





import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


a__							:							Optional[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__							:							Union[str, Any]			='''\
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__							:							Tuple			='''
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 					snake_case					(   datasets.Metric	):




 """simple docstring"""



 def 	_lowerCamelCase (	self	:							Optional[Any]     ):
        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 	_lowerCamelCase (	self	:							Optional[Any]							,     __A	:							Union[str, Any]							,     __A	:							int							,     __A	:							bool = False							,     __A	:							Optional[str] = None							,     __A	:							Optional[str] = None							,     __A	:							Optional[List[int]] = None							,     __A	:							Union[str, int] = "warn"							,     ):
        if scheme is not None:
               try:
                      __UpperCamelCase	=  importlib.import_module('seqeval.scheme'     )
                      __UpperCamelCase	=  getattr(__A							,     __A     )
               except AttributeError:
                      raise ValueError(f'''Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}'''     )
        __UpperCamelCase	=  classification_report(
            y_true=__A							,     y_pred=__A							,     suffix=__A							,     output_dict=__A							,     scheme=__A							,     mode=__A							,     sample_weight=__A							,     zero_division=__A							,     )
        report.pop('macro avg'     )
        report.pop('weighted avg'     )
        __UpperCamelCase	=  report.pop('micro avg'     )

        __UpperCamelCase	=  {
            type_name: {
                'precision': score['precision'],
                'recall': score['recall'],
                'f1': score['f1-score'],
                'number': score['support'],
            }
            for type_name, score in report.items()
        }
        __UpperCamelCase	=  overall_score['precision']
        __UpperCamelCase	=  overall_score['recall']
        __UpperCamelCase	=  overall_score['f1-score']
        __UpperCamelCase	=  accuracy_score(y_true=__A							,     y_pred=__A     )

        return scores







'''simple docstring'''
a__							:							int			='''
# Transformers installation
! pip install transformers datasets evaluate
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

a__							:							List[Any]			=[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
a__							:							Dict			={
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}
