


"""simple docstring"""


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 snake_case					(     datasets.Metric):







		def        a_       (    self					:				List[str]						)				->  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        a_       (    self					:				Tuple			,      a__					:				List[Any]			,      a__					:				Tuple			,      a__					:				bool = False			,      a__					:				Optional[str] = None			,      a__					:				Optional[str] = None			,      a__					:				Optional[List[int]] = None			,      a__					:				Union[str, int] = "warn"			,      )				->  List[Any]:





						'''simple docstring'''







						if scheme is not None:
										try:
														_A         =	importlib.import_module("seqeval.scheme"						)
														_A         =	getattr(a__			,      a__						)
										except AttributeError:
														raise ValueError(F"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}"""						)
						_A         =	classification_report(
						    y_true=a__			,      y_pred=a__			,      suffix=a__			,      output_dict=a__			,      scheme=a__			,      mode=a__			,      sample_weight=a__			,      zero_division=a__			,      )
						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=a__			,      y_pred=a__						)

						return scores





import doctest

import projectq
from projectq.ops import H, Measure

def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase)			->     int:
     a      =      quantum_engine.allocate_qubit()
     H | qubit
     Measure | qubit
     return int(__UpperCamelCase)


if __name__ == "__main__":
   doctest.testmod()

   # initialises a new quantum backend
   lowercase__      :			Optional[int]           =					projectq.MainEngine()

   # Generate a list of 10 random numbers
   lowercase__      :			List[str]           =					[get_random_number(quantum_engine) for _ in range(10)]

   # Flushes the quantum engine from memory
   quantum_engine.flush()

   print("Random numbers", random_numbers_list)

