






import argparse
import os
import shutil
from pathlib import Path

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

from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline


lowercase    				=   version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")





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





@torch.no_grad()
def 		__UpperCAmelCase     (	a_				,	a_				,	a_				,	a_ = False):
							snake_case_           =				torch.floataa if fpaa else torch.floataa
							if fpaa and torch.cuda.is_available():
														snake_case_           =				'cuda'
							elif fpaa and not torch.cuda.is_available():
														raise ValueError('`float16` model export is only supported on GPUs with CUDA')
							else:
														snake_case_           =				'cpu'
							snake_case_           =				StableDiffusionPipeline.from_pretrained(a_				,	torch_dtype=a_).to(a_)
							snake_case_           =				Path(a_)

							# TEXT ENCODER
							snake_case_           =				pipeline.text_encoder.config.max_position_embeddings
							snake_case_           =				pipeline.text_encoder.config.hidden_size
							snake_case_           =				pipeline.tokenizer(
							    'A sample prompt'				,	padding='max_length'				,	max_length=pipeline.tokenizer.model_max_length				,	truncation=a_				,	return_tensors='pt'				,	)
							onnx_export(
							    pipeline.text_encoder				,	model_args=(text_input.input_ids.to(device=a_				,	dtype=torch.intaa))				,	output_path=output_path / 'text_encoder' / 'model.onnx'				,	ordered_input_names=['input_ids']				,	output_names=['last_hidden_state', 'pooler_output']				,	dynamic_axes={
							        'input_ids': {0: 'batch', 1: 'sequence'},
							    }				,	opset=a_				,	)
							del pipeline.text_encoder

							# UNET
							snake_case_           =				pipeline.unet.config.in_channels
							snake_case_           =				pipeline.unet.config.sample_size
							snake_case_           =				output_path / 'unet' / 'model.onnx'
							onnx_export(
							    pipeline.unet				,	model_args=(
							        torch.randn(2				,	a_				,	a_				,	a_).to(device=a_				,	dtype=a_),
							        torch.randn(2).to(device=a_				,	dtype=a_),
							        torch.randn(2				,	a_				,	a_).to(device=a_				,	dtype=a_),
							        False,
							    )				,	output_path=a_				,	ordered_input_names=['sample', 'timestep', 'encoder_hidden_states', 'return_dict']				,	output_names=['out_sample']				,	dynamic_axes={
							        'sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
							        'timestep': {0: 'batch'},
							        'encoder_hidden_states': {0: 'batch', 1: 'sequence'},
							    }				,	opset=a_				,	use_external_data_format=a_				,	)
							snake_case_           =				str(unet_path.absolute().as_posix())
							snake_case_           =				os.path.dirname(a_)
							snake_case_           =				onnx.load(a_)
							# clean up existing tensor files
							shutil.rmtree(a_)
							os.mkdir(a_)
							# collate external tensor files into one
							onnx.save_model(
							    a_				,	a_				,	save_as_external_data=a_				,	all_tensors_to_one_file=a_				,	location='weights.pb'				,	convert_attribute=a_				,	)
							del pipeline.unet

							# VAE ENCODER
							snake_case_           =				pipeline.vae
							snake_case_           =				vae_encoder.config.in_channels
							snake_case_           =				vae_encoder.config.sample_size
							# need to get the raw tensor output (sample) from the encoder
							snake_case_           =				lambda a_				,	a_: vae_encoder.encode(a_				,	a_)[0].sample()
							onnx_export(
							    a_				,	model_args=(
							        torch.randn(1				,	a_				,	a_				,	a_).to(device=a_				,	dtype=a_),
							        False,
							    )				,	output_path=output_path / 'vae_encoder' / 'model.onnx'				,	ordered_input_names=['sample', 'return_dict']				,	output_names=['latent_sample']				,	dynamic_axes={
							        'sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
							    }				,	opset=a_				,	)

							# VAE DECODER
							snake_case_           =				pipeline.vae
							snake_case_           =				vae_decoder.config.latent_channels
							snake_case_           =				vae_decoder.config.out_channels
							# forward only through the decoder part
							snake_case_           =				vae_encoder.decode
							onnx_export(
							    a_				,	model_args=(
							        torch.randn(1				,	a_				,	a_				,	a_).to(device=a_				,	dtype=a_),
							        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=a_				,	)
							del pipeline.vae

