



"""simple docstring"""






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

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)



@flax.struct.dataclass
class 			A__   :




	'''simple docstring'''





	SCREAMING_SNAKE_CASE          =	42

	# setable values
	SCREAMING_SNAKE_CASE          =	42
	SCREAMING_SNAKE_CASE          =	42
	SCREAMING_SNAKE_CASE          =	None

	@classmethod
	def 							_SCREAMING_SNAKE_CASE   (     cls:		int       ,			_SCREAMING_SNAKE_CASE:		CommonSchedulerState       ,			_SCREAMING_SNAKE_CASE:		jnp.ndarray       ,			_SCREAMING_SNAKE_CASE:		jnp.ndarray)				->       Optional[Any]:

				"""simple docstring"""

				return cls(common=_SCREAMING_SNAKE_CASE       ,			init_noise_sigma=_SCREAMING_SNAKE_CASE       ,			timesteps=_SCREAMING_SNAKE_CASE)






@dataclass
class 			A__   (  _A							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE          =	42






class 			A__   (  _A  ,		_A							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE          =	[e.name for e in FlaxKarrasDiffusionSchedulers]

	SCREAMING_SNAKE_CASE          =	42

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

				"""simple docstring"""

				return True

	@register_to_config
	def __init__(     self:		str       ,			_SCREAMING_SNAKE_CASE:		int = 1000       ,			_SCREAMING_SNAKE_CASE:		float = 0.0001       ,			_SCREAMING_SNAKE_CASE:		float = 0.02       ,			_SCREAMING_SNAKE_CASE:		str = "linear"       ,			_SCREAMING_SNAKE_CASE:		Optional[jnp.ndarray] = None       ,			_SCREAMING_SNAKE_CASE:		str = "fixed_small"       ,			_SCREAMING_SNAKE_CASE:		bool = True       ,			_SCREAMING_SNAKE_CASE:		str = "epsilon"       ,			_SCREAMING_SNAKE_CASE:		jnp.dtype = jnp.floataa       ,			)				->       Any:

				"""simple docstring"""

				__lowerCAmelCase      :  List[str]												=       dtype

	def 							_SCREAMING_SNAKE_CASE   (     self:		int       ,			_SCREAMING_SNAKE_CASE:		Optional[CommonSchedulerState] = None)				->       DDPMSchedulerState:

				"""simple docstring"""

				if common is None:
							__lowerCAmelCase      :  str												=       CommonSchedulerState.create(self)

				# standard deviation of the initial noise distribution
				__lowerCAmelCase      :  Tuple												=       jnp.array(1.0       ,			dtype=self.dtype)

				__lowerCAmelCase      :  List[str]												=       jnp.arange(0       ,			self.config.num_train_timesteps).round()[::-1]

				return DDPMSchedulerState.create(
				    common=_SCREAMING_SNAKE_CASE       ,			init_noise_sigma=_SCREAMING_SNAKE_CASE       ,			timesteps=_SCREAMING_SNAKE_CASE       ,			)

	def 							_SCREAMING_SNAKE_CASE   (     self:		List[str]       ,			_SCREAMING_SNAKE_CASE:		DDPMSchedulerState       ,			_SCREAMING_SNAKE_CASE:		jnp.ndarray       ,			_SCREAMING_SNAKE_CASE:		Optional[int] = None)				->       jnp.ndarray:

				"""simple docstring"""

				return sample

	def 							_SCREAMING_SNAKE_CASE   (     self:		Dict       ,			_SCREAMING_SNAKE_CASE:		DDPMSchedulerState       ,			_SCREAMING_SNAKE_CASE:		int       ,			_SCREAMING_SNAKE_CASE:		Tuple = ())				->       DDPMSchedulerState:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[int]												=       self.config.num_train_timesteps // num_inference_steps
				# creates integer timesteps by multiplying by ratio
				# rounding to avoid issues when num_inference_step is power of 3
				__lowerCAmelCase      :  Tuple												=       (jnp.arange(0       ,			_SCREAMING_SNAKE_CASE) * step_ratio).round()[::-1]

				return state.replace(
				    num_inference_steps=_SCREAMING_SNAKE_CASE       ,			timesteps=_SCREAMING_SNAKE_CASE       ,			)

	def 							_SCREAMING_SNAKE_CASE   (     self:		int       ,			_SCREAMING_SNAKE_CASE:		DDPMSchedulerState       ,			_SCREAMING_SNAKE_CASE:		Any       ,			_SCREAMING_SNAKE_CASE:		Optional[int]=None       ,			_SCREAMING_SNAKE_CASE:		Any=None)				->       List[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       state.common.alphas_cumprod[t]
				__lowerCAmelCase      :  int												=       jnp.where(t > 0       ,			state.common.alphas_cumprod[t - 1]       ,			jnp.array(1.0       ,			dtype=self.dtype))

				# 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
				__lowerCAmelCase      :  int												=       (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

