

import numpy as np


def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						List[str]	,				_SCREAMING_SNAKE_CASE   :						str="utf-8"	,				_SCREAMING_SNAKE_CASE   :						Optional[Any]="surrogatepass"    ):





    """simple docstring"""


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


def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						int	,				_SCREAMING_SNAKE_CASE   :						int="utf-8"	,				_SCREAMING_SNAKE_CASE   :						List[Any]="surrogatepass"    ):





    """simple docstring"""


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


def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						Dict	,				_SCREAMING_SNAKE_CASE   :						Any    ):





    """simple docstring"""


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

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

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

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

        # Calculates parity
    SCREAMING_SNAKE_CASE_													=  0  # parity bit counter
    for bp in range(1	,				size_par + 1    ):
        # Bit counter one for a given parity
        SCREAMING_SNAKE_CASE_													=  0
        # counter to control the loop reading
        SCREAMING_SNAKE_CASE_													=  0
        for x in data_ord:
            if x is not None:
                try:
                    SCREAMING_SNAKE_CASE_													=  (bin_pos[cont_loop])[-1 * (bp)]
                except IndexError:
                    SCREAMING_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
    SCREAMING_SNAKE_CASE_													=  0  # parity bit counter
    for x in range(0	,				size_par + len(_lowerCamelCase    )    ):
        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    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						List[Any]	,				_SCREAMING_SNAKE_CASE   :						List[str]    ):





    """simple docstring"""


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

    for x in range(1	,				len(_lowerCamelCase    ) + 1    ):
        # Performs a template of bit positions - who should be given,
        #  and who should be parity
        if qtd_bp < size_par and (np.log(_lowerCamelCase    ) / np.log(2    )).is_integer():
            data_out_gab.append('P'    )
            SCREAMING_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
    SCREAMING_SNAKE_CASE_													=  []
    SCREAMING_SNAKE_CASE_													=  []
    SCREAMING_SNAKE_CASE_													=  [bin(_lowerCamelCase    )[2:] for x in range(1	,				size_par + len(_lowerCamelCase    ) + 1    )]

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

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

        # Calculates parity
    SCREAMING_SNAKE_CASE_													=  0  # parity bit counter
    for bp in range(1	,				size_par + 1    ):
        # Bit counter one for a certain parity
        SCREAMING_SNAKE_CASE_													=  0
        # Counter to control loop reading
        SCREAMING_SNAKE_CASE_													=  0
        for x in data_ord:
            if x is not None:
                try:
                    SCREAMING_SNAKE_CASE_													=  (bin_pos[cont_loop])[-1 * (bp)]
                except IndexError:
                    SCREAMING_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
    SCREAMING_SNAKE_CASE_													=  0  # Parity bit counter
    for x in range(0	,				size_par + len(_lowerCamelCase    )    ):
        if data_ord[x] is None:
            data_out.append(str(parity[cont_bp]    )    )
            cont_bp += 1
        else:
            data_out.append(data_ord[x]    )

    SCREAMING_SNAKE_CASE_													=  parity_received == parity
    return data_output, ack



from sklearn.metrics import mean_absolute_error

import datasets


UpperCamelCase__					: Union[str, Any]								=      "\\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"

UpperCamelCase__					: Optional[Any]								=      "\\nMean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual\nvalues.\n"


UpperCamelCase__					: Tuple								=      "\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n                 \"raw_values\" : Returns a full set of errors in case of multioutput input.\n\n                 \"uniform_average\" : Errors of all outputs are averaged with uniform weight.\n\nReturns:\n    mae : mean absolute error.\n        If multioutput is \"raw_values\", then mean absolute error is returned for each output separately. If multioutput is \"uniform_average\" or an ndarray of weights, then the weighted average of all output errors is returned.\n        MAE output is non-negative floating point. The best value is 0.0.\nExamples:\n\n    >>> mae_metric = datasets.load_metric(\"mae\")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mae': 0.5}\n\n    If you're using multi-dimensional lists, then set the config as follows :\n\n    >>> mae_metric = datasets.load_metric(\"mae\", \"multilist\")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mae': 0.75}\n    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput='raw_values')\n    >>> print(results)\n    {'mae': array([0.5, 1. ])}\n"



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

  def   lowerCAmelCase__       (   self):
      return datasets.MetricInfo(
          description=_DESCRIPTION  ,						citation=_CITATION  ,						inputs_description=_KWARGS_DESCRIPTION  ,						features=datasets.Features(self._get_feature_types())  ,						reference_urls=[
              'https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html'
          ]  ,						)

  def   lowerCAmelCase__       (   self):
      if self.config_name == "multilist":
          return {
              "predictions": datasets.Sequence(datasets.Value('float')),
              "references": datasets.Sequence(datasets.Value('float')),
          }
      else:
          return {
              "predictions": datasets.Value('float'),
              "references": datasets.Value('float'),
          }


  def   lowerCAmelCase__       (   self  ,						_A  ,						_A  ,						_A=None  ,						_A="uniform_average"):
      SCREAMING_SNAKE_CASE_													=  mean_absolute_error(_A  ,						_A  ,						sample_weight=_A  ,						multioutput=_A)

      return {"mae": mae_score}


