





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 					lowerCAmelCase_			(	__a     ,   __a     ,   __a     ,   __a     ,   __a     ,   __a     ,   __a     ,   __a=False     ,   )					->		Union[str, Any]:







  """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 					lowerCAmelCase_			(	__a     ,   __a     ,   __a     ,   __a = False		)					->		List[Any]:







  """simple docstring"""





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

  # TEXT ENCODER
  lowerCamelCase__:				Dict        =pipeline.text_encoder.config.max_position_embeddings
  lowerCamelCase__:				List[Any]        =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=__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__:				str        =pipeline.unet.config.in_channels
  lowerCamelCase__:				List[str]        =pipeline.unet.config.sample_size
  lowerCamelCase__:				str        =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        =str(unet_path.absolute().as_posix()		)
  lowerCamelCase__:				Optional[int]        =os.path.dirname(__a		)
  lowerCamelCase__:				Tuple        =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__:				Optional[Any]        =pipeline.vae
  lowerCamelCase__:				Optional[int]        =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__:				Union[str, Any]        =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__:				List[str]        =pipeline.vae
  lowerCamelCase__:				str        =vae_decoder.config.latent_channels
  lowerCamelCase__:				Any        =vae_decoder.config.out_channels
  # forward only through the decoder part
  lowerCamelCase__:				List[str]        =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[Any]        =pipeline.safety_checker
    lowerCamelCase__:				Tuple        =safety_checker.config.vision_config.num_channels
    lowerCamelCase__:				int        =safety_checker.config.vision_config.image_size
    lowerCamelCase__:				int        =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__:				str        =OnnxRuntimeModel.from_pretrained(output_path / "safety_checker"		)
    lowerCamelCase__:				Optional[int]        =pipeline.feature_extractor
  else:
    lowerCamelCase__:				Any        =None
    lowerCamelCase__:				Optional[Any]        =None

  lowerCamelCase__:				Dict        =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__:				int        =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=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)













from ..utils import DummyObject, requires_backends



class        _SCREAMING_SNAKE_CASE						(     metaclass=__SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''
 lowercase_		=      ["torch", "torchsde"]

 def __init__(self    :       Union[str, Any]	,			*UpperCAmelCase_    :       Dict	,			**UpperCAmelCase_    :       Tuple)	->Any:



   '''simple docstring'''



   requires_backends(self	,			["torch", "torchsde"])

 @classmethod
 def 							SCREAMING_SNAKE_CASE_ (cls    :       Dict	,			*UpperCAmelCase_    :       Optional[int]	,			**UpperCAmelCase_    :       Optional[int])	->List[Any]:



   '''simple docstring'''



   requires_backends(cls	,			["torch", "torchsde"])







 @classmethod
 def 							SCREAMING_SNAKE_CASE_ (cls    :       Tuple	,			*UpperCAmelCase_    :       Tuple	,			**UpperCAmelCase_    :       Any)	->Any:



   '''simple docstring'''



   requires_backends(cls	,			["torch", "torchsde"])

