from scipy.stats import pearsonr

import datasets


SCREAMING_SNAKE_CASE :	Tuple							     =		'\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.\nThe p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.\n'


SCREAMING_SNAKE_CASE :	Any							     =		'\nArgs:\n    predictions (`list` of `int`): Predicted class labels, as returned by a model.\n    references (`list` of `int`): Ground truth labels.\n    return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.\n\nReturns:\n    pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.\n    p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.\n\nExamples:\n\n    Example 1-A simple example using only predictions and references.\n        >>> pearsonr_metric = datasets.load_metric("pearsonr")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])\n        >>> print(round(results[\'pearsonr\'], 2))\n        -0.74\n\n    Example 2-The same as Example 1, but that also returns the `p-value`.\n        >>> pearsonr_metric = datasets.load_metric("pearsonr")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)\n        >>> print(sorted(list(results.keys())))\n        [\'p-value\', \'pearsonr\']\n        >>> print(round(results[\'pearsonr\'], 2))\n        -0.74\n        >>> print(round(results[\'p-value\'], 2))\n        0.15\n'


SCREAMING_SNAKE_CASE :	Tuple							     =		'\n@article{2020SciPy-NMeth,\nauthor  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and\n      Haberland, Matt and Reddy, Tyler and Cournapeau, David and\n      Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and\n      Bright, Jonathan and {van der Walt}, St{\'e}fan J. and\n      Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and\n      Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and\n      Kern, Robert and Larson, Eric and Carey, C J and\n      Polat, Ilhan and Feng, Yu and Moore, Eric W. and\n      {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and\n      Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and\n      Harris, Charles R. and Archibald, Anne M. and\n      Ribeiro, Antonio H. and Pedregosa, Fabian and\n      {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\ntitle   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n      Computing in Python}},\njournal = {Nature Methods},\nyear    = {2020},\nvolume  = {17},\npages   = {261--272},\nadsurl  = {https://rdcu.be/b08Wh},\ndoi = {10.1038/s41592-019-0686-2},\n}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,       _KWARGS_DESCRIPTION )
class    A_ (       datasets.Metric ):

  def 		_UpperCAmelCase   (		self :      List[str]       ):
   return datasets.MetricInfo(
       description=_DESCRIPTION     ,							citation=_CITATION     ,							inputs_description=_KWARGS_DESCRIPTION     ,							features=datasets.Features(
           {
               "predictions": datasets.Value("float"       ),
               "references": datasets.Value("float"       ),
           }       )     ,							reference_urls=["https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html"]     ,							)

  def 		_UpperCAmelCase   (		self :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      Any     ,							__SCREAMING_SNAKE_CASE :      Dict=False       ):





   if return_pvalue:
    __a       =  pearsonr(UpperCamelCase__     ,							UpperCamelCase__       )
    return {"pearsonr": results[0], "p-value": results[1]}
   else:
    return {"pearsonr": float(pearsonr(UpperCamelCase__     ,							UpperCamelCase__       )[0]       )}



class    A_ :

  def __init__(		self :      List[Any]       ):
   __a       =  {}  # Mapping from char to TrieNode
   __a       =  False

  def 		_UpperCAmelCase   (		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      list[str]       ):
   for word in words:
    self.insert(__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Any     ,							__SCREAMING_SNAKE_CASE :      str       ):
   __a       =  self
   for char in word:
    if char not in curr.nodes:
     __a       =  TrieNode()
    __a       =  curr.nodes[char]
   __a       =  True

  def 		_UpperCAmelCase   (		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      str       ):
   __a       =  self
   for char in word:
    if char not in curr.nodes:
     return False
    __a       =  curr.nodes[char]
   return curr.is_leaf

  def 		_UpperCAmelCase   (		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      str       ):

   def _delete(__SCREAMING_SNAKE_CASE :      TrieNode     ,							__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      int       ) -> bool:
    if index == len(__SCREAMING_SNAKE_CASE       ):
     # If word does not exist
     if not curr.is_leaf:
      return False
     __a       =  False
     return len(curr.nodes       ) == 0
    __a       =  word[index]
    __a       =  curr.nodes.get(__SCREAMING_SNAKE_CASE       )
    # If char not in current trie node
    if not char_node:
     return False
    # Flag to check if node can be deleted
    __a       =  _delete(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							index + 1       )
    if delete_curr:
     del curr.nodes[char]
     return len(curr.nodes       ) == 0
    return delete_curr

   _delete(self     ,							__SCREAMING_SNAKE_CASE     ,							0       )



def   __A		(			_A							,				_A					):



 """simple docstring"""


 if node.is_leaf:
  print(_A							,				end=" "					)

 for key, value in node.nodes.items():
  print_words(_A							,				word + key					)



def   __A		(			):



 """simple docstring"""


 __a       =  "banana bananas bandana band apple all beast".split()
 __a       =  TrieNode()
 root.insert_many(_A					)
 # print_words(root, "")
 assert all(root.find(_A					) for word in words					)
 assert root.find("banana"					)
 assert not root.find("bandanas"					)
 assert not root.find("apps"					)
 assert root.find("apple"					)
 assert root.find("all"					)
 root.delete("all"					)
 assert not root.find("all"					)
 root.delete("banana"					)
 assert not root.find("banana"					)
 assert root.find("bananas"					)
 return True



def   __A		(			_A							,				_A					):



 """simple docstring"""


 print(str(_A					)							,				"works!" if passes else "doesn't work :("					)



def   __A		(			):



 """simple docstring"""


 assert test_trie()



def   __A		(			):



 """simple docstring"""


 print_results("Testing trie functionality"							,				test_trie()					)


if __name__ == "__main__":
  main()


