





"""simple docstring"""





import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


lowerCAmelCase__     			=			version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')


def       a__					(					_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       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE = False    ):




		"""simple docstring"""


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

		# VAE DECODER
		UpperCamelCase   =	AutoencoderKL.from_pretrained(model_path + "/vae"    )
		UpperCamelCase   =	vae_decoder.config.latent_channels
		# forward only through the decoder part
		UpperCamelCase   =	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__":
						lowerCAmelCase__     			=			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''')

						lowerCAmelCase__     			=			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"""





import importlib
import os
import sys


# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('''.''')


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	test_file.split(os.path.sep    )
		if components[0:2] != ["tests", "models"]:
				raise ValueError(
				    "`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got "
				    F"{test_file} instead."    )
		UpperCamelCase   =	components[-1]
		if not test_fn.endswith("py"    ):
				raise ValueError(F"`test_file` should be a python file. Got {test_fn} instead."    )
		if not test_fn.startswith("test_modeling_"    ):
				raise ValueError(
				    F"`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead."    )

		UpperCamelCase   =	components[:-1] + [test_fn.replace(".py"  ,	""    )]
		UpperCamelCase   =	".".join(_SCREAMING_SNAKE_CASE    )

		return test_module_path


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	get_module_path(_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	importlib.import_module(_SCREAMING_SNAKE_CASE    )

		return test_module


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	[]
		UpperCamelCase   =	get_test_module(_SCREAMING_SNAKE_CASE    )
		for attr in dir(_SCREAMING_SNAKE_CASE    ):
				if attr.endswith("ModelTester"    ):
						tester_classes.append(getattr(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )    )

    # sort with class names
		return sorted(_SCREAMING_SNAKE_CASE  ,	key=lambda _SCREAMING_SNAKE_CASE    : x.__name__    )


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	[]
		UpperCamelCase   =	get_test_module(_SCREAMING_SNAKE_CASE    )
		for attr in dir(_SCREAMING_SNAKE_CASE    ):
				UpperCamelCase   =	getattr(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )
				# (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
				# `all_model_classes` is not empty (which also excludes other special classes).
				UpperCamelCase   =	getattr(_SCREAMING_SNAKE_CASE  ,	"all_model_classes"  ,	[]    )
				if len(_SCREAMING_SNAKE_CASE    ) > 0:
						test_classes.append(_SCREAMING_SNAKE_CASE    )

    # sort with class names
		return sorted(_SCREAMING_SNAKE_CASE  ,	key=lambda _SCREAMING_SNAKE_CASE    : x.__name__    )


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	get_test_classes(_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	set()
		for test_class in test_classes:
				model_classes.update(test_class.all_model_classes    )

		# sort with class names
		return sorted(_SCREAMING_SNAKE_CASE  ,	key=lambda _SCREAMING_SNAKE_CASE    : x.__name__    )


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	test_class()
		if hasattr(_SCREAMING_SNAKE_CASE  ,	"setUp"    ):
				test.setUp()

		UpperCamelCase   =	None
		if hasattr(_SCREAMING_SNAKE_CASE  ,	"model_tester"    ):
				# `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
				if test.model_tester is not None:
						UpperCamelCase   =	test.model_tester.__class__

		return model_tester


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	get_test_classes(_SCREAMING_SNAKE_CASE    )

		UpperCamelCase   =	[]
		for test_class in test_classes:
				if model_class in test_class.all_model_classes:
						target_test_classes.append(_SCREAMING_SNAKE_CASE    )

    # sort with class names
		return sorted(_SCREAMING_SNAKE_CASE  ,	key=lambda _SCREAMING_SNAKE_CASE    : x.__name__    )


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	get_test_classes_for_model(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )

		UpperCamelCase   =	[]
		for test_class in test_classes:
				UpperCamelCase   =	get_model_tester_from_test_class(_SCREAMING_SNAKE_CASE    )
				if tester_class is not None:
						tester_classes.append(_SCREAMING_SNAKE_CASE    )

    # sort with class names
		return sorted(_SCREAMING_SNAKE_CASE  ,	key=lambda _SCREAMING_SNAKE_CASE    : x.__name__    )


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	get_test_classes(_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	{test_class: get_model_tester_from_test_class(_SCREAMING_SNAKE_CASE    ) for test_class in test_classes}
		return test_tester_mapping


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	get_model_classes(_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	{
		    model_class: get_test_classes_for_model(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ) for model_class in model_classes
		}
		return model_test_mapping


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	get_model_classes(_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	{
		    model_class: get_tester_classes_for_model(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ) for model_class in model_classes
		}
		return model_to_tester_mapping






def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		if isinstance(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):
				return o
		elif isinstance(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):
				return o.__name__
		elif isinstance(_SCREAMING_SNAKE_CASE  ,	(list, tuple)    ):
				return [to_json(_SCREAMING_SNAKE_CASE    ) for x in o]
		elif isinstance(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):
				return {to_json(_SCREAMING_SNAKE_CASE    ): to_json(_SCREAMING_SNAKE_CASE    ) for k, v in o.items()}
		else:
				return o






