



"""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   UpperCAmelCase__		(   datasets.Metric  ):



		def        A_		(			self	:     Union[str, Any]					)      ->    Dict:






									'''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	:     Any   ,				snake_case	:     Dict   ,				snake_case	:     int   ,				snake_case	:     bool = False   ,				snake_case	:     Optional[str] = None   ,				snake_case	:     Optional[str] = None   ,				snake_case	:     Optional[List[int]] = None   ,				snake_case	:     Union[str, int] = "warn"   ,				)      ->    Optional[Any]:






									'''simple docstring'''

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

									return scores








'''simple docstring'''



from __future__ import annotations

# Define the Gravitational Constant G and the function
_lowercase       :						List[str]						=6.6743E-11  # unit of G : m^3 * kg^-1 * s^-2


def 							__UpperCAmelCase				(				UpperCamelCase__				:float   ,				UpperCamelCase__				:float   ,				UpperCamelCase__				:float   ,				UpperCamelCase__				:float			)		->						dict[str, float]:
				snake_case__      :   str      =    mass_a * mass_a

				if (force, mass_a, mass_a, distance).count(0			) != 1:
								raise ValueError('''One and only one argument must be 0'''			)
				if force < 0:
								raise ValueError('''Gravitational force can not be negative'''			)
				if distance < 0:
								raise ValueError('''Distance can not be negative'''			)
				if mass_a < 0 or mass_a < 0:
								raise ValueError('''Mass can not be negative'''			)
				if force == 0:
								snake_case__      :   Dict      =    GRAVITATIONAL_CONSTANT * product_of_mass / (distance**2)
								return {"force": force}
				elif mass_a == 0:
								snake_case__      :   List[str]      =    (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
								return {"mass_1": mass_a}
				elif mass_a == 0:
								snake_case__      :   List[Any]      =    (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
								return {"mass_2": mass_a}
				elif distance == 0:
								snake_case__      :   str      =    (GRAVITATIONAL_CONSTANT * product_of_mass / (force)) ** 0.5
								return {"distance": distance}
				raise ValueError('''One and only one argument must be 0'''			)


# Run doctest
if __name__ == "__main__":
				import doctest

				doctest.testmod()




