



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


SCREAMING_SNAKE_CASE_:List[str]             =						logging.get_logger(__name__)






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Optional[int]					=      ["pixel_values"]






    def __init__(       self,		lowerCamelCase__ = True,		lowerCamelCase__ = None,		lowerCamelCase__ = 0.9,		lowerCamelCase__ = PILImageResampling.BICUBIC,		lowerCamelCase__ = True,		lowerCamelCase__ = None,		lowerCamelCase__ = 1 / 255,		lowerCamelCase__ = True,		lowerCamelCase__ = True,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		**lowerCamelCase__,		):
        super().__init__(**lowerCamelCase__		)
        A :       Any          =   size if size is not None else {"""shortest_edge""": 224}
        A :       Union[str, Any]          =   get_size_dict(lowerCamelCase__,		default_to_square=lowerCamelCase__		)
        A :       int          =   crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
        A :       Union[str, Any]          =   get_size_dict(lowerCamelCase__,		param_name="""crop_size"""		)

        A :       int          =   do_resize
        A :       str          =   size
        A :       Optional[Any]          =   crop_pct
        A :       Any          =   resample
        A :       str          =   do_center_crop
        A :       List[Any]          =   crop_size
        A :       Dict          =   do_rescale
        A :       Optional[int]          =   rescale_factor
        A :       Optional[int]          =   do_normalize
        A :       Tuple          =   image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        A :       Union[str, Any]          =   image_std if image_std is not None else IMAGENET_DEFAULT_STD






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__ = None,		lowerCamelCase__ = PILImageResampling.BICUBIC,		lowerCamelCase__ = None,		**lowerCamelCase__,		):
        A :       int          =   get_size_dict(lowerCamelCase__,		default_to_square=lowerCamelCase__		)
        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:
                A :       Optional[int]          =   int(size["""shortest_edge"""] / crop_pct		)
            elif "height" in size and "width" in size:
                if size["height"] == size["width"]:
                    A :       Any          =   int(size["""height"""] / crop_pct		)
                else:
                    A :       Any          =   (int(size["""height"""] / crop_pct		), int(size["""width"""] / crop_pct		))
            else:
                raise ValueError("""Invalid size for resize: {}""".format(lowerCamelCase__		)		)

            A :       Dict          =   get_resize_output_image_size(lowerCamelCase__,		size=lowerCamelCase__,		default_to_square=lowerCamelCase__		)
        else:
            if "shortest_edge" in size:
                A :       Optional[Any]          =   get_resize_output_image_size(lowerCamelCase__,		size=size["""shortest_edge"""],		default_to_square=lowerCamelCase__		)
            elif "height" in size and "width" in size:
                A :       Any          =   (size["""height"""], size["""width"""])
            else:
                raise ValueError("""Invalid size for resize: {}""".format(lowerCamelCase__		)		)

        return resize(lowerCamelCase__,		size=lowerCamelCase__,		resample=lowerCamelCase__,		data_format=lowerCamelCase__,		**lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__ = None,		**lowerCamelCase__,		):
        A :       Dict          =   get_size_dict(lowerCamelCase__		)
        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(lowerCamelCase__,		size=(size["""height"""], size["""width"""]),		data_format=lowerCamelCase__,		**lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__ = None,		**lowerCamelCase__,		):
        return rescale(lowerCamelCase__,		scale=lowerCamelCase__,		data_format=lowerCamelCase__,		**lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__ = None,		**lowerCamelCase__,		):
        return normalize(lowerCamelCase__,		mean=lowerCamelCase__,		std=lowerCamelCase__,		data_format=lowerCamelCase__,		**lowerCamelCase__		)






    def 			_lowerCAmelCase					(       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__,		):
        A :       Tuple          =   do_resize if do_resize is not None else self.do_resize
        A :       str          =   crop_pct if crop_pct is not None else self.crop_pct
        A :       int          =   resample if resample is not None else self.resample
        A :       Optional[int]          =   do_center_crop if do_center_crop is not None else self.do_center_crop
        A :       Optional[int]          =   do_rescale if do_rescale is not None else self.do_rescale
        A :       Tuple          =   rescale_factor if rescale_factor is not None else self.rescale_factor
        A :       str          =   do_normalize if do_normalize is not None else self.do_normalize
        A :       str          =   image_mean if image_mean is not None else self.image_mean
        A :       Optional[int]          =   image_std if image_std is not None else self.image_std

        A :       Any          =   size if size is not None else self.size
        A :       Any          =   get_size_dict(lowerCamelCase__,		default_to_square=lowerCamelCase__		)
        A :       List[str]          =   crop_size if crop_size is not None else self.crop_size
        A :       Union[str, Any]          =   get_size_dict(lowerCamelCase__,		param_name="""crop_size"""		)

        A :       Any          =   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 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.
        A :       List[Any]          =   [to_numpy_array(lowerCamelCase__		) for image in images]

        if do_resize:
            A :       List[Any]          =   [self.resize(image=lowerCamelCase__,		size=lowerCamelCase__,		crop_pct=lowerCamelCase__,		resample=lowerCamelCase__		) for image in images]

        if do_center_crop:
            A :       str          =   [self.center_crop(image=lowerCamelCase__,		size=lowerCamelCase__		) for image in images]

        if do_rescale:
            A :       Union[str, Any]          =   [self.rescale(image=lowerCamelCase__,		scale=lowerCamelCase__		) for image in images]

        if do_normalize:
            A :       Union[str, Any]          =   [self.normalize(image=lowerCamelCase__,		mean=lowerCamelCase__,		std=lowerCamelCase__		) for image in images]

        A :       Optional[int]          =   [to_channel_dimension_format(lowerCamelCase__,		lowerCamelCase__		) for image in images]

        A :       List[str]          =   {"""pixel_values""": images}
        return BatchFeature(data=lowerCamelCase__,		tensor_type=lowerCamelCase__		)







import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


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




def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase=False      ,     )       ->  int:



    """simple docstring"""






    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 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase = False		)       ->  List[Any]:



    """simple docstring"""






    A :       Tuple          =   torch.floataa if fpaa else torch.floataa
    if fpaa and torch.cuda.is_available():
        A :       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:
        A :       Any          =   """cpu"""
    A :       Any          =   Path(_lowerCAmelCase		)

    # VAE DECODER
    A :       Union[str, Any]          =   AutoencoderKL.from_pretrained(model_path + """/vae"""		)
    A :       Any          =   vae_decoder.config.latent_channels
    # forward only through the decoder part
    A :       Optional[int]          =   vae_decoder.decode
    onnx_export(
        _lowerCAmelCase      ,     model_args=(
            torch.randn(1      ,     _lowerCAmelCase      ,     25      ,     25		).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 vae_decoder


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_: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""")

     SCREAMING_SNAKE_CASE_:Tuple             =						parser.parse_args()
     print(args.output_path)
     convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
     print("""SD: Done: ONNX""")


