





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

      '''simple docstring'''



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


        '''simple docstring'''




        super().__init__()
        __UpperCAmelCase    :						Optional[Any]       =						initial_learning_rate
        __UpperCAmelCase    :						Any       =						warmup_steps
        __UpperCAmelCase    :						List[str]       =						power
        __UpperCAmelCase    :						Dict       =						decay_schedule_fn
        __UpperCAmelCase    :						str       =						name

      def __call__(							self       :      str						,				__lowercase       :      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    :						Union[str, Any]       =						tf.cast(__lowercase						,				tf.floataa )
          __UpperCAmelCase    :						str       =						tf.cast(self.warmup_steps						,				tf.floataa )
          __UpperCAmelCase    :						Dict       =						global_step_float / warmup_steps_float
          __UpperCAmelCase    :						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       A_					(							self       :      Tuple ):


        '''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       lowerCamelCase_       (   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  ,			)						->List[Any]:





  """simple docstring"""


  __UpperCAmelCase    :						Optional[Any]       =						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[Any]       =						WarmUp(
        initial_learning_rate=UpperCAmelCase_  ,			decay_schedule_fn=UpperCAmelCase_  ,			warmup_steps=UpperCAmelCase_  ,			)
  if weight_decay_rate > 0.0:
    __UpperCAmelCase    :						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:
    __UpperCAmelCase    :						Dict       =						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 							snake_case							(			__UpperCAmelCase ):

      '''simple docstring'''



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


        '''simple docstring'''




        super().__init__(__lowercase						,				__lowercase						,				__lowercase						,				__lowercase						,				__lowercase						,				__lowercase						,				**__lowercase )
        __UpperCAmelCase    :						Any       =						weight_decay_rate
        __UpperCAmelCase    :						Dict       =						include_in_weight_decay
        __UpperCAmelCase    :						Optional[Any]       =						exclude_from_weight_decay

      @classmethod
      def       A_					(							cls       :      int						,				__lowercase       :      List[str] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Dict       =						{'''WarmUp''': WarmUp}
        return super(__lowercase						,				cls ).from_config(__lowercase						,				custom_objects=__lowercase )

      def       A_					(							self       :      List[Any]						,				__lowercase       :      Optional[int]						,				__lowercase       :      Optional[Any]						,				__lowercase       :      Optional[int] ):


        '''simple docstring'''




        super(__lowercase						,				self )._prepare_local(__lowercase						,				__lowercase						,				__lowercase )
        __UpperCAmelCase    :						Tuple       =						tf.constant(
            self.weight_decay_rate						,				name='''adam_weight_decay_rate''' )

      def       A_					(							self       :      List[str]						,				__lowercase       :      Dict						,				__lowercase       :      Any						,				__lowercase       :      str ):


        '''simple docstring'''




        __UpperCAmelCase    :						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       A_					(							self       :      List[Any]						,				__lowercase       :      Any						,				__lowercase       :      Any=None						,				**__lowercase       :      Union[str, Any] ):


        '''simple docstring'''




        __UpperCAmelCase       ,     __UpperCAmelCase    :						Tuple       =						list(zip(*__lowercase ) )
        return super(__lowercase						,				self ).apply_gradients(zip(__lowercase						,				__lowercase )						,				name=__lowercase						,				**__lowercase )

      def       A_					(							self       :      Tuple						,				__lowercase       :      Union[str, Any]						,				__lowercase       :      Any						,				__lowercase       :      List[str] ):


        '''simple docstring'''




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

        __UpperCAmelCase    :						Dict       =						apply_state or {}
        __UpperCAmelCase    :						str       =						apply_state.get((var_device, var_dtype) )
        if coefficients is None:
          __UpperCAmelCase    :						str       =						self._fallback_apply_state(__lowercase						,				__lowercase )
          __UpperCAmelCase    :						Optional[int]       =						coefficients

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

      def       A_					(							self       :      List[Any]						,				__lowercase       :      Any						,				__lowercase       :      List[str]						,				__lowercase       :      Dict=None ):


        '''simple docstring'''




        __UpperCAmelCase       ,     __UpperCAmelCase    :						Optional[Any]       =						self._get_lr(var.device						,				var.dtype.base_dtype						,				__lowercase )
        __UpperCAmelCase    :						List[Any]       =						self._decay_weights_op(__lowercase						,				__lowercase						,				__lowercase )
        with tf.control_dependencies([decay] ):
          return super(__lowercase						,				self )._resource_apply_dense(__lowercase						,				__lowercase						,				**__lowercase )

      def       A_					(							self       :      Dict						,				__lowercase       :      List[Any]						,				__lowercase       :      str						,				__lowercase       :      Tuple						,				__lowercase       :      Union[str, Any]=None ):


        '''simple docstring'''




        __UpperCAmelCase       ,     __UpperCAmelCase    :						str       =						self._get_lr(var.device						,				var.dtype.base_dtype						,				__lowercase )
        __UpperCAmelCase    :						List[Any]       =						self._decay_weights_op(__lowercase						,				__lowercase						,				__lowercase )
        with tf.control_dependencies([decay] ):
          return super(__lowercase						,				self )._resource_apply_sparse(__lowercase						,				__lowercase						,				__lowercase						,				**__lowercase )

      def       A_					(							self       :      Optional[Any] ):


        '''simple docstring'''




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







      def       A_					(							self       :      Optional[Any]						,				__lowercase       :      List[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(__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 							snake_case							(			__UpperCAmelCase ):

      '''simple docstring'''



      def __init__(							self       :      str ):


        '''simple docstring'''




        __UpperCAmelCase    :						List[Any]       =						[]
        __UpperCAmelCase    :						List[str]       =						None

      @property
      def       A_					(							self       :      int ):


        '''simple docstring'''




        if self._accum_steps is None:
          __UpperCAmelCase    :						List[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       A_					(							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       :      List[str]						,				__lowercase       :      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(__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       A_					(							self       :      Dict ):


        '''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(__lowercase ) )






"""simple docstring"""





# flake8: noqa
# Lint as: python3
lowercase__				:Tuple				   =       [
    'VerificationMode',
    'Version',
    'disable_progress_bar',
    'enable_progress_bar',
    'is_progress_bar_enabled',
    'experimental',
]

from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental