



import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


__A				    =						'''\
@inproceedings{ramshaw-marcus-1995-text,
    title = "Text Chunking using Transformation-Based Learning",
    author = "Ramshaw, Lance  and
      Marcus, Mitch",
    booktitle = "Third Workshop on Very Large Corpora",
    year = "1995",
    url = "https://www.aclweb.org/anthology/W95-0107",
}
@misc{seqeval,
  title={{seqeval}: A Python framework for sequence labeling evaluation},
  url={https://github.com/chakki-works/seqeval},
  note={Software available from https://github.com/chakki-works/seqeval},
  author={Hiroki Nakayama},
  year={2018},
}
'''

__A				    =						'''\
seqeval is a Python framework for sequence labeling evaluation.
seqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.

This is well-tested by using the Perl script conlleval, which can be used for
measuring the performance of a system that has processed the CoNLL-2000 shared task data.

seqeval supports following formats:
IOB1
IOB2
IOE1
IOE2
IOBES

See the [README.md] file at https://github.com/chakki-works/seqeval for more information.
'''

__A				    =						'''
Produces labelling scores along with its sufficient statistics
from a source against one or more references.

Args:
    predictions: List of List of predicted labels (Estimated targets as returned by a tagger)
    references: List of List of reference labels (Ground truth (correct) target values)
    suffix: True if the IOB prefix is after type, False otherwise. default: False
    scheme: Specify target tagging scheme. Should be one of ["IOB1", "IOB2", "IOE1", "IOE2", "IOBES", "BILOU"].
        default: None
    mode: Whether to count correct entity labels with incorrect I/B tags as true positives or not.
        If you want to only count exact matches, pass mode="strict". default: None.
    sample_weight: Array-like of shape (n_samples,), weights for individual samples. default: None
    zero_division: Which value to substitute as a metric value when encountering zero division. Should be on of 0, 1,
        "warn". "warn" acts as 0, but the warning is raised.

Returns:
    \'scores\': dict. Summary of the scores for overall and per type
        Overall:
            \'accuracy\': accuracy,
            \'precision\': precision,
            \'recall\': recall,
            \'f1\': F1 score, also known as balanced F-score or F-measure,
        Per type:
            \'precision\': precision,
            \'recall\': recall,
            \'f1\': F1 score, also known as balanced F-score or F-measure
Examples:

    >>> predictions = [[\'O\', \'O\', \'B-MISC\', \'I-MISC\', \'I-MISC\', \'I-MISC\', \'O\'], [\'B-PER\', \'I-PER\', \'O\']]
    >>> references = [[\'O\', \'O\', \'O\', \'B-MISC\', \'I-MISC\', \'I-MISC\', \'O\'], [\'B-PER\', \'I-PER\', \'O\']]
    >>> seqeval = datasets.load_metric("seqeval")
    >>> results = seqeval.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    [\'MISC\', \'PER\', \'overall_precision\', \'overall_recall\', \'overall_f1\', \'overall_accuracy\']
    >>> print(results["overall_f1"])
    0.5
    >>> print(results["PER"]["f1"])
    1.0
'''






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,    _KWARGS_DESCRIPTION)
class 		lowercase						( datasets.Metric):






	"""simple docstring"""


	def   _SCREAMING_SNAKE_CASE		(	self	:						Tuple     )			-> List[Any]:
							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   _SCREAMING_SNAKE_CASE		(	self	:						List[str]						,  __UpperCAmelCase	:						List[Any]						,  __UpperCAmelCase	:						Tuple						,  __UpperCAmelCase	:						bool = False						,  __UpperCAmelCase	:						Optional[str] = None						,  __UpperCAmelCase	:						Optional[str] = None						,  __UpperCAmelCase	:						Optional[List[int]] = None						,  __UpperCAmelCase	:						Union[str, int] = "warn"						,  )			-> Union[str, Any]:
							if scheme is not None:
													try:
																			UpperCAmelCase_=   importlib.import_module("""seqeval.scheme"""     )
																			UpperCAmelCase_=   getattr(__UpperCAmelCase						,  __UpperCAmelCase     )
													except AttributeError:
																			raise ValueError(F"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}"""     )
							UpperCAmelCase_=   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"""     )
							UpperCAmelCase_=   report.pop("""micro avg"""     )

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

							return scores









def        __a     (    lowerCAmelCase_   :					list				)      -> bool:






						'''simple docstring'''



						if not isinstance(lowerCAmelCase_			,lowerCAmelCase_				):
												raise ValueError("""Input series is not valid, valid series - [1, 2/3, 2]"""				)
						if len(lowerCAmelCase_				) == 0:
												raise ValueError("""Input list must be a non empty list"""				)
						if len(lowerCAmelCase_				) == 1 and series[0] != 0:
												return True
						UpperCAmelCase_=   []
						UpperCAmelCase_=   len(lowerCAmelCase_				)
						for i in range(0			,lowerCAmelCase_				):
												if series[i] == 0:
																		raise ValueError("""Input series cannot have 0 as an element"""				)
												rec_series.append(1 / series[i]				)
						UpperCAmelCase_=   rec_series[1] - rec_series[0]
						for index in range(2			,lowerCAmelCase_				):
												if rec_series[index] - rec_series[index - 1] != common_diff:
																		return False
						return True



def        __a     (    lowerCAmelCase_   :					list				)      -> float:






						'''simple docstring'''



						if not isinstance(lowerCAmelCase_			,lowerCAmelCase_				):
												raise ValueError("""Input series is not valid, valid series - [2, 4, 6]"""				)
						if len(lowerCAmelCase_				) == 0:
												raise ValueError("""Input list must be a non empty list"""				)
						UpperCAmelCase_=   0
						for val in series:
												answer += 1 / val
						return len(lowerCAmelCase_				) / answer


if __name__ == "__main__":
	import doctest

	doctest.testmod()




