



'''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       ):





	def __init__(			self :    List[Any]			,lowerCamelCase__ :    float			,lowerCamelCase__ :    Callable			,lowerCamelCase__ :    int			,lowerCamelCase__ :    float = 1.0			,lowerCamelCase__ :    str = None			,):







							'''simple docstring'''
							super().__init__()
							_UpperCamelCase :		Union[str, Any]			    =       initial_learning_rate
							_UpperCamelCase :		Optional[Any]			    =       warmup_steps
							_UpperCamelCase :		Tuple			    =       power
							_UpperCamelCase :		Union[str, Any]			    =       decay_schedule_fn
							_UpperCamelCase :		Any			    =       name





	def __call__(			self :    List[Any]			,lowerCamelCase__ :    Union[str, Any]			):







							'''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 :		int			    =       tf.cast(lowerCamelCase__			,tf.floataa			)
													_UpperCamelCase :		Optional[int]			    =       tf.cast(self.warmup_steps			,tf.floataa			)
													_UpperCamelCase :		Tuple			    =       global_step_float / warmup_steps_float
													_UpperCamelCase :		Optional[Any]			    =       self.initial_learning_rate * tf.math.pow(lowerCamelCase__			,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=lowerCamelCase__			,)





	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__   (					UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_ = 0.0  ,  UpperCAmelCase_ = 0.9  ,  UpperCAmelCase_ = 0.999  ,  UpperCAmelCase_ = 1E-8  ,  UpperCAmelCase_ = None  ,  UpperCAmelCase_ = None  ,  UpperCAmelCase_ = 0.0  ,  UpperCAmelCase_ = 1.0  ,  UpperCAmelCase_ = None  ,  ):
						_UpperCamelCase :		Optional[int]			    =       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:
												_UpperCamelCase :		Optional[int]			    =       WarmUp(
												    initial_learning_rate=UpperCAmelCase_  ,  decay_schedule_fn=UpperCAmelCase_  ,  warmup_steps=UpperCAmelCase_  ,  )
						if weight_decay_rate > 0.0:
												_UpperCamelCase :		Dict			    =       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:
												_UpperCamelCase :		Union[str, 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__     (							lowercase       ):





	def __init__(			self :    int			,lowerCamelCase__ :    Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.0_0_1			,lowerCamelCase__ :    float = 0.9			,lowerCamelCase__ :    float = 0.9_9_9			,lowerCamelCase__ :    float = 1E-7			,lowerCamelCase__ :    bool = False			,lowerCamelCase__ :    float = 0.0			,lowerCamelCase__ :    Optional[List[str]] = None			,lowerCamelCase__ :    Optional[List[str]] = None			,lowerCamelCase__ :    str = "AdamWeightDecay"			,**lowerCamelCase__ :    Optional[Any]			,):







							'''simple docstring'''
							super().__init__(lowerCamelCase__			,lowerCamelCase__			,lowerCamelCase__			,lowerCamelCase__			,lowerCamelCase__			,lowerCamelCase__			,**lowerCamelCase__			)
							_UpperCamelCase :		Union[str, Any]			    =       weight_decay_rate
							_UpperCamelCase :		Tuple			    =       include_in_weight_decay
							_UpperCamelCase :		int			    =       exclude_from_weight_decay





	@classmethod
	def      UpperCamelCase_  (			cls :    int			,lowerCamelCase__ :    List[Any]			):







							'''simple docstring'''
							_UpperCamelCase :		Tuple			    =       {'WarmUp': WarmUp}
							return super(lowerCamelCase__			,cls			).from_config(lowerCamelCase__			,custom_objects=lowerCamelCase__			)





	def      UpperCamelCase_  (			self :    List[str]			,lowerCamelCase__ :    List[Any]			,lowerCamelCase__ :    List[str]			,lowerCamelCase__ :    Union[str, Any]			):







							'''simple docstring'''
							super(lowerCamelCase__			,self			)._prepare_local(lowerCamelCase__			,lowerCamelCase__			,lowerCamelCase__			)
							_UpperCamelCase :		List[Any]			    =       tf.constant(
							    self.weight_decay_rate			,name='adam_weight_decay_rate'			)





	def      UpperCamelCase_  (			self :    str			,lowerCamelCase__ :    Union[str, Any]			,lowerCamelCase__ :    Optional[Any]			,lowerCamelCase__ :    Union[str, Any]			):







							'''simple docstring'''
							_UpperCamelCase :		Any			    =       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 :    Union[str, Any]			,lowerCamelCase__ :    Union[str, Any]			,lowerCamelCase__ :    Union[str, Any]=None			,**lowerCamelCase__ :    Dict			):







