





from sklearn.metrics import fa_score

import datasets


lowercase_          =		"""
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)
"""


lowercase_          =		"""
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. ])}
"""


lowercase_          =		"""
@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     SCREAMING_SNAKE_CASE       (datasets.Metric				):


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any	)->       Dict:





     """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 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[Any]      ,					a      :    Any      ,					a      :    Optional[Any]      ,					a      :    List[str]=None      ,					a      :    Optional[int]=1      ,					a      :    Union[str, Any]="binary"      ,					a      :    Any=None	)->       List[Any]:





     """simple docstring"""







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









from binascii import hexlify
from hashlib import shaaaa
from os import urandom

# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526

lowercase_          =		{
    # 1536-bit
    5: {
        """prime""": int(
            """FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"""
            + """29024E088A67CC74020BBEA63B139B22514A08798E3404DD"""
            + """EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"""
            + """E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"""
            + """EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"""
            + """C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"""
            + """83655D23DCA3AD961C62F356208552BB9ED529077096966D"""
            + """670C354E4ABC9804F1746C08CA237327FFFFFFFFFFFFFFFF""",
            base=16,
        ),
        """generator""": 2,
    },
    # 2048-bit
    14: {
        """prime""": int(
            """FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"""
            + """29024E088A67CC74020BBEA63B139B22514A08798E3404DD"""
            + """EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"""
            + """E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"""
            + """EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"""
            + """C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"""
            + """83655D23DCA3AD961C62F356208552BB9ED529077096966D"""
            + """670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"""
            + """E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"""
            + """DE2BCBF6955817183995497CEA956AE515D2261898FA0510"""
            + """15728E5A8AACAA68FFFFFFFFFFFFFFFF""",
            base=16,
        ),
        """generator""": 2,
    },
    # 3072-bit
    15: {
        """prime""": int(
            """FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"""
            + """29024E088A67CC74020BBEA63B139B22514A08798E3404DD"""
            + """EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"""
            + """E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"""
            + """EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"""
            + """C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"""
            + """83655D23DCA3AD961C62F356208552BB9ED529077096966D"""
            + """670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"""
            + """E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"""
            + """DE2BCBF6955817183995497CEA956AE515D2261898FA0510"""
            + """15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"""
            + """ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"""
            + """ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"""
            + """F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"""
            + """BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"""
            + """43DB5BFCE0FD108E4B82D120A93AD2CAFFFFFFFFFFFFFFFF""",
            base=16,
        ),
        """generator""": 2,
    },
    # 4096-bit
    16: {
        """prime""": int(
            """FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"""
            + """29024E088A67CC74020BBEA63B139B22514A08798E3404DD"""
            + """EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"""
            + """E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"""
            + """EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"""
            + """C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"""
            + """83655D23DCA3AD961C62F356208552BB9ED529077096966D"""
            + """670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"""
            + """E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"""
            + """DE2BCBF6955817183995497CEA956AE515D2261898FA0510"""
            + """15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"""
            + """ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"""
            + """ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"""
            + """F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"""
            + """BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"""
            + """43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"""
            + """88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"""
            + """2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"""
            + """287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"""
            + """1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"""
            + """93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934063199"""
            + """FFFFFFFFFFFFFFFF""",
            base=16,
        ),
        """generator""": 2,
    },
    # 6144-bit
    17: {
        """prime""": int(
            """FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD129024E08"""
            + """8A67CC74020BBEA63B139B22514A08798E3404DDEF9519B3CD3A431B"""
            + """302B0A6DF25F14374FE1356D6D51C245E485B576625E7EC6F44C42E9"""
            + """A637ED6B0BFF5CB6F406B7EDEE386BFB5A899FA5AE9F24117C4B1FE6"""
            + """49286651ECE45B3DC2007CB8A163BF0598DA48361C55D39A69163FA8"""
            + """FD24CF5F83655D23DCA3AD961C62F356208552BB9ED529077096966D"""
            + """670C354E4ABC9804F1746C08CA18217C32905E462E36CE3BE39E772C"""
            + """180E86039B2783A2EC07A28FB5C55DF06F4C52C9DE2BCBF695581718"""
            + """3995497CEA956AE515D2261898FA051015728E5A8AAAC42DAD33170D"""
            + """04507A33A85521ABDF1CBA64ECFB850458DBEF0A8AEA71575D060C7D"""
            + """B3970F85A6E1E4C7ABF5AE8CDB0933D71E8C94E04A25619DCEE3D226"""
            + """1AD2EE6BF12FFA06D98A0864D87602733EC86A64521F2B18177B200C"""
            + """BBE117577A615D6C770988C0BAD946E208E24FA074E5AB3143DB5BFC"""
            + """E0FD108E4B82D120A92108011A723C12A787E6D788719A10BDBA5B26"""
            + """99C327186AF4E23C1A946834B6150BDA2583E9CA2AD44CE8DBBBC2DB"""
            + """04DE8EF92E8EFC141FBECAA6287C59474E6BC05D99B2964FA090C3A2"""
            + """233BA186515BE7ED1F612970CEE2D7AFB81BDD762170481CD0069127"""
            + """D5B05AA993B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"""
            + """36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BDF8FF9406"""
            + """AD9E530EE5DB382F413001AEB06A53ED9027D831179727B0865A8918"""
            + """DA3EDBEBCF9B14ED44CE6CBACED4BB1BDB7F1447E6CC254B33205151"""
            + """2BD7AF426FB8F401378CD2BF5983CA01C64B92ECF032EA15D1721D03"""
            + """F482D7CE6E74FEF6D55E702F46980C82B5A84031900B1C9E59E7C97F"""
            + """BEC7E8F323A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"""
            + """CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE32806A1D58B"""
            + """B7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55CDA56C9EC2EF29632"""
            + """387FE8D76E3C0468043E8F663F4860EE12BF2D5B0B7474D6E694F91E"""
            + """6DCC4024FFFFFFFFFFFFFFFF""",
            base=16,
        ),
        """generator""": 2,
    },
    # 8192-bit
    18: {
        """prime""": int(
            """FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"""
            + """29024E088A67CC74020BBEA63B139B22514A08798E3404DD"""
            + """EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"""
            + """E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"""
            + """EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"""
            + """C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"""
            + """83655D23DCA3AD961C62F356208552BB9ED529077096966D"""
            + """670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"""
            + """E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"""
            + """DE2BCBF6955817183995497CEA956AE515D2261898FA0510"""
            + """15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"""
            + """ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"""
            + """ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"""
            + """F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"""
            + """BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"""
            + """43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"""
            + """88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"""
            + """2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"""
            + """287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"""
            + """1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"""
            + """93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"""
            + """36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BD"""
            + """F8FF9406AD9E530EE5DB382F413001AEB06A53ED9027D831"""
            + """179727B0865A8918DA3EDBEBCF9B14ED44CE6CBACED4BB1B"""
            + """DB7F1447E6CC254B332051512BD7AF426FB8F401378CD2BF"""
            + """5983CA01C64B92ECF032EA15D1721D03F482D7CE6E74FEF6"""
            + """D55E702F46980C82B5A84031900B1C9E59E7C97FBEC7E8F3"""
            + """23A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"""
            + """CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE328"""
            + """06A1D58BB7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55C"""
            + """DA56C9EC2EF29632387FE8D76E3C0468043E8F663F4860EE"""
            + """12BF2D5B0B7474D6E694F91E6DBE115974A3926F12FEE5E4"""
            + """38777CB6A932DF8CD8BEC4D073B931BA3BC832B68D9DD300"""
            + """741FA7BF8AFC47ED2576F6936BA424663AAB639C5AE4F568"""
            + """3423B4742BF1C978238F16CBE39D652DE3FDB8BEFC848AD9"""
            + """22222E04A4037C0713EB57A81A23F0C73473FC646CEA306B"""
            + """4BCBC8862F8385DDFA9D4B7FA2C087E879683303ED5BDD3A"""
            + """062B3CF5B3A278A66D2A13F83F44F82DDF310EE074AB6A36"""
            + """4597E899A0255DC164F31CC50846851DF9AB48195DED7EA1"""
            + """B1D510BD7EE74D73FAF36BC31ECFA268359046F4EB879F92"""
            + """4009438B481C6CD7889A002ED5EE382BC9190DA6FC026E47"""
            + """9558E4475677E9AA9E3050E2765694DFC81F56E880B96E71"""
            + """60C980DD98EDD3DFFFFFFFFFFFFFFFFF""",
            base=16,
        ),
        """generator""": 2,
    },
}
class     SCREAMING_SNAKE_CASE       :


