



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


__snake_case     :List[str]			       =version.parse(version.parse(torch.__version__).base_version) < version.parse('1.11')






def  lowerCamelCase_	(      lowerCAmelCase__	:	Optional[int]			,	lowerCAmelCase__	:	tuple			,	lowerCAmelCase__	:	Path			,	lowerCAmelCase__	:	Dict			,	lowerCAmelCase__	:	List[str]			,	lowerCAmelCase__	:	Any			,	lowerCAmelCase__	:	int			,	lowerCAmelCase__	:	str=False			,	)       ->							Dict:

							'''simple docstring'''






							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__	:	str			,	lowerCAmelCase__	:	str			,	lowerCAmelCase__	:	int			,	lowerCAmelCase__	:	bool = False				)       ->							int:

							'''simple docstring'''






							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      						=    StableDiffusionPipeline.from_pretrained(lowerCAmelCase__			,	torch_dtype=lowerCAmelCase__				).to(lowerCAmelCase__				)
							A      						=    Path(lowerCAmelCase__				)

							# TEXT ENCODER
							A      						=    pipeline.text_encoder.config.max_position_embeddings
							A      						=    pipeline.text_encoder.config.hidden_size
							A      						=    pipeline.tokenizer(
							    'A sample prompt'			,	padding='max_length'			,	max_length=pipeline.tokenizer.model_max_length			,	truncation=lowerCAmelCase__			,	return_tensors='pt'			,	)
							onnx_export(
							    pipeline.text_encoder			,	model_args=(text_input.input_ids.to(device=lowerCAmelCase__			,	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=lowerCAmelCase__			,	)
							del pipeline.text_encoder

							# UNET
							A      						=    pipeline.unet.config.in_channels
							A      						=    pipeline.unet.config.sample_size
							A      						=    output_path / 'unet' / 'model.onnx'
							onnx_export(
							    pipeline.unet			,	model_args=(
							        torch.randn(2			,	lowerCAmelCase__			,	lowerCAmelCase__			,	lowerCAmelCase__				).to(device=lowerCAmelCase__			,	dtype=lowerCAmelCase__				),
							        torch.randn(2				).to(device=lowerCAmelCase__			,	dtype=lowerCAmelCase__				),
							        torch.randn(2			,	lowerCAmelCase__			,	lowerCAmelCase__				).to(device=lowerCAmelCase__			,	dtype=lowerCAmelCase__				),
							        False,
							    )			,	output_path=lowerCAmelCase__			,	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=lowerCAmelCase__			,	use_external_data_format=lowerCAmelCase__			,	)
							A      						=    str(unet_path.absolute().as_posix()				)
							A      						=    os.path.dirname(lowerCAmelCase__				)
							A      						=    onnx.load(lowerCAmelCase__				)
							# clean up existing tensor files
							shutil.rmtree(lowerCAmelCase__				)
							os.mkdir(lowerCAmelCase__				)
							# collate external tensor files into one
							onnx.save_model(
							    lowerCAmelCase__			,	lowerCAmelCase__			,	save_as_external_data=lowerCAmelCase__			,	all_tensors_to_one_file=lowerCAmelCase__			,	location='weights.pb'			,	convert_attribute=lowerCAmelCase__			,	)
							del pipeline.unet

							# VAE ENCODER
							A      						=    pipeline.vae
							A      						=    vae_encoder.config.in_channels
							A      						=    vae_encoder.config.sample_size
							# need to get the raw tensor output (sample) from the encoder
							A      						=    lambda lowerCAmelCase__			,	lowerCAmelCase__				: vae_encoder.encode(lowerCAmelCase__			,	lowerCAmelCase__				)[0].sample()
							onnx_export(
							    lowerCAmelCase__			,	model_args=(
							        torch.randn(1			,	lowerCAmelCase__			,	lowerCAmelCase__			,	lowerCAmelCase__				).to(device=lowerCAmelCase__			,	dtype=lowerCAmelCase__				),
							        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=lowerCAmelCase__			,	)

							# VAE DECODER
							A      						=    pipeline.vae
							A      						=    vae_decoder.config.latent_channels
							A      						=    vae_decoder.config.out_channels
							# forward only through the decoder part
							A      						=    vae_encoder.decode
							onnx_export(
							    lowerCAmelCase__			,	model_args=(
							        torch.randn(1			,	lowerCAmelCase__			,	lowerCAmelCase__			,	lowerCAmelCase__				).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 pipeline.vae

