



import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
							import tensorflow as tf
							from tensorflow.python.eager import context
							from tensorflow.python.framework import ops

							from transformers import GradientAccumulator, create_optimizer



@require_tf
class  lowerCamelCase__    (							unittest.TestCase):




		'''simple docstring'''



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)  ->    Dict:

						"""simple docstring"""






						self.assertEqual(len(__lowercase				)	,len(__lowercase				)				)
						for a, b in zip(__lowercase	,__lowercase				):
										self.assertAlmostEqual(__lowercase	,__lowercase	,delta=__lowercase				)



		def 	lowerCAmelCase__   (self				)  ->    Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Tuple				=  GradientAccumulator()
						accumulator([tf.constant([1.0, 2.0]				)]				)
						accumulator([tf.constant([-2.0, 1.0]				)]				)
						accumulator([tf.constant([-1.0, 2.0]				)]				)
						with self.assertRaises(__lowercase				):
										accumulator([tf.constant([1.0, 1.0]				), tf.constant([2.0, 2.0]				)]				)
						self.assertEqual(accumulator.step	,3				)
						self.assertEqual(len(accumulator.gradients				)	,1				)
						self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()	,[-2.0, 5.0]	,tol=1e-2				)
						accumulator.reset()
						self.assertEqual(accumulator.step	,0				)
						self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()	,[0.0, 0.0]	,tol=1e-2				)



		def 	lowerCAmelCase__   (self				)  ->    int:

						"""simple docstring"""






						lowerCAmelCase__  :       Dict				=  None
						ops.enable_eager_execution_internal()
						lowerCAmelCase__  :       List[str]				=  tf.config.list_physical_devices('''CPU'''				)
						if len(__lowercase				) == 1:
										tf.config.set_logical_device_configuration(
										    physical_devices[0]	,[tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]				)
						lowerCAmelCase__  :       int				=  tf.config.list_logical_devices(device_type='''CPU'''				)
						lowerCAmelCase__  :       Any				=  tf.distribute.MirroredStrategy(devices=devices[:2]				)

						with strategy.scope():
										lowerCAmelCase__  :       Any				=  GradientAccumulator()
										lowerCAmelCase__  :       Union[str, Any]				=  tf.Variable([4.0, 3.0]				)
										lowerCAmelCase__		,	lowerCAmelCase__  :       Optional[Any]				=  create_optimizer(5e-5	,10	,5				)
										lowerCAmelCase__  :       Dict				=  tf.Variable([0.0, 0.0]	,trainable=__lowercase				)

						def accumulate_on_replica(__lowerCamelCase				):
										accumulator([gradient]				)

						def apply_on_replica():
										optimizer.apply_gradients(list(zip(accumulator.gradients	,[variable]				)				)				)

						@tf.function
						def accumulate(__lowerCamelCase	,__lowerCamelCase				):
										with strategy.scope():
														lowerCAmelCase__  :       int				=  strategy.experimental_local_results(__lowercase				)
														local_variables[0].assign(__lowercase				)
														local_variables[1].assign(__lowercase				)
														strategy.run(__lowercase	,args=(gradient_placeholder,)				)

						@tf.function
						def apply_grad():
										with strategy.scope():
														strategy.run(__lowercase				)

						def _check_local_values(__lowerCamelCase	,__lowerCamelCase				):
										lowerCAmelCase__  :       List[str]				=  strategy.experimental_local_results(accumulator._gradients[0]				)
										self.assertListAlmostEqual(values[0].value()	,__lowercase	,tol=1e-2				)
										self.assertListAlmostEqual(values[1].value()	,__lowercase	,tol=1e-2				)

						accumulate([1.0, 2.0]	,[-1.0, 1.0]				)
						accumulate([3.0, -1.0]	,[-1.0, -1.0]				)
						accumulate([-2.0, 2.0]	,[3.0, -2.0]				)
						self.assertEqual(accumulator.step	,3				)
						_check_local_values([2.0, 3.0]	,[1.0, -2.0]				)
						apply_grad()
						self.assertListAlmostEqual(variable.value()	,[4.0, 3.0]	,tol=1e-2				)
						accumulator.reset()
						self.assertEqual(accumulator.step	,0				)
						_check_local_values([0.0, 0.0]	,[0.0, 0.0]				)






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

import datasets


__snake_case       :	str            ='\\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'

__snake_case       :	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'

__snake_case       :	int            ='\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 			lowerCAmelCase__	(					lowerCamelCase_	:	List[str] ,lowerCamelCase_	:	Dict):





				'''simple docstring'''

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






def 			lowerCAmelCase__	(					lowerCamelCase_	:	List[Any] ,lowerCamelCase_	:	Optional[int]):





				'''simple docstring'''

				lowerCAmelCase__  :       int				=  simple_accuracy(lowerCamelCase_ ,lowerCamelCase_)
				lowerCAmelCase__  :       Any				=  float(fa_score(y_true=lowerCamelCase_ ,y_pred=lowerCamelCase_))
				return {
				    "accuracy": acc,
				    "f1": fa,
				}






def 			lowerCAmelCase__	(					lowerCamelCase_	:	List[Any] ,lowerCamelCase_	:	Optional[Any]):





				'''simple docstring'''

				lowerCAmelCase__  :       Union[str, Any]				=  float(pearsonr(lowerCamelCase_ ,lowerCamelCase_)[0])
				lowerCAmelCase__  :       int				=  float(spearmanr(lowerCamelCase_ ,lowerCamelCase_)[0])
				return {
				    "pearson": pearson_corr,
				    "spearmanr": spearman_corr,
				}



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




		'''simple docstring'''



		def 	lowerCAmelCase__   (self				)  ->    Union[str, Any]:

						"""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	,__lowerCamelCase	,__lowerCamelCase				)  ->    Tuple:

						"""simple docstring"""






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

