



'''simple docstring'''





import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput






class __A    (						A     ,     A		):



 '''simple docstring'''







 __lowerCamelCase					: Union[str, Any]    = 1



 @register_to_config
 def __init__(self				,     A = 1_000				,     A = None )					->  List[str]:


      """simple docstring"""



      self.set_timesteps(A )

      # standard deviation of the initial noise distribution
      _a											=						1.0

      # For now we only support F-PNDM, i.e. the runge-kutta method
      # For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
      # mainly at formula (9), (12), (13) and the Algorithm 2.
      _a											=						4

      # running values
      _a											=						[]



 def       a__						(self				,     A				,     A = None )					->  Union[str, Any]:


      """simple docstring"""



      _a											=						num_inference_steps
      _a											=						torch.linspace(1				,     0				,     num_inference_steps + 1 )[:-1]
      _a											=						torch.cat([steps, torch.tensor([0.0] )] )

      if self.config.trained_betas is not None:
           _a											=						torch.tensor(self.config.trained_betas				,     dtype=torch.floataa )
      else:
           _a											=						torch.sin(steps * math.pi / 2 ) ** 2

      _a											=						(1.0 - self.betas**2) ** 0.5

      _a											=						(torch.atana(self.betas				,     self.alphas ) / math.pi * 2)[:-1]
      _a											=						timesteps.to(A )

      _a											=						[]



 def       a__						(self				,     A				,     A				,     A				,     A = True				,     )					->  Union[SchedulerOutput, Tuple]:


      """simple docstring"""



      if self.num_inference_steps is None:
           raise ValueError(
               '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler''' )

      _a											=						(self.timesteps == timestep).nonzero().item()
      _a											=						timestep_index + 1

      _a											=						sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
      self.ets.append(A )

      if len(self.ets ) == 1:
           _a											=						self.ets[-1]
      elif len(self.ets ) == 2:
           _a											=						(3 * self.ets[-1] - self.ets[-2]) / 2
      elif len(self.ets ) == 3:
           _a											=						(23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
      else:
           _a											=						(1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

      _a											=						self._get_prev_sample(A				,     A				,     A				,     A )

      if not return_dict:
           return (prev_sample,)

      return SchedulerOutput(prev_sample=A )



 def       a__						(self				,     A				,     *A				,     **A )					->  torch.FloatTensor:


      """simple docstring"""



      return sample



 def       a__						(self				,     A				,     A				,     A				,     A )					->  Optional[int]:


      """simple docstring"""



      _a											=						self.alphas[timestep_index]
      _a											=						self.betas[timestep_index]

      _a											=						self.alphas[prev_timestep_index]
      _a											=						self.betas[prev_timestep_index]

      _a											=						(sample - sigma * ets) / max(A				,     1E-8 )
      _a											=						next_alpha * pred + ets * next_sigma

      return prev_sample



 def __len__(self )					->  Optional[int]:


      """simple docstring"""



      return self.config.num_train_timesteps








'''simple docstring'''





import pytest

import datasets


# Import fixture modules as plugins
lowercase_	 =						["tests.fixtures.files", "tests.fixtures.hub", "tests.fixtures.fsspec"]




def lowerCAmelCase						(__A						,			__A):



     """simple docstring"""



     for item in items:
          if any(marker in item.keywords for marker in ['''integration''', '''unit''']):
               continue
          item.add_marker(pytest.mark.unit)




def lowerCAmelCase						(__A):



     """simple docstring"""



     config.addinivalue_line('''markers'''						,			'''torchaudio_latest: mark test to run with torchaudio>=0.12''')




@pytest.fixture(autouse=__A)
def lowerCAmelCase						(__A						,			__A):



     """simple docstring"""



     _a											=						tmp_path_factory.getbasetemp() / '''cache'''
     _a											=						test_hf_cache_home / '''datasets'''
     _a											=						test_hf_cache_home / '''metrics'''
     _a											=						test_hf_cache_home / '''modules'''
     monkeypatch.setattr('''datasets.config.HF_DATASETS_CACHE'''						,			str(__A))
     monkeypatch.setattr('''datasets.config.HF_METRICS_CACHE'''						,			str(__A))
     monkeypatch.setattr('''datasets.config.HF_MODULES_CACHE'''						,			str(__A))
     _a											=						test_hf_datasets_cache / '''downloads'''
     monkeypatch.setattr('''datasets.config.DOWNLOADED_DATASETS_PATH'''						,			str(__A))
     _a											=						test_hf_datasets_cache / '''downloads''' / '''extracted'''
     monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH'''						,			str(__A))




@pytest.fixture(autouse=__A						,			scope='''session''')
def lowerCAmelCase						():



     """simple docstring"""



     datasets.disable_progress_bar()




@pytest.fixture(autouse=__A)
def lowerCAmelCase						(__A):



     """simple docstring"""



     monkeypatch.setattr('''datasets.config.HF_UPDATE_DOWNLOAD_COUNTS'''						,			__A)




@pytest.fixture
def lowerCAmelCase						(__A):



     """simple docstring"""



     monkeypatch.setattr('''sqlalchemy.util.deprecations.SILENCE_UBER_WARNING'''						,			__A)



