
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


lowercase__		:Optional[int]   =    version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__=False ,			):
					'''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        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__ = False		):
					'''simple docstring'''
					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__":
							lowercase__		:Optional[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")

							lowercase__		:List[Any]   =    parser.parse_args()

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






import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


lowercase__		:Optional[int]   =    logging.get_logger(__name__)  # pylint: disable=invalid-name







class 				lowercase		(					SCREAMING_SNAKE_CASE__		):




							def __init__(				self   ,A__   ,A__   ,A__   ,A__   ,A__   ,A__   ,A__   ,):
												super().__init__()
												self.register_modules(
												    vae=A__   ,text_encoder=A__   ,tokenizer=A__   ,unet=A__   ,scheduler=A__   ,safety_checker=A__   ,feature_extractor=A__   ,)




							def  A__      (				self   ,A__ = "auto"):
												if slice_size == "auto":
																	# half the attention head size is usually a good trade-off between
																	# speed and memory
																	lowercase							=						self.unet.config.attention_head_dim // 2
												self.unet.set_attention_slice(A__)




							def  A__      (				self):
												self.enable_attention_slicing(A__)





							@torch.no_grad()
							def __call__(				self   ,A__   ,A__ = 5_1_2   ,A__ = 5_1_2   ,A__ = 5_0   ,A__ = 7.5   ,A__ = None   ,A__ = 1   ,A__ = 0.0   ,A__ = None   ,A__ = None   ,A__ = "pil"   ,A__ = True   ,A__ = None   ,A__ = 1   ,A__ = None   ,**A__   ,):

												if isinstance(A__   ,A__):
																	lowercase							=						1
												elif isinstance(A__   ,A__):
																	lowercase							=						len(A__)
												else:
																	raise ValueError(f'`prompt` has to be of type `str` or `list` but is {type(A__)}')

												if height % 8 != 0 or width % 8 != 0:
																	raise ValueError(f'`height` and `width` have to be divisible by 8 but are {height} and {width}.')

												if (callback_steps is None) or (
												    callback_steps is not None and (not isinstance(A__   ,A__) or callback_steps <= 0)
												):
																	raise ValueError(
																	    f'`callback_steps` has to be a positive integer but is {callback_steps} of type'
																	    f' {type(A__)}.')

												# get prompt text embeddings
												lowercase							=						self.tokenizer(
												    A__   ,padding='''max_length'''   ,max_length=self.tokenizer.model_max_length   ,return_tensors='''pt'''   ,)
												lowercase							=						text_inputs.input_ids

												if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																	lowercase							=						self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
																	logger.warning(
																	    '''The following part of your input was truncated because CLIP can only handle sequences up to'''
																	    f' {self.tokenizer.model_max_length} tokens: {removed_text}')
																	lowercase							=						text_input_ids[:, : self.tokenizer.model_max_length]

												if text_embeddings is None:
																	lowercase							=						self.text_encoder(text_input_ids.to(self.device))[0]

												# duplicate text embeddings for each generation per prompt, using mps friendly method
												lowercase	,						lowercase	,						lowercase							=						text_embeddings.shape
												lowercase							=						text_embeddings.repeat(1   ,A__   ,1)
												lowercase							=						text_embeddings.view(bs_embed * num_images_per_prompt   ,A__   ,-1)

												# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
												# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
												# corresponds to doing no classifier free guidance.
												lowercase							=						guidance_scale > 1.0
												# get unconditional embeddings for classifier free guidance
												if do_classifier_free_guidance:
																	lowercase							=						42
																	if negative_prompt is None:
																						lowercase							=						['''''']
																	elif type(A__) is not type(A__):
																						raise TypeError(
																						    f'`negative_prompt` should be the same type to `prompt`, but got {type(A__)} !='
																						    f' {type(A__)}.')
																	elif isinstance(A__   ,A__):
																						lowercase							=						[negative_prompt]
																	elif batch_size != len(A__):
																						raise ValueError(
																						    f'`negative_prompt`: {negative_prompt} has batch size {len(A__)}, but `prompt`:'
																						    f' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
																						    ''' the batch size of `prompt`.''')
																	else:
																						lowercase							=						negative_prompt

																	lowercase							=						text_input_ids.shape[-1]
																	lowercase							=						self.tokenizer(
																	    A__   ,padding='''max_length'''   ,max_length=A__   ,truncation=A__   ,return_tensors='''pt'''   ,)
																	lowercase							=						self.text_encoder(uncond_input.input_ids.to(self.device))[0]

																	# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
																	lowercase							=						uncond_embeddings.shape[1]
																	lowercase							=						uncond_embeddings.repeat(A__   ,A__   ,1)
																	lowercase							=						uncond_embeddings.view(batch_size * num_images_per_prompt   ,A__   ,-1)

