



'''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        SCREAMING_SNAKE_CASE   :
  """simple docstring"""







  __A     =		None
  __A     =		None
  __A     =		None



  @classmethod
  def   a   (  cls   :					str					):


        """simple docstring"""



        return cls()





@dataclass
class        SCREAMING_SNAKE_CASE   (				__a ):
  """simple docstring"""







  __A     =		42
  __A     =		42
  __A     =		None





class        SCREAMING_SNAKE_CASE   (				__a	,						__a ):
  """simple docstring"""







  @property
  def   a   (  self   :					List[Any]					):


        """simple docstring"""



        return True



  @register_to_config
  def __init__(  self   :					Any					,					__lowerCAmelCase   :					int = 2000					,					__lowerCAmelCase   :					float = 0.1_5					,					__lowerCAmelCase   :					float = 0.0_1					,					__lowerCAmelCase   :					float = 1348.0					,					__lowerCAmelCase   :					float = 1e-5					,					__lowerCAmelCase   :					int = 1					,					):


        """simple docstring"""



        pass



  def   a   (  self   :					int					):


        """simple docstring"""



        _lowerCAmelCase		   =   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   a   (  self   :					int					,					__lowerCAmelCase   :					ScoreSdeVeSchedulerState					,					__lowerCAmelCase   :					int					,					__lowerCAmelCase   :					Tuple = ()					,					__lowerCAmelCase   :					float = None					):


        """simple docstring"""



        _lowerCAmelCase		   =   sampling_eps if sampling_eps is not None else self.config.sampling_eps

        _lowerCAmelCase		   =   jnp.linspace(1					,					__lowerCAmelCase					,					__lowerCAmelCase					)
        return state.replace(timesteps=__lowerCAmelCase					)



  def   a   (  self   :					List[str]					,					__lowerCAmelCase   :					ScoreSdeVeSchedulerState					,					__lowerCAmelCase   :					int					,					__lowerCAmelCase   :					float = None					,					__lowerCAmelCase   :					float = None					,					__lowerCAmelCase   :					float = None					,					):


        """simple docstring"""



        _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(__lowerCAmelCase					,					__lowerCAmelCase					,					__lowerCAmelCase					)

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

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



  def   a   (  self   :					Dict					,					__lowerCAmelCase   :					Union[str, Any]					,					__lowerCAmelCase   :					Tuple					,					__lowerCAmelCase   :					Dict					):


        """simple docstring"""



        return jnp.where(timesteps == 0					,					jnp.zeros_like(__lowerCAmelCase					)					,					state.discrete_sigmas[timesteps - 1]					)



  def   a   (  self   :					Union[str, Any]					,					__lowerCAmelCase   :					ScoreSdeVeSchedulerState					,					__lowerCAmelCase   :					jnp.ndarray					,					__lowerCAmelCase   :					int					,					__lowerCAmelCase   :					jnp.ndarray					,					__lowerCAmelCase   :					random.KeyArray					,					__lowerCAmelCase   :					bool = True					,					):


        """simple docstring"""



        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(__lowerCAmelCase					,					__lowerCAmelCase					,					__lowerCAmelCase					)
        _lowerCAmelCase		   =   jnp.zeros_like(__lowerCAmelCase					)
        _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(__lowerCAmelCase					,					sample.shape					)
        _lowerCAmelCase		   =   drift - diffusion**2 * model_output

        #  equation 6: sample noise for the diffusion term of
        _lowerCAmelCase		   =   random.split(__lowerCAmelCase					,					num=1					)
        _lowerCAmelCase		   =   random.normal(key=__lowerCAmelCase					,					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=__lowerCAmelCase					,					prev_sample_mean=__lowerCAmelCase					,					state=__lowerCAmelCase					)



  def   a   (  self   :					List[str]					,					__lowerCAmelCase   :					ScoreSdeVeSchedulerState					,					__lowerCAmelCase   :					jnp.ndarray					,					__lowerCAmelCase   :					jnp.ndarray					,					__lowerCAmelCase   :					random.KeyArray					,					__lowerCAmelCase   :					bool = True					,					):


        """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
        _lowerCAmelCase		   =   random.split(__lowerCAmelCase					,					num=1					)
        _lowerCAmelCase		   =   random.normal(key=__lowerCAmelCase					,					shape=sample.shape					)

        # compute step size from the model_output, the noise, and the snr
        _lowerCAmelCase		   =   jnp.linalg.norm(__lowerCAmelCase					)
        _lowerCAmelCase		   =   jnp.linalg.norm(__lowerCAmelCase					)
        _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(__lowerCAmelCase					,					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=__lowerCAmelCase					,					state=__lowerCAmelCase					)



  def __len__(  self   :					int					):


        """simple docstring"""



        return self.config.num_train_timesteps






'''simple docstring'''




import unittest

from transformers import is_torch_available, load_tool

from .test_tools_common import ToolTesterMixin


if is_torch_available():
 import torch





class        SCREAMING_SNAKE_CASE   (				unittest.TestCase	,						__a ):
  """simple docstring"""







  def   a   (  self   :					List[str]					):


        """simple docstring"""



        _lowerCAmelCase		   =   load_tool('speech-to-text'					)
        self.tool.setup()



  def   a   (  self   :					Any					):


        """simple docstring"""



        _lowerCAmelCase		   =   self.tool(torch.ones(3000					)					)
        self.assertEqual(__lowerCAmelCase					,					' you'					)



  def   a   (  self   :					Optional[int]					):


        """simple docstring"""



        _lowerCAmelCase		   =   self.tool(audio=torch.ones(3000					)					)
        self.assertEqual(__lowerCAmelCase					,					' you'					)





