





"""simple docstring"""


# 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 	__a   :
     # setable values
     SCREAMING_SNAKE_CASE__ :      Optional[jnp.ndarray]       		=			None
     SCREAMING_SNAKE_CASE__ :      Optional[jnp.ndarray]       		=			None
     SCREAMING_SNAKE_CASE__ :      Optional[jnp.ndarray]       		=			None



     @classmethod
     def 						snake_case_			(       cls   ):
            return cls()







@dataclass
class 	__a   (  lowerCAmelCase__		):
     SCREAMING_SNAKE_CASE__ :      ScoreSdeVeSchedulerState
     SCREAMING_SNAKE_CASE__ :      jnp.ndarray
     SCREAMING_SNAKE_CASE__ :      Optional[jnp.ndarray]       		=			None







class 	__a   (  lowerCAmelCase__	,						lowerCAmelCase__		):



     @property
     def 						snake_case_			(       self   ):
            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					,     ):
            pass



     def 						snake_case_			(       self   ):
            _lowerCamelCase    	=					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 						snake_case_			(       self					,     a__					,     a__					,     a__ = ()					,     a__ = None   ):
            _lowerCamelCase    	=					sampling_eps if sampling_eps is not None else self.config.sampling_eps

            _lowerCamelCase    	=					jnp.linspace(1					,     a__					,     a__   )
            return state.replace(timesteps=a__   )



     def 						snake_case_			(       self					,     a__					,     a__					,     a__ = None					,     a__ = None					,     a__ = None					,     ):
            _lowerCamelCase    	=					sigma_min if sigma_min is not None else self.config.sigma_min
            _lowerCamelCase    	=					sigma_max if sigma_max is not None else self.config.sigma_max
            _lowerCamelCase    	=					sampling_eps if sampling_eps is not None else self.config.sampling_eps
            if state.timesteps is None:
                   _lowerCamelCase    	=					self.set_timesteps(a__					,     a__					,     a__   )

            _lowerCamelCase    	=					jnp.exp(jnp.linspace(jnp.log(a__   )					,     jnp.log(a__   )					,     a__   )   )
            _lowerCamelCase    	=					jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]   )

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



     def 						snake_case_			(       self					,     a__					,     a__					,     a__   ):
            return jnp.where(timesteps == 0					,     jnp.zeros_like(a__   )					,     state.discrete_sigmas[timesteps - 1]   )



     def 						snake_case_			(       self					,     a__					,     a__					,     a__					,     a__					,     a__					,     a__ = True					,     ):
            if state.timesteps is None:
                   raise ValueError(
                       '`state.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'   )

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

            _lowerCamelCase    	=					state.discrete_sigmas[timesteps]
            _lowerCamelCase    	=					self.get_adjacent_sigma(a__					,     a__					,     a__   )
            _lowerCamelCase    	=					jnp.zeros_like(a__   )
            _lowerCamelCase    	=					(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
            _lowerCamelCase    	=					diffusion.flatten()
            _lowerCamelCase    	=					broadcast_to_shape_from_left(a__					,     sample.shape   )
            _lowerCamelCase    	=					drift - diffusion**2 * model_output

            #  equation 6: sample noise for the diffusion term of
            _lowerCamelCase    	=					random.split(a__					,     num=1   )
            _lowerCamelCase    	=					random.normal(key=a__					,     shape=sample.shape   )
            _lowerCamelCase    	=					sample - drift  # subtract because `dt` is a small negative timestep
            # TODO is the variable diffusion the correct scaling term for the noise?
            _lowerCamelCase    	=					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 						snake_case_			(       self					,     a__					,     a__					,     a__					,     a__					,     a__ = 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
            _lowerCamelCase    	=					random.split(a__					,     num=1   )
            _lowerCamelCase    	=					random.normal(key=a__					,     shape=sample.shape   )

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

            # compute corrected sample: model_output term and noise term
            _lowerCamelCase    	=					step_size.flatten()
            _lowerCamelCase    	=					broadcast_to_shape_from_left(a__					,     sample.shape   )
            _lowerCamelCase    	=					sample + step_size * model_output
            _lowerCamelCase    	=					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   ):
            return self.config.num_train_timesteps













"""simple docstring"""


def SCREAMING_SNAKE_CASE_   (       snake_case   :					str		)->		list:
       _lowerCamelCase    	=					[]
       _lowerCamelCase    	=					1

       for i in range(len(snake_case		)		):
              if i + 1 < len(snake_case		) and text[i] == text[i + 1]:
                     count += 1
              else:
                     encoded.append((text[i], count)		)
                     _lowerCamelCase    	=					1

       return encoded





def SCREAMING_SNAKE_CASE_   (       snake_case   :					list		)->		str:
       return "".join(char * length for char, length in encoded		)


if __name__ == "__main__":
      from doctest import testmod

      testmod(name="""run_length_encode""", verbose=True)
      testmod(name="""run_length_decode""", verbose=True)






