
'''simple docstring'''







import io
import json

import fsspec
import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Optional[int]				,				__SCREAMING_SNAKE_CASE :			List[str]							):



						"""simple docstring"""





						assert isinstance(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE							)
						assert dataset.num_rows == 4
						assert dataset.num_columns == 3
						assert dataset.column_names == ["col_1", "col_2", "col_3"]
						for feature, expected_dtype in expected_features.items():
												assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''keep_in_memory'''				,				[False, True]							)
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			List[Any]				,				__SCREAMING_SNAKE_CASE :			Optional[Any]				,				__SCREAMING_SNAKE_CASE :			int							):



						"""simple docstring"""





						lowercase_     :					Optional[int]													=	tmp_path / '''cache'''
						lowercase_     :					int													=	{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
						with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
												lowercase_     :					Tuple													=	JsonDatasetReader(__SCREAMING_SNAKE_CASE				,				cache_dir=__SCREAMING_SNAKE_CASE				,				keep_in_memory=__SCREAMING_SNAKE_CASE							).read()
						_check_json_dataset(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE							)
@pytest.mark.parametrize(
    '''features'''				,				[
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ]				,				)
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Optional[int]				,				__SCREAMING_SNAKE_CASE :			str				,				__SCREAMING_SNAKE_CASE :			str							):



						"""simple docstring"""





						lowercase_     :					Union[str, Any]													=	tmp_path / '''cache'''
						lowercase_     :					str													=	{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
						lowercase_     :					str													=	features.copy() if features else default_expected_features
						lowercase_     :					Tuple													=	(
						    Features({feature: Value(__SCREAMING_SNAKE_CASE							) for feature, dtype in features.items()}							) if features is not None else None
						)
						lowercase_     :					Any													=	JsonDatasetReader(__SCREAMING_SNAKE_CASE				,				features=__SCREAMING_SNAKE_CASE				,				cache_dir=__SCREAMING_SNAKE_CASE							).read()
						_check_json_dataset(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE							)
@pytest.mark.parametrize(
    '''features'''				,				[
        None,
        {'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''},
    ]				,				)
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			str				,				__SCREAMING_SNAKE_CASE :			Tuple				,				__SCREAMING_SNAKE_CASE :			Any							):



						"""simple docstring"""





						lowercase_     :					List[Any]													=	tmp_path / '''cache'''
						lowercase_     :					Union[str, Any]													=	{'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''}
						lowercase_     :					Optional[Any]													=	features.copy() if features else default_expected_features
						lowercase_     :					Any													=	(
						    Features({feature: Value(__SCREAMING_SNAKE_CASE							) for feature, dtype in features.items()}							) if features is not None else None
						)
						lowercase_     :					Tuple													=	JsonDatasetReader(__SCREAMING_SNAKE_CASE				,				features=__SCREAMING_SNAKE_CASE				,				cache_dir=__SCREAMING_SNAKE_CASE							).read()
						assert isinstance(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE							)
						assert dataset.num_rows == 2
						assert dataset.num_columns == 3
						assert dataset.column_names == ["col_3", "col_1", "col_2"]
						for feature, expected_dtype in expected_features.items():
												assert dataset.features[feature].dtype == expected_dtype
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Optional[Any]				,				__SCREAMING_SNAKE_CASE :			int							):



						"""simple docstring"""





						lowercase_     :					Tuple													=	{'''col_2''': '''int64''', '''col_3''': '''float64''', '''col_1''': '''string'''}
						lowercase_     :					Dict													=	features.copy()
						lowercase_     :					Tuple													=	(
						    Features({feature: Value(__SCREAMING_SNAKE_CASE							) for feature, dtype in features.items()}							) if features is not None else None
						)
						lowercase_     :					Dict													=	tmp_path / '''cache'''
						lowercase_     :					List[str]													=	JsonDatasetReader(__SCREAMING_SNAKE_CASE				,				features=__SCREAMING_SNAKE_CASE				,				cache_dir=__SCREAMING_SNAKE_CASE							).read()
						assert isinstance(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE							)
						assert dataset.num_rows == 2
						assert dataset.num_columns == 3
						assert dataset.column_names == ["col_2", "col_3", "col_1"]
						for feature, expected_dtype in expected_features.items():
												assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''split'''				,				[None, NamedSplit('''train'''							), '''train''', '''test''']							)
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Dict				,				__SCREAMING_SNAKE_CASE :			Optional[Any]				,				__SCREAMING_SNAKE_CASE :			List[str]							):



