

# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

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     SCREAMING_SNAKE_CASE__     :





       '''simple docstring'''




       UpperCamelCase__ :     CommonSchedulerState
       UpperCamelCase__ :     jnp.ndarray

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

       @classmethod
       def UpperCAmelCase_						(						cls       :						Any    ,		lowerCAmelCase__       :						CommonSchedulerState    ,		lowerCAmelCase__       :						jnp.ndarray    ,		lowerCAmelCase__       :						jnp.ndarray    ,		lowerCAmelCase__       :						jnp.ndarray    ,		)			->    str:
             return cls(
                 common=lowerCAmelCase__    ,		final_alpha_cumprod=lowerCAmelCase__    ,		init_noise_sigma=lowerCAmelCase__    ,		timesteps=lowerCAmelCase__    ,		)



@dataclass
class     SCREAMING_SNAKE_CASE__     (						__a		):





       '''simple docstring'''




       UpperCamelCase__ :     DDIMSchedulerState



class     SCREAMING_SNAKE_CASE__     (						__a ,	__a		):





       '''simple docstring'''




       UpperCamelCase__ :     List[str]				    =       [e.name for e in FlaxKarrasDiffusionSchedulers]

       UpperCamelCase__ :     jnp.dtype

       @property
       def UpperCAmelCase_						(						self       :						Union[str, Any]							)			->    Optional[int]:
             return True

       @register_to_config
       def __init__(						self       :						str    ,		lowerCAmelCase__       :						int = 1000    ,		lowerCAmelCase__       :						float = 0.0_001    ,		lowerCAmelCase__       :						float = 0.02    ,		lowerCAmelCase__       :						str = "linear"    ,		lowerCAmelCase__       :						Optional[jnp.ndarray] = None    ,		lowerCAmelCase__       :						bool = True    ,		lowerCAmelCase__       :						int = 0    ,		lowerCAmelCase__       :						str = "epsilon"    ,		lowerCAmelCase__       :						jnp.dtype = jnp.floataa    ,		)			->    List[Any]:
             snake_case__					=   dtype

       def UpperCAmelCase_						(						self       :						int    ,		lowerCAmelCase__       :						Optional[CommonSchedulerState] = None							)			->    DDIMSchedulerState:
             if common is None:
                   snake_case__					=   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.
             snake_case__					=   (
                 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
             snake_case__					=   jnp.array(1.0    ,		dtype=self.dtype							)

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

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

       def UpperCAmelCase_						(						self       :						int    ,		lowerCAmelCase__       :						DDIMSchedulerState    ,		lowerCAmelCase__       :						jnp.ndarray    ,		lowerCAmelCase__       :						Optional[int] = None							)			->    jnp.ndarray:
             return sample

       def UpperCAmelCase_						(						self       :						int    ,		lowerCAmelCase__       :						DDIMSchedulerState    ,		lowerCAmelCase__       :						int    ,		lowerCAmelCase__       :						Tuple = ()							)			->    DDIMSchedulerState:
             snake_case__					=   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
             snake_case__					=   (jnp.arange(0    ,		lowerCAmelCase__							) * step_ratio).round()[::-1] + self.config.steps_offset

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

       def UpperCAmelCase_						(						self       :						Union[str, Any]    ,		lowerCAmelCase__       :						DDIMSchedulerState    ,		lowerCAmelCase__       :						Dict    ,		lowerCAmelCase__       :						List[str]							)			->    Union[str, Any]:
             snake_case__					=   state.common.alphas_cumprod[timestep]
             snake_case__					=   jnp.where(
                 prev_timestep >= 0    ,		state.common.alphas_cumprod[prev_timestep]    ,		state.final_alpha_cumprod							)
             snake_case__					=   1 - alpha_prod_t
             snake_case__					=   1 - alpha_prod_t_prev

