



'''simple docstring'''



import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


_lowercase       :						List[str]						="\\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"

_lowercase       :						List[str]						="\\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"

_lowercase       :						int						="\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 			_SCREAMING_SNAKE_CASE				(datasets.Metric      ):



			def 				lowerCAmelCase (     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 				lowerCAmelCase (     self		:						Any    ,			__UpperCamelCase		:						Dict    ,			__UpperCamelCase		:						int    ,			__UpperCamelCase		:						bool = False    ,			__UpperCamelCase		:						Optional[str] = None    ,			__UpperCamelCase		:						Optional[str] = None    ,			__UpperCamelCase		:						Optional[List[int]] = None    ,			__UpperCamelCase		:						Union[str, int] = "warn"    ,			)      ->      Optional[Any]:
							"""simple docstring"""





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

							snake_case__      :   str      =    {
							    type_name: {
							        '''precision''': score['''precision'''],
							        '''recall''': score['''recall'''],
							        '''f1''': score['''f1-score'''],
							        '''number''': score['''support'''],
							    }
							    for type_name, score in report.items()
							}
							snake_case__      :   Tuple      =    overall_score['''precision''']
							snake_case__      :   Dict      =    overall_score['''recall''']
							snake_case__      :   Dict      =    overall_score['''f1-score''']
							snake_case__      :   Optional[int]      =    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()