							# SAFETY CHECKER
							if pipeline.safety_checker is not None:
														snake_case_           =				pipeline.safety_checker
														snake_case_           =				safety_checker.config.vision_config.num_channels
														snake_case_           =				safety_checker.config.vision_config.image_size
														snake_case_           =				safety_checker.forward_onnx
														onnx_export(
														    pipeline.safety_checker				,	model_args=(
														        torch.randn(
														            1				,	a_				,	a_				,	a_				,	).to(device=a_				,	dtype=a_),
														        torch.randn(1				,	a_				,	a_				,	a_).to(device=a_				,	dtype=a_),
														    )				,	output_path=output_path / 'safety_checker' / 'model.onnx'				,	ordered_input_names=['clip_input', 'images']				,	output_names=['out_images', 'has_nsfw_concepts']				,	dynamic_axes={
														        'clip_input': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
														        'images': {0: 'batch', 1: 'height', 2: 'width', 3: 'channels'},
														    }				,	opset=a_				,	)
														del pipeline.safety_checker
														snake_case_           =				OnnxRuntimeModel.from_pretrained(output_path / 'safety_checker')
														snake_case_           =				pipeline.feature_extractor
							else:
														snake_case_           =				None
														snake_case_           =				None

							snake_case_           =				OnnxStableDiffusionPipeline(
							    vae_encoder=OnnxRuntimeModel.from_pretrained(output_path / 'vae_encoder')				,	vae_decoder=OnnxRuntimeModel.from_pretrained(output_path / 'vae_decoder')				,	text_encoder=OnnxRuntimeModel.from_pretrained(output_path / 'text_encoder')				,	tokenizer=pipeline.tokenizer				,	unet=OnnxRuntimeModel.from_pretrained(output_path / 'unet')				,	scheduler=pipeline.scheduler				,	safety_checker=a_				,	feature_extractor=a_				,	requires_safety_checker=safety_checker is not None				,	)

							onnx_pipeline.save_pretrained(a_)
							print('ONNX pipeline saved to'				,	a_)

							del pipeline
							del onnx_pipeline
							snake_case_           =				OnnxStableDiffusionPipeline.from_pretrained(a_				,	provider='CPUExecutionProvider')
							print('ONNX pipeline is loadable')


if __name__ == "__main__":
	lowercase    				=   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")

	lowercase    				=   parser.parse_args()

	convert_models(args.model_path, args.output_path, args.opset, args.fpaa)













import unittest

import numpy as np





def 		__UpperCAmelCase     (	a_				,	a_				,	a_				,	a_ = None				,	):
							snake_case_           =				np.shape(a_)
							snake_case_           =				np.shape(a_)
							snake_case_           =				np.shape(a_)

							if shape_a[0] != shape_b[0]:
														snake_case_           =				(
														    'Expected the same number of rows for A and B. '
														    f'''Instead found A of size {shape_a} and B of size {shape_b}'''
														)
														raise ValueError(a_)

							if shape_b[1] != shape_c[1]:
														snake_case_           =				(
														    'Expected the same number of columns for B and C. '
														    f'''Instead found B of size {shape_b} and C of size {shape_c}'''
														)
														raise ValueError(a_)

							snake_case_           =				pseudo_inv
							if a_inv is None:
														try:
																					snake_case_           =				np.linalg.inv(a_)
														except np.linalg.LinAlgError:
																					raise ValueError(
																					    'Input matrix A is not invertible. Cannot compute Schur complement.')

							return mat_c - mat_b.T @ a_inv @ mat_b




class 					UpperCamelCase_ (     unittest.TestCase							):



	'''simple docstring'''




	def 				_UpperCamelCase     (			self   )			->       None:
								snake_case_           =				np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]   )
								snake_case_           =				np.array([[0, 3], [3, 0], [2, 3]]   )
								snake_case_           =				np.array([[2, 1], [6, 3]]   )

								snake_case_           =				schur_complement(a			,     a			,     a   )

								snake_case_           =				np.block([[a, b], [b.T, c]]   )

								snake_case_           =				np.linalg.det(a   )
								snake_case_           =				np.linalg.det(a   )
								snake_case_           =				np.linalg.det(a   )

								self.assertAlmostEqual(a			,     det_a * det_s   )




	def 				_UpperCamelCase     (			self   )			->       None:
								snake_case_           =				np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]   )
								snake_case_           =				np.array([[0, 3], [3, 0], [2, 3]]   )
								snake_case_           =				np.array([[2, 1], [6, 3]]   )

								with self.assertRaises(a   ):
															schur_complement(a			,     a			,     a   )






	def 				_UpperCamelCase     (			self   )			->       None:
								snake_case_           =				np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]   )
								snake_case_           =				np.array([[0, 3], [3, 0], [2, 3]]   )
								snake_case_           =				np.array([[2, 1, 3], [6, 3, 5]]   )

								with self.assertRaises(a   ):
															schur_complement(a			,     a			,     a   )


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	unittest.main()





