





"""simple docstring"""




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}









import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_          =		logging.get_logger(__name__)

lowercase_          =		{
    """xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/config.json""",
    """xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/config.json""",
}
class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):
   _UpperCamelCase   :		str			 =	'xlnet'
   _UpperCamelCase   :		Optional[Any]			 =	['mems']
   _UpperCamelCase   :		Union[str, Any]			 =	{
       'n_token': 'vocab_size',  # Backward compatibility
       'hidden_size': 'd_model',
       'num_attention_heads': 'n_head',
       'num_hidden_layers': 'n_layer',
   }


   def __init__(   self      :    str      ,					a      :    int=32_000      ,					a      :    Any=1_024      ,					a      :    Optional[Any]=24      ,					a      :    str=16      ,					a      :    int=4_096      ,					a      :    List[str]="gelu"      ,					a      :    Any=True      ,					a      :    Dict="bi"      ,					a      :    str=0.02      ,					a      :    List[str]=1E-1_2      ,					a      :    Tuple=0.1      ,					a      :    Optional[Any]=512      ,					a      :    Tuple=None      ,					a      :    Union[str, Any]=True      ,					a      :    int=False      ,					a      :    int=False      ,					a      :    Tuple=-1      ,					a      :    Any=False      ,					a      :    Tuple="last"      ,					a      :    List[str]=True      ,					a      :    Optional[Any]="tanh"      ,					a      :    List[Any]=0.1      ,					a      :    int=5      ,					a      :    List[Any]=5      ,					a      :    Optional[int]=5      ,					a      :    Dict=1      ,					a      :    Optional[Any]=2      ,					**a      :    List[Any]      ,					)->       List[str]:





     """simple docstring"""







     lowercase__           =  vocab_size
     lowercase__           =  d_model
     lowercase__           =  n_layer
     lowercase__           =  n_head
     if d_model % n_head != 0:
       raise ValueError(f"""'d_model % n_head' ({d_model % n_head}) should be equal to 0"""	)
     if "d_head" in kwargs:
       if kwargs["d_head"] != d_model // n_head:
         raise ValueError(
             f"""`d_head` ({kwargs["d_head"]}) should be equal to `d_model // n_head` ({d_model // n_head})"""	)
     lowercase__           =  d_model // n_head
     lowercase__           =  ff_activation
     lowercase__           =  d_inner
     lowercase__           =  untie_r
     lowercase__           =  attn_type

     lowercase__           =  initializer_range
     lowercase__           =  layer_norm_eps

     lowercase__           =  dropout
     lowercase__           =  mem_len
     lowercase__           =  reuse_len
     lowercase__           =  bi_data
     lowercase__           =  clamp_len
     lowercase__           =  same_length

     lowercase__           =  summary_type
     lowercase__           =  summary_use_proj
     lowercase__           =  summary_activation
     lowercase__           =  summary_last_dropout
     lowercase__           =  start_n_top
     lowercase__           =  end_n_top

     lowercase__           =  bos_token_id
     lowercase__           =  pad_token_id
     lowercase__           =  eos_token_id

     if "use_cache" in kwargs:
       warnings.warn(
           'The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`'
           ' instead.'      ,					a      ,					)
       lowercase__           =  kwargs['use_cache']

     lowercase__           =  use_mems_eval
     lowercase__           =  use_mems_train
     super().__init__(pad_token_id=a      ,					bos_token_id=a      ,					eos_token_id=a      ,					**a	)


   @property
   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[Any]	)->       List[str]:





     """simple docstring"""







     logger.info(f"""The model {self.model_type} is one of the few models that has no sequence length limit."""	)
     return -1


   @max_position_embeddings.setter
   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple      ,					a      :    Union[str, Any]	)->       List[Any]:





     """simple docstring"""







     raise NotImplementedError(
         f"""The model {self.model_type} is one of the few models that has no sequence length limit."""	)


