
import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


_A       =					"\\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       =					"\\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       =					"\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					)    ->				Dict:
							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:
													lowerCAmelCase_      =     importlib.import_module("seqeval.scheme"					)
													lowerCAmelCase_      =     getattr(_UpperCamelCase    ,    _UpperCamelCase					)
										except AttributeError:
													raise ValueError(f"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}"""					)
							lowerCAmelCase_      =     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_      =     report.pop("micro avg"					)

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

							return scores





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A       =					{"configuration_nat": ["NAT_PRETRAINED_CONFIG_ARCHIVE_MAP", "NatConfig"]}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A       =					[
			    "NAT_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "NatForImageClassification",
			    "NatModel",
			    "NatPreTrainedModel",
			    "NatBackbone",
			]

if TYPE_CHECKING:
			from .configuration_nat import NAT_PRETRAINED_CONFIG_ARCHIVE_MAP, NatConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_nat import (
						    NAT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    NatBackbone,
						    NatForImageClassification,
						    NatModel,
						    NatPreTrainedModel,
						)

else:
			import sys

			_A       =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



