"""simple docstring"""






import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


UpperCamelCase     					=     '\\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     					=     '\\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     					=     '\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 )    ->	Tuple:
							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       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE = False       ,     _SCREAMING_SNAKE_CASE = None       ,     _SCREAMING_SNAKE_CASE = None       ,     _SCREAMING_SNAKE_CASE = None       ,     _SCREAMING_SNAKE_CASE = "warn"       ,     )    ->	Any:
							if scheme is not None:
													try:
																			a_							:			Union[str, Any]     		=					importlib.import_module("seqeval.scheme" )
																			a_							:			Optional[int]     		=					getattr(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )
													except AttributeError:
																			raise ValueError(f'''Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}''' )
							a_							:			str     		=					classification_report(
							    y_true=_SCREAMING_SNAKE_CASE       ,     y_pred=_SCREAMING_SNAKE_CASE       ,     suffix=_SCREAMING_SNAKE_CASE       ,     output_dict=_SCREAMING_SNAKE_CASE       ,     scheme=_SCREAMING_SNAKE_CASE       ,     mode=_SCREAMING_SNAKE_CASE       ,     sample_weight=_SCREAMING_SNAKE_CASE       ,     zero_division=_SCREAMING_SNAKE_CASE       ,     )
							report.pop("macro avg" )
							report.pop("weighted avg" )
							a_							:			Union[str, Any]     		=					report.pop("micro avg" )

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

							return scores





"""simple docstring"""






def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:int  )				-> Optional[int]:
						a_							:			List[Any]     		=					len(_SCREAMING_SNAKE_CASE  )
						for _ in range(
						    0		,			int(((no_of_elements - 1) / 2) + 1  )  ):  # we don't need to traverse to end of list as
												for j in range(0		,			no_of_elements - 1  ):
																		if (
																		    lst[j + 1] < lst[j]
																		):  # applying bubble sort algorithm from left to right (or forwards)
																								a_							:			List[str]     		=					lst[j + 1]
																								a_							:			Optional[Any]     		=					lst[j]
																								a_							:			Optional[Any]     		=					temp
																		if (
																		    lst[no_of_elements - 1 - j] < lst[no_of_elements - 2 - j]
																		):  # applying bubble sort algorithm from right to left (or backwards)
																								a_							:			Dict     		=					lst[no_of_elements - 1 - j]
																								a_							:			Dict     		=					lst[no_of_elements - 2 - j]
																								a_							:			Any     		=					temp
						return lst


if __name__ == "__main__":
				print('enter the list to be sorted')
				UpperCamelCase     					=     [int(x) for x in input().split()]  # inputing elements of the list in one line
				UpperCamelCase     					=     double_sort(lst)
				print('the sorted list is')
				print(sorted_lst)




