




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				:     Tuple ,			UpperCAmelCase__				:     float ,			UpperCAmelCase__				:     Callable ,			UpperCAmelCase__				:     int ,			UpperCAmelCase__				:     float = 1.0 ,			UpperCAmelCase__				:     str = None ,			)						->Any:





           '''simple docstring'''

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


    def __call__(      self				:     List[Any] ,			UpperCAmelCase__				:     Optional[int])						->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`.
                  A__							=   tf.cast(UpperCAmelCase__ ,			tf.floataa)
                  A__							=   tf.cast(self.warmup_steps ,			tf.floataa)
                  A__							=   global_step_float / warmup_steps_float
                  A__							=   self.initial_learning_rate * tf.math.pow(UpperCAmelCase__ ,			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=UpperCAmelCase__ ,			)





    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int])						->str:





           '''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 SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_			,    lowercase_ = 0.0			,    lowercase_ = 0.9			,    lowercase_ = 0.9_99			,    lowercase_ = 1E-8			,    lowercase_ = None			,    lowercase_ = None			,    lowercase_ = 0.0			,    lowercase_ = 1.0			,    lowercase_ = None			,    )       ->	Tuple:



       """simple docstring"""

       A__							=   tf.keras.optimizers.schedules.PolynomialDecay(
           initial_learning_rate=lowercase_			,    decay_steps=num_train_steps - num_warmup_steps			,    end_learning_rate=init_lr * min_lr_ratio			,    power=lowercase_			,    )
       if num_warmup_steps:
              A__							=   WarmUp(
                  initial_learning_rate=lowercase_			,    decay_schedule_fn=lowercase_			,    warmup_steps=lowercase_			,    )
       if weight_decay_rate > 0.0:
              A__							=   AdamWeightDecay(
                  learning_rate=lowercase_			,    weight_decay_rate=lowercase_			,    beta_a=lowercase_			,    beta_a=lowercase_			,    epsilon=lowercase_			,    clipnorm=lowercase_			,    global_clipnorm=lowercase_			,    exclude_from_weight_decay=['''LayerNorm''', '''layer_norm''', '''bias''']			,    include_in_weight_decay=lowercase_			,    )
       else:
              A__							=   tf.keras.optimizers.Adam(
                  learning_rate=lowercase_			,    beta_a=lowercase_			,    beta_a=lowercase_			,    epsilon=lowercase_			,    clipnorm=lowercase_			,    global_clipnorm=lowercase_			,    )
       # 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_   (			UpperCAmelCase__					):

    '''simple docstring'''






    def __init__(      self				:     List[str] ,			UpperCAmelCase__				:     Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.001 ,			UpperCAmelCase__				:     float = 0.9 ,			UpperCAmelCase__				:     float = 0.999 ,			UpperCAmelCase__				:     float = 1e-7 ,			UpperCAmelCase__				:     bool = False ,			UpperCAmelCase__				:     float = 0.0 ,			UpperCAmelCase__				:     Optional[List[str]] = None ,			UpperCAmelCase__				:     Optional[List[str]] = None ,			UpperCAmelCase__				:     str = "AdamWeightDecay" ,			**UpperCAmelCase__				:     Optional[Any] ,			)						->List[str]:





           '''simple docstring'''

           super().__init__(UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__ ,			**UpperCAmelCase__)
           A__							=   weight_decay_rate
           A__							=   include_in_weight_decay
           A__							=   exclude_from_weight_decay


    @classmethod
    def 	SCREAMING_SNAKE_CASE							(      cls				:     Tuple ,			UpperCAmelCase__				:     Union[str, Any])						->Union[str, Any]:





           '''simple docstring'''

           A__							=   {'''WarmUp''': WarmUp}
           return super(UpperCAmelCase__ ,			cls).from_config(UpperCAmelCase__ ,			custom_objects=UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any] ,			UpperCAmelCase__				:     int ,			UpperCAmelCase__				:     Union[str, Any] ,			UpperCAmelCase__				:     Union[str, Any])						->int:





           '''simple docstring'''

           super(UpperCAmelCase__ ,			self)._prepare_local(UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__)
           A__							=   tf.constant(
               self.weight_decay_rate ,			name='''adam_weight_decay_rate''')


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int] ,			UpperCAmelCase__				:     Optional[int] ,			UpperCAmelCase__				:     Union[str, Any] ,			UpperCAmelCase__				:     Optional[int])						->Optional[int]:





           '''simple docstring'''

           A__							=   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 	SCREAMING_SNAKE_CASE							(      self				:     Dict ,			UpperCAmelCase__				:     Optional[int] ,			UpperCAmelCase__				:     Optional[Any]=None ,			**UpperCAmelCase__				:     Any)						->Any:





           '''simple docstring'''

           A__			,							A__							=   list(zip(*UpperCAmelCase__))
           return super(UpperCAmelCase__ ,			self).apply_gradients(zip(UpperCAmelCase__ ,			UpperCAmelCase__) ,			name=UpperCAmelCase__ ,			**UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     str ,			UpperCAmelCase__				:     Tuple ,			UpperCAmelCase__				:     List[Any] ,			UpperCAmelCase__				:     Optional[int])						->Any:





           '''simple docstring'''

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

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

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     Dict ,			UpperCAmelCase__				:     Union[str, Any] ,			UpperCAmelCase__				:     Tuple ,			UpperCAmelCase__				:     Union[str, Any]=None)						->Dict:





           '''simple docstring'''

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str] ,			UpperCAmelCase__				:     Union[str, Any] ,			UpperCAmelCase__				:     Tuple ,			UpperCAmelCase__				:     int ,			UpperCAmelCase__				:     Any=None)						->Tuple:





           '''simple docstring'''

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any])						->Union[str, Any]:





           '''simple docstring'''

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





    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any] ,			UpperCAmelCase__				:     Optional[Any])						->Optional[Any]:





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

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


class     UpperCamelCase_   (			UpperCAmelCase__					):

    '''simple docstring'''






    def __init__(      self				:     Any)						->int:





           '''simple docstring'''

           A__							=   []
           A__							=   None


    @property
    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any])						->List[str]:





           '''simple docstring'''

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

           return self._accum_steps.value()


    @property
    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int])						->Dict:





           '''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				:     str ,			UpperCAmelCase__				:     Optional[int])						->Optional[Any]:





           '''simple docstring'''

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

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

           self._accum_steps.assign_add(1)





    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any])						->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(UpperCAmelCase__))








from typing import TYPE_CHECKING

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


_lowerCamelCase     :		Any								=       {
    """configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase     :		str								=       [
      """NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """NezhaForNextSentencePrediction""",
      """NezhaForMaskedLM""",
      """NezhaForPreTraining""",
      """NezhaForMultipleChoice""",
      """NezhaForQuestionAnswering""",
      """NezhaForSequenceClassification""",
      """NezhaForTokenClassification""",
      """NezhaModel""",
      """NezhaPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_nezha import (
        NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
        NezhaForMaskedLM,
        NezhaForMultipleChoice,
        NezhaForNextSentencePrediction,
        NezhaForPreTraining,
        NezhaForQuestionAnswering,
        NezhaForSequenceClassification,
        NezhaForTokenClassification,
        NezhaModel,
        NezhaPreTrainedModel,
    )


else:
  import sys

  _lowerCamelCase     :		List[str]								=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


