





import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


UpperCAmelCase__  :	List[str]     ='''\
@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},
}
'''

UpperCAmelCase__  :	Dict     ='''\
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.
'''

UpperCAmelCase__  :	Optional[Any]     ='''
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 					__A   (  datasets.Metric			):


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

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

								return scores












def    _lowercase    (  _UpperCAmelCase = 99_80_01 )  ->     int:

				# fetches the next number
				for number in range(n - 1	,					99_99	,					-1 ):
								lowerCamelCase		       =str(_UpperCAmelCase )

								# checks whether 'str_number' is a palindrome.
								if str_number == str_number[::-1]:
												lowerCamelCase		       =9_99

												# if 'number' is a product of two 3-digit numbers
												# then number is the answer otherwise fetch next number.
												while divisor != 99:
																if (number % divisor == 0) and (len(str(number // divisor ) ) == 3.0):
																				return number
																divisor -= 1
				raise ValueError("""That number is larger than our acceptable range.""" )


if __name__ == "__main__":
							print(F"{solution() = }")





