





from __future__ import annotations

UpperCAmelCase_					=					tuple[int, int, int]
UpperCAmelCase_					=					tuple[str, str, str]


# used alphabet --------------------------
# from string.ascii_uppercase
UpperCAmelCase_					=					'ABCDEFGHIJKLMNOPQRSTUVWXYZ'

# -------------------------- default selection --------------------------
# rotors --------------------------
UpperCAmelCase_					=					'EGZWVONAHDCLFQMSIPJBYUKXTR'
UpperCAmelCase_					=					'FOBHMDKEXQNRAULPGSJVTYICZW'
UpperCAmelCase_					=					'ZJXESIUQLHAVRMDOYGTNFWPBKC'
# reflector --------------------------
UpperCAmelCase_					=					{
    'A': 'N',
    'N': 'A',
    'B': 'O',
    'O': 'B',
    'C': 'P',
    'P': 'C',
    'D': 'Q',
    'Q': 'D',
    'E': 'R',
    'R': 'E',
    'F': 'S',
    'S': 'F',
    'G': 'T',
    'T': 'G',
    'H': 'U',
    'U': 'H',
    'I': 'V',
    'V': 'I',
    'J': 'W',
    'W': 'J',
    'K': 'X',
    'X': 'K',
    'L': 'Y',
    'Y': 'L',
    'M': 'Z',
    'Z': 'M',
}

# -------------------------- extra rotors --------------------------
UpperCAmelCase_					=					'RMDJXFUWGISLHVTCQNKYPBEZOA'
UpperCAmelCase_					=					'SGLCPQWZHKXAREONTFBVIYJUDM'
UpperCAmelCase_					=					'HVSICLTYKQUBXDWAJZOMFGPREN'
UpperCAmelCase_					=					'RZWQHFMVDBKICJLNTUXAGYPSOE'
UpperCAmelCase_					=					'LFKIJODBEGAMQPXVUHYSTCZRWN'
UpperCAmelCase_					=					'KOAEGVDHXPQZMLFTYWJNBRCIUS'




def 						lowerCAmelCase_       (    __UpperCAmelCase:     str						,  __UpperCAmelCase:     List[str]						,  __UpperCAmelCase:     Tuple ) ->      str:
    # Checks if there are 3 unique rotors

    if (unique_rotsel := len(set(_lowerCAmelCase ) )) < 3:
        UpperCamelCase__				:							List[str]			  = f"Please use 3 unique rotors (not {unique_rotsel})"
        raise Exception(_lowerCAmelCase )

    # Checks if rotor positions are valid
    UpperCamelCase__  ,UpperCamelCase__  ,UpperCamelCase__				:							List[str]			  = rotpos
    if not 0 < rotorposa <= len(_lowerCAmelCase ):
        UpperCamelCase__				:							Optional[int]			  = f"First rotor position is not within range of 1..26 ({rotorposa}"
        raise ValueError(_lowerCAmelCase )
    if not 0 < rotorposa <= len(_lowerCAmelCase ):
        UpperCamelCase__				:							Tuple			  = f"Second rotor position is not within range of 1..26 ({rotorposa})"
        raise ValueError(_lowerCAmelCase )
    if not 0 < rotorposa <= len(_lowerCAmelCase ):
        UpperCamelCase__				:							Tuple			  = f"Third rotor position is not within range of 1..26 ({rotorposa})"
        raise ValueError(_lowerCAmelCase )

    # Validates string and returns dict
    UpperCamelCase__				:							Optional[int]			  = _plugboard(_lowerCAmelCase )

    return rotpos, rotsel, pbdict




def 						lowerCAmelCase_       (    __UpperCAmelCase:     Union[str, Any] ) ->      List[str]:

    # tests the input string if it
    # a) is type string
    # b) has even length (so pairs can be made)
    if not isinstance(_lowerCAmelCase						,  _lowerCAmelCase ):
        UpperCamelCase__				:							List[str]			  = f"Plugboard setting isn't type string ({type(_lowerCAmelCase )})"
        raise TypeError(_lowerCAmelCase )
    elif len(_lowerCAmelCase ) % 2 != 0:
        UpperCamelCase__				:							int			  = f"Odd number of symbols ({len(_lowerCAmelCase )})"
        raise Exception(_lowerCAmelCase )
    elif pbstring == "":
        return {}

    pbstring.replace(''' '''						,  '''''' )

    # Checks if all characters are unique
    UpperCamelCase__				:							Optional[int]			  = set()
    for i in pbstring:
        if i not in abc:
            UpperCamelCase__				:							Optional[Any]			  = f"'{i}' not in list of symbols"
            raise Exception(_lowerCAmelCase )
        elif i in tmppbl:
            UpperCamelCase__				:							List[Any]			  = f"Duplicate symbol ({i})"
            raise Exception(_lowerCAmelCase )
        else:
            tmppbl.add(_lowerCAmelCase )
    del tmppbl

