





'''simple docstring'''



import argparse
from pathlib import Path

import torch
from packaging import version
from torch.onnx import export

from diffusers import AutoencoderKL


__magic_name__   :						Tuple    =			version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=False					,	):







  '''simple docstring'''



  output_path.parent.mkdir(parents=SCREAMING_SNAKE_CASE__					,	exist_ok=SCREAMING_SNAKE_CASE__       )
  # 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(
        SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	f=output_path.as_posix()					,	input_names=SCREAMING_SNAKE_CASE__					,	output_names=SCREAMING_SNAKE_CASE__					,	dynamic_axes=SCREAMING_SNAKE_CASE__					,	do_constant_folding=SCREAMING_SNAKE_CASE__					,	use_external_data_format=SCREAMING_SNAKE_CASE__					,	enable_onnx_checker=SCREAMING_SNAKE_CASE__					,	opset_version=SCREAMING_SNAKE_CASE__					,	)
  else:
    export(
        SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	f=output_path.as_posix()					,	input_names=SCREAMING_SNAKE_CASE__					,	output_names=SCREAMING_SNAKE_CASE__					,	dynamic_axes=SCREAMING_SNAKE_CASE__					,	do_constant_folding=SCREAMING_SNAKE_CASE__					,	opset_version=SCREAMING_SNAKE_CASE__					,	)




@torch.no_grad()
def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__ = False       ):







  '''simple docstring'''



  _snake_case     		=				torch.floataa if fpaa else torch.floataa
  if fpaa and torch.cuda.is_available():
    _snake_case     		=				"cuda"
  elif fpaa and not torch.cuda.is_available():
    raise ValueError("`float16` model export is only supported on GPUs with CUDA"       )
  else:
    _snake_case     		=				"cpu"
  _snake_case     		=				Path(SCREAMING_SNAKE_CASE__       )

  # VAE DECODER
  _snake_case     		=				AutoencoderKL.from_pretrained(model_path + "/vae"       )
  _snake_case     		=				vae_decoder.config.latent_channels
  # forward only through the decoder part
  _snake_case     		=				vae_decoder.decode
  onnx_export(
      SCREAMING_SNAKE_CASE__					,	model_args=(
          torch.randn(1					,	SCREAMING_SNAKE_CASE__					,	25					,	25       ).to(device=SCREAMING_SNAKE_CASE__					,	dtype=SCREAMING_SNAKE_CASE__       ),
          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=SCREAMING_SNAKE_CASE__					,	)
  del vae_decoder


if __name__ == "__main__":
  __magic_name__   :						Tuple    =			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""")

  __magic_name__   :						Optional[Any]    =			parser.parse_args()
  print(args.output_path)
  convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
  print("""SD: Done: ONNX""")









'''simple docstring'''



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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


__magic_name__   :						Tuple    =			logging.get_logger(__name__)


if is_vision_available():
 import PIL


class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						str      =   ['''pixel_values''']

     def __init__(					self						,   lowerCamelCase = True						,   lowerCamelCase = None						,   lowerCamelCase = PILImageResampling.BICUBIC						,   lowerCamelCase = True						,   lowerCamelCase = None						,   lowerCamelCase = True						,   lowerCamelCase = 1 / 255						,   lowerCamelCase = True						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = True						,   **lowerCamelCase						,   ):
       super().__init__(**lowerCamelCase	)
       _snake_case     		=				size if size is not None else {"shortest_edge": 224}
       _snake_case     		=				get_size_dict(lowerCamelCase						,   default_to_square=lowerCamelCase	)
       _snake_case     		=				crop_size if crop_size is not None else {"height": 224, "width": 224}
       _snake_case     		=				get_size_dict(lowerCamelCase						,   default_to_square=lowerCamelCase						,   param_name="crop_size"	)

       _snake_case     		=				do_resize
       _snake_case     		=				size
       _snake_case     		=				resample
       _snake_case     		=				do_center_crop
       _snake_case     		=				crop_size
       _snake_case     		=				do_rescale
       _snake_case     		=				rescale_factor
       _snake_case     		=				do_normalize
       _snake_case     		=				image_mean if image_mean is not None else OPENAI_CLIP_MEAN
       _snake_case     		=				image_std if image_std is not None else OPENAI_CLIP_STD
       _snake_case     		=				do_convert_rgb

