



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        a						(      _UpperCAmelCase							:					Tuple ,   _UpperCAmelCase							:					tuple ,   _UpperCAmelCase							:					Path ,   _UpperCAmelCase							:					Union[str, Any] ,   _UpperCAmelCase							:					Optional[int] ,   _UpperCAmelCase							:					Union[str, Any] ,   _UpperCAmelCase							:					Any ,   _UpperCAmelCase							:					Optional[int]=False ,   ):







		'''simple docstring'''





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

@torch.no_grad()
def        a						(      _UpperCAmelCase							:					str ,   _UpperCAmelCase							:					str ,   _UpperCAmelCase							:					int ,   _UpperCAmelCase							:					bool = False						):







		'''simple docstring'''





		__UpperCAmelCase    :     Optional[Any]													=	torch.floataa if fpaa else torch.floataa
		if fpaa and torch.cuda.is_available():
				__UpperCAmelCase    :     Tuple													=	'''cuda'''
		elif fpaa and not torch.cuda.is_available():
				raise ValueError('''`float16` model export is only supported on GPUs with CUDA'''						)
		else:
				__UpperCAmelCase    :     List[str]													=	'''cpu'''
		__UpperCAmelCase    :     Union[str, Any]													=	StableDiffusionPipeline.from_pretrained(_UpperCAmelCase ,   torch_dtype=_UpperCAmelCase						).to(_UpperCAmelCase						)
		__UpperCAmelCase    :     List[str]													=	Path(_UpperCAmelCase						)

		# TEXT ENCODER
		__UpperCAmelCase    :     Union[str, Any]													=	pipeline.text_encoder.config.max_position_embeddings
		__UpperCAmelCase    :     Union[str, Any]													=	pipeline.text_encoder.config.hidden_size
		__UpperCAmelCase    :     Tuple													=	pipeline.tokenizer(
		    '''A sample prompt''' ,   padding='''max_length''' ,   max_length=pipeline.tokenizer.model_max_length ,   truncation=_UpperCAmelCase ,   return_tensors='''pt''' ,   )
		onnx_export(
		    pipeline.text_encoder ,   model_args=(text_input.input_ids.to(device=_UpperCAmelCase ,   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=_UpperCAmelCase ,   )
		del pipeline.text_encoder

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

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

		# VAE DECODER
		__UpperCAmelCase    :     List[Any]													=	pipeline.vae
		__UpperCAmelCase    :     str													=	vae_decoder.config.latent_channels
		__UpperCAmelCase    :     Dict													=	vae_decoder.config.out_channels
		# forward only through the decoder part
		__UpperCAmelCase    :     Union[str, Any]													=	vae_encoder.decode
		onnx_export(
		    _UpperCAmelCase ,   model_args=(
		        torch.randn(1 ,   _UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase						).to(device=_UpperCAmelCase ,   dtype=_UpperCAmelCase						),
		        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=_UpperCAmelCase ,   )
		del pipeline.vae

		# SAFETY CHECKER
		if pipeline.safety_checker is not None:
				__UpperCAmelCase    :     Any													=	pipeline.safety_checker
				__UpperCAmelCase    :     str													=	safety_checker.config.vision_config.num_channels
				__UpperCAmelCase    :     List[str]													=	safety_checker.config.vision_config.image_size
				__UpperCAmelCase    :     Dict													=	safety_checker.forward_onnx
				onnx_export(
				    pipeline.safety_checker ,   model_args=(
				        torch.randn(
				            1 ,   _UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase ,   ).to(device=_UpperCAmelCase ,   dtype=_UpperCAmelCase						),
				        torch.randn(1 ,   _UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase						).to(device=_UpperCAmelCase ,   dtype=_UpperCAmelCase						),
				    ) ,   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=_UpperCAmelCase ,   )
				del pipeline.safety_checker
				__UpperCAmelCase    :     List[str]													=	OnnxRuntimeModel.from_pretrained(output_path / '''safety_checker'''						)
				__UpperCAmelCase    :     Optional[Any]													=	pipeline.feature_extractor
		else:
				__UpperCAmelCase    :     Optional[int]													=	None
				__UpperCAmelCase    :     Optional[int]													=	None

		__UpperCAmelCase    :     List[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=_UpperCAmelCase ,   feature_extractor=_UpperCAmelCase ,   requires_safety_checker=safety_checker is not None ,   )

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

		del pipeline
		del onnx_pipeline
		__UpperCAmelCase    :     Tuple													=	OnnxStableDiffusionPipeline.from_pretrained(_UpperCAmelCase ,   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=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")

				__A   			=parser.parse_args()

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










from __future__ import annotations

class 		UpperCAmelCase__			:




			'''simple docstring'''






			def __init__(   self							: Optional[Any]       ,       a_							: Optional[int]=None ):
					'''simple docstring'''





					__UpperCAmelCase    :     Optional[Any]													=	data
					__UpperCAmelCase    :     Any													=	None




			def __repr__(   self							: Tuple ):
					'''simple docstring'''





					__UpperCAmelCase    :     Dict													=	[]
					__UpperCAmelCase    :     Any													=	self
					while temp:
							string_rep.append(F'{temp.data}' )
							__UpperCAmelCase    :     Dict													=	temp.next
					return "->".join(a_ )

def        a						(      _UpperCAmelCase							:					list						):







		'''simple docstring'''





		if not elements_list:
				raise Exception('''The Elements List is empty'''						)

		__UpperCAmelCase    :     int													=	Node(elements_list[0]						)
		for i in range(1 ,   len(_UpperCAmelCase						)						):
				__UpperCAmelCase    :     Any													=	Node(elements_list[i]						)
				__UpperCAmelCase    :     Optional[int]													=	current.next
		return head

def        a						(      _UpperCAmelCase							:					Node						):







		'''simple docstring'''





		if head_node is not None and isinstance(_UpperCAmelCase ,   _UpperCAmelCase						):
				print_reverse(head_node.next						)
				print(head_node.data						)

def        a						(      ):







		'''simple docstring'''





		from doctest import testmod

		testmod()

		__UpperCAmelCase    :     Tuple													=	make_linked_list([14, 52, 14, 12, 43]						)
		print('''Linked List:'''						)
		print(_UpperCAmelCase						)
		print('''Elements in Reverse:'''						)
		print_reverse(_UpperCAmelCase						)


if __name__ == "__main__":
				main()





