





'''simple docstring'''



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


_SCREAMING_SNAKE_CASE   =      version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')




def 				_lowerCAmelCase  (			lowerCamelCase_     :					Any						,    lowerCamelCase_     :					tuple						,    lowerCamelCase_     :					Path						,    lowerCamelCase_     :					Dict						,    lowerCamelCase_     :					Dict						,    lowerCamelCase_     :					Optional[Any]						,    lowerCamelCase_     :					Optional[int]						,    lowerCamelCase_     :					Union[str, Any]=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_     :					str						,    lowerCamelCase_     :					str						,    lowerCamelCase_     :					int						,    lowerCamelCase_     :					bool = False			):
			__lowercase			  =							torch.floataa if fpaa else torch.floataa
			if fpaa and torch.cuda.is_available():
						__lowercase			  =							'''cuda'''
			elif fpaa and not torch.cuda.is_available():
						raise ValueError('''`float16` model export is only supported on GPUs with CUDA'''			)
			else:
						__lowercase			  =							'''cpu'''
			__lowercase			  =							StableDiffusionPipeline.from_pretrained(lowerCamelCase_						,    torch_dtype=lowerCamelCase_			).to(lowerCamelCase_			)
			__lowercase			  =							Path(lowerCamelCase_			)

			# TEXT ENCODER
			__lowercase			  =							pipeline.text_encoder.config.max_position_embeddings
			__lowercase			  =							pipeline.text_encoder.config.hidden_size
			__lowercase			  =							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
			__lowercase			  =							pipeline.unet.config.in_channels
			__lowercase			  =							pipeline.unet.config.sample_size
			__lowercase			  =							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_						,    )
			__lowercase			  =							str(unet_path.absolute().as_posix()			)
			__lowercase			  =							os.path.dirname(lowerCamelCase_			)
			__lowercase			  =							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
			__lowercase			  =							pipeline.vae
			__lowercase			  =							vae_encoder.config.in_channels
			__lowercase			  =							vae_encoder.config.sample_size
			# need to get the raw tensor output (sample) from the encoder
			__lowercase			  =							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
			__lowercase			  =							pipeline.vae
			__lowercase			  =							vae_decoder.config.latent_channels
			__lowercase			  =							vae_decoder.config.out_channels
			# forward only through the decoder part
			__lowercase			  =							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:
						__lowercase			  =							pipeline.safety_checker
						__lowercase			  =							safety_checker.config.vision_config.num_channels
						__lowercase			  =							safety_checker.config.vision_config.image_size
						__lowercase			  =							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
						__lowercase			  =							OnnxRuntimeModel.from_pretrained(output_path / '''safety_checker'''			)
						__lowercase			  =							pipeline.feature_extractor
			else:
						__lowercase			  =							None
						__lowercase			  =							None

			__lowercase			  =							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
			__lowercase			  =							OnnxStableDiffusionPipeline.from_pretrained(lowerCamelCase_						,    provider='''CPUExecutionProvider'''			)
			print('''ONNX pipeline is loadable'''			)


if __name__ == "__main__":
					_SCREAMING_SNAKE_CASE   =      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''')

					_SCREAMING_SNAKE_CASE   =      parser.parse_args()

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












'''simple docstring'''



import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize






class 							__lowercase    :







	'''simple docstring'''

	def __init__(self       ,_lowerCamelCase       ,_lowerCamelCase=sys.maxsize	)							->							Optional[Any]:







				'''simple docstring'''





				__lowercase			  =							'''bilinear'''
				__lowercase			  =							max_size
				__lowercase			  =							short_edge_length


	def __call__(self       ,_lowerCamelCase	)							->							str:







				'''simple docstring'''





				__lowercase			  =							[]
				for img in imgs:
							__lowercase  ,						__lowercase			  =							img.shape[:2]
							# later: provide list and randomly choose index for resize
							__lowercase			  =							np.random.randint(self.short_edge_length[0]       ,self.short_edge_length[1] + 1	)
							if size == 0:
										return img
							__lowercase			  =							size * 1.0 / min(_lowerCamelCase       ,_lowerCamelCase	)
							if h < w:
										__lowercase  ,						__lowercase			  =							size, scale * w
							else:
										__lowercase  ,						__lowercase			  =							scale * h, size
							if max(_lowerCamelCase       ,_lowerCamelCase	) > self.max_size:
										__lowercase			  =							self.max_size * 1.0 / max(_lowerCamelCase       ,_lowerCamelCase	)
										__lowercase			  =							newh * scale
										__lowercase			  =							neww * scale
							__lowercase			  =							int(neww + 0.5	)
							__lowercase			  =							int(newh + 0.5	)

							if img.dtype == np.uinta:
										__lowercase			  =							Image.fromarray(_lowerCamelCase	)
										__lowercase			  =							pil_image.resize((neww, newh)       ,PILImageResampling.BILINEAR	)
										__lowercase			  =							np.asarray(_lowerCamelCase	)
							else:
										__lowercase			  =							img.permute(2       ,0       ,1	).unsqueeze(0	)  # 3, 0, 1)  # hw(c) -> nchw
										__lowercase			  =							nn.functional.interpolate(
										    _lowerCamelCase       ,(newh, neww)       ,mode=self.interp_method       ,align_corners=_lowerCamelCase	).squeeze(0	)
							img_augs.append(_lowerCamelCase	)

				return img_augs






class 							__lowercase    :