						"""simple docstring"""





						lowercase_     :					Dict													=	tmp_path / '''cache'''
						lowercase_     :					Any													=	{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
						lowercase_     :					Tuple													=	JsonDatasetReader(__SCREAMING_SNAKE_CASE				,				cache_dir=__SCREAMING_SNAKE_CASE				,				split=__SCREAMING_SNAKE_CASE							).read()
						_check_json_dataset(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE							)
						assert dataset.split == split if split else "train"
@pytest.mark.parametrize('''path_type'''				,				[str, list]							)
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Any				,				__SCREAMING_SNAKE_CASE :			Union[str, Any]				,				__SCREAMING_SNAKE_CASE :			List[Any]							):



						"""simple docstring"""





						if issubclass(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE							):
												lowercase_     :					List[str]													=	jsonl_path
						elif issubclass(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE							):
												lowercase_     :					str													=	[jsonl_path]
						lowercase_     :					Any													=	tmp_path / '''cache'''
						lowercase_     :					str													=	{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
						lowercase_     :					Optional[Any]													=	JsonDatasetReader(__SCREAMING_SNAKE_CASE				,				cache_dir=__SCREAMING_SNAKE_CASE							).read()
						_check_json_dataset(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE							)
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			str				,				__SCREAMING_SNAKE_CASE :			Dict				,				__SCREAMING_SNAKE_CASE :			Union[str, Any]=("train",)							):



						"""simple docstring"""





						assert isinstance(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE							)
						for split in splits:
												lowercase_     :					Dict													=	dataset_dict[split]
												assert dataset.num_rows == 4
												assert dataset.num_columns == 3
												assert dataset.column_names == ["col_1", "col_2", "col_3"]
												for feature, expected_dtype in expected_features.items():
																		assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''keep_in_memory'''				,				[False, True]							)
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Optional[int]				,				__SCREAMING_SNAKE_CASE :			int				,				__SCREAMING_SNAKE_CASE :			List[str]							):



						"""simple docstring"""





						lowercase_     :					List[Any]													=	tmp_path / '''cache'''
						lowercase_     :					Dict													=	{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
						with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
												lowercase_     :					Tuple													=	JsonDatasetReader({'''train''': jsonl_path}				,				cache_dir=__SCREAMING_SNAKE_CASE				,				keep_in_memory=__SCREAMING_SNAKE_CASE							).read()
						_check_json_datasetdict(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE							)
@pytest.mark.parametrize(
    '''features'''				,				[
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ]				,				)
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Optional[Any]				,				__SCREAMING_SNAKE_CASE :			Optional[Any]				,				__SCREAMING_SNAKE_CASE :			int							):



						"""simple docstring"""





						lowercase_     :					Dict													=	tmp_path / '''cache'''
						lowercase_     :					Dict													=	{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
						lowercase_     :					Dict													=	features.copy() if features else default_expected_features
						lowercase_     :					int													=	(
						    Features({feature: Value(__SCREAMING_SNAKE_CASE							) for feature, dtype in features.items()}							) if features is not None else None
						)
						lowercase_     :					str													=	JsonDatasetReader({'''train''': jsonl_path}				,				features=__SCREAMING_SNAKE_CASE				,				cache_dir=__SCREAMING_SNAKE_CASE							).read()
						_check_json_datasetdict(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE							)
@pytest.mark.parametrize('''split'''				,				[None, NamedSplit('''train'''							), '''train''', '''test''']							)
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Optional[Any]				,				__SCREAMING_SNAKE_CASE :			List[str]				,				__SCREAMING_SNAKE_CASE :			Optional[int]							):



