code: 
  
  import importlib
  from typing import List, Optional, Union
  
  from seqeval.metrics import accuracy_score, classification_report
  
  import datasets
  
  
  __A   :     Union[str, Any]      =			'''\
  @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},
  }
  '''
  
  __A   :     str      =			'''\
  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.
  '''
  
  __A   :     int      =			'''
  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    __A				(							datasets.Metric ):
  
  
  
  
  
  
      def 	lowercase__ (	self			:    str    )     ->   Optional[int]:
          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 	lowercase__ (	self			:    str    ,				UpperCAmelCase_			:    Union[str, Any]    ,				UpperCAmelCase_			:    int    ,				UpperCAmelCase_			:    bool = False    ,				UpperCAmelCase_			:    Optional[str] = None    ,				UpperCAmelCase_			:    Optional[str] = None    ,				UpperCAmelCase_			:    Optional[List[int]] = None    ,				UpperCAmelCase_			:    Union[str, int] = "warn"    ,				)     ->   Tuple:
          if scheme is not None:
              try:
                  lowerCAmelCase				:			int    			=      importlib.import_module('seqeval.scheme'    )
                  lowerCAmelCase				:			Dict    			=      getattr(UpperCAmelCase_    ,				UpperCAmelCase_    )
              except AttributeError:
                  raise ValueError(f"Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}"    )
          lowerCAmelCase				:			Optional[int]    			=      classification_report(
              y_true=UpperCAmelCase_    ,				y_pred=UpperCAmelCase_    ,				suffix=UpperCAmelCase_    ,				output_dict=UpperCAmelCase_    ,				scheme=UpperCAmelCase_    ,				mode=UpperCAmelCase_    ,				sample_weight=UpperCAmelCase_    ,				zero_division=UpperCAmelCase_    ,				)
          report.pop('macro avg'    )
          report.pop('weighted avg'    )
          lowerCAmelCase				:			List[Any]    			=      report.pop('micro avg'    )
  
          lowerCAmelCase				:			int    			=      {
              type_name: {
                  'precision': score['precision'],
                  'recall': score['recall'],
                  'f1': score['f1-score'],
                  'number': score['support'],
              }
              for type_name, score in report.items()
          }
          lowerCAmelCase				:			int    			=      overall_score['precision']
          lowerCAmelCase				:			List[str]    			=      overall_score['recall']
          lowerCAmelCase				:			Tuple    			=      overall_score['f1-score']
          lowerCAmelCase				:			int    			=      accuracy_score(y_true=UpperCAmelCase_    ,				y_pred=UpperCAmelCase_    )
  
          return scores
  
  
  
  
  
  
style_context: 
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available
  
  
  __A   :     Dict      =			{}
  
  try:
   if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   __A   :     List[Any]      =			['''BarthezTokenizer''']
  
  try:
   if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   __A   :     List[str]      =			['''BarthezTokenizerFast''']
  
  
  if TYPE_CHECKING:
   try:
    if not is_sentencepiece_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .tokenization_barthez import BarthezTokenizer
  
   try:
    if not is_tokenizers_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .tokenization_barthez_fast import BarthezTokenizerFast
  
  else:
   import sys
  
   __A   :     Tuple      =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  
  
  
  
  