


'''simple docstring'''
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets


__snake_case							      =     '''\
@inproceedings{wang2019glue,
  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},
  note={In the Proceedings of ICLR.},
  year={2019}
}
'''

__snake_case							      =     '''\
GLUE, the General Language Understanding Evaluation benchmark
(https://gluebenchmark.com/) is a collection of resources for training,
evaluating, and analyzing natural language understanding systems.
'''

__snake_case							      =     '''
Compute GLUE evaluation metric associated to each GLUE dataset.
Args:
    predictions: list of predictions to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
Returns: depending on the GLUE subset, one or several of:
    "accuracy": Accuracy
    "f1": F1 score
    "pearson": Pearson Correlation
    "spearmanr": Spearman Correlation
    "matthews_correlation": Matthew Correlation
Examples:

    >>> glue_metric = datasets.load_metric(\'glue\', \'sst2\')  # \'sst2\' or any of ["mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'accuracy\': 1.0}

    >>> glue_metric = datasets.load_metric(\'glue\', \'mrpc\')  # \'mrpc\' or \'qqp\'
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'accuracy\': 1.0, \'f1\': 1.0}

    >>> glue_metric = datasets.load_metric(\'glue\', \'stsb\')
    >>> references = [0., 1., 2., 3., 4., 5.]
    >>> predictions = [0., 1., 2., 3., 4., 5.]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print({"pearson": round(results["pearson"], 2), "spearmanr": round(results["spearmanr"], 2)})
    {\'pearson\': 1.0, \'spearmanr\': 1.0}

    >>> glue_metric = datasets.load_metric(\'glue\', \'cola\')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'matthews_correlation\': 1.0}
'''


def 			a      (			__a ,					__a   )  ->	List[str]:



					'''simple docstring'''






					return float((preds == labels).mean()   )


def 			a      (			__a ,					__a   )  ->	List[Any]:



					'''simple docstring'''






					UpperCamelCase__   :Dict												= simple_accuracy(__a ,					__a   )
					UpperCamelCase__   :List[str]												= float(fa_score(y_true=__a ,					y_pred=__a   )   )
					return {
					    "accuracy": acc,
					    "f1": fa,
					}


def 			a      (			__a ,					__a   )  ->	Union[str, Any]:



					'''simple docstring'''






					UpperCamelCase__   :int												= float(pearsonr(__a ,					__a   )[0]   )
					UpperCamelCase__   :List[str]												= float(spearmanr(__a ,					__a   )[0]   )
					return {
					    "pearson": pearson_corr,
					    "spearmanr": spearman_corr,
					}







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,		_KWARGS_DESCRIPTION					)
class    lowercase	(							datasets.Metric					):

		"""simple docstring"""

		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							if self.config_name not in [
							    "sst2",
							    "mnli",
							    "mnli_mismatched",
							    "mnli_matched",
							    "cola",
							    "stsb",
							    "mrpc",
							    "qqp",
							    "qnli",
							    "rte",
							    "wnli",
							    "hans",
							]:
												raise KeyError(
												    '''You should supply a configuration name selected in '''
												    '''["sst2", "mnli", "mnli_mismatched", "mnli_matched", '''
												    '''"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]'''							)
							return datasets.MetricInfo(
							    description=_DESCRIPTION    ,   citation=_CITATION    ,   inputs_description=_KWARGS_DESCRIPTION    ,   features=datasets.Features(
							        {
							            '''predictions''': datasets.Value('''int64''' if self.config_name != '''stsb''' else '''float32'''							),
							            '''references''': datasets.Value('''int64''' if self.config_name != '''stsb''' else '''float32'''							),
							        }							)    ,   codebase_urls=[]    ,   reference_urls=[]    ,   format='''numpy'''    ,   )




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_							):

							'''simple docstring'''


							if self.config_name == "cola":
												return {"matthews_correlation": matthews_corrcoef(UpperCamelCase_    ,   UpperCamelCase_							)}
							elif self.config_name == "stsb":
												return pearson_and_spearman(UpperCamelCase_    ,   UpperCamelCase_							)
							elif self.config_name in ["mrpc", "qqp"]:
												return acc_and_fa(UpperCamelCase_    ,   UpperCamelCase_							)
							elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
												return {"accuracy": simple_accuracy(UpperCamelCase_    ,   UpperCamelCase_							)}
							else:
												raise KeyError(
												    '''You should supply a configuration name selected in '''
												    '''["sst2", "mnli", "mnli_mismatched", "mnli_matched", '''
												    '''"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]'''							)



'''simple docstring'''
import unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class    lowercase	(							A__					):

		"""simple docstring"""

		_a     =				ComputeEnvironment.AMAZON_SAGEMAKER
		_a     =				True
		_a     =				'ml.p3.2xlarge'
		_a     =				'accelerate_sagemaker_execution_role'
		_a     =				'hf-sm'
		_a     =				'us-east-1'
		_a     =				1
		_a     =				'accelerate-sagemaker-1'
		_a     =				'1.6'
		_a     =				'4.4'
		_a     =				'train.py'
		_a     =				[
		    '--model_name_or_path',
		    'bert',
		    '--do_train',
		    'False',
		    '--epochs',
		    '3',
		    '--learning_rate',
		    '5e-5',
		    '--max_steps',
		    '50.5',
		]
		_a     =				[
		    '--model_name_or_path',
		    'bert',
		    '--do_train',
		    '--do_test',
		    'False',
		    '--do_predict',
		    '--epochs',
		    '3',
		    '--learning_rate',
		    '5e-5',
		    '--max_steps',
		    '50.5',
		]



class    lowercase	(							unittest.TestCase					):

		"""simple docstring"""

		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Union[str, Any]												= _convert_nargs_to_dict(MockLaunchConfig.success_training_script_args							)
							assert isinstance(converted_args['''model_name_or_path''']    ,   UpperCamelCase_							)
							assert isinstance(converted_args['''do_train''']    ,   UpperCamelCase_							)
							assert isinstance(converted_args['''epochs''']    ,   UpperCamelCase_							)
							assert isinstance(converted_args['''learning_rate''']    ,   UpperCamelCase_							)
							assert isinstance(converted_args['''max_steps''']    ,   UpperCamelCase_							)

							with pytest.raises(UpperCamelCase_							):
												_convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args							)