




import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			UpperCamelCase__								=42
			UpperCamelCase__								=None







def 		lowercase__   (		__A:	int							,__A:	Optional[Any]=0.999							,__A:	int="cosine"							,):





					'''simple docstring'''






					if alpha_transform_type == "cosine":

										def alpha_bar_fn(__A:	Tuple				):
															return math.cos((t + 0.008) / 1.008 * math.pi / 2				) ** 2

					elif alpha_transform_type == "exp":

										def alpha_bar_fn(__A:	List[str]				):
															return math.exp(t * -12.0				)

					else:
										raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}'''				)

					__magic_name__							:	List[Any]    							=							[]
					for i in range(__A				):
										__magic_name__							:	Tuple    							=							i / num_diffusion_timesteps
										__magic_name__							:	int    							=							(i + 1) / num_diffusion_timesteps
										betas.append(min(1 - alpha_bar_fn(__A				) / alpha_bar_fn(__A				)							,__A				)				)
					return torch.tensor(__A							,dtype=torch.floataa				)


class    lowerCamelCase    (		_lowerCamelCase				,_lowerCamelCase						):






			'''simple docstring'''





			@register_to_config
			def __init__( self			:   Union[str, Any]       ,						lowerCamelCase_			:   int = 1000       ,						lowerCamelCase_			:   str = "fixed_small_log"       ,						lowerCamelCase_			:   bool = True       ,						lowerCamelCase_			:   Optional[float] = 1.0       ,						lowerCamelCase_			:   str = "epsilon"       ,						lowerCamelCase_			:   str = "squaredcos_cap_v2"       ,						)						->   List[Any]:
								if beta_schedule != "squaredcos_cap_v2":
													raise ValueError('''UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''''  )

								__magic_name__							:	Optional[int]    							=							betas_for_alpha_bar(lowerCamelCase_  )

								__magic_name__							:	Union[str, Any]    							=							1.0 - self.betas
								__magic_name__							:	Optional[int]    							=							torch.cumprod(self.alphas       ,						dim=0  )
								__magic_name__							:	str    							=							torch.tensor(1.0  )

								# standard deviation of the initial noise distribution
								__magic_name__							:	Tuple    							=							1.0

								# setable values
								__magic_name__							:	List[str]    							=							None
								__magic_name__							:	List[str]    							=							torch.from_numpy(np.arange(0       ,						lowerCamelCase_  )[::-1].copy()  )

								__magic_name__							:	str    							=							variance_type

			def 							UpperCAmelCase__       ( self			:   Union[str, Any]       ,						lowerCamelCase_			:   torch.FloatTensor       ,						lowerCamelCase_			:   Optional[int] = None  )						->   torch.FloatTensor:
								return sample

			def 							UpperCAmelCase__       ( self			:   Any       ,						lowerCamelCase_			:   int       ,						lowerCamelCase_			:   Union[str, torch.device] = None  )						->   List[str]:
								__magic_name__							:	int    							=							num_inference_steps
								__magic_name__							:	Any    							=							(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
								__magic_name__							:	int    							=							(np.arange(0       ,						lowerCamelCase_  ) * step_ratio).round()[::-1].copy().astype(np.intaa  )
								__magic_name__							:	Tuple    							=							torch.from_numpy(lowerCamelCase_  ).to(lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   List[str]       ,						lowerCamelCase_			:   List[Any]       ,						lowerCamelCase_			:   List[str]=None       ,						lowerCamelCase_			:   Any=None       ,						lowerCamelCase_			:   str=None  )						->   Union[str, Any]:
								if prev_timestep is None:
													__magic_name__							:	Dict    							=							t - 1

								__magic_name__							:	Any    							=							self.alphas_cumprod[t]
								__magic_name__							:	List[Any]    							=							self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
								__magic_name__							:	Union[str, Any]    							=							1 - alpha_prod_t
								__magic_name__							:	int    							=							1 - alpha_prod_t_prev

								if prev_timestep == t - 1:
													__magic_name__							:	str    							=							self.betas[t]
								else:
													__magic_name__							:	str    							=							1 - alpha_prod_t / alpha_prod_t_prev

								# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
								# and sample from it to get previous sample
								# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
								__magic_name__							:	List[str]    							=							beta_prod_t_prev / beta_prod_t * beta

								if variance_type is None:
													__magic_name__							:	str    							=							self.config.variance_type

								# hacks - were probably added for training stability
								if variance_type == "fixed_small_log":
													__magic_name__							:	Any    							=							torch.log(torch.clamp(lowerCamelCase_       ,						min=1E-20  )  )
													__magic_name__							:	List[Any]    							=							torch.exp(0.5 * variance  )
								elif variance_type == "learned_range":
													# NOTE difference with DDPM scheduler
													__magic_name__							:	Any    							=							variance.log()
													__magic_name__							:	Union[str, Any]    							=							beta.log()

													__magic_name__							:	Any    							=							(predicted_variance + 1) / 2
													__magic_name__							:	str    							=							frac * max_log + (1 - frac) * min_log

								return variance

			def 							UpperCAmelCase__       ( self			:   List[Any]       ,						lowerCamelCase_			:   torch.FloatTensor       ,						lowerCamelCase_			:   int       ,						lowerCamelCase_			:   torch.FloatTensor       ,						lowerCamelCase_			:   Optional[int] = None       ,						lowerCamelCase_			:   Optional[int]=None       ,						lowerCamelCase_			:   bool = True       ,						)						->   Union[UnCLIPSchedulerOutput, Tuple]:
								__magic_name__							:	List[str]    							=							timestep

								if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
													__magic_name__							,    __magic_name__							:	List[Any]    							=							torch.split(lowerCamelCase_       ,						sample.shape[1]       ,						dim=1  )
								else:
													__magic_name__							:	Union[str, Any]    							=							None

								# 1. compute alphas, betas
								if prev_timestep is None:
													__magic_name__							:	Optional[Any]    							=							t - 1

								__magic_name__							:	Optional[int]    							=							self.alphas_cumprod[t]
								__magic_name__							:	Dict    							=							self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
								__magic_name__							:	Union[str, Any]    							=							1 - alpha_prod_t
								__magic_name__							:	Optional[int]    							=							1 - alpha_prod_t_prev

								if prev_timestep == t - 1:
													__magic_name__							:	List[Any]    							=							self.betas[t]
													__magic_name__							:	Union[str, Any]    							=							self.alphas[t]
								else:
													__magic_name__							:	List[str]    							=							1 - alpha_prod_t / alpha_prod_t_prev
													__magic_name__							:	List[Any]    							=							1 - beta

								# 2. compute predicted original sample from predicted noise also called
								# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
								if self.config.prediction_type == "epsilon":
													__magic_name__							:	List[str]    							=							(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
								elif self.config.prediction_type == "sample":
													__magic_name__							:	Union[str, Any]    							=							model_output
								else:
													raise ValueError(
													    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`'''
													    ''' for the UnCLIPScheduler.'''  )

								# 3. Clip "predicted x_0"
								if self.config.clip_sample:
													__magic_name__							:	Any    							=							torch.clamp(
													    lowerCamelCase_       ,						-self.config.clip_sample_range       ,						self.config.clip_sample_range  )

								# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
								# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
								__magic_name__							:	Optional[Any]    							=							(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
								__magic_name__							:	Dict    							=							alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

								# 5. Compute predicted previous sample µ_t
								# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
								__magic_name__							:	Optional[int]    							=							pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

								# 6. Add noise
								__magic_name__							:	List[Any]    							=							0
								if t > 0:
													__magic_name__							:	Tuple    							=							randn_tensor(
													    model_output.shape       ,						dtype=model_output.dtype       ,						generator=lowerCamelCase_       ,						device=model_output.device  )

													__magic_name__							:	Optional[Any]    							=							self._get_variance(
													    lowerCamelCase_       ,						predicted_variance=lowerCamelCase_       ,						prev_timestep=lowerCamelCase_       ,						)

													if self.variance_type == "fixed_small_log":
																		__magic_name__							:	List[Any]    							=							variance
													elif self.variance_type == "learned_range":
																		__magic_name__							:	Any    							=							(0.5 * variance).exp()
													else:
																		raise ValueError(
																		    F'''variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`'''
																		    ''' for the UnCLIPScheduler.'''  )

