
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   lowerCamelCase	(						unittest.TestCase						):





     @property
     def 	UpperCAmelCase(self   :					List[str]     )		->		List[str]:
         return (
             "CUDAExecutionProvider",
             {
                 "gpu_mem_limit": "15000000000",  # 15GB
                 "arena_extend_strategy": "kSameAsRequested",
             },
         )





     @property
     def 	UpperCAmelCase(self   :					Optional[Any]     )		->		Dict:
         snake_case				  =      ort.SessionOptions()
         snake_case				  =      False
         return options





     def 	UpperCAmelCase(self   :					Tuple     )		->		Union[str, Any]:
         snake_case				  =      load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/in_paint/overture-creations-5sI6fQgYIuo.png"     )
         snake_case				  =      load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"     )
         snake_case				  =      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				  =      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				  =      "A red cat sitting on a park bench"

         snake_case				  =      np.random.RandomState(0     )
         snake_case				  =      pipe(
             prompt=_A       ,					image=_A       ,					mask_image=_A       ,					strength=0.75       ,					guidance_scale=7.5       ,					num_inference_steps=1_5       ,					generator=_A       ,					output_type="np"       ,					)

         snake_case				  =      output.images[0]

         assert image.shape == (5_1_2, 5_1_2, 3)
         assert np.abs(expected_image - image     ).max() < 1E-2



import os

# Precomputes a list of the 100 first triangular numbers
_A						      =				[int(0.5 * n * (n + 1)) for n in range(1, 1_01)]



def 		lowercase_    (     )							->							Tuple:
    """simple docstring"""

    snake_case				  =      os.path.dirname(os.path.realpath(A__	)	)
    snake_case				  =      os.path.join(A__				,	"words.txt"	)

    snake_case				  =      ""
    with open(A__	) as f:
        snake_case				  =      f.readline()

    snake_case				  =      [word.strip("\""	) for word in words.strip("\r\n"	).split(","	)]
    snake_case				  =      [
        word
        for word in [sum(ord(A__	) - 64 for x in word	) for word in words]
        if word in TRIANGULAR_NUMBERS
    ]
    return len(A__	)


if __name__ == "__main__":
   print(solution())

