

from random import shuffle

import tensorflow as tf
from numpy import array





def       snake_case_ (__A	:		int   ,  __A	:		Optional[Any]	)     ->      int:
   __lowerCAmelCase    :     List[str]          =		int(__A	)
   assert noofclusters < len(__A	)

   # Find out the dimensionality
   __lowerCAmelCase    :     Union[str, Any]          =		len(vectors[0]	)

   # Will help select random centroids from among the available vectors
   __lowerCAmelCase    :     List[Any]          =		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.

   __lowerCAmelCase    :     Any          =		tf.Graph()

   with graph.as_default():
      # SESSION OF COMPUTATION

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

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

      ##Node for computing Euclidean distances
      # Placeholders for input
      __lowerCAmelCase    :     Union[str, Any]          =		tf.placeholder("""float"""   ,  [dim]	)
      __lowerCAmelCase    :     Any          =		tf.placeholder("""float"""   ,  [dim]	)
      __lowerCAmelCase    :     Any          =		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
      __lowerCAmelCase    :     Any          =		tf.placeholder("""float"""   ,  [noofclusters]	)
      __lowerCAmelCase    :     Tuple          =		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.
      __lowerCAmelCase    :     str          =		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.
      __lowerCAmelCase    :     List[str]          =		1_0_0
      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	)	):
            __lowerCAmelCase    :     List[str]          =		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    :     Optional[Any]          =		[
                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
            __lowerCAmelCase    :     Tuple          =		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
            __lowerCAmelCase    :     List[str]          =		[
                vectors[i]
                for i in range(len(__A	)	)
                if sess.run(assignments[i]	) == cluster_n
            ]
            # Compute new centroid location
            __lowerCAmelCase    :     Tuple          =		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
      __lowerCAmelCase    :     List[Any]          =		sess.run(__A	)
      __lowerCAmelCase    :     int          =		sess.run(__A	)
      return centroids, assignments








# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin




class 		__A   (      UpperCamelCase__       ,    UpperCamelCase__      ):
						UpperCamelCase      =       1

						@register_to_config
						def __init__(		self :Any     ,			__snake_case :Tuple=20_00     ,			__snake_case :Optional[Any]=0.1     ,			__snake_case :Any=20     ,			__snake_case :Optional[int]=1E-3			):
										'''simple docstring'''

										__magic_name__						:						Dict										=None
										__magic_name__						:						List[str]										=None
										__magic_name__						:						str										=None

						def      A__    (		self :Dict     ,			__snake_case :Optional[int]     ,			__snake_case :Union[str, torch.device] = None			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=torch.linspace(1     ,			self.config.sampling_eps     ,			__snake_case     ,			device=__snake_case			)

						def      A__    (		self :List[str]     ,			__snake_case :List[str]     ,			__snake_case :int     ,			__snake_case :int     ,			__snake_case :List[str]=None			):
										'''simple docstring'''

										if self.timesteps is None:
														raise ValueError(
														    """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""			)

										# TODO(Patrick) better comments + non-PyTorch
										# postprocess model score
										__magic_name__						:						int										=(
										    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
										)
										__magic_name__						:						Optional[int]										=torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff			)			)
										__magic_name__						:						str										=std.flatten()
										while len(std.shape			) < len(score.shape			):
														__magic_name__						:						List[str]										=std.unsqueeze(-1			)
										__magic_name__						:						Union[str, Any]										=-score / std

										# compute
										__magic_name__						:						Tuple										=-1.0 / len(self.timesteps			)

										__magic_name__						:						int										=self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
										__magic_name__						:						Dict										=beta_t.flatten()
										while len(beta_t.shape			) < len(x.shape			):
														__magic_name__						:						Any										=beta_t.unsqueeze(-1			)
										__magic_name__						:						Dict										=-0.5 * beta_t * x

										__magic_name__						:						Optional[int]										=torch.sqrt(__snake_case			)
										__magic_name__						:						int										=drift - diffusion**2 * score
										__magic_name__						:						List[str]										=x + drift * dt

										# add noise
										__magic_name__						:						Optional[int]										=randn_tensor(x.shape     ,			layout=x.layout     ,			generator=__snake_case     ,			device=x.device     ,			dtype=x.dtype			)
										__magic_name__						:						Optional[Any]										=x_mean + diffusion * math.sqrt(-dt			) * noise

										return x, x_mean


						def __len__(		self :List[Any]			):
										'''simple docstring'''

										return self.config.num_train_timesteps