	'''simple docstring'''

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







				'''simple docstring'''





				__lowercase			  =							ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]       ,cfg.INPUT.MAX_SIZE_TEST	)
				__lowercase			  =							cfg.INPUT.FORMAT
				__lowercase			  =							cfg.SIZE_DIVISIBILITY
				__lowercase			  =							cfg.PAD_VALUE
				__lowercase			  =							cfg.INPUT.MAX_SIZE_TEST
				__lowercase			  =							cfg.MODEL.DEVICE
				__lowercase			  =							torch.tensor(cfg.MODEL.PIXEL_STD	).to(self.device	).view(len(cfg.MODEL.PIXEL_STD	)       ,1       ,1	)
				__lowercase			  =							torch.tensor(cfg.MODEL.PIXEL_MEAN	).to(self.device	).view(len(cfg.MODEL.PIXEL_STD	)       ,1       ,1	)
				__lowercase			  =							lambda _lowerCamelCase	: (x - self.pixel_mean) / self.pixel_std


	def   _UpperCAmelCase       (self       ,_lowerCamelCase	)							->							Dict:







				'''simple docstring'''





				__lowercase			  =							tuple(max(_lowerCamelCase	) for s in zip(*[img.shape for img in images]	)	)
				__lowercase			  =							[im.shape[-2:] for im in images]
				__lowercase			  =							[
				    nn.functional.pad(
				        _lowerCamelCase       ,[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]       ,value=self.pad_value       ,)
				    for size, im in zip(_lowerCamelCase       ,_lowerCamelCase	)
				]

				return torch.stack(_lowerCamelCase	), torch.tensor(_lowerCamelCase	)


	def __call__(self       ,_lowerCamelCase       ,_lowerCamelCase=False	)							->							Tuple:







				'''simple docstring'''





				with torch.no_grad():
							if not isinstance(_lowerCamelCase       ,_lowerCamelCase	):
										__lowercase			  =							[images]
							if single_image:
										assert len(_lowerCamelCase	) == 1
							for i in range(len(_lowerCamelCase	)	):
										if isinstance(images[i]       ,torch.Tensor	):
													images.insert(_lowerCamelCase       ,images.pop(_lowerCamelCase	).to(self.device	).float()	)
										elif not isinstance(images[i]       ,torch.Tensor	):
													images.insert(
													    _lowerCamelCase       ,torch.as_tensor(img_tensorize(images.pop(_lowerCamelCase	)       ,input_format=self.input_format	)	)
													    .to(self.device	)
													    .float()       ,)
            # resize smallest edge
							__lowercase			  =							torch.tensor([im.shape[:2] for im in images]	)
							__lowercase			  =							self.aug(_lowerCamelCase	)
							# transpose images and convert to torch tensors
							# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
							# now normalize before pad to avoid useless arithmetic
							__lowercase			  =							[self.normalizer(_lowerCamelCase	) for x in images]
							# now pad them to do the following operations
							__lowercase  ,						__lowercase			  =							self.pad(_lowerCamelCase	)
							# Normalize

							if self.size_divisibility > 0:
										raise NotImplementedError()
							# pad
							__lowercase			  =							torch.true_divide(_lowerCamelCase       ,_lowerCamelCase	)
							if single_image:
										return images[0], sizes[0], scales_yx[0]
							else:
										return images, sizes, scales_yx





def 				_lowerCAmelCase  (			lowerCamelCase_     :					List[Any]						,    lowerCamelCase_     :					Tuple			):
			boxes[:, 0::2] *= scale_yx[:, 1]
			boxes[:, 1::2] *= scale_yx[:, 0]
			return boxes




def 				_lowerCAmelCase  (			lowerCamelCase_     :					str						,    lowerCamelCase_     :					Tuple[int, int]			):
			assert torch.isfinite(lowerCamelCase_			).all(), "Box tensor contains infinite or NaN!"
			__lowercase  ,						__lowercase			  =							box_size
			tensor[:, 0].clamp_(min=0						,    max=lowerCamelCase_			)
			tensor[:, 1].clamp_(min=0						,    max=lowerCamelCase_			)
			tensor[:, 2].clamp_(min=0						,    max=lowerCamelCase_			)
			tensor[:, 3].clamp_(min=0						,    max=lowerCamelCase_			)





