import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


lowerCamelCase_    :		Tuple							=	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						):
					__a		 =       torch.floataa if fpaa else torch.floataa
					if fpaa and torch.cuda.is_available():
										__a		 =       'cuda'
					elif fpaa and not torch.cuda.is_available():
										raise ValueError('`float16` model export is only supported on GPUs with CUDA'						)
					else:
										__a		 =       'cpu'
					__a		 =       Path(__lowerCamelCase						)

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

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





import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def 							lowerCAmelCase(     __lowerCamelCase						):
					return (data["data"], data["target"])
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
					__a		 =       XGBRegressor(verbosity=0				,    random_state=42						)
					xgb.fit(__lowerCamelCase				,    __lowerCamelCase						)
					# Predict target for test data
					__a		 =       xgb.predict(__lowerCamelCase						)
					__a		 =       predictions.reshape(len(__lowerCamelCase						)				,    1						)
					return predictions
def 							lowerCAmelCase(     ):
					__a		 =       fetch_california_housing()
					__a ,				__a		 =       data_handling(__lowerCamelCase						)
					__a ,				__a ,				__a ,				__a		 =       train_test_split(
					    __lowerCamelCase				,    __lowerCamelCase				,    test_size=0.25				,    random_state=1						)
					__a		 =       xgboost(__lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						)
					# Error printing
					print(f'''Mean Absolute Error : {mean_absolute_error(__lowerCamelCase				,    __lowerCamelCase						)}'''						)
					print(f'''Mean Square Error  : {mean_squared_error(__lowerCamelCase				,    __lowerCamelCase						)}'''						)


if __name__ == "__main__":
	import doctest

	doctest.testmod(verbose=True)
	main()




