





"""simple docstring"""





from sklearn.metrics import fa_score

import datasets


__A											=		"""
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
"""


__A											=		"""
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    labels (`list` of `int`): The set of labels to include when `average` is not set to `'binary'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.
    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.
    average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.

        - 'binary': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.
        - 'micro': Calculate metrics globally by counting the total true positives, false negatives and false positives.
        - 'macro': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
        - 'weighted': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.
        - 'samples': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.

Examples:

    Example 1-A simple binary example
        >>> f1_metric = datasets.load_metric(\"f1\")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])
        >>> print(results)
        {'f1': 0.5}

    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.
        >>> f1_metric = datasets.load_metric(\"f1\")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)
        >>> print(round(results['f1'], 2))
        0.67

    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.
        >>> f1_metric = datasets.load_metric(\"f1\")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])
        >>> print(round(results['f1'], 2))
        0.35

    Example 4-A multiclass example, with different values for the `average` input.
        >>> predictions = [0, 2, 1, 0, 0, 1]
        >>> references = [0, 1, 2, 0, 1, 2]
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"macro\")
        >>> print(round(results['f1'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"micro\")
        >>> print(round(results['f1'], 2))
        0.33
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"weighted\")
        >>> print(round(results['f1'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)
        >>> print(results)
        {'f1': array([0.8, 0. , 0. ])}
"""


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






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION  ,   _KWARGS_DESCRIPTION       )
class 					_lowerCAmelCase   (     datasets.Metric       ):
       """simple docstring"""




       def      snake_case						(    self      ):



         '''simple docstring'''

         return datasets.MetricInfo(
             description=_DESCRIPTION    , citation=_CITATION    , inputs_description=_KWARGS_DESCRIPTION    , features=datasets.Features(
                 {
                     'predictions': datasets.Sequence(datasets.Value('int32'      )      ),
                     'references': datasets.Sequence(datasets.Value('int32'      )      ),
                 }
                 if self.config_name == 'multilabel'
                 else {
                     'predictions': datasets.Value('int32'      ),
                     'references': datasets.Value('int32'      ),
                 }      )    , reference_urls=['https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html']    , )






       def      snake_case						(    self    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase=None    , __UpperCAmelCase=1    , __UpperCAmelCase="binary"    , __UpperCAmelCase=None      ):



         '''simple docstring'''

         lowerCAmelCase__			:int											=							fa_score(
             __UpperCAmelCase    , __UpperCAmelCase    , labels=__UpperCAmelCase    , pos_label=__UpperCAmelCase    , average=__UpperCAmelCase    , sample_weight=__UpperCAmelCase      )
         return {"f1": float(__UpperCAmelCase      ) if score.size == 1 else score}







"""simple docstring"""





from __future__ import annotations

__A											=		tuple[int, int, int]
__A											=		tuple[str, str, str]


# used alphabet --------------------------
# from string.ascii_uppercase
__A											=		"""ABCDEFGHIJKLMNOPQRSTUVWXYZ"""

# -------------------------- default selection --------------------------
# rotors --------------------------
__A											=		"""EGZWVONAHDCLFQMSIPJBYUKXTR"""
__A											=		"""FOBHMDKEXQNRAULPGSJVTYICZW"""
__A											=		"""ZJXESIUQLHAVRMDOYGTNFWPBKC"""
# reflector --------------------------
__A											=		{
    """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 --------------------------
__A											=		"""RMDJXFUWGISLHVTCQNKYPBEZOA"""
__A											=		"""SGLCPQWZHKXAREONTFBVIYJUDM"""
__A											=		"""HVSICLTYKQUBXDWAJZOMFGPREN"""
__A											=		"""RZWQHFMVDBKICJLNTUXAGYPSOE"""
__A											=		"""LFKIJODBEGAMQPXVUHYSTCZRWN"""
__A											=		"""KOAEGVDHXPQZMLFTYWJNBRCIUS"""
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)					->tuple[RotorPositionT, RotorSelectionT, dict[str, str]]:





  """simple docstring"""


  if (unique_rotsel := len(set(_SCREAMING_SNAKE_CASE			)			)) < 3:
    lowerCAmelCase__			:Union[str, Any]											=							F"Please use 3 unique rotors (not {unique_rotsel})"
    raise Exception(_SCREAMING_SNAKE_CASE			)

  # Checks if rotor positions are valid
  lowerCAmelCase__							,				lowerCAmelCase__							,				lowerCAmelCase__			:str											=							rotpos
  if not 0 < rotorposa <= len(_SCREAMING_SNAKE_CASE			):
    lowerCAmelCase__			:Tuple											=							F"First rotor position is not within range of 1..26 ({rotorposa}"
    raise ValueError(_SCREAMING_SNAKE_CASE			)
  if not 0 < rotorposa <= len(_SCREAMING_SNAKE_CASE			):
    lowerCAmelCase__			:Optional[Any]											=							F"Second rotor position is not within range of 1..26 ({rotorposa})"
    raise ValueError(_SCREAMING_SNAKE_CASE			)
  if not 0 < rotorposa <= len(_SCREAMING_SNAKE_CASE			):
    lowerCAmelCase__			:Union[str, Any]											=							F"Third rotor position is not within range of 1..26 ({rotorposa})"
    raise ValueError(_SCREAMING_SNAKE_CASE			)

