






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

from seqeval.metrics import accuracy_score, classification_report

import datasets


A_     :					List[Any]							=     "\\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"

A_     :					List[str]							=     "\\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"

A_     :					Union[str, 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					):







      '''simple docstring'''

      def      a__				(self   ):

       '''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,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_ = False,	lowerCamelCase_ = None,	lowerCamelCase_ = None,	lowerCamelCase_ = None,	lowerCamelCase_ = "warn",	):

       '''simple docstring'''



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

       lowerCamelCase__	:     Tuple								=		{
           type_name: {
               'precision': score['precision'],
               'recall': score['recall'],
               'f1': score['f1-score'],
               'number': score['support'],
           }
           for type_name, score in report.items()
       }
       lowerCamelCase__	:     Dict								=		overall_score['precision']
       lowerCamelCase__	:     Any								=		overall_score['recall']
       lowerCamelCase__	:     Any								=		overall_score['f1-score']
       lowerCamelCase__	:     Optional[Any]								=		accuracy_score(y_true=lowerCamelCase_,	y_pred=lowerCamelCase_   )

       return scores









"""simple docstring"""
import unittest

from cer import CER


A_     :					Union[str, Any]							=     CER()



class 						a_ (      unittest.TestCase					):







      '''simple docstring'''

      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     Optional[Any]								=		['White House']
       lowerCamelCase__	:     List[str]								=		['white house']
       # S = 2, D = 0, I = 0, N = 11, CER = 2 / 11
       lowerCamelCase__	:     Dict								=		cer.compute(predictions=lowerCamelCase_,	references=lowerCamelCase_   )
       self.assertTrue(abs(char_error_rate - 0.1_818_181_818   ) < 1e-6   )




      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     Union[str, Any]								=		['were wolf']
       lowerCamelCase__	:     Union[str, Any]								=		['werewolf']
       # S = 0, D = 0, I = 1, N = 9, CER = 1 / 9
       lowerCamelCase__	:     Dict								=		cer.compute(predictions=lowerCamelCase_,	references=lowerCamelCase_   )
       self.assertTrue(abs(char_error_rate - 0.1_111_111   ) < 1e-6   )

       lowerCamelCase__	:     List[Any]								=		['werewolf']
       lowerCamelCase__	:     Optional[Any]								=		['weae     wolf']
       # S = 1, D = 1, I = 0, N = 8, CER = 0.25
       lowerCamelCase__	:     List[str]								=		cer.compute(predictions=lowerCamelCase_,	references=lowerCamelCase_   )
       self.assertTrue(abs(char_error_rate - 0.25   ) < 1e-6   )

       # consecutive whitespaces case 1
       lowerCamelCase__	:     Optional[Any]								=		['were wolf']
       lowerCamelCase__	:     Tuple								=		['were               wolf']
       # S = 0, D = 0, I = 0, N = 9, CER = 0
       lowerCamelCase__	:     Tuple								=		cer.compute(predictions=lowerCamelCase_,	references=lowerCamelCase_   )
       self.assertTrue(abs(char_error_rate - 0.0   ) < 1e-6   )

       # consecutive whitespaces case 2
       lowerCamelCase__	:     Any								=		['were   wolf']
       lowerCamelCase__	:     int								=		['were               wolf']
       # S = 0, D = 0, I = 0, N = 9, CER = 0
       lowerCamelCase__	:     Optional[int]								=		cer.compute(predictions=lowerCamelCase_,	references=lowerCamelCase_   )
       self.assertTrue(abs(char_error_rate - 0.0   ) < 1e-6   )




      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     Optional[Any]								=		['werewolf']
       lowerCamelCase__	:     Optional[int]								=		['weaewolf']
       # S = 1, D = 0, I = 0, N = 8, CER = 0.125
       lowerCamelCase__	:     Union[str, Any]								=		cer.compute(predictions=lowerCamelCase_,	references=lowerCamelCase_   )
       self.assertTrue(abs(char_error_rate - 0.125   ) < 1e-6   )




