





'''simple docstring'''







import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


snake_case_  :							int            =	"\\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_  :							Dict            =	"\\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_  :							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        __a   (datasets.Metric  ):

							def      UpperCAmelCase__						(				self				:  List[str] )       ->							Union[str, 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      UpperCAmelCase__						(				self				:  List[Any]							,  __magic_name__				:  Dict							,  __magic_name__				:  Union[str, Any]							,  __magic_name__				:  bool = False							,  __magic_name__				:  Optional[str] = None							,  __magic_name__				:  Optional[str] = None							,  __magic_name__				:  Optional[List[int]] = None							,  __magic_name__				:  Union[str, int] = "warn"							,  )       ->							Tuple:







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

													UpperCAmelCase_      :   Tuple      =	{
													    type_name: {
													        '''precision''': score['''precision'''],
													        '''recall''': score['''recall'''],
													        '''f1''': score['''f1-score'''],
													        '''number''': score['''support'''],
													    }
													    for type_name, score in report.items()
													}
													UpperCAmelCase_      :   str      =	overall_score['''precision''']
													UpperCAmelCase_      :   Tuple      =	overall_score['''recall''']
													UpperCAmelCase_      :   Union[str, Any]      =	overall_score['''f1-score''']
													UpperCAmelCase_      :   int      =	accuracy_score(y_true=__magic_name__							,  y_pred=__magic_name__ )

													return scores












'''simple docstring'''







from __future__ import annotations





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      list[int | float] )			->						int | float:
						if len(SCREAMING_SNAKE_CASE__ ) == 0:
												raise ValueError('''find_min() arg is an empty sequence''' )
						UpperCAmelCase_      :   Optional[int]      =	nums[0]
						for num in nums:
												UpperCAmelCase_      :   Tuple      =	min(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ )
						return min_num


if __name__ == "__main__":
					import doctest

					doctest.testmod(verbose=True)





