



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        UpperCAmelCase_  (       tf.keras.optimizers.schedules.LearningRateSchedule	):

						'''simple docstring'''




						def __init__(   self				,							_A				,							_A				,							_A				,							_A = 1.0				,							_A = None				,							):







													'''simple docstring'''

													super().__init__()
													__SCREAMING_SNAKE_CASE									= initial_learning_rate
													__SCREAMING_SNAKE_CASE									= warmup_steps
													__SCREAMING_SNAKE_CASE									= power
													__SCREAMING_SNAKE_CASE									= decay_schedule_fn
													__SCREAMING_SNAKE_CASE									= name






						def __call__(   self				,							_A		):







													'''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`.
																				__SCREAMING_SNAKE_CASE									= tf.cast(_A				,							tf.floataa		)
																				__SCREAMING_SNAKE_CASE									= tf.cast(self.warmup_steps				,							tf.floataa		)
																				__SCREAMING_SNAKE_CASE									= global_step_float / warmup_steps_float
																				__SCREAMING_SNAKE_CASE									= self.initial_learning_rate * tf.math.pow(_A				,							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=_A				,							)






						def 	_A     (   self		):







													'''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      __lowercase       (			a__							,       a__							,       a__							,       a__ = 0.0							,       a__ = 0.9							,       a__ = 0.999							,       a__ = 1E-8							,       a__ = None							,       a__ = None							,       a__ = 0.0							,       a__ = 1.0							,       a__ = None							,       )    ->			Any:
							__SCREAMING_SNAKE_CASE									= tf.keras.optimizers.schedules.PolynomialDecay(
							    initial_learning_rate=A__							,       decay_steps=num_train_steps - num_warmup_steps							,       end_learning_rate=init_lr * min_lr_ratio							,       power=A__							,       )
							if num_warmup_steps:
														__SCREAMING_SNAKE_CASE									= WarmUp(
														    initial_learning_rate=A__							,       decay_schedule_fn=A__							,       warmup_steps=A__							,       )
							if weight_decay_rate > 0.0:
														__SCREAMING_SNAKE_CASE									= AdamWeightDecay(
														    learning_rate=A__							,       weight_decay_rate=A__							,       beta_a=A__							,       beta_a=A__							,       epsilon=A__							,       clipnorm=A__							,       global_clipnorm=A__							,       exclude_from_weight_decay=['LayerNorm', 'layer_norm', 'bias']							,       include_in_weight_decay=A__							,       )
							else:
														__SCREAMING_SNAKE_CASE									= tf.keras.optimizers.Adam(
														    learning_rate=A__							,       beta_a=A__							,       beta_a=A__							,       epsilon=A__							,       clipnorm=A__							,       global_clipnorm=A__							,       )
							# 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        UpperCAmelCase_  (       _SCREAMING_SNAKE_CASE	):

						'''simple docstring'''




						def __init__(   self				,							_A = 0.0_0_1				,							_A = 0.9				,							_A = 0.9_9_9				,							_A = 1e-7				,							_A = False				,							_A = 0.0				,							_A = None				,							_A = None				,							_A = "AdamWeightDecay"				,							**_A				,							):







													'''simple docstring'''

													super().__init__(_A				,							_A				,							_A				,							_A				,							_A				,							_A				,							**_A		)
													__SCREAMING_SNAKE_CASE									= weight_decay_rate
													__SCREAMING_SNAKE_CASE									= include_in_weight_decay
													__SCREAMING_SNAKE_CASE									= exclude_from_weight_decay






						@classmethod
						def 	_A     (   cls				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= {"""WarmUp""": WarmUp}
													return super(_A				,							cls		).from_config(_A				,							custom_objects=_A		)






						def 	_A     (   self				,							_A				,							_A				,							_A		):







													'''simple docstring'''

													super(_A				,							self		)._prepare_local(_A				,							_A				,							_A		)
													__SCREAMING_SNAKE_CASE									= tf.constant(
													    self.weight_decay_rate				,							name='adam_weight_decay_rate'		)






						def 	_A     (   self				,							_A				,							_A				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= 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 	_A     (   self				,							_A				,							_A=None				,							**_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= list(zip(*_A		)		)
													return super(_A				,							self		).apply_gradients(zip(_A				,							_A		)				,							name=_A				,							**_A		)






						def 	_A     (   self				,							_A				,							_A				,							_A		):







													'''simple docstring'''

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

													__SCREAMING_SNAKE_CASE									= apply_state or {}
													__SCREAMING_SNAKE_CASE									= apply_state.get((var_device, var_dtype)		)
													if coefficients is None:
																				__SCREAMING_SNAKE_CASE									= self._fallback_apply_state(_A				,							_A		)
																				__SCREAMING_SNAKE_CASE									= coefficients

													return coefficients["lr_t"], {"apply_state": apply_state}






						def 	_A     (   self				,							_A				,							_A				,							_A=None		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self._get_lr(var.device				,							var.dtype.base_dtype				,							_A		)
													__SCREAMING_SNAKE_CASE									= self._decay_weights_op(_A				,							_A				,							_A		)
													with tf.control_dependencies([decay]		):
																				return super(_A				,							self		)._resource_apply_dense(_A				,							_A				,							**_A		)






						def 	_A     (   self				,							_A				,							_A				,							_A				,							_A=None		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self._get_lr(var.device				,							var.dtype.base_dtype				,							_A		)
													__SCREAMING_SNAKE_CASE									= self._decay_weights_op(_A				,							_A				,							_A		)
													with tf.control_dependencies([decay]		):
																				return super(_A				,							self		)._resource_apply_sparse(_A				,							_A				,							_A				,							**_A		)






						def 	_A     (   self		):







													'''simple docstring'''

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






						def 	_A     (   self				,							_A		):







													'''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(_A				,							_A		) is not None:
																																		return True

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







class        UpperCAmelCase_  (       _SCREAMING_SNAKE_CASE	):

						'''simple docstring'''




						def __init__(   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= []
													__SCREAMING_SNAKE_CASE									= None






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													if self._accum_steps is None:
																				__SCREAMING_SNAKE_CASE									= tf.Variable(
																				    tf.constant(0				,							dtype=tf.intaa		)				,							trainable=_A				,							synchronization=tf.VariableSynchronization.ON_READ				,							aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA				,							)

													return self._accum_steps.value()






						@property
						def 	_A     (   self		):







													'''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				,							_A		):







													'''simple docstring'''

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

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

													self._accum_steps.assign_add(1		)






						def 	_A     (   self		):







													'''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(_A		)		)











lowerCAmelCase__		:					Optional[int]							=9.80_665






def      __lowercase       (			a__							,       a__							,       a__ = g       )    ->			float:

							if fluid_density <= 0:
														raise ValueError('Impossible fluid density'       )
							if volume < 0:
														raise ValueError('Impossible Object volume'       )
							if gravity <= 0:
														raise ValueError('Impossible Gravity'       )

							return fluid_density * gravity * volume


if __name__ == "__main__":
			import doctest

			# run doctest
			doctest.testmod()






