






import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
     import onnxruntime as ort




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

       '''simple docstring'''



       @property
       def        __lowercase    (				self     )			->   Any:






            '''simple docstring'''





            return (
                "CUDAExecutionProvider",
                {
                    "gpu_mem_limit": "15000000000",  # 15GB
                    "arena_extend_strategy": "kSameAsRequested",
                },
            )



       @property
       def        __lowercase    (				self     )			->   str:






            '''simple docstring'''





            __snake_case      :Optional[int] 							=    ort.SessionOptions()
            __snake_case      :Optional[int] 							=    False
            return options



       def        __lowercase    (				self     )			->   Optional[Any]:






            '''simple docstring'''





            __snake_case      :Any 							=    load_image(
                """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                """/in_paint/overture-creations-5sI6fQgYIuo.png"""     )
            __snake_case      :List[str] 							=    load_image(
                """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""     )
            __snake_case      :Optional[Any] 							=    load_numpy(
                """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                """/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy"""     )

            # using the PNDM scheduler by default
            __snake_case      :str 							=    OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
                """CompVis/stable-diffusion-v1-4"""      ,    revision="""onnx"""      ,    safety_checker=a__      ,    feature_extractor=a__      ,    provider=self.gpu_provider      ,    sess_options=self.gpu_options      ,    )
            pipe.set_progress_bar_config(disable=a__     )

            __snake_case      :Optional[int] 							=    """A red cat sitting on a park bench"""

            __snake_case      :List[Any] 							=    np.random.RandomState(0     )
            __snake_case      :List[Any] 							=    pipe(
                prompt=a__      ,    image=a__      ,    mask_image=a__      ,    strength=0.75      ,    guidance_scale=7.5      ,    num_inference_steps=15      ,    generator=a__      ,    output_type="""np"""      ,    )

            __snake_case      :Optional[Any] 							=    output.images[0]

            assert image.shape == (5_12, 5_12, 3)
            assert np.abs(expected_image - image     ).max() < 1e-2










def 	UpperCamelCase	(							snake_case__  : list	):




     '''simple docstring'''






     if not grid or not grid[0]:
          raise TypeError("""The grid does not contain the appropriate information"""	)

     for cell_n in range(1						,len(grid[0]	)	):
          grid[0][cell_n] += grid[0][cell_n - 1]
     __snake_case      :str 							=    grid[0]

     for row_n in range(1						,len(snake_case__	)	):
          __snake_case      :Optional[int] 							=    grid[row_n]
          __snake_case      :Optional[Any] 							=    fill_row(snake_case__						,snake_case__	)
          __snake_case      :Dict 							=    grid[row_n]

     return grid[-1][-1]






def 	UpperCamelCase	(							snake_case__  : list						,snake_case__  : list	):




     '''simple docstring'''






     current_row[0] += row_above[0]
     for cell_n in range(1						,len(snake_case__	)	):
          current_row[cell_n] += min(current_row[cell_n - 1]						,row_above[cell_n]	)

     return current_row


if __name__ == "__main__":
     import doctest

     doctest.testmod()


