from sklearn.metrics import accuracy_score

import datasets


UpperCAmelCase      		=					'''
Accuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:
Accuracy = (TP + TN) / (TP + TN + FP + FN)
 Where:
TP: True positive
TN: True negative
FP: False positive
FN: False negative
'''


UpperCAmelCase      		=					'''
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    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.

Examples:

    Example 1-A simple example
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
        >>> print(results)
        {\'accuracy\': 0.5}

    Example 2-The same as Example 1, except with `normalize` set to `False`.
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)
        >>> print(results)
        {\'accuracy\': 3.0}

    Example 3-The same as Example 1, except with `sample_weight` set.
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> 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])
        >>> print(results)
        {\'accuracy\': 0.8778625954198473}
'''


UpperCAmelCase      		=					'''
@article{scikit-learn,
  title={Scikit-learn: Machine Learning in {P}ython},
  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
  journal={Journal of Machine Learning Research},
  volume={12},
  pages={2825--2830},
  year={2011}
}
'''




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



 '''simple docstring'''

 def        __snake_case       (      self							:		Any	)			->				Union[str, 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        __snake_case       (      self							:		int							,      lowerCamelCase							:		Tuple							,      lowerCamelCase							:		int							,      lowerCamelCase							:		int=True							,      lowerCamelCase							:		Any=None	)			->				Union[str, Any]:
        return {
            "accuracy": float(
                accuracy_score(lowerCamelCase							,      lowerCamelCase							,      normalize=lowerCamelCase							,      sample_weight=lowerCamelCase	)	)
        }









'''simple docstring'''







import numpy as np






def 						_lowercase						(      lowercase_		,				lowercase_="utf-8"		,				lowercase_="surrogatepass"    ):



		'''simple docstring'''







		_snake_case      			=			bin(int.from_bytes(text.encode(lowercase_		,				lowercase_    )		,				"big"    )    )[2:]
		return bits.zfill(8 * ((len(lowercase_    ) + 7) // 8)    )






def 						_lowercase						(      lowercase_		,				lowercase_="utf-8"		,				lowercase_="surrogatepass"    ):



		'''simple docstring'''







		_snake_case      			=			int(lowercase_		,				2    )
		return n.to_bytes((n.bit_length() + 7) // 8		,				"big"    ).decode(lowercase_		,				lowercase_    ) or "\0"






def 						_lowercase						(      lowercase_		,				lowercase_    ):



		'''simple docstring'''







		if size_par + len(lowercase_    ) <= 2**size_par - (len(lowercase_    ) - 1):
				raise ValueError("size of parity don't match with size of data"    )

		_snake_case      			=			[]
		_snake_case      			=			[]
		_snake_case      			=			[bin(lowercase_    )[2:] for x in range(1		,				size_par + len(lowercase_    ) + 1    )]

		# sorted information data for the size of the output data
		_snake_case      			=			[]
		# data position template + parity
		_snake_case      			=			[]
		# parity bit counter
		_snake_case      			=			0
		# counter position of data bits
		_snake_case      			=			0

		for x in range(1		,				size_par + len(lowercase_    ) + 1    ):
				# Performs a template of bit positions - who should be given,
				# and who should be parity
				if qtd_bp < size_par:
						if (np.log(lowercase_    ) / np.log(2    )).is_integer():
								data_out_gab.append("P"    )
								_snake_case      			=			qtd_bp + 1
						else:
								data_out_gab.append("D"    )
				else:
						data_out_gab.append("D"    )

				# Sorts the data to the new output size
				if data_out_gab[-1] == "D":
						data_ord.append(data[cont_data]    )
						cont_data += 1
				else:
						data_ord.append(lowercase_    )

    # Calculates parity
		_snake_case      			=			0  # parity bit counter
		for bp in range(1		,				size_par + 1    ):
				# Bit counter one for a given parity
				_snake_case      			=			0
				# counter to control the loop reading
				_snake_case      			=			0
				for x in data_ord:
						if x is not None:
								try:
										_snake_case      			=			(bin_pos[cont_loop])[-1 * (bp)]
								except IndexError:
										_snake_case      			=			"0"
								if aux == "1" and x == "1":
										cont_bo += 1
						cont_loop += 1
				parity.append(cont_bo % 2    )

				qtd_bp += 1

		# Mount the message
		_snake_case      			=			0  # parity bit counter
		for x in range(0		,				size_par + len(lowercase_    )    ):
				if data_ord[x] is None:
						data_out.append(str(parity[cont_bp]    )    )
						cont_bp += 1
				else:
						data_out.append(data_ord[x]    )

		return data_out






def 						_lowercase						(      lowercase_		,				lowercase_    ):



		'''simple docstring'''







		_snake_case      			=			[]
		# Parity bit counter
		_snake_case      			=			0
		# Counter p data bit reading
		_snake_case      			=			0
		# list of parity received
		_snake_case      			=			[]
		_snake_case      			=			[]

		for x in range(1		,				len(lowercase_    ) + 1    ):
				# Performs a template of bit positions - who should be given,
				#  and who should be parity
				if qtd_bp < size_par and (np.log(lowercase_    ) / np.log(2    )).is_integer():
						data_out_gab.append("P"    )
						_snake_case      			=			qtd_bp + 1
				else:
						data_out_gab.append("D"    )

				# Sorts the data to the new output size
				if data_out_gab[-1] == "D":
						data_output.append(data[cont_data]    )
				else:
						parity_received.append(data[cont_data]    )
				cont_data += 1

		# -----------calculates the parity with the data
		_snake_case      			=			[]
		_snake_case      			=			[]
		_snake_case      			=			[bin(lowercase_    )[2:] for x in range(1		,				size_par + len(lowercase_    ) + 1    )]

		#  sorted information data for the size of the output data
		_snake_case      			=			[]
		# Data position feedback + parity
		_snake_case      			=			[]
		# Parity bit counter
		_snake_case      			=			0
		# Counter p data bit reading
		_snake_case      			=			0

		for x in range(1		,				size_par + len(lowercase_    ) + 1    ):
				# Performs a template position of bits - who should be given,
				# and who should be parity
				if qtd_bp < size_par and (np.log(lowercase_    ) / np.log(2    )).is_integer():
						data_out_gab.append("P"    )
						_snake_case      			=			qtd_bp + 1
				else:
						data_out_gab.append("D"    )

				# Sorts the data to the new output size
				if data_out_gab[-1] == "D":
						data_ord.append(data_output[cont_data]    )
						cont_data += 1
				else:
						data_ord.append(lowercase_    )

    # Calculates parity
		_snake_case      			=			0  # parity bit counter
		for bp in range(1		,				size_par + 1    ):
				# Bit counter one for a certain parity
				_snake_case      			=			0
				# Counter to control loop reading
				_snake_case      			=			0
				for x in data_ord:
						if x is not None:
								try:
										_snake_case      			=			(bin_pos[cont_loop])[-1 * (bp)]
								except IndexError:
										_snake_case      			=			"0"
								if aux == "1" and x == "1":
										cont_bo += 1
						cont_loop += 1
				parity.append(str(cont_bo % 2    )    )

				qtd_bp += 1

		# Mount the message
		_snake_case      			=			0  # Parity bit counter
		for x in range(0		,				size_par + len(lowercase_    )    ):
				if data_ord[x] is None:
						data_out.append(str(parity[cont_bp]    )    )
						cont_bp += 1
				else:
						data_out.append(data_ord[x]    )

		_snake_case      			=			parity_received == parity
		return data_output, ack





