


import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


UpperCAmelCase__            =							'''\
@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},
}
'''

UpperCAmelCase__            =							'''\
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.
'''

UpperCAmelCase__            =							'''
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 					__A (self	)					->       Union[str, Any]:
      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		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase = False		,					UpperCAmelCase = None		,					UpperCAmelCase = None		,					UpperCAmelCase = None		,					UpperCAmelCase = "warn"		,					)					->       Any:
      if scheme is not None:
       try:
        _lowercase											=importlib.import_module('''seqeval.scheme'''	)
        _lowercase											=getattr(UpperCAmelCase		,					UpperCAmelCase	)
       except AttributeError:
        raise ValueError(f"Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}"	)
      _lowercase											=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'''	)
      _lowercase											=report.pop('''micro avg'''	)

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

      return scores








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase__            =							{'''configuration_visual_bert''': ['''VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VisualBertConfig''']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__            =							[
      '''VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''VisualBertForMultipleChoice''',
      '''VisualBertForPreTraining''',
      '''VisualBertForQuestionAnswering''',
      '''VisualBertForRegionToPhraseAlignment''',
      '''VisualBertForVisualReasoning''',
      '''VisualBertLayer''',
      '''VisualBertModel''',
      '''VisualBertPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_visual_bert import VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, VisualBertConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_visual_bert import (
        VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        VisualBertForMultipleChoice,
        VisualBertForPreTraining,
        VisualBertForQuestionAnswering,
        VisualBertForRegionToPhraseAlignment,
        VisualBertForVisualReasoning,
        VisualBertLayer,
        VisualBertModel,
        VisualBertPreTrainedModel,
    )


else:
  import sys

  UpperCAmelCase__            =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

