


'''simple docstring'''

import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


a_  :    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_  :    Dict      =			"""\
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_  :    Any      =			"""
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 					snake_case   (   datasets.Metric      ):





  """simple docstring"""




  def   snake_case		(   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   snake_case		(   self      ,  UpperCamelCase      ,  UpperCamelCase      ,  UpperCamelCase = False      ,  UpperCamelCase = None      ,  UpperCamelCase = None      ,  UpperCamelCase = None      ,  UpperCamelCase = "warn"      ,  ):







   """simple docstring"""
   if scheme is not None:
    try:
     lowerCamelCase_								=  importlib.import_module("seqeval.scheme"					)
     lowerCamelCase_								=  getattr(UpperCamelCase      ,  UpperCamelCase					)
    except AttributeError:
     raise ValueError(f'''Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}'''					)
   lowerCamelCase_								=  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"					)
   lowerCamelCase_								=  report.pop("micro avg"					)

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

   return scores







'''simple docstring'''

class 					snake_case   :





  """simple docstring"""




  def __init__(   self      ,  UpperCamelCase					):







   """simple docstring"""
   lowerCamelCase_								=  data
   lowerCamelCase_								=  None
   lowerCamelCase_								=  None





def __snake_case (				UpperCAmelCase_						:						BinaryTreeNode | None ,    UpperCAmelCase_						:						int		):
 if node is None:
  lowerCamelCase_								=  BinaryTreeNode(UpperCAmelCase_		)
  return node

 # binary search tree is not empty,
 # so we will insert it into the tree
 # if new_value is less than value of data in node,
 #  add it to left subtree and proceed recursively
 if new_value < node.data:
  lowerCamelCase_								=  insert(node.left_child ,    UpperCAmelCase_		)
 else:
  # if new_value is greater than value of data in node,
  #  add it to right subtree and proceed recursively
  lowerCamelCase_								=  insert(node.right_child ,    UpperCAmelCase_		)
 return node





def __snake_case (				UpperCAmelCase_						:						None | BinaryTreeNode		):  # if node is None,return
 if node:
  lowerCamelCase_								=  inorder(node.left_child		)
  lowerCamelCase_								=  [*inorder_array, node.data]
  lowerCamelCase_								=  inorder_array + inorder(node.right_child		)
 else:
  lowerCamelCase_								=  []
 return inorder_array





def __snake_case (				):
 lowerCamelCase_								=  insert(UpperCAmelCase_ ,    15		)
 insert(UpperCAmelCase_ ,    10		)
 insert(UpperCAmelCase_ ,    25		)
 insert(UpperCAmelCase_ ,    6		)
 insert(UpperCAmelCase_ ,    14		)
 insert(UpperCAmelCase_ ,    20		)
 insert(UpperCAmelCase_ ,    60		)
 return root





def __snake_case (				):
 # main function
 lowerCamelCase_								=  make_tree()
 print("Printing values of binary search tree in Inorder Traversal."		)
 inorder(UpperCAmelCase_		)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()



