
'''simple docstring'''



UpperCamelCase__      :		List[str]       							=  256
# Modulus to hash a string
UpperCamelCase__      :		List[Any]       							=  1_000_003
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     bool:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				len(SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				len(SCREAMING_SNAKE_CASE_						)
   if p_len > t_len:
      return False

   _SCREAMING_SNAKE_CASE      =				0
   _SCREAMING_SNAKE_CASE      =				0
   _SCREAMING_SNAKE_CASE      =				1

   # Calculating the hash of pattern and substring of text
   for i in range(SCREAMING_SNAKE_CASE_						):
      _SCREAMING_SNAKE_CASE      =				(ord(pattern[i]						) + p_hash * alphabet_size) % modulus
      _SCREAMING_SNAKE_CASE      =				(ord(text[i]						) + text_hash * alphabet_size) % modulus
      if i == p_len - 1:
         continue
      _SCREAMING_SNAKE_CASE      =				(modulus_power * alphabet_size) % modulus

   for i in range(0    ,      t_len - p_len + 1						):
      if text_hash == p_hash and text[i : i + p_len] == pattern:
         return True
      if i == t_len - p_len:
         continue
      # Calculate the https://en.wikipedia.org/wiki/Rolling_hash
      _SCREAMING_SNAKE_CASE      =				(
          (text_hash - ord(text[i]						) * modulus_power) * alphabet_size
          + ord(text[i + p_len]						)
      ) % modulus
   return False
def    lowerCAmelCase_	(				)     ->     None:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				"""abc1abc12"""
   _SCREAMING_SNAKE_CASE      =				"""alskfjaldsabc1abc1abc12k23adsfabcabc"""
   _SCREAMING_SNAKE_CASE      =				"""alskfjaldsk23adsfabcabc"""
   assert rabin_karp(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						) and not rabin_karp(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)

   # Test 2)
   _SCREAMING_SNAKE_CASE      =				"""ABABX"""
   _SCREAMING_SNAKE_CASE      =				"""ABABZABABYABABX"""
   assert rabin_karp(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)

   # Test 3)
   _SCREAMING_SNAKE_CASE      =				"""AAAB"""
   _SCREAMING_SNAKE_CASE      =				"""ABAAAAAB"""
   assert rabin_karp(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)

   # Test 4)
   _SCREAMING_SNAKE_CASE      =				"""abcdabcy"""
   _SCREAMING_SNAKE_CASE      =				"""abcxabcdabxabcdabcdabcy"""
   assert rabin_karp(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)

   # Test 5)
   _SCREAMING_SNAKE_CASE      =				"""Lü"""
   _SCREAMING_SNAKE_CASE      =				"""Lüsai"""
   assert rabin_karp(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				"""Lue"""
   assert not rabin_karp(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
   print("""Success."""						)


if __name__ == "__main__":
     test_rabin_karp()








'''simple docstring'''



from sklearn.metrics import matthews_corrcoef

import datasets


UpperCamelCase__      :		List[str]       							=  "\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and false positives and negatives and is generally\nregarded as a balanced measure which can be used even if the classes are of\nvery different sizes. The MCC is in essence a correlation coefficient value\nbetween -1 and +1. A coefficient of +1 represents a perfect prediction, 0\nan average random prediction and -1 an inverse prediction.  The statistic\nis also known as the phi coefficient. [source: Wikipedia]\n"

UpperCamelCase__      :		List[Any]       							=  "\nArgs:\n    predictions (list of int): Predicted labels, as returned by a model.\n    references (list of int): Ground truth labels.\n    sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.\nReturns:\n    matthews_correlation (dict containing float): Matthews correlation.\nExamples:\n    Example 1, a basic example with only predictions and references as inputs:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3])\n        >>> print(round(results['matthews_correlation'], 2))\n        0.54\n\n    Example 2, the same example as above, but also including sample weights:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3],\n        ...                                     sample_weight=[0.5, 3, 1, 1, 1, 2])\n        >>> print(round(results['matthews_correlation'], 2))\n        0.1\n\n    Example 3, the same example as above, but with sample weights that cause a negative correlation:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3],\n        ...                                     sample_weight=[0.5, 1, 0, 0, 0, 1])\n        >>> print(round(results['matthews_correlation'], 2))\n        -0.25\n"

UpperCamelCase__      :		Any       							=  "\\n@article{scikit-learn,\n  title={Scikit-learn: Machine Learning in {P}ython},\n  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n  journal={Journal of Machine Learning Research},\n  volume={12},\n  pages={2825--2830},\n  year={2011}\n}\n"

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION  ,					_KWARGS_DESCRIPTION)
class    _a				(datasets.Metric):





       """simple docstring"""




       def        UpperCamelCase				(							self      )    ->			Optional[Any]:
          return datasets.MetricInfo(
              description=_DESCRIPTION       ,							citation=_CITATION       ,							inputs_description=_KWARGS_DESCRIPTION       ,							features=datasets.Features(
                  {
                      """predictions""": datasets.Value("""int32"""      ),
                      """references""": datasets.Value("""int32"""      ),
                  }      )       ,							reference_urls=[
                  """https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html"""
              ]       ,							)






       def        UpperCamelCase				(							self       ,							A__       ,							A__       ,							A__=None      )    ->			List[str]:
          return {
              "matthews_correlation": float(matthews_corrcoef(A__       ,							A__       ,							sample_weight=A__      )      ),
          }






