




import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


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

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

         return scores











def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						str      )  ->							int:
    UpperCAmelCase_							 =     5381
    for x in s:
        UpperCAmelCase_							 =     ((hash_value << 5) + hash_value) + ord(__UpperCamelCase      )
    return hash_value & 0xffff_ffff





