
"""simple docstring"""




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

import datasets


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

lowercase__		:Tuple			  =   "\\nGLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n"

lowercase__		:str			  =   "\nCompute GLUE evaluation metric associated to each GLUE dataset.\nArgs:\n    predictions: list of predictions to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\nReturns: depending on the GLUE subset, one or several of:\n    \"accuracy\": Accuracy\n    \"f1\": F1 score\n    \"pearson\": Pearson Correlation\n    \"spearmanr\": Spearman Correlation\n    \"matthews_correlation\": Matthew Correlation\nExamples:\n\n    >>> glue_metric = datasets.load_metric('glue', 'sst2')  # 'sst2' or any of [\"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'mrpc')  # 'mrpc' or 'qqp'\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0, 'f1': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'stsb')\n    >>> references = [0., 1., 2., 3., 4., 5.]\n    >>> predictions = [0., 1., 2., 3., 4., 5.]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print({\"pearson\": round(results[\"pearson\"], 2), \"spearmanr\": round(results[\"spearmanr\"], 2)})\n    {'pearson': 1.0, 'spearmanr': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'cola')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'matthews_correlation': 1.0}\n"



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					return float((preds == labels).mean()		)



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						simple_accuracy(lowerCAmelCase__			,      lowerCAmelCase__		)
					lowercase						     =						float(fa_score(y_true=lowerCAmelCase__			,      y_pred=lowerCAmelCase__		)		)
					return {
					    "accuracy": acc,
					    "f1": fa,
					}



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						float(pearsonr(lowerCAmelCase__			,      lowerCAmelCase__		)[0]		)
					lowercase						     =						float(spearmanr(lowerCAmelCase__			,      lowerCAmelCase__		)[0]		)
					return {
					    "pearson": pearson_corr,
					    "spearmanr": spearman_corr,
					}







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




							def 			A__				(    self):
												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 			A__				(    self       ,A__       ,A__):
												if self.config_name == "cola":
																	return {"matthews_correlation": matthews_corrcoef(A__       ,A__)}
												elif self.config_name == "stsb":
																	return pearson_and_spearman(A__       ,A__)
												elif self.config_name in ["mrpc", "qqp"]:
																	return acc_and_fa(A__       ,A__)
												elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
																	return {"accuracy": simple_accuracy(A__       ,A__)}
												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"]''')






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		( SCREAMING_SNAKE_CASE__		):
							lowercase_	:	int	 =ComputeEnvironment.AMAZON_SAGEMAKER
							lowercase_	:	Optional[int]	 =True
							lowercase_	:	Any	 ='''ml.p3.2xlarge'''
							lowercase_	:	Any	 ='''accelerate_sagemaker_execution_role'''
							lowercase_	:	Union[str, Any]	 ='''hf-sm'''
							lowercase_	:	Any	 ='''us-east-1'''
							lowercase_	:	List[str]	 =1
							lowercase_	:	Any	 ='''accelerate-sagemaker-1'''
							lowercase_	:	Union[str, Any]	 ='''1.6'''
							lowercase_	:	Any	 ='''4.4'''
							lowercase_	:	Any	 ='''train.py'''
							lowercase_	:	int	 =[
							    '''--model_name_or_path''',
							    '''bert''',
							    '''--do_train''',
							    '''False''',
							    '''--epochs''',
							    '''3''',
							    '''--learning_rate''',
							    '''5e-5''',
							    '''--max_steps''',
							    '''50.5''',
							]
							lowercase_	:	List[Any]	 =[
							    '''--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		):




							def 			A__				(    self):
												# If no defaults are changed, `to_kwargs` returns an empty dict.
												lowercase						     =						_convert_nargs_to_dict(MockLaunchConfig.success_training_script_args)
												assert isinstance(converted_args['''model_name_or_path''']       ,A__)
												assert isinstance(converted_args['''do_train''']       ,A__)
												assert isinstance(converted_args['''epochs''']       ,A__)
												assert isinstance(converted_args['''learning_rate''']       ,A__)
												assert isinstance(converted_args['''max_steps''']       ,A__)

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




