



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

    def __init__(				self			:				Optional[Any] ,  lowerCAmelCase_			:				Optional[int] ,  lowerCAmelCase_			:				Any ,  lowerCAmelCase_			:				List[str] ,  lowerCAmelCase_			:				Any = 1.0 ,  lowerCAmelCase_			:				List[str] = None ,  ):







      """simple docstring"""







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

    def __call__(				self			:				Optional[Any] ,  lowerCAmelCase_			:				Dict):







      """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_								= tf.cast(__lowerCAmelCase ,  tf.floataa)
        lowercase_								= tf.cast(self.warmup_steps ,  tf.floataa)
        lowercase_								= global_step_float / warmup_steps_float
        lowercase_								= 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			:				Optional[int]):







      """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						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase = 0.0 ,  __lowerCAmelCase = 0.9 ,  __lowerCAmelCase = 0.999 ,  __lowerCAmelCase = 1E-8 ,  __lowerCAmelCase = None ,  __lowerCAmelCase = None ,  __lowerCAmelCase = 0.0 ,  __lowerCAmelCase = 1.0 ,  __lowerCAmelCase = None ,  ) ->						Dict:







  '''simple docstring'''


  lowercase_								= 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_								= WarmUp(
        initial_learning_rate=__snake_case ,  decay_schedule_fn=__snake_case ,  warmup_steps=__snake_case ,  )
  if weight_decay_rate > 0.0:
    lowercase_								= 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_								= 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    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase   ):

    def __init__(				self			:				Dict ,  lowerCAmelCase_			:				str = 0.001 ,  lowerCAmelCase_			:				List[Any] = 0.9 ,  lowerCAmelCase_			:				Union[str, Any] = 0.999 ,  lowerCAmelCase_			:				Tuple = 1E-7 ,  lowerCAmelCase_			:				Any = False ,  lowerCAmelCase_			:				Tuple = 0.0 ,  lowerCAmelCase_			:				Optional[Any] = None ,  lowerCAmelCase_			:				Optional[int] = None ,  lowerCAmelCase_			:				Union[str, Any] = "AdamWeightDecay" ,  **lowerCAmelCase_			:				Tuple ,  ):







      """simple docstring"""







      super().__init__(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase ,  **__lowerCAmelCase)
      lowercase_								= weight_decay_rate
      lowercase_								= include_in_weight_decay
      lowercase_								= exclude_from_weight_decay

    @classmethod
    def 					_UpperCAmelCase	(				cls			:				List[str] ,  lowerCAmelCase_			:				int):







      """simple docstring"""







      lowercase_								= {"""WarmUp""": WarmUp}
      return super(__lowerCAmelCase ,  cls).from_config(__lowerCAmelCase ,  custom_objects=__lowerCAmelCase)

    def 					_UpperCAmelCase	(				self			:				Optional[Any] ,  lowerCAmelCase_			:				str ,  lowerCAmelCase_			:				Tuple ,  lowerCAmelCase_			:				Optional[Any]):







      """simple docstring"""







      super(__lowerCAmelCase ,  self)._prepare_local(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase)
      lowercase_								= tf.constant(
          self.weight_decay_rate ,  name="""adam_weight_decay_rate""")

    def 					_UpperCAmelCase	(				self			:				int ,  lowerCAmelCase_			:				Dict ,  lowerCAmelCase_			:				Union[str, Any] ,  lowerCAmelCase_			:				Dict):







      """simple docstring"""







      lowercase_								= 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			:				List[Any] ,  lowerCAmelCase_			:				str ,  lowerCAmelCase_			:				str=None ,  **lowerCAmelCase_			:				List[str]):







      """simple docstring"""







      lowercase_					,    lowercase_								= list(zip(*__lowerCAmelCase))
      return super(__lowerCAmelCase ,  self).apply_gradients(zip(__lowerCAmelCase ,  __lowerCAmelCase) ,  name=__lowerCAmelCase ,  **__lowerCAmelCase)

    def 					_UpperCAmelCase	(				self			:				Union[str, Any] ,  lowerCAmelCase_			:				List[Any] ,  lowerCAmelCase_			:				Optional[int] ,  lowerCAmelCase_			:				Union[str, Any]):







      """simple docstring"""







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

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

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

    def 					_UpperCAmelCase	(				self			:				Optional[Any] ,  lowerCAmelCase_			:				List[str] ,  lowerCAmelCase_			:				Union[str, Any] ,  lowerCAmelCase_			:				Union[str, Any]=None):







      """simple docstring"""







      lowercase_					,    lowercase_								= self._get_lr(var.device ,  var.dtype.base_dtype ,  __lowerCAmelCase)
      lowercase_								= 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			:				int ,  lowerCAmelCase_			:				int ,  lowerCAmelCase_			:				Dict ,  lowerCAmelCase_			:				Optional[int] ,  lowerCAmelCase_			:				List[Any]=None):







      """simple docstring"""







      lowercase_					,    lowercase_								= self._get_lr(var.device ,  var.dtype.base_dtype ,  __lowerCAmelCase)
      lowercase_								= 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"""







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




    def 					_UpperCAmelCase	(				self			:				Union[str, Any] ,  lowerCAmelCase_			:				Tuple):







      """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    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase   ):

    def __init__(				self			:				int):







      """simple docstring"""







      lowercase_								= []
      lowercase_								= None

    @property
    def 					_UpperCAmelCase	(				self			:				List[str]):







      """simple docstring"""







      if self._accum_steps is None:
        lowercase_								= 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			:				int):







      """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[Any] ,  lowerCAmelCase_			:				Optional[int]):







      """simple docstring"""







      if not self._gradients:
        lowercase_								= 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			:				Union[str, 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(__lowerCAmelCase))





"""simple docstring"""
def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						list[list[int]]:







  '''simple docstring'''


  lowercase_								= []
  if len(__lowerCAmelCase							) == 1:
    return [nums.copy()]
  for _ in range(len(__lowerCAmelCase							)							):
    lowercase_								= nums.pop(0							)
    lowercase_								= permute(__lowerCAmelCase							)
    for perm in permutations:
      perm.append(__lowerCAmelCase							)
    result.extend(__lowerCAmelCase							)
    nums.append(__lowerCAmelCase							)
  return result


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						Tuple:







  '''simple docstring'''


  def backtrack(__lowerCAmelCase							):
    if start == len(__lowerCAmelCase							) - 1:
      output.append(nums[:]							)
    else:
      for i in range(__lowerCAmelCase ,  len(__lowerCAmelCase							)							):
        lowercase_					,    lowercase_								= nums[i], nums[start]
        backtrack(start + 1							)
        lowercase_					,    lowercase_								= nums[i], nums[start]  # backtrack

  lowercase_								= []
  backtrack(0							)
  return output


if __name__ == "__main__":
       import doctest

       # use res to print the data in permute2 function
       UpperCAmelCase						:		Optional[Any]			= permutea([1, 2, 3])
       print(res)
       doctest.testmod()

