
import random
import unittest

from torch.utils.data import BatchSampler, DataLoader, IterableDataset

from accelerate import Accelerator
from accelerate.data_loader import (
    BatchSamplerShard,
    DataLoaderDispatcher,
    DataLoaderShard,
    IterableDatasetShard,
    SkipBatchSampler,
    SkipDataLoader,
    skip_first_batches,
)


class        lowercase      (	_SCREAMING_SNAKE_CASE     ):



		def __init__(				self       ,   A_=0.01       ,   A_=1_000 )						->      str:
				"""simple docstring"""
				UpperCamelCase						=  p_stop
				UpperCamelCase						=  max_length



		def __iter__(				self )						->      Tuple:
				"""simple docstring"""
				UpperCamelCase						=  0
				UpperCamelCase						=  False
				while not stop and count < self.max_length:
						yield count
						count += 1
						UpperCamelCase						=  random.random() < self.p_stop






class        lowercase      (	unittest.TestCase     ):



		def __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_=False       ,   A_=True )						->      List[Any]:
				"""simple docstring"""
				UpperCamelCase						=  [
				    BatchSamplerShard(A_       ,   2       ,   A_       ,   split_batches=A_       ,   even_batches=A_ )
				    for i in range(2 )
				]
				UpperCamelCase						=  [list(A_ ) for batch_sampler_shard in batch_sampler_shards]
				if not split_batches:
						self.assertListEqual([len(A_ ) for shard in batch_sampler_shards]       ,   [len(A_ ) for e in expected] )
				self.assertListEqual(A_       ,   A_ )



		def __UpperCamelCase				(				self )						->      List[str]:
				"""simple docstring"""
				UpperCamelCase						=  BatchSampler(range(24 )       ,   batch_size=3       ,   drop_last=A_ )
				UpperCamelCase						=  [
				    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
				    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
				]
				self.check_batch_sampler_shards(A_       ,   A_ )

				UpperCamelCase						=  BatchSampler(range(24 )       ,   batch_size=3       ,   drop_last=A_ )
				# Expected shouldn't change
				self.check_batch_sampler_shards(A_       ,   A_ )

				# Check the shards when the dataset is a round multiple of batch size but not total batch size.
				UpperCamelCase						=  BatchSampler(range(21 )       ,   batch_size=3       ,   drop_last=A_ )
				UpperCamelCase						=  [
				    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
				    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [0, 1, 2]],
				]
				self.check_batch_sampler_shards(A_       ,   A_ )

				UpperCamelCase						=  BatchSampler(range(21 )       ,   batch_size=3       ,   drop_last=A_ )
				UpperCamelCase						=  [
				    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
				    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
				]
				self.check_batch_sampler_shards(A_       ,   A_ )

				# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
				# num_processes batch.
				UpperCamelCase						=  BatchSampler(range(22 )       ,   batch_size=3       ,   drop_last=A_ )
				UpperCamelCase						=  [
				    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
				    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 0, 1]],
				]
				self.check_batch_sampler_shards(A_       ,   A_ )

				UpperCamelCase						=  BatchSampler(range(22 )       ,   batch_size=3       ,   drop_last=A_ )
				UpperCamelCase						=  [
				    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
				    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
				]
				self.check_batch_sampler_shards(A_       ,   A_ )

				# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
				# num_processes batch.
				UpperCamelCase						=  BatchSampler(range(20 )       ,   batch_size=3       ,   drop_last=A_ )
				UpperCamelCase						=  [
				    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 0]],
				    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [1, 2, 3]],
				]
				self.check_batch_sampler_shards(A_       ,   A_ )

				UpperCamelCase						=  BatchSampler(range(20 )       ,   batch_size=3       ,   drop_last=A_ )
				UpperCamelCase						=  [
				    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
				    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
				]
				self.check_batch_sampler_shards(A_       ,   A_ )

				# Check the shards when the dataset is very small.
				UpperCamelCase						=  BatchSampler(range(2 )       ,   batch_size=3       ,   drop_last=A_ )
				UpperCamelCase						=  [[[0, 1, 0]], [[1, 0, 1]]]
				self.check_batch_sampler_shards(A_       ,   A_ )

