


from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


_A			=		'\\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'

_A			=		'\\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'

_A			=		'\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 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : List[str]      ,					SCREAMING_SNAKE_CASE__      : Any    ):
     return float((preds == labels).mean()    )

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Optional[int]      ,					SCREAMING_SNAKE_CASE__      : Dict      ,					SCREAMING_SNAKE_CASE__      : Union[str, Any]="binary"    ):
     __UpperCamelCase   							=simple_accuracy(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )
     __UpperCamelCase   							=float(fa_score(y_true=SCREAMING_SNAKE_CASE__      ,					y_pred=SCREAMING_SNAKE_CASE__      ,					average=SCREAMING_SNAKE_CASE__    )    )
     return {
         "accuracy": acc,
         "f1": fa,
     }

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Dict      ,					SCREAMING_SNAKE_CASE__      : str    ):
     __UpperCamelCase   							={}
     for id_pred, label in zip(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    ):
          __UpperCamelCase   							=F'{id_pred["idx"]["paragraph"]}-{id_pred["idx"]["question"]}'
          __UpperCamelCase   							=id_pred['prediction']
          if question_id in question_map:
               question_map[question_id].append((pred, label)    )
          else:
               __UpperCamelCase   							=[(pred, label)]
     __UpperCamelCase							,				__UpperCamelCase   							=[], []
     for question, preds_labels in question_map.items():
          __UpperCamelCase							,				__UpperCamelCase   							=zip(*SCREAMING_SNAKE_CASE__    )
          __UpperCamelCase   							=fa_score(y_true=SCREAMING_SNAKE_CASE__      ,					y_pred=SCREAMING_SNAKE_CASE__      ,					average='macro'    )
          fas.append(SCREAMING_SNAKE_CASE__    )
          __UpperCamelCase   							=int(sum(pred == label for pred, label in preds_labels    ) == len(SCREAMING_SNAKE_CASE__    )    )
          ems.append(SCREAMING_SNAKE_CASE__    )
     __UpperCamelCase   							=float(sum(SCREAMING_SNAKE_CASE__    ) / len(SCREAMING_SNAKE_CASE__    )    )
     __UpperCamelCase   							=sum(SCREAMING_SNAKE_CASE__    ) / len(SCREAMING_SNAKE_CASE__    )
     __UpperCamelCase   							=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    UpperCAmelCase__	( datasets.Metric		):



    """simple docstring"""






    def _a (      self   )  ->	int:
         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 _a (      self   )  ->	List[str]:
         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 _a (      self ,       A_ ,       A_   )  ->	Union[str, Any]:
         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   							=[
                  {
                      'qas': [
                          {'id': ref['idx']['query'], 'answers': [{'text': ans} for ans in ref['answers']]}
                          for ref in references
                      ]
                  }
              ]
              __UpperCamelCase   							={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",]'   )









# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse

from ...utils.dataclasses import (
    ComputeEnvironment,
    DistributedType,
    DynamoBackend,
    PrecisionType,
    SageMakerDistributedType,
)
from ..menu import BulletMenu


_A			=						[
    'EAGER',
    'AOT_EAGER',
    'INDUCTOR',
    'NVFUSER',
    'AOT_NVFUSER',
    'AOT_CUDAGRAPHS',
    'OFI',
    'FX2TRT',
    'ONNXRT',
    'IPEX',
]

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Optional[int]      ,					SCREAMING_SNAKE_CASE__      : Optional[Any]=None      ,					SCREAMING_SNAKE_CASE__      : Optional[Any]=None      ,					SCREAMING_SNAKE_CASE__      : str=None    ):
     __UpperCamelCase   							=True
     while ask_again:
          __UpperCamelCase   							=input(SCREAMING_SNAKE_CASE__    )
          try:
               if default is not None and len(SCREAMING_SNAKE_CASE__    ) == 0:
                    return default
               return convert_value(SCREAMING_SNAKE_CASE__    ) if convert_value is not None else result
          except Exception:
               if error_message is not None:
                    print(SCREAMING_SNAKE_CASE__    )

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : List[Any]      ,					SCREAMING_SNAKE_CASE__      : List[str]=[]      ,					SCREAMING_SNAKE_CASE__      : Union[str, Any]=None      ,					SCREAMING_SNAKE_CASE__      : str=0    ):
     __UpperCamelCase   							=BulletMenu(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )
     __UpperCamelCase   							=menu.run(default_choice=SCREAMING_SNAKE_CASE__    )
     return convert_value(SCREAMING_SNAKE_CASE__    ) if convert_value is not None else result

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Union[str, Any]    ):
     __UpperCamelCase   							=int(SCREAMING_SNAKE_CASE__    )
     return ComputeEnvironment(['LOCAL_MACHINE', 'AMAZON_SAGEMAKER'][value]    )

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : List[str]    ):
     __UpperCamelCase   							=int(SCREAMING_SNAKE_CASE__    )
     return DistributedType(['NO', 'MULTI_CPU', 'MULTI_XPU', 'MULTI_GPU', 'MULTI_NPU', 'TPU'][value]    )

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Optional[int]    ):
     __UpperCamelCase   							=int(SCREAMING_SNAKE_CASE__    )
     return DynamoBackend(DYNAMO_BACKENDS[value]    ).value

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Dict    ):
     __UpperCamelCase   							=int(SCREAMING_SNAKE_CASE__    )
     return PrecisionType(['no', 'fp16', 'bf16', 'fp8'][value]    )

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Optional[Any]    ):
     __UpperCamelCase   							=int(SCREAMING_SNAKE_CASE__    )
     return SageMakerDistributedType(['NO', 'DATA_PARALLEL', 'MODEL_PARALLEL'][value]    )

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Any    ):
     return {"yes": True, "no": False}[value.lower()]






class    UpperCAmelCase__	( argparse.RawDescriptionHelpFormatter		):



    """simple docstring"""






    def _a (      self ,       A_ ,       A_ ,       A_ ,       A_   )  ->	Optional[int]:
         __UpperCamelCase   							=super()._format_usage(A_ ,       A_ ,       A_ ,       A_   )
         __UpperCamelCase   							=usage.replace('<command> [<args>] ' ,       ''   )
         return usage





