
'''simple docstring'''





from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


_SCREAMING_SNAKE_CASE    :      List[str]          =			'''\
@article{wang2019superglue,
  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
  author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},
  journal={arXiv preprint arXiv:1905.00537},
  year={2019}
}
'''

_SCREAMING_SNAKE_CASE    :      Optional[Any]          =			'''\
SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after
GLUE with a new set of more difficult language understanding tasks, improved
resources, and a new public leaderboard.
'''

_SCREAMING_SNAKE_CASE    :      str          =			'''
Compute SuperGLUE evaluation metric associated to each SuperGLUE dataset.
Args:
    predictions: list of predictions to score. Depending on the SuperGlUE subset:
        - for \'record\': list of question-answer dictionaries with the following keys:
            - \'idx\': index of the question as specified by the dataset
            - \'prediction_text\': the predicted answer text
        - for \'multirc\': list of question-answer dictionaries with the following keys:
            - \'idx\': index of the question-answer pair as specified by the dataset
            - \'prediction\': the predicted answer label
        - otherwise: list of predicted labels
    references: list of reference labels. Depending on the SuperGLUE subset:
        - for \'record\': list of question-answers dictionaries with the following keys:
            - \'idx\': index of the question as specified by the dataset
            - \'answers\': list of possible answers
        - otherwise: list of reference labels
Returns: depending on the SuperGLUE subset:
    - for \'record\':
        - \'exact_match\': Exact match between answer and gold answer
        - \'f1\': F1 score
    - for \'multirc\':
        - \'exact_match\': Exact match between answer and gold answer
        - \'f1_m\': Per-question macro-F1 score
        - \'f1_a\': Average F1 score over all answers
    - for \'axb\':
        \'matthews_correlation\': Matthew Correlation
    - for \'cb\':
        - \'accuracy\': Accuracy
        - \'f1\': F1 score
    - for all others:
        - \'accuracy\': Accuracy
Examples:

    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'copa\')  # any of ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]
    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'accuracy\': 1.0}

    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'cb\')
    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'accuracy\': 1.0, \'f1\': 1.0}

    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'record\')
    >>> predictions = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'prediction_text\': \'answer\'}]
    >>> references = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'answers\': [\'answer\', \'another_answer\']}]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'exact_match\': 1.0, \'f1\': 1.0}

    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'multirc\')
    >>> predictions = [{\'idx\': {\'answer\': 0, \'paragraph\': 0, \'question\': 0}, \'prediction\': 0}, {\'idx\': {\'answer\': 1, \'paragraph\': 2, \'question\': 3}, \'prediction\': 1}]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'exact_match\': 1.0, \'f1_m\': 1.0, \'f1_a\': 1.0}

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

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						List[str]							,						__UpperCamelCase :						Any       ) ->       Optional[int]:

				"""simple docstring"""







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

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Any							,						__UpperCamelCase :						Optional[int]							,						__UpperCamelCase :						str="binary"       ) ->       Optional[int]:

				"""simple docstring"""







				A__  :	Optional[Any]         =							simple_accuracy(__UpperCamelCase							,						__UpperCamelCase       )
				A__  :	Dict         =							float(fa_score(y_true=__UpperCamelCase							,						y_pred=__UpperCamelCase							,						average=__UpperCamelCase       )       )
				return {
				    "accuracy": acc,
				    "f1": fa,
				}

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						str							,						__UpperCamelCase :						int       ) ->       Any:

				"""simple docstring"""







				A__  :	List[Any]         =							{}
				for id_pred, label in zip(__UpperCamelCase							,						__UpperCamelCase       ):
								A__  :	Optional[int]         =							F"{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}"
								A__  :	Union[str, Any]         =							id_pred["""prediction"""]
								if question_id in question_map:
												question_map[question_id].append((pred, label)       )
								else:
												A__  :	str         =							[(pred, label)]
				A__  :	List[str]         =							[], []
				for question, preds_labels in question_map.items():
								A__  :	Optional[Any]         =							zip(*__UpperCamelCase       )
								A__  :	int         =							fa_score(y_true=__UpperCamelCase							,						y_pred=__UpperCamelCase							,						average='''macro'''       )
								fas.append(__UpperCamelCase       )
								A__  :	Dict         =							int(sum(pred == label for pred, label in preds_labels       ) == len(__UpperCamelCase       )       )
								ems.append(__UpperCamelCase       )
				A__  :	Optional[int]         =							float(sum(__UpperCamelCase       ) / len(__UpperCamelCase       )       )
				A__  :	Any         =							sum(__UpperCamelCase       ) / len(__UpperCamelCase       )
				A__  :	int         =							float(fa_score(y_true=__UpperCamelCase							,						y_pred=[id_pred['''prediction'''] for id_pred in ids_preds]       )       )
				return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}







