

'''simple docstring'''
import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


lowerCamelCase				:   List[str]      				=  '\\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'

lowerCamelCase				:   Any      				=  '\\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'

lowerCamelCase				:   Dict      				=  '\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 		A						(	self   :			Union[str, Any]				)			->					Tuple:





       '''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   :			Union[str, Any]							,      _a   :			int							,      _a   :			Any							,      _a   :			List[str] = False							,      _a   :			Tuple = None							,      _a   :			Optional[Any] = None							,      _a   :			Tuple = None							,      _a   :			Optional[int] = "warn"							,      )			->					str:





       '''simple docstring'''






       if scheme is not None:
            try:
                 _SCREAMING_SNAKE_CASE												=importlib.import_module('seqeval.scheme'				)
                 _SCREAMING_SNAKE_CASE												=getattr(__a							,      __a				)
            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=__a							,      y_pred=__a							,      suffix=__a							,      output_dict=__a							,      scheme=__a							,      mode=__a							,      sample_weight=__a							,      zero_division=__a							,      )
       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=__a							,      y_pred=__a				)

       return scores



'''simple docstring'''
def  _lowerCAmelCase      (		_UpperCamelCase		:  list       )   ->    list:
     """simple docstring"""




     for i, _ in enumerate(_UpperCamelCase       ):
          if (i % 2 == 1) == (nums[i - 1] > nums[i]):
               _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE												=nums[i], nums[i - 1]

     return nums


if __name__ == "__main__":
     print("Enter the array elements:")
     lowerCamelCase				:   Tuple      				=  list(map(int, input().split()))
     print("The unsorted array is:")
     print(array)
     print("Array after Wiggle sort:")
     print(wiggle_sort(array))
