


import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


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

A :       Any						=   "\\nseqeval is a Python framework for sequence labeling evaluation.\nseqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.\n\nThis is well-tested by using the Perl script conlleval, which can be used for\nmeasuring the performance of a system that has processed the CoNLL-2000 shared task data.\n\nseqeval supports following formats:\nIOB1\nIOB2\nIOE1\nIOE2\nIOBES\n\nSee the [README.md] file at https://github.com/chakki-works/seqeval for more information.\n"

A :       List[str]						=   "\nProduces labelling scores along with its sufficient statistics\nfrom a source against one or more references.\n\nArgs:\n    predictions: List of List of predicted labels (Estimated targets as returned by a tagger)\n    references: List of List of reference labels (Ground truth (correct) target values)\n    suffix: True if the IOB prefix is after type, False otherwise. default: False\n    scheme: Specify target tagging scheme. Should be one of [\"IOB1\", \"IOB2\", \"IOE1\", \"IOE2\", \"IOBES\", \"BILOU\"].\n        default: None\n    mode: Whether to count correct entity labels with incorrect I/B tags as true positives or not.\n        If you want to only count exact matches, pass mode=\"strict\". default: None.\n    sample_weight: Array-like of shape (n_samples,), weights for individual samples. default: None\n    zero_division: Which value to substitute as a metric value when encountering zero division. Should be on of 0, 1,\n        \"warn\". \"warn\" acts as 0, but the warning is raised.\n\nReturns:\n    'scores': dict. Summary of the scores for overall and per type\n        Overall:\n            'accuracy': accuracy,\n            'precision': precision,\n            'recall': recall,\n            'f1': F1 score, also known as balanced F-score or F-measure,\n        Per type:\n            'precision': precision,\n            'recall': recall,\n            'f1': F1 score, also known as balanced F-score or F-measure\nExamples:\n\n    >>> predictions = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]\n    >>> references = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]\n    >>> seqeval = datasets.load_metric(\"seqeval\")\n    >>> results = seqeval.compute(predictions=predictions, references=references)\n    >>> print(list(results.keys()))\n    ['MISC', 'PER', 'overall_precision', 'overall_recall', 'overall_f1', 'overall_accuracy']\n    >>> print(results[\"overall_f1\"])\n    0.5\n    >>> print(results[\"PER\"][\"f1\"])\n    1.0\n"







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


       """simple docstring"""




       def 		lowerCAmelCase	(  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 		lowerCAmelCase	(  self				:     Tuple   , __lowerCamelCase				:     Any   , __lowerCamelCase				:     List[str]   , __lowerCamelCase				:     bool = False   , __lowerCamelCase				:     Optional[str] = None   , __lowerCamelCase				:     Optional[str] = None   , __lowerCamelCase				:     Optional[List[int]] = None   , __lowerCamelCase				:     Union[str, int] = "warn"   , ):


             '''simple docstring'''



             if scheme is not None:
                   try:
                         lowerCamelCase__						:							Optional[int]       =    importlib.import_module("seqeval.scheme"			)
                         lowerCamelCase__						:							Optional[Any]       =    getattr(__lowerCamelCase   , __lowerCamelCase			)
                   except AttributeError:
                         raise ValueError(f"Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}"			)
             lowerCamelCase__						:							Optional[Any]       =    classification_report(
                 y_true=__lowerCamelCase   , y_pred=__lowerCamelCase   , suffix=__lowerCamelCase   , output_dict=__lowerCamelCase   , scheme=__lowerCamelCase   , mode=__lowerCamelCase   , sample_weight=__lowerCamelCase   , zero_division=__lowerCamelCase   , )
             report.pop("macro avg"			)
             report.pop("weighted avg"			)
             lowerCamelCase__						:							int       =    report.pop("micro avg"			)

             lowerCamelCase__						:							str       =    {
                 type_name: {
                     "precision": score["precision"],
                     "recall": score["recall"],
                     "f1": score["f1-score"],
                     "number": score["support"],
                 }
                 for type_name, score in report.items()
             }
             lowerCamelCase__						:							List[Any]       =    overall_score["precision"]
             lowerCamelCase__						:							int       =    overall_score["recall"]
             lowerCamelCase__						:							int       =    overall_score["f1-score"]
             lowerCamelCase__						:							Union[str, Any]       =    accuracy_score(y_true=__lowerCamelCase   , y_pred=__lowerCamelCase			)

             return scores






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A :       Optional[int]						=   {
    "configuration_mpnet": ["MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "MPNetConfig"],
    "tokenization_mpnet": ["MPNetTokenizer"],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A :       List[Any]						=   ["MPNetTokenizerFast"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A :       List[str]						=   [
        "MPNET_PRETRAINED_MODEL_ARCHIVE_LIST",
        "MPNetForMaskedLM",
        "MPNetForMultipleChoice",
        "MPNetForQuestionAnswering",
        "MPNetForSequenceClassification",
        "MPNetForTokenClassification",
        "MPNetLayer",
        "MPNetModel",
        "MPNetPreTrainedModel",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A :       Tuple						=   [
        "TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFMPNetEmbeddings",
        "TFMPNetForMaskedLM",
        "TFMPNetForMultipleChoice",
        "TFMPNetForQuestionAnswering",
        "TFMPNetForSequenceClassification",
        "TFMPNetForTokenClassification",
        "TFMPNetMainLayer",
        "TFMPNetModel",
        "TFMPNetPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
    from .tokenization_mpnet import MPNetTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_mpnet_fast import MPNetTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mpnet import (
            MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
            MPNetForMaskedLM,
            MPNetForMultipleChoice,
            MPNetForQuestionAnswering,
            MPNetForSequenceClassification,
            MPNetForTokenClassification,
            MPNetLayer,
            MPNetModel,
            MPNetPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_mpnet import (
            TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFMPNetEmbeddings,
            TFMPNetForMaskedLM,
            TFMPNetForMultipleChoice,
            TFMPNetForQuestionAnswering,
            TFMPNetForSequenceClassification,
            TFMPNetForTokenClassification,
            TFMPNetMainLayer,
            TFMPNetModel,
            TFMPNetPreTrainedModel,
        )

else:
    import sys

    A :       Union[str, Any]						=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


