

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


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

def        lowerCamelCase__       (    _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        lowerCamelCase__       (    _a							,	_a							,	_a							,	_a = False):
	SCREAMING_SNAKE_CASE      :					str    =				torch.floataa if fpaa else torch.floataa
	if fpaa and torch.cuda.is_available():
		SCREAMING_SNAKE_CASE      :					Dict    =				"cuda"
	elif fpaa and not torch.cuda.is_available():
		raise ValueError("`float16` model export is only supported on GPUs with CUDA")
	else:
		SCREAMING_SNAKE_CASE      :					Tuple    =				"cpu"
	SCREAMING_SNAKE_CASE      :					Any    =				StableDiffusionPipeline.from_pretrained(_a							,	torch_dtype=_a).to(_a)
	SCREAMING_SNAKE_CASE      :					List[Any]    =				Path(_a)

	# TEXT ENCODER
	SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				pipeline.text_encoder.config.max_position_embeddings
	SCREAMING_SNAKE_CASE      :					Tuple    =				pipeline.text_encoder.config.hidden_size
	SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				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
	SCREAMING_SNAKE_CASE      :					Dict    =				pipeline.unet.config.in_channels
	SCREAMING_SNAKE_CASE      :					str    =				pipeline.unet.config.sample_size
	SCREAMING_SNAKE_CASE      :					List[str]    =				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							,	)
	SCREAMING_SNAKE_CASE      :					List[Any]    =				str(unet_path.absolute().as_posix())
	SCREAMING_SNAKE_CASE      :					Tuple    =				os.path.dirname(_a)
	SCREAMING_SNAKE_CASE      :					List[Any]    =				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
	SCREAMING_SNAKE_CASE      :					Any    =				pipeline.vae
	SCREAMING_SNAKE_CASE      :					Tuple    =				vae_encoder.config.in_channels
	SCREAMING_SNAKE_CASE      :					Optional[Any]    =				vae_encoder.config.sample_size
	# need to get the raw tensor output (sample) from the encoder
	SCREAMING_SNAKE_CASE      :					str    =				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
	SCREAMING_SNAKE_CASE      :					str    =				pipeline.vae
	SCREAMING_SNAKE_CASE      :					Tuple    =				vae_decoder.config.latent_channels
	SCREAMING_SNAKE_CASE      :					str    =				vae_decoder.config.out_channels
	# forward only through the decoder part
	SCREAMING_SNAKE_CASE      :					List[Any]    =				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:
		SCREAMING_SNAKE_CASE      :					int    =				pipeline.safety_checker
		SCREAMING_SNAKE_CASE      :					List[str]    =				safety_checker.config.vision_config.num_channels
		SCREAMING_SNAKE_CASE      :					List[Any]    =				safety_checker.config.vision_config.image_size
		SCREAMING_SNAKE_CASE      :					List[str]    =				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
		SCREAMING_SNAKE_CASE      :					Optional[int]    =				OnnxRuntimeModel.from_pretrained(output_path / "safety_checker")
		SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				pipeline.feature_extractor
	else:
		SCREAMING_SNAKE_CASE      :					Tuple    =				None
		SCREAMING_SNAKE_CASE      :					Optional[int]    =				None

	SCREAMING_SNAKE_CASE      :					Any    =				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
	SCREAMING_SNAKE_CASE      :					Any    =				OnnxStableDiffusionPipeline.from_pretrained(_a							,	provider="CPUExecutionProvider")
	print("ONNX pipeline is loadable")


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

			a_     =			parser.parse_args()

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




"""simple docstring"""






import gc
import threading
import time

import psutil
import torch



class 			A__   :




	'''simple docstring'''





	def __init__(     self:		str)				->       Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       psutil.Process()
				__lowerCAmelCase      :  str												=       False

	def 							_SCREAMING_SNAKE_CASE   (     self:		int)				->       Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       -1

				while True:
							__lowerCAmelCase      :  str												=       max(self.process.memory_info().rss       ,			self.cpu_memory_peak)

							# can't sleep or will not catch the peak right (this comment is here on purpose)
							if not self.peak_monitoring:
										break

	def 							_SCREAMING_SNAKE_CASE   (     self:		Tuple)				->       int:

				"""simple docstring"""

				__lowerCAmelCase      :  List[str]												=       True
				__lowerCAmelCase      :  str												=       threading.Thread(target=self.peak_monitor)
				__lowerCAmelCase      :  Tuple												=       True
				self.thread.start()







	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[Any])				->       List[str]:

				"""simple docstring"""

				__lowerCAmelCase      :  Tuple												=       False
				self.thread.join()
				return self.cpu_memory_peak






__snake_case							:   Tuple      =    PeakCPUMemory()


def 			_lowercase				(  )						->   str:
			# Time
			__lowerCAmelCase      :  str												=       {"time": time.time()}

			gc.collect()
			torch.cuda.empty_cache()

			# CPU mem
			__lowerCAmelCase      :  Optional[Any]												=       psutil.Process().memory_info().rss
			cpu_peak_tracker.start()

			# GPU mem
			for i in range(torch.cuda.device_count()	):
						__lowerCAmelCase      :  Union[str, Any]												=       torch.cuda.memory_allocated(__snake_case	)
			torch.cuda.reset_peak_memory_stats()

			return measures


def 			_lowercase				(  __snake_case	)						->   Optional[Any]:
			# Time
			__lowerCAmelCase      :  str												=       {"time": time.time() - start_measures["time"]}

			gc.collect()
			torch.cuda.empty_cache()

			# CPU mem
			__lowerCAmelCase      :  str												=       (psutil.Process().memory_info().rss - start_measures["cpu"]) / 2**20
			__lowerCAmelCase      :  List[str]												=       (cpu_peak_tracker.stop() - start_measures["cpu"]) / 2**20

			# GPU mem
			for i in range(torch.cuda.device_count()	):
						__lowerCAmelCase      :  Union[str, Any]												=       (torch.cuda.memory_allocated(__snake_case	) - start_measures[str(__snake_case	)]) / 2**20
						__lowerCAmelCase      :  Any												=       (torch.cuda.max_memory_allocated(__snake_case	) - start_measures[str(__snake_case	)]) / 2**20

			return measures





def 			_lowercase				(  __snake_case					,__snake_case	)						->   Dict:
			print(F"""{description}:"""	)
			print(F"""- Time: {measures['time']:.2f}s"""	)
			for i in range(torch.cuda.device_count()	):
						print(F"""- GPU {i} allocated: {measures[str(__snake_case	)]:.2f}MiB"""	)
						__lowerCAmelCase      :  Optional[Any]												=       measures[F"""{i}-peak"""]
						print(F"""- GPU {i} peak: {peak:.2f}MiB"""	)
			print(F"""- CPU RAM allocated: {measures['cpu']:.2f}MiB"""	)
			print(F"""- CPU RAM peak: {measures['cpu-peak']:.2f}MiB"""	)