'''simple docstring'''





from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


lowerCAmelCase_	:		Any			=     '\\n@article{wang2019superglue,\n  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n  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},\n  journal={arXiv preprint arXiv:1905.00537},\n  year={2019}\n}\n'

lowerCAmelCase_	:		Optional[int]			=     '\\nSuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after\nGLUE with a new set of more difficult language understanding tasks, improved\nresources, and a new public leaderboard.\n'

lowerCAmelCase_	:		Any			=     '\nCompute SuperGLUE evaluation metric associated to each SuperGLUE dataset.\nArgs:\n    predictions: list of predictions to score. Depending on the SuperGlUE subset:\n        - for \'record\': list of question-answer dictionaries with the following keys:\n            - \'idx\': index of the question as specified by the dataset\n            - \'prediction_text\': the predicted answer text\n        - for \'multirc\': list of question-answer dictionaries with the following keys:\n            - \'idx\': index of the question-answer pair as specified by the dataset\n            - \'prediction\': the predicted answer label\n        - otherwise: list of predicted labels\n    references: list of reference labels. Depending on the SuperGLUE subset:\n        - for \'record\': list of question-answers dictionaries with the following keys:\n            - \'idx\': index of the question as specified by the dataset\n            - \'answers\': list of possible answers\n        - otherwise: list of reference labels\nReturns: depending on the SuperGLUE subset:\n    - for \'record\':\n        - \'exact_match\': Exact match between answer and gold answer\n        - \'f1\': F1 score\n    - for \'multirc\':\n        - \'exact_match\': Exact match between answer and gold answer\n        - \'f1_m\': Per-question macro-F1 score\n        - \'f1_a\': Average F1 score over all answers\n    - for \'axb\':\n        \'matthews_correlation\': Matthew Correlation\n    - for \'cb\':\n        - \'accuracy\': Accuracy\n        - \'f1\': F1 score\n    - for all others:\n        - \'accuracy\': Accuracy\nExamples:\n\n    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'copa\')  # any of ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]\n    >>> predictions = [0, 1]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'cb\')\n    >>> predictions = [0, 1]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0, \'f1\': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'record\')\n    >>> predictions = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'prediction_text\': \'answer\'}]\n    >>> references = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'answers\': [\'answer\', \'another_answer\']}]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'exact_match\': 1.0, \'f1\': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'multirc\')\n    >>> predictions = [{\'idx\': {\'answer\': 0, \'paragraph\': 0, \'question\': 0}, \'prediction\': 0}, {\'idx\': {\'answer\': 1, \'paragraph\': 2, \'question\': 3}, \'prediction\': 1}]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'exact_match\': 1.0, \'f1_m\': 1.0, \'f1_a\': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'axb\')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'matthews_correlation\': 1.0}\n'
def 						__A							(     UpperCAmelCase     ,UpperCAmelCase					)     ->			Any:




      '''simple docstring'''
      return float((preds == labels).mean()					)
def 						__A							(     UpperCAmelCase     ,UpperCAmelCase     ,UpperCAmelCase="binary"					)     ->			str:




      '''simple docstring'''
      _UpperCamelCase			:			Optional[Any]   			=						simple_accuracy(_SCREAMING_SNAKE_CASE     ,_SCREAMING_SNAKE_CASE					)
      _UpperCamelCase			:			Any   			=						float(fa_score(y_true=_SCREAMING_SNAKE_CASE     ,y_pred=_SCREAMING_SNAKE_CASE     ,average=_SCREAMING_SNAKE_CASE					)					)
      return {
          "accuracy": acc,
          "f1": fa,
      }
def 						__A							(     UpperCAmelCase     ,UpperCAmelCase					)     ->			Optional[Any]:




      '''simple docstring'''
      _UpperCamelCase			:			List[str]   			=						{}
      for id_pred, label in zip(_SCREAMING_SNAKE_CASE     ,_SCREAMING_SNAKE_CASE					):
            _UpperCamelCase			:			Tuple   			=						f'''{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}'''
            _UpperCamelCase			:			List[str]   			=						id_pred["prediction"]
            if question_id in question_map:
                  question_map[question_id].append((pred, label)					)
            else:
                  _UpperCamelCase			:			Dict   			=						[(pred, label)]
      _UpperCamelCase			,       _UpperCamelCase			:			Optional[int]   			=						[], []
      for question, preds_labels in question_map.items():
            _UpperCamelCase			,       _UpperCamelCase			:			Union[str, Any]   			=						zip(*_SCREAMING_SNAKE_CASE					)
            _UpperCamelCase			:			Any   			=						fa_score(y_true=_SCREAMING_SNAKE_CASE     ,y_pred=_SCREAMING_SNAKE_CASE     ,average="macro"					)
            fas.append(_SCREAMING_SNAKE_CASE					)
            _UpperCamelCase			:			Union[str, Any]   			=						int(sum(pred == label for pred, label in preds_labels					) == len(_SCREAMING_SNAKE_CASE					)					)
            ems.append(_SCREAMING_SNAKE_CASE					)
      _UpperCamelCase			:			int   			=						float(sum(_SCREAMING_SNAKE_CASE					) / len(_SCREAMING_SNAKE_CASE					)					)
      _UpperCamelCase			:			List[str]   			=						sum(_SCREAMING_SNAKE_CASE					) / len(_SCREAMING_SNAKE_CASE					)
      _UpperCamelCase			:			Any   			=						float(fa_score(y_true=_SCREAMING_SNAKE_CASE     ,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 		SCREAMING_SNAKE_CASE     (					datasets.Metric	):







    '''simple docstring'''





    def  snake_case__				(						self					:						Optional[int]					)							->Union[str, Any]:


          '''simple docstring'''




          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					:						List[Any]					)							->Any:


          '''simple docstring'''




          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					:						List[str]       ,		lowercase__					:						str       ,		lowercase__					:						Any					)							->Dict:


          '''simple docstring'''




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



'''simple docstring'''





from typing import TYPE_CHECKING

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


lowerCAmelCase_	:		Any			=     {
    """configuration_speech_to_text""": ["""SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Speech2TextConfig"""],
    """processing_speech_to_text""": ["""Speech2TextProcessor"""],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase_	:		int			=     ["""Speech2TextTokenizer"""]

try:
			if not is_speech_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase_	:		Optional[Any]			=     ["""Speech2TextFeatureExtractor"""]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase_	:		Optional[int]			=     [
			    """TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """TFSpeech2TextForConditionalGeneration""",
			    """TFSpeech2TextModel""",
			    """TFSpeech2TextPreTrainedModel""",
			]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase_	:		str			=     [
			    """SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """Speech2TextForConditionalGeneration""",
			    """Speech2TextModel""",
			    """Speech2TextPreTrainedModel""",
			]


if TYPE_CHECKING:
			from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
			from .processing_speech_to_text import SpeechaTextProcessor

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_speech_to_text import SpeechaTextTokenizer

			try:
						if not is_speech_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_speech_to_text import (
						    TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFSpeechaTextForConditionalGeneration,
						    TFSpeechaTextModel,
						    TFSpeechaTextPreTrainedModel,
						)

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_speech_to_text import (
						    SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SpeechaTextForConditionalGeneration,
						    SpeechaTextModel,
						    SpeechaTextPreTrainedModel,
						)

else:
			import sys

			lowerCAmelCase_	:		Union[str, Any]			=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


