






"""simple docstring"""




from random import shuffle

import tensorflow as tf
from numpy import array






def        lowerCAmelCase (      UpperCamelCase_:							int     , UpperCamelCase_:							Dict					)       ->							Dict:







  '''simple docstring'''







  _a							=	int(a__					)
  assert noofclusters < len(a__					)

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

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

  _a							=	tf.Graph()

  with graph.as_default():
    # SESSION OF COMPUTATION

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

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

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










"""simple docstring"""




import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest




class    lowercase_    (_UpperCAmelCase     ):
      A__	:		Union[str, Any]       			=				(PNDMScheduler,)
      A__	:		Optional[int]       			=				(('''num_inference_steps''', 50),)






      def 			lowerCamelCase__    (    self     ,     **a_	)	->Optional[Any]:







        '''simple docstring'''


        _a							=	{
            "num_train_timesteps": 1_0_0_0,
            "beta_start": 0.0_001,
            "beta_end": 0.02,
            "beta_schedule": "linear",
        }

        config.update(**a_	)
        return config






      def 			lowerCamelCase__    (    self     ,     a_=0     ,     **a_	)	->Tuple:







        '''simple docstring'''


        _a							=	dict(self.forward_default_kwargs	)
        _a							=	kwargs.pop("num_inference_steps"     ,     a_	)
        _a							=	self.dummy_sample
        _a							=	0.1 * sample
        _a							=	[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

        for scheduler_class in self.scheduler_classes:
          _a							=	self.get_scheduler_config(**a_	)
          _a							=	scheduler_class(**a_	)
          scheduler.set_timesteps(a_	)
          # copy over dummy past residuals
          _a							=	dummy_past_residuals[:]

          with tempfile.TemporaryDirectory() as tmpdirname:
            scheduler.save_config(a_	)
            _a							=	scheduler_class.from_pretrained(a_	)
            new_scheduler.set_timesteps(a_	)
            # copy over dummy past residuals
            _a							=	dummy_past_residuals[:]

          _a							=	scheduler.step_prk(a_     ,     a_     ,     a_     ,     **a_	).prev_sample
          _a							=	new_scheduler.step_prk(a_     ,     a_     ,     a_     ,     **a_	).prev_sample

          assert torch.sum(torch.abs(output - new_output	)	) < 1E-5, "Scheduler outputs are not identical"

          _a							=	scheduler.step_plms(a_     ,     a_     ,     a_     ,     **a_	).prev_sample
          _a							=	new_scheduler.step_plms(a_     ,     a_     ,     a_     ,     **a_	).prev_sample

          assert torch.sum(torch.abs(output - new_output	)	) < 1E-5, "Scheduler outputs are not identical"






      def 			lowerCamelCase__    (    self	)	->Dict:







        '''simple docstring'''


        pass






      def 			lowerCamelCase__    (    self     ,     a_=0     ,     **a_	)	->Tuple:







        '''simple docstring'''


        _a							=	dict(self.forward_default_kwargs	)
        _a							=	kwargs.pop("num_inference_steps"     ,     a_	)
        _a							=	self.dummy_sample
        _a							=	0.1 * sample
        _a							=	[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

        for scheduler_class in self.scheduler_classes:
          _a							=	self.get_scheduler_config()
          _a							=	scheduler_class(**a_	)
          scheduler.set_timesteps(a_	)

          # copy over dummy past residuals (must be after setting timesteps)
          _a							=	dummy_past_residuals[:]

          with tempfile.TemporaryDirectory() as tmpdirname:
            scheduler.save_config(a_	)
            _a							=	scheduler_class.from_pretrained(a_	)
            # copy over dummy past residuals
            new_scheduler.set_timesteps(a_	)

            # copy over dummy past residual (must be after setting timesteps)
            _a							=	dummy_past_residuals[:]

          _a							=	scheduler.step_prk(a_     ,     a_     ,     a_     ,     **a_	).prev_sample
          _a							=	new_scheduler.step_prk(a_     ,     a_     ,     a_     ,     **a_	).prev_sample

          assert torch.sum(torch.abs(output - new_output	)	) < 1E-5, "Scheduler outputs are not identical"

          _a							=	scheduler.step_plms(a_     ,     a_     ,     a_     ,     **a_	).prev_sample
          _a							=	new_scheduler.step_plms(a_     ,     a_     ,     a_     ,     **a_	).prev_sample

          assert torch.sum(torch.abs(output - new_output	)	) < 1E-5, "Scheduler outputs are not identical"






      def 			lowerCamelCase__    (    self     ,     **a_	)	->Optional[int]:







        '''simple docstring'''


        _a							=	self.scheduler_classes[0]
        _a							=	self.get_scheduler_config(**a_	)
        _a							=	scheduler_class(**a_	)

        _a							=	1_0
        _a							=	self.dummy_model()
        _a							=	self.dummy_sample_deter
        scheduler.set_timesteps(a_	)

        for i, t in enumerate(scheduler.prk_timesteps	):
          _a							=	model(a_     ,     a_	)
          _a							=	scheduler.step_prk(a_     ,     a_     ,     a_	).prev_sample

        for i, t in enumerate(scheduler.plms_timesteps	):
          _a							=	model(a_     ,     a_	)
          _a							=	scheduler.step_plms(a_     ,     a_     ,     a_	).prev_sample

        return sample






      def 			lowerCamelCase__    (    self	)	->Dict:







        '''simple docstring'''


        _a							=	dict(self.forward_default_kwargs	)

        _a							=	kwargs.pop("num_inference_steps"     ,     a_	)

        for scheduler_class in self.scheduler_classes:
          _a							=	self.get_scheduler_config()
          _a							=	scheduler_class(**a_	)

          _a							=	self.dummy_sample
          _a							=	0.1 * sample

          if num_inference_steps is not None and hasattr(a_     ,     "set_timesteps"	):
            scheduler.set_timesteps(a_	)
          elif num_inference_steps is not None and not hasattr(a_     ,     "set_timesteps"	):
            _a							=	num_inference_steps

          # copy over dummy past residuals (must be done after set_timesteps)
          _a							=	[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
          _a							=	dummy_past_residuals[:]

          _a							=	scheduler.step_prk(a_     ,     0     ,     a_     ,     **a_	).prev_sample
          _a							=	scheduler.step_prk(a_     ,     1     ,     a_     ,     **a_	).prev_sample

          self.assertEqual(output_a.shape     ,     sample.shape	)
          self.assertEqual(output_a.shape     ,     output_a.shape	)

          _a							=	scheduler.step_plms(a_     ,     0     ,     a_     ,     **a_	).prev_sample
          _a							=	scheduler.step_plms(a_     ,     1     ,     a_     ,     **a_	).prev_sample

          self.assertEqual(output_a.shape     ,     sample.shape	)
          self.assertEqual(output_a.shape     ,     output_a.shape	)






      def 			lowerCamelCase__    (    self	)	->int:







        '''simple docstring'''


        for timesteps in [1_0_0, 1_0_0_0]:
          self.check_over_configs(num_train_timesteps=a_	)






      def 			lowerCamelCase__    (    self	)	->Optional[Any]:







        '''simple docstring'''


        for steps_offset in [0, 1]:
          self.check_over_configs(steps_offset=a_	)

        _a							=	self.scheduler_classes[0]
        _a							=	self.get_scheduler_config(steps_offset=1	)
        _a							=	scheduler_class(**a_	)
        scheduler.set_timesteps(1_0	)
        assert torch.equal(
            scheduler.timesteps     ,     torch.LongTensor(
                [9_0_1, 8_5_1, 8_5_1, 8_0_1, 8_0_1, 7_5_1, 7_5_1, 7_0_1, 7_0_1, 6_5_1, 6_5_1, 6_0_1, 6_0_1, 5_0_1, 4_0_1, 3_0_1, 2_0_1, 1_0_1, 1]	)     ,     )






      def 			lowerCamelCase__    (    self	)	->Tuple:







