

# 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 (     SCREAMING_SNAKE_CASE	,			SCREAMING_SNAKE_CASE   ):
    __UpperCamelCase         =1

    @register_to_config
    def __init__(			self :      Union[str, Any]							,    snake_case__ :      Any=2_0_0_0							,    snake_case__ :      Optional[int]=0.1							,    snake_case__ :      List[Any]=2_0							,    snake_case__ :      Dict=1E-3    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			None
     SCREAMING_SNAKE_CASE								=			None
     SCREAMING_SNAKE_CASE								=			None

    def UpperCamelCase			(			self :      Optional[int]							,    snake_case__ :      str							,    snake_case__ :      Union[str, torch.device] = None    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			torch.linspace(1							,    self.config.sampling_eps							,    snake_case__							,    device=snake_case__    )

    def UpperCamelCase			(			self :      str							,    snake_case__ :      str							,    snake_case__ :      int							,    snake_case__ :      int							,    snake_case__ :      List[Any]=None    ):

     """simple docstring"""






     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
     SCREAMING_SNAKE_CASE								=			(
         -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
     )
     SCREAMING_SNAKE_CASE								=			torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff    )    )
     SCREAMING_SNAKE_CASE								=			std.flatten()
     while len(std.shape    ) < len(score.shape    ):
      SCREAMING_SNAKE_CASE								=			std.unsqueeze(-1    )
     SCREAMING_SNAKE_CASE								=			-score / std

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

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

     SCREAMING_SNAKE_CASE								=			torch.sqrt(snake_case__    )
     SCREAMING_SNAKE_CASE								=			drift - diffusion**2 * score
     SCREAMING_SNAKE_CASE								=			x + drift * dt

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

     return x, x_mean




    def __len__(			self :      Any    ):

     """simple docstring"""






     return self.config.num_train_timesteps




from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
      if not (is_transformers_available() and is_torch_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_torch_and_transformers_objects import (
          ImageTextPipelineOutput,
          UniDiffuserPipeline,
      )
else:
      from .modeling_text_decoder import UniDiffuserTextDecoder
      from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
      from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline

