code: 
  
  
  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
  
  
  
style_context: 
  
  
  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']				)
  
  
  