

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				):
							@property
							def     lowerCAmelCase     (		self		:			str):
								return (
								    "CUDAExecutionProvider",
								    {
								        "gpu_mem_limit": "15000000000",  # 15GB
								        "arena_extend_strategy": "kSameAsRequested",
								    },
								)
							@property
							def     lowerCAmelCase     (		self		:			Dict):
								__lowerCamelCase				:				Tuple					       = ort.SessionOptions()
								__lowerCamelCase				:				str					       = False
								return options


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

								__lowerCamelCase				:				List[str]					       = 'A red cat sitting on a park bench'

								__lowerCamelCase				:				Union[str, Any]					       = np.random.RandomState(0)
								__lowerCamelCase				:				Any					       = pipe(
								    prompt=SCREAMING_SNAKE_CASE__   ,image=SCREAMING_SNAKE_CASE__   ,mask_image=SCREAMING_SNAKE_CASE__   ,strength=0.75   ,guidance_scale=7.5   ,num_inference_steps=1_5   ,generator=SCREAMING_SNAKE_CASE__   ,output_type='np'   ,)

								__lowerCamelCase				:				Tuple					       = output.images[0]

								assert image.shape == (5_1_2, 5_1_2, 3)
								assert np.abs(expected_image - image).max() < 1E-2








from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

__lowerCAmelCase	: List[Any]  				=datasets.load_iris()

__lowerCAmelCase	: Tuple  				=np.array(data['data'])
__lowerCAmelCase	: Dict  				=np.array(data['target'])
__lowerCAmelCase	: List[str]  				=data['target_names']

__lowerCAmelCase				,__lowerCAmelCase				,__lowerCAmelCase				,__lowerCAmelCase	: str  				=train_test_split(X, y)


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						return np.linalg.norm(np.array(lowercase__      ) - np.array(lowercase__      )      )


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__=5      ):
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		zip(lowercase__    ,       lowercase__      )
						# List of distances of all points from the point to be classified
						__SCREAMING_SNAKE_CASE						:     Dict					=		[]
						for data_point in data:
												__SCREAMING_SNAKE_CASE						:     Tuple					=		euclidean_distance(data_point[0]    ,       lowercase__      )
												distances.append((distance, data_point[1])      )
						# Choosing 'k' points with the least distances.
						__SCREAMING_SNAKE_CASE						:     int					=		[i[1] for i in sorted(lowercase__      )[:k]]
						# Most commonly occurring class among them
						# is the class into which the point is classified
						__SCREAMING_SNAKE_CASE						:     Any					=		Counter(lowercase__      ).most_common(1      )[0][0]
						return classes[result]


if __name__ == "__main__":
						print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))