   def __init__(   self      :    Optional[Any]      ,					a      :    int = 14	)->       None:





     """simple docstring"""







     if group not in primes:
       raise ValueError('Unsupported Group'	)
     lowercase__           =  primes[group]['prime']
     lowercase__           =  primes[group]['generator']

     lowercase__           =  int(hexlify(urandom(32	)	)      ,					base=16	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]	)->       str:





     """simple docstring"""







     return hex(self.__private_key	)[2:]


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple	)->       str:





     """simple docstring"""







     lowercase__           =  pow(self.generator      ,					self.__private_key      ,					self.prime	)
     return hex(a	)[2:]


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple      ,					a      :    int	)->       bool:





     """simple docstring"""







     return (
         2 <= key <= self.prime - 2
         and pow(a      ,					(self.prime - 1) // 2      ,					self.prime	) == 1
     )


   def 	SCREAMING_SNAKE_CASE_							(   self      :    str      ,					a      :    str	)->       str:





     """simple docstring"""







     lowercase__           =  int(a      ,					base=16	)
     if not self.is_valid_public_key(a	):
       raise ValueError('Invalid public key'	)
     lowercase__           =  pow(a      ,					self.__private_key      ,					self.prime	)
     return shaaaa(str(a	).encode()	).hexdigest()


   @staticmethod
   def 	SCREAMING_SNAKE_CASE_							(   a      :    int      ,					a      :    int	)->       bool:





     """simple docstring"""







     return (
         2 <= remote_public_key_str <= prime - 2
         and pow(a      ,					(prime - 1) // 2      ,					a	) == 1
     )


   @staticmethod
   def 	SCREAMING_SNAKE_CASE_							(   a      :    str      ,					a      :    str      ,					a      :    int = 14	)->       str:





     """simple docstring"""







     lowercase__           =  int(a      ,					base=16	)
     lowercase__           =  int(a      ,					base=16	)
     lowercase__           =  primes[group]['prime']
     if not DiffieHellman.is_valid_public_key_static(a      ,					a	):
       raise ValueError('Invalid public key'	)
     lowercase__           =  pow(a      ,					a      ,					a	)
     return shaaaa(str(a	).encode()	).hexdigest()


if __name__ == "__main__":
      import doctest

      doctest.testmod()