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







	'''simple docstring'''





	def   __snake_case				(			self		):

					if self.config_name not in [
					    "boolq",
					    "cb",
					    "copa",
					    "multirc",
					    "record",
					    "rte",
					    "wic",
					    "wsc",
					    "wsc.fixed",
					    "axb",
					    "axg",
					]:
									raise KeyError(
									    '''You should supply a configuration name selected in '''
									    '''[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]'''		)
					return datasets.MetricInfo(
					    description=_DESCRIPTION				,       citation=_CITATION				,       inputs_description=_KWARGS_DESCRIPTION				,       features=datasets.Features(self._get_feature_types()		)				,       codebase_urls=[]				,       reference_urls=[]				,       format='''numpy''' if not self.config_name == '''record''' and not self.config_name == '''multirc''' else None				,       )





	def   __snake_case				(			self		):

					if self.config_name == "record":
									return {
									    "predictions": {
									        "idx": {
									            "passage": datasets.Value('''int64'''		),
									            "query": datasets.Value('''int64'''		),
									        },
									        "prediction_text": datasets.Value('''string'''		),
									    },
									    "references": {
									        "idx": {
									            "passage": datasets.Value('''int64'''		),
									            "query": datasets.Value('''int64'''		),
									        },
									        "answers": datasets.Sequence(datasets.Value('''string'''		)		),
									    },
									}
					elif self.config_name == "multirc":
									return {
									    "predictions": {
									        "idx": {
									            "answer": datasets.Value('''int64'''		),
									            "paragraph": datasets.Value('''int64'''		),
									            "question": datasets.Value('''int64'''		),
									        },
									        "prediction": datasets.Value('''int64'''		),
									    },
									    "references": datasets.Value('''int64'''		),
									}
					else:
									return {
									    "predictions": datasets.Value('''int64'''		),
									    "references": datasets.Value('''int64'''		),
									}





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__		):

					if self.config_name == "axb":
									return {"matthews_correlation": matthews_corrcoef(_lowercase				,       _lowercase		)}
					elif self.config_name == "cb":
									return acc_and_fa(_lowercase				,       _lowercase				,       fa_avg='''macro'''		)
					elif self.config_name == "record":
									A__  :	Optional[Any]         =							[
									    {
									        """qas""": [
									            {"""id""": ref["""idx"""]["""query"""], """answers""": [{"""text""": ans} for ans in ref["""answers"""]]}
									            for ref in references
									        ]
									    }
									]
									A__  :	Dict         =							{pred["""idx"""]["""query"""]: pred["""prediction_text"""] for pred in predictions}
									return evaluate_record(_lowercase				,       _lowercase		)[0]
					elif self.config_name == "multirc":
									return evaluate_multirc(_lowercase				,       _lowercase		)
					elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
									return {"accuracy": simple_accuracy(_lowercase				,       _lowercase		)}
					else:
									raise KeyError(
									    '''You should supply a configuration name selected in '''
									    '''[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]'''		)

import sacrebleu as scb
from packaging import version
from sacrebleu import TER

import datasets


_SCREAMING_SNAKE_CASE    :      Union[str, Any]          =			'\\n@inproceedings{snover-etal-2006-study,\n    title = "A Study of Translation Edit Rate with Targeted Human Annotation",\n    author = "Snover, Matthew  and\n      Dorr, Bonnie  and\n      Schwartz, Rich  and\n      Micciulla, Linnea  and\n      Makhoul, John",\n    booktitle = "Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers",\n    month = aug # " 8-12",\n    year = "2006",\n    address = "Cambridge, Massachusetts, USA",\n    publisher = "Association for Machine Translation in the Americas",\n    url = "https://aclanthology.org/2006.amta-papers.25",\n    pages = "223--231",\n}\n@inproceedings{post-2018-call,\n    title = "A Call for Clarity in Reporting {BLEU} Scores",\n    author = "Post, Matt",\n    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",\n    month = oct,\n    year = "2018",\n    address = "Belgium, Brussels",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/W18-6319",\n    pages = "186--191",\n}\n'

