'''simple docstring'''


import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_albert import AlbertTokenizer
else:
 snake_case__					:      Any				      =  None

snake_case__					:      List[Any]				      =  logging.get_logger(__name__)
snake_case__					:      Tuple				      =  {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case__					:      Union[str, Any]				      =  {
    '''vocab_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/spiece.model''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/spiece.model''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/spiece.model''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json''',
    },
}

snake_case__					:      str				      =  {
    '''albert-base-v1''': 512,
    '''albert-large-v1''': 512,
    '''albert-xlarge-v1''': 512,
    '''albert-xxlarge-v1''': 512,
    '''albert-base-v2''': 512,
    '''albert-large-v2''': 512,
    '''albert-xlarge-v2''': 512,
    '''albert-xxlarge-v2''': 512,
}

snake_case__					:      int				      =  '''▁'''






class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_	):







  '''simple docstring'''






  lowerCamelCase_		:Any						   =		VOCAB_FILES_NAMES
  lowerCamelCase_		:Dict						   =		PRETRAINED_VOCAB_FILES_MAP
  lowerCamelCase_		:Any						   =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowerCamelCase_		:str						   =		AlbertTokenizer


  def __init__(			self				,		snake_case_=None				,		snake_case_=None				,		snake_case_=True				,		snake_case_=True				,		snake_case_=False				,		snake_case_="[CLS]"				,		snake_case_="[SEP]"				,		snake_case_="<unk>"				,		snake_case_="[SEP]"				,		snake_case_="<pad>"				,		snake_case_="[CLS]"				,		snake_case_="[MASK]"				,		**snake_case_				,		):




       '''simple docstring'''

       UpperCAmelCase_							:       List[Any]       =				(
           AddedToken(snake_case_				,		lstrip=snake_case_				,		rstrip=snake_case_				,		normalized=snake_case_						)
           if isinstance(snake_case_				,		snake_case_						)
           else mask_token
       )

       super().__init__(
           snake_case_				,		tokenizer_file=snake_case_				,		do_lower_case=snake_case_				,		remove_space=snake_case_				,		keep_accents=snake_case_				,		bos_token=snake_case_				,		eos_token=snake_case_				,		unk_token=snake_case_				,		sep_token=snake_case_				,		pad_token=snake_case_				,		cls_token=snake_case_				,		mask_token=snake_case_				,		**snake_case_				,		)

       UpperCAmelCase_							:       Dict       =				do_lower_case
       UpperCAmelCase_							:       Optional[int]       =				remove_space
       UpperCAmelCase_							:       Union[str, Any]       =				keep_accents
       UpperCAmelCase_							:       Union[str, Any]       =				vocab_file
       UpperCAmelCase_							:       List[Any]       =				False if not self.vocab_file else True


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_ = None						):




       '''simple docstring'''

       UpperCAmelCase_							:       Any       =				[self.sep_token_id]
       UpperCAmelCase_							:       int       =				[self.cls_token_id]
       if token_ids_a is None:
            return cls + token_ids_a + sep
       return cls + token_ids_a + sep + token_ids_a + sep


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_ = None						):




       '''simple docstring'''

       UpperCAmelCase_							:       List[str]       =				[self.sep_token_id]
       UpperCAmelCase_							:       Optional[int]       =				[self.cls_token_id]

       if token_ids_a is None:
            return len(cls + token_ids_a + sep						) * [0]
       return len(cls + token_ids_a + sep						) * [0] + len(token_ids_a + sep						) * [1]






  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_ = None						):




       '''simple docstring'''

       if not self.can_save_slow_tokenizer:
            raise ValueError(
                'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
                'tokenizer.'						)

       if not os.path.isdir(snake_case_						):
            logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''						)
            return
       UpperCAmelCase_							:       Union[str, Any]       =				os.path.join(
           snake_case_				,		(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']						)

       if os.path.abspath(self.vocab_file						) != os.path.abspath(snake_case_						):
            copyfile(self.vocab_file				,		snake_case_						)

       return (out_vocab_file,)







'''simple docstring'''


from sklearn.metrics import fa_score

import datasets


snake_case__					:      str				      =  '''
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)
'''


snake_case__					:      int				      =  '''
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. ])}
'''


snake_case__					:      Tuple				      =  '''
@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	):







  '''simple docstring'''






  def _UpperCamelCase					(			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 _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_=None				,		snake_case_=1				,		snake_case_="binary"				,		snake_case_=None						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[int]       =				fa_score(
           snake_case_				,		snake_case_				,		labels=snake_case_				,		pos_label=snake_case_				,		average=snake_case_				,		sample_weight=snake_case_						)
       return {"f1": float(snake_case_						) if score.size == 1 else score}