				UpperCamelCase						=  BatchSampler(range(2 )       ,   batch_size=3       ,   drop_last=A_ )
				UpperCamelCase						=  [[], []]
				self.check_batch_sampler_shards(A_       ,   A_ )



		def __UpperCamelCase				(				self )						->      List[Any]:
				"""simple docstring"""
				UpperCamelCase						=  BatchSampler(range(24 )       ,   batch_size=4       ,   drop_last=A_ )
				UpperCamelCase						=  [
				    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
				    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
				]
				self.check_batch_sampler_shards(A_       ,   A_       ,   split_batches=A_ )

				UpperCamelCase						=  BatchSampler(range(24 )       ,   batch_size=4       ,   drop_last=A_ )
				# Expected shouldn't change
				self.check_batch_sampler_shards(A_       ,   A_       ,   split_batches=A_ )

				# Check the shards when the dataset is not a round multiple of batch size.
				UpperCamelCase						=  BatchSampler(range(22 )       ,   batch_size=4       ,   drop_last=A_ )
				UpperCamelCase						=  [
				    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
				    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [0, 1]],
				]
				self.check_batch_sampler_shards(A_       ,   A_       ,   split_batches=A_ )

				UpperCamelCase						=  BatchSampler(range(22 )       ,   batch_size=4       ,   drop_last=A_ )
				UpperCamelCase						=  [
				    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
				    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
				]
				self.check_batch_sampler_shards(A_       ,   A_       ,   split_batches=A_ )

				# Check the shards when the dataset is not a round multiple of batch size or num_processes.
				UpperCamelCase						=  BatchSampler(range(21 )       ,   batch_size=4       ,   drop_last=A_ )
				UpperCamelCase						=  [
				    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 0]],
				    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [1, 2]],
				]
				self.check_batch_sampler_shards(A_       ,   A_       ,   split_batches=A_ )

				UpperCamelCase						=  BatchSampler(range(21 )       ,   batch_size=4       ,   drop_last=A_ )
				UpperCamelCase						=  [
				    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
				    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
				]
				self.check_batch_sampler_shards(A_       ,   A_       ,   split_batches=A_ )

				# Check the shards when the dataset is very small.
				UpperCamelCase						=  BatchSampler(range(2 )       ,   batch_size=4       ,   drop_last=A_ )
				UpperCamelCase						=  [[[0, 1]], [[0, 1]]]
				self.check_batch_sampler_shards(A_       ,   A_       ,   split_batches=A_ )

				UpperCamelCase						=  BatchSampler(range(2 )       ,   batch_size=4       ,   drop_last=A_ )
				UpperCamelCase						=  [[], []]
				self.check_batch_sampler_shards(A_       ,   A_       ,   split_batches=A_ )



		def __UpperCamelCase				(				self )						->      List[Any]:
				"""simple docstring"""
				UpperCamelCase						=  BatchSampler(range(24 )       ,   batch_size=3       ,   drop_last=A_ )
				UpperCamelCase						=  [
				    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
				    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
				]
				self.check_batch_sampler_shards(A_       ,   A_       ,   even_batches=A_ )

				UpperCamelCase						=  BatchSampler(range(24 )       ,   batch_size=3       ,   drop_last=A_ )
				# Expected shouldn't change
				self.check_batch_sampler_shards(A_       ,   A_       ,   even_batches=A_ )

				# Check the shards when the dataset is a round multiple of batch size but not total batch size.
				UpperCamelCase						=  BatchSampler(range(21 )       ,   batch_size=3       ,   drop_last=A_ )
				UpperCamelCase						=  [
				    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
				    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
				]
				self.check_batch_sampler_shards(A_       ,   A_       ,   even_batches=A_ )

				UpperCamelCase						=  BatchSampler(range(21 )       ,   batch_size=3       ,   drop_last=A_ )
				UpperCamelCase						=  [
				    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
				    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
				]
				self.check_batch_sampler_shards(A_       ,   A_       ,   even_batches=A_ )

				# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
				# num_processes batch.
				UpperCamelCase						=  BatchSampler(range(22 )       ,   batch_size=3       ,   drop_last=A_ )
				UpperCamelCase						=  [
				    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
				    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21]],
				]
				self.check_batch_sampler_shards(A_       ,   A_       ,   even_batches=A_ )