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

             return variance

       def UpperCAmelCase_						(						self       :						List[str]    ,		lowerCAmelCase__       :						DDIMSchedulerState    ,		lowerCAmelCase__       :						jnp.ndarray    ,		lowerCAmelCase__       :						int    ,		lowerCAmelCase__       :						jnp.ndarray    ,		lowerCAmelCase__       :						float = 0.0    ,		lowerCAmelCase__       :						bool = 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)
             snake_case__					=   timestep - self.config.num_train_timesteps // state.num_inference_steps

             snake_case__					=   state.common.alphas_cumprod
             snake_case__					=   state.final_alpha_cumprod

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

             snake_case__					=   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":
                   snake_case__					=   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
                   snake_case__					=   model_output
             elif self.config.prediction_type == "sample":
                   snake_case__					=   model_output
                   snake_case__					=   (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
             elif self.config.prediction_type == "v_prediction":
                   snake_case__					=   (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
                   snake_case__					=   (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)
             snake_case__					=   self._get_variance(lowerCAmelCase__    ,		lowerCAmelCase__    ,		lowerCAmelCase__							)
             snake_case__					=   eta * variance ** 0.5

             # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
             snake_case__					=   (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
             snake_case__					=   alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

             if not return_dict:
                   return (prev_sample, state)

             return FlaxDDIMSchedulerOutput(prev_sample=lowerCAmelCase__    ,		state=lowerCAmelCase__							)

       def UpperCAmelCase_						(						self       :						Dict    ,		lowerCAmelCase__       :						DDIMSchedulerState    ,		lowerCAmelCase__       :						jnp.ndarray    ,		lowerCAmelCase__       :						jnp.ndarray    ,		lowerCAmelCase__       :						jnp.ndarray    ,		)			->    jnp.ndarray:
             return add_noise_common(state.common    ,		lowerCAmelCase__    ,		lowerCAmelCase__    ,		lowerCAmelCase__							)

       def UpperCAmelCase_						(						self       :						Optional[int]    ,		lowerCAmelCase__       :						DDIMSchedulerState    ,		lowerCAmelCase__       :						jnp.ndarray    ,		lowerCAmelCase__       :						jnp.ndarray    ,		lowerCAmelCase__       :						jnp.ndarray    ,		)			->    jnp.ndarray:
             return get_velocity_common(state.common    ,		lowerCAmelCase__    ,		lowerCAmelCase__    ,		lowerCAmelCase__							)

       def __len__(						self       :						int							)			->    Any:
             return self.config.num_train_timesteps









lowerCAmelCase						:   Optional[Any]										=    1000





def 			_lowercase    (      __UpperCamelCase     :		float ):
      return x * x





def 			_lowercase    (      __UpperCamelCase     :		int   ,       __UpperCamelCase     :		float   ,       __UpperCamelCase     :		float   ,       __UpperCamelCase     :		int = 4 ):
      assert callable(
          __UpperCamelCase ), F'''the function(object) passed should be callable your input : {function}'''
      assert isinstance(__UpperCamelCase   ,       (float, int) ), F'''a should be float or integer your input : {a}'''
      assert isinstance(function(__UpperCamelCase )   ,       (float, int) ), (
          "the function should return integer or float return type of your function, "
          F'''{type(__UpperCamelCase )}'''
      )
      assert isinstance(__UpperCamelCase   ,       (float, int) ), F'''b should be float or integer your input : {b}'''
      assert (
          isinstance(__UpperCamelCase   ,       __UpperCamelCase ) and precision > 0
      ), F'''precision should be positive integer your input : {precision}'''

      # just applying the formula of simpson for approximate integration written in
      # mentioned article in first comment of this file and above this function

      snake_case__					=   (b - a) / N_STEPS
      snake_case__					=   function(__UpperCamelCase ) + function(__UpperCamelCase )

      for i in range(1   ,       __UpperCamelCase ):
            snake_case__					=   a + h * i
            result += function(__UpperCamelCase ) * (4 if i % 2 else 2)

      result *= h / 3
      return round(__UpperCamelCase   ,       __UpperCamelCase )


if __name__ == "__main__":
       import doctest

       doctest.testmod()






