



'''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_ (							__magic_name__			):





	def  _lowercase(  self		,		A    )			->			float:
								return 0.0




def       __lowerCamelCase  (					_lowercase	,				_lowercase		)      ->			tuple[int | float, int | float]:
							UpperCAmelCase     :		Dict							     =		min([-2_0, np.min(fft_results[1 : samplerate // 2 - 1]		)]		)
							UpperCAmelCase     :		Any							     =		max([2_0, np.max(fft_results[1 : samplerate // 2 - 1]		)]		)
							return lowest, highest



def       __lowerCamelCase  (					_lowercase	,				_lowercase		)      ->			None:
							UpperCAmelCase     :		int							     =		5_1_2
							UpperCAmelCase     :		List[str]							     =		[1] + [0] * (size - 1)
							UpperCAmelCase     :		str							     =		[filter_type.process(_lowercase		) for item in inputs]

							UpperCAmelCase     :		Any							     =		[0] * (samplerate - size)  # zero-padding
							outputs += filler
							UpperCAmelCase     :		Optional[Any]							     =		np.abs(np.fft.fft(_lowercase		)		)
							UpperCAmelCase     :		Optional[int]							     =		2_0 * np.logaa(_lowercase		)

							# Frequencies on log scale from 24 to nyquist frequency
							plt.xlim(2_4	,				samplerate / 2 - 1		)
							plt.xlabel("""Frequency (Hz)"""		)
							plt.xscale("""log"""		)

							# Display within reasonable bounds
							UpperCAmelCase     :		Any							     =		get_bounds(_lowercase	,				_lowercase		)
							plt.ylim(max([-8_0, bounds[0]]		)	,				min([8_0, bounds[1]]		)		)
							plt.ylabel("""Gain (dB)"""		)

							plt.plot(_lowercase		)
							plt.show()



def       __lowerCamelCase  (					_lowercase	,				_lowercase		)      ->			None:
							UpperCAmelCase     :		Optional[Any]							     =		5_1_2
							UpperCAmelCase     :		Union[str, Any]							     =		[1] + [0] * (size - 1)
							UpperCAmelCase     :		Optional[int]							     =		[filter_type.process(_lowercase		) for item in inputs]

							UpperCAmelCase     :		List[Any]							     =		[0] * (samplerate - size)  # zero-padding
							outputs += filler
							UpperCAmelCase     :		Optional[Any]							     =		np.angle(np.fft.fft(_lowercase		)		)

							# Frequencies on log scale from 24 to nyquist frequency
							plt.xlim(2_4	,				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(_lowercase	,				-2 * pi		)		)
							plt.show()






'''simple docstring'''




import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory



def       __lowerCamelCase  (					)      ->			Any:
							raise RuntimeError("""CUDA out of memory."""		)

class 			UpperCamelCase_ (							nn.Module			):





	def __init__(  self    )			->			Any:
								super().__init__()
								UpperCAmelCase     :		Tuple							     =		nn.Linear(3		,		4    )
								UpperCAmelCase     :		Tuple							     =		nn.BatchNormad(4    )
								UpperCAmelCase     :		int							     =		nn.Linear(4		,		5    )






	def  _lowercase(  self		,		A    )			->			Any:
								return self.lineara(self.batchnorm(self.lineara(A    )    )    )




class 			UpperCamelCase_ (							unittest.TestCase			):





	def  _lowercase(  self    )			->			Optional[Any]:
								UpperCAmelCase     :		Optional[int]							     =		[]

								@find_executable_batch_size(starting_batch_size=128    )
								def mock_training_loop_function(A    ):
															nonlocal batch_sizes
															batch_sizes.append(A    )
															if batch_size != 8:
																						raise_fake_out_of_memory()

								mock_training_loop_function()
								self.assertListEqual(A		,		[128, 64, 32, 16, 8]    )





	def  _lowercase(  self    )			->			Any:
								UpperCAmelCase     :		Optional[Any]							     =		[]

								@find_executable_batch_size(starting_batch_size=128    )
								def mock_training_loop_function(A		,		A    ):
															nonlocal batch_sizes
															batch_sizes.append(A    )
															if batch_size != 8:
																						raise_fake_out_of_memory()
															return batch_size, arga

								UpperCAmelCase     ,     UpperCAmelCase     :		Optional[int]							     =		mock_training_loop_function("""hello"""    )
								self.assertListEqual(A		,		[128, 64, 32, 16, 8]    )
								self.assertListEqual([bs, arga]		,		[8, """hello"""]    )





	def  _lowercase(  self    )			->			Any:
								@find_executable_batch_size(starting_batch_size=0    )
								def mock_training_loop_function(A    ):
															pass

								with self.assertRaises(A    ) as cm:
															mock_training_loop_function()
															self.assertIn("""No executable batch size found, reached zero."""		,		cm.exception.args[0]    )





	def  _lowercase(  self    )			->			Optional[int]:
								@find_executable_batch_size(starting_batch_size=16    )
								def mock_training_loop_function(A    ):
															if batch_size > 0:
																						raise_fake_out_of_memory()
															pass

								with self.assertRaises(A    ) as cm:
															mock_training_loop_function()
															self.assertIn("""No executable batch size found, reached zero."""		,		cm.exception.args[0]    )





	def  _lowercase(  self    )			->			Optional[Any]:
								@find_executable_batch_size(starting_batch_size=128    )
								def mock_training_loop_function(A		,		A		,		A    ):
															if batch_size != 8:
																						raise raise_fake_out_of_memory()

								with self.assertRaises(A    ) as cm:
															mock_training_loop_function(128		,		"""hello"""		,		"""world"""    )
															self.assertIn("""Batch size was passed into `f`"""		,		cm.exception.args[0]    )
															self.assertIn("""`f(arg1='hello', arg2='world')"""		,		cm.exception.args[0]    )





	def  _lowercase(  self    )			->			int:
								@find_executable_batch_size(starting_batch_size=16    )
								def mock_training_loop_function(A    ):
															raise ValueError("""Oops, we had an error!"""    )

								with self.assertRaises(A    ) as cm:
															mock_training_loop_function()
															self.assertIn("""Oops, we had an error!"""		,		cm.exception.args[0]    )






	@require_cuda
	def  _lowercase(  self    )			->			Optional[int]:
								UpperCAmelCase     :		Optional[Any]							     =		torch.cuda.memory_allocated()
								UpperCAmelCase     :		List[str]							     =		ModelForTest()
								model.cuda()
								self.assertGreater(torch.cuda.memory_allocated()		,		A    )
								UpperCAmelCase     :		Tuple							     =		release_memory(A    )
								self.assertEqual(torch.cuda.memory_allocated()		,		A    )

