
'''simple docstring'''



import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						_lowercase		:				Optional[Any]									=       (EulerDiscreteScheduler,)
						_lowercase		:				Optional[Any]									=       1_0




						def  _lowercase     ( self   ,       **_lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			{
												    """num_train_timesteps""": 1_100,
												    """beta_start""": 0.0001,
												    """beta_end""": 0.02,
												    """beta_schedule""": """linear""",
												}

												config.update(**_lowercase				)
												return config




						def  _lowercase     ( self				):

												"""simple docstring"""



												for timesteps in [10, 50, 100, 1_000]:
																		self.check_over_configs(num_train_timesteps=_lowercase				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]   ,       [0.0002, 0.002, 0.02]				):
																		self.check_over_configs(beta_start=_lowercase   ,       beta_end=_lowercase				)




						def  _lowercase     ( self				):

												"""simple docstring"""



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




						def  _lowercase     ( self				):

												"""simple docstring"""



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




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.scheduler_classes[0]
												_lowerCAmelCase  						=			self.get_scheduler_config()
												_lowerCAmelCase  						=			scheduler_class(**_lowercase				)

												scheduler.set_timesteps(self.num_inference_steps				)

												_lowerCAmelCase  						=			torch.manual_seed(0				)

												_lowerCAmelCase  						=			self.dummy_model()
												_lowerCAmelCase  						=			self.dummy_sample_deter * scheduler.init_noise_sigma
												_lowerCAmelCase  						=			sample.to(_lowercase				)

												for i, t in enumerate(scheduler.timesteps				):
																		_lowerCAmelCase  						=			scheduler.scale_model_input(_lowercase   ,       _lowercase				)

																		_lowerCAmelCase  						=			model(_lowercase   ,       _lowercase				)

																		_lowerCAmelCase  						=			scheduler.step(_lowercase   ,       _lowercase   ,       _lowercase   ,       generator=_lowercase				)
																		_lowerCAmelCase  						=			output.prev_sample

												_lowerCAmelCase  						=			torch.sum(torch.abs(_lowercase				)				)
												_lowerCAmelCase  						=			torch.mean(torch.abs(_lowercase				)				)

												assert abs(result_sum.item() - 10.0807				) < 1e-2
												assert abs(result_mean.item() - 0.0131				) < 1e-3




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.scheduler_classes[0]
												_lowerCAmelCase  						=			self.get_scheduler_config(prediction_type="""v_prediction"""				)
												_lowerCAmelCase  						=			scheduler_class(**_lowercase				)

												scheduler.set_timesteps(self.num_inference_steps				)

												_lowerCAmelCase  						=			torch.manual_seed(0				)

												_lowerCAmelCase  						=			self.dummy_model()
												_lowerCAmelCase  						=			self.dummy_sample_deter * scheduler.init_noise_sigma
												_lowerCAmelCase  						=			sample.to(_lowercase				)

												for i, t in enumerate(scheduler.timesteps				):
																		_lowerCAmelCase  						=			scheduler.scale_model_input(_lowercase   ,       _lowercase				)

																		_lowerCAmelCase  						=			model(_lowercase   ,       _lowercase				)

																		_lowerCAmelCase  						=			scheduler.step(_lowercase   ,       _lowercase   ,       _lowercase   ,       generator=_lowercase				)
																		_lowerCAmelCase  						=			output.prev_sample

												_lowerCAmelCase  						=			torch.sum(torch.abs(_lowercase				)				)
												_lowerCAmelCase  						=			torch.mean(torch.abs(_lowercase				)				)

												assert abs(result_sum.item() - 0.0002				) < 1e-2
												assert abs(result_mean.item() - 2.2676e-06				) < 1e-3




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.scheduler_classes[0]
												_lowerCAmelCase  						=			self.get_scheduler_config()
												_lowerCAmelCase  						=			scheduler_class(**_lowercase				)

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

												_lowerCAmelCase  						=			torch.manual_seed(0				)

												_lowerCAmelCase  						=			self.dummy_model()
												_lowerCAmelCase  						=			self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
												_lowerCAmelCase  						=			sample.to(_lowercase				)

												for t in scheduler.timesteps:
																		_lowerCAmelCase  						=			scheduler.scale_model_input(_lowercase   ,       _lowercase				)

																		_lowerCAmelCase  						=			model(_lowercase   ,       _lowercase				)

																		_lowerCAmelCase  						=			scheduler.step(_lowercase   ,       _lowercase   ,       _lowercase   ,       generator=_lowercase				)
																		_lowerCAmelCase  						=			output.prev_sample

												_lowerCAmelCase  						=			torch.sum(torch.abs(_lowercase				)				)
												_lowerCAmelCase  						=			torch.mean(torch.abs(_lowercase				)				)

												assert abs(result_sum.item() - 10.0807				) < 1e-2
												assert abs(result_mean.item() - 0.0131				) < 1e-3




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.scheduler_classes[0]
												_lowerCAmelCase  						=			self.get_scheduler_config()
												_lowerCAmelCase  						=			scheduler_class(**_lowercase   ,       use_karras_sigmas=_lowercase				)

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

												_lowerCAmelCase  						=			torch.manual_seed(0				)

												_lowerCAmelCase  						=			self.dummy_model()
												_lowerCAmelCase  						=			self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
												_lowerCAmelCase  						=			sample.to(_lowercase				)

												for t in scheduler.timesteps:
																		_lowerCAmelCase  						=			scheduler.scale_model_input(_lowercase   ,       _lowercase				)

																		_lowerCAmelCase  						=			model(_lowercase   ,       _lowercase				)

																		_lowerCAmelCase  						=			scheduler.step(_lowercase   ,       _lowercase   ,       _lowercase   ,       generator=_lowercase				)
																		_lowerCAmelCase  						=			output.prev_sample

												_lowerCAmelCase  						=			torch.sum(torch.abs(_lowercase				)				)
												_lowerCAmelCase  						=			torch.mean(torch.abs(_lowercase				)				)

												assert abs(result_sum.item() - 124.52_2994_9951_1719				) < 1e-2
												assert abs(result_mean.item() - 0.1_6213_9326_3339_9963				) < 1e-3



'''simple docstring'''



# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position

_lowercase    =		"""2.13.1"""

import platform

import pyarrow
from packaging import version


if version.parse(platform.python_version()) < version.parse("""3.7"""):
						raise ImportWarning(
						    """To use `datasets`, Python>=3.7 is required, and the current version of Python doesn't match this condition."""
						)

if version.parse(pyarrow.__version__).major < 8:
						raise ImportWarning(
						    """To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn't match this condition.\n"""
						    """If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`."""
						)

del platform
del pyarrow
del version

from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
    list_datasets,
    list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
    NamedSplit,
    NamedSplitAll,
    Split,
    SplitBase,
    SplitDict,
    SplitGenerator,
    SplitInfo,
    SubSplitInfo,
    percent,
)
from .tasks import *
from .utils import *
from .utils import logging


# deprecated modules
from datasets import arrow_dataset as _arrow_dataset  # isort:skip
from datasets import utils as _utils  # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager  # isort:skip

_lowercase    =		concatenate_datasets
_lowercase    =		DownloadConfig
_lowercase    =		DownloadManager
_lowercase    =		DownloadMode
_lowercase    =		DownloadConfig
_lowercase    =		DownloadMode
_lowercase    =		DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager
