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:			Dict				,				__lowerCAmelCase:			float				,				__lowerCAmelCase:			Callable				,				__lowerCAmelCase:			int				,				__lowerCAmelCase:			float = 1.0				,				__lowerCAmelCase:			str = None				,				) -> List[Any]:




    '''simple docstring'''




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






  def __call__(				self:			List[str]				,				__lowerCAmelCase:			Union[str, Any]   ) -> 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						    =       tf.cast(__lowerCAmelCase				,				tf.floataa   )
      __UpperCAmelCase						    =       tf.cast(self.warmup_steps				,				tf.floataa   )
      __UpperCAmelCase						    =       global_step_float / warmup_steps_float
      __UpperCAmelCase						    =       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:			Union[str, Any]   ) -> 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       __lowerCAmelCase      (						A_   : float						,	A_   : int						,	A_   : int						,	A_   : float = 0.0						,	A_   : float = 0.9						,	A_   : float = 0.9_99						,	A_   : float = 1e-8						,	A_   : Optional[float] = None						,	A_   : Optional[float] = None						,	A_   : float = 0.0						,	A_   : float = 1.0						,	A_   : Optional[List[str]] = None						,	)    ->  Dict:
  __UpperCAmelCase						    =       tf.keras.optimizers.schedules.PolynomialDecay(
      initial_learning_rate=A_						,	decay_steps=num_train_steps - num_warmup_steps						,	end_learning_rate=init_lr * min_lr_ratio						,	power=A_						,	)
  if num_warmup_steps:
    __UpperCAmelCase						    =       WarmUp(
        initial_learning_rate=A_						,	decay_schedule_fn=A_						,	warmup_steps=A_						,	)
  if weight_decay_rate > 0.0:
    __UpperCAmelCase						    =       AdamWeightDecay(
        learning_rate=A_						,	weight_decay_rate=A_						,	beta_a=A_						,	beta_a=A_						,	epsilon=A_						,	clipnorm=A_						,	global_clipnorm=A_						,	exclude_from_weight_decay=["LayerNorm", "layer_norm", "bias"]						,	include_in_weight_decay=A_						,	)
  else:
    __UpperCAmelCase						    =       tf.keras.optimizers.Adam(
        learning_rate=A_						,	beta_a=A_						,	beta_a=A_						,	epsilon=A_						,	clipnorm=A_						,	global_clipnorm=A_						,	)
  # 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__    (		snake_case					):



  """simple docstring"""






  def __init__(				self:			Dict				,				__lowerCAmelCase:			Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.001				,				__lowerCAmelCase:			float = 0.9				,				__lowerCAmelCase:			float = 0.999				,				__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:			List[str]				,				) -> Optional[int]:




    '''simple docstring'''




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






  @classmethod
  def 	_UpperCAmelCase				(				cls:			Union[str, Any]				,				__lowerCAmelCase:			str   ) -> int:




    '''simple docstring'''




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






  def 	_UpperCAmelCase				(				self:			int				,				__lowerCAmelCase:			str				,				__lowerCAmelCase:			Any				,				__lowerCAmelCase:			Tuple   ) -> Tuple:




    '''simple docstring'''




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






  def 	_UpperCAmelCase				(				self:			Dict				,				__lowerCAmelCase:			int				,				__lowerCAmelCase:			Tuple				,				__lowerCAmelCase:			List[Any]   ) -> List[str]:




    '''simple docstring'''




    __UpperCAmelCase						    =       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:			Tuple				,				__lowerCAmelCase:			List[str]=None				,				**__lowerCAmelCase:			Tuple   ) -> Optional[int]:




    '''simple docstring'''




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






  def 	_UpperCAmelCase				(				self:			int				,				__lowerCAmelCase:			Any				,				__lowerCAmelCase:			Tuple				,				__lowerCAmelCase:			List[str]   ) -> int:




    '''simple docstring'''




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

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

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






  def 	_UpperCAmelCase				(				self:			List[Any]				,				__lowerCAmelCase:			List[Any]				,				__lowerCAmelCase:			Union[str, Any]				,				__lowerCAmelCase:			str=None   ) -> Union[str, Any]:




    '''simple docstring'''




    __UpperCAmelCase    ,		__UpperCAmelCase						    =       self._get_lr(var.device				,				var.dtype.base_dtype				,				__lowerCAmelCase   )
    __UpperCAmelCase						    =       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[Any]				,				__lowerCAmelCase:			Optional[int]				,				__lowerCAmelCase:			int				,				__lowerCAmelCase:			Any				,				__lowerCAmelCase:			Union[str, Any]=None   ) -> Union[str, Any]:




    '''simple docstring'''




    __UpperCAmelCase    ,		__UpperCAmelCase						    =       self._get_lr(var.device				,				var.dtype.base_dtype				,				__lowerCAmelCase   )
    __UpperCAmelCase						    =       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   ) -> Optional[Any]:




    '''simple docstring'''




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






  def 	_UpperCAmelCase				(				self:			Dict				,				__lowerCAmelCase:			str   ) -> 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 			UpperCAmelCase__    (		snake_case					):



  """simple docstring"""






  def __init__(				self:			int   ) -> Optional[int]:




    '''simple docstring'''




    __UpperCAmelCase						    =       []
    __UpperCAmelCase						    =       None






  @property
  def 	_UpperCAmelCase				(				self:			Union[str, Any]   ) -> Optional[Any]:




    '''simple docstring'''




    if self._accum_steps is None:
      __UpperCAmelCase						    =       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:			str   ) -> Union[str, Any]:




    '''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				,				__lowerCAmelCase:			Dict   ) -> int:




    '''simple docstring'''




    if not self._gradients:
      __UpperCAmelCase						    =       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:			Any   ) -> List[str]:




    '''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   )   )




import inspect
import unittest


class 			UpperCAmelCase__    (		unittest.TestCase					):



  """simple docstring"""






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




    '''simple docstring'''




    try:
      import diffusers  # noqa: F401
    except ImportError:
      assert False






  def 	_UpperCAmelCase				(				self:			Dict   ) -> Dict:




    '''simple docstring'''




    import diffusers
    from diffusers.dependency_versions_table import deps

    __UpperCAmelCase						    =       inspect.getmembers(__lowerCAmelCase				,				inspect.isclass   )

    for cls_name, cls_module in all_classes:
      if "dummy_" in cls_module.__module__:
        for backend in cls_module._backends:
          if backend == "k_diffusion":
            __UpperCAmelCase						    =       "k-diffusion"
          elif backend == "invisible_watermark":
            __UpperCAmelCase						    =       "invisible-watermark"
          assert backend in deps, F'''{backend} is not in the deps table!'''



