

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			:
			__a :       Dict									=							None
			__a :       Tuple									=							None
			__a :       List[Any]									=							None
			@classmethod
			def 							__snake_case (		cls     )    ->    Any:






				'''simple docstring'''







				return cls()






@dataclass
class     _UpperCAmelCase			(		A__):
			__a :       Any									=							4_2
			__a :       int									=							4_2
			__a :       Any									=							None






class     _UpperCAmelCase			(		A__    ,     A__):
			@property
			def 							__snake_case (		self     )    ->    str:






				'''simple docstring'''







				return True
			@register_to_config
			def __init__(		self      ,		_A = 20_00      ,		_A = 0.15      ,		_A = 0.01      ,		_A = 13_48.0      ,		_A = 1e-5      ,		_A = 1      ,		)    ->    List[Any]:






				'''simple docstring'''







				pass
			def 							__snake_case (		self     )    ->    Tuple:






				'''simple docstring'''







				_UpperCAmelCase    :							List[str]									=					ScoreSdeVeSchedulerState.create()
				return self.set_sigmas(
				    _lowerCAmelCase      ,		self.config.num_train_timesteps      ,		self.config.sigma_min      ,		self.config.sigma_max      ,		self.config.sampling_eps      ,		)
			def 							__snake_case (		self      ,		_A      ,		_A      ,		_A = ()      ,		_A = None     )    ->    List[str]:






				'''simple docstring'''







				_UpperCAmelCase    :							Optional[int]									=					sampling_eps if sampling_eps is not None else self.config.sampling_eps

				_UpperCAmelCase    :							int									=					jnp.linspace(1      ,		_lowerCAmelCase      ,		_lowerCAmelCase     )
				return state.replace(timesteps=_lowerCAmelCase     )
			def 							__snake_case (		self      ,		_A      ,		_A      ,		_A = None      ,		_A = None      ,		_A = None      ,		)    ->    Optional[int]:






				'''simple docstring'''







				_UpperCAmelCase    :							Any									=					sigma_min if sigma_min is not None else self.config.sigma_min
				_UpperCAmelCase    :							Tuple									=					sigma_max if sigma_max is not None else self.config.sigma_max
				_UpperCAmelCase    :							Dict									=					sampling_eps if sampling_eps is not None else self.config.sampling_eps
				if state.timesteps is None:
					_UpperCAmelCase    :							List[Any]									=					self.set_timesteps(_lowerCAmelCase      ,		_lowerCAmelCase      ,		_lowerCAmelCase     )

				_UpperCAmelCase    :							List[Any]									=					jnp.exp(jnp.linspace(jnp.log(_lowerCAmelCase     )      ,		jnp.log(_lowerCAmelCase     )      ,		_lowerCAmelCase     )     )
				_UpperCAmelCase    :							Any									=					jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]     )

				return state.replace(discrete_sigmas=_lowerCAmelCase      ,		sigmas=_lowerCAmelCase     )
			def 							__snake_case (		self      ,		_A      ,		_A      ,		_A     )    ->    Tuple:






				'''simple docstring'''







				return jnp.where(timesteps == 0      ,		jnp.zeros_like(_lowerCAmelCase     )      ,		state.discrete_sigmas[timesteps - 1]     )
			def 							__snake_case (		self      ,		_A      ,		_A      ,		_A      ,		_A      ,		_A      ,		_A = True      ,		)    ->    Union[str, Any]:






				'''simple docstring'''







				if state.timesteps is None:
					raise ValueError(
					    """`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""     )

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

				_UpperCAmelCase    :							List[str]									=					state.discrete_sigmas[timesteps]
				_UpperCAmelCase    :							Optional[Any]									=					self.get_adjacent_sigma(_lowerCAmelCase      ,		_lowerCAmelCase      ,		_lowerCAmelCase     )
				_UpperCAmelCase    :							Optional[Any]									=					jnp.zeros_like(_lowerCAmelCase     )
				_UpperCAmelCase    :							Tuple									=					(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    :							int									=					broadcast_to_shape_from_left(_lowerCAmelCase      ,		sample.shape     )
				_UpperCAmelCase    :							Dict									=					drift - diffusion**2 * model_output

				#  equation 6: sample noise for the diffusion term of
				_UpperCAmelCase    :							Any									=					random.split(_lowerCAmelCase      ,		num=1     )
				_UpperCAmelCase    :							Tuple									=					random.normal(key=_lowerCAmelCase      ,		shape=sample.shape     )
				_UpperCAmelCase    :							Tuple									=					sample - drift  # subtract because `dt` is a small negative timestep
				# TODO is the variable diffusion the correct scaling term for the noise?
				_UpperCAmelCase    :							Optional[Any]									=					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=_lowerCAmelCase      ,		prev_sample_mean=_lowerCAmelCase      ,		state=_lowerCAmelCase     )
			def 							__snake_case (		self      ,		_A      ,		_A      ,		_A      ,		_A      ,		_A = True      ,		)    ->    Dict:






				'''simple docstring'''







				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    :							List[str]									=					random.split(_lowerCAmelCase      ,		num=1     )
				_UpperCAmelCase    :							List[str]									=					random.normal(key=_lowerCAmelCase      ,		shape=sample.shape     )

				# compute step size from the model_output, the noise, and the snr
				_UpperCAmelCase    :							Union[str, Any]									=					jnp.linalg.norm(_lowerCAmelCase     )
				_UpperCAmelCase    :							List[str]									=					jnp.linalg.norm(_lowerCAmelCase     )
				_UpperCAmelCase    :							int									=					(self.config.snr * noise_norm / grad_norm) ** 2 * 2
				_UpperCAmelCase    :							Dict									=					step_size * jnp.ones(sample.shape[0]     )

				# compute corrected sample: model_output term and noise term
				_UpperCAmelCase    :							Dict									=					step_size.flatten()
				_UpperCAmelCase    :							Optional[int]									=					broadcast_to_shape_from_left(_lowerCAmelCase      ,		sample.shape     )
				_UpperCAmelCase    :							int									=					sample + step_size * model_output
				_UpperCAmelCase    :							str									=					prev_sample_mean + ((step_size * 2) ** 0.5) * noise

				if not return_dict:
					return (prev_sample, state)

				return FlaxSdeVeOutput(prev_sample=_lowerCAmelCase      ,		state=_lowerCAmelCase     )
			def __len__(		self     )    ->    Optional[int]:






				'''simple docstring'''







				return self.config.num_train_timesteps








"""simple docstring"""






def       UpperCamelCase					(			_lowerCAmelCase     :						float,  _lowerCAmelCase     :						float )				->			float:
	_UpperCAmelCase    :							List[Any]									=					float()
	try:
		_UpperCAmelCase    :							Optional[Any]									=					mass * acceleration
	except Exception:
		return -0.0
	return force


if __name__ == "__main__":
						import doctest

						# run doctest
						doctest.testmod()

						# demo
						lowerCamelCase__				:  Any													=  12.5
						lowerCamelCase__				:  Union[str, Any]													=  10
						lowerCamelCase__				:  Optional[Any]													=  newtons_second_law_of_motion(mass, acceleration)
						print('''The force is ''', force, '''N''')





