


"""simple docstring"""






import re
from typing import Callable, List, Optional, Union

import tensorflow as tf


try:
					from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
					from tensorflow.keras.optimizers import Adam






class      A(  tf.keras.optimizers.schedules.LearningRateSchedule		):

		"""simple docstring"""





		def __init__( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__ = 1.0					,				SCREAMING_SNAKE_CASE__ = None					,				)      ->	str:

					"""simple docstring"""



					super().__init__()
					_UpperCamelCase					:List[Any]					     =	initial_learning_rate
					_UpperCamelCase					:Optional[int]					     =	warmup_steps
					_UpperCamelCase					:Optional[int]					     =	power
					_UpperCamelCase					:int					     =	decay_schedule_fn
					_UpperCamelCase					:Optional[Any]					     =	name
		def __call__( self					,				SCREAMING_SNAKE_CASE__   )      ->	str:

					"""simple docstring"""



					with tf.name_scope(self.name or '''WarmUp'''   ) as name:
								# Implements polynomial warmup. i.e., if global_step < warmup_steps, the
								# learning rate will be `global_step/num_warmup_steps * init_lr`.
								_UpperCamelCase					:List[str]					     =	tf.cast(SCREAMING_SNAKE_CASE__					,				tf.floataa   )
								_UpperCamelCase					:List[Any]					     =	tf.cast(self.warmup_steps					,				tf.floataa   )
								_UpperCamelCase					:Union[str, Any]					     =	global_step_float / warmup_steps_float
								_UpperCamelCase					:List[Any]					     =	self.initial_learning_rate * tf.math.pow(SCREAMING_SNAKE_CASE__					,				self.power   )
								return tf.cond(
								    global_step_float < warmup_steps_float					,				lambda: warmup_learning_rate					,				lambda: self.decay_schedule_fn(step - self.warmup_steps   )					,				name=SCREAMING_SNAKE_CASE__					,				)





		def   _UpperCamelCase( self   )      ->	Dict:

					"""simple docstring"""



					return {
					    "initial_learning_rate": self.initial_learning_rate,
					    "decay_schedule_fn": self.decay_schedule_fn,
					    "warmup_steps": self.warmup_steps,
					    "power": self.power,
					    "name": self.name,
					}





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__ = 0.0       ,					snake_case__ = 0.9       ,					snake_case__ = 0.9_99       ,					snake_case__ = 1E-8       ,					snake_case__ = None       ,					snake_case__ = None       ,					snake_case__ = 0.0       ,					snake_case__ = 1.0       ,					snake_case__ = None       ,					)					->  Tuple:
			_UpperCamelCase					:List[str]					     =	tf.keras.optimizers.schedules.PolynomialDecay(
			    initial_learning_rate=snake_case__       ,					decay_steps=num_train_steps - num_warmup_steps       ,					end_learning_rate=init_lr * min_lr_ratio       ,					power=snake_case__       ,					)
			if num_warmup_steps:
						_UpperCamelCase					:List[Any]					     =	WarmUp(
						    initial_learning_rate=snake_case__       ,					decay_schedule_fn=snake_case__       ,					warmup_steps=snake_case__       ,					)
			if weight_decay_rate > 0.0:
						_UpperCamelCase					:Tuple					     =	AdamWeightDecay(
						    learning_rate=snake_case__       ,					weight_decay_rate=snake_case__       ,					beta_a=snake_case__       ,					beta_a=snake_case__       ,					epsilon=snake_case__       ,					clipnorm=snake_case__       ,					global_clipnorm=snake_case__       ,					exclude_from_weight_decay=['''LayerNorm''', '''layer_norm''', '''bias''']       ,					include_in_weight_decay=snake_case__       ,					)
			else:
						_UpperCamelCase					:Optional[Any]					     =	tf.keras.optimizers.Adam(
						    learning_rate=snake_case__       ,					beta_a=snake_case__       ,					beta_a=snake_case__       ,					epsilon=snake_case__       ,					clipnorm=snake_case__       ,					global_clipnorm=snake_case__       ,					)
			# We return the optimizer and the LR scheduler in order to better track the
			# evolution of the LR independently of the optimizer.
			return optimizer, lr_schedule






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		def __init__( self					,				SCREAMING_SNAKE_CASE__ = 0.0_0_1					,				SCREAMING_SNAKE_CASE__ = 0.9					,				SCREAMING_SNAKE_CASE__ = 0.9_9_9					,				SCREAMING_SNAKE_CASE__ = 1E-7					,				SCREAMING_SNAKE_CASE__ = False					,				SCREAMING_SNAKE_CASE__ = 0.0					,				SCREAMING_SNAKE_CASE__ = None					,				SCREAMING_SNAKE_CASE__ = None					,				SCREAMING_SNAKE_CASE__ = "AdamWeightDecay"					,				**SCREAMING_SNAKE_CASE__					,				)      ->	Tuple:

					"""simple docstring"""



					super().__init__(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Optional[int]					     =	weight_decay_rate
					_UpperCamelCase					:Optional[int]					     =	include_in_weight_decay
					_UpperCamelCase					:List[str]					     =	exclude_from_weight_decay
		@classmethod
		def   _UpperCamelCase( cls					,				SCREAMING_SNAKE_CASE__   )      ->	Tuple:

					"""simple docstring"""



