
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets


lowercase__          ='\\n@inproceedings{wang2019glue,\n  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n  note={In the Proceedings of ICLR.},\n  year={2019}\n}\n'

lowercase__          ='\\nGLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n'

lowercase__          ='\nCompute GLUE evaluation metric associated to each GLUE dataset.\nArgs:\n    predictions: list of predictions to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\nReturns: depending on the GLUE subset, one or several of:\n    "accuracy": Accuracy\n    "f1": F1 score\n    "pearson": Pearson Correlation\n    "spearmanr": Spearman Correlation\n    "matthews_correlation": Matthew Correlation\nExamples:\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'sst2\')  # \'sst2\' or any of ["mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0}\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'mrpc\')  # \'mrpc\' or \'qqp\'\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0, \'f1\': 1.0}\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'stsb\')\n    >>> references = [0., 1., 2., 3., 4., 5.]\n    >>> predictions = [0., 1., 2., 3., 4., 5.]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print({"pearson": round(results["pearson"], 2), "spearmanr": round(results["spearmanr"], 2)})\n    {\'pearson\': 1.0, \'spearmanr\': 1.0}\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'cola\')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'matthews_correlation\': 1.0}\n'




def        __UpperCamelCase					(    lowerCAmelCase__							:    List[str]  ,       lowerCAmelCase__							:    List[str]  ):
 return float((preds == labels).mean()  )




def        __UpperCamelCase					(    lowerCAmelCase__							:    List[str]  ,       lowerCAmelCase__							:    List[Any]  ):
 __a			:  Optional[int]		    =  simple_accuracy(lowerCAmelCase__  ,       lowerCAmelCase__  )
 __a			:  Union[str, Any]		    =  float(fa_score(y_true=lowerCAmelCase__  ,       y_pred=lowerCAmelCase__  )  )
 return {
     "accuracy": acc,
     "f1": fa,
 }




def        __UpperCamelCase					(    lowerCAmelCase__							:    List[str]  ,       lowerCAmelCase__							:    Union[str, Any]  ):
 __a			:  str		    =  float(pearsonr(lowerCAmelCase__  ,       lowerCAmelCase__  )[0]  )
 __a			:  List[str]		    =  float(spearmanr(lowerCAmelCase__  ,       lowerCAmelCase__  )[0]  )
 return {
     "pearson": pearson_corr,
     "spearmanr": spearman_corr,
 }



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,_KWARGS_DESCRIPTION   )
class 		UpperCamelCase__  (	datasets.Metric   ):
   def 				lowerCAmelCase      (self			: Dict       ):
    if self.config_name not in [
        "sst2",
        "mnli",
        "mnli_mismatched",
        "mnli_matched",
        "cola",
        "stsb",
        "mrpc",
        "qqp",
        "qnli",
        "rte",
        "wnli",
        "hans",
    ]:
     raise KeyError(
         '''You should supply a configuration name selected in '''
         '''["sst2", "mnli", "mnli_mismatched", "mnli_matched", '''
         '''"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]'''       )
    return datasets.MetricInfo(
        description=_DESCRIPTION ,    citation=_CITATION ,    inputs_description=_KWARGS_DESCRIPTION ,    features=datasets.Features(
            {
                '''predictions''': datasets.Value('''int64''' if self.config_name != '''stsb''' else '''float32'''       ),
                '''references''': datasets.Value('''int64''' if self.config_name != '''stsb''' else '''float32'''       ),
            }       ) ,    codebase_urls=[] ,    reference_urls=[] ,    format='''numpy''' ,    )







   def 				lowerCAmelCase      (self			: Dict ,    snake_case_			: Any ,    snake_case_			: int       ):
    if self.config_name == "cola":
     return {"matthews_correlation": matthews_corrcoef(snake_case_ ,    snake_case_       )}
    elif self.config_name == "stsb":
     return pearson_and_spearman(snake_case_ ,    snake_case_       )
    elif self.config_name in ["mrpc", "qqp"]:
     return acc_and_fa(snake_case_ ,    snake_case_       )
    elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
     return {"accuracy": simple_accuracy(snake_case_ ,    snake_case_       )}
    else:
     raise KeyError(
         '''You should supply a configuration name selected in '''
         '''["sst2", "mnli", "mnli_mismatched", "mnli_matched", '''
         '''"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]'''       )


from __future__ import annotations

# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
lowercase__          =10




def        __UpperCamelCase					(    lowerCAmelCase__							:    int  ,       lowerCAmelCase__							:    int  ,       lowerCAmelCase__							:    list[int]  ,       lowerCAmelCase__							:    int  ):
 for i in range(lowerCAmelCase__  ,       lowerCAmelCase__  ):
  if array[i] == target:
   return i
 return -1




def        __UpperCamelCase					(    lowerCAmelCase__							:    list[int]  ,       lowerCAmelCase__							:    int  ):
 __a			:  List[Any]		    =  0
 __a			:  Union[str, Any]		    =  len(lowerCAmelCase__  )
 while left <= right:
  if right - left < precision:
   return lin_search(lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  )

  __a			:  str		    =  (left + right) // 3 + 1
  __a			:  List[Any]		    =  2 * (left + right) // 3 + 1

  if array[one_third] == target:
   return one_third
  elif array[two_third] == target:
   return two_third

  elif target < array[one_third]:
   __a			:  List[str]		    =  one_third - 1
  elif array[two_third] < target:
   __a			:  List[str]		    =  two_third + 1

  else:
   __a			:  Dict		    =  one_third + 1
   __a			:  Any		    =  two_third - 1
 else:
  return -1




def        __UpperCamelCase					(    lowerCAmelCase__							:    int  ,       lowerCAmelCase__							:    int  ,       lowerCAmelCase__							:    list[int]  ,       lowerCAmelCase__							:    int  ):
 if left < right:
  if right - left < precision:
   return lin_search(lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  )
  __a			:  Union[str, Any]		    =  (left + right) // 3 + 1
  __a			:  Optional[Any]		    =  2 * (left + right) // 3 + 1

  if array[one_third] == target:
   return one_third
  elif array[two_third] == target:
   return two_third

  elif target < array[one_third]:
   return rec_ternary_search(lowerCAmelCase__  ,       one_third - 1  ,       lowerCAmelCase__  ,       lowerCAmelCase__  )
  elif array[two_third] < target:
   return rec_ternary_search(two_third + 1  ,       lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  )
  else:
   return rec_ternary_search(one_third + 1  ,       two_third - 1  ,       lowerCAmelCase__  ,       lowerCAmelCase__  )
 else:
  return -1


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      lowercase__          =input('Enter numbers separated by comma:\n').strip()
      lowercase__          =[int(item.strip()) for item in user_input.split(',')]
      assert collection == sorted(collection), F"List must be ordered.\n{collection}."
      lowercase__          =int(input('Enter the number to be found in the list:\n').strip())
      lowercase__          =ite_ternary_search(collection, target)
      lowercase__          =rec_ternary_search(0, len(collection) - 1, collection, target)
      if resulta != -1:
            print(F"""Iterative search: {target} found at positions: {resulta}""")
            print(F"""Recursive search: {target} found at positions: {resulta}""")
      else:
            print('Not found')
