



"""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  _snake_case		:







				"""simple docstring"""






				a			=						42

				# setable values
				a			=						42
				a			=						42
				a			=						None





				@classmethod
				def 		_lowerCAmelCase		(      cls							:							str      ,    _A							:							CommonSchedulerState      ,    _A							:							jnp.ndarray      ,    _A							:							jnp.ndarray):



											"""simple docstring"""







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




@dataclass
class  _snake_case		(    __snake_case ):







				"""simple docstring"""






				a			=						42




class  _snake_case		(    __snake_case   ,       __snake_case ):







				"""simple docstring"""






				a			=						[e.name for e in FlaxKarrasDiffusionSchedulers]

				a			=						42





				@property
				def 		_lowerCAmelCase		(      self							:							Union[str, Any]):



											"""simple docstring"""







											return True





				@register_to_config
				def __init__(      self							:							Optional[int]      ,    _A							:							int = 1_0_0_0      ,    _A							:							float = 0.0_001      ,    _A							:							float = 0.02      ,    _A							:							str = "linear"      ,    _A							:							Optional[jnp.ndarray] = None      ,    _A							:							str = "fixed_small"      ,    _A							:							bool = True      ,    _A							:							str = "epsilon"      ,    _A							:							jnp.dtype = jnp.floataa      ,    ):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    dtype





				def 		_lowerCAmelCase		(      self							:							Dict      ,    _A							:							Optional[CommonSchedulerState] = None):



											"""simple docstring"""







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

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

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

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





				def 		_lowerCAmelCase		(      self							:							List[str]      ,    _A							:							DDPMSchedulerState      ,    _A							:							jnp.ndarray      ,    _A							:							Optional[int] = None):



											"""simple docstring"""







											return sample





				def 		_lowerCAmelCase		(      self							:							Any      ,    _A							:							DDPMSchedulerState      ,    _A							:							int      ,    _A							:							Tuple = ()):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[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
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    (jnp.arange(0      ,    _A) * step_ratio).round()[::-1]

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





				def 		_lowerCAmelCase		(      self							:							Dict      ,    _A							:							DDPMSchedulerState      ,    _A							:							Union[str, Any]      ,    _A							:							Tuple=None      ,    _A							:							List[Any]=None):



											"""simple docstring"""







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

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

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

											return variance





				def 		_lowerCAmelCase		(      self							:							Dict      ,    _A							:							DDPMSchedulerState      ,    _A							:							jnp.ndarray      ,    _A							:							int      ,    _A							:							jnp.ndarray      ,    _A							:							Optional[jax.random.KeyArray] = None      ,    _A							:							bool = True      ,    ):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    timestep

											if key is None:
																		_SCREAMING_SNAKE_CASE		:						Tuple			  =    jax.random.PRNGKey(0)

											if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
																		_SCREAMING_SNAKE_CASE		:						List[str]			  =    jnp.split(_A      ,    sample.shape[1]      ,    axis=1)
											else:
																		_SCREAMING_SNAKE_CASE		:						Dict			  =    None

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

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

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

											_SCREAMING_SNAKE_CASE		:						List[str]			  =    pred_prev_sample + variance

											if not return_dict:
																		return (pred_prev_sample, state)

											return FlaxDDPMSchedulerOutput(prev_sample=_A      ,    state=_A)





				def 		_lowerCAmelCase		(      self							:							Optional[Any]      ,    _A							:							DDPMSchedulerState      ,    _A							:							jnp.ndarray      ,    _A							:							jnp.ndarray      ,    _A							:							jnp.ndarray      ,    ):



											"""simple docstring"""







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





				def 		_lowerCAmelCase		(      self							:							Any      ,    _A							:							DDPMSchedulerState      ,    _A							:							jnp.ndarray      ,    _A							:							jnp.ndarray      ,    _A							:							jnp.ndarray      ,    ):



											"""simple docstring"""







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





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



											"""simple docstring"""







											return self.config.num_train_timesteps



"""simple docstring"""





def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    int:

							if not isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ):
														raise TypeError("""only integers accepted as input"""  )
							else:
														_SCREAMING_SNAKE_CASE		:						List[Any]			  =    str(abs(__SCREAMING_SNAKE_CASE  )  )
														_SCREAMING_SNAKE_CASE		:						List[str]			  =    [list(__SCREAMING_SNAKE_CASE  ) for char in range(len(__SCREAMING_SNAKE_CASE  )  )]
														for index in range(len(__SCREAMING_SNAKE_CASE  )  ):
																					num_transpositions[index].pop(__SCREAMING_SNAKE_CASE  )
														return max(
														    int("""""".join(list(__SCREAMING_SNAKE_CASE  )  )  ) for transposition in num_transpositions  )


if __name__ == "__main__":
			__import__('''doctest''').testmod()


