
"""simple docstring"""







def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     Any:







							"""simple docstring"""


							__snake_case     			=       0
							__snake_case     			=       len(SCREAMING_SNAKE_CASE ) - 1

							while left <= right:
														# avoid divided by 0 during interpolation
														if sorted_collection[left] == sorted_collection[right]:
																					if sorted_collection[left] == item:
																												return left
																					else:
																												return None

														__snake_case     			=       left + ((item - sorted_collection[left]) * (right - left)) // (
														    sorted_collection[right] - sorted_collection[left]
														)

														# out of range check
														if point < 0 or point >= len(SCREAMING_SNAKE_CASE ):
																					return None

														__snake_case     			=       sorted_collection[point]
														if current_item == item:
																					return point
														else:
																					if point < left:
																												__snake_case     			=       left
																												__snake_case     			=       point
																					elif point > right:
																												__snake_case     			=       right
																												__snake_case     			=       point
																					else:
																												if item < current_item:
																																			__snake_case     			=       point - 1
																												else:
																																			__snake_case     			=       point + 1
							return None
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     Tuple:







							"""simple docstring"""


							if sorted_collection[left] == sorted_collection[right]:
														if sorted_collection[left] == item:
																					return left
														else:
																					return None

							__snake_case     			=       left + ((item - sorted_collection[left]) * (right - left)) // (
							    sorted_collection[right] - sorted_collection[left]
							)

							# out of range check
							if point < 0 or point >= len(SCREAMING_SNAKE_CASE ):
														return None

							if sorted_collection[point] == item:
														return point
							elif point < left:
														return interpolation_search_by_recursion(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
							elif point > right:
														return interpolation_search_by_recursion(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
							else:
														if sorted_collection[point] > item:
																					return interpolation_search_by_recursion(
																					    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    point - 1 )
														else:
																					return interpolation_search_by_recursion(
																					    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    point + 1							,    SCREAMING_SNAKE_CASE )
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     Union[str, Any]:







							"""simple docstring"""


							if collection != sorted(SCREAMING_SNAKE_CASE ):
														raise ValueError("Collection must be ascending sorted" )
							return True


if __name__ == "__main__":
		import sys
		_SCREAMING_SNAKE_CASE	       =      0
		if debug == 1:
				_SCREAMING_SNAKE_CASE	       =      [10, 30, 40, 45, 50, 66, 77, 93]
				try:
						__assert_sorted(collection)
				except ValueError:
						sys.exit("""Sequence must be ascending sorted to apply interpolation search""")
				_SCREAMING_SNAKE_CASE	       =      67

		_SCREAMING_SNAKE_CASE	       =      interpolation_search(collection, target)
		if result is not None:
				print(F"""{target} found at positions: {result}""")
		else:
				print("""Not found""")


"""simple docstring"""







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 							__magic_name__     (		unittest.TestCase   ):





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





					@property
					def 							lowerCAmelCase      (				self   :							str    ):
												__snake_case     			=       ort.SessionOptions()
												__snake_case     			=       False
												return options





					def 							lowerCAmelCase      (				self   :							Tuple    ):
												__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=snake_case_							,						feature_extractor=snake_case_							,						provider=self.gpu_provider							,						sess_options=self.gpu_options							,						)
												pipe.set_progress_bar_config(disable=snake_case_    )

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

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

												__snake_case     			=       output.images[0]

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