





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

import datasets


lowercase_		:				Tuple				=						'''\
@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}
}
'''

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

lowercase_		:				Dict				=						'''
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__(		__lowerCAmelCase	,			__lowerCAmelCase ):
				return float((preds == labels).mean() )

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			Tuple  						=   simple_accuracy(__lowerCAmelCase	,			__lowerCAmelCase )
				_snake_case :			int  						=   float(fa_score(y_true=__lowerCAmelCase	,			y_pred=__lowerCAmelCase ) )
				return {
				    "accuracy": acc,
				    "f1": fa,
				}

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			Optional[int]  						=   float(pearsonr(__lowerCAmelCase	,			__lowerCAmelCase )[0] )
				_snake_case :			str  						=   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     ):



						"""simple docstring"""

						def 		__UpperCAmelCase       (    self			:		Dict					):





										'''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 		__UpperCAmelCase       (    self			:		Dict ,				lowerCamelCase_			:		Tuple ,				lowerCamelCase_			:		str					):





										'''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"]'					)













import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
lowercase_		:				Optional[int]				=						object()

# For specifying empty leaf dict `{}`
lowercase_		:				List[Any]				=						object()

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			Optional[Any]  						=   tuple((re.compile(x + '$' ) for x in qs) )
				for i in range(len(__lowerCAmelCase ) - len(__lowerCAmelCase ) + 1 ):
								_snake_case :			Tuple  						=   [x.match(__lowerCAmelCase ) for x, y in zip(__lowerCAmelCase	,			ks[i:] )]
								if matches and all(__lowerCAmelCase ):
												return True
				return False

def       A__(		__lowerCAmelCase ):
				def replace(__lowerCAmelCase	,			__lowerCAmelCase ):
								for rule, replacement in rules:
												if _match(__lowerCAmelCase	,			__lowerCAmelCase ):
																return replacement
								return val

				return replace

def       A__(		):
				return [
				    # embeddings
				    (("transformer", "wpe", "embedding"), P('mp'	,			__lowerCAmelCase )),
				    (("transformer", "wte", "embedding"), P('mp'	,			__lowerCAmelCase )),
				    # atention
				    (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(__lowerCAmelCase	,			'mp' )),
				    (("attention", "out_proj", "kernel"), P('mp'	,			__lowerCAmelCase )),
				    (("attention", "out_proj", "bias"), None),
				    # mlp
				    (("mlp", "c_fc", "kernel"), P(__lowerCAmelCase	,			'mp' )),
				    (("mlp", "c_fc", "bias"), P('mp' )),
				    (("mlp", "c_proj", "kernel"), P('mp'	,			__lowerCAmelCase )),
				    (("mlp", "c_proj", "bias"), None),
				    # layer norms
				    ((r"ln_\d+", "bias"), None),
				    ((r"\d+", r"ln_\d+", "scale"), None),
				    (("ln_f", "bias"), None),
				    (("ln_f", "scale"), None),
				]

def       A__(		__lowerCAmelCase ):
				_snake_case :			Optional[Any]  						=   _get_partition_rules()
				_snake_case :			Optional[Any]  						=   _replacement_rules(__lowerCAmelCase )
				_snake_case :			str  						=   {k: _unmatched for k in flatten_dict(__lowerCAmelCase )}
				_snake_case :			str  						=   {k: replace(__lowerCAmelCase	,			__lowerCAmelCase ) for k, v in initd.items()}
				assert _unmatched not in result.values(), "Incomplete partition spec."
				return freeze(unflatten_dict(__lowerCAmelCase ) )






