






"""simple docstring"""


# 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 __a :
  __lowercase     :     Union[str, Any]       						=       42
  __lowercase     :     Optional[Any]       						=       42

  # setable values
  __lowercase     :     Optional[int]       						=       42
  __lowercase     :     Optional[Any]       						=       42
  __lowercase     :     Any       						=       None



  @classmethod
  def     A_					(cls	:		Any					,      lowercase_	:		int					,      lowercase_	:		List[str]					,      lowercase_	:		Tuple					,      lowercase_	:		Any					,      )		->				Union[str, Any]:
   return cls(
       common=SCREAMING_SNAKE_CASE_					,      final_alpha_cumprod=SCREAMING_SNAKE_CASE_					,      init_noise_sigma=SCREAMING_SNAKE_CASE_					,      timesteps=SCREAMING_SNAKE_CASE_					,      )






@dataclass
class __a ( SCREAMING_SNAKE_CASE_					):
  __lowercase     :     Optional[int]       						=       42






class __a ( SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_					):
  __lowercase     :     Tuple       						=       [e.name for e in FlaxKarrasDiffusionSchedulers]

  __lowercase     :     Optional[Any]       						=       42



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



  @register_to_config
  def __init__(self	:		Optional[Any]					,      lowercase_	:		Any = 1_000					,      lowercase_	:		Union[str, Any] = 0.0001					,      lowercase_	:		str = 0.02					,      lowercase_	:		Optional[Any] = "linear"					,      lowercase_	:		List[Any] = None					,      lowercase_	:		int = True					,      lowercase_	:		List[Any] = 0					,      lowercase_	:		Optional[Any] = "epsilon"					,      lowercase_	:		Optional[Any] = jnp.floataa					,      )		->				str:
   __A						:							Optional[Any]      =						dtype



  def     A_					(self	:		List[str]					,      lowercase_	:		Any = None )		->				DDIMSchedulerState:






   if common is None:
    __A						:							Any      =						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.
   __A						:							int      =						(
       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
   __A						:							Optional[Any]      =						jnp.array(1.0					,      dtype=self.dtype )

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

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



  def     A_					(self	:		Tuple					,      lowercase_	:		str					,      lowercase_	:		Tuple					,      lowercase_	:		str = None )		->				jnp.ndarray:
   return sample



  def     A_					(self	:		Dict					,      lowercase_	:		int					,      lowercase_	:		str					,      lowercase_	:		List[Any] = () )		->				DDIMSchedulerState:
   __A						:							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
   __A						:							List[str]      =						(jnp.arange(0					,      SCREAMING_SNAKE_CASE_ ) * step_ratio).round()[::-1] + self.config.steps_offset

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



  def     A_					(self	:		Dict					,      lowercase_	:		Dict					,      lowercase_	:		str					,      lowercase_	:		Any )		->				Optional[int]:
   __A						:							Tuple      =						state.common.alphas_cumprod[timestep]
   __A						:							Dict      =						jnp.where(
       prev_timestep >= 0					,      state.common.alphas_cumprod[prev_timestep]					,      state.final_alpha_cumprod )
   __A						:							Tuple      =						1 - alpha_prod_t
   __A						:							Dict      =						1 - alpha_prod_t_prev

   __A						:							Optional[int]      =						(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

   return variance



  def     A_					(self	:		int					,      lowercase_	:		str					,      lowercase_	:		Any					,      lowercase_	:		int					,      lowercase_	:		List[str]					,      lowercase_	:		List[Any] = 0.0					,      lowercase_	:		Optional[int] = 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)
   __A						:							List[str]      =						timestep - self.config.num_train_timesteps // state.num_inference_steps

   __A						:							Optional[int]      =						state.common.alphas_cumprod
   __A						:							Dict      =						state.final_alpha_cumprod

   # 2. compute alphas, betas
   __A						:							List[Any]      =						alphas_cumprod[timestep]
   __A						:							str      =						jnp.where(prev_timestep >= 0					,      alphas_cumprod[prev_timestep]					,      SCREAMING_SNAKE_CASE_ )

   __A						:							str      =						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":
    __A						:							Optional[Any]      =						(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
    __A						:							Tuple      =						model_output
   elif self.config.prediction_type == "sample":
    __A						:							str      =						model_output
    __A						:							List[str]      =						(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
   elif self.config.prediction_type == "v_prediction":
    __A						:							Tuple      =						(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
    __A						:							str      =						(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)
   __A						:							Union[str, Any]      =						self._get_variance(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_ )
   __A						:							List[str]      =						eta * variance ** 0.5

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

   if not return_dict:
    return (prev_sample, state)

   return FlaxDDIMSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_					,      state=SCREAMING_SNAKE_CASE_ )



  def     A_					(self	:		List[Any]					,      lowercase_	:		Any					,      lowercase_	:		List[str]					,      lowercase_	:		Optional[Any]					,      lowercase_	:		str					,      )		->				jnp.ndarray:
   return add_noise_common(state.common					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_ )



  def     A_					(self	:		str					,      lowercase_	:		Union[str, Any]					,      lowercase_	:		Optional[Any]					,      lowercase_	:		Optional[Any]					,      lowercase_	:		List[str]					,      )		->				jnp.ndarray:
   return get_velocity_common(state.common					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_ )



  def __len__(self	:		Tuple )		->				Union[str, Any]:
   return self.config.num_train_timesteps







def 						A_			(				snake_case				: Union[str, Any]			,   snake_case				: Dict			)   ->     Dict:



						'''simple docstring'''

						__UpperCamelCase  	=      (boundary[1] - boundary[0]) / steps
						__UpperCamelCase  	=      boundary[0]
						__UpperCamelCase  	=      boundary[1]
						__UpperCamelCase  	=      make_points(snake_case			,   snake_case			,   snake_case			)
						__UpperCamelCase  	=      0.0
						y += (h / 3.0) * f(snake_case			)
						__UpperCamelCase  	=      2
						for i in x_i:
												y += (h / 3) * (4 - 2 * (cnt % 2)) * f(snake_case			)
												cnt += 1
						y += (h / 3.0) * f(snake_case			)
						return y




def 						A_			(				snake_case				: str			,   snake_case				: Optional[int]			,   snake_case				: Any			)   ->     Optional[int]:



						'''simple docstring'''

						__UpperCamelCase  	=      a + h
						while x < (b - h):
												yield x
												__UpperCamelCase  	=      x + h




def 						A_			(				snake_case				: Union[str, Any]			)   ->     str:  # enter your function here



						'''simple docstring'''

						__UpperCamelCase  	=      (x - 0) * (x - 0)
						return y




def 						A_			(				)   ->     Dict:



						'''simple docstring'''

						__UpperCamelCase  	=      0.0  # Lower bound of integration
						__UpperCamelCase  	=      1.0  # Upper bound of integration
						__UpperCamelCase  	=      10.0  # define number of steps or resolution
						__UpperCamelCase  	=      [a, b]  # define boundary of integration
						__UpperCamelCase  	=      method_a(snake_case			,   snake_case			)
						print(f"y = {y}"			)


if __name__ == "__main__":
				main()




