


"""simple docstring"""







from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np






class 						_A  (  _a  ):







    """simple docstring"""




    def      __snake_case							( self    :   int    ,			__UpperCAmelCase    :   float):
      return 0.0






def 					lowercase      (    A_  ,       A_	)->				tuple[int | float, int | float]:
  '''simple docstring'''







  a						:		Tuple											=      min([-20, np.min(fft_results[1 : samplerate // 2 - 1]	)]	)
  a						:		List[str]											=      max([20, np.max(fft_results[1 : samplerate // 2 - 1]	)]	)
  return lowest, highest


def 					lowercase      (    A_  ,       A_	)->				None:
  '''simple docstring'''







  a						:		List[str]											=      512
  a						:		Optional[Any]											=      [1] + [0] * (size - 1)
  a						:		Tuple											=      [filter_type.process(A_	) for item in inputs]

  a						:		Optional[int]											=      [0] * (samplerate - size)  # zero-padding
  outputs += filler
  a						:		int											=      np.abs(np.fft.fft(A_	)	)
  a						:		Dict											=      20 * np.logaa(A_	)

  # Frequencies on log scale from 24 to nyquist frequency
  plt.xlim(24  ,       samplerate / 2 - 1	)
  plt.xlabel("Frequency (Hz)"	)
  plt.xscale("log"	)

  # Display within reasonable bounds
  a						:		Union[str, Any]											=      get_bounds(A_  ,       A_	)
  plt.ylim(max([-80, bounds[0]]	)  ,       min([80, bounds[1]]	)	)
  plt.ylabel("Gain (dB)"	)

  plt.plot(A_	)
  plt.show()







def 					lowercase      (    A_  ,       A_	)->				None:
  '''simple docstring'''







  a						:		Dict											=      512
  a						:		str											=      [1] + [0] * (size - 1)
  a						:		Any											=      [filter_type.process(A_	) for item in inputs]

  a						:		Tuple											=      [0] * (samplerate - size)  # zero-padding
  outputs += filler
  a						:		Optional[Any]											=      np.angle(np.fft.fft(A_	)	)

  # Frequencies on log scale from 24 to nyquist frequency
  plt.xlim(24  ,       samplerate / 2 - 1	)
  plt.xlabel("Frequency (Hz)"	)
  plt.xscale("log"	)

  plt.ylim(-2 * pi  ,       2 * pi	)
  plt.ylabel("Phase shift (Radians)"	)
  plt.plot(np.unwrap(A_  ,       -2 * pi	)	)
  plt.show()










"""simple docstring"""







import datasets


__lowercase    =				"""\
@InProceedings{conneau2018xnli,
  author = \"Conneau, Alexis
                 and Rinott, Ruty
                 and Lample, Guillaume
                 and Williams, Adina
                 and Bowman, Samuel R.
                 and Schwenk, Holger
                 and Stoyanov, Veselin\",
  title = \"XNLI: Evaluating Cross-lingual Sentence Representations\",
  booktitle = \"Proceedings of the 2018 Conference on Empirical Methods
               in Natural Language Processing\",
  year = \"2018\",
  publisher = \"Association for Computational Linguistics\",
  location = \"Brussels, Belgium\",
}
"""

__lowercase    =				"""\
XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels).
"""

__lowercase    =				"""
Computes XNLI score which is just simple accuracy.
Args:
    predictions: Predicted labels.
    references: Ground truth labels.
Returns:
    'accuracy': accuracy
Examples:

    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> xnli_metric = datasets.load_metric(\"xnli\")
    >>> results = xnli_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}
"""


def 					lowercase      (    A_  ,       A_	)->				List[str]:
  '''simple docstring'''







  return (preds == labels).mean()







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







    """simple docstring"""




    def      __snake_case							( self    :   List[str]):
      return datasets.MetricInfo(
          description=_DESCRIPTION    ,			citation=_CITATION    ,			inputs_description=_KWARGS_DESCRIPTION    ,			features=datasets.Features(
              {
                  "predictions": datasets.Value("int64" if self.config_name != "sts-b" else "float32"),
                  "references": datasets.Value("int64" if self.config_name != "sts-b" else "float32"),
              })    ,			codebase_urls=[]    ,			reference_urls=[]    ,			format="numpy"    ,			)






    def      __snake_case							( self    :   str    ,			__UpperCAmelCase    :   List[Any]    ,			__UpperCAmelCase    :   Tuple):
      return {"accuracy": simple_accuracy(__UpperCAmelCase    ,			__UpperCAmelCase)}






