




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

import flax
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"""

     _snake_case:		CommonSchedulerState
     _snake_case:		jnp.ndarray

     # setable values
     _snake_case:		jnp.ndarray
     _snake_case:		jnp.ndarray
     _snake_case:		Optional[int]       =None




     @classmethod
     def 	__snake_case				(      cls					:				List[Any]		,					A__					:				Optional[Any]		,					A__					:				Optional[Any]		,					A__					:				Union[str, Any]		,					A__					:				str		,					)			->    List[str]:
           return cls(
               common=lowercase__		,					final_alpha_cumprod=lowercase__		,					init_noise_sigma=lowercase__		,					timesteps=lowercase__		,					)






@dataclass
class      A     (						_lowerCAmelCase						):
     """simple docstring"""

     _snake_case:		DDIMSchedulerState






class      A     (						_lowerCAmelCase     ,  _lowerCAmelCase						):
     """simple docstring"""

     _snake_case:		Optional[int]       =[e.name for e in FlaxKarrasDiffusionSchedulers]

     _snake_case:		jnp.dtype




     @property
     def 	__snake_case				(      self					:				List[Any]     )			->    str:
           return True




     @register_to_config
     def __init__(      self					:				Union[str, Any]		,					A__					:				Dict = 1_0_0_0		,					A__					:				Tuple = 0.0001		,					A__					:				List[Any] = 0.02		,					A__					:				Dict = "linear"		,					A__					:				Any = None		,					A__					:				Optional[Any] = True		,					A__					:				Tuple = 0		,					A__					:				Dict = "epsilon"		,					A__					:				Dict = jnp.floataa		,					)			->    int:
           lowerCamelCase__ 					=			dtype




     def 	__snake_case				(      self					:				Dict		,					A__					:				str = None     )			->    DDIMSchedulerState:







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

           # At every step in ddim, we are looking into the previous alphas_cumprod
           # For the final step, there is no previous alphas_cumprod because we are already at 0
           # `set_alpha_to_one` decides whether we set this parameter simply to one or
           # whether we use the final alpha of the "non-previous" one.
           lowerCamelCase__ 					=			(
               jnp.array(1.0		,					dtype=self.dtype     ) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
           )

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

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

           return DDIMSchedulerState.create(
               common=lowercase__		,					final_alpha_cumprod=lowercase__		,					init_noise_sigma=lowercase__		,					timesteps=lowercase__		,					)




     def 	__snake_case				(      self					:				Optional[int]		,					A__					:				Tuple		,					A__					:				Optional[int]		,					A__					:				str = None     )			->    jnp.ndarray:
           return sample




     def 	__snake_case				(      self					:				int		,					A__					:				Optional[Any]		,					A__					:				Dict		,					A__					:				int = ()     )			->    DDIMSchedulerState:
           lowerCamelCase__ 					=			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__ 					=			(jnp.arange(0		,					lowercase__     ) * step_ratio).round()[::-1] + self.config.steps_offset

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




     def 	__snake_case				(      self					:				Union[str, Any]		,					A__					:				Optional[Any]		,					A__					:				Optional[Any]		,					A__					:				List[Any]     )			->    Optional[Any]:
           lowerCamelCase__ 					=			state.common.alphas_cumprod[timestep]
           lowerCamelCase__ 					=			jnp.where(
               prev_timestep >= 0		,					state.common.alphas_cumprod[prev_timestep]		,					state.final_alpha_cumprod     )
           lowerCamelCase__ 					=			1 - alpha_prod_t
           lowerCamelCase__ 					=			1 - alpha_prod_t_prev

           lowerCamelCase__ 					=			(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

           return variance




     def 	__snake_case				(      self					:				Dict		,					A__					:				int		,					A__					:				Optional[Any]		,					A__					:				Union[str, Any]		,					A__					:				str		,					A__					:				Optional[int] = 0.0		,					A__					:				Optional[Any] = True		,					)			->    Union[FlaxDDIMSchedulerOutput, Tuple]:







           if state.num_inference_steps is None:
                 raise ValueError(
                     'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'     )

           # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
           # Ideally, read DDIM paper in-detail understanding

           # Notation (<variable name> -> <name in paper>
           # - pred_noise_t -> e_theta(x_t, t)
           # - pred_original_sample -> f_theta(x_t, t) or x_0
           # - std_dev_t -> sigma_t
           # - eta -> η
           # - pred_sample_direction -> "direction pointing to x_t"
           # - pred_prev_sample -> "x_t-1"

           # 1. get previous step value (=t-1)
           lowerCamelCase__ 					=			timestep - self.config.num_train_timesteps // state.num_inference_steps

           lowerCamelCase__ 					=			state.common.alphas_cumprod
           lowerCamelCase__ 					=			state.final_alpha_cumprod

           # 2. compute alphas, betas
           lowerCamelCase__ 					=			alphas_cumprod[timestep]
           lowerCamelCase__ 					=			jnp.where(prev_timestep >= 0		,					alphas_cumprod[prev_timestep]		,					lowercase__     )

           lowerCamelCase__ 					=			1 - alpha_prod_t

           # 3. compute predicted original sample from predicted noise also called
           # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
           if self.config.prediction_type == "epsilon":
                 lowerCamelCase__ 					=			(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
                 lowerCamelCase__ 					=			model_output
           elif self.config.prediction_type == "sample":
                 lowerCamelCase__ 					=			model_output
                 lowerCamelCase__ 					=			(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
           elif self.config.prediction_type == "v_prediction":
                 lowerCamelCase__ 					=			(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
                 lowerCamelCase__ 					=			(alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
           else:
                 raise ValueError(
                     F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
                     ' `v_prediction`'     )

           # 4. compute variance: "sigma_t(η)" -> see formula (16)
           # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
           lowerCamelCase__ 					=			self._get_variance(lowercase__		,					lowercase__		,					lowercase__     )
           lowerCamelCase__ 					=			eta * variance ** 0.5

           # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
           lowerCamelCase__ 					=			(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

           # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
           lowerCamelCase__ 					=			alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

           if not return_dict:
                 return (prev_sample, state)

           return FlaxDDIMSchedulerOutput(prev_sample=lowercase__		,					state=lowercase__     )




     def 	__snake_case				(      self					:				Tuple		,					A__					:				int		,					A__					:				Dict		,					A__					:				Union[str, Any]		,					A__					:				Optional[Any]		,					)			->    jnp.ndarray:
           return add_noise_common(state.common		,					lowercase__		,					lowercase__		,					lowercase__     )




     def 	__snake_case				(      self					:				Dict		,					A__					:				Any		,					A__					:				Optional[Any]		,					A__					:				str		,					A__					:				Any		,					)			->    jnp.ndarray:
           return get_velocity_common(state.common		,					lowercase__		,					lowercase__		,					lowercase__     )




     def __len__(      self					:				Optional[int]     )			->    int:
           return self.config.num_train_timesteps






from __future__ import annotations

def      lowerCAmelCase_     (					lowerCamelCase:    list[int | float]  )   ->     int | float:


      '''simple docstring'''




      if len(lowerCamelCase  ) == 0:
            raise ValueError('find_max() arg is an empty sequence'  )
      lowerCamelCase__ 					=			nums[0]
      for x in nums:
            if x > max_num:
                  lowerCamelCase__ 					=			x
      return max_num


if __name__ == "__main__":
      import doctest

      doctest.testmod(verbose=True)