							# SAFETY CHECKER
							if pipeline.safety_checker is not None:
														A      						=    pipeline.safety_checker
														A      						=    safety_checker.config.vision_config.num_channels
														A      						=    safety_checker.config.vision_config.image_size
														A      						=    safety_checker.forward_onnx
														onnx_export(
														    pipeline.safety_checker			,	model_args=(
														        torch.randn(
														            1			,	lowerCAmelCase__			,	lowerCAmelCase__			,	lowerCAmelCase__			,	).to(device=lowerCAmelCase__			,	dtype=lowerCAmelCase__				),
														        torch.randn(1			,	lowerCAmelCase__			,	lowerCAmelCase__			,	lowerCAmelCase__				).to(device=lowerCAmelCase__			,	dtype=lowerCAmelCase__				),
														    )			,	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=lowerCAmelCase__			,	)
														del pipeline.safety_checker
														A      						=    OnnxRuntimeModel.from_pretrained(output_path / 'safety_checker'				)
														A      						=    pipeline.feature_extractor
							else:
														A      						=    None
														A      						=    None

							A      						=    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=lowerCAmelCase__			,	feature_extractor=lowerCAmelCase__			,	requires_safety_checker=safety_checker is not None			,	)

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

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


if __name__ == "__main__":
				__snake_case     :Union[str, Any]			       =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')

				__snake_case     :str			       =parser.parse_args()

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




'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class       __magic_name__		(       _UpperCamelCase     ,		_UpperCamelCase     ,		_UpperCamelCase     ,		unittest.TestCase  ):
							lowerCAmelCase			:       Any  							=   StableDiffusionInpaintPipeline
							lowerCAmelCase			:       int  							=   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
							lowerCAmelCase			:       Optional[Any]  							=   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							lowerCAmelCase			:       List[str]  							=   frozenset(
							    []  )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
							lowerCAmelCase			:       Optional[Any]  							=   frozenset([]  )







							def 				__lowercase     (				self   :			Dict						):
									torch.manual_seed(0						)
									_a	: Optional[int]								=				UNetaDConditionModel(
									    block_out_channels=(32, 64)      ,layers_per_block=2      ,sample_size=32      ,in_channels=9      ,out_channels=4      ,down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')      ,up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')      ,cross_attention_dim=32      ,attention_head_dim=(2, 4)      ,use_linear_projection=_UpperCAmelCase      ,)
									_a	: Optional[int]								=				PNDMScheduler(skip_prk_steps=_UpperCAmelCase						)
									torch.manual_seed(0						)
									_a	: Dict								=				AutoencoderKL(
									    block_out_channels=[32, 64]      ,in_channels=3      ,out_channels=3      ,down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']      ,up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']      ,latent_channels=4      ,sample_size=128      ,)
									torch.manual_seed(0						)
									_a	: Union[str, Any]								=				CLIPTextConfig(
									    bos_token_id=0      ,eos_token_id=2      ,hidden_size=32      ,intermediate_size=37      ,layer_norm_eps=1E-05      ,num_attention_heads=4      ,num_hidden_layers=5      ,pad_token_id=1      ,vocab_size=1000      ,hidden_act='gelu'      ,projection_dim=512      ,)
									_a	: str								=				CLIPTextModel(_UpperCAmelCase						)
									_a	: Any								=				CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

									_a	: Optional[Any]								=				{
									    'unet': unet,
									    'scheduler': scheduler,
									    'vae': vae,
									    'text_encoder': text_encoder,
									    'tokenizer': tokenizer,
									    'safety_checker': None,
									    'feature_extractor': None,
									}
									return components







							def 				__lowercase     (				self   :			List[Any]      ,_UpperCAmelCase   :			List[Any]      ,_UpperCAmelCase   :			int=0						):
									# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
									_a	: Any								=				floats_tensor((1, 3, 32, 32)      ,rng=random.Random(_UpperCAmelCase						)						).to(_UpperCAmelCase						)
									_a	: Any								=				image.cpu().permute(0      ,2      ,3      ,1						)[0]
									_a	: Optional[int]								=				Image.fromarray(np.uinta(_UpperCAmelCase						)						).convert('RGB'						).resize((64, 64)						)
									_a	: Tuple								=				Image.fromarray(np.uinta(image + 4						)						).convert('RGB'						).resize((64, 64)						)
									if str(_UpperCAmelCase						).startswith('mps'						):
											_a	: str								=				torch.manual_seed(_UpperCAmelCase						)
									else:
											_a	: Dict								=				torch.Generator(device=_UpperCAmelCase						).manual_seed(_UpperCAmelCase						)
									_a	: Any								=				{
									    'prompt': 'A painting of a squirrel eating a burger',
									    'image': init_image,
									    'mask_image': mask_image,
									    'generator': generator,
									    'num_inference_steps': 2,
									    'guidance_scale': 6.0,
									    'output_type': 'numpy',
									}
									return inputs







							def 				__lowercase     (				self   :			List[str]						):
									_a	: Optional[int]								=				'cpu'  # ensure determinism for the device-dependent torch.Generator
									_a	: Dict								=				self.get_dummy_components()
									_a	: int								=				StableDiffusionInpaintPipeline(**_UpperCAmelCase						)
									_a	: Any								=				sd_pipe.to(_UpperCAmelCase						)
									sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase						)