						"""simple docstring"""





						if split:
												lowercase_     :					Dict													=	{split: jsonl_path}
						else:
												lowercase_     :					Optional[Any]													=	'''train'''
												lowercase_     :					Dict													=	{'''train''': jsonl_path, '''test''': jsonl_path}
						lowercase_     :					Union[str, Any]													=	tmp_path / '''cache'''
						lowercase_     :					Optional[Any]													=	{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
						lowercase_     :					Union[str, Any]													=	JsonDatasetReader(__SCREAMING_SNAKE_CASE				,				cache_dir=__SCREAMING_SNAKE_CASE							).read()
						_check_json_datasetdict(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE				,				splits=list(path.keys()							)							)
						assert all(dataset[split].split == split for split in path.keys()							)
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Dict							):



						"""simple docstring"""





						return json.load(__SCREAMING_SNAKE_CASE							)
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			List[str]							):



						"""simple docstring"""





						return [json.loads(__SCREAMING_SNAKE_CASE							) for line in buffer]






class 			lowerCAmelCase__       :






					@pytest.mark.parametrize('''lines, load_json_function'''    ,							[(True, load_json_lines), (False, load_json)]		)
					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											with io.BytesIO() as buffer:
																	JsonDatasetWriter(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							lines=__SCREAMING_SNAKE_CASE		).write()
																	buffer.seek(0		)
																	lowercase_     :					List[str]													=	load_json_function(__SCREAMING_SNAKE_CASE		)
											assert isinstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)
											assert isinstance(exported_content[0]    ,							__SCREAMING_SNAKE_CASE		)
											assert len(__SCREAMING_SNAKE_CASE		) == 10






					@pytest.mark.parametrize(
					    '''orient, container, keys, len_at'''    ,							[
					        ('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None),
					        ('''split''', dict, {'''columns''', '''data'''}, '''data'''),
					        ('''index''', dict, set('''0123456789'''		), None),
					        ('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''),
					        ('''values''', list, None, None),
					        ('''table''', dict, {'''schema''', '''data'''}, '''data'''),
					    ]    ,							)
					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											with io.BytesIO() as buffer:
																	JsonDatasetWriter(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							lines=__SCREAMING_SNAKE_CASE    ,							orient=__SCREAMING_SNAKE_CASE		).write()
																	buffer.seek(0		)
																	lowercase_     :					Union[str, Any]													=	load_json(__SCREAMING_SNAKE_CASE		)
											assert isinstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)
											if keys:
																	if container is dict:
																							assert exported_content.keys() == keys
																	else:
																							assert exported_content[0].keys() == keys
											else:
																	assert not hasattr(__SCREAMING_SNAKE_CASE    ,							'''keys'''		) and not hasattr(exported_content[0]    ,							'''keys'''		)
											if len_at:
																	assert len(exported_content[len_at]		) == 10
											else:
																	assert len(__SCREAMING_SNAKE_CASE		) == 10






					@pytest.mark.parametrize('''lines, load_json_function'''    ,							[(True, load_json_lines), (False, load_json)]		)
					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											with io.BytesIO() as buffer:
																	JsonDatasetWriter(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							lines=__SCREAMING_SNAKE_CASE    ,							num_proc=2		).write()
																	buffer.seek(0		)
																	lowercase_     :					List[Any]													=	load_json_function(__SCREAMING_SNAKE_CASE		)
											assert isinstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)
											assert isinstance(exported_content[0]    ,							__SCREAMING_SNAKE_CASE		)
											assert len(__SCREAMING_SNAKE_CASE		) == 10






					@pytest.mark.parametrize(
					    '''orient, container, keys, len_at'''    ,							[
					        ('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None),
					        ('''split''', dict, {'''columns''', '''data'''}, '''data'''),
					        ('''index''', dict, set('''0123456789'''		), None),
					        ('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''),
					        ('''values''', list, None, None),
					        ('''table''', dict, {'''schema''', '''data'''}, '''data'''),
					    ]    ,							)
					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											with io.BytesIO() as buffer:
																	JsonDatasetWriter(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							lines=__SCREAMING_SNAKE_CASE    ,							orient=__SCREAMING_SNAKE_CASE    ,							num_proc=2		).write()
																	buffer.seek(0		)
																	lowercase_     :					Optional[Any]													=	load_json(__SCREAMING_SNAKE_CASE		)
											assert isinstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)
											if keys:
																	if container is dict:
																							assert exported_content.keys() == keys
																	else:
																							assert exported_content[0].keys() == keys
											else:
																	assert not hasattr(__SCREAMING_SNAKE_CASE    ,							'''keys'''		) and not hasattr(exported_content[0]    ,							'''keys'''		)
											if len_at:
																	assert len(exported_content[len_at]		) == 10
											else:
																	assert len(__SCREAMING_SNAKE_CASE		) == 10






