
import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
					import jax
					import jax.numpy as jnp





@slow
@require_flax
class 							UpperCAmelCase				(  unittest.TestCase  ):




	'''simple docstring'''





	def     UpperCamelCase_  (	self :			Dict							,A :			Dict							,A :			Tuple			):
					return f'''gaussian_noise_s={seed}_shape={'_'.join([str(A			) for s in shape]			)}.npy'''






	def     UpperCamelCase_  (	self :			Tuple			):
					# clean up the VRAM after each test
					super().tearDown()
					gc.collect()






	def     UpperCamelCase_  (	self :			Any							,A :			Any=0							,A :			str=(4, 4, 64, 64)							,A :			Tuple=False			):
					__A             =			jnp.bfloataa if fpaa else jnp.floataa
					__A             =			jnp.array(load_hf_numpy(self.get_file_format(A							,A			)			)							,dtype=A			)
					return image






	def     UpperCamelCase_  (	self :			Optional[int]							,A :			Dict=False							,A :			List[str]="CompVis/stable-diffusion-v1-4"			):
					__A             =			jnp.bfloataa if fpaa else jnp.floataa
					__A             =			"bf16" if fpaa else None

					__A      ,							__A             =			FlaxUNetaDConditionModel.from_pretrained(
					    A							,subfolder="unet"							,dtype=A							,revision=A			)
					return model, params






	def     UpperCamelCase_  (	self :			Optional[Any]							,A :			str=0							,A :			Union[str, Any]=(4, 77, 7_68)							,A :			int=False			):
					__A             =			jnp.bfloataa if fpaa else jnp.floataa
					__A             =			jnp.array(load_hf_numpy(self.get_file_format(A							,A			)			)							,dtype=A			)
					return hidden_states






	@parameterized.expand(
	    [
	        # fmt: off
	        [83, 4, [-0.23_23, -0.13_04, 0.08_13, -0.30_93, -0.09_19, -0.15_71, -0.11_25, -0.58_06]],
	        [17, 0.55, [-0.08_31, -0.24_43, 0.09_01, -0.09_19, 0.33_96, 0.01_03, -0.37_43, 0.07_01]],
	        [8, 0.89, [-0.48_63, 0.08_59, 0.08_75, -0.16_58, 0.91_99, -0.01_14, 0.48_39, 0.46_39]],
	        [3, 10_00, [-0.56_49, 0.24_02, -0.55_18, 0.12_48, 1.13_28, -0.24_43, -0.03_25, -1.00_78]],
	        # fmt: on
	    ]			)
	def     UpperCamelCase_  (	self :			List[Any]							,A :			str							,A :			Any							,A :			Optional[int]			):
					__A      ,							__A             =			self.get_unet_model(model_id="CompVis/stable-diffusion-v1-4"							,fpaa=A			)
					__A             =			self.get_latents(A							,fpaa=A			)
					__A             =			self.get_encoder_hidden_states(A							,fpaa=A			)

					__A             =			model.apply(
					    {"params": params}							,A							,jnp.array(A							,dtype=jnp.intaa			)							,encoder_hidden_states=A							,).sample

					assert sample.shape == latents.shape

					__A             =			jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())			)							,dtype=jnp.floataa			)
					__A             =			jnp.array(A							,dtype=jnp.floataa			)

					# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
					assert jnp.allclose(A							,A							,atol=1E-2			)







	@parameterized.expand(
	    [
	        # fmt: off
	        [83, 4, [0.15_14, 0.08_07, 0.16_24, 0.10_16, -0.18_96, 0.02_63, 0.06_77, 0.23_10]],
	        [17, 0.55, [0.11_64, -0.02_16, 0.01_70, 0.15_89, -0.31_20, 0.10_05, -0.05_81, -0.14_58]],
	        [8, 0.89, [-0.17_58, -0.01_69, 0.10_04, -0.14_11, 0.13_12, 0.11_03, -0.19_96, 0.21_39]],
	        [3, 10_00, [0.12_14, 0.03_52, -0.07_31, -0.15_62, -0.09_94, -0.09_06, -0.23_40, -0.05_39]],
	        # fmt: on
	    ]			)
	def     UpperCamelCase_  (	self :			str							,A :			Tuple							,A :			List[Any]							,A :			List[str]			):
					__A      ,							__A             =			self.get_unet_model(model_id="stabilityai/stable-diffusion-2"							,fpaa=A			)
					__A             =			self.get_latents(A							,shape=(4, 4, 96, 96)							,fpaa=A			)
					__A             =			self.get_encoder_hidden_states(A							,shape=(4, 77, 10_24)							,fpaa=A			)

					__A             =			model.apply(
					    {"params": params}							,A							,jnp.array(A							,dtype=jnp.intaa			)							,encoder_hidden_states=A							,).sample

					assert sample.shape == latents.shape

					__A             =			jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())			)							,dtype=jnp.floataa			)
					__A             =			jnp.array(A							,dtype=jnp.floataa			)

					# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
					assert jnp.allclose(A							,A							,atol=1E-2			)


from collections import Counter

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

SCREAMING_SNAKE_CASE							:Tuple										=    datasets.load_iris()

SCREAMING_SNAKE_CASE							:Dict										=    np.array(data['data'])
SCREAMING_SNAKE_CASE							:Optional[int]										=    np.array(data['target'])
SCREAMING_SNAKE_CASE							:List[str]										=    data['target_names']

SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE							:List[Any]										=    train_test_split(X, y)


def 						UpperCAmelCase      (     a_		,       a_						)		->     Tuple:







				"""simple docstring"""



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


def 						UpperCAmelCase      (     a_		,       a_		,       a_		,       a_		,       a_=5						)		->     Dict:







				"""simple docstring"""



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