
from random import shuffle

import tensorflow as tf
from numpy import array



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__		):
					'''simple docstring'''
					lowercase							=						int(lowerCAmelCase__		)
					assert noofclusters < len(lowerCAmelCase__		)

					# Find out the dimensionality
					lowercase							=						len(vectors[0]		)

					# Will help select random centroids from among the available vectors
					lowercase							=						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.

					lowercase							=						tf.Graph()

					with graph.as_default():
										# SESSION OF COMPUTATION

										lowercase							=						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
										lowercase							=						[
										    tf.Variable(vectors[vector_indices[i]]		) for i in range(lowerCAmelCase__		)
										]
										##These nodes will assign the centroid Variables the appropriate
										##values
										lowercase							=						tf.placeholder('''float64''' ,			[dim]		)
										lowercase							=						[]
										for centroid in centroids:
															cent_assigns.append(tf.assign(lowerCAmelCase__ ,			lowerCAmelCase__		)		)

										##Variables for cluster assignments of individual vectors(initialized
										##to 0 at first)
										lowercase							=						[tf.Variable(0		) for i in range(len(lowerCAmelCase__		)		)]
										##These nodes will assign an assignment Variable the appropriate
										##value
										lowercase							=						tf.placeholder('''int32'''		)
										lowercase							=						[]
										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
										lowercase							=						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
										lowercase							=						tf.reduce_mean(lowerCAmelCase__ ,			0		)

										##Node for computing Euclidean distances
										# Placeholders for input
										lowercase							=						tf.placeholder('''float''' ,			[dim]		)
										lowercase							=						tf.placeholder('''float''' ,			[dim]		)
										lowercase							=						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
										lowercase							=						tf.placeholder('''float''' ,			[noofclusters]		)
										lowercase							=						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.
										lowercase							=						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.
										lowercase							=						100
										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__		)		):
																				lowercase							=						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.
																				lowercase							=						[
																				    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
																				lowercase							=						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
																				lowercase							=						[
																				    vectors[i]
																				    for i in range(len(lowerCAmelCase__		)		)
																				    if sess.run(assignments[i]		) == cluster_n
																				]
																				# Compute new centroid location
																				lowercase							=						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
										lowercase							=						sess.run(lowerCAmelCase__		)
										lowercase							=						sess.run(lowerCAmelCase__		)
										return centroids, assignments







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 	snake_case_   (		unittest.TestCase					):







			'''simple docstring'''

			def 				snake_case__(     self :				Any							,   _UpperCamelCase :				Any							,   _UpperCamelCase :				Tuple							)      ->List[Any]:
									return f'''gaussian_noise_s={seed}_shape={'_'.join([str(_UpperCamelCase							) for s in shape]							)}.npy'''







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







			def 				snake_case__(     self :				int							,   _UpperCamelCase :				Union[str, Any]=0							,   _UpperCamelCase :				int=(4, 4, 6_4, 6_4)							,   _UpperCamelCase :				Optional[int]=False							)      ->Tuple:
									snake_case_   =	jnp.bfloataa if fpaa else jnp.floataa
									snake_case_   =	jnp.array(load_hf_numpy(self.get_file_format(_UpperCamelCase							,   _UpperCamelCase							)							)							,   dtype=_UpperCamelCase							)
									return image







			def 				snake_case__(     self :				List[Any]							,   _UpperCamelCase :				Optional[Any]=False							,   _UpperCamelCase :				Optional[int]="CompVis/stable-diffusion-v1-4"							)      ->Optional[Any]:
									snake_case_   =	jnp.bfloataa if fpaa else jnp.floataa
									snake_case_   =	'''bf16''' if fpaa else None

									snake_case_,    snake_case_   =	FlaxUNetaDConditionModel.from_pretrained(
									    _UpperCamelCase							,   subfolder='''unet'''							,   dtype=_UpperCamelCase							,   revision=_UpperCamelCase							)
									return model, params







			def 				snake_case__(     self :				Dict							,   _UpperCamelCase :				List[Any]=0							,   _UpperCamelCase :				Tuple=(4, 7_7, 7_6_8)							,   _UpperCamelCase :				List[Any]=False							)      ->int:
									snake_case_   =	jnp.bfloataa if fpaa else jnp.floataa
									snake_case_   =	jnp.array(load_hf_numpy(self.get_file_format(_UpperCamelCase							,   _UpperCamelCase							)							)							,   dtype=_UpperCamelCase							)
									return hidden_states







			@parameterized.expand(
			    [
			        # fmt: off
			        [8_3, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
			        [1_7, 0.55, [-0.0831, -0.2443, 0.0901, -0.0919, 0.3396, 0.0103, -0.3743, 0.0701]],
			        [8, 0.89, [-0.4863, 0.0859, 0.0875, -0.1658, 0.9199, -0.0114, 0.4839, 0.4639]],
			        [3, 1_0_0_0, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
			        # fmt: on
			    ]							)
			def 				snake_case__(     self :				Optional[Any]							,   _UpperCamelCase :				List[Any]							,   _UpperCamelCase :				List[str]							,   _UpperCamelCase :				Optional[int]							)      ->Union[str, Any]:
									snake_case_,    snake_case_   =	self.get_unet_model(model_id='''CompVis/stable-diffusion-v1-4'''							,   fpaa=_UpperCamelCase							)
									snake_case_   =	self.get_latents(_UpperCamelCase							,   fpaa=_UpperCamelCase							)
									snake_case_   =	self.get_encoder_hidden_states(_UpperCamelCase							,   fpaa=_UpperCamelCase							)

									snake_case_   =	model.apply(
									    {'''params''': params}							,   _UpperCamelCase							,   jnp.array(_UpperCamelCase							,   dtype=jnp.intaa							)							,   encoder_hidden_states=_UpperCamelCase							,   ).sample

									assert sample.shape == latents.shape

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

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







			@parameterized.expand(
			    [
			        # fmt: off
			        [8_3, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],
			        [1_7, 0.55, [0.1164, -0.0216, 0.0170, 0.1589, -0.3120, 0.1005, -0.0581, -0.1458]],
			        [8, 0.89, [-0.1758, -0.0169, 0.1004, -0.1411, 0.1312, 0.1103, -0.1996, 0.2139]],
			        [3, 1_0_0_0, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
			        # fmt: on
			    ]							)
			def 				snake_case__(     self :				Optional[int]							,   _UpperCamelCase :				Optional[Any]							,   _UpperCamelCase :				Union[str, Any]							,   _UpperCamelCase :				str							)      ->Dict:
									snake_case_,    snake_case_   =	self.get_unet_model(model_id='''stabilityai/stable-diffusion-2'''							,   fpaa=_UpperCamelCase							)
									snake_case_   =	self.get_latents(_UpperCamelCase							,   shape=(4, 4, 9_6, 9_6)							,   fpaa=_UpperCamelCase							)
									snake_case_   =	self.get_encoder_hidden_states(_UpperCamelCase							,   shape=(4, 7_7, 1_0_2_4)							,   fpaa=_UpperCamelCase							)

									snake_case_   =	model.apply(
									    {'''params''': params}							,   _UpperCamelCase							,   jnp.array(_UpperCamelCase							,   dtype=jnp.intaa							)							,   encoder_hidden_states=_UpperCamelCase							,   ).sample

									assert sample.shape == latents.shape

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

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