


from random import shuffle

import tensorflow as tf
from numpy import array






def 						lowercase_(    SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_      ):


  '''simple docstring'''

  lowerCamelCase					:   Optional[Any]       =							int(UpperCAmelCase__      )
  assert noofclusters < len(UpperCAmelCase__      )

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

  # Will help select random centroids from among the available vectors
  lowerCamelCase					:   Optional[Any]       =							list(range(len(UpperCAmelCase__      )      )      )
  shuffle(UpperCAmelCase__      )

  # 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.

  lowerCamelCase					:   List[Any]       =							tf.Graph()

  with graph.as_default():
    # SESSION OF COMPUTATION

    lowerCamelCase					:   List[str]       =							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
    lowerCamelCase					:   List[Any]       =							[
        tf.Variable(vectors[vector_indices[i]]      ) for i in range(UpperCAmelCase__      )
    ]
    ##These nodes will assign the centroid Variables the appropriate
    ##values
    lowerCamelCase					:   Optional[int]       =							tf.placeholder("float64" ,		[dim]      )
    lowerCamelCase					:   List[Any]       =							[]
    for centroid in centroids:
      cent_assigns.append(tf.assign(UpperCAmelCase__ ,		UpperCAmelCase__      )      )

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

    ##Now lets construct the node that will compute the mean
    # The placeholder for the input
    lowerCamelCase					:   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
    lowerCamelCase					:   List[str]       =							tf.reduce_mean(UpperCAmelCase__ ,		0      )

    ##Node for computing Euclidean distances
    # Placeholders for input
    lowerCamelCase					:   Optional[Any]       =							tf.placeholder("float" ,		[dim]      )
    lowerCamelCase					:   Optional[int]       =							tf.placeholder("float" ,		[dim]      )
    lowerCamelCase					:   Optional[int]       =							tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(UpperCAmelCase__ ,		UpperCAmelCase__      ) ,		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
    lowerCamelCase					:   List[str]       =							tf.placeholder("float" ,		[noofclusters]      )
    lowerCamelCase					:   Optional[int]       =							tf.argmin(UpperCAmelCase__ ,		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.
    lowerCamelCase					:   str       =							tf.initialize_all_variables()

    # Initialize all variables
    sess.run(UpperCAmelCase__      )

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

        # Return centroids and assignments
    lowerCamelCase					:   Dict       =							sess.run(UpperCAmelCase__      )
    lowerCamelCase					:   Optional[Any]       =							sess.run(UpperCAmelCase__      )
    return centroids, assignments






'''simple docstring'''







import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class        A__   (   unittest.TestCase      ):



    @property
    def        snake_case_      (       self							)    ->   int:





           '''simple docstring'''
           torch.manual_seed(0							)
           A_						       =   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        snake_case_      (       self							)    ->   str:





           '''simple docstring'''
           A_						       =   self.dummy_uncond_unet
           A_						       =   PNDMScheduler()

           A_						       =   PNDMPipeline(unet=UpperCamelCase__		,					scheduler=UpperCamelCase__							)
           pndm.to(UpperCamelCase__							)
           pndm.set_progress_bar_config(disable=UpperCamelCase__							)

           A_						       =   torch.manual_seed(0							)
           A_						       =   pndm(generator=UpperCamelCase__		,					num_inference_steps=20		,					output_type="""numpy"""							).images

           A_						       =   torch.manual_seed(0							)
           A_						       =   pndm(generator=UpperCamelCase__		,					num_inference_steps=20		,					output_type="""numpy"""		,					return_dict=UpperCamelCase__							)[0]

           A_						       =   image[0, -3:, -3:, -1]
           A_						       =   image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           A_						       =   np.array([1.0, 1.0, 0.0, 1.0, 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        snake_case_      (       self							)    ->   Tuple:





           '''simple docstring'''
           A_						       =   """google/ddpm-cifar10-32"""

           A_						       =   UNetaDModel.from_pretrained(UpperCamelCase__							)
           A_						       =   PNDMScheduler()

           A_						       =   PNDMPipeline(unet=UpperCamelCase__		,					scheduler=UpperCamelCase__							)
           pndm.to(UpperCamelCase__							)
           pndm.set_progress_bar_config(disable=UpperCamelCase__							)
           A_						       =   torch.manual_seed(0							)
           A_						       =   pndm(generator=UpperCamelCase__		,					output_type="""numpy"""							).images

           A_						       =   image[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           A_						       =   np.array([0.1564, 0.14645, 0.1406, 0.14715, 0.12425, 0.14045, 0.13115, 0.12175, 0.125]							)

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