





"""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 			_a						(				_lowerCAmelCase     ,    unittest.TestCase    ):
				# FIXME: add fast tests
				pass




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






				@property
				def     snake_case     (			self		:   int							)     ->				Any:




										'''simple docstring'''



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






				@property
				def     snake_case     (			self		:   Dict							)     ->				Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase      : List[str]         =   ort.SessionOptions()
										_UpperCamelCase      : Optional[Any]         =   False
										return options






				def     snake_case     (			self		:   List[Any]							)     ->				Dict:




										'''simple docstring'''



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

										_UpperCamelCase      : List[Any]         =   '''A red cat sitting on a park bench'''

										_UpperCamelCase      : Dict         =   np.random.RandomState(0							)
										_UpperCamelCase      : List[Any]         =   pipe(
										    prompt=lowerCAmelCase__,			image=lowerCAmelCase__,			mask_image=lowerCAmelCase__,			guidance_scale=7.5,			num_inference_steps=1_0,			generator=lowerCAmelCase__,			output_type='''np''',			)
										_UpperCamelCase      : Any         =   output.images
										_UpperCamelCase      : List[str]         =   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)
										_UpperCamelCase      : int         =   np.array([0.2_514, 0.3_007, 0.3_517, 0.1_790, 0.2_382, 0.3_167, 0.1_944, 0.2_273, 0.2_464]							)

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






				def     snake_case     (			self		:   Tuple							)     ->				Union[str, Any]:




										'''simple docstring'''



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

										_UpperCamelCase      : Optional[Any]         =   '''A red cat sitting on a park bench'''

										_UpperCamelCase      : List[Any]         =   np.random.RandomState(0							)
										_UpperCamelCase      : Union[str, Any]         =   pipe(
										    prompt=lowerCAmelCase__,			image=lowerCAmelCase__,			mask_image=lowerCAmelCase__,			guidance_scale=7.5,			num_inference_steps=2_0,			generator=lowerCAmelCase__,			output_type='''np''',			)
										_UpperCamelCase      : Optional[Any]         =   output.images
										_UpperCamelCase      : Tuple         =   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)
										_UpperCamelCase      : Dict         =   np.array([0.0_086, 0.0_077, 0.0_083, 0.0_093, 0.0_107, 0.0_139, 0.0_094, 0.0_097, 0.0_125]							)

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










"""simple docstring"""




from __future__ import annotations

import math





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase	,						_lowercase	,						_lowercase       ):

						if depth < 0:
												raise ValueError('''Depth cannot be less than 0'''       )

						if len(_lowercase       ) == 0:
												raise ValueError('''Scores cannot be empty'''       )

						if depth == height:
												return scores[node_index]

						if is_max:
												return max(
												    minimax(depth + 1	,						node_index * 2	,						_lowercase	,						_lowercase	,						_lowercase       )	,						minimax(depth + 1	,						node_index * 2 + 1	,						_lowercase	,						_lowercase	,						_lowercase       )	,						)

						return min(
						    minimax(depth + 1	,						node_index * 2	,						_lowercase	,						_lowercase	,						_lowercase       )	,						minimax(depth + 1	,						node_index * 2 + 1	,						_lowercase	,						_lowercase	,						_lowercase       )	,						)





def 	a_    (							):
						_UpperCamelCase      : Dict         =   [90, 23, 6, 33, 21, 65, 123, 3_4423]
						_UpperCamelCase      : int         =   math.log(len(_lowercase       )	,						2       )
						print('''Optimal value : '''	,						end=''''''       )
						print(minimax(0	,						0	,						_lowercase	,						_lowercase	,						_lowercase       )       )


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						main()



