






import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


UpperCAmelCase_					:		List[str]           = '''\
@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_					:		Optional[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        _SCREAMING_SNAKE_CASE			(     datasets.Metric ):





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

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

		return scores








class        _SCREAMING_SNAKE_CASE			:





	def __init__(						self							:   Tuple      ):
		UpperCamelCase	:List[str]		=      [0]
		UpperCamelCase	:Optional[int]		=      0





	def 		_A					(						self							:   Dict					,    __lowerCamelCase							:   int      ):
		UpperCamelCase	:List[str]		=      self.__heap[i]
		while i // 2 > 0:
			if self.__heap[i] > self.__heap[i // 2]:
				UpperCamelCase	:Any		=      self.__heap[i // 2]
				UpperCamelCase	:Optional[Any]		=      temporary
			i //= 2





	def 		_A					(						self							:   Optional[int]					,    __lowerCamelCase							:   int      ):
		self.__heap.append(__lowerCamelCase      )
		self.__size += 1
		self.__swap_up(self.__size      )





	def 		_A					(						self							:   Optional[Any]					,    __lowerCamelCase							:   int      ):
		while self.__size >= 2 * i:
			if 2 * i + 1 > self.__size:
				UpperCamelCase	:List[Any]		=      2 * i
			else:
				if self.__heap[2 * i] > self.__heap[2 * i + 1]:
					UpperCamelCase	:Union[str, Any]		=      2 * i
				else:
					UpperCamelCase	:Dict		=      2 * i + 1
			UpperCamelCase	:Optional[Any]		=      self.__heap[i]
			if self.__heap[i] < self.__heap[bigger_child]:
				UpperCamelCase	:List[str]		=      self.__heap[bigger_child]
				UpperCamelCase	:List[str]		=      temporary
			UpperCamelCase	:Optional[int]		=      bigger_child





	def 		_A					(						self							:   Optional[Any]      ):
		UpperCamelCase	:str		=      self.__heap[1]
		UpperCamelCase	:Tuple		=      self.__heap[self.__size]
		self.__size -= 1
		self.__heap.pop()
		self.__swap_down(1      )
		return max_value





	@property
	def 		_A					(						self							:   int      ):
		return self.__heap[1:]





	def __len__(						self							:   str      ):
		return self.__size


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	# create an instance of BinaryHeap
	UpperCAmelCase_					:		Optional[int]           = BinaryHeap()
	binary_heap.insert(6)
	binary_heap.insert(10)
	binary_heap.insert(15)
	binary_heap.insert(12)
	# pop root(max-values because it is max heap)
	print(binary_heap.pop())  # 15
	print(binary_heap.pop())  # 12
	# get the list and size after operations
	print(binary_heap.get_list)
	print(len(binary_heap))
