






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__						:      Optional[int]								=					"""\
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__						:      Tuple								=					"""
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 				lowerCamelCase_     (	datasets.Metric     ):
							def  SCREAMING_SNAKE_CASE_ (						self		:					Any					):





								'''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  SCREAMING_SNAKE_CASE_ (						self		:					Tuple							,__lowerCamelCase		:					str							,__lowerCamelCase		:					Any							,__lowerCamelCase		:					bool = False							,__lowerCamelCase		:					Optional[str] = None							,__lowerCamelCase		:					Optional[str] = None							,__lowerCamelCase		:					Optional[List[int]] = None							,__lowerCamelCase		:					Union[str, int] = "warn"							,):





								'''simple docstring'''



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

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

								return scores













UpperCamelCase__						:      Dict								=					10**8





def   SCREAMING_SNAKE_CASE__						(   snake_case_  )   ->  bool:


	"""simple docstring"""



	if n % 1_0 == 0:
		return False
	a		=			str(snake_case_  )
	return s == s[::-1]





def   SCREAMING_SNAKE_CASE__						(   )   ->  int:


	"""simple docstring"""



	a		=			set()
	a		=			1
	a		=			5
	while sum_squares < LIMIT:
		a		=			first_square + 1
		while sum_squares < LIMIT:
			if is_palindrome(snake_case_  ):
				answer.add(snake_case_  )
			last_square += 1
			sum_squares += last_square**2
		first_square += 1
		a		=			first_square**2 + (first_square + 1) ** 2

	return sum(snake_case_  )


if __name__ == "__main__":
				print(solution())





