





'''simple docstring'''


import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


_SCREAMING_SNAKE_CASE			=							"\\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"

_SCREAMING_SNAKE_CASE			=							"\\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"

_SCREAMING_SNAKE_CASE			=							"\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 							_snake_case				(		self						)      ->							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 							_snake_case				(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase = False     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = "warn"     ,       )      ->							Union[str, Any]:
     if scheme is not None:
         try:
             _lowerCAmelCase		     = importlib.import_module("seqeval.scheme"						)
             _lowerCAmelCase		     = getattr(_lowerCAmelCase     ,       _lowerCAmelCase						)
         except AttributeError:
             raise ValueError(f'''Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}'''						)
     _lowerCAmelCase		     = 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"						)
     _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=_lowerCAmelCase     ,       y_pred=_lowerCAmelCase						)

     return scores









'''simple docstring'''


def 		__a(SCREAMING_SNAKE_CASE_					:   List[Any]						,    SCREAMING_SNAKE_CASE_					:   str      ):



    '''simple docstring'''





    if n < 0:
        return 0
    # table[i] represents the number of ways to get to amount i
    _lowerCAmelCase		     = [0] * (n + 1)

    # There is exactly 1 way to get to zero(You pick no coins).
    _lowerCAmelCase		     = 1

    # Pick all coins one by one and update table[] values
    # after the index greater than or equal to the value of the
    # picked coin
    for coin_val in s:
        for j in range(SCREAMING_SNAKE_CASE_						,    n + 1      ):
            table[j] += table[j - coin_val]

    return table[n]


if __name__ == "__main__":
       import doctest

       doctest.testmod()


