



import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


_lowercase:				Optional[Any]       =	"\\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:				List[str]       =	"\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 			_lowercase							(     datasets.Metric			):





	"""simple docstring"""




	def   UpperCamelCase_ (self	):
						"""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   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_ = False	, lowerCamelCase_ = None	, lowerCamelCase_ = None	, lowerCamelCase_ = None	, lowerCamelCase_ = "warn"	, ):
						"""simple docstring"""

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

						return scores





def 					a(   A :					Optional[Any]				,  A :					List[str] )	-> List[Any]:
					"""simple docstring"""





					print("\nVertex Distance" )
					for i in range(A ):
										if dist[i] != float("inf" ):
															print(A				,  "\t"				,  int(dist[i] )				,  end="\t" )
										else:
															print(A				,  "\t"				,  "INF"				,  end="\t" )
										print()






def 					a(   A :					str				,  A :					int				,  A :					int )	-> Optional[int]:
					"""simple docstring"""





					a        =     float("inf" )
					a        =     -1
					for i in range(A ):
										if (not vset[i]) and mdist[i] < min_val:
															a        =     i
															a        =     mdist[i]
					return min_ind






def 					a(   A :					List[str]				,  A :					Union[str, Any]				,  A :					Dict )	-> Tuple:
					"""simple docstring"""





					a        =     [float("inf" ) for _ in range(A )]
					a        =     [False for _ in range(A )]
					a        =     0.0

					for _ in range(v - 1 ):
										a        =     min_dist(A				,  A				,  A )
										a        =     True

										for i in range(A ):
															if (
															    (not vset[i])
															    and graph[u][i] != float("inf" )
															    and mdist[u] + graph[u][i] < mdist[i]
															):
																				a        =     mdist[u] + graph[u][i]

					print_dist(A				,  A )


if __name__ == "__main__":
				_lowercase:				Any       =	int(input("Enter number of vertices: ").strip())
				_lowercase:				Optional[Any]       =	int(input("Enter number of edges: ").strip())

				_lowercase:				List[str]       =	[[float("inf") for i in range(V)] for j in range(V)]

				for i in range(V):
								_lowercase:				List[Any]       =	0.0

				for i in range(E):
								print("\nEdge ", i + 1)
								_lowercase:				Tuple       =	int(input("Enter source:").strip())
								_lowercase:				Tuple       =	int(input("Enter destination:").strip())
								_lowercase:				int       =	float(input("Enter weight:").strip())
								_lowercase:				str       =	weight

				_lowercase:				Union[str, Any]       =	int(input("\nEnter shortest path source:").strip())
				dijkstra(graph, V, gsrc)
