code: 
  
  
  '''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			:		Any =			'\\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			:		Dict =			'\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    __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		,       __a		,       __a		,       __a = False		,       __a = None		,       __a = None		,       __a = None		,       __a = "warn"		,       ):
  
  						'''simple docstring'''
  
  
  
  
  
  						if scheme is not None:
  											try:
  																__a    : List[str]      					=						importlib.import_module('seqeval.scheme'							)
  																__a    : Dict      					=						getattr(__a		,       __a							)
  											except AttributeError:
  																raise ValueError(f"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}"""							)
  						__a    : Optional[int]      					=						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    : str      					=						report.pop('micro avg'							)
  
  						__a    : str      					=						{
  						    type_name: {
  						        'precision': score['precision'],
  						        'recall': score['recall'],
  						        'f1': score['f1-score'],
  						        'number': score['support'],
  						    }
  						    for type_name, score in report.items()
  						}
  						__a    : Optional[Any]      					=						overall_score['precision']
  						__a    : str      					=						overall_score['recall']
  						__a    : Any      					=						overall_score['f1-score']
  						__a    : Optional[Any]      					=						accuracy_score(y_true=__a		,       y_pred=__a							)
  
  						return scores
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     int = 30     ):
  					__a    : Dict      					=						exponent + 2
  					__a    : List[Any]      					=						(1 + 5**0.5) / 2
  					__a    : Any      					=						(phi**fibonacci_index - (phi - 1) ** fibonacci_index) / 5**0.5
  
  					return int(_SCREAMING_SNAKE_CASE     )
  
  
  if __name__ == "__main__":
  	print(f'''{solution() = }''')
  
  
  
  