
def       A       (					lowercase    ,       lowercase )   ->  List[Any]:
		'''simple docstring'''

		UpperCamelCase		=     0
		UpperCamelCase		=     len(lowercase ) - 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

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

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

				UpperCamelCase		=     sorted_collection[point]
				if current_item == item:
						return point
				else:
						if point < left:
								UpperCamelCase		=     left
								UpperCamelCase		=     point
						elif point > right:
								UpperCamelCase		=     right
								UpperCamelCase		=     point
						else:
								if item < current_item:
										UpperCamelCase		=     point - 1
								else:
										UpperCamelCase		=     point + 1
		return None


def       A       (					lowercase    ,       lowercase    ,       lowercase    ,       lowercase )   ->  Optional[Any]:
		'''simple docstring'''

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

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

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

		if sorted_collection[point] == item:
				return point
		elif point < left:
				return interpolation_search_by_recursion(lowercase    ,       lowercase    ,       lowercase    ,       lowercase )
		elif point > right:
				return interpolation_search_by_recursion(lowercase    ,       lowercase    ,       lowercase    ,       lowercase )
		else:
				if sorted_collection[point] > item:
						return interpolation_search_by_recursion(
						    lowercase    ,       lowercase    ,       lowercase    ,       point - 1 )
				else:
						return interpolation_search_by_recursion(
						    lowercase    ,       lowercase    ,       point + 1    ,       lowercase )


def       A       (					lowercase )   ->  str:
		'''simple docstring'''

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


if __name__ == "__main__":
					import sys
					_UpperCAmelCase					: List[Any]      =       0
					if debug == 1:
										_UpperCAmelCase					: Union[str, Any]      =       [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")
										_UpperCAmelCase					: Any      =       67

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


import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()


@slow
@require_torch_gpu
class 				lowercase					(		unittest.TestCase   ):



		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				# clean up the VRAM after each test
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()



		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				UpperCamelCase		=     StableDiffusionKDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'       )
				UpperCamelCase		=     sd_pipe.to(A_       )
				sd_pipe.set_progress_bar_config(disable=A_       )

				sd_pipe.set_scheduler('sample_euler'       )

				UpperCamelCase		=     'A painting of a squirrel eating a burger'
				UpperCamelCase		=     torch.manual_seed(0       )
				UpperCamelCase		=     sd_pipe([prompt]				,      generator=A_				,      guidance_scale=9.0				,      num_inference_steps=20				,      output_type='np'       )

				UpperCamelCase		=     output.images

				UpperCamelCase		=     image[0, -3:, -3:, -1]

				assert image.shape == (1, 512, 512, 3)
				UpperCamelCase		=     np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339]       )

				assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2



		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				UpperCamelCase		=     StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'       )
				UpperCamelCase		=     sd_pipe.to(A_       )
				sd_pipe.set_progress_bar_config(disable=A_       )

				sd_pipe.set_scheduler('sample_euler'       )

				UpperCamelCase		=     'A painting of a squirrel eating a burger'
				UpperCamelCase		=     torch.manual_seed(0       )
				UpperCamelCase		=     sd_pipe([prompt]				,      generator=A_				,      guidance_scale=9.0				,      num_inference_steps=20				,      output_type='np'       )

				UpperCamelCase		=     output.images

				UpperCamelCase		=     image[0, -3:, -3:, -1]

				assert image.shape == (1, 512, 512, 3)
				UpperCamelCase		=     np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112]       )

				assert np.abs(image_slice.flatten() - expected_slice       ).max() < 5e-1



		def __UpperCamelCase ( self       )  ->					Optional[int]:
				"""simple docstring"""
				UpperCamelCase		=     StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'       )
				UpperCamelCase		=     sd_pipe.to(A_       )
				sd_pipe.set_progress_bar_config(disable=A_       )

				sd_pipe.set_scheduler('sample_dpmpp_2m'       )

				UpperCamelCase		=     'A painting of a squirrel eating a burger'
				UpperCamelCase		=     torch.manual_seed(0       )
				UpperCamelCase		=     sd_pipe(
				    [prompt]				,      generator=A_				,      guidance_scale=7.5				,      num_inference_steps=15				,      output_type='np'				,      use_karras_sigmas=A_				,      )

				UpperCamelCase		=     output.images

				UpperCamelCase		=     image[0, -3:, -3:, -1]

				assert image.shape == (1, 512, 512, 3)
				UpperCamelCase		=     np.array(
				    [0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048]       )

				assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2
