

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 			_SCREAMING_SNAKE_CASE (   unittest.TestCase		):







   '''simple docstring'''




   @property
   def      _snake_case		(       self					:			List[Any]			):
       return (
           "CUDAExecutionProvider",
           {
               "gpu_mem_limit": "15000000000",  # 15GB
               "arena_extend_strategy": "kSameAsRequested",
           },
       )





   @property
   def      _snake_case		(       self					:			Optional[Any]			):
       SCREAMING_SNAKE_CASE										=   ort.SessionOptions()
       SCREAMING_SNAKE_CASE										=   False
       return options





   def      _snake_case		(       self					:			Union[str, Any]			):
       SCREAMING_SNAKE_CASE										=   load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/in_paint/overture-creations-5sI6fQgYIuo.png"			)
       SCREAMING_SNAKE_CASE										=   load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"			)
       SCREAMING_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
       SCREAMING_SNAKE_CASE										=   OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
           "CompVis/stable-diffusion-v1-4"     , revision="onnx"     , safety_checker=__lowerCamelCase     , feature_extractor=__lowerCamelCase     , provider=self.gpu_provider     , sess_options=self.gpu_options     , )
       pipe.set_progress_bar_config(disable=__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   "A red cat sitting on a park bench"

       SCREAMING_SNAKE_CASE										=   np.random.RandomState(0			)
       SCREAMING_SNAKE_CASE										=   pipe(
           prompt=__lowerCamelCase     , image=__lowerCamelCase     , mask_image=__lowerCamelCase     , strength=0.75     , guidance_scale=7.5     , num_inference_steps=15     , generator=__lowerCamelCase     , output_type="np"     , )

       SCREAMING_SNAKE_CASE										=   output.images[0]

       assert image.shape == (512, 512, 3)
       assert np.abs(expected_image - image			).max() < 1e-2


'''simple docstring'''

SCREAMING_SNAKE_CASE							:				int     	=			8.3_1_4_4_5_9_8




def 							_UpperCamelCase				(   lowerCAmelCase__:					float		,lowerCAmelCase__:					float   )      -> float:
							if temperature < 0:
														raise Exception('Temperature cannot be less than 0 K'   )
							if molar_mass <= 0:
														raise Exception('Molar mass cannot be less than or equal to 0 kg/mol'   )
							else:
														return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5


if __name__ == "__main__":
							import doctest

							# run doctest
							doctest.testmod()

							# example
							SCREAMING_SNAKE_CASE							:				List[str]     	=			300
							SCREAMING_SNAKE_CASE							:				List[Any]     	=			28
							SCREAMING_SNAKE_CASE							:				Optional[Any]     	=			rms_speed_of_molecule(temperature, molar_mass)
							print(f"Vrms of Nitrogen gas at 300 K is {vrms} m/s")





