






import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler





class __lowerCamelCase			(				unittest.TestCase):





	"""simple docstring"""






	UpperCamelCase__								=				(ScoreSdeVeScheduler,)
	UpperCamelCase__								=				()

	@property
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      4
							_UpperCAmelCase     =      3
							_UpperCAmelCase     =      8
							_UpperCAmelCase     =      8

							_UpperCAmelCase     =      torch.rand((batch_size, num_channels, height, width)   )

							return sample

	@property
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      4
							_UpperCAmelCase     =      3
							_UpperCAmelCase     =      8
							_UpperCAmelCase     =      8

							_UpperCAmelCase     =      batch_size * num_channels * height * width
							_UpperCAmelCase     =      torch.arange(UpperCAmelCase   )
							_UpperCAmelCase     =      sample.reshape(UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase   )
							_UpperCAmelCase     =      sample / num_elems
							_UpperCAmelCase     =      sample.permute(3	,   0	,   1	,   2   )

							return sample

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							def model(UpperCAmelCase	,   UpperCAmelCase	,   *UpperCAmelCase   ):
													return sample * t / (t + 1)

							return model

	def 	UpperCamelCase   (							self	,   **UpperCAmelCase   ):
							"""simple docstring"""







							_UpperCAmelCase     =      {
							    'num_train_timesteps': 2000,
							    'snr': 0.15,
							    'sigma_min': 0.01,
							    'sigma_max': 1348,
							    'sampling_eps': 1e-5,
							}

							config.update(**UpperCAmelCase   )
							return config

	def 	UpperCamelCase   (							self	,   UpperCAmelCase=0	,   **UpperCAmelCase   ):
							"""simple docstring"""







							_UpperCAmelCase     =      dict(self.forward_default_kwargs   )

							for scheduler_class in self.scheduler_classes:
													_UpperCAmelCase     =      self.dummy_sample
													_UpperCAmelCase     =      0.1 * sample

													_UpperCAmelCase     =      self.get_scheduler_config(**UpperCAmelCase   )
													_UpperCAmelCase     =      scheduler_class(**UpperCAmelCase   )

													with tempfile.TemporaryDirectory() as tmpdirname:
																			scheduler.save_config(UpperCAmelCase   )
																			_UpperCAmelCase     =      scheduler_class.from_pretrained(UpperCAmelCase   )

													_UpperCAmelCase     =      scheduler.step_pred(
													    UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase	,   generator=torch.manual_seed(0   )	,   **UpperCAmelCase   ).prev_sample
													_UpperCAmelCase     =      new_scheduler.step_pred(
													    UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase	,   generator=torch.manual_seed(0   )	,   **UpperCAmelCase   ).prev_sample

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

													_UpperCAmelCase     =      scheduler.step_correct(UpperCAmelCase	,   UpperCAmelCase	,   generator=torch.manual_seed(0   )	,   **UpperCAmelCase   ).prev_sample
													_UpperCAmelCase     =      new_scheduler.step_correct(
													    UpperCAmelCase	,   UpperCAmelCase	,   generator=torch.manual_seed(0   )	,   **UpperCAmelCase   ).prev_sample

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

	def 	UpperCamelCase   (							self	,   UpperCAmelCase=0	,   **UpperCAmelCase   ):
							"""simple docstring"""







							_UpperCAmelCase     =      dict(self.forward_default_kwargs   )
							kwargs.update(UpperCAmelCase   )

							for scheduler_class in self.scheduler_classes:
													_UpperCAmelCase     =      self.dummy_sample
													_UpperCAmelCase     =      0.1 * sample

													_UpperCAmelCase     =      self.get_scheduler_config()
													_UpperCAmelCase     =      scheduler_class(**UpperCAmelCase   )

													with tempfile.TemporaryDirectory() as tmpdirname:
																			scheduler.save_config(UpperCAmelCase   )
																			_UpperCAmelCase     =      scheduler_class.from_pretrained(UpperCAmelCase   )