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase = PILImageResampling.BICUBIC						,   lowerCamelCase = None						,   **lowerCamelCase						,   ):
       _snake_case     		=				get_size_dict(lowerCamelCase						,   default_to_square=lowerCamelCase	)
       if "shortest_edge" not in size:
         raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''	)
       _snake_case     		=				get_resize_output_image_size(lowerCamelCase						,   size=size["shortest_edge"]						,   default_to_square=lowerCamelCase	)
       return resize(lowerCamelCase						,   size=lowerCamelCase						,   resample=lowerCamelCase						,   data_format=lowerCamelCase						,   **lowerCamelCase	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase = None						,   **lowerCamelCase						,   ):
       _snake_case     		=				get_size_dict(lowerCamelCase	)
       if "height" not in size or "width" not in size:
         raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''	)
       return center_crop(lowerCamelCase						,   size=(size["height"], size["width"])						,   data_format=lowerCamelCase						,   **lowerCamelCase	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase = None						,   **lowerCamelCase						,   ):
       return rescale(lowerCamelCase						,   scale=lowerCamelCase						,   data_format=lowerCamelCase						,   **lowerCamelCase	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase = None						,   **lowerCamelCase						,   ):
       return normalize(lowerCamelCase						,   mean=lowerCamelCase						,   std=lowerCamelCase						,   data_format=lowerCamelCase						,   **lowerCamelCase	)






     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = ChannelDimension.FIRST						,   **lowerCamelCase						,   ):
       _snake_case     		=				do_resize if do_resize is not None else self.do_resize
       _snake_case     		=				size if size is not None else self.size
       _snake_case     		=				get_size_dict(lowerCamelCase						,   param_name="size"						,   default_to_square=lowerCamelCase	)
       _snake_case     		=				resample if resample is not None else self.resample
       _snake_case     		=				do_center_crop if do_center_crop is not None else self.do_center_crop
       _snake_case     		=				crop_size if crop_size is not None else self.crop_size
       _snake_case     		=				get_size_dict(lowerCamelCase						,   param_name="crop_size"						,   default_to_square=lowerCamelCase	)
       _snake_case     		=				do_rescale if do_rescale is not None else self.do_rescale
       _snake_case     		=				rescale_factor if rescale_factor is not None else self.rescale_factor
       _snake_case     		=				do_normalize if do_normalize is not None else self.do_normalize
       _snake_case     		=				image_mean if image_mean is not None else self.image_mean
       _snake_case     		=				image_std if image_std is not None else self.image_std
       _snake_case     		=				do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

       _snake_case     		=				make_list_of_images(lowerCamelCase	)

       if not valid_images(lowerCamelCase	):
         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:
         raise ValueError("Size must be specified if do_resize is True."	)

       if do_center_crop and crop_size is None:
         raise ValueError("Crop size 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."	)

       # PIL RGBA images are converted to RGB
       if do_convert_rgb:
         _snake_case     		=				[convert_to_rgb(lowerCamelCase	) for image in images]

       # All transformations expect numpy arrays.
       _snake_case     		=				[to_numpy_array(lowerCamelCase	) for image in images]

       if do_resize:
         _snake_case     		=				[self.resize(image=lowerCamelCase						,   size=lowerCamelCase						,   resample=lowerCamelCase	) for image in images]

       if do_center_crop:
         _snake_case     		=				[self.center_crop(image=lowerCamelCase						,   size=lowerCamelCase	) for image in images]

       if do_rescale:
         _snake_case     		=				[self.rescale(image=lowerCamelCase						,   scale=lowerCamelCase	) for image in images]

       if do_normalize:
         _snake_case     		=				[self.normalize(image=lowerCamelCase						,   mean=lowerCamelCase						,   std=lowerCamelCase	) for image in images]

       _snake_case     		=				[to_channel_dimension_format(lowerCamelCase						,   lowerCamelCase	) for image in images]

       _snake_case     		=				{"pixel_values": images}
       return BatchFeature(data=lowerCamelCase						,   tensor_type=lowerCamelCase	)


