






"""simple docstring"""





class   _lowercase			:


      def __init__(     self					:						Optional[Any]				, a					:						Union[str, Any]    ):





            """simple docstring"""







            __snake_case   :     Union[str, Any]	      =data
            __snake_case   :     Optional[Any]	      =None
            __snake_case   :     Any	      =None







      def __str__(     self					:						Any    ):





            """simple docstring"""







            return f'''{self.data}'''



class   _lowercase			:


      def __init__(     self					:						Optional[int]    ):





            """simple docstring"""







            __snake_case   :     Any	      =None
            __snake_case   :     int	      =None


      def __iter__(     self					:						int    ):





            """simple docstring"""







            __snake_case   :     List[str]	      =self.head
            while node:
                  yield node.data
                  __snake_case   :     str	      =node.next


      def __str__(     self					:						Optional[int]    ):





            """simple docstring"""







            return "->".join([str(a    ) for item in self]    )


      def __len__(     self					:						str    ):





            """simple docstring"""







            return sum(1 for _ in self    )


      def _UpperCamelCase    (     self					:						int				, a					:						List[Any]    ):





            """simple docstring"""







            self.insert_at_nth(0				, a    )


      def _UpperCamelCase    (     self					:						str				, a					:						Optional[int]    ):





            """simple docstring"""







            self.insert_at_nth(len(self    )				, a    )


      def _UpperCamelCase    (     self					:						Optional[Any]				, a					:						int				, a					:						Tuple    ):





            """simple docstring"""







            __snake_case   :     List[str]	      =len(self    )

            if not 0 <= index <= length:
                  raise IndexError('''list index out of range'''    )
            __snake_case   :     List[Any]	      =Node(a    )
            if self.head is None:
                  __snake_case   :     Dict	      =new_node
            elif index == 0:
                  __snake_case   :     Union[str, Any]	      =new_node
                  __snake_case   :     Any	      =self.head
                  __snake_case   :     List[Any]	      =new_node
            elif index == length:
                  __snake_case   :     int	      =new_node
                  __snake_case   :     Optional[Any]	      =self.tail
                  __snake_case   :     Optional[Any]	      =new_node
            else:
                  __snake_case   :     Any	      =self.head
                  for _ in range(0				, a    ):
                        __snake_case   :     Optional[Any]	      =temp.next
                  __snake_case   :     Optional[Any]	      =new_node
                  __snake_case   :     Union[str, Any]	      =temp.previous
                  __snake_case   :     List[Any]	      =temp
                  __snake_case   :     str	      =new_node


      def _UpperCamelCase    (     self					:						List[str]    ):





            """simple docstring"""







            return self.delete_at_nth(0    )


      def _UpperCamelCase    (     self					:						List[Any]    ):





            """simple docstring"""







            return self.delete_at_nth(len(self    ) - 1    )


      def _UpperCamelCase    (     self					:						Optional[int]				, a					:						int    ):





            """simple docstring"""







            __snake_case   :     Tuple	      =len(self    )

            if not 0 <= index <= length - 1:
                  raise IndexError('''list index out of range'''    )
            __snake_case   :     str	      =self.head  # default first node
            if length == 1:
                  __snake_case   :     List[Any]	      =None
            elif index == 0:
                  __snake_case   :     List[Any]	      =self.head.next
                  __snake_case   :     Optional[int]	      =None
            elif index == length - 1:
                  __snake_case   :     str	      =self.tail
                  __snake_case   :     Union[str, Any]	      =self.tail.previous
                  __snake_case   :     Optional[Any]	      =None
            else:
                  __snake_case   :     Optional[int]	      =self.head
                  for _ in range(0				, a    ):
                        __snake_case   :     List[Any]	      =temp.next
                  __snake_case   :     Union[str, Any]	      =temp
                  __snake_case   :     Optional[int]	      =temp.previous
                  __snake_case   :     int	      =temp.next
            return delete_node.data


      def _UpperCamelCase    (     self					:						List[Any]				, a					:						Any    ):





            """simple docstring"""







            __snake_case   :     Optional[int]	      =self.head

            while current.data != data:  # Find the position to delete
                  if current.next:
                        __snake_case   :     Dict	      =current.next
                  else:  # We have reached the end an no value matches
                        raise ValueError('''No data matching given value'''    )

            if current == self.head:
                  self.delete_head()

            elif current == self.tail:
                  self.delete_tail()

            else:  # Before: 1 <--> 2(current) <--> 3
                  __snake_case   :     Tuple	      =current.next  # 1 --> 3
                  __snake_case   :     Optional[Any]	      =current.previous  # 1 <--> 3
            return data







      def _UpperCamelCase    (     self					:						int    ):





            """simple docstring"""







            return len(self    ) == 0



def      __lowercase       (     )       ->       None:
      __snake_case   :     List[str]	      =DoublyLinkedList()
      assert linked_list.is_empty() is True
      assert str(a			) == ""

      try:
            linked_list.delete_head()
            raise AssertionError  # This should not happen.
      except IndexError:
            assert True  # This should happen.

      try:
            linked_list.delete_tail()
            raise AssertionError  # This should not happen.
      except IndexError:
            assert True  # This should happen.

      for i in range(10			):
            assert len(a			) == i
            linked_list.insert_at_nth(a							,		i + 1			)
      assert str(a			) == "->".join(str(a			) for i in range(1							,		11			)			)

      linked_list.insert_at_head(0			)
      linked_list.insert_at_tail(11			)
      assert str(a			) == "->".join(str(a			) for i in range(0							,		12			)			)

      assert linked_list.delete_head() == 0
      assert linked_list.delete_at_nth(9			) == 10
      assert linked_list.delete_tail() == 11
      assert len(a			) == 9
      assert str(a			) == "->".join(str(a			) for i in range(1							,		10			)			)


if __name__ == "__main__":
    from doctest import testmod

    testmod()










"""simple docstring"""





import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


UpperCamelCase_						:					Optional[int]      				=			"""\
@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},
}
"""

UpperCamelCase_						:					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.
"""

UpperCamelCase_						:					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   _lowercase			(  datasets.Metric    ):


      def _UpperCamelCase    (     self					:						int    ):





            """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					:						List[Any]				, a					:						List[str]				, a					:						Optional[Any]				, a					:						bool = False				, a					:						Optional[str] = None				, a					:						Optional[str] = None				, a					:						Optional[List[int]] = None				, a					:						Union[str, int] = "warn"				, ):





            """simple docstring"""







            if scheme is not None:
                  try:
                        __snake_case   :     List[str]	      =importlib.import_module('''seqeval.scheme'''    )
                        __snake_case   :     int	      =getattr(a				, a    )
                  except AttributeError:
                        raise ValueError(f'''Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}'''    )
            __snake_case   :     Optional[Any]	      =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'''    )
            __snake_case   :     Union[str, Any]	      =report.pop('''micro avg'''    )

            __snake_case   :     str	      ={
                type_name: {
                    '''precision''': score['''precision'''],
                    '''recall''': score['''recall'''],
                    '''f1''': score['''f1-score'''],
                    '''number''': score['''support'''],
                }
                for type_name, score in report.items()
            }
            __snake_case   :     str	      =overall_score['''precision''']
            __snake_case   :     int	      =overall_score['''recall''']
            __snake_case   :     str	      =overall_score['''f1-score''']
            __snake_case   :     Union[str, Any]	      =accuracy_score(y_true=a				, y_pred=a    )

            return scores


