






"""simple docstring"""
import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


lowerCAmelCase__							= '\\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'

lowerCAmelCase__							= '\\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'

lowerCAmelCase__							= '\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 _lowerCAmelCase	(						datasets.Metric					):







						def  A			(						self       )		->       List[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      ,							lowerCAmelCase_      ,							lowerCAmelCase_      ,							lowerCAmelCase_ = False      ,							lowerCAmelCase_ = None      ,							lowerCAmelCase_ = None      ,							lowerCAmelCase_ = None      ,							lowerCAmelCase_ = "warn"      ,							)		->       List[Any]:
										if scheme is not None:
														try:
																		_SCREAMING_SNAKE_CASE      :		List[str]							=						importlib.import_module('seqeval.scheme'       )
																		_SCREAMING_SNAKE_CASE      :		List[Any]							=						getattr(lowerCAmelCase_      ,							lowerCAmelCase_       )
														except AttributeError:
																		raise ValueError(F"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}"""       )
										_SCREAMING_SNAKE_CASE      :		Optional[int]							=						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'       )
										_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						report.pop('micro avg'       )

										_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						{
										    type_name: {
										        'precision': score['precision'],
										        'recall': score['recall'],
										        'f1': score['f1-score'],
										        'number': score['support'],
										    }
										    for type_name, score in report.items()
										}
										_SCREAMING_SNAKE_CASE      :		List[str]							=						overall_score['precision']
										_SCREAMING_SNAKE_CASE      :		List[Any]							=						overall_score['recall']
										_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						overall_score['f1-score']
										_SCREAMING_SNAKE_CASE      :		Any							=						accuracy_score(y_true=lowerCAmelCase_      ,							y_pred=lowerCAmelCase_       )

										return scores









"""simple docstring"""
from typing import TYPE_CHECKING

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


lowerCAmelCase__							= {
    'configuration_big_bird': ['BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BigBirdConfig', 'BigBirdOnnxConfig'],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__							= ['BigBirdTokenizer']

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__							= ['BigBirdTokenizerFast']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__							= [
			    'BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'BigBirdForCausalLM',
			    'BigBirdForMaskedLM',
			    'BigBirdForMultipleChoice',
			    'BigBirdForPreTraining',
			    'BigBirdForQuestionAnswering',
			    'BigBirdForSequenceClassification',
			    'BigBirdForTokenClassification',
			    'BigBirdLayer',
			    'BigBirdModel',
			    'BigBirdPreTrainedModel',
			    'load_tf_weights_in_big_bird',
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__							= [
			    'FlaxBigBirdForCausalLM',
			    'FlaxBigBirdForMaskedLM',
			    'FlaxBigBirdForMultipleChoice',
			    'FlaxBigBirdForPreTraining',
			    'FlaxBigBirdForQuestionAnswering',
			    'FlaxBigBirdForSequenceClassification',
			    'FlaxBigBirdForTokenClassification',
			    'FlaxBigBirdModel',
			    'FlaxBigBirdPreTrainedModel',
			]

if TYPE_CHECKING:
			from .configuration_big_bird import BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP, BigBirdConfig, BigBirdOnnxConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_big_bird import BigBirdTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_big_bird_fast import BigBirdTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_big_bird import (
						    BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST,
						    BigBirdForCausalLM,
						    BigBirdForMaskedLM,
						    BigBirdForMultipleChoice,
						    BigBirdForPreTraining,
						    BigBirdForQuestionAnswering,
						    BigBirdForSequenceClassification,
						    BigBirdForTokenClassification,
						    BigBirdLayer,
						    BigBirdModel,
						    BigBirdPreTrainedModel,
						    load_tf_weights_in_big_bird,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_big_bird import (
						    FlaxBigBirdForCausalLM,
						    FlaxBigBirdForMaskedLM,
						    FlaxBigBirdForMultipleChoice,
						    FlaxBigBirdForPreTraining,
						    FlaxBigBirdForQuestionAnswering,
						    FlaxBigBirdForSequenceClassification,
						    FlaxBigBirdForTokenClassification,
						    FlaxBigBirdModel,
						    FlaxBigBirdPreTrainedModel,
						)

else:
			import sys

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