				UpperCamelCase						=  BatchSampler(range(22 )       ,   batch_size=3       ,   drop_last=A_ )
				UpperCamelCase						=  [
				    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
				    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
				]
				self.check_batch_sampler_shards(A_       ,   A_       ,   even_batches=A_ )

				# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
				# num_processes batch.
				UpperCamelCase						=  BatchSampler(range(20 )       ,   batch_size=3       ,   drop_last=A_ )
				UpperCamelCase						=  [
				    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19]],
				    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
				]
				self.check_batch_sampler_shards(A_       ,   A_       ,   even_batches=A_ )

				UpperCamelCase						=  BatchSampler(range(20 )       ,   batch_size=3       ,   drop_last=A_ )
				UpperCamelCase						=  [
				    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
				    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
				]
				self.check_batch_sampler_shards(A_       ,   A_       ,   even_batches=A_ )

				# Check the shards when the dataset is very small.
				UpperCamelCase						=  BatchSampler(range(2 )       ,   batch_size=3       ,   drop_last=A_ )
				UpperCamelCase						=  [[[0, 1]], []]
				self.check_batch_sampler_shards(A_       ,   A_       ,   even_batches=A_ )

				UpperCamelCase						=  BatchSampler(range(2 )       ,   batch_size=3       ,   drop_last=A_ )
				UpperCamelCase						=  [[], []]
				self.check_batch_sampler_shards(A_       ,   A_       ,   even_batches=A_ )



		def __UpperCamelCase				(				self )						->      Optional[Any]:
				"""simple docstring"""
				UpperCamelCase						=  BatchSampler(range(24 )       ,   batch_size=4       ,   drop_last=A_ )
				UpperCamelCase						=  [
				    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
				    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
				]
				self.check_batch_sampler_shards(A_       ,   A_       ,   split_batches=A_       ,   even_batches=A_ )

				UpperCamelCase						=  BatchSampler(range(24 )       ,   batch_size=4       ,   drop_last=A_ )
				# Expected shouldn't change
				self.check_batch_sampler_shards(A_       ,   A_       ,   split_batches=A_       ,   even_batches=A_ )

				# Check the shards when the dataset is not a round multiple of batch size.
				UpperCamelCase						=  BatchSampler(range(22 )       ,   batch_size=4       ,   drop_last=A_ )
				UpperCamelCase						=  [
				    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
				    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
				]
				self.check_batch_sampler_shards(A_       ,   A_       ,   split_batches=A_       ,   even_batches=A_ )

				UpperCamelCase						=  BatchSampler(range(22 )       ,   batch_size=4       ,   drop_last=A_ )
				UpperCamelCase						=  [
				    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
				    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
				]
				self.check_batch_sampler_shards(A_       ,   A_       ,   split_batches=A_       ,   even_batches=A_ )

				# Check the shards when the dataset is not a round multiple of batch size or num_processes.
				UpperCamelCase						=  BatchSampler(range(21 )       ,   batch_size=4       ,   drop_last=A_ )
				UpperCamelCase						=  [
				    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20]],
				    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
				]
				self.check_batch_sampler_shards(A_       ,   A_       ,   split_batches=A_       ,   even_batches=A_ )

				UpperCamelCase						=  BatchSampler(range(21 )       ,   batch_size=4       ,   drop_last=A_ )
				UpperCamelCase						=  [
				    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
				    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
				]
				self.check_batch_sampler_shards(A_       ,   A_       ,   split_batches=A_       ,   even_batches=A_ )

				# Check the shards when the dataset is very small.
				UpperCamelCase						=  BatchSampler(range(2 )       ,   batch_size=4       ,   drop_last=A_ )
				UpperCamelCase						=  [[[0, 1]], []]
				self.check_batch_sampler_shards(A_       ,   A_       ,   split_batches=A_       ,   even_batches=A_ )