					_UpperCamelCase					:int					     =	{'''WarmUp''': WarmUp}
					return super(SCREAMING_SNAKE_CASE__					,				cls   ).from_config(SCREAMING_SNAKE_CASE__					,				custom_objects=SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	Tuple:

					"""simple docstring"""



					super(SCREAMING_SNAKE_CASE__					,				self   )._prepare_local(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Any					     =	tf.constant(
					    self.weight_decay_rate					,				name='''adam_weight_decay_rate'''   )
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	str:

					"""simple docstring"""



					_UpperCamelCase					:str					     =	self._do_use_weight_decay(var.name   )
					if do_decay:
								return var.assign_sub(
								    learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]['''weight_decay_rate''']					,				use_locking=self._use_locking					,				)
					return tf.no_op()
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__=None					,				**SCREAMING_SNAKE_CASE__   )      ->	str:

					"""simple docstring"""



					_UpperCamelCase     ,      _UpperCamelCase					:Optional[Any]					     =	list(zip(*SCREAMING_SNAKE_CASE__   )   )
					return super(SCREAMING_SNAKE_CASE__					,				self   ).apply_gradients(zip(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )					,				name=SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	Dict:

					"""simple docstring"""



					if apply_state is None:
								return self._decayed_lr_t[var_dtype], {}

					_UpperCamelCase					:Tuple					     =	apply_state or {}
					_UpperCamelCase					:Union[str, Any]					     =	apply_state.get((var_device, var_dtype)   )
					if coefficients is None:
								_UpperCamelCase					:Tuple					     =	self._fallback_apply_state(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
								_UpperCamelCase					:str					     =	coefficients

					return coefficients["lr_t"], {"apply_state": apply_state}
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__=None   )      ->	Union[str, Any]:

					"""simple docstring"""



					_UpperCamelCase     ,      _UpperCamelCase					:List[str]					     =	self._get_lr(var.device					,				var.dtype.base_dtype					,				SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Tuple					     =	self._decay_weights_op(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
					with tf.control_dependencies([decay]   ):
								return super(SCREAMING_SNAKE_CASE__					,				self   )._resource_apply_dense(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__=None   )      ->	Tuple:

					"""simple docstring"""



					_UpperCamelCase     ,      _UpperCamelCase					:Optional[Any]					     =	self._get_lr(var.device					,				var.dtype.base_dtype					,				SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Any					     =	self._decay_weights_op(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
					with tf.control_dependencies([decay]   ):
								return super(SCREAMING_SNAKE_CASE__					,				self   )._resource_apply_sparse(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self   )      ->	Any:

					"""simple docstring"""



					_UpperCamelCase					:Tuple					     =	super().get_config()
					config.update({'''weight_decay_rate''': self.weight_decay_rate}   )
					return config





		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	str:

					"""simple docstring"""



					if self.weight_decay_rate == 0:
								return False

					if self._include_in_weight_decay:
								for r in self._include_in_weight_decay:
											if re.search(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   ) is not None:
														return True

					if self._exclude_from_weight_decay:
								for r in self._exclude_from_weight_decay:
											if re.search(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   ) is not None:
														return False
					return True






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		def __init__( self   )      ->	Dict:

					"""simple docstring"""



					_UpperCamelCase					:List[str]					     =	[]
					_UpperCamelCase					:Union[str, Any]					     =	None
		@property
		def   _UpperCamelCase( self   )      ->	Optional[Any]:

					"""simple docstring"""



					if self._accum_steps is None:
								_UpperCamelCase					:List[str]					     =	tf.Variable(
								    tf.constant(0					,				dtype=tf.intaa   )					,				trainable=SCREAMING_SNAKE_CASE__					,				synchronization=tf.VariableSynchronization.ON_READ					,				aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA					,				)

					return self._accum_steps.value()
		@property
		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



					if not self._gradients:
								raise ValueError('''The accumulator should be called first to initialize the gradients'''   )
					return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]
		def __call__( self					,				SCREAMING_SNAKE_CASE__   )      ->	Optional[int]:

					"""simple docstring"""



					if not self._gradients:
								_UpperCamelCase					:List[str]					     =	self.step  # Create the step variable.
								self._gradients.extend(
								    [
								        tf.Variable(
								            tf.zeros_like(SCREAMING_SNAKE_CASE__   )					,				trainable=SCREAMING_SNAKE_CASE__					,				synchronization=tf.VariableSynchronization.ON_READ					,				aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA					,				)
								        if gradient is not None
								        else gradient
								        for gradient in gradients
								    ]   )
					if len(SCREAMING_SNAKE_CASE__   ) != len(self._gradients   ):
								raise ValueError(f"Expected {len(self._gradients   )} gradients, but got {len(SCREAMING_SNAKE_CASE__   )}"   )

					for accum_gradient, gradient in zip(self._gradients					,				SCREAMING_SNAKE_CASE__   ):
								if accum_gradient is not None and gradient is not None:
											accum_gradient.assign_add(SCREAMING_SNAKE_CASE__   )

					self._accum_steps.assign_add(1   )





		def   _UpperCamelCase( self   )      ->	List[Any]:

					"""simple docstring"""



					if not self._gradients:
								return
					self._accum_steps.assign(0   )
					for gradient in self._gradients:
								if gradient is not None:
											gradient.assign(tf.zeros_like(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()

