

'''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


__snake_case			:     Dict										=      version.parse(version.parse(torch.__version__).base_version) < version.parse('1.11')
def 						__lowerCamelCase	(       __snake_case					:		Optional[Any],		__snake_case					:		List[str],		__snake_case					:		Any,		__snake_case					:		Dict,		__snake_case					:		Union[str, Any],		__snake_case					:		Any,		__snake_case					:		int,		__snake_case					:		Optional[int]=False,		)	->							List[str]:






						"""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	(       __snake_case					:		List[Any],		__snake_case					:		Optional[Any],		__snake_case					:		Dict,		__snake_case					:		str = False	)	->							Optional[int]:






						"""simple docstring"""





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

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

						A__					:       int     =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__					:       Dict     =OnnxStableDiffusionPipeline.from_pretrained(_lowerCAmelCase,		provider="""CPUExecutionProvider"""	)
						print("""ONNX pipeline is loadable"""	)


if __name__ == "__main__":
				__snake_case			:     int										=      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 torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest



class lowerCamelCase			(  lowercase_						):



			'''simple docstring'''






			__snake_case        =	(CMStochasticIterativeScheduler,)
			__snake_case        =	10


			def lowercase__						(				self	:						List[str]		,      **lowerCAmelCase_	:						Dict			)      ->		Dict:





									'''simple docstring'''
									A__					:       int     ={
									    """num_train_timesteps""": 2_01,
									    """sigma_min""": 0.002,
									    """sigma_max""": 80.0,
									}

									config.update(**lowerCAmelCase_			)
									return config


			def lowercase__						(				self	:						Optional[Any]			)      ->		Optional[int]:





									'''simple docstring'''
									A__					:       Dict     =10

									A__					:       str     =self.get_scheduler_config()
									A__					:       Any     =self.scheduler_classes[0](**lowerCAmelCase_			)

									scheduler.set_timesteps(lowerCAmelCase_			)

									A__					:       List[Any]     =scheduler.timesteps[0]
									A__					:       Union[str, Any]     =scheduler.timesteps[1]

									A__					:       Optional[int]     =self.dummy_sample
									A__					:       Union[str, Any]     =0.1 * sample

									A__					:       Dict     =scheduler.step(lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_			).prev_sample
									A__					:       int     =scheduler.step(lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_			).prev_sample

									self.assertEqual(output_a.shape		,      sample.shape			)
									self.assertEqual(output_a.shape		,      output_a.shape			)


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





									'''simple docstring'''
									for timesteps in [10, 50, 1_00, 10_00]:
															self.check_over_configs(num_train_timesteps=lowerCAmelCase_			)


			def lowercase__						(				self	:						Tuple			)      ->		Optional[int]:





									'''simple docstring'''
									for clip_denoised in [True, False]:
															self.check_over_configs(clip_denoised=lowerCAmelCase_			)


			def lowercase__						(				self	:						List[Any]			)      ->		Dict:





									'''simple docstring'''
									A__					:       Union[str, Any]     =self.scheduler_classes[0]
									A__					:       Dict     =self.get_scheduler_config()
									A__					:       Any     =scheduler_class(**lowerCAmelCase_			)

									A__					:       int     =1
									scheduler.set_timesteps(lowerCAmelCase_			)
									A__					:       Any     =scheduler.timesteps

									A__					:       Optional[int]     =torch.manual_seed(0			)

									A__					:       List[Any]     =self.dummy_model()
									A__					:       Optional[int]     =self.dummy_sample_deter * scheduler.init_noise_sigma

									for i, t in enumerate(lowerCAmelCase_			):
															# 1. scale model input
															A__					:       Optional[Any]     =scheduler.scale_model_input(lowerCAmelCase_		,      lowerCAmelCase_			)

															# 2. predict noise residual
															A__					:       Union[str, Any]     =model(lowerCAmelCase_		,      lowerCAmelCase_			)

