


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

UpperCAmelCase_				:			Optional[Any]   =	'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

UpperCAmelCase_				:			Any   =	concatenate_datasets
UpperCAmelCase_				:			Dict   =	DownloadConfig
UpperCAmelCase_				:			Optional[int]   =	DownloadManager
UpperCAmelCase_				:			Dict   =	DownloadMode
UpperCAmelCase_				:			str   =	DownloadConfig
UpperCAmelCase_				:			List[Any]   =	DownloadMode
UpperCAmelCase_				:			Any   =	DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
   import torch

   from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class 			SCREAMING_SNAKE_CASE__  (    unittest.TestCase      ):
      def 	SCREAMING_SNAKE_CASE							(	self    :	Dict					)		->      List[str]:
             a_ :		List[str]							=  torch.tensor([-1_0_0, -1, -0.1, 0, 0.1, 1.0, 1_0_0]					)
             a_ :		Union[str, Any]							=  get_activation('gelu'					)
             self.assertTrue(torch.allclose(gelu_python(SCREAMING_SNAKE_CASE__					)					,							torch_builtin(SCREAMING_SNAKE_CASE__					)					)					)
             self.assertFalse(torch.allclose(gelu_python(SCREAMING_SNAKE_CASE__					)					,							gelu_new(SCREAMING_SNAKE_CASE__					)					)					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					)		->      Any:
             a_ :		List[str]							=  torch.tensor([-1_0_0, -1, -0.1, 0, 0.1, 1.0, 1_0_0]					)
             a_ :		Union[str, Any]							=  get_activation('gelu'					)
             a_ :		str							=  get_activation('gelu_10'					)

             a_ :		Tuple							=  torch_builtin(SCREAMING_SNAKE_CASE__					)
             a_ :		str							=  geluaa(SCREAMING_SNAKE_CASE__					)

             a_ :		List[Any]							=  torch.where(y_gelu_aa < 10.0					,							1					,							0					)

             self.assertTrue(torch.max(SCREAMING_SNAKE_CASE__					).item() == 10.0					)
             self.assertTrue(torch.allclose(y_gelu * clipped_mask					,							y_gelu_aa * clipped_mask					)					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Dict					)		->      List[Any]:
             get_activation('gelu'					)
             get_activation('gelu_10'					)
             get_activation('gelu_fast'					)
             get_activation('gelu_new'					)
             get_activation('gelu_python'					)
             get_activation('gelu_pytorch_tanh'					)
             get_activation('linear'					)
             get_activation('mish'					)
             get_activation('quick_gelu'					)
             get_activation('relu'					)
             get_activation('sigmoid'					)
             get_activation('silu'					)
             get_activation('swish'					)
             get_activation('tanh'					)
             with self.assertRaises(SCREAMING_SNAKE_CASE__					):
                    get_activation('bogus'					)
             with self.assertRaises(SCREAMING_SNAKE_CASE__					):
                    get_activation(SCREAMING_SNAKE_CASE__					)

      def 	SCREAMING_SNAKE_CASE							(	self    :	str					)		->      str:
             a_ :		Any							=  get_activation('gelu'					)
             a_ :		Any							=  1
             a_ :		int							=  get_activation('gelu'					)
             self.assertEqual(acta.a					,							1					)
             with self.assertRaises(SCREAMING_SNAKE_CASE__					):
                    a_ :		Tuple							=  acta.a


