


import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
			import jax
			import jax.numpy as jnp
			from flax.jax_utils import replicate
			from flax.training.common_utils import shard

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




							"""simple docstring"""


							def    __snake_case (							self	:  List[str]    )     ->   Any:
														# clean up the VRAM after each test
														super().tearDown()
														gc.collect()

							def    __snake_case (							self	:  Any    )     ->   Union[str, Any]:
														__snake_case			,      __snake_case		:      Union[str, Any]										=       FlaxControlNetModel.from_pretrained(
														    "lllyasviel/sd-controlnet-canny"   ,     from_pt=lowerCamelCase   ,     dtype=jnp.bfloataa    )
														__snake_case			,      __snake_case		:      Tuple										=       FlaxStableDiffusionControlNetPipeline.from_pretrained(
														    "runwayml/stable-diffusion-v1-5"   ,     controlnet=lowerCamelCase   ,     from_pt=lowerCamelCase   ,     dtype=jnp.bfloataa    )
														__snake_case		:      Tuple										=       controlnet_params

														__snake_case		:      Optional[Any]										=       "bird"
														__snake_case		:      str										=       jax.device_count()
														__snake_case		:      Optional[Any]										=       pipe.prepare_text_inputs([prompts] * num_samples    )

														__snake_case		:      Optional[Any]										=       load_image(
														    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"    )
														__snake_case		:      Union[str, Any]										=       pipe.prepare_image_inputs([canny_image] * num_samples    )

														__snake_case		:      int										=       jax.random.PRNGKey(0    )
														__snake_case		:      int										=       jax.random.split(lowerCamelCase   ,     jax.device_count()    )

														__snake_case		:      int										=       replicate(lowerCamelCase    )
														__snake_case		:      List[Any]										=       shard(lowerCamelCase    )
														__snake_case		:      str										=       shard(lowerCamelCase    )

														__snake_case		:      int										=       pipe(
														    prompt_ids=lowerCamelCase   ,     image=lowerCamelCase   ,     params=lowerCamelCase   ,     prng_seed=lowerCamelCase   ,     num_inference_steps=50   ,     jit=lowerCamelCase   ,     ).images
														assert images.shape == (jax.device_count(), 1, 768, 512, 3)

														__snake_case		:      Dict										=       images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]    )
														__snake_case		:      Optional[Any]										=       images[0, 253:256, 253:256, -1]

														__snake_case		:      int										=       jnp.asarray(jax.device_get(image_slice.flatten()    )    )
														__snake_case		:      List[Any]										=       jnp.array(
														    [0.16_79_69, 0.11_66_99, 0.08_15_43, 0.15_42_97, 0.13_28_12, 0.10_88_87, 0.16_99_22, 0.16_99_22, 0.20_50_78]    )
														print(F'output_slice: {output_slice}'    )
														assert jnp.abs(output_slice - expected_slice    ).max() < 1E-2






							def    __snake_case (							self	:  Optional[Any]    )     ->   Dict:
														__snake_case			,      __snake_case		:      Optional[int]										=       FlaxControlNetModel.from_pretrained(
														    "lllyasviel/sd-controlnet-openpose"   ,     from_pt=lowerCamelCase   ,     dtype=jnp.bfloataa    )
														__snake_case			,      __snake_case		:      Dict										=       FlaxStableDiffusionControlNetPipeline.from_pretrained(
														    "runwayml/stable-diffusion-v1-5"   ,     controlnet=lowerCamelCase   ,     from_pt=lowerCamelCase   ,     dtype=jnp.bfloataa    )
														__snake_case		:      List[str]										=       controlnet_params

														__snake_case		:      Dict										=       "Chef in the kitchen"
														__snake_case		:      List[str]										=       jax.device_count()
														__snake_case		:      Any										=       pipe.prepare_text_inputs([prompts] * num_samples    )

														__snake_case		:      str										=       load_image(
														    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"    )
														__snake_case		:      str										=       pipe.prepare_image_inputs([pose_image] * num_samples    )

														__snake_case		:      Union[str, Any]										=       jax.random.PRNGKey(0    )
														__snake_case		:      Dict										=       jax.random.split(lowerCamelCase   ,     jax.device_count()    )

														__snake_case		:      str										=       replicate(lowerCamelCase    )
														__snake_case		:      Optional[Any]										=       shard(lowerCamelCase    )
														__snake_case		:      Union[str, Any]										=       shard(lowerCamelCase    )

														__snake_case		:      Tuple										=       pipe(
														    prompt_ids=lowerCamelCase   ,     image=lowerCamelCase   ,     params=lowerCamelCase   ,     prng_seed=lowerCamelCase   ,     num_inference_steps=50   ,     jit=lowerCamelCase   ,     ).images
														assert images.shape == (jax.device_count(), 1, 768, 512, 3)

														__snake_case		:      Tuple										=       images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]    )
														__snake_case		:      str										=       images[0, 253:256, 253:256, -1]

														__snake_case		:      List[str]										=       jnp.asarray(jax.device_get(image_slice.flatten()    )    )
														__snake_case		:      Dict										=       jnp.array(
														    [[0.27_14_84, 0.26_17_19, 0.27_53_91, 0.27_73_44, 0.27_92_97, 0.29_10_16, 0.29_49_22, 0.30_27_34, 0.30_27_34]]    )
														print(F'output_slice: {output_slice}'    )
														assert jnp.abs(output_slice - expected_slice    ).max() < 1E-2





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]))

