


'''simple docstring'''

import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


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

def 							__magic_name__(						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 							__magic_name__(						lowerCamelCase,				lowerCamelCase,				lowerCamelCase,				lowerCamelCase = False):
						__lowerCAmelCase							       =				torch.floataa if fpaa else torch.floataa
						if fpaa and torch.cuda.is_available():
												__lowerCAmelCase							       =				'''cuda'''
						elif fpaa and not torch.cuda.is_available():
												raise ValueError('''`float16` model export is only supported on GPUs with CUDA''')
						else:
												__lowerCAmelCase							       =				'''cpu'''
						__lowerCAmelCase							       =				Path(lowerCamelCase)

						# VAE DECODER
						__lowerCAmelCase							       =				AutoencoderKL.from_pretrained(model_path + '''/vae''')
						__lowerCAmelCase							       =				vae_decoder.config.latent_channels
						# forward only through the decoder part
						__lowerCAmelCase							       =				vae_decoder.decode
						onnx_export(
						    lowerCamelCase,				model_args=(
						        torch.randn(1,				lowerCamelCase,				2_5,				2_5).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				: Dict							    =				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=1_4,
	    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				: int							    =				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 .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    LineByLineWithSOPTextDataset,
    TextDataset,
    TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments

