






import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


__snake_case     :     Optional[int]       =  """\
@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},
}
"""

__snake_case     :     Any       =  """\
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.
"""

__snake_case     :     Optional[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      __SCREAMING_SNAKE_CASE       ( datasets.Metric):






						def    UpperCamelCase__   (						self ):






												"""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    UpperCamelCase__   (						self  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase = False  ,    _UpperCamelCase = None  ,    _UpperCamelCase = None  ,    _UpperCamelCase = None  ,    _UpperCamelCase = "warn"  ,    ):






												"""simple docstring"""
												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 __future__ import annotations







def 						_UpperCamelCase (     UpperCamelCase_							:       list[int]		)	-> list[int]:

						"""simple docstring"""



						if not list_of_ints:
												return []

						if min(UpperCamelCase_		) < 0:
												raise ValueError('All numbers must be positive'		)

						lowerCAmelCase__         =    max(len(bin(UpperCamelCase_		)[2:]		) for x in list_of_ints		)
						return _msd_radix_sort(UpperCamelCase_     ,							UpperCamelCase_		)







def 						_UpperCamelCase (     UpperCamelCase_							:       list[int]     ,							UpperCamelCase_							:       int		)	-> list[int]:

						"""simple docstring"""



						if bit_position == 0 or len(UpperCamelCase_		) in [0, 1]:
												return list_of_ints

						lowerCAmelCase__         =    []
						lowerCAmelCase__         =    []
						# Split numbers based on bit at bit_position from the right
						for number in list_of_ints:
												if (number >> (bit_position - 1)) & 1:
																		# number has a one at bit bit_position
																		ones.append(UpperCamelCase_		)
												else:
																		# number has a zero at bit bit_position
																		zeros.append(UpperCamelCase_		)

    # recursively split both lists further
						lowerCAmelCase__         =    _msd_radix_sort(UpperCamelCase_     ,							bit_position - 1		)
						lowerCAmelCase__         =    _msd_radix_sort(UpperCamelCase_     ,							bit_position - 1		)

						# recombine lists
						lowerCAmelCase__         =    zeros
						res.extend(UpperCamelCase_		)

						return res







def 						_UpperCamelCase (     UpperCamelCase_							:       list[int]		)	-> Any:

						"""simple docstring"""



						lowerCAmelCase__         =    len(UpperCamelCase_		)
						if not list_of_ints or length == 1:
												return

						if min(UpperCamelCase_		) < 0:
												raise ValueError('All numbers must be positive'		)

						lowerCAmelCase__         =    max(len(bin(UpperCamelCase_		)[2:]		) for x in list_of_ints		)
						_msd_radix_sort_inplace(UpperCamelCase_     ,							UpperCamelCase_     ,							0     ,							UpperCamelCase_		)







def 						_UpperCamelCase (     UpperCamelCase_							:       list[int]     ,							UpperCamelCase_							:       int     ,							UpperCamelCase_							:       int     ,							UpperCamelCase_							:       int		)	-> int:

						"""simple docstring"""



						if bit_position == 0 or end_index - begin_index <= 1:
												return

						bit_position -= 1

						lowerCAmelCase__         =    begin_index
						lowerCAmelCase__         =    end_index - 1
						while i <= j:
												lowerCAmelCase__         =    False
												if not (list_of_ints[i] >> bit_position) & 1:
																		# found zero at the beginning
																		i += 1
																		lowerCAmelCase__         =    True
												if (list_of_ints[j] >> bit_position) & 1:
																		# found one at the end
																		j -= 1
																		lowerCAmelCase__         =    True

												if changed:
																		continue

												lowerCAmelCase__    ,							lowerCAmelCase__         =    list_of_ints[j], list_of_ints[i]
												j -= 1
												if j != i:
																		i += 1

						_msd_radix_sort_inplace(UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_		)
						_msd_radix_sort_inplace(UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_		)


if __name__ == "__main__":
					import doctest

					doctest.testmod()