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											with pytest.raises(__SCREAMING_SNAKE_CASE		):
																	with io.BytesIO() as buffer:
																							JsonDatasetWriter(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							num_proc=0		)






					@pytest.mark.parametrize('''compression, extension'''    ,							[('''gzip''', '''gz'''), ('''bz2''', '''bz2'''), ('''xz''', '''xz''')]		)
					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					Any													=	tmp_path_factory.mktemp('''data'''		) / F'''test.json.{extension}'''
											lowercase_     :					Any													=	str(shared_datadir / F'''test_file.json.{extension}'''		)
											JsonDatasetWriter(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							compression=__SCREAMING_SNAKE_CASE		).write()

											with fsspec.open(__SCREAMING_SNAKE_CASE    ,							'''rb'''    ,							compression='''infer'''		) as f:
																	lowercase_     :					Tuple													=	f.read()
											with fsspec.open(__SCREAMING_SNAKE_CASE    ,							'''rb'''    ,							compression='''infer'''		) as f:
																	lowercase_     :					Union[str, Any]													=	f.read()
											assert exported_content == original_content





'''simple docstring'''







import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest






class 			lowerCAmelCase__       (  lowerCamelCase_					):
					lowerCAmelCase_					       =					(DDPMScheduler,)






					def      _snake_case   (       self    ,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					Optional[Any]													=	{
											    '''num_train_timesteps''': 10_00,
											    '''beta_start''': 0.0_001,
											    '''beta_end''': 0.02,
											    '''beta_schedule''': '''linear''',
											    '''variance_type''': '''fixed_small''',
											    '''clip_sample''': True,
											}

											config.update(**__SCREAMING_SNAKE_CASE		)
											return config






					def      _snake_case   (       self		):


											"""simple docstring"""





											for timesteps in [1, 5, 1_00, 10_00]:
																	self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]    ,							[0.002, 0.02, 0.2, 2]		):
																	self.check_over_configs(beta_start=__SCREAMING_SNAKE_CASE    ,							beta_end=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											for schedule in ["linear", "squaredcos_cap_v2"]:
																	self.check_over_configs(beta_schedule=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											for variance in ["fixed_small", "fixed_large", "other"]:
																	self.check_over_configs(variance_type=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											for clip_sample in [True, False]:
																	self.check_over_configs(clip_sample=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											self.check_over_configs(thresholding=__SCREAMING_SNAKE_CASE		)
											for threshold in [0.5, 1.0, 2.0]:
																	for prediction_type in ["epsilon", "sample", "v_prediction"]:
																							self.check_over_configs(
																							    thresholding=__SCREAMING_SNAKE_CASE    ,							prediction_type=__SCREAMING_SNAKE_CASE    ,							sample_max_value=__SCREAMING_SNAKE_CASE    ,							)






					def      _snake_case   (       self		):


											"""simple docstring"""





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






					def      _snake_case   (       self		):


											"""simple docstring"""





											for t in [0, 5_00, 9_99]:
																	self.check_over_forward(time_step=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					List[str]													=	self.scheduler_classes[0]
											lowercase_     :					int													=	self.get_scheduler_config()
											lowercase_     :					str													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											assert torch.sum(torch.abs(scheduler._get_variance(0		) - 0.0		)		) < 1E-5
											assert torch.sum(torch.abs(scheduler._get_variance(4_87		) - 0.00_979		)		) < 1E-5
											assert torch.sum(torch.abs(scheduler._get_variance(9_99		) - 0.02		)		) < 1E-5