																	# For classifier free guidance, we need to do two forward passes.
																	# Here we concatenate the unconditional and text embeddings into a single batch
																	# to avoid doing two forward passes
																	lowercase							=						torch.cat([uncond_embeddings, text_embeddings])

												# get the initial random noise unless the user supplied it

												# Unlike in other pipelines, latents need to be generated in the target device
												# for 1-to-1 results reproducibility with the CompVis implementation.
												# However this currently doesn't work in `mps`.
												lowercase							=						(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
												lowercase							=						(batch_size * num_images_per_prompt, self.unet.config.in_channels, 6_4, 6_4)
												lowercase							=						text_embeddings.dtype
												if latents is None:
																	if self.device.type == "mps":
																						# randn does not exist on mps
																						lowercase							=						torch.randn(
																						    A__   ,generator=A__   ,device='''cpu'''   ,dtype=A__).to(self.device)
																						lowercase							=						torch.randn(A__   ,generator=A__   ,device='''cpu'''   ,dtype=A__).to(
																						    self.device)
																	else:
																						lowercase							=						torch.randn(
																						    A__   ,generator=A__   ,device=self.device   ,dtype=A__)
																						lowercase							=						torch.randn(A__   ,generator=A__   ,device=self.device   ,dtype=A__)
												else:
																	if latents_reference.shape != latents_shape:
																						raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {latents_shape}')
																	lowercase							=						latents_reference.to(self.device)
																	lowercase							=						latents.to(self.device)

												# This is the key part of the pipeline where we
												# try to ensure that the generated images w/ the same seed
												# but different sizes actually result in similar images
												lowercase							=						(latents_shape[3] - latents_shape_reference[3]) // 2
												lowercase							=						(latents_shape[2] - latents_shape_reference[2]) // 2
												lowercase							=						latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
												lowercase							=						latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
												lowercase							=						0 if dx < 0 else dx
												lowercase							=						0 if dy < 0 else dy
												lowercase							=						max(-dx   ,0)
												lowercase							=						max(-dy   ,0)
												# import pdb
												# pdb.set_trace()
												lowercase							=						latents_reference[:, :, dy : dy + h, dx : dx + w]

												# set timesteps
												self.scheduler.set_timesteps(A__)

												# Some schedulers like PNDM have timesteps as arrays
												# It's more optimized to move all timesteps to correct device beforehand
												lowercase							=						self.scheduler.timesteps.to(self.device)

												# scale the initial noise by the standard deviation required by the scheduler
												lowercase							=						latents * self.scheduler.init_noise_sigma

												# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
												# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
												# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
												# and should be between [0, 1]
												lowercase							=						'''eta''' in set(inspect.signature(self.scheduler.step).parameters.keys())
												lowercase							=						{}
												if accepts_eta:
																	lowercase							=						eta

												for i, t in enumerate(self.progress_bar(A__)):
																	# expand the latents if we are doing classifier free guidance
																	lowercase							=						torch.cat([latents] * 2) if do_classifier_free_guidance else latents
																	lowercase							=						self.scheduler.scale_model_input(A__   ,A__)

																	# predict the noise residual
																	lowercase							=						self.unet(A__   ,A__   ,encoder_hidden_states=A__).sample

																	# perform guidance
																	if do_classifier_free_guidance:
																						lowercase	,						lowercase							=						noise_pred.chunk(2)
																						lowercase							=						noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																	# compute the previous noisy sample x_t -> x_t-1
																	lowercase							=						self.scheduler.step(A__   ,A__   ,A__   ,**A__).prev_sample

																	# call the callback, if provided
																	if callback is not None and i % callback_steps == 0:
																						callback(A__   ,A__   ,A__)

												lowercase							=						1 / 0.18215 * latents
												lowercase							=						self.vae.decode(A__).sample

												lowercase							=						(image / 2 + 0.5).clamp(0   ,1)

												# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
												lowercase							=						image.cpu().permute(0   ,2   ,3   ,1).float().numpy()

												if self.safety_checker is not None:
																	lowercase							=						self.feature_extractor(self.numpy_to_pil(A__)   ,return_tensors='''pt''').to(
																	    self.device)
																	lowercase	,						lowercase							=						self.safety_checker(
																	    images=A__   ,clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype))
												else:
																	lowercase							=						None

												if output_type == "pil":
																	lowercase							=						self.numpy_to_pil(A__)

												if not return_dict:
																	return (image, has_nsfw_concept)

												return StableDiffusionPipelineOutput(images=A__   ,nsfw_content_detected=A__)