  # Validates string and returns dict
  lowerCAmelCase__			:int											=							_plugboard(_SCREAMING_SNAKE_CASE			)

  return rotpos, rotsel, pbdict
def  __A   (_SCREAMING_SNAKE_CASE			)					->dict[str, str]:





  """simple docstring"""


  if not isinstance(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			):
    lowerCAmelCase__			:str											=							F"Plugboard setting isn't type string ({type(_SCREAMING_SNAKE_CASE			)})"
    raise TypeError(_SCREAMING_SNAKE_CASE			)
  elif len(_SCREAMING_SNAKE_CASE			) % 2 != 0:
    lowerCAmelCase__			:str											=							F"Odd number of symbols ({len(_SCREAMING_SNAKE_CASE			)})"
    raise Exception(_SCREAMING_SNAKE_CASE			)
  elif pbstring == "":
    return {}

  pbstring.replace(' '     ,   ''			)

  # Checks if all characters are unique
  lowerCAmelCase__			:Any											=							set()
  for i in pbstring:
    if i not in abc:
      lowerCAmelCase__			:Any											=							F"'{i}' not in list of symbols"
      raise Exception(_SCREAMING_SNAKE_CASE			)
    elif i in tmppbl:
      lowerCAmelCase__			:Dict											=							F"Duplicate symbol ({i})"
      raise Exception(_SCREAMING_SNAKE_CASE			)
    else:
      tmppbl.add(_SCREAMING_SNAKE_CASE			)
  del tmppbl

  # Created the dictionary
  lowerCAmelCase__			:List[Any]											=							{}
  for j in range(0     ,   len(_SCREAMING_SNAKE_CASE			) - 1     ,   2			):
    lowerCAmelCase__			:Optional[int]											=							pbstring[j + 1]
    lowerCAmelCase__			:Union[str, Any]											=							pbstring[j]

  return pb
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE = (rotora, rotora, rotora)     ,   _SCREAMING_SNAKE_CASE = ""     ,   )					->str:





  """simple docstring"""


  lowerCAmelCase__			:Tuple											=							text.upper()
  lowerCAmelCase__							,				lowerCAmelCase__							,				lowerCAmelCase__			:Tuple											=							_validator(
      _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   plugb.upper()			)

  lowerCAmelCase__							,				lowerCAmelCase__							,				lowerCAmelCase__			:Tuple											=							rotor_position
  lowerCAmelCase__							,				lowerCAmelCase__							,				lowerCAmelCase__			:Union[str, Any]											=							rotor_selection
  rotorposa -= 1
  rotorposa -= 1
  rotorposa -= 1

  lowerCAmelCase__			:Dict											=							[]

  # encryption/decryption process --------------------------
  for symbol in text:
    if symbol in abc:
      # 1st plugboard --------------------------
      if symbol in plugboard:
        lowerCAmelCase__			:Dict											=							plugboard[symbol]

      # rotor ra --------------------------
      lowerCAmelCase__			:Optional[int]											=							abc.index(_SCREAMING_SNAKE_CASE			) + rotorposa
      lowerCAmelCase__			:str											=							rotora[index % len(_SCREAMING_SNAKE_CASE			)]

      # rotor rb --------------------------
      lowerCAmelCase__			:Optional[int]											=							abc.index(_SCREAMING_SNAKE_CASE			) + rotorposa
      lowerCAmelCase__			:int											=							rotora[index % len(_SCREAMING_SNAKE_CASE			)]

      # rotor rc --------------------------
      lowerCAmelCase__			:str											=							abc.index(_SCREAMING_SNAKE_CASE			) + rotorposa
      lowerCAmelCase__			:Optional[Any]											=							rotora[index % len(_SCREAMING_SNAKE_CASE			)]

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

      lowerCAmelCase__			:str											=							reflector[symbol]

      # 2nd rotors
      lowerCAmelCase__			:Tuple											=							abc[rotora.index(_SCREAMING_SNAKE_CASE			) - rotorposa]
      lowerCAmelCase__			:Optional[int]											=							abc[rotora.index(_SCREAMING_SNAKE_CASE			) - rotorposa]
      lowerCAmelCase__			:Any											=							abc[rotora.index(_SCREAMING_SNAKE_CASE			) - rotorposa]

      # 2nd plugboard
      if symbol in plugboard:
        lowerCAmelCase__			:Union[str, Any]											=							plugboard[symbol]

      # moves/resets rotor positions
      rotorposa += 1
      if rotorposa >= len(_SCREAMING_SNAKE_CASE			):
        lowerCAmelCase__			:str											=							0
        rotorposa += 1
      if rotorposa >= len(_SCREAMING_SNAKE_CASE			):
        lowerCAmelCase__			:List[Any]											=							0
        rotorposa += 1
      if rotorposa >= len(_SCREAMING_SNAKE_CASE			):
        lowerCAmelCase__			:Optional[Any]											=							0

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

  return "".join(_SCREAMING_SNAKE_CASE			)


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

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