													__magic_name__							:	int    							=							variance * variance_noise

								__magic_name__							:	Tuple    							=							pred_prev_sample + variance

								if not return_dict:
													return (pred_prev_sample,)

								return UnCLIPSchedulerOutput(prev_sample=lowerCamelCase_       ,						pred_original_sample=lowerCamelCase_  )


			def 							UpperCAmelCase__       ( self			:   Optional[int]       ,						lowerCamelCase_			:   torch.FloatTensor       ,						lowerCamelCase_			:   torch.FloatTensor       ,						lowerCamelCase_			:   torch.IntTensor       ,						)						->   torch.FloatTensor:
								# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
								__magic_name__							:	Optional[Any]    							=							self.alphas_cumprod.to(device=original_samples.device       ,						dtype=original_samples.dtype  )
								__magic_name__							:	Union[str, Any]    							=							timesteps.to(original_samples.device  )

								__magic_name__							:	str    							=							alphas_cumprod[timesteps] ** 0.5
								__magic_name__							:	Any    							=							sqrt_alpha_prod.flatten()
								while len(sqrt_alpha_prod.shape  ) < len(original_samples.shape  ):
													__magic_name__							:	Any    							=							sqrt_alpha_prod.unsqueeze(-1  )

								__magic_name__							:	List[str]    							=							(1 - alphas_cumprod[timesteps]) ** 0.5
								__magic_name__							:	List[str]    							=							sqrt_one_minus_alpha_prod.flatten()
								while len(sqrt_one_minus_alpha_prod.shape  ) < len(original_samples.shape  ):
													__magic_name__							:	Union[str, Any]    							=							sqrt_one_minus_alpha_prod.unsqueeze(-1  )

								__magic_name__							:	Dict    							=							sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
								return noisy_samples









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    lowerCamelCase    (		tf.keras.optimizers.schedules.LearningRateSchedule						):






			'''simple docstring'''





			def __init__( self			:   Union[str, Any]       ,						lowerCamelCase_			:   float       ,						lowerCamelCase_			:   Callable       ,						lowerCamelCase_			:   int       ,						lowerCamelCase_			:   float = 1.0       ,						lowerCamelCase_			:   str = None       ,						)						->   Optional[int]:
								super().__init__()
								__magic_name__							:	Union[str, Any]    							=							initial_learning_rate
								__magic_name__							:	Optional[int]    							=							warmup_steps
								__magic_name__							:	Union[str, Any]    							=							power
								__magic_name__							:	List[Any]    							=							decay_schedule_fn
								__magic_name__							:	Dict    							=							name

			def __call__( self			:   Any       ,						lowerCamelCase_			:   Dict  )						->   Optional[Any]:
								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`.
													__magic_name__							:	Union[str, Any]    							=							tf.cast(lowerCamelCase_       ,						tf.floataa  )
													__magic_name__							:	Dict    							=							tf.cast(self.warmup_steps       ,						tf.floataa  )
													__magic_name__							:	Dict    							=							global_step_float / warmup_steps_float
													__magic_name__							:	List[str]    							=							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]  )						->   str:
								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__   (		__A:	float							,__A:	int							,__A:	int							,__A:	float = 0.0							,__A:	float = 0.9							,__A:	float = 0.999							,__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							,):





					'''simple docstring'''






					__magic_name__							:	Any    							=							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:
										__magic_name__							:	Tuple    							=							WarmUp(
										    initial_learning_rate=__A							,decay_schedule_fn=__A							,warmup_steps=__A							,)
					if weight_decay_rate > 0.0:
										__magic_name__							:	Union[str, Any]    							=							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:
										__magic_name__							:	str    							=							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    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			def __init__( self			:   Optional[int]       ,						lowerCamelCase_			:   Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.0_0_1       ,						lowerCamelCase_			:   float = 0.9       ,						lowerCamelCase_			:   float = 0.9_9_9       ,						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_			:   int       ,						)						->   List[str]:
								super().__init__(lowerCamelCase_       ,						lowerCamelCase_       ,						lowerCamelCase_       ,						lowerCamelCase_       ,						lowerCamelCase_       ,						lowerCamelCase_       ,						**lowerCamelCase_  )
								__magic_name__							:	int    							=							weight_decay_rate
								__magic_name__							:	Tuple    							=							include_in_weight_decay
								__magic_name__							:	Union[str, Any]    							=							exclude_from_weight_decay

