
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left


@flax.struct.dataclass
class 	UpperCamelCase__       :

						'''simple docstring'''



						__a     :    Optional[jnp.ndarray]												=			None
						__a     :    Optional[jnp.ndarray]												=			None
						__a     :    Optional[jnp.ndarray]												=			None


						@classmethod
						def    A__       (  cls	)  ->List[Any]:
									return cls()


@dataclass
class 	UpperCamelCase__       (  UpperCAmelCase__):

						'''simple docstring'''



						__a     :    ScoreSdeVeSchedulerState
						__a     :    jnp.ndarray
						__a     :    Optional[jnp.ndarray]												=			None


class 	UpperCamelCase__       (  UpperCAmelCase__						,			UpperCAmelCase__):

						'''simple docstring'''



						@property
						def    A__       (  self	)  ->Any:
									return True


						@register_to_config
						def __init__(  self  , A = 20_00  , A = 0.15  , A = 0.01  , A = 1348.0  , A = 1e-5  , A = 1  , )  ->int:
									pass


						def    A__       (  self	)  ->List[Any]:
									UpperCAmelCase__		:Optional[int] 		=    ScoreSdeVeSchedulerState.create()
									return self.set_sigmas(
									    A  , self.config.num_train_timesteps  , self.config.sigma_min  , self.config.sigma_max  , self.config.sampling_eps  , )


						def    A__       (  self  , A  , A  , A = ()  , A = None	)  ->ScoreSdeVeSchedulerState:
									UpperCAmelCase__		:str 		=    sampling_eps if sampling_eps is not None else self.config.sampling_eps

									UpperCAmelCase__		:Optional[Any] 		=    jnp.linspace(1  , A  , A	)
									return state.replace(timesteps=A	)


						def    A__       (  self  , A  , A  , A = None  , A = None  , A = None  , )  ->ScoreSdeVeSchedulerState:
									UpperCAmelCase__		:List[Any] 		=    sigma_min if sigma_min is not None else self.config.sigma_min
									UpperCAmelCase__		:List[str] 		=    sigma_max if sigma_max is not None else self.config.sigma_max
									UpperCAmelCase__		:List[str] 		=    sampling_eps if sampling_eps is not None else self.config.sampling_eps
									if state.timesteps is None:
												UpperCAmelCase__		:Union[str, Any] 		=    self.set_timesteps(A  , A  , A	)

									UpperCAmelCase__		:str 		=    jnp.exp(jnp.linspace(jnp.log(A	)  , jnp.log(A	)  , A	)	)
									UpperCAmelCase__		:Optional[int] 		=    jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]	)

									return state.replace(discrete_sigmas=A  , sigmas=A	)


						def    A__       (  self  , A  , A  , A	)  ->List[Any]:
									return jnp.where(timesteps == 0  , jnp.zeros_like(A	)  , state.discrete_sigmas[timesteps - 1]	)


						def    A__       (  self  , A  , A  , A  , A  , A  , A = True  , )  ->Union[FlaxSdeVeOutput, Tuple]:
									if state.timesteps is None:
												raise ValueError(
												    '`state.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'	)

									UpperCAmelCase__		:Tuple 		=    timestep * jnp.ones(
									    sample.shape[0]  , )
									UpperCAmelCase__		:Optional[int] 		=    (timestep * (len(state.timesteps	) - 1)).long()

									UpperCAmelCase__		:List[str] 		=    state.discrete_sigmas[timesteps]
									UpperCAmelCase__		:str 		=    self.get_adjacent_sigma(A  , A  , A	)
									UpperCAmelCase__		:Optional[Any] 		=    jnp.zeros_like(A	)
									UpperCAmelCase__		:List[str] 		=    (sigma**2 - adjacent_sigma**2) ** 0.5

									# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
									# also equation 47 shows the analog from SDE models to ancestral sampling methods
									UpperCAmelCase__		:str 		=    diffusion.flatten()
									UpperCAmelCase__		:str 		=    broadcast_to_shape_from_left(A  , sample.shape	)
									UpperCAmelCase__		:Tuple 		=    drift - diffusion**2 * model_output

									#  equation 6: sample noise for the diffusion term of
									UpperCAmelCase__		:str 		=    random.split(A  , num=1	)
									UpperCAmelCase__		:Dict 		=    random.normal(key=A  , shape=sample.shape	)
									UpperCAmelCase__		:Any 		=    sample - drift  # subtract because `dt` is a small negative timestep
									# TODO is the variable diffusion the correct scaling term for the noise?
									UpperCAmelCase__		:List[str] 		=    prev_sample_mean + diffusion * noise  # add impact of diffusion field g

									if not return_dict:
												return (prev_sample, prev_sample_mean, state)

									return FlaxSdeVeOutput(prev_sample=A  , prev_sample_mean=A  , state=A	)


						def    A__       (  self  , A  , A  , A  , A  , A = True  , )  ->Union[FlaxSdeVeOutput, Tuple]:
									if state.timesteps is None:
												raise ValueError(
												    '`state.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'	)

									# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
									# sample noise for correction
									UpperCAmelCase__		:Any 		=    random.split(A  , num=1	)
									UpperCAmelCase__		:Optional[int] 		=    random.normal(key=A  , shape=sample.shape	)

									# compute step size from the model_output, the noise, and the snr
									UpperCAmelCase__		:str 		=    jnp.linalg.norm(A	)
									UpperCAmelCase__		:Dict 		=    jnp.linalg.norm(A	)
									UpperCAmelCase__		:Tuple 		=    (self.config.snr * noise_norm / grad_norm) ** 2 * 2
									UpperCAmelCase__		:Tuple 		=    step_size * jnp.ones(sample.shape[0]	)

									# compute corrected sample: model_output term and noise term
									UpperCAmelCase__		:Optional[Any] 		=    step_size.flatten()
									UpperCAmelCase__		:Optional[Any] 		=    broadcast_to_shape_from_left(A  , sample.shape	)
									UpperCAmelCase__		:List[str] 		=    sample + step_size * model_output
									UpperCAmelCase__		:Any 		=    prev_sample_mean + ((step_size * 2) ** 0.5) * noise

									if not return_dict:
												return (prev_sample, state)

									return FlaxSdeVeOutput(prev_sample=A  , state=A	)


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







from __future__ import annotations




def 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						):




			"""simple docstring"""


			if not all((nth_term, start_term_a, common_ratio_r)							):
						return []
			UpperCAmelCase__		:list[float | int] 		=    []
			UpperCAmelCase__		:str 		=    1
			UpperCAmelCase__		:Any 		=    common_ratio_r
			for _ in range(int(SCREAMING_SNAKE_CASE							)							):
						if not series:
									series.append(SCREAMING_SNAKE_CASE							)
						else:
									power += 1
									series.append(float(start_term_a * multiple							)							)
									UpperCAmelCase__		:str 		=    pow(float(SCREAMING_SNAKE_CASE							)     ,						SCREAMING_SNAKE_CASE							)
			return series


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						__snake_case					:							Optional[int]           =							float(input('Enter the last number (n term) of the Geometric Series'))
						__snake_case					:							List[str]           =							float(input('Enter the starting term (a) of the Geometric Series'))
						__snake_case					:							Optional[int]           =							float(
						    input('Enter the common ratio between two terms (r) of the Geometric Series')
						)
						print('Formula of Geometric Series => a + ar + ar^2 ... +ar^n')
						print(geometric_series(nth_term, start_term_a, common_ratio_r))





