




from random import shuffle

import tensorflow as tf
from numpy import array


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase )->	Union[str, Any]:
		SCREAMING_SNAKE_CASE_					      =							int(lowerCAmelCase )
		assert noofclusters < len(lowerCAmelCase )

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

		# Will help select random centroids from among the available vectors
		SCREAMING_SNAKE_CASE_					      =							list(range(len(lowerCAmelCase ) ) )
		shuffle(lowerCAmelCase )

		# 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.

		SCREAMING_SNAKE_CASE_					      =							tf.Graph()

		with graph.as_default():
				# SESSION OF COMPUTATION

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

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

				##Now lets construct the node that will compute the mean
				# The placeholder for the input
				SCREAMING_SNAKE_CASE_					      =							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
				SCREAMING_SNAKE_CASE_					      =							tf.reduce_mean(lowerCAmelCase    ,						0 )

				##Node for computing Euclidean distances
				# Placeholders for input
				SCREAMING_SNAKE_CASE_					      =							tf.placeholder('float'    ,						[dim] )
				SCREAMING_SNAKE_CASE_					      =							tf.placeholder('float'    ,						[dim] )
				SCREAMING_SNAKE_CASE_					      =							tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(lowerCAmelCase    ,						lowerCAmelCase )    ,						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
				SCREAMING_SNAKE_CASE_					      =							tf.placeholder('float'    ,						[noofclusters] )
				SCREAMING_SNAKE_CASE_					      =							tf.argmin(lowerCAmelCase    ,						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.
				SCREAMING_SNAKE_CASE_					      =							tf.initialize_all_variables()

				# Initialize all variables
				sess.run(lowerCAmelCase )

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

        # Return centroids and assignments
				SCREAMING_SNAKE_CASE_					      =							sess.run(lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							sess.run(lowerCAmelCase )
				return centroids, assignments





import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class    lowercase_		(SCREAMING_SNAKE_CASE__ ):
						lowerCAmelCase__			      =(KDPMaDiscreteScheduler,)
						lowerCAmelCase__			      =10



						def 	__a					(			self	:   Dict					,							**snake_case__	:   str  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							{
								    'num_train_timesteps': 11_00,
								    'beta_start': 0.00_01,
								    'beta_end': 0.02,
								    'beta_schedule': 'linear',
								}

								config.update(**snake_case__  )
								return config



						def 	__a					(			self	:   str  ):

								"""simple docstring"""

								for timesteps in [10, 50, 1_00, 10_00]:
										self.check_over_configs(num_train_timesteps=snake_case__  )



						def 	__a					(			self	:   List[Any]  ):

								"""simple docstring"""

								for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]					,							[0.00_02, 0.0_02, 0.02]  ):
										self.check_over_configs(beta_start=snake_case__					,							beta_end=snake_case__  )



						def 	__a					(			self	:   Optional[int]  ):

								"""simple docstring"""

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



						def 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

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



						def 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.scheduler_classes[0]
								SCREAMING_SNAKE_CASE_					      =							self.get_scheduler_config(prediction_type='v_prediction'  )
								SCREAMING_SNAKE_CASE_					      =							scheduler_class(**snake_case__  )

								scheduler.set_timesteps(self.num_inference_steps  )

								SCREAMING_SNAKE_CASE_					      =							self.dummy_model()
								SCREAMING_SNAKE_CASE_					      =							self.dummy_sample_deter * scheduler.init_noise_sigma
								SCREAMING_SNAKE_CASE_					      =							sample.to(snake_case__  )

								for i, t in enumerate(scheduler.timesteps  ):
										SCREAMING_SNAKE_CASE_					      =							scheduler.scale_model_input(snake_case__					,							snake_case__  )

										SCREAMING_SNAKE_CASE_					      =							model(snake_case__					,							snake_case__  )

										SCREAMING_SNAKE_CASE_					      =							scheduler.step(snake_case__					,							snake_case__					,							snake_case__  )
										SCREAMING_SNAKE_CASE_					      =							output.prev_sample

