


import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


__snake_case             =	'''\
@inproceedings{ramshaw-marcus-1995-text,
    title = "Text Chunking using Transformation-Based Learning",
    author = "Ramshaw, Lance  and
      Marcus, Mitch",
    booktitle = "Third Workshop on Very Large Corpora",
    year = "1995",
    url = "https://www.aclweb.org/anthology/W95-0107",
}
@misc{seqeval,
  title={{seqeval}: A Python framework for sequence labeling evaluation},
  url={https://github.com/chakki-works/seqeval},
  note={Software available from https://github.com/chakki-works/seqeval},
  author={Hiroki Nakayama},
  year={2018},
}
'''

__snake_case             =	'''\
seqeval is a Python framework for sequence labeling evaluation.
seqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.

This is well-tested by using the Perl script conlleval, which can be used for
measuring the performance of a system that has processed the CoNLL-2000 shared task data.

seqeval supports following formats:
IOB1
IOB2
IOE1
IOE2
IOBES

See the [README.md] file at https://github.com/chakki-works/seqeval for more information.
'''

__snake_case             =	'''
Produces labelling scores along with its sufficient statistics
from a source against one or more references.

Args:
    predictions: List of List of predicted labels (Estimated targets as returned by a tagger)
    references: List of List of reference labels (Ground truth (correct) target values)
    suffix: True if the IOB prefix is after type, False otherwise. default: False
    scheme: Specify target tagging scheme. Should be one of ["IOB1", "IOB2", "IOE1", "IOE2", "IOBES", "BILOU"].
        default: None
    mode: Whether to count correct entity labels with incorrect I/B tags as true positives or not.
        If you want to only count exact matches, pass mode="strict". default: None.
    sample_weight: Array-like of shape (n_samples,), weights for individual samples. default: None
    zero_division: Which value to substitute as a metric value when encountering zero division. Should be on of 0, 1,
        "warn". "warn" acts as 0, but the warning is raised.

Returns:
    \'scores\': dict. Summary of the scores for overall and per type
        Overall:
            \'accuracy\': accuracy,
            \'precision\': precision,
            \'recall\': recall,
            \'f1\': F1 score, also known as balanced F-score or F-measure,
        Per type:
            \'precision\': precision,
            \'recall\': recall,
            \'f1\': F1 score, also known as balanced F-score or F-measure
Examples:

    >>> predictions = [[\'O\', \'O\', \'B-MISC\', \'I-MISC\', \'I-MISC\', \'I-MISC\', \'O\'], [\'B-PER\', \'I-PER\', \'O\']]
    >>> references = [[\'O\', \'O\', \'O\', \'B-MISC\', \'I-MISC\', \'I-MISC\', \'O\'], [\'B-PER\', \'I-PER\', \'O\']]
    >>> seqeval = datasets.load_metric("seqeval")
    >>> results = seqeval.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    [\'MISC\', \'PER\', \'overall_precision\', \'overall_recall\', \'overall_f1\', \'overall_accuracy\']
    >>> print(results["overall_f1"])
    0.5
    >>> print(results["PER"]["f1"])
    1.0
'''



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION							,			_KWARGS_DESCRIPTION	)
class   __lowerCamelCase (datasets.Metric	):







       def 		snake_case_  ( self:   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 		snake_case_  ( self:   Optional[int],A_:   Optional[Any],A_:   Dict,A_:   bool = False,A_:   Optional[str] = None,A_:   Optional[str] = None,A_:   Optional[List[int]] = None,A_:   Union[str, int] = "warn",):






            '''simple docstring'''




            if scheme is not None:
                 try:
                      __UpperCamelCase   = importlib.import_module('seqeval.scheme'				)
                      __UpperCamelCase   = getattr(A_,A_				)
                 except AttributeError:
                      raise ValueError(F'''Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}'''				)
            __UpperCamelCase   = 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'				)
            __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=A_,y_pred=A_				)

            return scores






from ..utils import DummyObject, requires_backends



class   __lowerCamelCase (metaclass=_a	):
       _lowercase       							=    ["""torch""", """transformers""", """k_diffusion"""]







       def __init__( self:   Tuple,*A_:   Union[str, Any],**A_:   Tuple				):






            '''simple docstring'''




            requires_backends(self,['torch', 'transformers', 'k_diffusion']				)







       @classmethod
       def 		snake_case_  ( cls:   int,*A_:   Optional[Any],**A_:   Tuple				):






            '''simple docstring'''




            requires_backends(cls,['torch', 'transformers', 'k_diffusion']				)







       @classmethod
       def 		snake_case_  ( cls:   int,*A_:   Tuple,**A_:   Tuple				):






            '''simple docstring'''




            requires_backends(cls,['torch', 'transformers', 'k_diffusion']				)


