


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






def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase=False     ,			)	->	Union[str, Any]:
				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      a__ (				__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase = False   )	->	Optional[Any]:
				_A					  =			torch.floataa if fpaa else torch.floataa
				if fpaa and torch.cuda.is_available():
								_A					  =			"cuda"
				elif fpaa and not torch.cuda.is_available():
								raise ValueError("`float16` model export is only supported on GPUs with CUDA"   )
				else:
								_A					  =			"cpu"
				_A					  =			StableDiffusionPipeline.from_pretrained(__lowercase     ,			torch_dtype=__lowercase   ).to(__lowercase   )
				_A					  =			Path(__lowercase   )

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

				_A					  =			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
				_A					  =			OnnxStableDiffusionPipeline.from_pretrained(__lowercase     ,			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=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_       							=    parser.parse_args()

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



"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


a_       							=    logging.get_logger(__name__)
class snake_case      (  _UpperCamelCase):







		def __init__(					self      :       str    ,   *a__      :       Dict    ,   **a__      :       Optional[int]		)       ->						None:





						'''simple docstring'''







						warnings.warn(
						    "The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
						    " use GLPNImageProcessor instead."    ,   a__    ,   )
						super().__init__(*a__    ,   **a__		)