
'''simple docstring'''





UpperCAmelCase            = [
    [0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0],
    [0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0],
    [0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0],
]

def   A			(							A_				:       int     ,				A_				:       int     ,				A_				:       int     ,				A_				:       int							):
						return 0 <= row < rows and 0 <= col < cols

def   A			(							A_				:       int     ,				A_				:       int     ,				A_				:       set     ,				A_				:       list[list[int]]							):
						snake_case							:  int			=     len(A_							)
						snake_case							:  List[Any]			=     len(mat[0]							)
						if is_safe(A_     ,				A_     ,				A_     ,				A_							) and (row, col) not in seen and mat[row][col] == 1:
												seen.add((row, col)							)
												return (
												    1
												    + depth_first_search(row + 1     ,				A_     ,				A_     ,				A_							)
												    + depth_first_search(row - 1     ,				A_     ,				A_     ,				A_							)
												    + depth_first_search(A_     ,				col + 1     ,				A_     ,				A_							)
												    + depth_first_search(A_     ,				col - 1     ,				A_     ,				A_							)
												)
						else:
												return 0

def   A			(							A_				:       list[list[int]]							):
						snake_case							:  set			=     set()

						snake_case							:  List[Any]			=     0
						for row, line in enumerate(A_							):
												for col, item in enumerate(A_							):
																		if item == 1 and (row, col) not in seen:
																								# Maximizing the area
																								snake_case							:  Union[str, Any]			=     max(A_     ,				depth_first_search(A_     ,				A_     ,				A_     ,				A_							)							)
						return max_area


if __name__ == "__main__":
						import doctest

						print(find_max_area(matrix))  # Output -> 6
						doctest.testmod()



'''simple docstring'''







import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


_lowercase					:    Optional[int]       				=			"\\n@inproceedings{ramshaw-marcus-1995-text,\n    title = \"Text Chunking using Transformation-Based Learning\",\n    author = \"Ramshaw, Lance  and\n      Marcus, Mitch\",\n    booktitle = \"Third Workshop on Very Large Corpora\",\n    year = \"1995\",\n    url = \"https://www.aclweb.org/anthology/W95-0107\",\n}\n@misc{seqeval,\n  title={{seqeval}: A Python framework for sequence labeling evaluation},\n  url={https://github.com/chakki-works/seqeval},\n  note={Software available from https://github.com/chakki-works/seqeval},\n  author={Hiroki Nakayama},\n  year={2018},\n}\n"

_lowercase					:    List[Any]       				=			"\\nseqeval is a Python framework for sequence labeling evaluation.\nseqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.\n\nThis is well-tested by using the Perl script conlleval, which can be used for\nmeasuring the performance of a system that has processed the CoNLL-2000 shared task data.\n\nseqeval supports following formats:\nIOB1\nIOB2\nIOE1\nIOE2\nIOBES\n\nSee the [README.md] file at https://github.com/chakki-works/seqeval for more information.\n"

_lowercase					:    int       				=			"\nProduces labelling scores along with its sufficient statistics\nfrom a source against one or more references.\n\nArgs:\n    predictions: List of List of predicted labels (Estimated targets as returned by a tagger)\n    references: List of List of reference labels (Ground truth (correct) target values)\n    suffix: True if the IOB prefix is after type, False otherwise. default: False\n    scheme: Specify target tagging scheme. Should be one of [\"IOB1\", \"IOB2\", \"IOE1\", \"IOE2\", \"IOBES\", \"BILOU\"].\n        default: None\n    mode: Whether to count correct entity labels with incorrect I/B tags as true positives or not.\n        If you want to only count exact matches, pass mode=\"strict\". default: None.\n    sample_weight: Array-like of shape (n_samples,), weights for individual samples. default: None\n    zero_division: Which value to substitute as a metric value when encountering zero division. Should be on of 0, 1,\n        \"warn\". \"warn\" acts as 0, but the warning is raised.\n\nReturns:\n    'scores': dict. Summary of the scores for overall and per type\n        Overall:\n            'accuracy': accuracy,\n            'precision': precision,\n            'recall': recall,\n            'f1': F1 score, also known as balanced F-score or F-measure,\n        Per type:\n            'precision': precision,\n            'recall': recall,\n            'f1': F1 score, also known as balanced F-score or F-measure\nExamples:\n\n    >>> predictions = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]\n    >>> references = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]\n    >>> seqeval = datasets.load_metric(\"seqeval\")\n    >>> results = seqeval.compute(predictions=predictions, references=references)\n    >>> print(list(results.keys()))\n    ['MISC', 'PER', 'overall_precision', 'overall_recall', 'overall_f1', 'overall_accuracy']\n    >>> print(results[\"overall_f1\"])\n    0.5\n    >>> print(results[\"PER\"][\"f1\"])\n    1.0\n"






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,		_KWARGS_DESCRIPTION	)
class 					lowerCAmelCase__  (    datasets.Metric	):






					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							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE = False							,				__SCREAMING_SNAKE_CASE = None							,				__SCREAMING_SNAKE_CASE = None							,				__SCREAMING_SNAKE_CASE = None							,				__SCREAMING_SNAKE_CASE = "warn"							,				):


											"""simple docstring"""





											if scheme is not None:
																	try:
																							lowercase_      :	List[str]						=			importlib.import_module('''seqeval.scheme'''						)
																							lowercase_      :	int						=			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}'''						)
											lowercase_      :	List[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'''						)
											lowercase_      :	int						=			report.pop('''micro avg'''						)

											lowercase_      :	List[Any]						=			{
											    type_name: {
											        '''precision''': score['''precision'''],
											        '''recall''': score['''recall'''],
											        '''f1''': score['''f1-score'''],
											        '''number''': score['''support'''],
											    }
											    for type_name, score in report.items()
											}
											lowercase_      :	Optional[Any]						=			overall_score['''precision''']
											lowercase_      :	Optional[Any]						=			overall_score['''recall''']
											lowercase_      :	Optional[int]						=			overall_score['''f1-score''']
											lowercase_      :	Optional[int]						=			accuracy_score(y_true=__SCREAMING_SNAKE_CASE							,				y_pred=__SCREAMING_SNAKE_CASE						)

											return scores