				UpperCamelCase						=  BatchSampler(range(2 )       ,   batch_size=4       ,   drop_last=A_ )
				UpperCamelCase						=  [[], []]
				self.check_batch_sampler_shards(A_       ,   A_       ,   split_batches=A_       ,   even_batches=A_ )



		def __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase						=  [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
				UpperCamelCase						=  [BatchSamplerShard(A_       ,   2       ,   A_       ,   even_batches=A_ ) for i in range(2 )]

				self.assertEqual(len(batch_sampler_shards[0] )       ,   3 )
				self.assertEqual(len(batch_sampler_shards[1] )       ,   2 )

				self.assertListEqual(list(batch_sampler_shards[0] )       ,   [[0, 1, 2], [5, 6, 7, 8], [12, 13]] )
				self.assertListEqual(list(batch_sampler_shards[1] )       ,   [[3, 4], [9, 10, 11]] )



		def __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_       ,   A_=False       ,   A_=2       ,   A_=False )						->      Union[str, Any]:
				"""simple docstring"""
				random.seed(A_ )
				UpperCamelCase						=  list(A_ )

				UpperCamelCase						=  [
				    IterableDatasetShard(
				        A_       ,   batch_size=A_       ,   drop_last=A_       ,   num_processes=A_       ,   process_index=A_       ,   split_batches=A_       ,   )
				    for i in range(A_ )
				]
				UpperCamelCase						=  []
				for iterable_dataset_shard in iterable_dataset_shards:
						# Since our random iterable dataset will be... random... we need to use a seed to get reproducible results.
						random.seed(A_ )
						iterable_dataset_lists.append(list(A_ ) )

				UpperCamelCase						=  batch_size // num_processes if split_batches else batch_size
				# All iterable dataset shard should have the same length, a round multiple of shard_batch_size
				UpperCamelCase						=  iterable_dataset_lists[0]
				for l in iterable_dataset_lists[1:]:
						self.assertEqual(len(A_ )       ,   len(A_ ) )
						self.assertTrue(len(A_ ) % shard_batch_size == 0 )

				UpperCamelCase						=  []
				for idx in range(0       ,   len(A_ )       ,   A_ ):
						for l in iterable_dataset_lists:
								observed += l[idx : idx + shard_batch_size]

				if not drop_last:
						while len(A_ ) < len(A_ ):
								reference += reference
				self.assertListEqual(A_       ,   reference[: len(A_ )] )



		def __UpperCamelCase				(				self )						->      Dict:
				"""simple docstring"""
				UpperCamelCase						=  42
				UpperCamelCase						=  RandomIterableDataset()

				self.check_iterable_dataset_shards(A_       ,   A_       ,   batch_size=4       ,   drop_last=A_       ,   split_batches=A_ )
				self.check_iterable_dataset_shards(A_       ,   A_       ,   batch_size=4       ,   drop_last=A_       ,   split_batches=A_ )
				self.check_iterable_dataset_shards(A_       ,   A_       ,   batch_size=4       ,   drop_last=A_       ,   split_batches=A_ )
				self.check_iterable_dataset_shards(A_       ,   A_       ,   batch_size=4       ,   drop_last=A_       ,   split_batches=A_ )

				# Edge case with a very small dataset
				UpperCamelCase						=  RandomIterableDataset(max_length=2 )

				self.check_iterable_dataset_shards(A_       ,   A_       ,   batch_size=4       ,   drop_last=A_       ,   split_batches=A_ )
				self.check_iterable_dataset_shards(A_       ,   A_       ,   batch_size=4       ,   drop_last=A_       ,   split_batches=A_ )
				self.check_iterable_dataset_shards(A_       ,   A_       ,   batch_size=4       ,   drop_last=A_       ,   split_batches=A_ )
				self.check_iterable_dataset_shards(A_       ,   A_       ,   batch_size=4       ,   drop_last=A_       ,   split_batches=A_ )



		def __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase						=  BatchSampler(range(16 )       ,   batch_size=4       ,   drop_last=A_ )
				UpperCamelCase						=  SkipBatchSampler(A_       ,   2 )
				self.assertListEqual(list(A_ )       ,   [[8, 9, 10, 11], [12, 13, 14, 15]] )



		def __UpperCamelCase				(				self )						->      List[str]:
				"""simple docstring"""
				UpperCamelCase						=  SkipDataLoader(list(range(16 ) )       ,   batch_size=4       ,   skip_batches=2 )
				self.assertListEqual([t.tolist() for t in dataloader]       ,   [[8, 9, 10, 11], [12, 13, 14, 15]] )



		def __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase						=  DataLoader(list(range(16 ) )       ,   batch_size=4 )
				UpperCamelCase						=  skip_first_batches(A_       ,   num_batches=2 )
				self.assertListEqual([t.tolist() for t in new_dataloader]       ,   [[8, 9, 10, 11], [12, 13, 14, 15]] )



		def __UpperCamelCase				(				self )						->      Optional[int]:
				"""simple docstring"""
				UpperCamelCase						=  DataLoaderShard(list(range(16 ) )       ,   batch_size=4 )
				for idx, _ in enumerate(A_ ):
						self.assertEqual(dataloader.end_of_dataloader       ,   idx == 3 )

