

'''simple docstring'''

import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


UpperCAmelCase							=					'''\
@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},
}
'''

UpperCAmelCase							=					'''\
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.
'''

UpperCAmelCase							=					'''
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 					lowerCAmelCase       ( datasets.Metric				):







       def      snake_case				(							self     :	str ):







            """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      snake_case				(							self     :	Any  , __lowercase     :	List[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"  , ):







            """simple docstring"""







            if scheme is not None:
                 try:
                      __lowercase     						=importlib.import_module('seqeval.scheme' )
                      __lowercase     						=getattr(__lowercase  , __lowercase )
                 except AttributeError:
                      raise ValueError(f'''Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}''' )
            __lowercase     						=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' )
            __lowercase     						=report.pop('micro avg' )

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

            return scores





'''simple docstring'''

from .number_theory.prime_numbers import next_prime


class 					lowerCAmelCase       :







       def __init__(							self     :	int  , __lowercase     :	int  , __lowercase     :	int | None = None  , __lowercase     :	float | None = None  , ):







            """simple docstring"""







            __lowercase     						=size_table
            __lowercase     						=[None] * self.size_table
            __lowercase     						=0.7_5 if lim_charge is None else lim_charge
            __lowercase     						=1 if charge_factor is None else charge_factor
            __lowercase     						=[]
            __lowercase     						={}







       def      snake_case				(							self     :	Dict ):







            """simple docstring"""







            return self._keys







       def      snake_case				(							self     :	Optional[Any] ):







            """simple docstring"""







            return sum(1 for slot in self.values if slot is not None ) / (
                self.size_table * self.charge_factor
            )







       def      snake_case				(							self     :	int  , __lowercase     :	int ):







            """simple docstring"""







            return key % self.size_table







       def      snake_case				(							self     :	int  , __lowercase     :	Any ):







            """simple docstring"""







            print(f'''step {step_ord}''' )
            print(list(range(len(self.values ) ) ) )
            print(self.values )







       def      snake_case				(							self     :	int  , __lowercase     :	int ):







            """simple docstring"""







            __lowercase     						=1
            __lowercase     						=values
            for value in values:
                 self.insert_data(__lowercase )
                 self._step_by_step(__lowercase )
                 i += 1







       def      snake_case				(							self     :	List[str]  , __lowercase     :	Optional[int]  , __lowercase     :	List[str] ):







            """simple docstring"""







            __lowercase     						=data
            __lowercase     						=data







       def      snake_case				(							self     :	int  , __lowercase     :	Any  , __lowercase     :	Optional[Any]=None ):







            """simple docstring"""







            __lowercase     						=self.hash_function(key + 1 )

            while self.values[new_key] is not None and self.values[new_key] != key:
                 if self.values.count(__lowercase ) > 0:
                      __lowercase     						=self.hash_function(new_key + 1 )
                 else:
                      __lowercase     						=None
                      break

            return new_key







       def      snake_case				(							self     :	Any ):







            """simple docstring"""







            __lowercase     						=[value for value in self.values if value is not None]
            __lowercase     						=next_prime(self.size_table  , factor=2 )
            self._keys.clear()
            __lowercase     						=[None] * self.size_table  # hell's pointers D: don't DRY ;/
            for value in survivor_values:
                 self.insert_data(__lowercase )







       def      snake_case				(							self     :	Optional[Any]  , __lowercase     :	Any ):







            """simple docstring"""







            __lowercase     						=self.hash_function(__lowercase )

            if self.values[key] is None:
                 self._set_value(__lowercase  , __lowercase )

            elif self.values[key] == data:
                 pass

            else:
                 __lowercase     						=self._collision_resolution(__lowercase  , __lowercase )
                 if collision_resolution is not None:
                      self._set_value(__lowercase  , __lowercase )
                 else:
                      self.rehashing()
                      self.insert_data(__lowercase )