													_UpperCAmelCase     =      scheduler.step_pred(
													    UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase	,   generator=torch.manual_seed(0   )	,   **UpperCAmelCase   ).prev_sample
													_UpperCAmelCase     =      new_scheduler.step_pred(
													    UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase	,   generator=torch.manual_seed(0   )	,   **UpperCAmelCase   ).prev_sample

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

													_UpperCAmelCase     =      scheduler.step_correct(UpperCAmelCase	,   UpperCAmelCase	,   generator=torch.manual_seed(0   )	,   **UpperCAmelCase   ).prev_sample
													_UpperCAmelCase     =      new_scheduler.step_correct(
													    UpperCAmelCase	,   UpperCAmelCase	,   generator=torch.manual_seed(0   )	,   **UpperCAmelCase   ).prev_sample

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

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







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

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							for sigma_min, sigma_max in zip([0.00_01, 0.0_01, 0.01]	,   [1, 100, 1000]   ):
													self.check_over_configs(sigma_min=UpperCAmelCase	,   sigma_max=UpperCAmelCase   )

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							for t in [0.1, 0.5, 0.75]:
													self.check_over_forward(time_step=UpperCAmelCase   )

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      dict(self.forward_default_kwargs   )

							_UpperCAmelCase     =      self.scheduler_classes[0]
							_UpperCAmelCase     =      self.get_scheduler_config()
							_UpperCAmelCase     =      scheduler_class(**UpperCAmelCase   )

							_UpperCAmelCase     =      3

							_UpperCAmelCase     =      self.dummy_model()
							_UpperCAmelCase     =      self.dummy_sample_deter

							scheduler.set_sigmas(UpperCAmelCase   )
							scheduler.set_timesteps(UpperCAmelCase   )
							_UpperCAmelCase     =      torch.manual_seed(0   )

							for i, t in enumerate(scheduler.timesteps   ):
													_UpperCAmelCase     =      scheduler.sigmas[i]

													for _ in range(scheduler.config.correct_steps   ):
																			with torch.no_grad():
																									_UpperCAmelCase     =      model(UpperCAmelCase	,   UpperCAmelCase   )
																			_UpperCAmelCase     =      scheduler.step_correct(UpperCAmelCase	,   UpperCAmelCase	,   generator=UpperCAmelCase	,   **UpperCAmelCase   ).prev_sample

													with torch.no_grad():
																			_UpperCAmelCase     =      model(UpperCAmelCase	,   UpperCAmelCase   )

													_UpperCAmelCase     =      scheduler.step_pred(UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase	,   generator=UpperCAmelCase	,   **UpperCAmelCase   )
													_UpperCAmelCase ,			_UpperCAmelCase     =      output.prev_sample, output.prev_sample_mean

							_UpperCAmelCase     =      torch.sum(torch.abs(UpperCAmelCase   )   )
							_UpperCAmelCase     =      torch.mean(torch.abs(UpperCAmelCase   )   )

							assert np.isclose(result_sum.item()	,   1_43_72_75_85_28.0   )
							assert np.isclose(result_mean.item()	,   18_71_45_30.0   )

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      dict(self.forward_default_kwargs   )

							_UpperCAmelCase     =      kwargs.pop('num_inference_steps'	,   UpperCAmelCase   )

							for scheduler_class in self.scheduler_classes:
													_UpperCAmelCase     =      self.get_scheduler_config()
													_UpperCAmelCase     =      scheduler_class(**UpperCAmelCase   )

													_UpperCAmelCase     =      self.dummy_sample
													_UpperCAmelCase     =      0.1 * sample

													if num_inference_steps is not None and hasattr(UpperCAmelCase	,   'set_timesteps'   ):
																			scheduler.set_timesteps(UpperCAmelCase   )
													elif num_inference_steps is not None and not hasattr(UpperCAmelCase	,   'set_timesteps'   ):
																			_UpperCAmelCase     =      num_inference_steps

													_UpperCAmelCase     =      scheduler.step_pred(UpperCAmelCase	,   0	,   UpperCAmelCase	,   generator=torch.manual_seed(0   )	,   **UpperCAmelCase   ).prev_sample
													_UpperCAmelCase     =      scheduler.step_pred(UpperCAmelCase	,   1	,   UpperCAmelCase	,   generator=torch.manual_seed(0   )	,   **UpperCAmelCase   ).prev_sample