				# Test it also works on the second iteration
				for idx, _ in enumerate(A_ ):
						self.assertEqual(dataloader.end_of_dataloader       ,   idx == 3 )



		def __UpperCamelCase				(				self )						->      List[str]:
				"""simple docstring"""
				Accelerator()
				UpperCamelCase						=  DataLoaderDispatcher(range(16 )       ,   batch_size=4 )
				for idx, _ in enumerate(A_ ):
						self.assertEqual(dataloader.end_of_dataloader       ,   idx == 3 )

				# Test it also works on the second iteration
				for idx, _ in enumerate(A_ ):
						self.assertEqual(dataloader.end_of_dataloader       ,   idx == 3 )

import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest


class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : Dict			  =       (DDPMScheduler,)



		def __UpperCamelCase				(				self       ,   **A_ )						->      Dict:
				"""simple docstring"""
				UpperCamelCase						=  {
				    'num_train_timesteps': 1_000,
				    'beta_start': 0.0001,
				    'beta_end': 0.02,
				    'beta_schedule': 'linear',
				    'variance_type': 'fixed_small',
				    'clip_sample': True,
				}

				config.update(**A_ )
				return config



		def __UpperCamelCase				(				self )						->      List[Any]:
				"""simple docstring"""
				for timesteps in [1, 5, 100, 1_000]:
						self.check_over_configs(num_train_timesteps=A_ )



		def __UpperCamelCase				(				self )						->      str:
				"""simple docstring"""
				for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]       ,   [0.002, 0.02, 0.2, 2] ):
						self.check_over_configs(beta_start=A_       ,   beta_end=A_ )



		def __UpperCamelCase				(				self )						->      Any:
				"""simple docstring"""
				for schedule in ["linear", "squaredcos_cap_v2"]:
						self.check_over_configs(beta_schedule=A_ )



		def __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				for variance in ["fixed_small", "fixed_large", "other"]:
						self.check_over_configs(variance_type=A_ )



		def __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				for clip_sample in [True, False]:
						self.check_over_configs(clip_sample=A_ )



		def __UpperCamelCase				(				self )						->      Optional[Any]:
				"""simple docstring"""
				self.check_over_configs(thresholding=A_ )
				for threshold in [0.5, 1.0, 2.0]:
						for prediction_type in ["epsilon", "sample", "v_prediction"]:
								self.check_over_configs(
								    thresholding=A_       ,   prediction_type=A_       ,   sample_max_value=A_       ,   )



		def __UpperCamelCase				(				self )						->      Optional[int]:
				"""simple docstring"""
				for prediction_type in ["epsilon", "sample", "v_prediction"]:
						self.check_over_configs(prediction_type=A_ )



		def __UpperCamelCase				(				self )						->      Optional[Any]:
				"""simple docstring"""
				for t in [0, 500, 999]:
						self.check_over_forward(time_step=A_ )



		def __UpperCamelCase				(				self )						->      List[str]:
				"""simple docstring"""
				UpperCamelCase						=  self.scheduler_classes[0]
				UpperCamelCase						=  self.get_scheduler_config()
				UpperCamelCase						=  scheduler_class(**A_ )

				assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1e-5
				assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.0_0979 ) ) < 1e-5
				assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.02 ) ) < 1e-5



		def __UpperCamelCase				(				self )						->      List[Any]:
				"""simple docstring"""
				UpperCamelCase						=  self.scheduler_classes[0]
				UpperCamelCase						=  self.get_scheduler_config()
				UpperCamelCase						=  scheduler_class(**A_ )

				UpperCamelCase						=  len(A_ )