    # Created the dictionary
    UpperCamelCase__				:							int			  = {}
    for j in range(0						,  len(_lowerCAmelCase ) - 1						,  2 ):
        UpperCamelCase__				:							int			  = pbstring[j + 1]
        UpperCamelCase__				:							Optional[int]			  = pbstring[j]

    return pb




def 						lowerCAmelCase_       (    __UpperCAmelCase:     int						,  __UpperCAmelCase:     List[Any]						,  __UpperCAmelCase:     List[str] = (rotora, rotora, rotora)						,  __UpperCAmelCase:     str = ""						,  ) ->      Optional[int]:
    UpperCamelCase__				:							Dict			  = text.upper()
    UpperCamelCase__  ,UpperCamelCase__  ,UpperCamelCase__				:							List[Any]			  = _validator(
        _lowerCAmelCase						,  _lowerCAmelCase						,  plugb.upper() )

    UpperCamelCase__  ,UpperCamelCase__  ,UpperCamelCase__				:							List[Any]			  = rotor_position
    UpperCamelCase__  ,UpperCamelCase__  ,UpperCamelCase__				:							List[Any]			  = rotor_selection
    rotorposa -= 1
    rotorposa -= 1
    rotorposa -= 1

    UpperCamelCase__				:							List[Any]			  = []

    # encryption/decryption process --------------------------
    for symbol in text:
        if symbol in abc:
            # 1st plugboard --------------------------
            if symbol in plugboard:
                UpperCamelCase__				:							List[str]			  = plugboard[symbol]

            # rotor ra --------------------------
            UpperCamelCase__				:							Optional[Any]			  = abc.index(_lowerCAmelCase ) + rotorposa
            UpperCamelCase__				:							List[str]			  = rotora[index % len(_lowerCAmelCase )]

            # rotor rb --------------------------
            UpperCamelCase__				:							int			  = abc.index(_lowerCAmelCase ) + rotorposa
            UpperCamelCase__				:							Dict			  = rotora[index % len(_lowerCAmelCase )]

            # rotor rc --------------------------
            UpperCamelCase__				:							Optional[Any]			  = abc.index(_lowerCAmelCase ) + rotorposa
            UpperCamelCase__				:							Dict			  = rotora[index % len(_lowerCAmelCase )]

            # reflector --------------------------
            # this is the reason you don't need another machine to decipher

            UpperCamelCase__				:							Tuple			  = reflector[symbol]

            # 2nd rotors
            UpperCamelCase__				:							Any			  = abc[rotora.index(_lowerCAmelCase ) - rotorposa]
            UpperCamelCase__				:							Tuple			  = abc[rotora.index(_lowerCAmelCase ) - rotorposa]
            UpperCamelCase__				:							str			  = abc[rotora.index(_lowerCAmelCase ) - rotorposa]

            # 2nd plugboard
            if symbol in plugboard:
                UpperCamelCase__				:							str			  = plugboard[symbol]

            # moves/resets rotor positions
            rotorposa += 1
            if rotorposa >= len(_lowerCAmelCase ):
                UpperCamelCase__				:							List[Any]			  = 0
                rotorposa += 1
            if rotorposa >= len(_lowerCAmelCase ):
                UpperCamelCase__				:							int			  = 0
                rotorposa += 1
            if rotorposa >= len(_lowerCAmelCase ):
                UpperCamelCase__				:							Union[str, Any]			  = 0

        # else:
        #    pass
        #    Error could be also raised
        #    raise ValueError(
        #       'Invalid symbol('+repr(symbol)+')')
        result.append(_lowerCAmelCase )

    return "".join(_lowerCAmelCase )


if __name__ == "__main__":
  UpperCAmelCase_					=					'This is my Python script that emulates the Enigma machine from WWII.'
  UpperCAmelCase_					=					(1, 1, 1)
  UpperCAmelCase_					=					'pictures'
  UpperCAmelCase_					=					(rotora, rotora, rotora)
  UpperCAmelCase_					=					enigma(message, rotor_pos, rotor_sel, pb)

  print('Encrypted message:', en)
  print('Decrypted message:', enigma(en, rotor_pos, rotor_sel, pb))



"""simple docstring"""
from scipy.stats import pearsonr

import datasets


SCREAMING_SNAKE_CASE_					     =	'''
Pearson correlation coefficient and p-value for testing non-correlation.
The 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.
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.
'''


SCREAMING_SNAKE_CASE_					     =	'''
Args:
    predictions (`list` of `int`): Predicted class labels, as returned by a model.
    references (`list` of `int`): Ground truth labels.
    return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.

Returns:
    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.
    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.

Examples:

    Example 1-A simple example using only predictions and references.
        >>> pearsonr_metric = datasets.load_metric("pearsonr")
        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])
        >>> print(round(results[\'pearsonr\'], 2))
        -0.74

    Example 2-The same as Example 1, but that also returns the `p-value`.
        >>> pearsonr_metric = datasets.load_metric("pearsonr")
        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)
        >>> print(sorted(list(results.keys())))
        [\'p-value\', \'pearsonr\']
        >>> print(round(results[\'pearsonr\'], 2))
        -0.74
        >>> print(round(results[\'p-value\'], 2))
        0.15
'''


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



							'''simple docstring'''





							def    A__						( self				)       ->  Union[str, Any]:
													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    A__						( self    ,				snake_case_    ,				snake_case_    ,				snake_case_=False				)       ->  Dict:
													if return_pvalue:
																			__lowerCAmelCase     =  pearsonr(snake_case_    ,				snake_case_				)
																			return {"pearsonr": results[0], "p-value": results[1]}
													else:
																			return {"pearsonr": float(pearsonr(snake_case_    ,				snake_case_				)[0]				)}
