code: 
  
  
  '''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   lowerCAmelCase__ :
  
  
  
  
       '''simple docstring'''
       # setable values
       __SCREAMING_SNAKE_CASE						    =				None
       __SCREAMING_SNAKE_CASE						    =				None
       __SCREAMING_SNAKE_CASE						    =				None
  
  
       @classmethod
       def 				_lowerCamelCase				(		cls)    ->       Union[str, Any]:
              return cls()
  
  
  
  
  
  
  
  @dataclass
  class   lowerCAmelCase__ (       A__):
  
  
  
  
       '''simple docstring'''
       __SCREAMING_SNAKE_CASE						    =				42
       __SCREAMING_SNAKE_CASE						    =				42
       __SCREAMING_SNAKE_CASE						    =				None
  
  
  
  
  
  
  
  class   lowerCAmelCase__ (       A__  ,					A__):
  
  
  
  
       '''simple docstring'''
  
  
       @property
       def 				_lowerCamelCase				(		self)    ->       Dict:
              return True
  
  
       @register_to_config
       def __init__(		self				,		__lowerCamelCase = 2_0_0_0				,		__lowerCamelCase = 0.1_5				,		__lowerCamelCase = 0.0_1				,		__lowerCamelCase = 1_3_4_8.0				,		__lowerCamelCase = 1e-5				,		__lowerCamelCase = 1				,		)    ->       Dict:
              pass
  
  
       def 				_lowerCamelCase				(		self)    ->       Tuple:
              _A   : Dict       					=				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 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase = ()				,		__lowerCamelCase = None)    ->       Any:
              _A   : Union[str, Any]       					=				sampling_eps if sampling_eps is not None else self.config.sampling_eps
  
              _A   : Optional[Any]       					=				jnp.linspace(1				,		__lowerCamelCase				,		__lowerCamelCase)
              return state.replace(timesteps=__lowerCamelCase)
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase = None				,		__lowerCamelCase = None				,		__lowerCamelCase = None				,		)    ->       Optional[int]:
              _A   : Any       					=				sigma_min if sigma_min is not None else self.config.sigma_min
              _A   : Any       					=				sigma_max if sigma_max is not None else self.config.sigma_max
              _A   : int       					=				sampling_eps if sampling_eps is not None else self.config.sampling_eps
              if state.timesteps is None:
                     _A   : int       					=				self.set_timesteps(__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase)
  
              _A   : str       					=				jnp.exp(jnp.linspace(jnp.log(__lowerCamelCase)				,		jnp.log(__lowerCamelCase)				,		__lowerCamelCase))
              _A   : Tuple       					=				jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps])
  
              return state.replace(discrete_sigmas=__lowerCamelCase				,		sigmas=__lowerCamelCase)
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase)    ->       Dict:
              return jnp.where(timesteps == 0				,		jnp.zeros_like(__lowerCamelCase)				,		state.discrete_sigmas[timesteps - 1])
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase = True				,		)    ->       int:
  
  
  
  
  
  
              if state.timesteps is None:
                     raise ValueError(
                         "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler")
  
              _A   : Optional[int]       					=				timestep * jnp.ones(
                  sample.shape[0]				,		)
              _A   : Tuple       					=				(timestep * (len(state.timesteps) - 1)).long()
  
              _A   : List[str]       					=				state.discrete_sigmas[timesteps]
              _A   : Dict       					=				self.get_adjacent_sigma(__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase)
              _A   : Optional[int]       					=				jnp.zeros_like(__lowerCamelCase)
              _A   : Optional[Any]       					=				(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
              _A   : Union[str, Any]       					=				diffusion.flatten()
              _A   : Optional[int]       					=				broadcast_to_shape_from_left(__lowerCamelCase				,		sample.shape)
              _A   : Optional[int]       					=				drift - diffusion**2 * model_output
  
              #  equation 6: sample noise for the diffusion term of
              _A   : Any       					=				random.split(__lowerCamelCase				,		num=1)
              _A   : str       					=				random.normal(key=__lowerCamelCase				,		shape=sample.shape)
              _A   : Tuple       					=				sample - drift  # subtract because `dt` is a small negative timestep
              # TODO is the variable diffusion the correct scaling term for the noise?
              _A   : 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 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase = True				,		)    ->       Any:
  
  
  
  
  
  
              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
              _A   : Optional[int]       					=				random.split(__lowerCamelCase				,		num=1)
              _A   : str       					=				random.normal(key=__lowerCamelCase				,		shape=sample.shape)
  
              # compute step size from the model_output, the noise, and the snr
              _A   : List[Any]       					=				jnp.linalg.norm(__lowerCamelCase)
              _A   : Optional[Any]       					=				jnp.linalg.norm(__lowerCamelCase)
              _A   : Any       					=				(self.config.snr * noise_norm / grad_norm) ** 2 * 2
              _A   : Optional[int]       					=				step_size * jnp.ones(sample.shape[0])
  
              # compute corrected sample: model_output term and noise term
              _A   : Any       					=				step_size.flatten()
              _A   : str       					=				broadcast_to_shape_from_left(__lowerCamelCase				,		sample.shape)
              _A   : int       					=				sample + step_size * model_output
              _A   : Tuple       					=				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)    ->       str:
              return self.config.num_train_timesteps
  
style_context: 
  
  
  lowerCAmelCase__   	=				'\n# Transformers installation\n! pip install transformers datasets evaluate\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'
  
  lowerCAmelCase__   	=				[{'type': 'code', 'content': INSTALL_CONTENT}]
  lowerCAmelCase__   	=				{
      '{processor_class}': 'FakeProcessorClass',
      '{model_class}': 'FakeModelClass',
      '{object_class}': 'FakeObjectClass',
  }
  