


# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin


class     __UpperCAmelCase							(       __A ,					__A							):


     """simple docstring"""
     _lowerCamelCase   =      1


     @register_to_config
     def __init__(	self  ,       __A=2000  ,       __A=0.1  ,       __A=20  ,       __A=1E-3    ):
        __a					=  None
        __a					=  None
        __a					=  None


     def 	snake_case_  (	self  ,       __A  ,       __A = None    ):
        __a					=  torch.linspace(1  ,       self.config.sampling_eps  ,       __A  ,       device=__A    )


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

        # TODO(Patrick) better comments + non-PyTorch
        # postprocess model score
        __a					=  (
            -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
        )
        __a					=  torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff    )    )
        __a					=  std.flatten()
        while len(std.shape    ) < len(score.shape    ):
           __a					=  std.unsqueeze(-1    )
        __a					=  -score / std

        # compute
        __a					=  -1.0 / len(self.timesteps    )

        __a					=  self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
        __a					=  beta_t.flatten()
        while len(beta_t.shape    ) < len(x.shape    ):
           __a					=  beta_t.unsqueeze(-1    )
        __a					=  -0.5 * beta_t * x

        __a					=  torch.sqrt(__A    )
        __a					=  drift - diffusion**2 * score
        __a					=  x + drift * dt

        # add noise
        __a					=  randn_tensor(x.shape  ,       layout=x.layout  ,       generator=__A  ,       device=x.device  ,       dtype=x.dtype    )
        __a					=  x_mean + diffusion * math.sqrt(-dt    ) * noise

        return x, x_mean


     def __len__(	self    ):
        return self.config.num_train_timesteps







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


SCREAMING_SNAKE_CASE							    =							{}


try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE							    =							['MLukeTokenizer']

if TYPE_CHECKING:
  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mluke import MLukeTokenizer


else:
  import sys

  SCREAMING_SNAKE_CASE							    =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



