

'''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        a__  (							tf.keras.optimizers.schedules.LearningRateSchedule  ):


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





      """simple docstring"""





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


   def __call__(			self      ,    _UpperCamelCase  ):





      """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`.
         _lowercase      :				Any      					=       tf.cast(_UpperCamelCase      ,    tf.floataa  )
         _lowercase      :				Union[str, Any]      					=       tf.cast(self.warmup_steps      ,    tf.floataa  )
         _lowercase      :				Tuple      					=       global_step_float / warmup_steps_float
         _lowercase      :				Dict      					=       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 						_lowerCamelCase       (			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  _A		(     snake_case		,							snake_case		,							snake_case		,							snake_case = 0.0		,							snake_case = 0.9		,							snake_case = 0.999		,							snake_case = 1E-8		,							snake_case = None		,							snake_case = None		,							snake_case = 0.0		,							snake_case = 1.0		,							snake_case = None		,							)      ->     Optional[int]:
   _lowercase      :				Optional[int]      					=       tf.keras.optimizers.schedules.PolynomialDecay(
       initial_learning_rate=snake_case		,							decay_steps=num_train_steps - num_warmup_steps		,							end_learning_rate=init_lr * min_lr_ratio		,							power=snake_case		,							)
   if num_warmup_steps:
      _lowercase      :				Union[str, Any]      					=       WarmUp(
          initial_learning_rate=snake_case		,							decay_schedule_fn=snake_case		,							warmup_steps=snake_case		,							)
   if weight_decay_rate > 0.0:
      _lowercase      :				Dict      					=       AdamWeightDecay(
          learning_rate=snake_case		,							weight_decay_rate=snake_case		,							beta_a=snake_case		,							beta_a=snake_case		,							epsilon=snake_case		,							clipnorm=snake_case		,							global_clipnorm=snake_case		,							exclude_from_weight_decay=["LayerNorm", "layer_norm", "bias"]		,							include_in_weight_decay=snake_case		,							)
   else:
      _lowercase      :				List[str]      					=       tf.keras.optimizers.Adam(
          learning_rate=snake_case		,							beta_a=snake_case		,							beta_a=snake_case		,							epsilon=snake_case		,							clipnorm=snake_case		,							global_clipnorm=snake_case		,							)
   # 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        a__  (							lowerCamelCase_  ):


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





      """simple docstring"""





      super().__init__(_UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    **_UpperCamelCase  )
      _lowercase      :				Any      					=       weight_decay_rate
      _lowercase      :				Union[str, Any]      					=       include_in_weight_decay
      _lowercase      :				Dict      					=       exclude_from_weight_decay


   @classmethod
   def 						_lowerCamelCase       (			cls      ,    _UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				Union[str, Any]      					=       {"WarmUp": WarmUp}
      return super(_UpperCamelCase      ,    cls  ).from_config(_UpperCamelCase      ,    custom_objects=_UpperCamelCase  )


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase  ):





      """simple docstring"""





      super(_UpperCamelCase      ,    self  )._prepare_local(_UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase  )
      _lowercase      :				Optional[Any]      					=       tf.constant(
          self.weight_decay_rate      ,    name="adam_weight_decay_rate"  )


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				str      					=       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 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase=None      ,    **_UpperCamelCase  ):





      """simple docstring"""





      _lowercase					,				_lowercase      :				Optional[Any]      					=       list(zip(*_UpperCamelCase  )  )
      return super(_UpperCamelCase      ,    self  ).apply_gradients(zip(_UpperCamelCase      ,    _UpperCamelCase  )      ,    name=_UpperCamelCase      ,    **_UpperCamelCase  )


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase  ):





      """simple docstring"""





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

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

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


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase=None  ):





      """simple docstring"""





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


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase=None  ):





      """simple docstring"""





      _lowercase					,				_lowercase      :				Dict      					=       self._get_lr(var.device      ,    var.dtype.base_dtype      ,    _UpperCamelCase  )
      _lowercase      :				Optional[int]      					=       self._decay_weights_op(_UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase  )
      with tf.control_dependencies([decay]  ):
         return super(_UpperCamelCase      ,    self  )._resource_apply_sparse(_UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    **_UpperCamelCase  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





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






   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """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        a__  (							lowerCamelCase_  ):


   def __init__(			self  ):





      """simple docstring"""





      _lowercase      :				List[Any]      					=       []
      _lowercase      :				Dict      					=       None


   @property
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      if self._accum_steps is None:
         _lowercase      :				str      					=       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 						_lowerCamelCase       (			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      ,    _UpperCamelCase  ):





      """simple docstring"""





      if not self._gradients:
         _lowercase      :				Optional[Any]      					=       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 						_lowerCamelCase       (			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(_UpperCamelCase  )  )









'''simple docstring'''
def  _A		(     snake_case		,							snake_case					)      ->     int:
   return int((input_a, input_a).count(0					) != 0					)







def  _A		(     )      ->     None:
   assert nand_gate(0		,							0					) == 1
   assert nand_gate(0		,							1					) == 1
   assert nand_gate(1		,							0					) == 1
   assert nand_gate(1		,							1					) == 0


if __name__ == "__main__":
    print(nand_gate(0, 0))
    print(nand_gate(0, 1))
    print(nand_gate(1, 0))
    print(nand_gate(1, 1))