															# 3. predict previous sample x_t-1
															A__					:       Tuple     =scheduler.step(lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_		,      generator=lowerCAmelCase_			).prev_sample

															A__					:       Dict     =pred_prev_sample

									A__					:       Union[str, Any]     =torch.sum(torch.abs(lowerCAmelCase_			)			)
									A__					:       Optional[Any]     =torch.mean(torch.abs(lowerCAmelCase_			)			)

									assert abs(result_sum.item() - 192.7614			) < 1e-2
									assert abs(result_mean.item() - 0.2510			) < 1e-3


			def lowercase__						(				self	:						Dict			)      ->		Union[str, Any]:





									'''simple docstring'''
									A__					:       Dict     =self.scheduler_classes[0]
									A__					:       Dict     =self.get_scheduler_config()
									A__					:       Tuple     =scheduler_class(**lowerCAmelCase_			)

									A__					:       Tuple     =[1_06, 0]
									scheduler.set_timesteps(timesteps=lowerCAmelCase_			)
									A__					:       List[Any]     =scheduler.timesteps

									A__					:       Optional[Any]     =torch.manual_seed(0			)

									A__					:       int     =self.dummy_model()
									A__					:       int     =self.dummy_sample_deter * scheduler.init_noise_sigma

									for t in timesteps:
															# 1. scale model input
															A__					:       Any     =scheduler.scale_model_input(lowerCAmelCase_		,      lowerCAmelCase_			)

															# 2. predict noise residual
															A__					:       List[str]     =model(lowerCAmelCase_		,      lowerCAmelCase_			)

															# 3. predict previous sample x_t-1
															A__					:       Dict     =scheduler.step(lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_		,      generator=lowerCAmelCase_			).prev_sample

															A__					:       Union[str, Any]     =pred_prev_sample

									A__					:       Union[str, Any]     =torch.sum(torch.abs(lowerCAmelCase_			)			)
									A__					:       List[Any]     =torch.mean(torch.abs(lowerCAmelCase_			)			)

									assert abs(result_sum.item() - 347.6357			) < 1e-2
									assert abs(result_mean.item() - 0.4527			) < 1e-3


			def lowercase__						(				self	:						Optional[Any]			)      ->		Dict:





									'''simple docstring'''
									A__					:       Optional[Any]     =self.scheduler_classes[0]
									A__					:       Union[str, Any]     =self.get_scheduler_config()
									A__					:       List[Any]     =scheduler_class(**lowerCAmelCase_			)

									A__					:       Tuple     =[39, 30, 12, 15, 0]

									with self.assertRaises(lowerCAmelCase_		,      msg="""`timesteps` must be in descending order."""			):
															scheduler.set_timesteps(timesteps=lowerCAmelCase_			)


			def lowercase__						(				self	:						Union[str, Any]			)      ->		List[str]:





									'''simple docstring'''
									A__					:       Union[str, Any]     =self.scheduler_classes[0]
									A__					:       List[str]     =self.get_scheduler_config()
									A__					:       Tuple     =scheduler_class(**lowerCAmelCase_			)

									A__					:       Dict     =[39, 30, 12, 1, 0]
									A__					:       int     =len(lowerCAmelCase_			)

									with self.assertRaises(lowerCAmelCase_		,      msg="""Can only pass one of `num_inference_steps` or `timesteps`."""			):
															scheduler.set_timesteps(num_inference_steps=lowerCAmelCase_		,      timesteps=lowerCAmelCase_			)



			def lowercase__						(				self	:						Any			)      ->		Optional[int]:





									'''simple docstring'''
									A__					:       Optional[Any]     =self.scheduler_classes[0]
									A__					:       Any     =self.get_scheduler_config()
									A__					:       Optional[int]     =scheduler_class(**lowerCAmelCase_			)

									A__					:       List[str]     =[scheduler.config.num_train_timesteps]

									with self.assertRaises(
									    lowerCAmelCase_		,      msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""		,      ):
															scheduler.set_timesteps(timesteps=lowerCAmelCase_			)

