"""simple docstring"""




import warnings

from ...utils import is_sklearn_available, requires_backends


if is_sklearn_available():
						from scipy.stats import pearsonr, spearmanr
						from sklearn.metrics import fa_score, matthews_corrcoef


__lowerCamelCase											=  (
    "This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate "
    "library. You can have a look at this example script for pointers: "
    "https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"
)






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							warnings.warn(UpperCamelCase__       ,    UpperCamelCase__				)
							requires_backends(UpperCamelCase__       ,    'sklearn'				)
							return (preds == labels).mean()






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							warnings.warn(UpperCamelCase__       ,    UpperCamelCase__				)
							requires_backends(UpperCamelCase__       ,    'sklearn'				)
							A__	     =							simple_accuracy(UpperCamelCase__       ,    UpperCamelCase__				)
							A__	     =							fa_score(y_true=UpperCamelCase__       ,    y_pred=UpperCamelCase__				)
							return {
							    "acc": acc,
							    "f1": fa,
							    "acc_and_f1": (acc + fa) / 2,
							}






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							warnings.warn(UpperCamelCase__       ,    UpperCamelCase__				)
							requires_backends(UpperCamelCase__       ,    'sklearn'				)
							A__	     =							pearsonr(UpperCamelCase__       ,    UpperCamelCase__				)[0]
							A__	     =							spearmanr(UpperCamelCase__       ,    UpperCamelCase__				)[0]
							return {
							    "pearson": pearson_corr,
							    "spearmanr": spearman_corr,
							    "corr": (pearson_corr + spearman_corr) / 2,
							}






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							warnings.warn(UpperCamelCase__       ,    UpperCamelCase__				)
							requires_backends(UpperCamelCase__       ,    'sklearn'				)
							assert len(UpperCamelCase__				) == len(UpperCamelCase__				), F'''Predictions and labels have mismatched lengths {len(UpperCamelCase__				)} and {len(UpperCamelCase__				)}'''
							if task_name == "cola":
														return {"mcc": matthews_corrcoef(UpperCamelCase__       ,    UpperCamelCase__				)}
							elif task_name == "sst-2":
														return {"acc": simple_accuracy(UpperCamelCase__       ,    UpperCamelCase__				)}
							elif task_name == "mrpc":
														return acc_and_fa(UpperCamelCase__       ,    UpperCamelCase__				)
							elif task_name == "sts-b":
														return pearson_and_spearman(UpperCamelCase__       ,    UpperCamelCase__				)
							elif task_name == "qqp":
														return acc_and_fa(UpperCamelCase__       ,    UpperCamelCase__				)
							elif task_name == "mnli":
														return {"mnli/acc": simple_accuracy(UpperCamelCase__       ,    UpperCamelCase__				)}
							elif task_name == "mnli-mm":
														return {"mnli-mm/acc": simple_accuracy(UpperCamelCase__       ,    UpperCamelCase__				)}
							elif task_name == "qnli":
														return {"acc": simple_accuracy(UpperCamelCase__       ,    UpperCamelCase__				)}
							elif task_name == "rte":
														return {"acc": simple_accuracy(UpperCamelCase__       ,    UpperCamelCase__				)}
							elif task_name == "wnli":
														return {"acc": simple_accuracy(UpperCamelCase__       ,    UpperCamelCase__				)}
							elif task_name == "hans":
														return {"acc": simple_accuracy(UpperCamelCase__       ,    UpperCamelCase__				)}
							else:
														raise KeyError(UpperCamelCase__				)






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							warnings.warn(UpperCamelCase__       ,    UpperCamelCase__				)
							requires_backends(UpperCamelCase__       ,    'sklearn'				)
							if len(UpperCamelCase__				) != len(UpperCamelCase__				):
														raise ValueError(F'''Predictions and labels have mismatched lengths {len(UpperCamelCase__				)} and {len(UpperCamelCase__				)}'''				)
							if task_name == "xnli":
														return {"acc": simple_accuracy(UpperCamelCase__       ,    UpperCamelCase__				)}
							else:
														raise KeyError(UpperCamelCase__				)






"""simple docstring"""




from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np
class     UpperCamelCase__(			__A ):





						def 				snake_case__	(       self		,__UpperCAmelCase     )					->				float:
													return 0.0






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





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






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							512
							A__	     =							[1] + [0] * (size - 1)
							A__	     =							[filter_type.process(UpperCamelCase__				) for item in inputs]

							A__	     =							[0] * (samplerate - size)  # zero-padding
							outputs += filler
							A__	     =							np.abs(np.fft.fft(UpperCamelCase__				)				)
							A__	     =							20 * np.logaa(UpperCamelCase__				)

							# 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__	     =							get_bounds(UpperCamelCase__       ,    UpperCamelCase__				)
							plt.ylim(max([-80, bounds[0]]				)       ,    min([80, bounds[1]]				)				)
							plt.ylabel('Gain (dB)'				)

							plt.plot(UpperCamelCase__				)
							plt.show()






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							512
							A__	     =							[1] + [0] * (size - 1)
							A__	     =							[filter_type.process(UpperCamelCase__				) for item in inputs]

							A__	     =							[0] * (samplerate - size)  # zero-padding
							outputs += filler
							A__	     =							np.angle(np.fft.fft(UpperCamelCase__				)				)

							# 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(UpperCamelCase__       ,    -2 * pi				)				)
							plt.show()





