






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		:       List[Any]			     = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")






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






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

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

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

  # VAE ENCODER
  __UpperCAmelCase							:	str           =  pipeline.vae
  __UpperCAmelCase							:	Any           =  vae_encoder.config.in_channels
  __UpperCAmelCase							:	Union[str, Any]           =  vae_encoder.config.sample_size
  # need to get the raw tensor output (sample) from the encoder
  __UpperCAmelCase							:	str           =  lambda __lowerCamelCase  ,		__lowerCamelCase		: vae_encoder.encode(__lowerCamelCase  ,		__lowerCamelCase		)[0].sample()
  onnx_export(
      __lowerCamelCase  ,		model_args=(
          torch.randn(1  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase		).to(device=__lowerCamelCase  ,		dtype=__lowerCamelCase		),
          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=__lowerCamelCase  ,		)

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

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

  __UpperCAmelCase							:	Optional[int]           =  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=__lowerCamelCase  ,		feature_extractor=__lowerCamelCase  ,		requires_safety_checker=safety_checker is not None  ,		)

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

  del pipeline
  del onnx_pipeline
  __UpperCAmelCase							:	Optional[Any]           =  OnnxStableDiffusionPipeline.from_pretrained(__lowerCamelCase  ,		provider="""CPUExecutionProvider"""		)
  print("""ONNX pipeline is loadable"""		)


if __name__ == "__main__":
       a		:       str			     = 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		:       Any			     = parser.parse_args()

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










from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
       import PIL


a		:       Union[str, Any]			     = logging.get_logger(__name__)
class     a    ( lowercase__   ):



      """simple docstring"""


      a  : Any							       =	['pixel_values']
      def __init__(      self					:			Optional[int]		,      __lowercase					:			bool = True		,      __lowercase					:			Dict[str, int] = None		,      __lowercase					:			int = 0.9		,      __lowercase					:			PILImageResampling = PILImageResampling.BICUBIC		,      __lowercase					:			bool = True		,      __lowercase					:			Dict[str, int] = None		,      __lowercase					:			Union[int, float] = 1 / 255		,      __lowercase					:			bool = True		,      __lowercase					:			bool = True		,      __lowercase					:			Optional[Union[float, List[float]]] = None		,      __lowercase					:			Optional[Union[float, List[float]]] = None		,      **__lowercase					:			Any		,      )							->      None:
        super().__init__(**__lowercase					)
        __UpperCAmelCase							:	Tuple           =  size if size is not None else {"""shortest_edge""": 224}
        __UpperCAmelCase							:	Union[str, Any]           =  get_size_dict(__lowercase		,      default_to_square=__lowercase					)
        __UpperCAmelCase							:	Union[str, Any]           =  crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
        __UpperCAmelCase							:	Any           =  get_size_dict(__lowercase		,      param_name="""crop_size"""					)

        __UpperCAmelCase							:	Dict           =  do_resize
        __UpperCAmelCase							:	Dict           =  size
        __UpperCAmelCase							:	Tuple           =  crop_pct
        __UpperCAmelCase							:	List[Any]           =  resample
        __UpperCAmelCase							:	List[Any]           =  do_center_crop
        __UpperCAmelCase							:	List[Any]           =  crop_size
        __UpperCAmelCase							:	Any           =  do_rescale
        __UpperCAmelCase							:	Tuple           =  rescale_factor
        __UpperCAmelCase							:	int           =  do_normalize
        __UpperCAmelCase							:	List[Any]           =  image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        __UpperCAmelCase							:	List[str]           =  image_std if image_std is not None else IMAGENET_DEFAULT_STD
      def 	UpperCAmelCase      (      self					:			Tuple		,      __lowercase					:			np.ndarray		,      __lowercase					:			Dict[str, int]		,      __lowercase					:			Optional[float] = None		,      __lowercase					:			PILImageResampling = PILImageResampling.BICUBIC		,      __lowercase					:			Optional[Union[str, ChannelDimension]] = None		,      **__lowercase					:			Optional[int]		,      )							->      np.ndarray:
        __UpperCAmelCase							:	Tuple           =  get_size_dict(__lowercase		,      default_to_square=__lowercase					)
        if "shortest_edge" not in size and ("height" not in size or "width" not in size):
          raise ValueError(f"""size must contain 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""					)
        if crop_pct is not None:
          if "shortest_edge" in size:
            __UpperCAmelCase							:	Union[str, Any]           =  int(size["""shortest_edge"""] / crop_pct					)
          elif "height" in size and "width" in size:
            if size["height"] == size["width"]:
              __UpperCAmelCase							:	Tuple           =  int(size["""height"""] / crop_pct					)
            else:
              __UpperCAmelCase							:	str           =  (int(size["""height"""] / crop_pct					), int(size["""width"""] / crop_pct					))
          else:
            raise ValueError("""Invalid size for resize: {}""".format(__lowercase					)					)

