




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  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__=False			,		)			->							Any:





					'''simple docstring'''


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






@torch.no_grad()
def  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__ = False   )			->							Optional[int]:





					'''simple docstring'''


					lowercase :       Optional[int]       =			torch.floataa if fpaa else torch.floataa
					if fpaa and torch.cuda.is_available():
										lowercase :       Tuple       =			'''cuda'''
					elif fpaa and not torch.cuda.is_available():
										raise ValueError('''`float16` model export is only supported on GPUs with CUDA'''   )
					else:
										lowercase :       Optional[Any]       =			'''cpu'''
					lowercase :       Tuple       =			Path(__magic_name__   )

					# VAE DECODER
					lowercase :       Tuple       =			AutoencoderKL.from_pretrained(model_path + '''/vae'''   )
					lowercase :       str       =			vae_decoder.config.latent_channels
					# forward only through the decoder part
					lowercase :       int       =			vae_decoder.decode
					onnx_export(
					    __magic_name__			,		model_args=(
					        torch.randn(1			,		__magic_name__			,		25			,		25   ).to(device=__magic_name__			,		dtype=__magic_name__   ),
					        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=__magic_name__			,		)
					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')





import os
import sys
import unittest


lowerCAmelCase_						=			os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
lowerCAmelCase_						=			os.path.join(git_repo_path, 'src', 'diffusers')
class 						_A (  unittest.TestCase				):






  def  __a      (  self		:     Any				)		->   str:






       """simple docstring"""







       lowercase :       List[str]       =			find_backend('''    if not is_torch_available():'''				)
       self.assertEqual(_A				,						'''torch'''				)

       # backend_with_underscore = find_backend("    if not is_tensorflow_text_available():")
       # self.assertEqual(backend_with_underscore, "tensorflow_text")

       lowercase :       str       =			find_backend('''    if not (is_torch_available() and is_transformers_available()):'''				)
       self.assertEqual(_A				,						'''torch_and_transformers'''				)

       # double_backend_with_underscore = find_backend(
       #    "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
       # )
       # self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")

       lowercase :       str       =			find_backend(
           '''    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):'''				)
       self.assertEqual(_A				,						'''torch_and_transformers_and_onnx'''				)






  def  __a      (  self		:     Optional[int]				)		->   Any:






       """simple docstring"""







       lowercase :       Dict       =			read_init()
       # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
       self.assertIn('''torch'''				,						_A				)
       self.assertIn('''torch_and_transformers'''				,						_A				)
       self.assertIn('''flax_and_transformers'''				,						_A				)
       self.assertIn('''torch_and_transformers_and_onnx'''				,						_A				)

       # Likewise, we can't assert on the exact content of a key
       self.assertIn('''UNet2DModel'''				,						objects['''torch''']				)
       self.assertIn('''FlaxUNet2DConditionModel'''				,						objects['''flax''']				)
       self.assertIn('''StableDiffusionPipeline'''				,						objects['''torch_and_transformers''']				)
       self.assertIn('''FlaxStableDiffusionPipeline'''				,						objects['''flax_and_transformers''']				)
       self.assertIn('''LMSDiscreteScheduler'''				,						objects['''torch_and_scipy''']				)
       self.assertIn('''OnnxStableDiffusionPipeline'''				,						objects['''torch_and_transformers_and_onnx''']				)






  def  __a      (  self		:     Union[str, Any]				)		->   str:






       """simple docstring"""







       lowercase :       List[str]       =			create_dummy_object('''CONSTANT'''				,						'''\'torch\''''				)
       self.assertEqual(_A				,						'''\nCONSTANT = None\n'''				)

       lowercase :       List[Any]       =			create_dummy_object('''function'''				,						'''\'torch\''''				)
       self.assertEqual(
           _A				,						'''\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n'''				)

       lowercase :       Tuple       =			'''
class FakeClass(metaclass=DummyObject):
    _backends = \'torch\'

    def __init__(self, *args, **kwargs):
        requires_backends(self, \'torch\')

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, \'torch\')

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, \'torch\')
'''
       lowercase :       List[Any]       =			create_dummy_object('''FakeClass'''				,						'''\'torch\''''				)
       self.assertEqual(_A				,						_A				)






  def  __a      (  self		:     Union[str, Any]				)		->   str:






       """simple docstring"""







       lowercase :       Optional[int]       =			'''# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, ["torch"])


class FakeClass(metaclass=DummyObject):
    _backends = ["torch"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
'''
       lowercase :       int       =			create_dummy_files({'''torch''': ['''CONSTANT''', '''function''', '''FakeClass''']}				)
       self.assertEqual(dummy_files['''torch''']				,						_A				)