



'''simple docstring'''





from sklearn.metrics import mean_squared_error

import datasets


SCREAMING_SNAKE_CASE__          =  "\\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"

SCREAMING_SNAKE_CASE__          =  "\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n"


SCREAMING_SNAKE_CASE__          =  "\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n        \"raw_values\" : Returns a full set of errors in case of multioutput input.\n\n        \"uniform_average\" : Errors of all outputs are averaged with uniform weight.\n\n    squared : bool, default=True\n        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.\n\nReturns:\n    mse : mean squared error.\nExamples:\n\n    >>> mse_metric = datasets.load_metric(\"mse\")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mse': 0.375}\n    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)\n    >>> print(rmse_result)\n    {'mse': 0.6123724356957945}\n\n    If you're using multi-dimensional lists, then set the config as follows :\n\n    >>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mse': 0.7083333333333334}\n    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values')\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {'mse': array([0.41666667, 1. ])}\n"


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



       def 				_a   (					self   )	->				Optional[int]:
           return datasets.MetricInfo(
               description=_DESCRIPTION  ,citation=_CITATION  ,inputs_description=_KWARGS_DESCRIPTION  ,features=datasets.Features(self._get_feature_types()   )  ,reference_urls=[
                   "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html"
               ]  ,)



       def 				_a   (					self   )	->				Tuple:
           if self.config_name == "multilist":
               return {
                   "predictions": datasets.Sequence(datasets.Value("float"   )   ),
                   "references": datasets.Sequence(datasets.Value("float"   )   ),
               }
           else:
               return {
                   "predictions": datasets.Value("float"   ),
                   "references": datasets.Value("float"   ),
               }







       def 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_=None  ,UpperCAmelCase_="uniform_average"  ,UpperCAmelCase_=True   )	->				Tuple:
           lowercase__									=    mean_squared_error(
               UpperCAmelCase_  ,UpperCAmelCase_  ,sample_weight=UpperCAmelCase_  ,multioutput=UpperCAmelCase_  ,squared=UpperCAmelCase_   )

           return {"mse": mse}









'''simple docstring'''





SCREAMING_SNAKE_CASE__          =  256
# Modulus to hash a string
SCREAMING_SNAKE_CASE__          =  100_0003




def 							lowerCamelCase	(					_snake_case	:    str  ,_snake_case	:    str							):





    '''simple docstring'''


    lowercase__									=    len(_snake_case							)
    lowercase__									=    len(_snake_case							)
    if p_len > t_len:
        return False

    lowercase__									=    0
    lowercase__									=    0
    lowercase__									=    1

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




def 							lowerCamelCase	(					):





    '''simple docstring'''


    lowercase__									=    "abc1abc12"
    lowercase__									=    "alskfjaldsabc1abc1abc12k23adsfabcabc"
    lowercase__									=    "alskfjaldsk23adsfabcabc"
    assert rabin_karp(_snake_case  ,_snake_case							) and not rabin_karp(_snake_case  ,_snake_case							)

    # Test 2)
    lowercase__									=    "ABABX"
    lowercase__									=    "ABABZABABYABABX"
    assert rabin_karp(_snake_case  ,_snake_case							)

    # Test 3)
    lowercase__									=    "AAAB"
    lowercase__									=    "ABAAAAAB"
    assert rabin_karp(_snake_case  ,_snake_case							)

    # Test 4)
    lowercase__									=    "abcdabcy"
    lowercase__									=    "abcxabcdabxabcdabcdabcy"
    assert rabin_karp(_snake_case  ,_snake_case							)

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


if __name__ == "__main__":
 test_rabin_karp()




