

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						):




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


		'''simple docstring'''







		super().__init__()
		UpperCAmelCase_       				=			initial_learning_rate
		UpperCAmelCase_       				=			warmup_steps
		UpperCAmelCase_       				=			power
		UpperCAmelCase_       				=			decay_schedule_fn
		UpperCAmelCase_       				=			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`.
			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       __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      (						A       ,     A       ,     A       ,     A = 0.0       ,     A = 0.9       ,     A = 0.999       ,     A = 1E-8       ,     A = None       ,     A = None       ,     A = 0.0       ,     A = 1.0       ,     A = None       ,     ):
	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    (					lowercase						):




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


		'''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       __A					(		cls	:						Any		,	lowerCAmelCase	:						Optional[int]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			{"WarmUp": WarmUp}
		return super(lowerCAmelCase		,	cls							).from_config(lowerCAmelCase		,	custom_objects=lowerCAmelCase							)




	def       __A					(		self	:						Optional[Any]		,	lowerCAmelCase	:						List[Any]		,	lowerCAmelCase	:						List[str]		,	lowerCAmelCase	:						Union[str, Any]							):


		'''simple docstring'''







		super(lowerCAmelCase		,	self							)._prepare_local(lowerCAmelCase		,	lowerCAmelCase		,	lowerCAmelCase							)
		UpperCAmelCase_       				=			tf.constant(
		    self.weight_decay_rate		,	name="adam_weight_decay_rate"							)




	def       __A					(		self	:						Tuple		,	lowerCAmelCase	:						Dict		,	lowerCAmelCase	:						int		,	lowerCAmelCase	:						Optional[Any]							):


		'''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       __A					(		self	:						List[Any]		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						List[Any]=None		,	**lowerCAmelCase	:						List[Any]							):


		'''simple docstring'''







		UpperCAmelCase_				,   UpperCAmelCase_       				=			list(zip(*lowerCAmelCase							)							)
		return super(lowerCAmelCase		,	self							).apply_gradients(zip(lowerCAmelCase		,	lowerCAmelCase							)		,	name=lowerCAmelCase		,	**lowerCAmelCase							)




	def       __A					(		self	:						Optional[int]		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						Dict		,	lowerCAmelCase	:						Tuple							):


		'''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       __A					(		self	:						Optional[Any]		,	lowerCAmelCase	:						int		,	lowerCAmelCase	:						Optional[int]		,	lowerCAmelCase	:						Tuple=None							):


		'''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       __A					(		self	:						List[Any]		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						List[Any]		,	lowerCAmelCase	:						List[Any]		,	lowerCAmelCase	:						List[Any]=None							):


		'''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       __A					(		self	:						Optional[Any]							):


		'''simple docstring'''







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




	def       __A					(		self	:						Union[str, Any]		,	lowerCAmelCase	:						Optional[int]							):


		'''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    (					lowercase						):




	def __init__(		self	:						List[str]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			[]
		UpperCAmelCase_       				=			None




	@property
	def       __A					(		self	:						Dict							):


		'''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       __A					(		self	:						List[str]							):


		'''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	:						Union[str, Any]							):


		'''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       __A					(		self	:						List[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							)							)


import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class 						__UpperCamelCase    (					lowercase						):
	SCREAMING_SNAKE_CASE__    				=     (KDPMaDiscreteScheduler,)
	SCREAMING_SNAKE_CASE__    				=     10




	def       __A					(		self	:						Union[str, Any]		,	**lowerCAmelCase	:						Optional[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			{
		    "num_train_timesteps": 1_100,
		    "beta_start": 0.0_001,
		    "beta_end": 0.02,
		    "beta_schedule": "linear",
		}

		config.update(**lowerCAmelCase							)
		return config




	def       __A					(		self	:						List[Any]							):


		'''simple docstring'''







		for timesteps in [10, 50, 100, 1_000]:
			self.check_over_configs(num_train_timesteps=lowerCAmelCase							)




	def       __A					(		self	:						Optional[Any]							):


		'''simple docstring'''







		for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]		,	[0.0_002, 0.002, 0.02]							):
			self.check_over_configs(beta_start=lowerCAmelCase		,	beta_end=lowerCAmelCase							)




	def       __A					(		self	:						List[str]							):


		'''simple docstring'''







		for schedule in ["linear", "scaled_linear"]:
			self.check_over_configs(beta_schedule=lowerCAmelCase							)




	def       __A					(		self	:						List[str]							):


		'''simple docstring'''







		for prediction_type in ["epsilon", "v_prediction"]:
			self.check_over_configs(prediction_type=lowerCAmelCase							)




	def       __A					(		self	:						List[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.scheduler_classes[0]
		UpperCAmelCase_       				=			self.get_scheduler_config(prediction_type="v_prediction"							)
		UpperCAmelCase_       				=			scheduler_class(**lowerCAmelCase							)

		scheduler.set_timesteps(self.num_inference_steps							)

