
'''simple docstring'''
from random import shuffle

import tensorflow as tf
from numpy import array







def 		A_(     A						:		Any		,     A						:		List[Any]):
							UpperCamelCase   			=     int(A)
							assert noofclusters < len(A)

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

							# Will help select random centroids from among the available vectors
							UpperCamelCase   			=     list(range(len(A)))
							shuffle(A)

							# 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.

							UpperCamelCase   			=     tf.Graph()

							with graph.as_default():
														# SESSION OF COMPUTATION

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

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

														##Now lets construct the node that will compute the mean
														# The placeholder for the input
														UpperCamelCase   			=     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
														UpperCamelCase   			=     tf.reduce_mean(A		,     0)

														##Node for computing Euclidean distances
														# Placeholders for input
														UpperCamelCase   			=     tf.placeholder('float'		,     [dim])
														UpperCamelCase   			=     tf.placeholder('float'		,     [dim])
														UpperCamelCase   			=     tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(A		,     A)		,     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
														UpperCamelCase   			=     tf.placeholder('float'		,     [noofclusters])
														UpperCamelCase   			=     tf.argmin(A		,     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.
														UpperCamelCase   			=     tf.initialize_all_variables()

														# Initialize all variables
														sess.run(A)

														##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.
														UpperCamelCase   			=     100
														for _ in range(A):
																					##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(A)):
																												UpperCamelCase   			=     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.
																												UpperCamelCase   			=     [
																												    sess.run(A		,     feed_dict={va: vect, va: sess.run(A)})
																												    for centroid in centroids
																												]
																												# Now use the cluster assignment node, with the distances
																												# as the input
																												UpperCamelCase   			=     sess.run(
																												    A		,     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(A):
																												# Collect all the vectors assigned to this cluster
																												UpperCamelCase   			=     [
																												    vectors[i]
																												    for i in range(len(A))
																												    if sess.run(assignments[i]) == cluster_n
																												]
																												# Compute new centroid location
																												UpperCamelCase   			=     sess.run(
																												    A		,     feed_dict={mean_input: array(A)})
																												# Assign value to appropriate variable
																												sess.run(
																												    cent_assigns[cluster_n]		,     feed_dict={centroid_value: new_location})

        # Return centroids and assignments
														UpperCamelCase   			=     sess.run(A)
														UpperCamelCase   			=     sess.run(A)
														return centroids, assignments






"""simple docstring"""




import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()

class 						a__		( unittest.TestCase		):
   @property
   def      __magic_name__     (     self							):
          torch.manual_seed(0							)
          lowercase       :							str									=				UNetaDModel(
              block_out_channels=(32, 64)      ,       layers_per_block=2      ,       sample_size=32      ,       in_channels=3      ,       out_channels=3      ,       down_block_types=("DownBlock2D", "AttnDownBlock2D")      ,       up_block_types=("AttnUpBlock2D", "UpBlock2D")      ,       )
          return model

   def      __magic_name__     (     self							):
          lowercase       :							List[str]									=				self.dummy_uncond_unet
          lowercase       :							Dict									=				KarrasVeScheduler()

          lowercase       :							Any									=				KarrasVePipeline(unet=_a      ,       scheduler=_a							)
          pipe.to(_a							)
          pipe.set_progress_bar_config(disable=_a							)

          lowercase       :							Tuple									=				torch.manual_seed(0							)
          lowercase       :							Tuple									=				pipe(num_inference_steps=2      ,       generator=_a      ,       output_type="numpy"							).images

          lowercase       :							int									=				torch.manual_seed(0							)
          lowercase       :							Tuple									=				pipe(num_inference_steps=2      ,       generator=_a      ,       output_type="numpy"      ,       return_dict=_a							)[0]

          lowercase       :							Any									=				image[0, -3:, -3:, -1]
          lowercase       :							Dict									=				image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          lowercase       :							Tuple									=				np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]							)

          assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2
          assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1E-2


@slow
@require_torch
class 						a__		( unittest.TestCase		):
   def      __magic_name__     (     self							):
          lowercase       :							Tuple									=				"google/ncsnpp-celebahq-256"
          lowercase       :							str									=				UNetaDModel.from_pretrained(_a							)
          lowercase       :							Optional[Any]									=				KarrasVeScheduler()

          lowercase       :							Union[str, Any]									=				KarrasVePipeline(unet=_a      ,       scheduler=_a							)
          pipe.to(_a							)
          pipe.set_progress_bar_config(disable=_a							)

          lowercase       :							Tuple									=				torch.manual_seed(0							)
          lowercase       :							Dict									=				pipe(num_inference_steps=20      ,       generator=_a      ,       output_type="numpy"							).images

          lowercase       :							Optional[Any]									=				image[0, -3:, -3:, -1]
          assert image.shape == (1, 256, 256, 3)
          lowercase       :							Optional[Any]									=				np.array([0.5_7_8, 0.5_8_1_1, 0.5_9_2_4, 0.5_8_0_9, 0.5_8_7, 0.5_8_8_6, 0.5_8_6_1, 0.5_8_0_2, 0.5_8_6]							)

          assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2


