
'''simple docstring'''

from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


__snake_case:  str								=			"\\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"

__snake_case:  List[Any]								=			"\\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"

__snake_case:  Optional[int]								=			"\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 	_snake_case						(      A_       :    int     ,		A_       :    Tuple   ):
  """simple docstring"""







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






def 	_snake_case						(      A_       :    Optional[Any]     ,		A_       :    int     ,		A_       :    Optional[Any]="binary"   ):
  """simple docstring"""







  a_      :	Dict      =							simple_accuracy(A_     ,		A_   )
  a_      :	str      =							float(fa_score(y_true=A_     ,		y_pred=A_     ,		average=A_   )   )
  return {
      "accuracy": acc,
      "f1": fa,
  }






def 	_snake_case						(      A_       :    Any     ,		A_       :    Any   ):
  """simple docstring"""







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

       '''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 							_lowerCAmelCase  (      self ):

       '''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 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_ ):

       '''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_      :	List[Any]      =							[
             {
                 """qas""": [
                     {"""id""": ref["""idx"""]["""query"""], """answers""": [{"""text""": ans} for ans in ref["""answers"""]]}
                     for ref in references
                 ]
             }
         ]
         a_      :	List[str]      =							{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'''

import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow


@require_torch
class  _UpperCAmelCase ( unittest.TestCase    ):

     """simple docstring"""

     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	int      =							"""ylacombe/bark-small"""
       a_      :	Dict      =							tempfile.mkdtemp()
       a_      :	Union[str, Any]      =							"""en_speaker_1"""
       a_      :	Dict      =							"""This is a test string"""
       a_      :	Optional[int]      =							"""speaker_embeddings_path.json"""
       a_      :	int      =							"""speaker_embeddings"""


     def 							_lowerCAmelCase  (      self							,		**lowerCAmelCase_ ):

       '''simple docstring'''

       return AutoTokenizer.from_pretrained(self.checkpoint							,		**lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       shutil.rmtree(self.tmpdirname )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							self.get_tokenizer()

       a_      :	Optional[Any]      =							BarkProcessor(tokenizer=lowerCAmelCase_ )

       processor.save_pretrained(self.tmpdirname )
       a_      :	List[str]      =							BarkProcessor.from_pretrained(self.tmpdirname )

       self.assertEqual(processor.tokenizer.get_vocab()							,		tokenizer.get_vocab() )


     @slow
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Any      =							BarkProcessor.from_pretrained(
           pretrained_processor_name_or_path=self.checkpoint							,		speaker_embeddings_dict_path=self.speaker_embeddings_dict_path							,		)
       processor.save_pretrained(
           self.tmpdirname							,		speaker_embeddings_dict_path=self.speaker_embeddings_dict_path							,		speaker_embeddings_directory=self.speaker_embeddings_directory							,		)

       a_      :	List[str]      =							self.get_tokenizer(bos_token="""(BOS)"""							,		eos_token="""(EOS)""" )

       a_      :	Any      =							BarkProcessor.from_pretrained(
           self.tmpdirname							,		self.speaker_embeddings_dict_path							,		bos_token="""(BOS)"""							,		eos_token="""(EOS)"""							,		)

       self.assertEqual(processor.tokenizer.get_vocab()							,		tokenizer_add_kwargs.get_vocab() )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[Any]      =							BarkProcessor.from_pretrained(
           pretrained_processor_name_or_path=self.checkpoint							,		speaker_embeddings_dict_path=self.speaker_embeddings_dict_path							,		)

       a_      :	Dict      =							35
       a_      :	List[Any]      =							2
       a_      :	Optional[int]      =							8

       a_      :	int      =							{
           """semantic_prompt""": np.ones(lowerCAmelCase_ ),
           """coarse_prompt""": np.ones((nb_codebooks_coarse, seq_len) ),
           """fine_prompt""": np.ones((nb_codebooks_total, seq_len) ),
       }

       # test providing already loaded voice_preset
       a_      :	Optional[Any]      =							processor(text=self.input_string							,		voice_preset=lowerCAmelCase_ )

       a_      :	Tuple      =							inputs["""history_prompt"""]
       for key in voice_preset:
         self.assertListEqual(voice_preset[key].tolist()							,		processed_voice_preset.get(lowerCAmelCase_							,		np.array([] ) ).tolist() )

       # test loading voice preset from npz file
       a_      :	Any      =							os.path.join(self.tmpdirname							,		"""file.npz""" )
       np.savez(lowerCAmelCase_							,		**lowerCAmelCase_ )
       a_      :	Any      =							processor(text=self.input_string							,		voice_preset=lowerCAmelCase_ )
       a_      :	List[Any]      =							inputs["""history_prompt"""]

       for key in voice_preset:
         self.assertListEqual(voice_preset[key].tolist()							,		processed_voice_preset.get(lowerCAmelCase_							,		np.array([] ) ).tolist() )

       # test loading voice preset from the hub
       a_      :	Any      =							processor(text=self.input_string							,		voice_preset=self.voice_preset )






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Tuple      =							self.get_tokenizer()

       a_      :	Union[str, Any]      =							BarkProcessor(tokenizer=lowerCAmelCase_ )

       a_      :	Optional[int]      =							processor(text=self.input_string )

       a_      :	Optional[int]      =							tokenizer(
           self.input_string							,		padding="""max_length"""							,		max_length=2_56							,		add_special_tokens=lowerCAmelCase_							,		return_attention_mask=lowerCAmelCase_							,		return_token_type_ids=lowerCAmelCase_							,		)

       for key in encoded_tok.keys():
         self.assertListEqual(encoded_tok[key]							,		encoded_processor[key].squeeze().tolist() )


