



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

					"""simple docstring"""

					super().__init__()
					SCREAMING_SNAKE_CASE  :						Tuple      					= initial_learning_rate
					SCREAMING_SNAKE_CASE  :						int      					= warmup_steps
					SCREAMING_SNAKE_CASE  :						int      					= power
					SCREAMING_SNAKE_CASE  :						Tuple      					= decay_schedule_fn
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= name


	def __call__(self : Dict,					__UpperCAmelCase : List[Any]      )     ->			Optional[int]:

					"""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`.
									SCREAMING_SNAKE_CASE  :						Tuple      					= tf.cast(__UpperCAmelCase,					tf.floataa      )
									SCREAMING_SNAKE_CASE  :						Any      					= tf.cast(self.warmup_steps,					tf.floataa      )
									SCREAMING_SNAKE_CASE  :						Any      					= global_step_float / warmup_steps_float
									SCREAMING_SNAKE_CASE  :						Tuple      					= 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 				lowercase__  (self : Tuple      )     ->			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       __lowercase  (_SCREAMING_SNAKE_CASE      :float    ,	_SCREAMING_SNAKE_CASE      :int    ,	_SCREAMING_SNAKE_CASE      :int    ,	_SCREAMING_SNAKE_CASE      :float = 0.0    ,	_SCREAMING_SNAKE_CASE      :float = 0.9    ,	_SCREAMING_SNAKE_CASE      :float = 0.999    ,	_SCREAMING_SNAKE_CASE      :float = 1E-8    ,	_SCREAMING_SNAKE_CASE      :Optional[float] = None    ,	_SCREAMING_SNAKE_CASE      :Optional[float] = None    ,	_SCREAMING_SNAKE_CASE      :float = 0.0    ,	_SCREAMING_SNAKE_CASE      :float = 1.0    ,	_SCREAMING_SNAKE_CASE      :Optional[List[str]] = None    ,	):
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= tf.keras.optimizers.schedules.PolynomialDecay(
				    initial_learning_rate=_SCREAMING_SNAKE_CASE    ,	decay_steps=num_train_steps - num_warmup_steps    ,	end_learning_rate=init_lr * min_lr_ratio    ,	power=_SCREAMING_SNAKE_CASE    ,	)
				if num_warmup_steps:
								SCREAMING_SNAKE_CASE  :						Any      					= WarmUp(
								    initial_learning_rate=_SCREAMING_SNAKE_CASE    ,	decay_schedule_fn=_SCREAMING_SNAKE_CASE    ,	warmup_steps=_SCREAMING_SNAKE_CASE    ,	)
				if weight_decay_rate > 0.0:
								SCREAMING_SNAKE_CASE  :						Optional[Any]      					= AdamWeightDecay(
								    learning_rate=_SCREAMING_SNAKE_CASE    ,	weight_decay_rate=_SCREAMING_SNAKE_CASE    ,	beta_a=_SCREAMING_SNAKE_CASE    ,	beta_a=_SCREAMING_SNAKE_CASE    ,	epsilon=_SCREAMING_SNAKE_CASE    ,	clipnorm=_SCREAMING_SNAKE_CASE    ,	global_clipnorm=_SCREAMING_SNAKE_CASE    ,	exclude_from_weight_decay=['''LayerNorm''', '''layer_norm''', '''bias''']    ,	include_in_weight_decay=_SCREAMING_SNAKE_CASE    ,	)
				else:
								SCREAMING_SNAKE_CASE  :						int      					= tf.keras.optimizers.Adam(
								    learning_rate=_SCREAMING_SNAKE_CASE    ,	beta_a=_SCREAMING_SNAKE_CASE    ,	beta_a=_SCREAMING_SNAKE_CASE    ,	epsilon=_SCREAMING_SNAKE_CASE    ,	clipnorm=_SCREAMING_SNAKE_CASE    ,	global_clipnorm=_SCREAMING_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__     (				_lowercase	):


	def __init__(self : 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 : Tuple,					)     ->			List[str]:

					"""simple docstring"""

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


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

					"""simple docstring"""

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


	def 				lowercase__  (self : str,					__UpperCAmelCase : List[str],					__UpperCAmelCase : List[str],					__UpperCAmelCase : Tuple      )     ->			str:

					"""simple docstring"""

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


	def 				lowercase__  (self : Optional[Any],					__UpperCAmelCase : Any,					__UpperCAmelCase : Any,					__UpperCAmelCase : Tuple      )     ->			str:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= 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 				lowercase__  (self : Union[str, Any],					__UpperCAmelCase : int,					__UpperCAmelCase : List[str]=None,					**__UpperCAmelCase : int      )     ->			Optional[int]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Any      					= list(zip(*__UpperCAmelCase      )      )
					return super(__UpperCAmelCase,					self      ).apply_gradients(zip(__UpperCAmelCase,					__UpperCAmelCase      ),					name=__UpperCAmelCase,					**__UpperCAmelCase      )


	def 				lowercase__  (self : Tuple,					__UpperCAmelCase : int,					__UpperCAmelCase : Union[str, Any],					__UpperCAmelCase : Dict      )     ->			Union[str, Any]:

					"""simple docstring"""

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

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

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


	def 				lowercase__  (self : Tuple,					__UpperCAmelCase : int,					__UpperCAmelCase : Union[str, Any],					__UpperCAmelCase : List[str]=None      )     ->			Optional[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						int      					= self._get_lr(var.device,					var.dtype.base_dtype,					__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Dict      					= self._decay_weights_op(__UpperCAmelCase,					__UpperCAmelCase,					__UpperCAmelCase      )
					with tf.control_dependencies([decay]      ):
									return super(__UpperCAmelCase,					self      )._resource_apply_dense(__UpperCAmelCase,					__UpperCAmelCase,					**__UpperCAmelCase      )


	def 				lowercase__  (self : List[str],					__UpperCAmelCase : Union[str, Any],					__UpperCAmelCase : Any,					__UpperCAmelCase : int,					__UpperCAmelCase : Optional[int]=None      )     ->			Union[str, Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						List[str]      					= self._get_lr(var.device,					var.dtype.base_dtype,					__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						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 				lowercase__  (self : List[str]      )     ->			Optional[Any]:

					"""simple docstring"""

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






	def 				lowercase__  (self : List[str],					__UpperCAmelCase : Union[str, Any]      )     ->			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(__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__     (				_lowercase	):


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						int      					= []
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= None


	@property
	def 				lowercase__  (self : Optional[Any]      )     ->			List[str]:

					"""simple docstring"""

					if self._accum_steps is None:
									SCREAMING_SNAKE_CASE  :						List[Any]      					= 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 				lowercase__  (self : List[Any]      )     ->			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 : Optional[Any],					__UpperCAmelCase : Optional[Any]      )     ->			Union[str, Any]:

					"""simple docstring"""

					if not self._gradients:
									SCREAMING_SNAKE_CASE  :						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 				lowercase__  (self : List[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(__UpperCAmelCase      )      )









'''simple docstring'''

from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
	from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline




