



'''simple docstring'''




from random import shuffle

import tensorflow as tf
from numpy import array


def        lowerCAmelCase_     (			_lowerCamelCase:     Any ,   _lowerCamelCase:     int      ):
		__SCREAMING_SNAKE_CASE :	Any					 =	int(_lowerCamelCase      )
		assert noofclusters < len(_lowerCamelCase      )

		# Find out the dimensionality
		__SCREAMING_SNAKE_CASE :	int					 =	len(vectors[0]      )

		# Will help select random centroids from among the available vectors
		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	list(range(len(_lowerCamelCase      )      )      )
		shuffle(_lowerCamelCase      )

		# GRAPH OF COMPUTATION
		# We initialize a new graph and set it as the default during each run
		# of this algorithm. This ensures that as this function is called
		# multiple times, the default graph doesn't keep getting crowded with
		# unused ops and Variables from previous function calls.

		__SCREAMING_SNAKE_CASE :	List[str]					 =	tf.Graph()

		with graph.as_default():
				# SESSION OF COMPUTATION

				__SCREAMING_SNAKE_CASE :	Tuple					 =	tf.Session()

				##CONSTRUCTING THE ELEMENTS OF COMPUTATION

				##First lets ensure we have a Variable vector for each centroid,
				##initialized to one of the vectors from the available data points
				__SCREAMING_SNAKE_CASE :	Dict					 =	[
				    tf.Variable(vectors[vector_indices[i]]      ) for i in range(_lowerCamelCase      )
				]
				##These nodes will assign the centroid Variables the appropriate
				##values
				__SCREAMING_SNAKE_CASE :	str					 =	tf.placeholder("""float64""" ,   [dim]      )
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	[]
				for centroid in centroids:
						cent_assigns.append(tf.assign(_lowerCamelCase ,   _lowerCamelCase      )      )

				##Variables for cluster assignments of individual vectors(initialized
				##to 0 at first)
				__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	[tf.Variable(0      ) for i in range(len(_lowerCamelCase      )      )]
				##These nodes will assign an assignment Variable the appropriate
				##value
				__SCREAMING_SNAKE_CASE :	List[str]					 =	tf.placeholder("""int32"""      )
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	[]
				for assignment in assignments:
						cluster_assigns.append(tf.assign(_lowerCamelCase ,   _lowerCamelCase      )      )

				##Now lets construct the node that will compute the mean
				# The placeholder for the input
				__SCREAMING_SNAKE_CASE :	List[Any]					 =	tf.placeholder("""float""" ,   [None, dim]      )
				# The Node/op takes the input and computes a mean along the 0th
				# dimension, i.e. the list of input vectors
				__SCREAMING_SNAKE_CASE :	str					 =	tf.reduce_mean(_lowerCamelCase ,   0      )

				##Node for computing Euclidean distances
				# Placeholders for input
				__SCREAMING_SNAKE_CASE :	str					 =	tf.placeholder("""float""" ,   [dim]      )
				__SCREAMING_SNAKE_CASE :	Any					 =	tf.placeholder("""float""" ,   [dim]      )
				__SCREAMING_SNAKE_CASE :	Any					 =	tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(_lowerCamelCase ,   _lowerCamelCase      ) ,   2      )      )      )

				##This node will figure out which cluster to assign a vector to,
				##based on Euclidean distances of the vector from the centroids.
				# Placeholder for input
				__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	tf.placeholder("""float""" ,   [noofclusters]      )
				__SCREAMING_SNAKE_CASE :	List[Any]					 =	tf.argmin(_lowerCamelCase ,   0      )

				##INITIALIZING STATE VARIABLES

				##This will help initialization of all Variables defined with respect
				##to the graph. The Variable-initializer should be defined after
				##all the Variables have been constructed, so that each of them
				##will be included in the initialization.
				__SCREAMING_SNAKE_CASE :	int					 =	tf.initialize_all_variables()

				# Initialize all variables
				sess.run(_lowerCamelCase      )

				##CLUSTERING ITERATIONS

				# Now perform the Expectation-Maximization steps of K-Means clustering
				# iterations. To keep things simple, we will only do a set number of
				# iterations, instead of using a Stopping Criterion.
				__SCREAMING_SNAKE_CASE :	str					 =	1_00
				for _ in range(_lowerCamelCase      ):
						##EXPECTATION STEP
						##Based on the centroid locations till last iteration, compute
						##the _expected_ centroid assignments.
						# Iterate over each vector
						for vector_n in range(len(_lowerCamelCase      )      ):
								__SCREAMING_SNAKE_CASE :	str					 =	vectors[vector_n]
								# Compute Euclidean distance between this vector and each
								# centroid. Remember that this list cannot be named
								#'centroid_distances', since that is the input to the
								# cluster assignment node.
								__SCREAMING_SNAKE_CASE :	List[str]					 =	[
								    sess.run(_lowerCamelCase ,   feed_dict={va: vect, va: sess.run(_lowerCamelCase      )}      )
								    for centroid in centroids
								]
								# Now use the cluster assignment node, with the distances
								# as the input
								__SCREAMING_SNAKE_CASE :	str					 =	sess.run(
								    _lowerCamelCase ,   feed_dict={centroid_distances: distances}      )
								# Now assign the value to the appropriate state variable
								sess.run(
								    cluster_assigns[vector_n] ,   feed_dict={assignment_value: assignment}      )

						##MAXIMIZATION STEP
						# Based on the expected state computed from the Expectation Step,
						# compute the locations of the centroids so as to maximize the
						# overall objective of minimizing within-cluster Sum-of-Squares
						for cluster_n in range(_lowerCamelCase      ):
								# Collect all the vectors assigned to this cluster
								__SCREAMING_SNAKE_CASE :	Dict					 =	[
								    vectors[i]
								    for i in range(len(_lowerCamelCase      )      )
								    if sess.run(assignments[i]      ) == cluster_n
								]
								# Compute new centroid location
								__SCREAMING_SNAKE_CASE :	Any					 =	sess.run(
								    _lowerCamelCase ,   feed_dict={mean_input: array(_lowerCamelCase      )}      )
								# Assign value to appropriate variable
								sess.run(
								    cent_assigns[cluster_n] ,   feed_dict={centroid_value: new_location}      )

        # Return centroids and assignments
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	sess.run(_lowerCamelCase      )
				__SCREAMING_SNAKE_CASE :	Dict					 =	sess.run(_lowerCamelCase      )
				return centroids, assignments




'''simple docstring'''




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 		_UpperCamelCase						(							unittest.TestCase					):


			'''simple docstring'''

			def UpperCamelCase__      (							self  :				Optional[Any]		):





					"""simple docstring"""






					super().tearDown()
					gc.collect()

			def UpperCamelCase__      (							self  :				List[Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	int					 =	FlaxControlNetModel.from_pretrained(
					    """lllyasviel/sd-controlnet-canny"""							,  from_pt=lowerCAmelCase__							,  dtype=jnp.bfloataa		)
					__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	Tuple					 =	FlaxStableDiffusionControlNetPipeline.from_pretrained(
					    """runwayml/stable-diffusion-v1-5"""							,  controlnet=lowerCAmelCase__							,  from_pt=lowerCAmelCase__							,  dtype=jnp.bfloataa		)
					__SCREAMING_SNAKE_CASE :	str					 =	controlnet_params

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

					__SCREAMING_SNAKE_CASE :	Tuple					 =	load_image(
					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"""		)
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	pipe.prepare_image_inputs([canny_image] * num_samples		)

					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	jax.random.PRNGKey(0		)
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	jax.random.split(lowerCAmelCase__							,  jax.device_count()		)

					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	replicate(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	shard(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	shard(lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	List[Any]					 =	pipe(
					    prompt_ids=lowerCAmelCase__							,  image=lowerCAmelCase__							,  params=lowerCAmelCase__							,  prng_seed=lowerCAmelCase__							,  num_inference_steps=5_0							,  jit=lowerCAmelCase__							,  ).images
					assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)

					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]		)
					__SCREAMING_SNAKE_CASE :	Tuple					 =	images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	jnp.asarray(jax.device_get(image_slice.flatten()		)		)
					__SCREAMING_SNAKE_CASE :	Tuple					 =	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 UpperCamelCase__      (							self  :				str		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	FlaxControlNetModel.from_pretrained(
					    """lllyasviel/sd-controlnet-openpose"""							,  from_pt=lowerCAmelCase__							,  dtype=jnp.bfloataa		)
					__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	Any					 =	FlaxStableDiffusionControlNetPipeline.from_pretrained(
					    """runwayml/stable-diffusion-v1-5"""							,  controlnet=lowerCAmelCase__							,  from_pt=lowerCAmelCase__							,  dtype=jnp.bfloataa		)
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	controlnet_params

					__SCREAMING_SNAKE_CASE :	List[str]					 =	"""Chef in the kitchen"""
					__SCREAMING_SNAKE_CASE :	int					 =	jax.device_count()
					__SCREAMING_SNAKE_CASE :	Tuple					 =	pipe.prepare_text_inputs([prompts] * num_samples		)

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

					__SCREAMING_SNAKE_CASE :	Dict					 =	jax.random.PRNGKey(0		)
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	jax.random.split(lowerCAmelCase__							,  jax.device_count()		)

					__SCREAMING_SNAKE_CASE :	List[Any]					 =	replicate(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	shard(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	shard(lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	Tuple					 =	pipe(
					    prompt_ids=lowerCAmelCase__							,  image=lowerCAmelCase__							,  params=lowerCAmelCase__							,  prng_seed=lowerCAmelCase__							,  num_inference_steps=5_0							,  jit=lowerCAmelCase__							,  ).images
					assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)

					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]		)
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

					__SCREAMING_SNAKE_CASE :	Tuple					 =	jnp.asarray(jax.device_get(image_slice.flatten()		)		)
					__SCREAMING_SNAKE_CASE :	Tuple					 =	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