




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

import datasets


lowercase_	:				List[Any]  				= "\\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_	:				Optional[int]  				= "\\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_	:				Dict  				= "\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 							__SCREAMING_SNAKE_CASE  (			snake_case_	,    snake_case_						):

						'''simple docstring'''


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


def 							__SCREAMING_SNAKE_CASE  (			snake_case_	,    snake_case_						):

						'''simple docstring'''


						_UpperCAmelCase													=     simple_accuracy(_UpperCamelCase	,    _UpperCamelCase						)
						_UpperCAmelCase													=     float(fa_score(y_true=_UpperCamelCase	,    y_pred=_UpperCamelCase						)						)
						return {
						    "accuracy": acc,
						    "f1": fa,
						}


def 							__SCREAMING_SNAKE_CASE  (			snake_case_	,    snake_case_						):

						'''simple docstring'''


						_UpperCAmelCase													=     float(pearsonr(_UpperCamelCase	,    _UpperCamelCase						)[0]						)
						_UpperCAmelCase													=     float(spearmanr(_UpperCamelCase	,    _UpperCamelCase						)[0]						)
						return {
						    "pearson": pearson_corr,
						    "spearmanr": spearman_corr,
						}






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







			def UpperCamelCase			(       self    :			List[str]      ):




									"""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    :			int    ,    snake_case__    :			Any    ,    snake_case__    :			int      ):




									"""simple docstring"""


									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\"]"      )







'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase :      int					 =     {"configuration_mbart": ["MBART_PRETRAINED_CONFIG_ARCHIVE_MAP", "MBartConfig", "MBartOnnxConfig"]}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      List[Any]					 =     ["MBartTokenizer"]

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      Dict					 =     ["MBartTokenizerFast"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      int					 =     [
         "MBART_PRETRAINED_MODEL_ARCHIVE_LIST",
         "MBartForCausalLM",
         "MBartForConditionalGeneration",
         "MBartForQuestionAnswering",
         "MBartForSequenceClassification",
         "MBartModel",
         "MBartPreTrainedModel",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      Union[str, Any]					 =     [
         "TFMBartForConditionalGeneration",
         "TFMBartModel",
         "TFMBartPreTrainedModel",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      Optional[Any]					 =     [
         "FlaxMBartForConditionalGeneration",
         "FlaxMBartForQuestionAnswering",
         "FlaxMBartForSequenceClassification",
         "FlaxMBartModel",
         "FlaxMBartPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_mbart import MBartTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_mbart_fast import MBartTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mbart import (
              MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
              MBartForCausalLM,
              MBartForConditionalGeneration,
              MBartForQuestionAnswering,
              MBartForSequenceClassification,
              MBartModel,
              MBartPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_mbart import (
              FlaxMBartForConditionalGeneration,
              FlaxMBartForQuestionAnswering,
              FlaxMBartForSequenceClassification,
              FlaxMBartModel,
              FlaxMBartPreTrainedModel,
          )

else:
     import sys

     lowerCamelCase :      List[Any]					 =     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
