


import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


_A		:					List[Any]          =							'''\
@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		:					Optional[Any]          =							'''\
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		:					Optional[int]          =							'''
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 							a       (      datasets.Metric ):





     def 			SCREAMING_SNAKE_CASE__							(     self       :					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       :					Tuple     ,						SCREAMING_SNAKE_CASE_       :					int     ,						SCREAMING_SNAKE_CASE_       :					Optional[int]     ,						SCREAMING_SNAKE_CASE_       :					bool = False     ,						SCREAMING_SNAKE_CASE_       :					Optional[str] = None     ,						SCREAMING_SNAKE_CASE_       :					Optional[str] = None     ,						SCREAMING_SNAKE_CASE_       :					Optional[List[int]] = None     ,						SCREAMING_SNAKE_CASE_       :					Union[str, int] = "warn"     ,						):
        if scheme is not None:
           try:
              __lowerCamelCase:					Dict	 =						importlib.import_module("""seqeval.scheme"""				)
              __lowerCamelCase:					List[Any]	 =						getattr(SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_				)
           except AttributeError:
              raise ValueError(F'Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}'				)
        __lowerCamelCase:					Union[str, Any]	 =						classification_report(
            y_true=SCREAMING_SNAKE_CASE_     ,						y_pred=SCREAMING_SNAKE_CASE_     ,						suffix=SCREAMING_SNAKE_CASE_     ,						output_dict=SCREAMING_SNAKE_CASE_     ,						scheme=SCREAMING_SNAKE_CASE_     ,						mode=SCREAMING_SNAKE_CASE_     ,						sample_weight=SCREAMING_SNAKE_CASE_     ,						zero_division=SCREAMING_SNAKE_CASE_     ,						)
        report.pop("""macro avg"""				)
        report.pop("""weighted avg"""				)
        __lowerCamelCase:					Optional[int]	 =						report.pop("""micro avg"""				)

        __lowerCamelCase:					int	 =						{
            type_name: {
                """precision""": score["""precision"""],
                """recall""": score["""recall"""],
                """f1""": score["""f1-score"""],
                """number""": score["""support"""],
            }
            for type_name, score in report.items()
        }
        __lowerCamelCase:					str	 =						overall_score["""precision"""]
        __lowerCamelCase:					Tuple	 =						overall_score["""recall"""]
        __lowerCamelCase:					str	 =						overall_score["""f1-score"""]
        __lowerCamelCase:					int	 =						accuracy_score(y_true=SCREAMING_SNAKE_CASE_     ,						y_pred=SCREAMING_SNAKE_CASE_				)

        return scores






_A		:					Union[str, Any]          =							1_000



def      __lowerCAmelCase    (				snake_case						:     float			)							->				float:
   return x * x



def      __lowerCAmelCase    (				snake_case						:     Optional[int]    ,   snake_case						:     float    ,   snake_case						:     float    ,   snake_case						:     int = 4			)							->				float:
   assert callable(
       snake_case			), f'the function(object) passed should be callable your input : {function}'
   assert isinstance(snake_case    ,   (float, int)			), f'a should be float or integer your input : {a}'
   assert isinstance(function(snake_case			)    ,   (float, int)			), (
       "the function should return integer or float return type of your function, "
       f'{type(snake_case			)}'
   )
   assert isinstance(snake_case    ,   (float, int)			), f'b should be float or integer your input : {b}'
   assert (
       isinstance(snake_case    ,   snake_case			) and precision > 0
   ), f'precision should be positive integer your input : {precision}'

   # just applying the formula of simpson for approximate integration written in
   # mentioned article in first comment of this file and above this function

   __lowerCamelCase:					List[str]	 =						(b - a) / N_STEPS
   __lowerCamelCase:					Optional[Any]	 =						function(snake_case			) + function(snake_case			)

   for i in range(1    ,   snake_case			):
      __lowerCamelCase:					Dict	 =						a + h * i
      result += function(snake_case			) * (4 if i % 2 else 2)

   result *= h / 3
   return round(snake_case    ,   snake_case			)


if __name__ == "__main__":
   import doctest

   doctest.testmod()