								SCREAMING_SNAKE_CASE_					      =							torch.sum(torch.abs(snake_case__  )  )
								SCREAMING_SNAKE_CASE_					      =							torch.mean(torch.abs(snake_case__  )  )

								if torch_device in ["cpu", "mps"]:
										assert abs(result_sum.item() - 4.69_34e-07  ) < 1e-2
										assert abs(result_mean.item() - 6.11_12e-10  ) < 1e-3
								else:
										# CUDA
										assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72e-07  ) < 1e-2
										assert abs(result_mean.item() - 0.00_02  ) < 1e-3



						def 	__a					(			self	:   Union[str, Any]  ):

								"""simple docstring"""

								if torch_device == "mps":
										return
								SCREAMING_SNAKE_CASE_					      =							self.scheduler_classes[0]
								SCREAMING_SNAKE_CASE_					      =							self.get_scheduler_config()
								SCREAMING_SNAKE_CASE_					      =							scheduler_class(**snake_case__  )

								scheduler.set_timesteps(self.num_inference_steps  )

								SCREAMING_SNAKE_CASE_					      =							self.dummy_model()
								SCREAMING_SNAKE_CASE_					      =							self.dummy_sample_deter * scheduler.init_noise_sigma
								SCREAMING_SNAKE_CASE_					      =							sample.to(snake_case__  )

								for i, t in enumerate(scheduler.timesteps  ):
										SCREAMING_SNAKE_CASE_					      =							scheduler.scale_model_input(snake_case__					,							snake_case__  )

										SCREAMING_SNAKE_CASE_					      =							model(snake_case__					,							snake_case__  )

										SCREAMING_SNAKE_CASE_					      =							scheduler.step(snake_case__					,							snake_case__					,							snake_case__  )
										SCREAMING_SNAKE_CASE_					      =							output.prev_sample

								SCREAMING_SNAKE_CASE_					      =							torch.sum(torch.abs(snake_case__  )  )
								SCREAMING_SNAKE_CASE_					      =							torch.mean(torch.abs(snake_case__  )  )

								if torch_device in ["cpu", "mps"]:
										assert abs(result_sum.item() - 20.41_25  ) < 1e-2
										assert abs(result_mean.item() - 0.02_66  ) < 1e-3
								else:
										# CUDA
										assert abs(result_sum.item() - 20.41_25  ) < 1e-2
										assert abs(result_mean.item() - 0.02_66  ) < 1e-3






						def 	__a					(			self	:   Optional[int]  ):

								"""simple docstring"""

								if torch_device == "mps":
										return
								SCREAMING_SNAKE_CASE_					      =							self.scheduler_classes[0]
								SCREAMING_SNAKE_CASE_					      =							self.get_scheduler_config()
								SCREAMING_SNAKE_CASE_					      =							scheduler_class(**snake_case__  )

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

								SCREAMING_SNAKE_CASE_					      =							self.dummy_model()
								SCREAMING_SNAKE_CASE_					      =							self.dummy_sample_deter.to(snake_case__  ) * scheduler.init_noise_sigma

								for t in scheduler.timesteps:
										SCREAMING_SNAKE_CASE_					      =							scheduler.scale_model_input(snake_case__					,							snake_case__  )

										SCREAMING_SNAKE_CASE_					      =							model(snake_case__					,							snake_case__  )

										SCREAMING_SNAKE_CASE_					      =							scheduler.step(snake_case__					,							snake_case__					,							snake_case__  )
										SCREAMING_SNAKE_CASE_					      =							output.prev_sample

								SCREAMING_SNAKE_CASE_					      =							torch.sum(torch.abs(snake_case__  )  )
								SCREAMING_SNAKE_CASE_					      =							torch.mean(torch.abs(snake_case__  )  )

								if str(snake_case__  ).startswith('cpu'  ):
										# The following sum varies between 148 and 156 on mps. Why?
										assert abs(result_sum.item() - 20.41_25  ) < 1e-2
										assert abs(result_mean.item() - 0.02_66  ) < 1e-3
								else:
										# CUDA
										assert abs(result_sum.item() - 20.41_25  ) < 1e-2
										assert abs(result_mean.item() - 0.02_66  ) < 1e-3