


"""simple docstring"""



import argparse
from pathlib import Path

import torch
from packaging import version
from torch.onnx import export

from diffusers import AutoencoderKL


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






def       lowerCamelCase					(a_						:Union[str, Any]				,				a_						:tuple				,				a_						:Path				,				a_						:str				,				a_						:Optional[int]				,				a_						:int				,				a_						:Union[str, Any]				,				a_						:Tuple=False				,				)     ->							str:
       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_						:str				,				a_						:str				,				a_						:int				,				a_						:bool = False)     ->							Any:
       lowercase		:Dict       							=				torch.floataa if fpaa else torch.floataa
       if fpaa and torch.cuda.is_available():
              lowercase		:Any       							=				'''cuda'''
       elif fpaa and not torch.cuda.is_available():
              raise ValueError('''`float16` model export is only supported on GPUs with CUDA''')
       else:
              lowercase		:Tuple       							=				'''cpu'''
       lowercase		:Tuple       							=				Path(a_)

       # VAE DECODER
       lowercase		:Dict       							=				AutoencoderKL.from_pretrained(model_path + '''/vae''')
       lowercase		:Optional[int]       							=				vae_decoder.config.latent_channels
       # forward only through the decoder part
       lowercase		:Dict       							=				vae_decoder.decode
       onnx_export(
           a_				,				model_args=(
               torch.randn(1				,				a_				,				25				,				25).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 vae_decoder


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

 UpperCAmelCase      		=			parser.parse_args()
 print(args.output_path)
 convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
 print('''SD: Done: ONNX''')





"""simple docstring"""



from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class 					__magic_name__		(			__UpperCAmelCase     ):
       __A			:      UNetaDModel
       __A			:      ScoreSdeVeScheduler






       def __init__(   self  :    Optional[Any]  ,     snake_case__  :    UNetaDModel  ,     snake_case__  :    ScoreSdeVeScheduler				):

              '''simple docstring'''






              super().__init__()
              self.register_modules(unet=snake_case__  ,     scheduler=snake_case__				)







       @torch.no_grad()
       def __call__(   self  :    Tuple  ,     snake_case__  :    int = 1  ,     snake_case__  :    int = 2_0_0_0  ,     snake_case__  :    Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,     snake_case__  :    Optional[str] = "pil"  ,     snake_case__  :    bool = True  ,     **snake_case__  :    Optional[Any]  ,     ):

              '''simple docstring'''






              lowercase		:str       							=				self.unet.config.sample_size
              lowercase		:str       							=				(batch_size, 3, img_size, img_size)

              lowercase		:Any       							=				self.unet

              lowercase		:Dict       							=				randn_tensor(snake_case__  ,     generator=snake_case__				) * self.scheduler.init_noise_sigma
              lowercase		:int       							=				sample.to(self.device				)

              self.scheduler.set_timesteps(snake_case__				)
              self.scheduler.set_sigmas(snake_case__				)

              for i, t in enumerate(self.progress_bar(self.scheduler.timesteps				)				):
                     lowercase		:Optional[int]       							=				self.scheduler.sigmas[i] * torch.ones(shape[0]  ,     device=self.device				)

                     # correction step
                     for _ in range(self.scheduler.config.correct_steps				):
                            lowercase		:str       							=				self.unet(snake_case__  ,     snake_case__				).sample
                            lowercase		:Optional[Any]       							=				self.scheduler.step_correct(snake_case__  ,     snake_case__  ,     generator=snake_case__				).prev_sample

                     # prediction step
                     lowercase		:Optional[Any]       							=				model(snake_case__  ,     snake_case__				).sample
                     lowercase		:Dict       							=				self.scheduler.step_pred(snake_case__  ,     snake_case__  ,     snake_case__  ,     generator=snake_case__				)

                     lowercase							,  lowercase		:Tuple       							=				output.prev_sample, output.prev_sample_mean

              lowercase		:str       							=				sample_mean.clamp(0  ,     1				)
              lowercase		:int       							=				sample.cpu().permute(0  ,     2  ,     3  ,     1				).numpy()
              if output_type == "pil":
                     lowercase		:str       							=				self.numpy_to_pil(snake_case__				)

              if not return_dict:
                     return (sample,)

              return ImagePipelineOutput(images=snake_case__				)

