





from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


lowerCamelCase__				  =   '\\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__				  =   '\\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__				  =   '\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 					lowercase__    (					lowercase_		,lowercase_			)					->		Any:




      """simple docstring"""

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




def 					lowercase__    (					lowercase_		,lowercase_		,lowercase_="binary"			)					->		str:




      """simple docstring"""

      _UpperCamelCase      :	int						=       simple_accuracy(_SCREAMING_SNAKE_CASE		,_SCREAMING_SNAKE_CASE			)
      _UpperCamelCase      :	List[Any]						=       float(fa_score(y_true=_SCREAMING_SNAKE_CASE		,y_pred=_SCREAMING_SNAKE_CASE		,average=_SCREAMING_SNAKE_CASE			)			)
      return {
          "accuracy": acc,
          "f1": fa,
      }




def 					lowercase__    (					lowercase_		,lowercase_			)					->		Optional[Any]:




      """simple docstring"""

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



        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    __SCREAMING_SNAKE_CASE				(		self   :       List[Any]	)		->				Any:



        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    __SCREAMING_SNAKE_CASE				(		self   :       List[str]					,   __a   :       str					,   __a   :       Any	)		->				Dict:



        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      :	Optional[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_torch_available, is_vision_available


lowerCamelCase__				  =   {
    "configuration_mask2former": [
        "MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Mask2FormerConfig",
    ],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase__				  =   ["Mask2FormerImageProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase__				  =   [
      "MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "Mask2FormerForUniversalSegmentation",
      "Mask2FormerModel",
      "Mask2FormerPreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_maskaformer import MaskaFormerImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_maskaformer import (
        MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        MaskaFormerForUniversalSegmentation,
        MaskaFormerModel,
        MaskaFormerPreTrainedModel,
    )


else:
  import sys

  lowerCamelCase__				  =   _LazyModule(__name__, globals()["__file__"], _import_structure)