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

				# hacks - were probably added for training stability
				if variance_type == "fixed_small":
							__lowerCAmelCase      :  Dict												=       jnp.clip(_SCREAMING_SNAKE_CASE       ,			a_min=1e-20)
				# for rl-diffuser https://arxiv.org/abs/2205.09991
				elif variance_type == "fixed_small_log":
							__lowerCAmelCase      :  Union[str, Any]												=       jnp.log(jnp.clip(_SCREAMING_SNAKE_CASE       ,			a_min=1e-20))
				elif variance_type == "fixed_large":
							__lowerCAmelCase      :  Any												=       state.common.betas[t]
				elif variance_type == "fixed_large_log":
							# Glide max_log
							__lowerCAmelCase      :  Tuple												=       jnp.log(state.common.betas[t])
				elif variance_type == "learned":
							return predicted_variance
				elif variance_type == "learned_range":
							__lowerCAmelCase      :  Union[str, Any]												=       variance
							__lowerCAmelCase      :  Optional[Any]												=       state.common.betas[t]
							__lowerCAmelCase      :  List[Any]												=       (predicted_variance + 1) / 2
							__lowerCAmelCase      :  Optional[int]												=       frac * max_log + (1 - frac) * min_log

				return variance

	def 							_SCREAMING_SNAKE_CASE   (     self:		List[Any]       ,			_SCREAMING_SNAKE_CASE:		DDPMSchedulerState       ,			_SCREAMING_SNAKE_CASE:		jnp.ndarray       ,			_SCREAMING_SNAKE_CASE:		int       ,			_SCREAMING_SNAKE_CASE:		jnp.ndarray       ,			_SCREAMING_SNAKE_CASE:		Optional[jax.random.KeyArray] = None       ,			_SCREAMING_SNAKE_CASE:		bool = True       ,			)				->       Union[FlaxDDPMSchedulerOutput, Tuple]:

				"""simple docstring"""

				__lowerCAmelCase      :  Any												=       timestep

				if key is None:
							__lowerCAmelCase      :  str												=       jax.random.PRNGKey(0)

				if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
							__lowerCAmelCase      :  Tuple												=       jnp.split(_SCREAMING_SNAKE_CASE       ,			sample.shape[1]       ,			axis=1)
				else:
							__lowerCAmelCase      :  Optional[int]												=       None

				# 1. compute alphas, betas
				__lowerCAmelCase      :  Union[str, Any]												=       state.common.alphas_cumprod[t]
				__lowerCAmelCase      :  int												=       jnp.where(t > 0       ,			state.common.alphas_cumprod[t - 1]       ,			jnp.array(1.0       ,			dtype=self.dtype))
				__lowerCAmelCase      :  Union[str, Any]												=       1 - alpha_prod_t
				__lowerCAmelCase      :  Tuple												=       1 - alpha_prod_t_prev

				# 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":
							__lowerCAmelCase      :  List[str]												=       (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
				elif self.config.prediction_type == "sample":
							__lowerCAmelCase      :  Dict												=       model_output
				elif self.config.prediction_type == "v_prediction":
							__lowerCAmelCase      :  List[str]												=       (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
				else:
							raise ValueError(
							    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` """
							    " for the FlaxDDPMScheduler.")

				# 3. Clip "predicted x_0"
				if self.config.clip_sample:
							__lowerCAmelCase      :  List[str]												=       jnp.clip(_SCREAMING_SNAKE_CASE       ,			-1       ,			1)

				# 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
				__lowerCAmelCase      :  Dict												=       (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
				__lowerCAmelCase      :  List[str]												=       state.common.alphas[t] ** 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
				__lowerCAmelCase      :  Tuple												=       pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

				# 6. Add noise
				def random_variance():
							__lowerCAmelCase      :  str												=       jax.random.split(_SCREAMING_SNAKE_CASE       ,			num=1)
							__lowerCAmelCase      :  List[Any]												=       jax.random.normal(_SCREAMING_SNAKE_CASE       ,			shape=model_output.shape       ,			dtype=self.dtype)
							return (self._get_variance(_SCREAMING_SNAKE_CASE       ,			_SCREAMING_SNAKE_CASE       ,			predicted_variance=_SCREAMING_SNAKE_CASE) ** 0.5) * noise

				__lowerCAmelCase      :  int												=       jnp.where(t > 0       ,			random_variance()       ,			jnp.zeros(model_output.shape       ,			dtype=self.dtype))

				__lowerCAmelCase      :  Union[str, Any]												=       pred_prev_sample + variance

				if not return_dict:
							return (pred_prev_sample, state)

				return FlaxDDPMSchedulerOutput(prev_sample=_SCREAMING_SNAKE_CASE       ,			state=_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE   (     self:		int       ,			_SCREAMING_SNAKE_CASE:		DDPMSchedulerState       ,			_SCREAMING_SNAKE_CASE:		jnp.ndarray       ,			_SCREAMING_SNAKE_CASE:		jnp.ndarray       ,			_SCREAMING_SNAKE_CASE:		jnp.ndarray       ,			)				->       jnp.ndarray:

				"""simple docstring"""

				return add_noise_common(state.common       ,			_SCREAMING_SNAKE_CASE       ,			_SCREAMING_SNAKE_CASE       ,			_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE   (     self:		Dict       ,			_SCREAMING_SNAKE_CASE:		DDPMSchedulerState       ,			_SCREAMING_SNAKE_CASE:		jnp.ndarray       ,			_SCREAMING_SNAKE_CASE:		jnp.ndarray       ,			_SCREAMING_SNAKE_CASE:		jnp.ndarray       ,			)				->       jnp.ndarray:

				"""simple docstring"""

				return get_velocity_common(state.common       ,			_SCREAMING_SNAKE_CASE       ,			_SCREAMING_SNAKE_CASE       ,			_SCREAMING_SNAKE_CASE)







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

				"""simple docstring"""

				return self.config.num_train_timesteps







"""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



