






'''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


lowerCamelCase_				: int    	=							(
    '''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 	__magic_name__( _A						,      _A					):
						'''simple docstring'''





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







def 	__magic_name__( _A						,      _A					):
						'''simple docstring'''





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







def 	__magic_name__( _A						,      _A					):
						'''simple docstring'''





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







def 	__magic_name__( _A						,      _A						,      _A					):
						'''simple docstring'''





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







def 	__magic_name__( _A						,      _A						,      _A					):
						'''simple docstring'''





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








'''simple docstring'''


from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





			def __init__(   self							:						Tuple ,      lowercase							:						pyspark.sql.DataFrame ,      lowercase							:						Optional[NamedSplit] = None ,      lowercase							:						Optional[Features] = None ,      lowercase							:						bool = True ,      lowercase							:						str = None ,      lowercase							:						bool = False ,      lowercase							:						str = None ,      lowercase							:						bool = True ,      lowercase							:						str = "arrow" ,      **lowercase							:						Any ,      )							->    Optional[int]:

									'''simple docstring'''







									super().__init__(
									    split=lowercase ,      features=lowercase ,      cache_dir=lowercase ,      keep_in_memory=lowercase ,      streaming=lowercase ,      **lowercase ,      )
									UpperCamelCase__         = load_from_cache_file
									UpperCamelCase__         = file_format
									UpperCamelCase__         = Spark(
									    df=lowercase ,      features=lowercase ,      cache_dir=lowercase ,      working_dir=lowercase ,      **lowercase ,      )







			def 				A      (   self							:						int			)							->    Any:

									'''simple docstring'''







									if self.streaming:
															return self.builder.as_streaming_dataset(split=self.split			)
									UpperCamelCase__         = None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
									self.builder.download_and_prepare(
									    download_mode=lowercase ,      file_format=self._file_format ,      )
									return self.builder.as_dataset(split=self.split			)
