



import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin


@skip_mps
class 	__snake_case   ( SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, unittest.TestCase ):
		SCREAMING_SNAKE_CASE__								=	IFInpaintingPipeline
		SCREAMING_SNAKE_CASE__								=	TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
		SCREAMING_SNAKE_CASE__								=	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
		SCREAMING_SNAKE_CASE__								=	PipelineTesterMixin.required_optional_params - {'latents'}
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return self._get_dummy_components()
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_=0						):







						"""simple docstring"""
						if str(a_						).startswith('mps'						):
										lowerCAmelCase__		  =  torch.manual_seed(a_						)
						else:
										lowerCAmelCase__		  =  torch.Generator(device=a_						).manual_seed(a_						)

						lowerCAmelCase__		  =  floats_tensor((1, 3, 32, 32)    ,rng=random.Random(a_						)						).to(a_						)
						lowerCAmelCase__		  =  floats_tensor((1, 3, 32, 32)    ,rng=random.Random(a_						)						).to(a_						)

						lowerCAmelCase__		  =  {
						    'prompt': 'A painting of a squirrel eating a burger',
						    'image': image,
						    'mask_image': mask_image,
						    'generator': generator,
						    'num_inference_steps': 2,
						    'output_type': 'numpy',
						}

						return inputs
		@unittest.skipIf(
		    torch_device != 'cuda' or not is_xformers_available()    ,reason='XFormers attention is only available with CUDA and `xformers` installed'    ,)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						self._test_save_load_optional_components()
		@unittest.skipIf(torch_device != 'cuda'    ,reason='float16 requires CUDA'						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
						super().test_save_load_floataa(expected_max_diff=1e-1						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						self._test_attention_slicing_forward_pass(expected_max_diff=1e-2						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						self._test_save_load_local()



		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						self._test_inference_batch_single_identical(
						    expected_max_diff=1e-2    ,)





from collections import Counter

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

_lowerCAmelCase	:   int    =   datasets.load_iris()

_lowerCAmelCase	:   Tuple    =   np.array(data["data"])
_lowerCAmelCase	:   Any    =   np.array(data["target"])
_lowerCAmelCase	:   Union[str, Any]    =   data["target_names"]

_lowerCAmelCase,		_lowerCAmelCase,		_lowerCAmelCase,		_lowerCAmelCase	:   Optional[Any]    =   train_test_split(X, y)







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     Tuple:



				"""simple docstring"""





				return np.linalg.norm(np.array(snake_case__					) - np.array(snake_case__					)					)







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__=5					)	->     Dict:



				"""simple docstring"""





				lowerCAmelCase__		  =  zip(snake_case__    ,			snake_case__					)
				# List of distances of all points from the point to be classified
				lowerCAmelCase__		  =  []
				for data_point in data:
								lowerCAmelCase__		  =  euclidean_distance(data_point[0]    ,			snake_case__					)
								distances.append((distance, data_point[1])					)
				# Choosing 'k' points with the least distances.
				lowerCAmelCase__		  =  [i[1] for i in sorted(snake_case__					)[:k]]
				# Most commonly occurring class among them
				# is the class into which the point is classified
				lowerCAmelCase__		  =  Counter(snake_case__					).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]))