        '''simple docstring'''


        for beta_start, beta_end in zip([0.0_001, 0.001]     ,     [0.002, 0.02]	):
          self.check_over_configs(beta_start=a_     ,     beta_end=a_	)






      def 			lowerCamelCase__    (    self	)	->Dict:







        '''simple docstring'''


        for schedule in ["linear", "squaredcos_cap_v2"]:
          self.check_over_configs(beta_schedule=a_	)






      def 			lowerCamelCase__    (    self	)	->str:







        '''simple docstring'''


        for prediction_type in ["epsilon", "v_prediction"]:
          self.check_over_configs(prediction_type=a_	)






      def 			lowerCamelCase__    (    self	)	->Any:







        '''simple docstring'''


        for t in [1, 5, 1_0]:
          self.check_over_forward(time_step=a_	)






      def 			lowerCamelCase__    (    self	)	->List[Any]:







        '''simple docstring'''


        for t, num_inference_steps in zip([1, 5, 1_0]     ,     [1_0, 5_0, 1_0_0]	):
          self.check_over_forward(num_inference_steps=a_	)






      def 			lowerCamelCase__    (    self	)	->Union[str, Any]:







        '''simple docstring'''


        _a							=	2_7

        for scheduler_class in self.scheduler_classes:
          _a							=	self.dummy_sample
          _a							=	0.1 * sample

          _a							=	self.get_scheduler_config()
          _a							=	scheduler_class(**a_	)

          scheduler.set_timesteps(a_	)

          # before power of 3 fix, would error on first step, so we only need to do two
          for i, t in enumerate(scheduler.prk_timesteps[:2]	):
            _a							=	scheduler.step_prk(a_     ,     a_     ,     a_	).prev_sample






      def 			lowerCamelCase__    (    self	)	->Any:







        '''simple docstring'''


        with self.assertRaises(a_	):
          _a							=	self.scheduler_classes[0]
          _a							=	self.get_scheduler_config()
          _a							=	scheduler_class(**a_	)

          scheduler.step_plms(self.dummy_sample     ,     1     ,     self.dummy_sample	).prev_sample






      def 			lowerCamelCase__    (    self	)	->Any:







        '''simple docstring'''


        _a							=	self.full_loop()
        _a							=	torch.sum(torch.abs(a_	)	)
        _a							=	torch.mean(torch.abs(a_	)	)

        assert abs(result_sum.item() - 198.1_318	) < 1E-2
        assert abs(result_mean.item() - 0.2_580	) < 1E-3






      def 			lowerCamelCase__    (    self	)	->Optional[Any]:







        '''simple docstring'''


        _a							=	self.full_loop(prediction_type="v_prediction"	)
        _a							=	torch.sum(torch.abs(a_	)	)
        _a							=	torch.mean(torch.abs(a_	)	)

        assert abs(result_sum.item() - 67.3_986	) < 1E-2
        assert abs(result_mean.item() - 0.0_878	) < 1E-3






      def 			lowerCamelCase__    (    self	)	->int:







        '''simple docstring'''


        _a							=	self.full_loop(set_alpha_to_one=a_     ,     beta_start=0.01	)
        _a							=	torch.sum(torch.abs(a_	)	)
        _a							=	torch.mean(torch.abs(a_	)	)

        assert abs(result_sum.item() - 230.0_399	) < 1E-2
        assert abs(result_mean.item() - 0.2_995	) < 1E-3






      def 			lowerCamelCase__    (    self	)	->str:







        '''simple docstring'''


        _a							=	self.full_loop(set_alpha_to_one=a_     ,     beta_start=0.01	)
        _a							=	torch.sum(torch.abs(a_	)	)
        _a							=	torch.mean(torch.abs(a_	)	)

        assert abs(result_sum.item() - 186.9_482	) < 1E-2
        assert abs(result_mean.item() - 0.2_434	) < 1E-3


