"""simple docstring"""



import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()



class      _A	(						unittest.TestCase					):




    """simple docstring"""




    @property
    def  lowercase					(		self							:     Any    )    ->       Tuple:
         torch.manual_seed(0    )
         __snake_case				=							UNetaDModel(
             block_out_channels=(32, 64)	,      layers_per_block=2	,      sample_size=32	,      in_channels=3	,      out_channels=3	,      down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')	,      up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')	,      )
         return model







    def  lowercase					(		self							:     List[Any]    )    ->       Optional[int]:
         __snake_case				=							self.dummy_uncond_unet
         __snake_case				=							ScoreSdeVeScheduler()

         __snake_case				=							ScoreSdeVePipeline(unet=A_	,      scheduler=A_    )
         sde_ve.to(A_    )
         sde_ve.set_progress_bar_config(disable=A_    )

         __snake_case				=							torch.manual_seed(0    )
         __snake_case				=							sde_ve(num_inference_steps=2	,      output_type='''numpy'''	,      generator=A_    ).images

         __snake_case				=							torch.manual_seed(0    )
         __snake_case				=							sde_ve(num_inference_steps=2	,      output_type='''numpy'''	,      generator=A_	,      return_dict=A_    )[
             0
         ]

         __snake_case				=							image[0, -3:, -3:, -1]
         __snake_case				=							image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         __snake_case				=							np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]    )

         assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1E-2






@slow
@require_torch
class      _A	(						unittest.TestCase					):




    """simple docstring"""




    def  lowercase					(		self							:     Union[str, Any]    )    ->       Dict:
         __snake_case				=							'''google/ncsnpp-church-256'''
         __snake_case				=							UNetaDModel.from_pretrained(A_    )

         __snake_case				=							ScoreSdeVeScheduler.from_pretrained(A_    )

         __snake_case				=							ScoreSdeVePipeline(unet=A_	,      scheduler=A_    )
         sde_ve.to(A_    )
         sde_ve.set_progress_bar_config(disable=A_    )

         __snake_case				=							torch.manual_seed(0    )
         __snake_case				=							sde_ve(num_inference_steps=10	,      output_type='''numpy'''	,      generator=A_    ).images

         __snake_case				=							image[0, -3:, -3:, -1]

         assert image.shape == (1, 256, 256, 3)

         __snake_case				=							np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]    )

         assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2

"""simple docstring"""



import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


__lowercase		:		int														=							version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")





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





@torch.no_grad()
def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case,      snake_case,      snake_case = False):
     __snake_case				=							torch.floataa if fpaa else torch.floataa
     if fpaa and torch.cuda.is_available():
          __snake_case				=							'''cuda'''
     elif fpaa and not torch.cuda.is_available():
          raise ValueError('''`float16` model export is only supported on GPUs with CUDA''')
     else:
          __snake_case				=							'''cpu'''
     __snake_case				=							Path(snake_case)

     # VAE DECODER
     __snake_case				=							AutoencoderKL.from_pretrained(model_path + '''/vae''')
     __snake_case				=							vae_decoder.config.latent_channels
     # forward only through the decoder part
     __snake_case				=							vae_decoder.decode
     onnx_export(
         snake_case,      model_args=(
             torch.randn(1,      snake_case,      25,      25).to(device=snake_case,      dtype=snake_case),
             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=snake_case,      )
     del vae_decoder


if __name__ == "__main__":
       __lowercase		:		Union[str, Any]														=							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()
       print(args.output_path)
       convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
       print("SD: Done: ONNX")