


import unittest

from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax

from .test_modeling_common_flax import FlaxModelTesterMixin


if is_flax_available():
			import jax

@require_flax
class     a		(SCREAMING_SNAKE_CASE__					,    unittest.TestCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Tuple					  =  FlaxAutoencoderKL

							@property
							def    __snake_case (							self	:  Optional[int]    )     ->   Dict:
														__snake_case		:      Optional[int]										=       4
														__snake_case		:      List[str]										=       3
														__snake_case		:      str										=       (32, 32)

														__snake_case		:      str										=       jax.random.PRNGKey(0    )
														__snake_case		:      Dict										=       jax.random.uniform(a_   ,     ((batch_size, num_channels) + sizes)    )

														return {"sample": image, "prng_key": prng_key}






							def    __snake_case (							self	:  Dict    )     ->   Optional[int]:
														__snake_case		:      Optional[Any]										=       {
														    "block_out_channels": [32, 64],
														    "in_channels": 3,
														    "out_channels": 3,
														    "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
														    "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
														    "latent_channels": 4,
														}
														__snake_case		:      int										=       self.dummy_input
														return init_dict, inputs_dict





from collections import Counter

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

_snake_case					:		Union[str, Any]      =    datasets.load_iris()

_snake_case					:		Tuple      =    np.array(data["data"])
_snake_case					:		int      =    np.array(data["target"])
_snake_case					:		int      =    data["target_names"]

_snake_case ,				_snake_case ,				_snake_case ,				_snake_case					:		Any      =    train_test_split(X, y)







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							return np.linalg.norm(np.array(__lowerCamelCase     ) - np.array(__lowerCamelCase     )     )







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