      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     int								=		['werewolf']
       lowerCamelCase__	:     Union[str, Any]								=		['wereawolf']
       # S = 0, D = 1, I = 0, N = 8, CER = 0.125
       lowerCamelCase__	:     Any								=		cer.compute(predictions=lowerCamelCase_,	references=lowerCamelCase_   )
       self.assertTrue(abs(char_error_rate - 0.125   ) < 1e-6   )




      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     List[Any]								=		['werewolf']
       lowerCamelCase__	:     Dict								=		['wereolf']
       # S = 0, D = 0, I = 1, N = 8, CER = 0.125
       lowerCamelCase__	:     int								=		cer.compute(predictions=lowerCamelCase_,	references=lowerCamelCase_   )
       self.assertTrue(abs(char_error_rate - 0.125   ) < 1e-6   )




      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     Optional[int]								=		['werewolf']
       lowerCamelCase__	:     Tuple								=		cer.compute(predictions=lowerCamelCase_,	references=lowerCamelCase_   )
       self.assertEqual(lowerCamelCase_,	0.0   )




      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     List[str]								=		['werewolf', 'I am your father']
       lowerCamelCase__	:     List[Any]								=		cer.compute(predictions=lowerCamelCase_,	references=lowerCamelCase_   )
       self.assertEqual(lowerCamelCase_,	0.0   )

       lowerCamelCase__	:     Dict								=		['werewolf', 'I am your father', 'doge']
       lowerCamelCase__	:     int								=		['werxwolf', 'I       am your father', 'doge']
       # S = 1, D = 0, I = 0, N = 28, CER = 1 / 28
       lowerCamelCase__	:     Optional[int]								=		cer.compute(predictions=lowerCamelCase_,	references=lowerCamelCase_   )
       self.assertTrue(abs(char_error_rate - 0.03_571_428   ) < 1e-6   )




      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     Union[str, Any]								=		['My hovercraft', 'is full of eels']
       lowerCamelCase__	:     List[Any]								=		['My hovercraft is full', ' of eels']
       # S = 0, D = 0, I = 2, N = 28, CER = 2 / 28
       # whitespace at the front of " of eels" will be strip during preporcessing
       # so need to insert 2 whitespaces
       lowerCamelCase__	:     Dict								=		cer.compute(predictions=lowerCamelCase_,	references=lowerCamelCase_,	concatenate_texts=lowerCamelCase_   )
       self.assertTrue(abs(char_error_rate - 0.071_428   ) < 1e-6   )




      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     Optional[Any]								=		['我能吞下玻璃而不伤身体']
       lowerCamelCase__	:     Any								=		[' 能吞虾玻璃而 不霜身体啦']
       # S = 3, D = 2, I = 0, N = 11, CER = 5 / 11
       lowerCamelCase__	:     List[str]								=		cer.compute(predictions=lowerCamelCase_,	references=lowerCamelCase_   )
       self.assertTrue(abs(char_error_rate - 0.4_545_454_545   ) < 1e-6   )

       lowerCamelCase__	:     Optional[Any]								=		['我能吞下玻璃', '而不伤身体']
       lowerCamelCase__	:     Optional[Any]								=		['我    能 吞 下 玻 璃', '而不伤身体']
       # S = 0, D = 5, I = 0, N = 11, CER = 5 / 11
       lowerCamelCase__	:     Dict								=		cer.compute(predictions=lowerCamelCase_,	references=lowerCamelCase_   )
       self.assertTrue(abs(char_error_rate - 0.454_545_454_545   ) < 1e-6   )

       lowerCamelCase__	:     Optional[int]								=		['我能吞下玻璃而不伤身体']
       lowerCamelCase__	:     Union[str, Any]								=		cer.compute(predictions=lowerCamelCase_,	references=lowerCamelCase_   )
       self.assertFalse(lowerCamelCase_,	0.0   )





      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     Optional[int]								=		['']
       lowerCamelCase__	:     Optional[int]								=		['Hypothesis']
       with self.assertRaises(lowerCamelCase_   ):
        cer.compute(predictions=lowerCamelCase_,	references=lowerCamelCase_   )


if __name__ == "__main__":
      unittest.main()

