




import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest





class     lowerCamelCase__(					__lowerCamelCase):
      UpperCAmelCase__      :	Tuple    		=   (UnCLIPScheduler,)
      def 			lowerCAmelCase__						(				self: List[Any]    ,       **UpperCamelCase_: Any   ):
             __lowerCamelCase   =						{
                 """num_train_timesteps""": 10_00,
                 """variance_type""": """fixed_small_log""",
                 """clip_sample""": True,
                 """clip_sample_range""": 1.0,
                 """prediction_type""": """epsilon""",
             }

             config.update(**UpperCamelCase_   )
             return config
      def 			lowerCAmelCase__						(				self: Optional[Any]   ):
             for timesteps in [1, 5, 1_00, 10_00]:
                    self.check_over_configs(num_train_timesteps=UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Optional[Any]   ):
             for variance in ["fixed_small_log", "learned_range"]:
                    self.check_over_configs(variance_type=UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: List[Any]   ):
             for clip_sample in [True, False]:
                    self.check_over_configs(clip_sample=UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Any   ):
             for clip_sample_range in [1, 5, 10, 20]:
                    self.check_over_configs(clip_sample_range=UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: List[Any]   ):
             for prediction_type in ["epsilon", "sample"]:
                    self.check_over_configs(prediction_type=UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: List[Any]   ):
             for time_step in [0, 5_00, 9_99]:
                    for prev_timestep in [None, 5, 1_00, 2_50, 5_00, 7_50]:
                           if prev_timestep is not None and prev_timestep >= time_step:
                                  continue

                           self.check_over_forward(time_step=UpperCamelCase_    ,       prev_timestep=UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Union[str, Any]   ):
             __lowerCamelCase   =						self.scheduler_classes[0]
             __lowerCamelCase   =						self.get_scheduler_config(variance_type="""fixed_small_log"""   )
             __lowerCamelCase   =						scheduler_class(**UpperCamelCase_   )

             assert torch.sum(torch.abs(scheduler._get_variance(0   ) - 1.0000E-10   )   ) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(4_87   ) - 0.054_9625   )   ) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(9_99   ) - 0.999_4987   )   ) < 1E-5
      def 			lowerCAmelCase__						(				self: List[Any]   ):
             __lowerCamelCase   =						self.scheduler_classes[0]
             __lowerCamelCase   =						self.get_scheduler_config(variance_type="""learned_range"""   )
             __lowerCamelCase   =						scheduler_class(**UpperCamelCase_   )

             __lowerCamelCase   =						0.5

             assert scheduler._get_variance(1    ,       predicted_variance=UpperCamelCase_   ) - -10.171_2790 < 1E-5
             assert scheduler._get_variance(4_87    ,       predicted_variance=UpperCamelCase_   ) - -5.799_8052 < 1E-5
             assert scheduler._get_variance(9_99    ,       predicted_variance=UpperCamelCase_   ) - -0.001_0011 < 1E-5
      def 			lowerCAmelCase__						(				self: Dict   ):
             __lowerCamelCase   =						self.scheduler_classes[0]
             __lowerCamelCase   =						self.get_scheduler_config()
             __lowerCamelCase   =						scheduler_class(**UpperCamelCase_   )

             __lowerCamelCase   =						scheduler.timesteps

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

             for i, t in enumerate(UpperCamelCase_   ):
                    # 1. predict noise residual
                    __lowerCamelCase   =						model(UpperCamelCase_    ,       UpperCamelCase_   )

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

                    __lowerCamelCase   =						pred_prev_sample

             __lowerCamelCase   =						torch.sum(torch.abs(UpperCamelCase_   )   )
             __lowerCamelCase   =						torch.mean(torch.abs(UpperCamelCase_   )   )

             assert abs(result_sum.item() - 252.268_2495   ) < 1E-2
             assert abs(result_mean.item() - 0.328_4743   ) < 1E-3
      def 			lowerCAmelCase__						(				self: int   ):
             __lowerCamelCase   =						self.scheduler_classes[0]
             __lowerCamelCase   =						self.get_scheduler_config()
             __lowerCamelCase   =						scheduler_class(**UpperCamelCase_   )

             scheduler.set_timesteps(25   )

             __lowerCamelCase   =						scheduler.timesteps

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

             for i, t in enumerate(UpperCamelCase_   ):
                    # 1. predict noise residual
                    __lowerCamelCase   =						model(UpperCamelCase_    ,       UpperCamelCase_   )

                    if i + 1 == timesteps.shape[0]:
                           __lowerCamelCase   =						None
                    else:
                           __lowerCamelCase   =						timesteps[i + 1]

                    # 2. predict previous mean of sample x_t-1
                    __lowerCamelCase   =						scheduler.step(
                        UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       prev_timestep=UpperCamelCase_    ,       generator=UpperCamelCase_   ).prev_sample

                    __lowerCamelCase   =						pred_prev_sample

             __lowerCamelCase   =						torch.sum(torch.abs(UpperCamelCase_   )   )
             __lowerCamelCase   =						torch.mean(torch.abs(UpperCamelCase_   )   )

             assert abs(result_sum.item() - 258.204_4983   ) < 1E-2
             assert abs(result_mean.item() - 0.336_2038   ) < 1E-3
      def 			lowerCAmelCase__						(				self: Any   ):
             pass




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



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

__A				='''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

__A				=concatenate_datasets
__A				=DownloadConfig
__A				=DownloadManager
__A				=DownloadMode
__A				=DownloadConfig
__A				=DownloadMode
__A				=DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager


