





def    SCREAMING_SNAKE_CASE							(			snake_case       )		->						int:
		__lowercase					=    str(snake_case       ).strip()
		if not bin_string:
				raise ValueError('Empty string was passed to the function'       )
		__lowercase					=    bin_string[0] == '-'
		if is_negative:
				__lowercase					=    bin_string[1:]
		if not all(char in '01' for char in bin_string       ):
				raise ValueError('Non-binary value was passed to the function'       )
		__lowercase					=    0
		for char in bin_string:
				__lowercase					=    2 * decimal_number + int(snake_case       )
		return -decimal_number if is_negative else decimal_number


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

		testmod()











import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


SCREAMING_SNAKE_CASE_      :			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},
}
'''

SCREAMING_SNAKE_CASE_      :			Tuple		  =      '''\
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.
'''

SCREAMING_SNAKE_CASE_      :			Dict		  =      '''
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       UpperCAmelCase (	self       :    Any      )					->   str:

			'''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       UpperCAmelCase (	self       :    Optional[Any]				,			__lowerCamelCase       :    int				,			__lowerCamelCase       :    str				,			__lowerCamelCase       :    bool = False				,			__lowerCamelCase       :    Optional[str] = None				,			__lowerCamelCase       :    Optional[str] = None				,			__lowerCamelCase       :    Optional[List[int]] = None				,			__lowerCamelCase       :    Union[str, int] = "warn"				,			)					->   Tuple:

			'''simple docstring'''




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

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

			return scores




