


"""simple docstring"""







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

a__				:							Dict						=     '''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__				:							List[Any]						=     concatenate_datasets
a__				:							List[str]						=     DownloadConfig
a__				:							Optional[Any]						=     DownloadManager
a__				:							Dict						=     DownloadMode
a__				:							List[str]						=     DownloadConfig
a__				:							List[str]						=     DownloadMode
a__				:							Tuple						=     DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager




"""simple docstring"""







import warnings

from ...utils import is_sklearn_available, requires_backends


if is_sklearn_available():
		from scipy.stats import pearsonr, spearmanr
		from sklearn.metrics import fa_score, matthews_corrcoef


a__				:							Any						=     (
    '''This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate '''
    '''library. You can have a look at this example script for pointers: '''
    '''https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'''
)




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					warnings.warn(lowerCAmelCase_  ,						lowerCAmelCase_   )
					requires_backends(lowerCAmelCase_  ,						"sklearn"   )
					return (preds == labels).mean()




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					warnings.warn(lowerCAmelCase_  ,						lowerCAmelCase_   )
					requires_backends(lowerCAmelCase_  ,						"sklearn"   )
					__SCREAMING_SNAKE_CASE						   =				simple_accuracy(lowerCAmelCase_  ,						lowerCAmelCase_   )
					__SCREAMING_SNAKE_CASE						   =				fa_score(y_true=lowerCAmelCase_  ,						y_pred=lowerCAmelCase_   )
					return {
					    "acc": acc,
					    "f1": fa,
					    "acc_and_f1": (acc + fa) / 2,
					}




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					warnings.warn(lowerCAmelCase_  ,						lowerCAmelCase_   )
					requires_backends(lowerCAmelCase_  ,						"sklearn"   )
					__SCREAMING_SNAKE_CASE						   =				pearsonr(lowerCAmelCase_  ,						lowerCAmelCase_   )[0]
					__SCREAMING_SNAKE_CASE						   =				spearmanr(lowerCAmelCase_  ,						lowerCAmelCase_   )[0]
					return {
					    "pearson": pearson_corr,
					    "spearmanr": spearman_corr,
					    "corr": (pearson_corr + spearman_corr) / 2,
					}




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					warnings.warn(lowerCAmelCase_  ,						lowerCAmelCase_   )
					requires_backends(lowerCAmelCase_  ,						"sklearn"   )
					assert len(lowerCAmelCase_   ) == len(lowerCAmelCase_   ), f"""Predictions and labels have mismatched lengths {len(lowerCAmelCase_   )} and {len(lowerCAmelCase_   )}"""
					if task_name == "cola":
										return {"mcc": matthews_corrcoef(lowerCAmelCase_  ,						lowerCAmelCase_   )}
					elif task_name == "sst-2":
										return {"acc": simple_accuracy(lowerCAmelCase_  ,						lowerCAmelCase_   )}
					elif task_name == "mrpc":
										return acc_and_fa(lowerCAmelCase_  ,						lowerCAmelCase_   )
					elif task_name == "sts-b":
										return pearson_and_spearman(lowerCAmelCase_  ,						lowerCAmelCase_   )
					elif task_name == "qqp":
										return acc_and_fa(lowerCAmelCase_  ,						lowerCAmelCase_   )
					elif task_name == "mnli":
										return {"mnli/acc": simple_accuracy(lowerCAmelCase_  ,						lowerCAmelCase_   )}
					elif task_name == "mnli-mm":
										return {"mnli-mm/acc": simple_accuracy(lowerCAmelCase_  ,						lowerCAmelCase_   )}
					elif task_name == "qnli":
										return {"acc": simple_accuracy(lowerCAmelCase_  ,						lowerCAmelCase_   )}
					elif task_name == "rte":
										return {"acc": simple_accuracy(lowerCAmelCase_  ,						lowerCAmelCase_   )}
					elif task_name == "wnli":
										return {"acc": simple_accuracy(lowerCAmelCase_  ,						lowerCAmelCase_   )}
					elif task_name == "hans":
										return {"acc": simple_accuracy(lowerCAmelCase_  ,						lowerCAmelCase_   )}
					else:
										raise KeyError(lowerCAmelCase_   )




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					warnings.warn(lowerCAmelCase_  ,						lowerCAmelCase_   )
					requires_backends(lowerCAmelCase_  ,						"sklearn"   )
					if len(lowerCAmelCase_   ) != len(lowerCAmelCase_   ):
										raise ValueError(f"""Predictions and labels have mismatched lengths {len(lowerCAmelCase_   )} and {len(lowerCAmelCase_   )}"""   )
					if task_name == "xnli":
										return {"acc": simple_accuracy(lowerCAmelCase_  ,						lowerCAmelCase_   )}
					else:
										raise KeyError(lowerCAmelCase_   )
