


"""simple docstring"""



from random import shuffle

import tensorflow as tf
from numpy import array







def     __lowerCAmelCase		(		lowercase    :    int		,				lowercase    :    Optional[int]					)      ->		Union[str, Any]:


    """simple docstring"""





    snake_case       : Optional[int]    =       int(lowercase					)
    assert noofclusters < len(lowercase					)

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

    # Will help select random centroids from among the available vectors
    snake_case       : Any    =       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.

    snake_case       : Optional[int]    =       tf.Graph()

    with graph.as_default():
        # SESSION OF COMPUTATION

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

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

        ##Node for computing Euclidean distances
        # Placeholders for input
        snake_case       : Dict    =       tf.placeholder("float"		,				[dim]					)
        snake_case       : Any    =       tf.placeholder("float"		,				[dim]					)
        snake_case       : Dict    =       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
        snake_case       : Optional[int]    =       tf.placeholder("float"		,				[noofclusters]					)
        snake_case       : Optional[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.
        snake_case       : Tuple    =       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.
        snake_case       : Any    =       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					)					):
                snake_case       : 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.
                snake_case       : int    =       [
                    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
                snake_case       : Optional[int]    =       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
                snake_case       : List[str]    =       [
                    vectors[i]
                    for i in range(len(lowercase					)					)
                    if sess.run(assignments[i]					) == cluster_n
                ]
                # Compute new centroid location
                snake_case       : List[str]    =       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
        snake_case       : Optional[int]    =       sess.run(lowercase					)
        snake_case       : List[Any]    =       sess.run(lowercase					)
        return centroids, assignments









"""simple docstring"""



import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class _lowerCAmelCase							( snake_case_      ):
   __UpperCAmelCase					:			List[str]				    =    (KDPMaDiscreteScheduler,)
   __UpperCAmelCase					:			List[Any]				    =    10







   def 					lowerCamelCase						(      self		,      **UpperCamelCase__		)      ->						Optional[int]:

       '''simple docstring'''




       snake_case       : List[str]    =       {
           "num_train_timesteps": 1100,
           "beta_start": 0.0001,
           "beta_end": 0.02,
           "beta_schedule": "linear",
       }

       config.update(**UpperCamelCase__		)
       return config







   def 					lowerCamelCase						(      self		)      ->						Optional[int]:

       '''simple docstring'''




       for timesteps in [10, 50, 100, 1000]:
           self.check_over_configs(num_train_timesteps=UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						str:

       '''simple docstring'''




       for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]		,      [0.0002, 0.002, 0.02]		):
           self.check_over_configs(beta_start=UpperCamelCase__		,      beta_end=UpperCamelCase__		)







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

       '''simple docstring'''




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







   def 					lowerCamelCase						(      self		)      ->						List[str]:

       '''simple docstring'''




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







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

       '''simple docstring'''




       snake_case       : str    =       self.scheduler_classes[0]
       snake_case       : str    =       self.get_scheduler_config(prediction_type="v_prediction"		)
       snake_case       : Dict    =       scheduler_class(**UpperCamelCase__		)

       scheduler.set_timesteps(self.num_inference_steps		)

       snake_case       : List[Any]    =       self.dummy_model()
       snake_case       : List[str]    =       self.dummy_sample_deter * scheduler.init_noise_sigma
       snake_case       : Optional[Any]    =       sample.to(UpperCamelCase__		)

       for i, t in enumerate(scheduler.timesteps		):
           snake_case       : int    =       scheduler.scale_model_input(UpperCamelCase__		,      UpperCamelCase__		)

           snake_case       : int    =       model(UpperCamelCase__		,      UpperCamelCase__		)

           snake_case       : Optional[Any]    =       scheduler.step(UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		)
           snake_case       : Optional[Any]    =       output.prev_sample

       snake_case       : Optional[Any]    =       torch.sum(torch.abs(UpperCamelCase__		)		)
       snake_case       : Any    =       torch.mean(torch.abs(UpperCamelCase__		)		)

       if torch_device in ["cpu", "mps"]:
           assert abs(result_sum.item() - 4.6934e-07		) < 1e-2
           assert abs(result_mean.item() - 6.1112e-10		) < 1e-3
       else:
           # CUDA
           assert abs(result_sum.item() - 4.693_4286_5017_0972e-07		) < 1e-2
           assert abs(result_mean.item() - 0.0002		) < 1e-3







   def 					lowerCamelCase						(      self		)      ->						Dict:

       '''simple docstring'''




       if torch_device == "mps":
           return
       snake_case       : Optional[Any]    =       self.scheduler_classes[0]
       snake_case       : Optional[int]    =       self.get_scheduler_config()
       snake_case       : Dict    =       scheduler_class(**UpperCamelCase__		)

       scheduler.set_timesteps(self.num_inference_steps		)

       snake_case       : List[Any]    =       self.dummy_model()
       snake_case       : Union[str, Any]    =       self.dummy_sample_deter * scheduler.init_noise_sigma
       snake_case       : List[str]    =       sample.to(UpperCamelCase__		)

       for i, t in enumerate(scheduler.timesteps		):
           snake_case       : List[Any]    =       scheduler.scale_model_input(UpperCamelCase__		,      UpperCamelCase__		)

           snake_case       : Optional[int]    =       model(UpperCamelCase__		,      UpperCamelCase__		)

           snake_case       : Optional[int]    =       scheduler.step(UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		)
           snake_case       : int    =       output.prev_sample

       snake_case       : int    =       torch.sum(torch.abs(UpperCamelCase__		)		)
       snake_case       : Union[str, Any]    =       torch.mean(torch.abs(UpperCamelCase__		)		)

       if torch_device in ["cpu", "mps"]:
           assert abs(result_sum.item() - 20.4125		) < 1e-2
           assert abs(result_mean.item() - 0.0266		) < 1e-3
       else:
           # CUDA
           assert abs(result_sum.item() - 20.4125		) < 1e-2
           assert abs(result_mean.item() - 0.0266		) < 1e-3







   def 					lowerCamelCase						(      self		)      ->						Optional[int]:

       '''simple docstring'''




       if torch_device == "mps":
           return
       snake_case       : Any    =       self.scheduler_classes[0]
       snake_case       : Any    =       self.get_scheduler_config()
       snake_case       : Union[str, Any]    =       scheduler_class(**UpperCamelCase__		)

       scheduler.set_timesteps(self.num_inference_steps		,      device=UpperCamelCase__		)

       snake_case       : Optional[Any]    =       self.dummy_model()
       snake_case       : Optional[int]    =       self.dummy_sample_deter.to(UpperCamelCase__		) * scheduler.init_noise_sigma

       for t in scheduler.timesteps:
           snake_case       : Optional[Any]    =       scheduler.scale_model_input(UpperCamelCase__		,      UpperCamelCase__		)

           snake_case       : Any    =       model(UpperCamelCase__		,      UpperCamelCase__		)

           snake_case       : List[Any]    =       scheduler.step(UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		)
           snake_case       : Dict    =       output.prev_sample

       snake_case       : Dict    =       torch.sum(torch.abs(UpperCamelCase__		)		)
       snake_case       : str    =       torch.mean(torch.abs(UpperCamelCase__		)		)

       if str(UpperCamelCase__		).startswith("cpu"		):
           # The following sum varies between 148 and 156 on mps. Why?
           assert abs(result_sum.item() - 20.4125		) < 1e-2
           assert abs(result_mean.item() - 0.0266		) < 1e-3
       else:
           # CUDA
           assert abs(result_sum.item() - 20.4125		) < 1e-2
           assert abs(result_mean.item() - 0.0266		) < 1e-3





