






'''simple docstring'''







import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


__A      :       Union[str, Any]      =	'\\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      :       List[str]      =	'\\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      :       int      =	'\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 					a__    (			datasets.Metric						):
     '''simple docstring'''







     def     _UpperCamelCase   (self    :	Any					)  ->       List[str]:
           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     _UpperCamelCase   (self    :	str				,				lowercase__    :	Union[str, Any]				,				lowercase__    :	List[Any]				,				lowercase__    :	bool = False				,				lowercase__    :	Optional[str] = None				,				lowercase__    :	Optional[str] = None				,				lowercase__    :	Optional[List[int]] = None				,				lowercase__    :	Union[str, int] = "warn"				,				)  ->       str:
           if scheme is not None:
                 try:
                       __A		   =      importlib.import_module("seqeval.scheme"					)
                       __A		   =      getattr(lowercase__				,				lowercase__					)
                 except AttributeError:
                       raise ValueError(f"Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}"					)
           __A		   =      classification_report(
               y_true=lowercase__				,				y_pred=lowercase__				,				suffix=lowercase__				,				output_dict=lowercase__				,				scheme=lowercase__				,				mode=lowercase__				,				sample_weight=lowercase__				,				zero_division=lowercase__				,				)
           report.pop("macro avg"					)
           report.pop("weighted avg"					)
           __A		   =      report.pop("micro avg"					)

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

           return scores










'''simple docstring'''







from typing import List

import datasets
from datasets.tasks import ImageClassification

from ..folder_based_builder import folder_based_builder


__A      :       Tuple      =	datasets.utils.logging.get_logger(__name__)






class 					a__    (			folder_based_builder.FolderBasedBuilderConfig						):
     '''simple docstring'''







     _snake_case             =  None
     _snake_case             =  None







class 					a__    (			folder_based_builder.FolderBasedBuilder						):
     '''simple docstring'''







     _snake_case             =  datasets.Image()
     _snake_case             =  'image'
     _snake_case             =  ImageFolderConfig
     _snake_case             =  42  # definition at the bottom of the script
     _snake_case             =  ImageClassification(image_column='image' ,						label_column='label'						)







__A      :       Any      =	[
    '.blp',
    '.bmp',
    '.dib',
    '.bufr',
    '.cur',
    '.pcx',
    '.dcx',
    '.dds',
    '.ps',
    '.eps',
    '.fit',
    '.fits',
    '.fli',
    '.flc',
    '.ftc',
    '.ftu',
    '.gbr',
    '.gif',
    '.grib',
    '.h5',
    '.hdf',
    '.png',
    '.apng',
    '.jp2',
    '.j2k',
    '.jpc',
    '.jpf',
    '.jpx',
    '.j2c',
    '.icns',
    '.ico',
    '.im',
    '.iim',
    '.tif',
    '.tiff',
    '.jfif',
    '.jpe',
    '.jpg',
    '.jpeg',
    '.mpg',
    '.mpeg',
    '.msp',
    '.pcd',
    '.pxr',
    '.pbm',
    '.pgm',
    '.ppm',
    '.pnm',
    '.psd',
    '.bw',
    '.rgb',
    '.rgba',
    '.sgi',
    '.ras',
    '.tga',
    '.icb',
    '.vda',
    '.vst',
    '.webp',
    '.wmf',
    '.emf',
    '.xbm',
    '.xpm',
]
__A      :       List[Any]      =	IMAGE_EXTENSIONS


