




import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


__a          =			False

class   __a(					unittest.TestCase       ):






    """simple docstring"""





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE=32  )			->		Union[str, Any]:
         set_seed(0  )
         UpperCAmelCase_      :							List[str]       =      UNetaDModel(sample_size=lowerCamelCase_       ,in_channels=3       ,out_channels=3  )
         UpperCAmelCase_      :							Tuple       =      torch.optim.SGD(model.parameters()       ,lr=0.00_01  )
         return model, optimizer





    @slow
    def 							a__				(				self  )			->		List[Any]:
         UpperCAmelCase_      :							Union[str, Any]       =      '''cpu'''  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
         UpperCAmelCase_      :							int       =      DDPMScheduler(
             num_train_timesteps=1_000       ,beta_start=0.00_01       ,beta_end=0.02       ,beta_schedule='''linear'''       ,clip_sample=lowerCamelCase_       ,)
         UpperCAmelCase_      :							Dict       =      DDIMScheduler(
             num_train_timesteps=1_000       ,beta_start=0.00_01       ,beta_end=0.02       ,beta_schedule='''linear'''       ,clip_sample=lowerCamelCase_       ,)

         assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

         # shared batches for DDPM and DDIM
         set_seed(0  )
         UpperCAmelCase_      :							Optional[Any]       =      [torch.randn((4, 3, 32, 32)  ).clip(-1       ,1  ).to(lowerCamelCase_  ) for _ in range(4  )]
         UpperCAmelCase_      :							List[Any]       =      [torch.randn((4, 3, 32, 32)  ).to(lowerCamelCase_  ) for _ in range(4  )]
         UpperCAmelCase_      :							Optional[Any]       =      [torch.randint(0       ,1_000       ,(4,)  ).long().to(lowerCamelCase_  ) for _ in range(4  )]

         # train with a DDPM scheduler
         UpperCAmelCase_,   UpperCAmelCase_      :							List[str]       =      self.get_model_optimizer(resolution=32  )
         model.train().to(lowerCamelCase_  )
         for i in range(4  ):
              optimizer.zero_grad()
              UpperCAmelCase_      :							Union[str, Any]       =      ddpm_scheduler.add_noise(clean_images[i]       ,noise[i]       ,timesteps[i]  )
              UpperCAmelCase_      :							List[str]       =      model(lowerCamelCase_       ,timesteps[i]  ).sample
              UpperCAmelCase_      :							Union[str, Any]       =      torch.nn.functional.mse_loss(lowerCamelCase_       ,noise[i]  )
              loss.backward()
              optimizer.step()
         del model, optimizer

         # recreate the model and optimizer, and retry with DDIM
         UpperCAmelCase_,   UpperCAmelCase_      :							str       =      self.get_model_optimizer(resolution=32  )
         model.train().to(lowerCamelCase_  )
         for i in range(4  ):
              optimizer.zero_grad()
              UpperCAmelCase_      :							Optional[int]       =      ddim_scheduler.add_noise(clean_images[i]       ,noise[i]       ,timesteps[i]  )
              UpperCAmelCase_      :							str       =      model(lowerCamelCase_       ,timesteps[i]  ).sample
              UpperCAmelCase_      :							Dict       =      torch.nn.functional.mse_loss(lowerCamelCase_       ,noise[i]  )
              loss.backward()
              optimizer.step()
         del model, optimizer

         self.assertTrue(torch.allclose(lowerCamelCase_       ,lowerCamelCase_       ,atol=1e-5  )  )
         self.assertTrue(torch.allclose(lowerCamelCase_       ,lowerCamelCase_       ,atol=1e-5  )  )





from random import shuffle

import tensorflow as tf
from numpy import array







def lowerCamelCase__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Union[str, Any]       =      int(_lowercase				)
     assert noofclusters < len(_lowercase				)

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

     # Will help select random centroids from among the available vectors
     UpperCAmelCase_      :							Optional[int]       =      list(range(len(_lowercase				)				)				)
     shuffle(_lowercase				)

     # 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_      :							List[str]       =      tf.Graph()

     with graph.as_default():
          # SESSION OF COMPUTATION

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

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

          ##Now lets construct the node that will compute the mean
          # The placeholder for the input
          UpperCAmelCase_      :							Tuple       =      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_      :							str       =      tf.reduce_mean(_lowercase			,						0				)

          ##Node for computing Euclidean distances
          # Placeholders for input
          UpperCAmelCase_      :							List[Any]       =      tf.placeholder('''float'''			,						[dim]				)
          UpperCAmelCase_      :							Tuple       =      tf.placeholder('''float'''			,						[dim]				)
          UpperCAmelCase_      :							Optional[Any]       =      tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(_lowercase			,						_lowercase				)			,						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_      :							int       =      tf.placeholder('''float'''			,						[noofclusters]				)
          UpperCAmelCase_      :							Any       =      tf.argmin(_lowercase			,						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_      :							List[Any]       =      tf.initialize_all_variables()

          # Initialize all variables
          sess.run(_lowercase				)

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

        # Return centroids and assignments
          UpperCAmelCase_      :							Optional[int]       =      sess.run(_lowercase				)
          UpperCAmelCase_      :							int       =      sess.run(_lowercase				)
          return centroids, assignments