			@classmethod
			def 							UpperCAmelCase__       ( cls			:   int       ,						lowerCamelCase_			:   int  )						->   Optional[Any]:
								__magic_name__							:	Tuple    							=							{'''WarmUp''': WarmUp}
								return super(lowerCamelCase_       ,						cls  ).from_config(lowerCamelCase_       ,						custom_objects=lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   Union[str, Any]       ,						lowerCamelCase_			:   int       ,						lowerCamelCase_			:   Optional[int]       ,						lowerCamelCase_			:   Optional[int]  )						->   int:
								super(lowerCamelCase_       ,						self  )._prepare_local(lowerCamelCase_       ,						lowerCamelCase_       ,						lowerCamelCase_  )
								__magic_name__							:	Any    							=							tf.constant(
								    self.weight_decay_rate       ,						name='''adam_weight_decay_rate'''  )

			def 							UpperCAmelCase__       ( self			:   Optional[int]       ,						lowerCamelCase_			:   List[str]       ,						lowerCamelCase_			:   Optional[Any]       ,						lowerCamelCase_			:   Any  )						->   Optional[Any]:
								__magic_name__							:	Optional[int]    							=							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			:   str       ,						lowerCamelCase_			:   Optional[Any]       ,						lowerCamelCase_			:   int=None       ,						**lowerCamelCase_			:   Union[str, Any]  )						->   Optional[int]:
								__magic_name__							,    __magic_name__							:	Dict    							=							list(zip(*lowerCamelCase_  )  )
								return super(lowerCamelCase_       ,						self  ).apply_gradients(zip(lowerCamelCase_       ,						lowerCamelCase_  )       ,						name=lowerCamelCase_       ,						**lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   List[Any]       ,						lowerCamelCase_			:   Optional[int]       ,						lowerCamelCase_			:   int       ,						lowerCamelCase_			:   Union[str, Any]  )						->   Union[str, Any]:
								if apply_state is None:
													return self._decayed_lr_t[var_dtype], {}

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

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

			def 							UpperCAmelCase__       ( self			:   List[str]       ,						lowerCamelCase_			:   List[str]       ,						lowerCamelCase_			:   List[str]       ,						lowerCamelCase_			:   Dict=None  )						->   Optional[Any]:
								__magic_name__							,    __magic_name__							:	Union[str, Any]    							=							self._get_lr(var.device       ,						var.dtype.base_dtype       ,						lowerCamelCase_  )
								__magic_name__							:	Optional[Any]    							=							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			:   Dict       ,						lowerCamelCase_			:   List[Any]       ,						lowerCamelCase_			:   List[Any]       ,						lowerCamelCase_			:   Optional[Any]       ,						lowerCamelCase_			:   Union[str, Any]=None  )						->   Any:
								__magic_name__							,    __magic_name__							:	Tuple    							=							self._get_lr(var.device       ,						var.dtype.base_dtype       ,						lowerCamelCase_  )
								__magic_name__							:	Optional[int]    							=							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			:   int  )						->   Dict:
								__magic_name__							:	List[Any]    							=							super().get_config()
								config.update({'''weight_decay_rate''': self.weight_decay_rate}  )
								return config


			def 							UpperCAmelCase__       ( self			:   Dict       ,						lowerCamelCase_			:   Union[str, Any]  )						->   Union[str, Any]:
								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    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			def __init__( self			:   Optional[Any]  )						->   Any:
								__magic_name__							:	Optional[int]    							=							[]
								__magic_name__							:	Dict    							=							None

			@property
			def 							UpperCAmelCase__       ( self			:   Optional[int]  )						->   Any:
								if self._accum_steps is None:
													__magic_name__							:	Optional[Any]    							=							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			:   Optional[Any]  )						->   Optional[Any]:
								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[int]       ,						lowerCamelCase_			:   Any  )						->   List[str]:
								if not self._gradients:
													__magic_name__							:	int    							=							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			:   str  )						->   Optional[int]:
								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_  )  )