									_a	: Dict								=				self.get_dummy_inputs(_UpperCAmelCase						)
									_a	: Tuple								=				sd_pipe(**_UpperCAmelCase						).images
									_a	: Dict								=				image[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)
									_a	: List[Any]								=				np.array([0.47_27, 0.57_35, 0.39_41, 0.54_46, 0.59_26, 0.43_94, 0.50_62, 0.46_54, 0.44_76]						)

									assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2







							def 				__lowercase     (				self   :			List[Any]						):
									super().test_inference_batch_single_identical(expected_max_diff=3E-3						)

@slow
@require_torch_gpu
class       __magic_name__		(       unittest.TestCase  ):







							def 				__lowercase     (				self   :			List[str]						):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







							def 				__lowercase     (				self   :			int						):
									_a	: Tuple								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/sd2-inpaint/init_image.png'						)
									_a	: Union[str, Any]								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'						)
									_a	: int								=				load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
									    '/yellow_cat_sitting_on_a_park_bench.npy'						)

									_a	: List[Any]								=				'stabilityai/stable-diffusion-2-inpainting'
									_a	: Tuple								=				StableDiffusionInpaintPipeline.from_pretrained(_UpperCAmelCase      ,safety_checker=_UpperCAmelCase						)
									pipe.to(_UpperCAmelCase						)
									pipe.set_progress_bar_config(disable=_UpperCAmelCase						)
									pipe.enable_attention_slicing()

									_a	: int								=				'Face of a yellow cat, high resolution, sitting on a park bench'

									_a	: Dict								=				torch.manual_seed(0						)
									_a	: Any								=				pipe(
									    prompt=_UpperCAmelCase      ,image=_UpperCAmelCase      ,mask_image=_UpperCAmelCase      ,generator=_UpperCAmelCase      ,output_type='np'      ,)
									_a	: List[str]								=				output.images[0]

									assert image.shape == (512, 512, 3)
									assert np.abs(expected_image - image						).max() < 9E-3







							def 				__lowercase     (				self   :			Tuple						):
									_a	: str								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/sd2-inpaint/init_image.png'						)
									_a	: Optional[Any]								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'						)
									_a	: int								=				load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
									    '/yellow_cat_sitting_on_a_park_bench_fp16.npy'						)

									_a	: int								=				'stabilityai/stable-diffusion-2-inpainting'
									_a	: List[str]								=				StableDiffusionInpaintPipeline.from_pretrained(
									    _UpperCAmelCase      ,torch_dtype=torch.floataa      ,safety_checker=_UpperCAmelCase      ,)
									pipe.to(_UpperCAmelCase						)
									pipe.set_progress_bar_config(disable=_UpperCAmelCase						)
									pipe.enable_attention_slicing()

									_a	: Dict								=				'Face of a yellow cat, high resolution, sitting on a park bench'

									_a	: int								=				torch.manual_seed(0						)
									_a	: Dict								=				pipe(
									    prompt=_UpperCAmelCase      ,image=_UpperCAmelCase      ,mask_image=_UpperCAmelCase      ,generator=_UpperCAmelCase      ,output_type='np'      ,)
									_a	: str								=				output.images[0]

									assert image.shape == (512, 512, 3)
									assert np.abs(expected_image - image						).max() < 5E-1







							def 				__lowercase     (				self   :			Tuple						):
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									_a	: Dict								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/sd2-inpaint/init_image.png'						)
									_a	: Optional[int]								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'						)

									_a	: List[str]								=				'stabilityai/stable-diffusion-2-inpainting'
									_a	: Any								=				PNDMScheduler.from_pretrained(_UpperCAmelCase      ,subfolder='scheduler'						)
									_a	: Optional[Any]								=				StableDiffusionInpaintPipeline.from_pretrained(
									    _UpperCAmelCase      ,safety_checker=_UpperCAmelCase      ,scheduler=_UpperCAmelCase      ,torch_dtype=torch.floataa      ,)
									pipe.to(_UpperCAmelCase						)
									pipe.set_progress_bar_config(disable=_UpperCAmelCase						)
									pipe.enable_attention_slicing(1						)
									pipe.enable_sequential_cpu_offload()

									_a	: Any								=				'Face of a yellow cat, high resolution, sitting on a park bench'

									_a	: Optional[Any]								=				torch.manual_seed(0						)
									_a	: int								=				pipe(
									    prompt=_UpperCAmelCase      ,image=_UpperCAmelCase      ,mask_image=_UpperCAmelCase      ,generator=_UpperCAmelCase      ,num_inference_steps=2      ,output_type='np'      ,)

									_a	: int								=				torch.cuda.max_memory_allocated()
									# make sure that less than 2.65 GB is allocated
									assert mem_bytes < 2.65 * 10**9




