



"""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    __lowercase     (					tf.keras.optimizers.schedules.LearningRateSchedule):







						"""simple docstring"""







						def __init__(self   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__ = 1.0   ,		lowercase__ = None   ,		):
											super().__init__()
											snake_case_					:		Any 					=					initial_learning_rate
											snake_case_					:		Dict 					=					warmup_steps
											snake_case_					:		Tuple 					=					power
											snake_case_					:		Dict 					=					decay_schedule_fn
											snake_case_					:		Union[str, Any] 					=					name







						def __call__(self   ,		lowercase__       ):

											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`.
																snake_case_					:		Dict 					=					tf.cast(lowercase__   ,		tf.floataa       )
																snake_case_					:		List[str] 					=					tf.cast(self.warmup_steps   ,		tf.floataa       )
																snake_case_					:		Any 					=					global_step_float / warmup_steps_float
																snake_case_					:		Optional[Any] 					=					self.initial_learning_rate * tf.math.pow(lowercase__   ,		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=lowercase__   ,		)







						def 						__UpperCamelCase							(self       ):
											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 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Optional[int]				,    SCREAMING_SNAKE_CASE__ :  List[str]				,    SCREAMING_SNAKE_CASE__ :  Union[str, Any]				,    SCREAMING_SNAKE_CASE__ :  Optional[int] = 0.0				,    SCREAMING_SNAKE_CASE__ :  Tuple = 0.9				,    SCREAMING_SNAKE_CASE__ :  List[str] = 0.999				,    SCREAMING_SNAKE_CASE__ :  str = 1E-8				,    SCREAMING_SNAKE_CASE__ :  Union[str, Any] = None				,    SCREAMING_SNAKE_CASE__ :  Any = None				,    SCREAMING_SNAKE_CASE__ :  Any = 0.0				,    SCREAMING_SNAKE_CASE__ :  int = 1.0				,    SCREAMING_SNAKE_CASE__ :  List[Any] = None				,    ):



					"""simple docstring"""





					snake_case_					:		Tuple 					=					tf.keras.optimizers.schedules.PolynomialDecay(
					    initial_learning_rate=__UpperCAmelCase				,    decay_steps=num_train_steps - num_warmup_steps				,    end_learning_rate=init_lr * min_lr_ratio				,    power=__UpperCAmelCase				,    )
					if num_warmup_steps:
										snake_case_					:		str 					=					WarmUp(
										    initial_learning_rate=__UpperCAmelCase				,    decay_schedule_fn=__UpperCAmelCase				,    warmup_steps=__UpperCAmelCase				,    )
					if weight_decay_rate > 0.0:
										snake_case_					:		int 					=					AdamWeightDecay(
										    learning_rate=__UpperCAmelCase				,    weight_decay_rate=__UpperCAmelCase				,    beta_a=__UpperCAmelCase				,    beta_a=__UpperCAmelCase				,    epsilon=__UpperCAmelCase				,    clipnorm=__UpperCAmelCase				,    global_clipnorm=__UpperCAmelCase				,    exclude_from_weight_decay=["""LayerNorm""", """layer_norm""", """bias"""]				,    include_in_weight_decay=__UpperCAmelCase				,    )
					else:
										snake_case_					:		Any 					=					tf.keras.optimizers.Adam(
										    learning_rate=__UpperCAmelCase				,    beta_a=__UpperCAmelCase				,    beta_a=__UpperCAmelCase				,    epsilon=__UpperCAmelCase				,    clipnorm=__UpperCAmelCase				,    global_clipnorm=__UpperCAmelCase				,    )
					# 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    __lowercase     (					_snake_case):







						"""simple docstring"""







						def __init__(self   ,		lowercase__ = 0.001   ,		lowercase__ = 0.9   ,		lowercase__ = 0.999   ,		lowercase__ = 1e-7   ,		lowercase__ = False   ,		lowercase__ = 0.0   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = "AdamWeightDecay"   ,		**lowercase__   ,		):
											super().__init__(lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		**lowercase__       )
											snake_case_					:		Union[str, Any] 					=					weight_decay_rate
											snake_case_					:		List[str] 					=					include_in_weight_decay
											snake_case_					:		List[Any] 					=					exclude_from_weight_decay







						@classmethod
						def 						__UpperCamelCase							(cls   ,		lowercase__       ):
											snake_case_					:		List[str] 					=					{"""WarmUp""": WarmUp}
											return super(lowercase__   ,		cls       ).from_config(lowercase__   ,		custom_objects=lowercase__       )







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__       ):
											super(lowercase__   ,		self       )._prepare_local(lowercase__   ,		lowercase__   ,		lowercase__       )
											snake_case_					:		List[Any] 					=					tf.constant(
											    self.weight_decay_rate   ,		name="""adam_weight_decay_rate"""       )







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__       ):
											snake_case_					:		Tuple 					=					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   ,		lowercase__   ,		lowercase__=None   ,		**lowercase__       ):
											snake_case_					,			snake_case_					:		Tuple 					=					list(zip(*lowercase__       )       )
											return super(lowercase__   ,		self       ).apply_gradients(zip(lowercase__   ,		lowercase__       )   ,		name=lowercase__   ,		**lowercase__       )







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__       ):

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

											snake_case_					:		List[Any] 					=					apply_state or {}
											snake_case_					:		str 					=					apply_state.get((var_device, var_dtype)       )
											if coefficients is None:
																snake_case_					:		int 					=					self._fallback_apply_state(lowercase__   ,		lowercase__       )
																snake_case_					:		Tuple 					=					coefficients

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







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







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__=None       ):
											snake_case_					,			snake_case_					:		Union[str, Any] 					=					self._get_lr(var.device   ,		var.dtype.base_dtype   ,		lowercase__       )
											snake_case_					:		str 					=					self._decay_weights_op(lowercase__   ,		lowercase__   ,		lowercase__       )
											with tf.control_dependencies([decay]       ):
																return super(lowercase__   ,		self       )._resource_apply_sparse(lowercase__   ,		lowercase__   ,		lowercase__   ,		**lowercase__       )







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Tuple 					=					super().get_config()
											config.update({"""weight_decay_rate""": self.weight_decay_rate}       )
											return config







						def 						__UpperCamelCase							(self   ,		lowercase__       ):

											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(lowercase__   ,		lowercase__       ) is not None:
																										return True

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







class    __lowercase     (					_snake_case):







						"""simple docstring"""







						def __init__(self       ):
											snake_case_					:		str 					=					[]
											snake_case_					:		str 					=					None







						@property
						def 						__UpperCamelCase							(self       ):

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

											return self._accum_steps.value()







						@property
						def 						__UpperCamelCase							(self       ):

											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   ,		lowercase__       ):

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

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

											self._accum_steps.assign_add(1       )







						def 						__UpperCamelCase							(self       ):

											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(lowercase__       )       )









"""simple docstring"""

def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  int				,    SCREAMING_SNAKE_CASE__ :  int			):



					"""simple docstring"""





					return numa ^ numa < 0


if __name__ == "__main__":
			import doctest

			doctest.testmod()