		UpperCAmelCase_       				=			self.dummy_model()
		UpperCAmelCase_       				=			self.dummy_sample_deter * scheduler.init_noise_sigma
		UpperCAmelCase_       				=			sample.to(lowerCAmelCase							)

		for i, t in enumerate(scheduler.timesteps							):
			UpperCAmelCase_       				=			scheduler.scale_model_input(lowerCAmelCase		,	lowerCAmelCase							)

			UpperCAmelCase_       				=			model(lowerCAmelCase		,	lowerCAmelCase							)

			UpperCAmelCase_       				=			scheduler.step(lowerCAmelCase		,	lowerCAmelCase		,	lowerCAmelCase							)
			UpperCAmelCase_       				=			output.prev_sample

		UpperCAmelCase_       				=			torch.sum(torch.abs(lowerCAmelCase							)							)
		UpperCAmelCase_       				=			torch.mean(torch.abs(lowerCAmelCase							)							)

		if torch_device in ["cpu", "mps"]:
			assert abs(result_sum.item() - 4.6_934e-07							) < 1e-2
			assert abs(result_mean.item() - 6.1_112e-10							) < 1e-3
		else:
			# CUDA
			assert abs(result_sum.item() - 4.693_428_650_170_972e-07							) < 1e-2
			assert abs(result_mean.item() - 0.0_002							) < 1e-3




	def       __A					(		self	:						int							):


		'''simple docstring'''







		if torch_device == "mps":
			return
		UpperCAmelCase_       				=			self.scheduler_classes[0]
		UpperCAmelCase_       				=			self.get_scheduler_config()
		UpperCAmelCase_       				=			scheduler_class(**lowerCAmelCase							)

		scheduler.set_timesteps(self.num_inference_steps							)

		UpperCAmelCase_       				=			self.dummy_model()
		UpperCAmelCase_       				=			self.dummy_sample_deter * scheduler.init_noise_sigma
		UpperCAmelCase_       				=			sample.to(lowerCAmelCase							)

		for i, t in enumerate(scheduler.timesteps							):
			UpperCAmelCase_       				=			scheduler.scale_model_input(lowerCAmelCase		,	lowerCAmelCase							)

			UpperCAmelCase_       				=			model(lowerCAmelCase		,	lowerCAmelCase							)

			UpperCAmelCase_       				=			scheduler.step(lowerCAmelCase		,	lowerCAmelCase		,	lowerCAmelCase							)
			UpperCAmelCase_       				=			output.prev_sample

		UpperCAmelCase_       				=			torch.sum(torch.abs(lowerCAmelCase							)							)
		UpperCAmelCase_       				=			torch.mean(torch.abs(lowerCAmelCase							)							)

		if torch_device in ["cpu", "mps"]:
			assert abs(result_sum.item() - 20.4_125							) < 1e-2
			assert abs(result_mean.item() - 0.0_266							) < 1e-3
		else:
			# CUDA
			assert abs(result_sum.item() - 20.4_125							) < 1e-2
			assert abs(result_mean.item() - 0.0_266							) < 1e-3




	def       __A					(		self	:						Dict							):


		'''simple docstring'''







		if torch_device == "mps":
			return
		UpperCAmelCase_       				=			self.scheduler_classes[0]
		UpperCAmelCase_       				=			self.get_scheduler_config()
		UpperCAmelCase_       				=			scheduler_class(**lowerCAmelCase							)

		scheduler.set_timesteps(self.num_inference_steps		,	device=lowerCAmelCase							)

		UpperCAmelCase_       				=			self.dummy_model()
		UpperCAmelCase_       				=			self.dummy_sample_deter.to(lowerCAmelCase							) * scheduler.init_noise_sigma

		for t in scheduler.timesteps:
			UpperCAmelCase_       				=			scheduler.scale_model_input(lowerCAmelCase		,	lowerCAmelCase							)

			UpperCAmelCase_       				=			model(lowerCAmelCase		,	lowerCAmelCase							)

			UpperCAmelCase_       				=			scheduler.step(lowerCAmelCase		,	lowerCAmelCase		,	lowerCAmelCase							)
			UpperCAmelCase_       				=			output.prev_sample

		UpperCAmelCase_       				=			torch.sum(torch.abs(lowerCAmelCase							)							)
		UpperCAmelCase_       				=			torch.mean(torch.abs(lowerCAmelCase							)							)

		if str(lowerCAmelCase							).startswith("cpu"							):
			# The following sum varies between 148 and 156 on mps. Why?
			assert abs(result_sum.item() - 20.4_125							) < 1e-2
			assert abs(result_mean.item() - 0.0_266							) < 1e-3
		else:
			# CUDA
			assert abs(result_sum.item() - 20.4_125							) < 1e-2
			assert abs(result_mean.item() - 0.0_266							) < 1e-3