





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 						_A(					unittest.TestCase   ):


				"""simple docstring"""


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



				@property
				def 					UpperCAmelCase_		(			self     ):
									__A	:						List[str]       =				ort.SessionOptions()
									__A	:						Optional[int]       =				False
									return options





				def 					UpperCAmelCase_		(			self     ):
									__A	:						Tuple       =				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/in_paint/overture-creations-5sI6fQgYIuo.png'     )
									__A	:						Any       =				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'     )
									__A	:						Tuple       =				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
									__A	:						List[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     )

									__A	:						Dict       =				'A red cat sitting on a park bench'

									__A	:						Union[str, Any]       =				np.random.RandomState(0     )
									__A	:						List[str]       =				pipe(
									    prompt=_A     ,					image=_A     ,					mask_image=_A     ,					strength=0.7_5     ,					guidance_scale=7.5     ,					num_inference_steps=15     ,					generator=_A     ,					output_type='np'     ,					)

									__A	:						int       =				output.images[0]

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











from __future__ import annotations







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a  )      ->      float:
					if days_between_payments <= 0:
										raise ValueError('days_between_payments must be > 0'  )
					if daily_interest_rate < 0:
										raise ValueError('daily_interest_rate must be >= 0'  )
					if principal <= 0:
										raise ValueError('principal must be > 0'  )
					return principal * daily_interest_rate * days_between_payments







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a					,						)      ->      float:
					if number_of_compounding_periods <= 0:
										raise ValueError('number_of_compounding_periods must be > 0'  )
					if nominal_annual_interest_rate_percentage < 0:
										raise ValueError('nominal_annual_interest_rate_percentage must be >= 0'  )
					if principal <= 0:
										raise ValueError('principal must be > 0'  )

					return principal * (
					    (1 + nominal_annual_interest_rate_percentage) ** number_of_compounding_periods
					    - 1
					)







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a					,						)      ->      float:
					if number_of_years <= 0:
										raise ValueError('number_of_years must be > 0'  )
					if nominal_annual_percentage_rate < 0:
										raise ValueError('nominal_annual_percentage_rate must be >= 0'  )
					if principal <= 0:
										raise ValueError('principal must be > 0'  )

					return compound_interest(
					    a					,						nominal_annual_percentage_rate / 3_65					,						number_of_years * 3_65  )


if __name__ == "__main__":
				import doctest

				doctest.testmod()