_SCREAMING_SNAKE_CASE    :      Tuple          =			'\\nTER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a\nhypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu\n(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found\nhere: https://github.com/jhclark/tercom.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.\n'

_SCREAMING_SNAKE_CASE    :      Optional[Any]          =			'\nProduces TER scores alongside the number of edits and reference length.\n\nArgs:\n    predictions (list of str): The system stream (a sequence of segments).\n    references (list of list of str): A list of one or more reference streams (each a sequence of segments).\n    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,\n                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.\n                                    Only applies if `normalized = True`. Defaults to `False`.\n    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.\n\nReturns:\n    \'score\' (float): TER score (num_edits / sum_ref_lengths * 100)\n    \'num_edits\' (int): The cumulative number of edits\n    \'ref_length\' (float): The cumulative average reference length\n\nExamples:\n    Example 1:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?",\n        ...                     "What did the TER metric user say to the developer?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],\n        ...             ["Your jokes are...", "...TERrible"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {\'score\': 150.0, \'num_edits\': 15, \'ref_length\': 10.0}\n\n    Example 2:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {\'score\': 62.5, \'num_edits\': 5, \'ref_length\': 8.0}\n\n    Example 3:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         normalized=True,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {\'score\': 57.14285714285714, \'num_edits\': 6, \'ref_length\': 10.5}\n\n    Example 4:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {\'score\': 0.0, \'num_edits\': 0, \'ref_length\': 8.0}\n\n    Example 5:\n        >>> predictions = ["does this sentence match??",\n        ...                    "what about this sentence?",\n        ...                    "What did the TER metric user say to the developer?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],\n        ...             ["Your jokes are...", "...TERrible"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {\'score\': 100.0, \'num_edits\': 10, \'ref_length\': 10.0}\n'







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







	'''simple docstring'''





	def   __snake_case				(			self		):
					if version.parse(scb.__version__		) < version.parse('''1.4.12'''		):
									raise ImportWarning(
									    '''To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'''
									    '''You can install it with `pip install "sacrebleu>=1.4.12"`.'''		)
					return datasets.MetricInfo(
					    description=_DESCRIPTION				,       citation=_CITATION				,       homepage='''http://www.cs.umd.edu/~snover/tercom/'''				,       inputs_description=_KWARGS_DESCRIPTION				,       features=datasets.Features(
					        {
					            '''predictions''': datasets.Value('''string'''				,       id='''sequence'''		),
					            '''references''': datasets.Sequence(datasets.Value('''string'''				,       id='''sequence'''		)				,       id='''references'''		),
					        }		)				,       codebase_urls=['''https://github.com/mjpost/sacreBLEU#ter''']				,       reference_urls=[
					        '''https://github.com/jhclark/tercom''',
					    ]				,       )





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__ = False				,       UpperCamelCase__ = False				,       UpperCamelCase__ = False				,       UpperCamelCase__ = False				,       ):
					A__  :	List[Any]         =							len(references[0]		)
					if any(len(UpperCamelCase__		) != references_per_prediction for refs in references		):
									raise ValueError('''Sacrebleu requires the same number of references for each prediction'''		)
					A__  :	Dict         =							[[refs[i] for refs in references] for i in range(UpperCamelCase__		)]

					A__  :	Optional[Any]         =							TER(
					    normalized=UpperCamelCase__				,       no_punct=UpperCamelCase__				,       asian_support=UpperCamelCase__				,       case_sensitive=UpperCamelCase__				,       )
					A__  :	str         =							sb_ter.corpus_score(UpperCamelCase__				,       UpperCamelCase__		)

					return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}