






'''simple docstring'''



from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


__UpperCamelCase      :       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}
}
"""

__UpperCamelCase      :       Optional[int]      			=      """\
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.
"""

__UpperCamelCase      :       List[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    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Union[str, Any],					SCREAMING_SNAKE_CASE__:	Union[str, Any]    )  ->			Tuple:

   """simple docstring"""
   return float((preds == labels).mean()    )



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	List[str],					SCREAMING_SNAKE_CASE__:	int,					SCREAMING_SNAKE_CASE__:	Tuple="binary"    )  ->			Tuple:

   """simple docstring"""
   __a				     =    simple_accuracy(SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__    )
   __a				     =    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__:	List[Any],					SCREAMING_SNAKE_CASE__:	List[Any]    )  ->			Tuple:

   """simple docstring"""
   __a				     =    {}
   for id_pred, label in zip(SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__    ):
      __a				     =    f"""{id_pred["idx"]["paragraph"]}-{id_pred["idx"]["question"]}"""
      __a				     =    id_pred['prediction']
      if question_id in question_map:
         question_map[question_id].append((pred, label)    )
      else:
         __a				     =    [(pred, label)]
   __a     ,			__a				     =    [], []
   for question, preds_labels in question_map.items():
      __a     ,			__a				     =    zip(*SCREAMING_SNAKE_CASE__    )
      __a				     =    fa_score(y_true=SCREAMING_SNAKE_CASE__,					y_pred=SCREAMING_SNAKE_CASE__,					average='macro'    )
      fas.append(SCREAMING_SNAKE_CASE__    )
      __a				     =    int(sum(pred == label for pred, label in preds_labels    ) == len(SCREAMING_SNAKE_CASE__    )    )
      ems.append(SCREAMING_SNAKE_CASE__    )
   __a				     =    float(sum(SCREAMING_SNAKE_CASE__    ) / len(SCREAMING_SNAKE_CASE__    )    )
   __a				     =    sum(SCREAMING_SNAKE_CASE__    ) / len(SCREAMING_SNAKE_CASE__    )
   __a				     =    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     ):




      def  __UpperCamelCase							(  self      )							->Dict:



         '''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  __UpperCamelCase							(  self      )							->Tuple:



         '''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  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase      )							->Tuple:



         '''simple docstring'''
         if self.config_name == "axb":
            return {"matthews_correlation": matthews_corrcoef(lowerCamelCase ,   lowerCamelCase      )}
         elif self.config_name == "cb":
            return acc_and_fa(lowerCamelCase ,   lowerCamelCase ,   fa_avg='macro'      )
         elif self.config_name == "record":
            __a				     =    [
                {
                    'qas': [
                        {'id': ref['idx']['query'], 'answers': [{'text': ans} for ans in ref['answers']]}
                        for ref in references
                    ]
                }
            ]
            __a				     =    {pred['idx']['query']: pred['prediction_text'] for pred in predictions}
            return evaluate_record(lowerCamelCase ,   lowerCamelCase      )[0]
         elif self.config_name == "multirc":
            return evaluate_multirc(lowerCamelCase ,   lowerCamelCase      )
         elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
            return {"accuracy": simple_accuracy(lowerCamelCase ,   lowerCamelCase      )}
         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'''



# Copyright 2023 The HuggingFace Inc. 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 torch

from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool


if is_datasets_available():
 from datasets import load_dataset
class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):
      __a  					="microsoft/speecht5_tts"
      __a  					=(
          "This is a tool that reads an English text out loud. It takes an input named `text` which should contain the "
          "text to read (in English) and returns a waveform object containing the sound."
      )
      __a  					="text_reader"
      __a  					=SpeechTaProcessor
      __a  					=SpeechTaForTextToSpeech
      __a  					=SpeechTaHifiGan

      __a  					=["text"]
      __a  					=["audio"]




      def  __UpperCamelCase							(  self      )							->str:



         '''simple docstring'''
         if self.post_processor is None:
            __a				     =    'microsoft/speecht5_hifigan'
         super().setup()




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase=None      )							->str:



         '''simple docstring'''
         __a				     =    self.pre_processor(text=lowerCamelCase ,   return_tensors='pt' ,   truncation=lowerCamelCase      )

         if speaker_embeddings is None:
            if not is_datasets_available():
               raise ImportError('Datasets needs to be installed if not passing speaker embeddings.'      )

            __a				     =    load_dataset('Matthijs/cmu-arctic-xvectors' ,   split='validation'      )
            __a				     =    torch.tensor(embeddings_dataset[7305]['xvector']      ).unsqueeze(0      )

         return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}




      def  __UpperCamelCase							(  self ,   lowerCamelCase      )							->List[str]:



         '''simple docstring'''
         with torch.no_grad():
            return self.model.generate_speech(**lowerCamelCase      )






      def  __UpperCamelCase							(  self ,   lowerCamelCase      )							->List[str]:



         '''simple docstring'''
         with torch.no_grad():
            return self.post_processor(lowerCamelCase      ).cpu().detach()