          __UpperCAmelCase							:	str           =  get_resize_output_image_size(__lowercase		,      size=__lowercase		,      default_to_square=__lowercase					)
        else:
          if "shortest_edge" in size:
            __UpperCAmelCase							:	List[str]           =  get_resize_output_image_size(__lowercase		,      size=size["""shortest_edge"""]		,      default_to_square=__lowercase					)
          elif "height" in size and "width" in size:
            __UpperCAmelCase							:	int           =  (size["""height"""], size["""width"""])
          else:
            raise ValueError("""Invalid size for resize: {}""".format(__lowercase					)					)

        return resize(__lowercase		,      size=__lowercase		,      resample=__lowercase		,      data_format=__lowercase		,      **__lowercase					)
      def 	UpperCAmelCase      (      self					:			Dict		,      __lowercase					:			np.ndarray		,      __lowercase					:			Dict[str, int]		,      __lowercase					:			Optional[Union[str, ChannelDimension]] = None		,      **__lowercase					:			Union[str, Any]		,      )							->      np.ndarray:
        __UpperCAmelCase							:	Optional[Any]           =  get_size_dict(__lowercase					)
        if "height" not in size or "width" not in size:
          raise ValueError(f"""size must contain 'height' and 'width' as keys. Got {size.keys()}"""					)
        return center_crop(__lowercase		,      size=(size["""height"""], size["""width"""])		,      data_format=__lowercase		,      **__lowercase					)
      def 	UpperCAmelCase      (      self					:			List[str]		,      __lowercase					:			np.ndarray		,      __lowercase					:			Union[int, float]		,      __lowercase					:			Optional[Union[str, ChannelDimension]] = None		,      **__lowercase					:			int		,      )							->      int:
        return rescale(__lowercase		,      scale=__lowercase		,      data_format=__lowercase		,      **__lowercase					)
      def 	UpperCAmelCase      (      self					:			List[Any]		,      __lowercase					:			np.ndarray		,      __lowercase					:			Union[float, List[float]]		,      __lowercase					:			Union[float, List[float]]		,      __lowercase					:			Optional[Union[str, ChannelDimension]] = None		,      **__lowercase					:			List[Any]		,      )							->      np.ndarray:
        return normalize(__lowercase		,      mean=__lowercase		,      std=__lowercase		,      data_format=__lowercase		,      **__lowercase					)




      def 	UpperCAmelCase      (      self					:			Any		,      __lowercase					:			ImageInput		,      __lowercase					:			bool = None		,      __lowercase					:			Dict[str, int] = None		,      __lowercase					:			int = None		,      __lowercase					:			PILImageResampling = None		,      __lowercase					:			bool = None		,      __lowercase					:			Dict[str, int] = None		,      __lowercase					:			bool = None		,      __lowercase					:			float = None		,      __lowercase					:			bool = None		,      __lowercase					:			Optional[Union[float, List[float]]] = None		,      __lowercase					:			Optional[Union[float, List[float]]] = None		,      __lowercase					:			Optional[Union[str, TensorType]] = None		,      __lowercase					:			ChannelDimension = ChannelDimension.FIRST		,      **__lowercase					:			List[str]		,      )							->      PIL.Image.Image:
        __UpperCAmelCase							:	Any           =  do_resize if do_resize is not None else self.do_resize
        __UpperCAmelCase							:	Optional[int]           =  crop_pct if crop_pct is not None else self.crop_pct
        __UpperCAmelCase							:	Optional[Any]           =  resample if resample is not None else self.resample
        __UpperCAmelCase							:	List[Any]           =  do_center_crop if do_center_crop is not None else self.do_center_crop
        __UpperCAmelCase							:	Dict           =  do_rescale if do_rescale is not None else self.do_rescale
        __UpperCAmelCase							:	Tuple           =  rescale_factor if rescale_factor is not None else self.rescale_factor
        __UpperCAmelCase							:	Union[str, Any]           =  do_normalize if do_normalize is not None else self.do_normalize
        __UpperCAmelCase							:	Tuple           =  image_mean if image_mean is not None else self.image_mean
        __UpperCAmelCase							:	Any           =  image_std if image_std is not None else self.image_std

        __UpperCAmelCase							:	Optional[int]           =  size if size is not None else self.size
        __UpperCAmelCase							:	Dict           =  get_size_dict(__lowercase		,      default_to_square=__lowercase					)
        __UpperCAmelCase							:	Tuple           =  crop_size if crop_size is not None else self.crop_size
        __UpperCAmelCase							:	Tuple           =  get_size_dict(__lowercase		,      param_name="""crop_size"""					)

        __UpperCAmelCase							:	Dict           =  make_list_of_images(__lowercase					)

        if not valid_images(__lowercase					):
          raise ValueError(
              """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
              """torch.Tensor, tf.Tensor or jax.ndarray."""					)

        if do_resize and size is None or resample is None:
          raise ValueError("""Size and resample must be specified if do_resize is True."""					)

        if do_center_crop and crop_pct is None:
          raise ValueError("""Crop_pct must be specified if do_center_crop is True."""					)

        if do_rescale and rescale_factor is None:
          raise ValueError("""Rescale factor must be specified if do_rescale is True."""					)

        if do_normalize and (image_mean is None or image_std is None):
          raise ValueError("""Image mean and std must be specified if do_normalize is True."""					)

        # All transformations expect numpy arrays.
        __UpperCAmelCase							:	str           =  [to_numpy_array(__lowercase					) for image in images]

        if do_resize:
          __UpperCAmelCase							:	str           =  [self.resize(image=__lowercase		,      size=__lowercase		,      crop_pct=__lowercase		,      resample=__lowercase					) for image in images]

        if do_center_crop:
          __UpperCAmelCase							:	Any           =  [self.center_crop(image=__lowercase		,      size=__lowercase					) for image in images]

        if do_rescale:
          __UpperCAmelCase							:	List[str]           =  [self.rescale(image=__lowercase		,      scale=__lowercase					) for image in images]

        if do_normalize:
          __UpperCAmelCase							:	str           =  [self.normalize(image=__lowercase		,      mean=__lowercase		,      std=__lowercase					) for image in images]

        __UpperCAmelCase							:	List[str]           =  [to_channel_dimension_format(__lowercase		,      __lowercase					) for image in images]

        __UpperCAmelCase							:	Any           =  {"""pixel_values""": images}
        return BatchFeature(data=__lowercase		,      tensor_type=__lowercase					)


