

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








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__)





