





'''simple docstring'''







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 				__SCREAMING_SNAKE_CASE  (_UpperCAmelCase       ,							_UpperCAmelCase      ):






				"""simple docstring"""







				__a            =1




				@register_to_config
				def __init__(  self     :					Optional[int]					,				__a     :					List[Any]=20_00					,				__a     :					List[Any]=0.1					,				__a     :					Optional[int]=20					,				__a     :					Optional[Any]=1e-3					):
								_a								= None
								_a								= None
								_a								= None




				def   UpperCamelCase__   (  self     :					Union[str, Any]					,				__a     :					Optional[Any]					,				__a     :					List[str] = None					):
								_a								= torch.linspace(1					,				self.config.sampling_eps					,				_UpperCAmelCase					,				device=_UpperCAmelCase					)




				def   UpperCamelCase__   (  self     :					Tuple					,				__a     :					str					,				__a     :					Optional[Any]					,				__a     :					Any					,				__a     :					int=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(_UpperCAmelCase					)
								_a								= drift - diffusion**2 * score
								_a								= x + drift * dt

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

								return x, x_mean




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

"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A			   = {
    "configuration_lilt": ["LILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LiltConfig"],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __A			   = [
           "LILT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "LiltForQuestionAnswering",
           "LiltForSequenceClassification",
           "LiltForTokenClassification",
           "LiltModel",
           "LiltPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_lilt import (
                  LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  LiltForQuestionAnswering,
                  LiltForSequenceClassification,
                  LiltForTokenClassification,
                  LiltModel,
                  LiltPreTrainedModel,
              )

else:
       import sys

       __A			   = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
