from dataclasses import dataclass

lowerCAmelCase__  :  Any											=     '0.1'
lowerCAmelCase__  :  str											=     'Lucia Harcekova'
@dataclass
class     snake_case	:




					"""simple docstring"""

					snake_case__       =			42
					snake_case__       =			42
					snake_case__       =			42







					def __repr__(    self       :     List[Any]      ):
									return f'''({self.offset}, {self.length}, {self.indicator})'''






class     snake_case	:




					"""simple docstring"""

					def __init__(    self       :     List[Any]   ,lowerCamelCase__       :     int = 13   ,lowerCamelCase__       :     int = 6      ):
									UpperCAmelCase__					=							window_size
									UpperCAmelCase__					=							lookahead_buffer_size
									UpperCAmelCase__					=							self.window_size - self.lookahead_buffer_size







					def 		__lowerCAmelCase						(    self       :     Optional[Any]   ,lowerCamelCase__       :     str      ):
									UpperCAmelCase__					=							[]
									UpperCAmelCase__					=							''

									# while there are still characters in text to compress
									while text:
													# find the next encoding phrase
													# - triplet with offset, length, indicator (the next encoding character)
													UpperCAmelCase__					=							self._find_encoding_token(lowerCamelCase__   ,lowerCamelCase__      )

													# update the search buffer:
													# - add new characters from text into it
													# - check if size exceed the max search buffer size, if so, drop the
													#   oldest elements
													search_buffer += text[: token.length + 1]
													if len(lowerCamelCase__      ) > self.search_buffer_size:
																	UpperCAmelCase__					=							search_buffer[-self.search_buffer_size :]

													# update the text
													UpperCAmelCase__					=							text[token.length + 1 :]

													# append the token to output
													output.append(lowerCamelCase__      )

									return output







					def 		__lowerCAmelCase						(    self       :     Optional[int]   ,lowerCamelCase__       :     list[Token]      ):
									UpperCAmelCase__					=							''

									for token in tokens:
													for _ in range(token.length      ):
																	output += output[-token.offset]
													output += token.indicator

									return output







					def 		__lowerCAmelCase						(    self       :     List[str]   ,lowerCamelCase__       :     str   ,lowerCamelCase__       :     str      ):

									if not text:
													raise ValueError('We need some text to work with.'      )

									# Initialise result parameters to default values
									UpperCAmelCase__				,     UpperCAmelCase__					=							0, 0

									if not search_buffer:
													return Token(lowerCamelCase__   ,lowerCamelCase__   ,text[length]      )

									for i, character in enumerate(lowerCamelCase__      ):
													UpperCAmelCase__					=							len(lowerCamelCase__      ) - i
													if character == text[0]:
																	UpperCAmelCase__					=							self._match_length_from_index(lowerCamelCase__   ,lowerCamelCase__   ,0   ,lowerCamelCase__      )
																	# if the found length is bigger than the current or if it's equal,
																	# which means it's offset is smaller: update offset and length
																	if found_length >= length:
																					UpperCAmelCase__				,     UpperCAmelCase__					=							found_offset, found_length

									return Token(lowerCamelCase__   ,lowerCamelCase__   ,text[length]      )







					def 		__lowerCAmelCase						(    self       :     int   ,lowerCamelCase__       :     str   ,lowerCamelCase__       :     str   ,lowerCamelCase__       :     int   ,lowerCamelCase__       :     int      ):
									if not text or text[text_index] != window[window_index]:
													return 0
									return 1 + self._match_length_from_index(
									    lowerCamelCase__   ,window + text[text_index]   ,text_index + 1   ,window_index + 1      )


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

						testmod()
						# Initialize compressor class
						lowerCAmelCase__  :  Union[str, Any]											=     LZaaCompressor(window_size=13, lookahead_buffer_size=6)

						# Example
						lowerCAmelCase__  :  List[str]											=     'cabracadabrarrarrad'
						lowerCAmelCase__  :  List[str]											=     lzaa_compressor.compress(TEXT)
						print(lzaa_compressor.compress('ababcbababaa'))
						lowerCAmelCase__  :  Optional[Any]											=     lzaa_compressor.decompress(compressed_text)
						assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."




"""simple docstring"""






from sklearn.metrics import accuracy_score

import datasets


lowerCAmelCase__  :  List[str]											=     '\nAccuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:\nAccuracy = (TP + TN) / (TP + TN + FP + FN)\n Where:\nTP: True positive\nTN: True negative\nFP: False positive\nFN: False negative\n'


lowerCAmelCase__  :  List[str]											=     '\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.\n\nExamples:\n\n    Example 1-A simple example\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])\n        >>> print(results)\n        {\'accuracy\': 0.5}\n\n    Example 2-The same as Example 1, except with `normalize` set to `False`.\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)\n        >>> print(results)\n        {\'accuracy\': 3.0}\n\n    Example 3-The same as Example 1, except with `sample_weight` set.\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])\n        >>> print(results)\n        {\'accuracy\': 0.8778625954198473}\n'


lowerCAmelCase__  :  Dict											=     '\n@article{scikit-learn,\n  title={Scikit-learn: Machine Learning in {P}ython},\n  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n  journal={Journal of Machine Learning Research},\n  volume={12},\n  pages={2825--2830},\n  year={2011}\n}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				,   _KWARGS_DESCRIPTION    )
class     snake_case	(			datasets.Metric    ):




					"""simple docstring"""

					def 		__lowerCAmelCase						(    self       :     List[Any]      ):
									return datasets.MetricInfo(
									    description=_DESCRIPTION   ,citation=_CITATION   ,inputs_description=_KWARGS_DESCRIPTION   ,features=datasets.Features(
									        {
									            'predictions': datasets.Sequence(datasets.Value('int32'      )      ),
									            'references': datasets.Sequence(datasets.Value('int32'      )      ),
									        }
									        if self.config_name == 'multilabel'
									        else {
									            'predictions': datasets.Value('int32'      ),
									            'references': datasets.Value('int32'      ),
									        }      )   ,reference_urls=['https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html']   ,)







					def 		__lowerCAmelCase						(    self       :     int   ,lowerCamelCase__       :     Any   ,lowerCamelCase__       :     List[str]   ,lowerCamelCase__       :     Dict=True   ,lowerCamelCase__       :     Tuple=None      ):
									return {
									    "accuracy": float(
									        accuracy_score(lowerCamelCase__   ,lowerCamelCase__   ,normalize=lowerCamelCase__   ,sample_weight=lowerCamelCase__      )      )
									}