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Union[str, Any]													=	self.scheduler_classes[0]
											lowercase_     :					Any													=	self.get_scheduler_config()
											lowercase_     :					Optional[int]													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											lowercase_     :					Any													=	len(__SCREAMING_SNAKE_CASE		)

											lowercase_     :					Optional[int]													=	self.dummy_model()
											lowercase_     :					Any													=	self.dummy_sample_deter
											lowercase_     :					List[str]													=	torch.manual_seed(0		)

											for t in reversed(range(__SCREAMING_SNAKE_CASE		)		):
																	# 1. predict noise residual
																	lowercase_     :					Optional[int]													=	model(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)

																	# 2. predict previous mean of sample x_t-1
																	lowercase_     :					Union[str, Any]													=	scheduler.step(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							generator=__SCREAMING_SNAKE_CASE		).prev_sample

																	# if t > 0:
																	#     noise = self.dummy_sample_deter
																	#     variance = scheduler.get_variance(t) ** (0.5) * noise
																	#
																	# sample = pred_prev_sample + variance
																	lowercase_     :					List[str]													=	pred_prev_sample

											lowercase_     :					str													=	torch.sum(torch.abs(__SCREAMING_SNAKE_CASE		)		)
											lowercase_     :					Tuple													=	torch.mean(torch.abs(__SCREAMING_SNAKE_CASE		)		)

											assert abs(result_sum.item() - 258.9_606		) < 1E-2
											assert abs(result_mean.item() - 0.3_372		) < 1E-3






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Optional[Any]													=	self.scheduler_classes[0]
											lowercase_     :					Tuple													=	self.get_scheduler_config(prediction_type='''v_prediction'''		)
											lowercase_     :					Any													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											lowercase_     :					Optional[int]													=	len(__SCREAMING_SNAKE_CASE		)

											lowercase_     :					List[str]													=	self.dummy_model()
											lowercase_     :					int													=	self.dummy_sample_deter
											lowercase_     :					str													=	torch.manual_seed(0		)

											for t in reversed(range(__SCREAMING_SNAKE_CASE		)		):
																	# 1. predict noise residual
																	lowercase_     :					Optional[Any]													=	model(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)

																	# 2. predict previous mean of sample x_t-1
																	lowercase_     :					Optional[Any]													=	scheduler.step(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							generator=__SCREAMING_SNAKE_CASE		).prev_sample

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

											lowercase_     :					str													=	torch.sum(torch.abs(__SCREAMING_SNAKE_CASE		)		)
											lowercase_     :					Tuple													=	torch.mean(torch.abs(__SCREAMING_SNAKE_CASE		)		)

											assert abs(result_sum.item() - 202.0_296		) < 1E-2
											assert abs(result_mean.item() - 0.2_631		) < 1E-3






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Optional[int]													=	self.scheduler_classes[0]
											lowercase_     :					int													=	self.get_scheduler_config()
											lowercase_     :					int													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											lowercase_     :					int													=	[1_00, 87, 50, 1, 0]

											scheduler.set_timesteps(timesteps=__SCREAMING_SNAKE_CASE		)

											lowercase_     :					List[str]													=	scheduler.timesteps

											for i, timestep in enumerate(__SCREAMING_SNAKE_CASE		):
																	if i == len(__SCREAMING_SNAKE_CASE		) - 1:
																							lowercase_     :					str													=	-1
																	else:
																							lowercase_     :					Any													=	timesteps[i + 1]

																	lowercase_     :					Optional[Any]													=	scheduler.previous_timestep(__SCREAMING_SNAKE_CASE		)
																	lowercase_     :					int													=	prev_t.item()

																	self.assertEqual(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Tuple													=	self.scheduler_classes[0]
											lowercase_     :					List[Any]													=	self.get_scheduler_config()
											lowercase_     :					str													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											lowercase_     :					Optional[int]													=	[1_00, 87, 50, 51, 0]

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






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Optional[Any]													=	self.scheduler_classes[0]
											lowercase_     :					str													=	self.get_scheduler_config()
											lowercase_     :					Tuple													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											lowercase_     :					Tuple													=	[1_00, 87, 50, 1, 0]
											lowercase_     :					List[str]													=	len(__SCREAMING_SNAKE_CASE		)

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






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					List[Any]													=	self.scheduler_classes[0]
											lowercase_     :					Optional[int]													=	self.get_scheduler_config()
											lowercase_     :					Any													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											lowercase_     :					str													=	[scheduler.config.num_train_timesteps]

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



