


from sklearn.metrics import mean_squared_error

import datasets


A   :					Optional[int]	=      "\\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"

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


A   :					Optional[int]	=      "\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     _lowercase				(							datasets.Metric):


       """simple docstring"""




       def 		lowerCAmelCase  (     self							:    Tuple ):


             '''simple docstring'''



             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 		lowerCAmelCase  (     self							:    Optional[int] ):


             '''simple docstring'''



             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 		lowerCAmelCase  (     self							:    Union[str, Any]      ,   __lowerCamelCase							:    Union[str, Any]      ,   __lowerCamelCase							:    Tuple      ,   __lowerCamelCase							:    Any=None      ,   __lowerCamelCase							:    Optional[Any]="uniform_average"      ,   __lowerCamelCase							:    Any=True ):


             '''simple docstring'''



             lowerCamelCase__	:       str					=						mean_squared_error(
                 __lowerCamelCase      ,   __lowerCamelCase      ,   sample_weight=__lowerCamelCase      ,   multioutput=__lowerCamelCase      ,   squared=__lowerCamelCase )

             return {"mse": mse}






from __future__ import annotations

def    lowercase_	(	_A    :					str					,      _A    :					list[str] | None = None					,      _A    :					dict[str, float] | None = None					,      _A    :					bool = False					,      ):

      """simple docstring"""




      lowerCamelCase__	:       Tuple					=						cipher_alphabet or [chr(_A		) for i in range(97					,      123		)]

      # If the argument is None or the user provided an empty dictionary
      if not frequencies_dict:
            # Frequencies of letters in the english language (how much they show up)
            lowerCamelCase__	:       Dict					=						{
                "a": 0.08_497,
                "b": 0.01_492,
                "c": 0.02_202,
                "d": 0.04_253,
                "e": 0.11_162,
                "f": 0.02_228,
                "g": 0.02_015,
                "h": 0.06_094,
                "i": 0.07_546,
                "j": 0.00_153,
                "k": 0.01_292,
                "l": 0.04_025,
                "m": 0.02_406,
                "n": 0.06_749,
                "o": 0.07_507,
                "p": 0.01_929,
                "q": 0.00_095,
                "r": 0.07_587,
                "s": 0.06_327,
                "t": 0.09_356,
                "u": 0.02_758,
                "v": 0.00_978,
                "w": 0.02_560,
                "x": 0.00_150,
                "y": 0.01_994,
                "z": 0.00_077,
            }
      else:
            # Custom frequencies dictionary
            lowerCamelCase__	:       Optional[int]					=						frequencies_dict

      if not case_sensitive:
            lowerCamelCase__	:       str					=						ciphertext.lower()

      # Chi squared statistic values
      lowerCamelCase__	:       dict[int, tuple[float, str]]					=						{}

      # cycle through all of the shifts
      for shift in range(len(_A		)		):
            lowerCamelCase__	:       Optional[Any]					=						""

            # decrypt the message with the shift
            for letter in ciphertext:
                  try:
                        # Try to index the letter in the alphabet
                        lowerCamelCase__	:       Dict					=						(alphabet_letters.index(letter.lower()		) - shift) % len(
                            _A		)
                        decrypted_with_shift += (
                            alphabet_letters[new_key].upper()
                            if case_sensitive and letter.isupper()
                            else alphabet_letters[new_key]
                        )
                  except ValueError:
                        # Append the character if it isn't in the alphabet
                        decrypted_with_shift += letter

            lowerCamelCase__	:       str					=						0.0

            # Loop through each letter in the decoded message with the shift
            for letter in decrypted_with_shift:
                  if case_sensitive:
                        lowerCamelCase__	:       List[str]					=						letter.lower()
                        if letter in frequencies:
                              # Get the amount of times the letter occurs in the message
                              lowerCamelCase__	:       List[str]					=						decrypted_with_shift.lower().count(_A		)

                              # Get the excepcted amount of times the letter should appear based
                              # on letter frequencies
                              lowerCamelCase__	:       List[Any]					=						frequencies[letter] * occurrences

                              # Complete the chi squared statistic formula
                              lowerCamelCase__	:       str					=						((occurrences - expected) ** 2) / expected

                              # Add the margin of error to the total chi squared statistic
                              chi_squared_statistic += chi_letter_value
                  else:
                        if letter.lower() in frequencies:
                              # Get the amount of times the letter occurs in the message
                              lowerCamelCase__	:       Any					=						decrypted_with_shift.count(_A		)

                              # Get the excepcted amount of times the letter should appear based
                              # on letter frequencies
                              lowerCamelCase__	:       str					=						frequencies[letter] * occurrences

                              # Complete the chi squared statistic formula
                              lowerCamelCase__	:       int					=						((occurrences - expected) ** 2) / expected

                              # Add the margin of error to the total chi squared statistic
                              chi_squared_statistic += chi_letter_value

        # Add the data to the chi_squared_statistic_values dictionary
            lowerCamelCase__	:       Optional[int]					=						(
                chi_squared_statistic,
                decrypted_with_shift,
            )

      # Get the most likely cipher by finding the cipher with the smallest chi squared
      # statistic
      def chi_squared_statistic_values_sorting_key(_A    :					int		) -> tuple[float, str]:
            return chi_squared_statistic_values[key]

      lowerCamelCase__	:       int					=						min(
          _A					,      key=_A					,      )

      # Get all the data from the most likely cipher (key, decoded message)
      (
          (
          lowerCamelCase__
      )					,     (
          lowerCamelCase__
      )					,     
      )	:       int					=						chi_squared_statistic_values[most_likely_cipher]

      # Return the data on the most likely shift
      return (
          most_likely_cipher,
          most_likely_cipher_chi_squared_value,
          decoded_most_likely_cipher,
      )


