import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


lowerCAmelCase__  	=      "\\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"

lowerCAmelCase__  	=      "\\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"

lowerCAmelCase__  	=      "\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 	_a  (							datasets.Metric					):



	"""simple docstring"""



	def __lowerCAmelCase      (   self       ):
		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       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_ = False       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = "warn"       ,  ):
		if scheme is not None:
			try:
				_lowercase   =importlib.import_module("seqeval.scheme"       )
				_lowercase   =getattr(lowerCAmelCase_       ,  lowerCAmelCase_       )
			except AttributeError:
				raise ValueError(F'''Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}'''       )
		_lowercase   =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"       )
		_lowercase   =report.pop("micro avg"       )

		_lowercase   ={
		    type_name: {
		        "precision": score["precision"],
		        "recall": score["recall"],
		        "f1": score["f1-score"],
		        "number": score["support"],
		    }
		    for type_name, score in report.items()
		}
		_lowercase   =overall_score["precision"]
		_lowercase   =overall_score["recall"]
		_lowercase   =overall_score["f1-score"]
		_lowercase   =accuracy_score(y_true=lowerCAmelCase_       ,  y_pred=lowerCAmelCase_       )

		return scores












'''simple docstring'''







def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase = 13 ) ->			str:
							lowerCamelCase__   :      Dict			    =			""""""
							for c in s:
														if "A" <= c <= "Z":
																					out += chr(ord("""A""" ) + (ord(UpperCamelCase ) - ord("""A""" ) + n) % 26 )
														elif "a" <= c <= "z":
																					out += chr(ord("""a""" ) + (ord(UpperCamelCase ) - ord("""a""" ) + n) % 26 )
														else:
																					out += c
							return out





def 					SCREAMING_SNAKE_CASE_			() ->			None:
							lowerCamelCase__   :      Dict			    =			input("""Enter message: """ )

							lowerCamelCase__   :      str			    =			dencrypt(UpperCamelCase							,     13 )
							print("""Encryption:"""							,     UpperCamelCase )

							lowerCamelCase__   :      List[str]			    =			dencrypt(UpperCamelCase							,     13 )
							print("""Decryption: """							,     UpperCamelCase )


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			main()

