


"""simple docstring"""






# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings("""ignore""", category=UserWarning, module="""torch.optim.lr_scheduler""")






class      A:

		"""simple docstring"""





		def __init__( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__ = True					,				SCREAMING_SNAKE_CASE__ = False   )      ->	List[Any]:

					"""simple docstring"""



					_UpperCamelCase					:Any					     =	scheduler
					_UpperCamelCase					:List[str]					     =	optimizers if isinstance(SCREAMING_SNAKE_CASE__					,				(list, tuple)   ) else [optimizers]
					_UpperCamelCase					:int					     =	split_batches
					_UpperCamelCase					:Union[str, Any]					     =	step_with_optimizer
					_UpperCamelCase					:List[str]					     =	GradientState()
		def   _UpperCamelCase( self					,				*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )      ->	int:

					"""simple docstring"""



					if not self.step_with_optimizer:
								# No link between scheduler and optimizer -> just step
								self.scheduler.step(*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )
								return

					# Otherwise, first make sure the optimizer was stepped.
					if not self.gradient_state.sync_gradients:
								if self.gradient_state.adjust_scheduler:
											self.scheduler._step_count += 1
								return

					for opt in self.optimizers:
								if opt.step_was_skipped:
											return
					if self.split_batches:
								# Split batches -> the training dataloader batch size is not changed so one step per training step
								self.scheduler.step(*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )
					else:
								# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
								# num_processes steps per training step
								_UpperCamelCase					:Any					     =	AcceleratorState().num_processes
								for _ in range(SCREAMING_SNAKE_CASE__   ):
											# Special case when using OneCycle and `drop_last` was not used
											if hasattr(self.scheduler					,				'''total_steps'''   ):
														if self.scheduler._step_count <= self.scheduler.total_steps:
																	self.scheduler.step(*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )
											else:
														self.scheduler.step(*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self   )      ->	str:

					"""simple docstring"""



					return self.scheduler.get_last_lr()
		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



					return self.scheduler.state_dict()
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	int:

					"""simple docstring"""



					self.scheduler.load_state_dict(SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self   )      ->	Dict:

					"""simple docstring"""



					return self.scheduler.get_lr()





		def   _UpperCamelCase( self					,				*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )      ->	Dict:

					"""simple docstring"""



					return self.scheduler.print_lr(*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )





"""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(  lowerCamelCase__		):

		"""simple docstring"""





		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	float:

					"""simple docstring"""



					return 0.0





def 				A_       ( snake_case__       ,					snake_case__		)					->  tuple[int | float, int | float]:
			_UpperCamelCase					:Union[str, Any]					     =	min([-20, np.min(fft_results[1 : samplerate // 2 - 1]		)]		)
			_UpperCamelCase					:Optional[int]					     =	max([20, np.max(fft_results[1 : samplerate // 2 - 1]		)]		)
			return lowest, highest





def 				A_       ( snake_case__       ,					snake_case__		)					->  None:
			_UpperCamelCase					:List[str]					     =	5_12
			_UpperCamelCase					:int					     =	[1] + [0] * (size - 1)
			_UpperCamelCase					:Union[str, Any]					     =	[filter_type.process(snake_case__		) for item in inputs]

			_UpperCamelCase					:Any					     =	[0] * (samplerate - size)  # zero-padding
			outputs += filler
			_UpperCamelCase					:int					     =	np.abs(np.fft.fft(snake_case__		)		)
			_UpperCamelCase					:str					     =	20 * np.logaa(snake_case__		)

			# 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
			_UpperCamelCase					:Tuple					     =	get_bounds(snake_case__       ,					snake_case__		)
			plt.ylim(max([-80, bounds[0]]		)       ,					min([80, bounds[1]]		)		)
			plt.ylabel('''Gain (dB)'''		)

			plt.plot(snake_case__		)
			plt.show()





def 				A_       ( snake_case__       ,					snake_case__		)					->  None:
			_UpperCamelCase					:Tuple					     =	5_12
			_UpperCamelCase					:Union[str, Any]					     =	[1] + [0] * (size - 1)
			_UpperCamelCase					:List[Any]					     =	[filter_type.process(snake_case__		) for item in inputs]

			_UpperCamelCase					:str					     =	[0] * (samplerate - size)  # zero-padding
			outputs += filler
			_UpperCamelCase					:Tuple					     =	np.angle(np.fft.fft(snake_case__		)		)

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

