



import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


UpperCAmelCase__			:   Tuple      			=    '\\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'

UpperCAmelCase__			:   Optional[int]      			=    '\\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'

UpperCAmelCase__			:   List[Any]      			=    '\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     ):







		def 					a_  (    self  :   Tuple					)       ->		Optional[Any]:


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


						'''simple docstring'''







						if scheme is not None:
										try:
														_UpperCAmelCase						:  Any     					=	importlib.import_module('''seqeval.scheme'''					)
														_UpperCAmelCase						:  str     					=	getattr(UpperCAmelCase_       ,		UpperCAmelCase_					)
										except AttributeError:
														raise ValueError(F'''Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}'''					)
						_UpperCAmelCase						:  Dict     					=	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'''					)
						_UpperCAmelCase						:  Any     					=	report.pop('''micro avg'''					)

						_UpperCAmelCase						:  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()
						}
						_UpperCAmelCase						:  Optional[Any]     					=	overall_score['''precision''']
						_UpperCAmelCase						:  Tuple     					=	overall_score['''recall''']
						_UpperCAmelCase						:  int     					=	overall_score['''f1-score''']
						_UpperCAmelCase						:  Optional[int]     					=	accuracy_score(y_true=UpperCAmelCase_       ,		y_pred=UpperCAmelCase_					)

						return scores





import math
from timeit import timeit


def 	_A	(   _UpperCamelCase			):
				_UpperCAmelCase						:  str     					=	0
				_UpperCAmelCase						:  Tuple     					=	abs(_UpperCamelCase			)
				while True:
								_UpperCAmelCase						:  str     					=	n // 10
								digits += 1
								if n == 0:
												break
				return digits


def 	_A	(   _UpperCamelCase			):
				return 1 if n == 0 else math.floor(math.log(abs(_UpperCamelCase			)      ,					10			) + 1			)


def 	_A	(   _UpperCamelCase			):
				return len(str(abs(_UpperCamelCase			)			)			)


def 	_A	(   ):
				from collections.abc import Callable

				def benchmark_a_function(_UpperCamelCase      ,					_UpperCamelCase			) -> None:
								_UpperCAmelCase						:  List[str]     					=	F'''{func.__name__}({value})'''
								_UpperCAmelCase						:  Any     					=	timeit(F'''__main__.{call}'''      ,					setup='''import __main__'''			)
								print(F'''{call}: {func(_UpperCamelCase			)} -- {timing} seconds'''			)

				for value in (262_144, 1_125_899_906_842_624, 1_267_650_600_228_229_401_496_703_205_376):
								for func in (num_digits, num_digits_fast, num_digits_faster):
												benchmark_a_function(_UpperCamelCase      ,					_UpperCamelCase			)
								print()


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					benchmark()
