





# 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 					A__   (       __snake_case       ,   __snake_case			):
      _UpperCAmelCase	:Dict						    =   1





      @register_to_config
      def __init__( self       ,							A_=2000       ,							A_=0.1       ,							A_=20       ,							A_=1e-3   ):
       '''simple docstring'''




       UpperCamelCase	:					List[Any]       	=   None
       UpperCamelCase	:					Any       	=   None
       UpperCamelCase	:					Dict       	=   None





      def        __UpperCamelCase( self       ,							A_       ,							A_ = None   ):
       '''simple docstring'''




       UpperCamelCase	:					Dict       	=   torch.linspace(1       ,							self.config.sampling_eps       ,							A_       ,							device=A_   )





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_       ,							A_=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
       UpperCamelCase	:					int       	=   (
           -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
       )
       UpperCamelCase	:					List[str]       	=   torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff   )   )
       UpperCamelCase	:					List[str]       	=   std.flatten()
       while len(std.shape   ) < len(score.shape   ):
        UpperCamelCase	:					Optional[Any]       	=   std.unsqueeze(-1   )
       UpperCamelCase	:					Union[str, Any]       	=   -score / std

       # compute
       UpperCamelCase	:					str       	=   -1.0 / len(self.timesteps   )

       UpperCamelCase	:					Optional[int]       	=   self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
       UpperCamelCase	:					Tuple       	=   beta_t.flatten()
       while len(beta_t.shape   ) < len(x.shape   ):
        UpperCamelCase	:					Optional[int]       	=   beta_t.unsqueeze(-1   )
       UpperCamelCase	:					List[Any]       	=   -0.5 * beta_t * x

       UpperCamelCase	:					List[Any]       	=   torch.sqrt(A_   )
       UpperCamelCase	:					Any       	=   drift - diffusion**2 * score
       UpperCamelCase	:					List[Any]       	=   x + drift * dt

       # add noise
       UpperCamelCase	:					str       	=   randn_tensor(x.shape       ,							layout=x.layout       ,							generator=A_       ,							device=x.device       ,							dtype=x.dtype   )
       UpperCamelCase	:					Union[str, Any]       	=   x_mean + diffusion * math.sqrt(-dt   ) * noise

       return x, x_mean





      def __len__( self   ):
       '''simple docstring'''




       return self.config.num_train_timesteps





'''simple docstring'''





import os
import time

import pytest

from datasets.utils.filelock import FileLock, Timeout





def 						__snake_case(				_lowerCAmelCase		)		-> Optional[int]:
      snake_case__  :    int    						=      FileLock(str(tmpdir / """foo.lock"""		)		)
      snake_case__  :    Dict    						=      FileLock(str(tmpdir / """foo.lock"""		)		)
      snake_case__  :    List[str]    						=      0.01
      with locka.acquire():
            with pytest.raises(_lowerCAmelCase		):
                  snake_case__  :    str    						=      time.time()
                  locka.acquire(_lowerCAmelCase		)
                  assert time.time() - _start > timeout





def 						__snake_case(				_lowerCAmelCase		)		-> Tuple:
      snake_case__  :    Dict    						=      """a""" * 1_000 + """.lock"""
      snake_case__  :    int    						=      FileLock(str(tmpdir / filename		)		)
      assert locka._lock_file.endswith(""".lock"""		)
      assert not locka._lock_file.endswith(_lowerCAmelCase		)
      assert len(os.path.basename(locka._lock_file		)		) <= 255
      snake_case__  :    Dict    						=      FileLock(tmpdir / filename		)
      with locka.acquire():
            with pytest.raises(_lowerCAmelCase		):
                  locka.acquire(0		)
