
'''simple docstring'''





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     a :
				# setable values
				_snake_case		     =			None
				_snake_case		     =			None
				_snake_case		     =			None




				@classmethod
				def 						__snake_case				(				cls						:					List[str]   ):
										return cls()




@dataclass
class     a (					lowerCamelCase__     ):
				_snake_case		     =			42
				_snake_case		     =			42
				_snake_case		     =			None




class     a (					lowerCamelCase__					,lowerCamelCase__     ):




				@property
				def 						__snake_case				(				self						:					Tuple   ):
										return True




				@register_to_config
				def __init__(				self						:					Any,     SCREAMING_SNAKE_CASE_						:					str = 20_00,     SCREAMING_SNAKE_CASE_						:					Union[str, Any] = 0.15,     SCREAMING_SNAKE_CASE_						:					List[str] = 0.01,     SCREAMING_SNAKE_CASE_						:					Tuple = 13_48.0,     SCREAMING_SNAKE_CASE_						:					List[Any] = 1e-5,     SCREAMING_SNAKE_CASE_						:					int = 1,     ):
										pass




				def 						__snake_case				(				self						:					List[str]   ):
										snake_case							:  str			=     ScoreSdeVeSchedulerState.create()
										return self.set_sigmas(
										    __UpperCamelCase,     self.config.num_train_timesteps,     self.config.sigma_min,     self.config.sigma_max,     self.config.sampling_eps,     )




				def 						__snake_case				(				self						:					List[str],     SCREAMING_SNAKE_CASE_						:					int,     SCREAMING_SNAKE_CASE_						:					Union[str, Any],     SCREAMING_SNAKE_CASE_						:					Optional[Any] = (),     SCREAMING_SNAKE_CASE_						:					List[str] = None   ):
										snake_case							:  int			=     sampling_eps if sampling_eps is not None else self.config.sampling_eps

										snake_case							:  Optional[int]			=     jnp.linspace(1,     __UpperCamelCase,     __UpperCamelCase   )
										return state.replace(timesteps=__UpperCamelCase   )




				def 						__snake_case				(				self						:					Union[str, Any],     SCREAMING_SNAKE_CASE_						:					Optional[int],     SCREAMING_SNAKE_CASE_						:					List[str],     SCREAMING_SNAKE_CASE_						:					Optional[int] = None,     SCREAMING_SNAKE_CASE_						:					Optional[Any] = None,     SCREAMING_SNAKE_CASE_						:					str = None,     ):
										snake_case							:  int			=     sigma_min if sigma_min is not None else self.config.sigma_min
										snake_case							:  List[Any]			=     sigma_max if sigma_max is not None else self.config.sigma_max
										snake_case							:  Optional[int]			=     sampling_eps if sampling_eps is not None else self.config.sampling_eps
										if state.timesteps is None:
																snake_case							:  Optional[Any]			=     self.set_timesteps(__UpperCamelCase,     __UpperCamelCase,     __UpperCamelCase   )

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

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




				def 						__snake_case				(				self						:					int,     SCREAMING_SNAKE_CASE_						:					Tuple,     SCREAMING_SNAKE_CASE_						:					Any,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]   ):
										return jnp.where(timesteps == 0,     jnp.zeros_like(__UpperCamelCase   ),     state.discrete_sigmas[timesteps - 1]   )




				def 						__snake_case				(				self						:					str,     SCREAMING_SNAKE_CASE_						:					Any,     SCREAMING_SNAKE_CASE_						:					Tuple,     SCREAMING_SNAKE_CASE_						:					Any,     SCREAMING_SNAKE_CASE_						:					Dict,     SCREAMING_SNAKE_CASE_						:					int,     SCREAMING_SNAKE_CASE_						:					Union[str, Any] = True,     ):
										if state.timesteps is None:
																raise ValueError(
																    '''`state.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''   )

										snake_case							:  Optional[int]			=     timestep * jnp.ones(
										    sample.shape[0],     )
										snake_case							:  Union[str, Any]			=     (timestep * (len(state.timesteps   ) - 1)).long()

										snake_case							:  Optional[Any]			=     state.discrete_sigmas[timesteps]
										snake_case							:  Optional[int]			=     self.get_adjacent_sigma(__UpperCamelCase,     __UpperCamelCase,     __UpperCamelCase   )
										snake_case							:  Any			=     jnp.zeros_like(__UpperCamelCase   )
										snake_case							:  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
										snake_case							:  Union[str, Any]			=     diffusion.flatten()
										snake_case							:  Union[str, Any]			=     broadcast_to_shape_from_left(__UpperCamelCase,     sample.shape   )
										snake_case							:  Union[str, Any]			=     drift - diffusion**2 * model_output

										#  equation 6: sample noise for the diffusion term of
										snake_case							:  str			=     random.split(__UpperCamelCase,     num=1   )
										snake_case							:  Optional[Any]			=     random.normal(key=__UpperCamelCase,     shape=sample.shape   )
										snake_case							:  Any			=     sample - drift  # subtract because `dt` is a small negative timestep
										# TODO is the variable diffusion the correct scaling term for the noise?
										snake_case							:  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=__UpperCamelCase,     prev_sample_mean=__UpperCamelCase,     state=__UpperCamelCase   )




				def 						__snake_case				(				self						:					List[Any],     SCREAMING_SNAKE_CASE_						:					Dict,     SCREAMING_SNAKE_CASE_						:					str,     SCREAMING_SNAKE_CASE_						:					Dict,     SCREAMING_SNAKE_CASE_						:					Dict,     SCREAMING_SNAKE_CASE_						:					int = True,     ):
										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
										snake_case							:  Tuple			=     random.split(__UpperCamelCase,     num=1   )
										snake_case							:  int			=     random.normal(key=__UpperCamelCase,     shape=sample.shape   )

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

										# compute corrected sample: model_output term and noise term
										snake_case							:  Any			=     step_size.flatten()
										snake_case							:  int			=     broadcast_to_shape_from_left(__UpperCamelCase,     sample.shape   )
										snake_case							:  Optional[Any]			=     sample + step_size * model_output
										snake_case							:  Optional[int]			=     prev_sample_mean + ((step_size * 2) ** 0.5) * noise

										if not return_dict:
																return (prev_sample, state)

										return FlaxSdeVeOutput(prev_sample=__UpperCamelCase,     state=__UpperCamelCase   )




				def __len__(				self						:					Optional[Any]   ):
										return self.config.num_train_timesteps





'''simple docstring'''

def __UpperCamelCase (      __lowerCamelCase		:			int					)   ->  int:







     '''simple docstring'''






     if not isinstance(__lowerCamelCase   ,	__lowerCamelCase					) or number < 0:
          raise ValueError("Input must be a non-negative integer"					)

     _a            =		number & ~(number - 1)
     _a            =		0
     while intermediate:
          intermediate >>= 1
          index += 1
     return index - 1


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)

