,						lowerCamelCase__  ):
														raise TypeError("only integers accepted as input"  )
							else:
														lowerCamelCase_	:							str									=str(abs(lowerCamelCase__  )  )
														lowerCamelCase_	:							Tuple									=[list(lowerCamelCase__  ) for char in range(len(lowerCamelCase__  )  )]
														for index in range(len(lowerCamelCase__  )  ):
																					num_transpositions[index].pop(lowerCamelCase__  )
														return max(
														    int("".join(list(lowerCamelCase__  )  )  ) for transposition in num_transpositions  )


if __name__ == "__main__":
						__import__('doctest').testmod()



