




'''simple docstring'''

import math




def    snake_case__     (  a	)			->       Dict:





			'''simple docstring'''






			snake_case__          =						0
			if n % 2 == 0:
						count += 1
						while n % 2 == 0:
									snake_case__          =						int(n / 2	)
    # the n input value must be odd so that
    # we can skip one element (ie i += 2)

			snake_case__          =						3

			while i <= int(math.sqrt(a	)	):
						if n % i == 0:
									count += 1
									while n % i == 0:
												snake_case__          =						int(n / i	)
						snake_case__          =						i + 2

			# this condition checks the prime
			# number n is greater than 2

			if n > 2:
						count += 1
			return count


if __name__ == "__main__":
		a__         =     51_242_183
		print(F"The number of distinct prime factors is/are {exact_prime_factor_count(n)}")
		print(F"The value of log(log(n)) is {math.log(math.log(n)):.4f}")


"""simple docstring"""





import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


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

_a					 =				'\\nseqeval is a Python framework for sequence labeling evaluation.\nseqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.\n\nThis is well-tested by using the Perl script conlleval, which can be used for\nmeasuring the performance of a system that has processed the CoNLL-2000 shared task data.\n\nseqeval supports following formats:\nIOB1\nIOB2\nIOE1\nIOE2\nIOBES\n\nSee the [README.md] file at https://github.com/chakki-works/seqeval for more information.\n'

_a					 =				'\nProduces labelling scores along with its sufficient statistics\nfrom a source against one or more references.\n\nArgs:\n    predictions: List of List of predicted labels (Estimated targets as returned by a tagger)\n    references: List of List of reference labels (Ground truth (correct) target values)\n    suffix: True if the IOB prefix is after type, False otherwise. default: False\n    scheme: Specify target tagging scheme. Should be one of ["IOB1", "IOB2", "IOE1", "IOE2", "IOBES", "BILOU"].\n        default: None\n    mode: Whether to count correct entity labels with incorrect I/B tags as true positives or not.\n        If you want to only count exact matches, pass mode="strict". default: None.\n    sample_weight: Array-like of shape (n_samples,), weights for individual samples. default: None\n    zero_division: Which value to substitute as a metric value when encountering zero division. Should be on of 0, 1,\n        "warn". "warn" acts as 0, but the warning is raised.\n\nReturns:\n    \'scores\': dict. Summary of the scores for overall and per type\n        Overall:\n            \'accuracy\': accuracy,\n            \'precision\': precision,\n            \'recall\': recall,\n            \'f1\': F1 score, also known as balanced F-score or F-measure,\n        Per type:\n            \'precision\': precision,\n            \'recall\': recall,\n            \'f1\': F1 score, also known as balanced F-score or F-measure\nExamples:\n\n    >>> predictions = [[\'O\', \'O\', \'B-MISC\', \'I-MISC\', \'I-MISC\', \'I-MISC\', \'O\'], [\'B-PER\', \'I-PER\', \'O\']]\n    >>> references = [[\'O\', \'O\', \'O\', \'B-MISC\', \'I-MISC\', \'I-MISC\', \'O\'], [\'B-PER\', \'I-PER\', \'O\']]\n    >>> seqeval = datasets.load_metric("seqeval")\n    >>> results = seqeval.compute(predictions=predictions, references=references)\n    >>> print(list(results.keys()))\n    [\'MISC\', \'PER\', \'overall_precision\', \'overall_recall\', \'overall_f1\', \'overall_accuracy\']\n    >>> print(results["overall_f1"])\n    0.5\n    >>> print(results["PER"]["f1"])\n    1.0\n'




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







 """simple docstring"""







 def    _lowercase					(     self				:     Optional[int]      ):
    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    _lowercase					(     self				:     Optional[Any],   UpperCAmelCase__				:     List[str],   UpperCAmelCase__				:     str,   UpperCAmelCase__				:     bool = False,   UpperCAmelCase__				:     Optional[str] = None,   UpperCAmelCase__				:     Optional[str] = None,   UpperCAmelCase__				:     Optional[List[int]] = None,   UpperCAmelCase__				:     Union[str, int] = "warn",   ):
    if scheme is not None:
       try:
          __lowercase								=		importlib.import_module("seqeval.scheme"      )
          __lowercase								=		getattr(UpperCAmelCase__,   UpperCAmelCase__      )
       except AttributeError:
          raise ValueError(F"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}"""      )
    __lowercase								=		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"      )
    __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=UpperCAmelCase__,   y_pred=UpperCAmelCase__      )

    return scores