				UpperCamelCase						=  self.dummy_model()
				UpperCamelCase						=  self.dummy_sample_deter
				UpperCamelCase						=  torch.manual_seed(0 )

				for t in reversed(range(A_ ) ):
						# 1. predict noise residual
						UpperCamelCase						=  model(A_       ,   A_ )

						# 2. predict previous mean of sample x_t-1
						UpperCamelCase						=  scheduler.step(A_       ,   A_       ,   A_       ,   generator=A_ ).prev_sample

						# if t > 0:
						#     noise = self.dummy_sample_deter
						#     variance = scheduler.get_variance(t) ** (0.5) * noise
						#
						# sample = pred_prev_sample + variance
						UpperCamelCase						=  pred_prev_sample

				UpperCamelCase						=  torch.sum(torch.abs(A_ ) )
				UpperCamelCase						=  torch.mean(torch.abs(A_ ) )

				assert abs(result_sum.item() - 258.9606 ) < 1e-2
				assert abs(result_mean.item() - 0.3372 ) < 1e-3



		def __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				UpperCamelCase						=  self.scheduler_classes[0]
				UpperCamelCase						=  self.get_scheduler_config(prediction_type='v_prediction' )
				UpperCamelCase						=  scheduler_class(**A_ )

				UpperCamelCase						=  len(A_ )

				UpperCamelCase						=  self.dummy_model()
				UpperCamelCase						=  self.dummy_sample_deter
				UpperCamelCase						=  torch.manual_seed(0 )

				for t in reversed(range(A_ ) ):
						# 1. predict noise residual
						UpperCamelCase						=  model(A_       ,   A_ )

						# 2. predict previous mean of sample x_t-1
						UpperCamelCase						=  scheduler.step(A_       ,   A_       ,   A_       ,   generator=A_ ).prev_sample

						# if t > 0:
						#     noise = self.dummy_sample_deter
						#     variance = scheduler.get_variance(t) ** (0.5) * noise
						#
						# sample = pred_prev_sample + variance
						UpperCamelCase						=  pred_prev_sample

				UpperCamelCase						=  torch.sum(torch.abs(A_ ) )
				UpperCamelCase						=  torch.mean(torch.abs(A_ ) )

				assert abs(result_sum.item() - 202.0296 ) < 1e-2
				assert abs(result_mean.item() - 0.2631 ) < 1e-3



		def __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase						=  self.scheduler_classes[0]
				UpperCamelCase						=  self.get_scheduler_config()
				UpperCamelCase						=  scheduler_class(**A_ )

				UpperCamelCase						=  [100, 87, 50, 1, 0]

				scheduler.set_timesteps(timesteps=A_ )

				UpperCamelCase						=  scheduler.timesteps

				for i, timestep in enumerate(A_ ):
						if i == len(A_ ) - 1:
								UpperCamelCase						=  -1
						else:
								UpperCamelCase						=  timesteps[i + 1]

						UpperCamelCase						=  scheduler.previous_timestep(A_ )
						UpperCamelCase						=  prev_t.item()

						self.assertEqual(A_       ,   A_ )



		def __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				UpperCamelCase						=  self.scheduler_classes[0]
				UpperCamelCase						=  self.get_scheduler_config()
				UpperCamelCase						=  scheduler_class(**A_ )

				UpperCamelCase						=  [100, 87, 50, 51, 0]

				with self.assertRaises(A_       ,   msg='`custom_timesteps` must be in descending order.' ):
						scheduler.set_timesteps(timesteps=A_ )



		def __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase						=  self.scheduler_classes[0]
				UpperCamelCase						=  self.get_scheduler_config()
				UpperCamelCase						=  scheduler_class(**A_ )

				UpperCamelCase						=  [100, 87, 50, 1, 0]
				UpperCamelCase						=  len(A_ )

				with self.assertRaises(A_       ,   msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.' ):
						scheduler.set_timesteps(num_inference_steps=A_       ,   timesteps=A_ )



		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  self.scheduler_classes[0]
				UpperCamelCase						=  self.get_scheduler_config()
				UpperCamelCase						=  scheduler_class(**A_ )

				UpperCamelCase						=  [scheduler.config.num_train_timesteps]

				with self.assertRaises(
				    A_       ,   msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'       ,   ):
						scheduler.set_timesteps(timesteps=A_ )
