



'''simple docstring'''





import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


A			:   Union[str, Any]			=      """\
@inproceedings{ramshaw-marcus-1995-text,
    title = \"Text Chunking using Transformation-Based Learning\",
    author = \"Ramshaw, Lance  and
      Marcus, Mitch\",
    booktitle = \"Third Workshop on Very Large Corpora\",
    year = \"1995\",
    url = \"https://www.aclweb.org/anthology/W95-0107\",
}
@misc{seqeval,
  title={{seqeval}: A Python framework for sequence labeling evaluation},
  url={https://github.com/chakki-works/seqeval},
  note={Software available from https://github.com/chakki-works/seqeval},
  author={Hiroki Nakayama},
  year={2018},
}
"""

A			:   int			=      """\
seqeval is a Python framework for sequence labeling evaluation.
seqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.

This is well-tested by using the Perl script conlleval, which can be used for
measuring the performance of a system that has processed the CoNLL-2000 shared task data.

seqeval supports following formats:
IOB1
IOB2
IOE1
IOE2
IOBES

See the [README.md] file at https://github.com/chakki-works/seqeval for more information.
"""

A			:   Optional[int]			=      """
Produces labelling scores along with its sufficient statistics
from a source against one or more references.

Args:
    predictions: List of List of predicted labels (Estimated targets as returned by a tagger)
    references: List of List of reference labels (Ground truth (correct) target values)
    suffix: True if the IOB prefix is after type, False otherwise. default: False
    scheme: Specify target tagging scheme. Should be one of [\"IOB1\", \"IOB2\", \"IOE1\", \"IOE2\", \"IOBES\", \"BILOU\"].
        default: None
    mode: Whether to count correct entity labels with incorrect I/B tags as true positives or not.
        If you want to only count exact matches, pass mode=\"strict\". default: None.
    sample_weight: Array-like of shape (n_samples,), weights for individual samples. default: None
    zero_division: Which value to substitute as a metric value when encountering zero division. Should be on of 0, 1,
        \"warn\". \"warn\" acts as 0, but the warning is raised.

Returns:
    'scores': dict. Summary of the scores for overall and per type
        Overall:
            'accuracy': accuracy,
            'precision': precision,
            'recall': recall,
            'f1': F1 score, also known as balanced F-score or F-measure,
        Per type:
            'precision': precision,
            'recall': recall,
            'f1': F1 score, also known as balanced F-score or F-measure
Examples:

    >>> predictions = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
    >>> references = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
    >>> seqeval = datasets.load_metric(\"seqeval\")
    >>> results = seqeval.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    ['MISC', 'PER', 'overall_precision', 'overall_recall', 'overall_f1', 'overall_accuracy']
    >>> print(results[\"overall_f1\"])
    0.5
    >>> print(results[\"PER\"][\"f1\"])
    1.0
"""





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION  ,      _KWARGS_DESCRIPTION						)
class 							lowerCAmelCase_		(	datasets.Metric						):





    def 					__snake_case       (    self    :    Optional[int]     ):



       '''simple docstring'''







       return datasets.MetricInfo(
           description=_DESCRIPTION,    citation=_CITATION,    homepage='''https://github.com/chakki-works/seqeval''',    inputs_description=_KWARGS_DESCRIPTION,    features=datasets.Features(
               {
                   '''predictions''': datasets.Sequence(datasets.Value('''string''',    id='''label'''     ),    id='''sequence'''     ),
                   '''references''': datasets.Sequence(datasets.Value('''string''',    id='''label'''     ),    id='''sequence'''     ),
               }     ),    codebase_urls=['''https://github.com/chakki-works/seqeval'''],    reference_urls=['''https://github.com/chakki-works/seqeval'''],    )






    def 					__snake_case       (    self    :    List[str],    _snake_case    :    List[Any],    _snake_case    :    Tuple,    _snake_case    :    bool = False,    _snake_case    :    Optional[str] = None,    _snake_case    :    Optional[str] = None,    _snake_case    :    Optional[List[int]] = None,    _snake_case    :    Union[str, int] = "warn",    ):



       '''simple docstring'''







       if scheme is not None:
          try:
             snake_case   :							int						  =importlib.import_module('''seqeval.scheme'''     )
             snake_case   :							Tuple						  =getattr(_snake_case,    _snake_case     )
          except AttributeError:
             raise ValueError(f'''Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}'''     )
       snake_case   :							Dict						  =classification_report(
           y_true=_snake_case,    y_pred=_snake_case,    suffix=_snake_case,    output_dict=_snake_case,    scheme=_snake_case,    mode=_snake_case,    sample_weight=_snake_case,    zero_division=_snake_case,    )
       report.pop('''macro avg'''     )
       report.pop('''weighted avg'''     )
       snake_case   :							str						  =report.pop('''micro avg'''     )

       snake_case   :							Union[str, Any]						  ={
           type_name: {
               '''precision''': score['''precision'''],
               '''recall''': score['''recall'''],
               '''f1''': score['''f1-score'''],
               '''number''': score['''support'''],
           }
           for type_name, score in report.items()
       }
       snake_case   :							int						  =overall_score['''precision''']
       snake_case   :							Optional[Any]						  =overall_score['''recall''']
       snake_case   :							Tuple						  =overall_score['''f1-score''']
       snake_case   :							str						  =accuracy_score(y_true=_snake_case,    y_pred=_snake_case     )

       return scores










'''simple docstring'''





def 		_a   (  lowerCamelCase_							):
   return "".join(chr(ord(lowerCamelCase_							) + 32							) if '''A''' <= char <= '''Z''' else char for char in word							)


if __name__ == "__main__":
      from doctest import testmod

      testmod()





