




'''simple docstring'''





import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest




class 			_snake_case		(     lowercase_							):
			lowerCAmelCase_					:				List[str]							=   (DEISMultistepScheduler,)
			lowerCAmelCase_					:				List[Any]							=   (("num_inference_steps", 25),)




			def        lowerCAmelCase__    ( self    ,							**a__							)    ->    List[str]:

										'''simple docstring'''




										snake_case_      =				{
										    "num_train_timesteps": 1_000,
										    "beta_start": 0.0_0_0_1,
										    "beta_end": 0.0_2,
										    "beta_schedule": "linear",
										    "solver_order": 2,
										}

										config.update(**a__							)
										return config




			def        lowerCAmelCase__    ( self    ,							a__=0    ,							**a__							)    ->    Dict:

										'''simple docstring'''




										snake_case_      =				dict(self.forward_default_kwargs							)
										snake_case_      =				kwargs.pop("num_inference_steps"    ,							a__							)
										snake_case_      =				self.dummy_sample
										snake_case_      =				0.1 * sample
										snake_case_      =				[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

										for scheduler_class in self.scheduler_classes:
																	snake_case_      =				self.get_scheduler_config(**a__							)
																	snake_case_      =				scheduler_class(**a__							)
																	scheduler.set_timesteps(a__							)
																	# copy over dummy past residuals
																	snake_case_      =				dummy_past_residuals[: scheduler.config.solver_order]

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

																	snake_case_ ,				snake_case_      =				sample, sample
																	for t in range(a__    ,							time_step + scheduler.config.solver_order + 1							):
																								snake_case_      =				scheduler.step(a__    ,							a__    ,							a__    ,							**a__							).prev_sample
																								snake_case_      =				new_scheduler.step(a__    ,							a__    ,							a__    ,							**a__							).prev_sample

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




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

										'''simple docstring'''




										pass




			def        lowerCAmelCase__    ( self    ,							a__=0    ,							**a__							)    ->    Tuple:

										'''simple docstring'''




										snake_case_      =				dict(self.forward_default_kwargs							)
										snake_case_      =				kwargs.pop("num_inference_steps"    ,							a__							)
										snake_case_      =				self.dummy_sample
										snake_case_      =				0.1 * sample
										snake_case_      =				[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

										for scheduler_class in self.scheduler_classes:
																	snake_case_      =				self.get_scheduler_config()
																	snake_case_      =				scheduler_class(**a__							)
																	scheduler.set_timesteps(a__							)

																	# copy over dummy past residuals (must be after setting timesteps)
																	snake_case_      =				dummy_past_residuals[: scheduler.config.solver_order]

																	with tempfile.TemporaryDirectory() as tmpdirname:
																								scheduler.save_config(a__							)
																								snake_case_      =				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)
																								snake_case_      =				dummy_past_residuals[: new_scheduler.config.solver_order]

																	snake_case_      =				scheduler.step(a__    ,							a__    ,							a__    ,							**a__							).prev_sample
																	snake_case_      =				new_scheduler.step(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__=None    ,							**a__							)    ->    Any:

										'''simple docstring'''




										if scheduler is None:
																	snake_case_      =				self.scheduler_classes[0]
																	snake_case_      =				self.get_scheduler_config(**a__							)
																	snake_case_      =				scheduler_class(**a__							)

										snake_case_      =				self.scheduler_classes[0]
										snake_case_      =				self.get_scheduler_config(**a__							)
										snake_case_      =				scheduler_class(**a__							)

										snake_case_      =				10
										snake_case_      =				self.dummy_model()
										snake_case_      =				self.dummy_sample_deter
										scheduler.set_timesteps(a__							)

										for i, t in enumerate(scheduler.timesteps							):
																	snake_case_      =				model(a__    ,							a__							)
																	snake_case_      =				scheduler.step(a__    ,							a__    ,							a__							).prev_sample

										return sample




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

										'''simple docstring'''




										snake_case_      =				dict(self.forward_default_kwargs							)

										snake_case_      =				kwargs.pop("num_inference_steps"    ,							a__							)

										for scheduler_class in self.scheduler_classes:
																	snake_case_      =				self.get_scheduler_config()
																	snake_case_      =				scheduler_class(**a__							)

																	snake_case_      =				self.dummy_sample
																	snake_case_      =				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"							):
																								snake_case_      =				num_inference_steps

																	# copy over dummy past residuals (must be done after set_timesteps)
																	snake_case_      =				[residual + 0.2, residual + 0.1_5, residual + 0.1_0]
																	snake_case_      =				dummy_past_residuals[: scheduler.config.solver_order]

																	snake_case_      =				scheduler.timesteps[5]
																	snake_case_      =				scheduler.timesteps[6]

																	snake_case_      =				scheduler.step(a__    ,							a__    ,							a__    ,							**a__							).prev_sample
																	snake_case_      =				scheduler.step(a__    ,							a__    ,							a__    ,							**a__							).prev_sample

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




			def        lowerCAmelCase__    ( self							)    ->    Dict:

										'''simple docstring'''




										snake_case_      =				DEISMultistepScheduler(**self.get_scheduler_config()							)
										snake_case_      =				self.full_loop(scheduler=a__							)
										snake_case_      =				torch.mean(torch.abs(a__							)							)

										assert abs(result_mean.item() - 0.2_3_9_1_6							) < 1e-3

										snake_case_      =				DPMSolverSinglestepScheduler.from_config(scheduler.config							)
										snake_case_      =				DPMSolverMultistepScheduler.from_config(scheduler.config							)
										snake_case_      =				UniPCMultistepScheduler.from_config(scheduler.config							)
										snake_case_      =				DEISMultistepScheduler.from_config(scheduler.config							)

										snake_case_      =				self.full_loop(scheduler=a__							)
										snake_case_      =				torch.mean(torch.abs(a__							)							)

										assert abs(result_mean.item() - 0.2_3_9_1_6							) < 1e-3




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

										'''simple docstring'''




										for timesteps in [25, 50, 100, 999, 1_000]:
																	self.check_over_configs(num_train_timesteps=a__							)




			def        lowerCAmelCase__    ( self							)    ->    Tuple:

										'''simple docstring'''




										self.check_over_configs(thresholding=a__							)
										for order in [1, 2, 3]:
																	for solver_type in ["logrho"]:
																								for threshold in [0.5, 1.0, 2.0]:
																															for prediction_type in ["epsilon", "sample"]:
																																						self.check_over_configs(
																																						    thresholding=a__    ,							prediction_type=a__    ,							sample_max_value=a__    ,							algorithm_type="deis"    ,							solver_order=a__    ,							solver_type=a__    ,							)




			def        lowerCAmelCase__    ( self							)    ->    Any:

										'''simple docstring'''




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




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

										'''simple docstring'''




										for algorithm_type in ["deis"]:
																	for solver_type in ["logrho"]:
																								for order in [1, 2, 3]:
																															for prediction_type in ["epsilon", "sample"]:
																																						self.check_over_configs(
																																						    solver_order=a__    ,							solver_type=a__    ,							prediction_type=a__    ,							algorithm_type=a__    ,							)
																																						snake_case_      =				self.full_loop(
																																						    solver_order=a__    ,							solver_type=a__    ,							prediction_type=a__    ,							algorithm_type=a__    ,							)
																																						assert not torch.isnan(a__							).any(), "Samples have nan numbers"




			def        lowerCAmelCase__    ( self							)    ->    Tuple:

										'''simple docstring'''




										self.check_over_configs(lower_order_final=a__							)
										self.check_over_configs(lower_order_final=a__							)




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

										'''simple docstring'''




										for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
																	self.check_over_forward(num_inference_steps=a__    ,							time_step=0							)




			def        lowerCAmelCase__    ( self							)    ->    int:

										'''simple docstring'''




										snake_case_      =				self.full_loop()
										snake_case_      =				torch.mean(torch.abs(a__							)							)

										assert abs(result_mean.item() - 0.2_3_9_1_6							) < 1e-3




			def        lowerCAmelCase__    ( self							)    ->    str:

										'''simple docstring'''




										snake_case_      =				self.full_loop(prediction_type="v_prediction"							)
										snake_case_      =				torch.mean(torch.abs(a__							)							)

										assert abs(result_mean.item() - 0.0_9_1							) < 1e-3




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

										'''simple docstring'''




										snake_case_      =				self.scheduler_classes[0]
										snake_case_      =				self.get_scheduler_config(thresholding=a__    ,							dynamic_thresholding_ratio=0							)
										snake_case_      =				scheduler_class(**a__							)

										snake_case_      =				10
										snake_case_      =				self.dummy_model()
										snake_case_      =				self.dummy_sample_deter.half()
										scheduler.set_timesteps(a__							)

										for i, t in enumerate(scheduler.timesteps							):
																	snake_case_      =				model(a__    ,							a__							)
																	snake_case_      =				scheduler.step(a__    ,							a__    ,							a__							).prev_sample

										assert sample.dtype == torch.floataa










'''simple docstring'''





from random import shuffle

import tensorflow as tf
from numpy import array


def 					UpperCamelCase_(     snake_case							:	Optional[int]							,    snake_case							:	Optional[int]					):

							'''simple docstring'''






							snake_case_      =				int(snake_case					)
							assert noofclusters < len(snake_case					)

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

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

							# 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_      =				tf.Graph()

							with graph.as_default():
														# SESSION OF COMPUTATION

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

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

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

														##Node for computing Euclidean distances
														# Placeholders for input
														snake_case_      =				tf.placeholder("float"							,    [dim]					)
														snake_case_      =				tf.placeholder("float"							,    [dim]					)
														snake_case_      =				tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(snake_case							,    snake_case					)							,    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_      =				tf.placeholder("float"							,    [noofclusters]					)
														snake_case_      =				tf.argmin(snake_case							,    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_      =				tf.initialize_all_variables()

														# Initialize all variables
														sess.run(snake_case					)

														##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_      =				1_0_0
														for _ in range(snake_case					):
																					##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(snake_case					)					):
																												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.
																												snake_case_      =				[
																												    sess.run(snake_case							,    feed_dict={va: vect, va: sess.run(snake_case					)}					)
																												    for centroid in centroids
																												]
																												# Now use the cluster assignment node, with the distances
																												# as the input
																												snake_case_      =				sess.run(
																												    snake_case							,    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(snake_case					):
																												# Collect all the vectors assigned to this cluster
																												snake_case_      =				[
																												    vectors[i]
																												    for i in range(len(snake_case					)					)
																												    if sess.run(assignments[i]					) == cluster_n
																												]
																												# Compute new centroid location
																												snake_case_      =				sess.run(
																												    snake_case							,    feed_dict={mean_input: array(snake_case					)}					)
																												# Assign value to appropriate variable
																												sess.run(
																												    cent_assigns[cluster_n]							,    feed_dict={centroid_value: new_location}					)

        # Return centroids and assignments
														snake_case_      =				sess.run(snake_case					)
														snake_case_      =				sess.run(snake_case					)
														return centroids, assignments




