




# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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        __snake_case  :
				lowerCAmelCase__				=     42

				# setable values
				lowerCAmelCase__				=     42
				lowerCAmelCase__				=     42
				lowerCAmelCase__				=     None



				@classmethod
				def       SCREAMING_SNAKE_CASE       (      cls      :		Tuple		,	_UpperCAmelCase      :		CommonSchedulerState		,	_UpperCAmelCase      :		jnp.ndarray		,	_UpperCAmelCase      :		jnp.ndarray      )  ->    Dict:





								'''simple docstring'''




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






@dataclass
class        __snake_case  (_a		):
				lowerCAmelCase__				=     42






class        __snake_case  (_a						,	_a		):
				lowerCAmelCase__				=     [e.name for e in FlaxKarrasDiffusionSchedulers]

				lowerCAmelCase__				=     42



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





								'''simple docstring'''




								return True



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





								'''simple docstring'''




								_lowerCAmelCase    :    Tuple										=      dtype



				def       SCREAMING_SNAKE_CASE       (      self      :		Any		,	_UpperCAmelCase      :		Optional[CommonSchedulerState] = None      )  ->    DDPMSchedulerState:





								'''simple docstring'''




								if common is None:
												_lowerCAmelCase    :    Any										=      CommonSchedulerState.create(self      )

								# standard deviation of the initial noise distribution
								_lowerCAmelCase    :    Optional[Any]										=      jnp.array(1.0		,	dtype=self.dtype      )

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

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



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict		,	_UpperCAmelCase      :		DDPMSchedulerState		,	_UpperCAmelCase      :		jnp.ndarray		,	_UpperCAmelCase      :		Optional[int] = None      )  ->    jnp.ndarray:





								'''simple docstring'''




								return sample



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]		,	_UpperCAmelCase      :		DDPMSchedulerState		,	_UpperCAmelCase      :		int		,	_UpperCAmelCase      :		Tuple = ()      )  ->    DDPMSchedulerState:





								'''simple docstring'''




								_lowerCAmelCase    :    Union[str, Any]										=      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    :    Optional[Any]										=      (jnp.arange(0		,	_UpperCAmelCase      ) * step_ratio).round()[::-1]

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



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple		,	_UpperCAmelCase      :		DDPMSchedulerState		,	_UpperCAmelCase      :		Any		,	_UpperCAmelCase      :		int=None		,	_UpperCAmelCase      :		str=None      )  ->    List[str]:





								'''simple docstring'''




								_lowerCAmelCase    :    Union[str, Any]										=      state.common.alphas_cumprod[t]
								_lowerCAmelCase    :    Union[str, Any]										=      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    :    str										=      (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

								if variance_type is None:
												_lowerCAmelCase    :    Any										=      self.config.variance_type

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

								return variance



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





								'''simple docstring'''




								_lowerCAmelCase    :    Dict										=      timestep

								if key is None:
												_lowerCAmelCase    :    List[str]										=      jax.random.PRNGKey(0      )

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

								# 1. compute alphas, betas
								_lowerCAmelCase    :    Dict										=      state.common.alphas_cumprod[t]
								_lowerCAmelCase    :    List[Any]										=      jnp.where(t > 0		,	state.common.alphas_cumprod[t - 1]		,	jnp.array(1.0		,	dtype=self.dtype      )      )
								_lowerCAmelCase    :    Dict										=      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    :    Tuple										=      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
								elif self.config.prediction_type == "sample":
												_lowerCAmelCase    :    Optional[int]										=      model_output
								elif self.config.prediction_type == "v_prediction":
												_lowerCAmelCase    :    List[Any]										=      (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    :    str										=      jnp.clip(_UpperCAmelCase		,	-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    :    List[Any]										=      (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
								_lowerCAmelCase    :    Optional[int]										=      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    :    List[Any]										=      pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

								_lowerCAmelCase    :    Optional[Any]										=      jnp.where(t > 0		,	random_variance()		,	jnp.zeros(model_output.shape		,	dtype=self.dtype      )      )

								_lowerCAmelCase    :    int										=      pred_prev_sample + variance

								if not return_dict:
												return (pred_prev_sample, state)

								return FlaxDDPMSchedulerOutput(prev_sample=_UpperCAmelCase		,	state=_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple		,	_UpperCAmelCase      :		DDPMSchedulerState		,	_UpperCAmelCase      :		jnp.ndarray		,	_UpperCAmelCase      :		jnp.ndarray		,	_UpperCAmelCase      :		jnp.ndarray		,	)  ->    jnp.ndarray:





								'''simple docstring'''




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



				def       SCREAMING_SNAKE_CASE       (      self      :		Any		,	_UpperCAmelCase      :		DDPMSchedulerState		,	_UpperCAmelCase      :		jnp.ndarray		,	_UpperCAmelCase      :		jnp.ndarray		,	_UpperCAmelCase      :		jnp.ndarray		,	)  ->    jnp.ndarray:





								'''simple docstring'''




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



				def __len__(      self      :		Optional[int]      )  ->    List[Any]:





								'''simple docstring'''




								return self.config.num_train_timesteps







def 							_UpperCAmelCase  (UpperCamelCase_ : str       ):
				'''simple docstring'''
				_lowerCAmelCase    :    Dict										=      [0] * len(UpperCamelCase_       )

				for i in range(1      ,    len(UpperCamelCase_       )       ):
								# use last results for better performance - dynamic programming
								_lowerCAmelCase    :    Union[str, Any]										=      prefix_result[i - 1]
								while j > 0 and input_string[i] != input_string[j]:
												_lowerCAmelCase    :    str										=      prefix_result[j - 1]

								if input_string[i] == input_string[j]:
												j += 1
								_lowerCAmelCase    :    Optional[int]										=      j

				return prefix_result




def 							_UpperCAmelCase  (UpperCamelCase_ : str       ):
				'''simple docstring'''
				return max(prefix_function(UpperCamelCase_       )       )


if __name__ == "__main__":
	import doctest

	doctest.testmod()

