
"""simple docstring"""




import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


snake_case					  =						'\\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'

snake_case					  =						'\\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'

snake_case					  =						'\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 	UpperCamelCase      (			datasets.Metric ):

				"""simple docstring"""



				def 				A   (							self     )       ->       int:
									"""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						,      lowercase__						,      lowercase__						,      lowercase__ = False						,      lowercase__ = None						,      lowercase__ = None						,      lowercase__ = None						,      lowercase__ = "warn"						,      )       ->       Any:
									"""simple docstring"""







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

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

									return scores


"""simple docstring"""




from itertools import permutations

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_ = 5     ):
					if gon_side < 3 or gon_side > 5:
										raise ValueError('gon_side must be in the range [3, 5]'     )

					# Since it's 16, we know 10 is on the outer ring
					# Put the big numbers at the end so that they are never the first number
					SCREAMING_SNAKE_CASE           =      list(range(gon_side + 1,	0,	-1     )     )
					SCREAMING_SNAKE_CASE           =      list(range(gon_side + 2,	gon_side * 2 + 1     )     )

					for perm in permutations(small_numbers + big_numbers     ):
										SCREAMING_SNAKE_CASE           =      generate_gon_ring(SCREAMING_SNAKE_CASE_,	list(SCREAMING_SNAKE_CASE_     )     )
										if is_magic_gon(SCREAMING_SNAKE_CASE_     ):
															return int(''.join(str(SCREAMING_SNAKE_CASE_     ) for n in numbers     )     )

					SCREAMING_SNAKE_CASE           =      f'''Magic {gon_side}-gon ring is impossible'''
					raise ValueError(SCREAMING_SNAKE_CASE_     )

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					SCREAMING_SNAKE_CASE           =      [0] * (gon_side * 3)
					SCREAMING_SNAKE_CASE           =      perm[0:3]
					perm.append(perm[1]     )

					SCREAMING_SNAKE_CASE           =      1 if gon_side < 5 else 2

					for i in range(1,	len(SCREAMING_SNAKE_CASE_     ) // 3 + magic_number     ):
										SCREAMING_SNAKE_CASE           =      perm[2 * i + 1]
										SCREAMING_SNAKE_CASE           =      result[3 * i - 1]
										SCREAMING_SNAKE_CASE           =      perm[2 * i + 2]

					return result

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_     ):
					if len(SCREAMING_SNAKE_CASE_     ) % 3 != 0:
										raise ValueError('a gon ring should have a length that is a multiple of 3'     )

					if min(numbers[::3]     ) != numbers[0]:
										return False

					SCREAMING_SNAKE_CASE           =      sum(numbers[:3]     )

					return all(sum(numbers[i : i + 3]     ) == total for i in range(3,	len(SCREAMING_SNAKE_CASE_     ),	3     )     )


if __name__ == "__main__":
	print(solution())