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








from __future__ import annotations

_a				     =  [2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]





class __lowerCamelCase			:





	"""simple docstring"""






	def __init__(							self	,   UpperCAmelCase   ):
							"""simple docstring"""







							_UpperCAmelCase     =      -1
							_UpperCAmelCase     =      -1
							_UpperCAmelCase     =      [-1] * length
							_UpperCAmelCase     =      None
							_UpperCAmelCase     =      None

	def __repr__(							self   ):
							"""simple docstring"""







							return F"""Node(min_value={self.minn} max_value={self.maxx})"""



def __A   (				__lowerCAmelCase     )->  Node | None:





						"""simple docstring"""



						_UpperCAmelCase     =      Node(len(__lowerCAmelCase     )     )
						_UpperCAmelCase ,			_UpperCAmelCase     =      min(__lowerCAmelCase     ), max(__lowerCAmelCase     )
						# Leaf node case where the node contains only one unique value
						if root.minn == root.maxx:
												return root
						_UpperCAmelCase     =      (root.minn + root.maxx) // 2

						_UpperCAmelCase     =      []
						_UpperCAmelCase     =      []

						for index, num in enumerate(__lowerCAmelCase     ):
												if num <= pivot:
																		left_arr.append(__lowerCAmelCase     )
												else:
																		right_arr.append(__lowerCAmelCase     )
												_UpperCAmelCase     =      len(__lowerCAmelCase     )
						_UpperCAmelCase     =      build_tree(__lowerCAmelCase     )
						_UpperCAmelCase     =      build_tree(__lowerCAmelCase     )
						return root


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  int:





						"""simple docstring"""



						if index < 0 or node is None:
												return 0
						# Leaf node cases
						if node.minn == node.maxx:
												return index + 1 if node.minn == num else 0
						_UpperCAmelCase     =      (node.minn + node.maxx) // 2
						if num <= pivot:
												# go the left subtree and map index to the left subtree
												return rank_till_index(node.left			,		__lowerCAmelCase			,		node.map_left[index] - 1     )
						else:
												# go to the right subtree and map index to the right subtree
												return rank_till_index(node.right			,		__lowerCAmelCase			,		index - node.map_left[index]     )


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  int:





						"""simple docstring"""



						if start > end:
												return 0
						_UpperCAmelCase     =      rank_till_index(__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )
						_UpperCAmelCase     =      rank_till_index(__lowerCAmelCase			,		__lowerCAmelCase			,		start - 1     )
						return rank_till_end - rank_before_start


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  int:





						"""simple docstring"""



						if index > (end - start) or start > end or node is None:
												return -1
						# Leaf node case
						if node.minn == node.maxx:
												return node.minn
						# Number of elements in the left subtree in interval [start, end]
						_UpperCAmelCase     =      node.map_left[end] - (
						    node.map_left[start - 1] if start else 0
						)
						if num_elements_in_left_tree > index:
												return quantile(
												    node.left			,		__lowerCAmelCase			,		(node.map_left[start - 1] if start else 0)			,		node.map_left[end] - 1			,		)
						else:
												return quantile(
												    node.right			,		index - num_elements_in_left_tree			,		start - (node.map_left[start - 1] if start else 0)			,		end - node.map_left[end]			,		)


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  int:





						"""simple docstring"""



						if (
						    start > end
						    or node is None
						    or start_num > end_num
						    or node.minn > end_num
						    or node.maxx < start_num
						):
												return 0
						if start_num <= node.minn and node.maxx <= end_num:
												return end - start + 1
						_UpperCAmelCase     =      range_counting(
						    node.left			,		(node.map_left[start - 1] if start else 0)			,		node.map_left[end] - 1			,		__lowerCAmelCase			,		__lowerCAmelCase			,		)
						_UpperCAmelCase     =      range_counting(
						    node.right			,		start - (node.map_left[start - 1] if start else 0)			,		end - node.map_left[end]			,		__lowerCAmelCase			,		__lowerCAmelCase			,		)
						return left + right


if __name__ == "__main__":
		import doctest

		doctest.testmod()
