




import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


UpperCAmelCase_		:				Any									=     version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase=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	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase = False   ):
    __magic_name__	:	Dict								=torch.floataa if fpaa else torch.floataa
    if fpaa and torch.cuda.is_available():
        __magic_name__	:	Dict								="""cuda"""
    elif fpaa and not torch.cuda.is_available():
        raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""   )
    else:
        __magic_name__	:	Optional[Any]								="""cpu"""
    __magic_name__	:	Optional[int]								=Path(lowerCAmelCase__   )

    # VAE DECODER
    __magic_name__	:	Dict								=AutoencoderKL.from_pretrained(model_path + """/vae"""   )
    __magic_name__	:	Dict								=vae_decoder.config.latent_channels
    # forward only through the decoder part
    __magic_name__	:	Tuple								=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__":
       UpperCAmelCase_		:				Optional[Any]									=     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")

       UpperCAmelCase_		:				List[str]									=     parser.parse_args()
       print(args.output_path)
       convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
       print("SD: Done: ONNX")










import pytest

import datasets


# Import fixture modules as plugins
UpperCAmelCase_		:				Union[str, Any]									=     ["tests.fixtures.files", "tests.fixtures.hub", "tests.fixtures.fsspec"]




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase   ):
				# Mark tests as "unit" by default if not marked as "integration" (or already marked as "unit")
				for item in items:
								if any(marker in item.keywords for marker in ["""integration""", """unit"""]   ):
												continue
								item.add_marker(pytest.mark.unit   )




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				config.addinivalue_line("""markers"""	,     """torchaudio_latest: mark test to run with torchaudio>=0.12"""   )




@pytest.fixture(autouse=lowerCamelCase   )
def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase   ):
				# test_hf_cache_home = tmp_path_factory.mktemp("cache")  # TODO: why a cache dir per test function does not work?
				__magic_name__	:	Tuple								=tmp_path_factory.getbasetemp() / """cache"""
				__magic_name__	:	Optional[int]								=test_hf_cache_home / """datasets"""
				__magic_name__	:	List[str]								=test_hf_cache_home / """metrics"""
				__magic_name__	:	List[str]								=test_hf_cache_home / """modules"""
				monkeypatch.setattr("""datasets.config.HF_DATASETS_CACHE"""	,     str(lowerCamelCase   )   )
				monkeypatch.setattr("""datasets.config.HF_METRICS_CACHE"""	,     str(lowerCamelCase   )   )
				monkeypatch.setattr("""datasets.config.HF_MODULES_CACHE"""	,     str(lowerCamelCase   )   )
				__magic_name__	:	Optional[Any]								=test_hf_datasets_cache / """downloads"""
				monkeypatch.setattr("""datasets.config.DOWNLOADED_DATASETS_PATH"""	,     str(lowerCamelCase   )   )
				__magic_name__	:	Tuple								=test_hf_datasets_cache / """downloads""" / """extracted"""
				monkeypatch.setattr("""datasets.config.EXTRACTED_DATASETS_PATH"""	,     str(lowerCamelCase   )   )




@pytest.fixture(autouse=lowerCamelCase	,     scope="""session"""   )
def 		lowerCAmelCase_   (	):
				datasets.disable_progress_bar()




@pytest.fixture(autouse=lowerCamelCase   )
def 		lowerCAmelCase_   (	lowerCamelCase   ):
				# don't take tests into account when counting downloads
				monkeypatch.setattr("""datasets.config.HF_UPDATE_DOWNLOAD_COUNTS"""	,     lowerCamelCase   )




@pytest.fixture
def 		lowerCAmelCase_   (	lowerCamelCase   ):
				# Required to suppress RemovedIn20Warning when feature(s) are not compatible with SQLAlchemy 2.0
				# To be removed once SQLAlchemy 2.0 supported
				monkeypatch.setattr("""sqlalchemy.util.deprecations.SILENCE_UBER_WARNING"""	,     lowerCamelCase   )




