

import json
import os
import tempfile

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features import ArrayaD
from utils import generate_examples, get_duration


UpperCamelCase__					: Optional[Any]								=      (30, 487)
UpperCamelCase__					: str								=      (36, 1_024)
UpperCamelCase__					: Optional[Any]								=      (100, 100)
UpperCamelCase__					: Optional[int]								=      100

UpperCamelCase__					: Tuple								=      datasets.Features(
    {"text": ArrayaD(SHAPE_TEST_1, dtype="float32"), "image": ArrayaD(SHAPE_TEST_2, dtype="float32")}
)

UpperCamelCase__		,	UpperCamelCase__					: str								=      os.path.split(__file__)
UpperCamelCase__					: Tuple								=      os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))


@get_duration
def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						Optional[int]	,				_SCREAMING_SNAKE_CASE   :						Union[str, Any]	,				_SCREAMING_SNAKE_CASE   :						Union[str, Any]    ):





    """simple docstring"""


    with ArrowWriter(features=_SCREAMING_SNAKE_CASE	,				path=os.path.join(_SCREAMING_SNAKE_CASE	,				'beta.arrow'    )    ) as writer:
        for key, record in dummy_data:
            SCREAMING_SNAKE_CASE_													=  my_features.encode_example(_SCREAMING_SNAKE_CASE    )
            writer.write(_SCREAMING_SNAKE_CASE    )
        SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_													=  writer.finalize()


@get_duration
def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						int	,				_SCREAMING_SNAKE_CASE   :						Dict    ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_													=  datasets.Dataset.from_file(
        filename=os.path.join(_SCREAMING_SNAKE_CASE	,				'beta.arrow'    )	,				info=datasets.DatasetInfo(features=_SCREAMING_SNAKE_CASE    )    )
    for _ in dataset:
        pass


@get_duration
def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						List[Any]	,				_SCREAMING_SNAKE_CASE   :						Dict    ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_													=  datasets.Dataset.from_file(
        filename=os.path.join(_SCREAMING_SNAKE_CASE	,				'beta.arrow'    )	,				info=datasets.DatasetInfo(features=_SCREAMING_SNAKE_CASE    )    )
    dataset.set_format('numpy'    )
    for _ in dataset:
        pass


@get_duration
def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						Optional[Any]	,				_SCREAMING_SNAKE_CASE   :						Union[str, Any]    ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_													=  10
    SCREAMING_SNAKE_CASE_													=  datasets.Dataset.from_file(
        filename=os.path.join(_SCREAMING_SNAKE_CASE	,				'beta.arrow'    )	,				info=datasets.DatasetInfo(features=_SCREAMING_SNAKE_CASE    )    )
    for i in range(0	,				len(_SCREAMING_SNAKE_CASE    )	,				_SCREAMING_SNAKE_CASE    ):
        SCREAMING_SNAKE_CASE_													=  dataset[i : i + batch_size]


@get_duration
def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						Optional[Any]	,				_SCREAMING_SNAKE_CASE   :						Dict    ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_													=  10
    SCREAMING_SNAKE_CASE_													=  datasets.Dataset.from_file(
        filename=os.path.join(_SCREAMING_SNAKE_CASE	,				'beta.arrow'    )	,				info=datasets.DatasetInfo(features=_SCREAMING_SNAKE_CASE    )    )
    dataset.set_format('numpy'    )
    for i in range(0	,				len(_SCREAMING_SNAKE_CASE    )	,				_SCREAMING_SNAKE_CASE    ):
        SCREAMING_SNAKE_CASE_													=  dataset[i : i + batch_size]


@get_duration
def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						List[Any]	,				_SCREAMING_SNAKE_CASE   :						int    ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_													=  datasets.Dataset.from_file(
        filename=os.path.join(_SCREAMING_SNAKE_CASE	,				'beta.arrow'    )	,				info=datasets.DatasetInfo(features=_SCREAMING_SNAKE_CASE    )    )
    for col in feats:
        SCREAMING_SNAKE_CASE_													=  dataset[col]


@get_duration
def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						int	,				_SCREAMING_SNAKE_CASE   :						int    ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_													=  datasets.Dataset.from_file(
        filename=os.path.join(_SCREAMING_SNAKE_CASE	,				'beta.arrow'    )	,				info=datasets.DatasetInfo(features=_SCREAMING_SNAKE_CASE    )    )
    dataset.set_format('numpy'    )
    for col in feats:
        SCREAMING_SNAKE_CASE_													=  dataset[col]


def    _UpperCAmelCase    (					):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_													=  {}
    SCREAMING_SNAKE_CASE_													=  (
        read_unformated,
        read_formatted_as_numpy,
        read_batch_unformated,
        read_batch_formatted_as_numpy,
        read_col_unformated,
        read_col_formatted_as_numpy,
    )
    with tempfile.TemporaryDirectory() as tmp_dir:
        SCREAMING_SNAKE_CASE_													=  datasets.Features({'image': ArrayaD(_SCREAMING_SNAKE_CASE	,				dtype='float32'    )}    )
        SCREAMING_SNAKE_CASE_													=  generate_examples(features=_SCREAMING_SNAKE_CASE	,				num_examples=_SCREAMING_SNAKE_CASE    )
        SCREAMING_SNAKE_CASE_													=  write(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE    )
        for read_func in read_functions:
            SCREAMING_SNAKE_CASE_													=  read_func(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE    )

    with tempfile.TemporaryDirectory() as tmp_dir:
        # don't use fixed length for fair comparison
        # feats = datasets.Features(
        #     {"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[1]), SPEED_TEST_SHAPE[0])}
        # )
        SCREAMING_SNAKE_CASE_													=  datasets.Features({'image': datasets.Sequence(datasets.Sequence(datasets.Value('float32'    )    )    )}    )
        SCREAMING_SNAKE_CASE_													=  generate_examples(
            features=_SCREAMING_SNAKE_CASE	,				num_examples=_SCREAMING_SNAKE_CASE	,				seq_shapes={'image': SPEED_TEST_SHAPE}    )
        SCREAMING_SNAKE_CASE_													=  write(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE    )
        for read_func in read_functions:
            SCREAMING_SNAKE_CASE_													=  read_func(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE    )

    with tempfile.TemporaryDirectory() as tmp_dir:
        # don't use fixed length for fair comparison
        # feats = datasets.Features(
        #     {"image": datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1])}
        # )
        SCREAMING_SNAKE_CASE_													=  datasets.Features({'image': datasets.Sequence(datasets.Value('float32'    )    )}    )
        SCREAMING_SNAKE_CASE_													=  generate_examples(
            features=_SCREAMING_SNAKE_CASE	,				num_examples=_SCREAMING_SNAKE_CASE	,				seq_shapes={'image': [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}	,				)
        SCREAMING_SNAKE_CASE_													=  write(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE    )
        for read_func in read_functions:
            SCREAMING_SNAKE_CASE_													=  read_func(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE    )

    with open(_SCREAMING_SNAKE_CASE	,				'wb'    ) as f:
        f.write(json.dumps(_SCREAMING_SNAKE_CASE    ).encode('utf-8'    )    )


if __name__ == "__main__":  # useful to run the profiler
       benchmark_array_xd()





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}


