

'''simple docstring'''






import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
 import onnxruntime as ort






class 			lowercase_	(lowerCamelCase__				,       unittest.TestCase      ):




  """simple docstring"""




  pass






@nightly
@require_onnxruntime
@require_torch_gpu
class 			lowercase_	(unittest.TestCase      ):




  """simple docstring"""




  @property
  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple			):
   return (
       "CUDAExecutionProvider",
       {
           "gpu_mem_limit": "15000000000",  # 15GB
           "arena_extend_strategy": "kSameAsRequested",
       },
   )






  @property
  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple			):
   __lowercase							  = ort.SessionOptions()
   __lowercase							  = False
   return options






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   __lowercase							  = load_image(
       '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
       '''/in_paint/overture-creations-5sI6fQgYIuo.png'''			)
   __lowercase							  = load_image(
       '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
       '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''			)
   __lowercase							  = OnnxStableDiffusionInpaintPipeline.from_pretrained(
       '''runwayml/stable-diffusion-inpainting'''					,revision='''onnx'''					,safety_checker=lowercase__					,feature_extractor=lowercase__					,provider=self.gpu_provider					,sess_options=self.gpu_options					,)
   pipe.set_progress_bar_config(disable=lowercase__			)

   __lowercase							  = '''A red cat sitting on a park bench'''

   __lowercase							  = np.random.RandomState(0			)
   __lowercase							  = pipe(
       prompt=lowercase__					,image=lowercase__					,mask_image=lowercase__					,guidance_scale=7.5					,num_inference_steps=1_0					,generator=lowercase__					,output_type='''np'''					,)
   __lowercase							  = output.images
   __lowercase							  = images[0, 2_5_5:2_5_8, 2_5_5:2_5_8, -1]

   assert images.shape == (1, 5_1_2, 5_1_2, 3)
   __lowercase							  = np.array([0.2_5_1_4, 0.3_0_0_7, 0.3_5_1_7, 0.1_7_9_0, 0.2_3_8_2, 0.3_1_6_7, 0.1_9_4_4, 0.2_2_7_3, 0.2_4_6_4]			)

   assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-3






  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple			):
   __lowercase							  = load_image(
       '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
       '''/in_paint/overture-creations-5sI6fQgYIuo.png'''			)
   __lowercase							  = load_image(
       '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
       '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''			)
   __lowercase							  = LMSDiscreteScheduler.from_pretrained(
       '''runwayml/stable-diffusion-inpainting'''					,subfolder='''scheduler'''					,revision='''onnx'''			)
   __lowercase							  = OnnxStableDiffusionInpaintPipeline.from_pretrained(
       '''runwayml/stable-diffusion-inpainting'''					,revision='''onnx'''					,scheduler=lowercase__					,safety_checker=lowercase__					,feature_extractor=lowercase__					,provider=self.gpu_provider					,sess_options=self.gpu_options					,)
   pipe.set_progress_bar_config(disable=lowercase__			)

   __lowercase							  = '''A red cat sitting on a park bench'''

   __lowercase							  = np.random.RandomState(0			)
   __lowercase							  = pipe(
       prompt=lowercase__					,image=lowercase__					,mask_image=lowercase__					,guidance_scale=7.5					,num_inference_steps=2_0					,generator=lowercase__					,output_type='''np'''					,)
   __lowercase							  = output.images
   __lowercase							  = images[0, 2_5_5:2_5_8, 2_5_5:2_5_8, -1]

   assert images.shape == (1, 5_1_2, 5_1_2, 3)
   __lowercase							  = np.array([0.0_0_8_6, 0.0_0_7_7, 0.0_0_8_3, 0.0_0_9_3, 0.0_1_0_7, 0.0_1_3_9, 0.0_0_9_4, 0.0_0_9_7, 0.0_1_2_5]			)

   assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-3




'''simple docstring'''






import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


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


def 		_A     (    A__     ,				A__     ,				A__     ,				A__     ,				A__     ,				A__     ,				A__     ,				A__=False     ,				):






 """simple docstring"""



 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 		_A     (    A__     ,				A__     ,				A__     ,				A__ = 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							  = Path(A__      )

 # VAE DECODER
 __lowercase							  = AutoencoderKL.from_pretrained(model_path + '''/vae'''      )
 __lowercase							  = vae_decoder.config.latent_channels
 # forward only through the decoder part
 __lowercase							  = 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__":
 lowerCAmelCase__											=	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''')

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