							'''simple docstring'''
							_UpperCamelCase    ,      _UpperCamelCase :		Tuple			    =       list(zip(*lowerCamelCase__			)			)
							return super(lowerCamelCase__			,self			).apply_gradients(zip(lowerCamelCase__			,lowerCamelCase__			)			,name=lowerCamelCase__			,**lowerCamelCase__			)





	def      UpperCamelCase_  (			self :    Union[str, Any]			,lowerCamelCase__ :    List[str]			,lowerCamelCase__ :    Tuple			,lowerCamelCase__ :    Dict			):







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

							_UpperCamelCase :		Tuple			    =       apply_state or {}
							_UpperCamelCase :		Any			    =       apply_state.get((var_device, var_dtype)			)
							if coefficients is None:
													_UpperCamelCase :		Dict			    =       self._fallback_apply_state(lowerCamelCase__			,lowerCamelCase__			)
													_UpperCamelCase :		int			    =       coefficients

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





	def      UpperCamelCase_  (			self :    int			,lowerCamelCase__ :    Optional[Any]			,lowerCamelCase__ :    Any			,lowerCamelCase__ :    Union[str, Any]=None			):







							'''simple docstring'''
							_UpperCamelCase    ,      _UpperCamelCase :		str			    =       self._get_lr(var.device			,var.dtype.base_dtype			,lowerCamelCase__			)
							_UpperCamelCase :		Optional[int]			    =       self._decay_weights_op(lowerCamelCase__			,lowerCamelCase__			,lowerCamelCase__			)
							with tf.control_dependencies([decay]			):
													return super(lowerCamelCase__			,self			)._resource_apply_dense(lowerCamelCase__			,lowerCamelCase__			,**lowerCamelCase__			)





	def      UpperCamelCase_  (			self :    Optional[int]			,lowerCamelCase__ :    Dict			,lowerCamelCase__ :    int			,lowerCamelCase__ :    Dict			,lowerCamelCase__ :    Tuple=None			):







							'''simple docstring'''
							_UpperCamelCase    ,      _UpperCamelCase :		int			    =       self._get_lr(var.device			,var.dtype.base_dtype			,lowerCamelCase__			)
							_UpperCamelCase :		Any			    =       self._decay_weights_op(lowerCamelCase__			,lowerCamelCase__			,lowerCamelCase__			)
							with tf.control_dependencies([decay]			):
													return super(lowerCamelCase__			,self			)._resource_apply_sparse(lowerCamelCase__			,lowerCamelCase__			,lowerCamelCase__			,**lowerCamelCase__			)





	def      UpperCamelCase_  (			self :    str			):







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





	def      UpperCamelCase_  (			self :    Union[str, Any]			,lowerCamelCase__ :    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(lowerCamelCase__			,lowerCamelCase__			) is not None:
																									return True

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






class 							lowercase__     (							lowercase       ):





	def __init__(			self :    Optional[Any]			):







							'''simple docstring'''
							_UpperCamelCase :		Any			    =       []
							_UpperCamelCase :		Optional[Any]			    =       None





	@property
	def      UpperCamelCase_  (			self :    Optional[int]			):







							'''simple docstring'''
							if self._accum_steps is None:
													_UpperCamelCase :		Union[str, Any]			    =       tf.Variable(
													    tf.constant(0			,dtype=tf.intaa			)			,trainable=lowerCamelCase__			,synchronization=tf.VariableSynchronization.ON_READ			,aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA			,)

							return self._accum_steps.value()





	@property
	def      UpperCamelCase_  (			self :    Tuple			):







							'''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 :    Optional[int]			,lowerCamelCase__ :    Optional[Any]			):







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

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

							self._accum_steps.assign_add(1			)





	def      UpperCamelCase_  (			self :    Optional[int]			):







							'''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(lowerCamelCase__			)			)







'''simple docstring'''
from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable


snake_case_			:     Any  	=  {
    'configuration_gpt_neox_japanese': ['GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoXJapaneseConfig'],
    'tokenization_gpt_neox_japanese': ['GPTNeoXJapaneseTokenizer'],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_			:     Optional[int]  	=  [
					    'GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'GPTNeoXJapaneseForCausalLM',
					    'GPTNeoXJapaneseLayer',
					    'GPTNeoXJapaneseModel',
					    'GPTNeoXJapanesePreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
					from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_gpt_neox_japanese import (
										    GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GPTNeoXJapaneseForCausalLM,
										    GPTNeoXJapaneseLayer,
										    GPTNeoXJapaneseModel,
										    GPTNeoXJapanesePreTrainedModel,
										)


else:
					import sys

					snake_case_			:     Any  	=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


