code: 
  
  """simple docstring"""
  import math
  from collections import OrderedDict
  
  import torch
  from packaging import version
  from torch import Tensor, nn
  
  from .utils import logging
  
  
  _A						=     logging.get_logger(__name__)
  
  class 			lowerCamelCase  (					nn.Module      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def __init__(self  ):
  
  
          """simple docstring"""
  
          super().__init__()
          if version.parse(torch.__version__  ) < version.parse("""1.12.0"""  ):
                raise ImportError(
                    F"""You are using torch=={torch.__version__}, but torch>=1.12.0 is required to use """
                    """PytorchGELUTanh. Please upgrade torch."""  )
  
  
  
  
    def    _a			(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          return nn.functional.gelu(_lowerCamelCase       ,					approximate="""tanh"""  )
  
  
  
  
  class 			lowerCamelCase  (					nn.Module      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def    _a			(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi  ) * (input + 0.044_715 * torch.pow(_lowerCamelCase       ,					3.0  ))  ))
  
  
  
  
  class 			lowerCamelCase  (					nn.Module      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def __init__(self       ,					_lowerCamelCase = False  ):
  
  
          """simple docstring"""
  
          super().__init__()
          if use_gelu_python:
                UpperCAmelCase__ :       int						=						self._gelu_python
          else:
                UpperCAmelCase__ :       Dict						=						nn.functional.gelu
  
  
  
    def    _a			(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          return input * 0.5 * (1.0 + torch.erf(input / math.sqrt(2.0  )  ))
  
  
  
  
    def    _a			(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          return self.act(_lowerCamelCase  )
  
  
  
  
  class 			lowerCamelCase  (					nn.Module      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def    _a			(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          return 0.5 * input * (1.0 + torch.tanh(input * 0.7_978_845_608 * (1.0 + 0.044_715 * input * input)  ))
  
  
  
  
  class 			lowerCamelCase  (					nn.Module      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def    _a			(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          return input * torch.sigmoid(1.702 * input  )
  
  
  
  
  class 			lowerCamelCase  (					nn.Module      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def __init__(self       ,					_lowerCamelCase       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          if min > max:
                raise ValueError(F"""min should be < max (got min: {min}, max: {max})"""  )
  
          super().__init__()
          UpperCAmelCase__ :       Dict						=						min
          UpperCAmelCase__ :       Dict						=						max
  
  
  
  
    def    _a			(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          return torch.clip(gelu(_lowerCamelCase  )       ,					self.min       ,					self.max  )
  
  
  
  
  class 			lowerCamelCase  (					nn.Module      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def __init__(self  ):
  
  
          """simple docstring"""
  
          super().__init__()
          UpperCAmelCase__ :       Dict						=						math.sqrt(2 / math.pi  )
  
  
  
  
    def    _a			(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.044_715 * torch.pow(_lowerCamelCase       ,					3  ))  ))
  
  
  
  
  class 			lowerCamelCase  (					nn.Module      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def    _a			(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          return nn.functional.silu(_lowerCamelCase  )
  
  
  
  
  class 			lowerCamelCase  (					nn.Module      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def __init__(self  ):
  
  
          """simple docstring"""
  
          super().__init__()
          if version.parse(torch.__version__  ) < version.parse("""1.9.0"""  ):
                UpperCAmelCase__ :       List[str]						=						self._mish_python
          else:
                UpperCAmelCase__ :       Optional[int]						=						nn.functional.mish
  
  
  
    def    _a			(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          return input * torch.tanh(nn.functional.softplus(_lowerCamelCase  )  )
  
  
  
  
    def    _a			(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          return self.act(_lowerCamelCase  )
  
  
  
  
  class 			lowerCamelCase  (					nn.Module      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def    _a			(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          return input
  
  
  
  
  class 			lowerCamelCase  (					nn.Module      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase=0.707_107       ,					_lowerCamelCase=0.282_095  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Tuple						=						(input - mu).div(sigma * math.sqrt(2.0  )  )
          return 0.5 * (1.0 + torch.erf(_lowerCamelCase  ))
  
  
  
  
  class 			lowerCamelCase  (					nn.Module      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def    _a			(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       int						=						nn.functional.relu(_lowerCamelCase  )
          UpperCAmelCase__ :       int						=						torch.square(_lowerCamelCase  )
          return squared
  
  
  
  
  class 			lowerCamelCase  (					lowerCAmelCase__      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def __getitem__(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Dict						=						super().__getitem__(_lowerCamelCase  )
          UpperCAmelCase__				,		UpperCAmelCase__ :       List[str]						=						content if isinstance(_lowerCamelCase       ,					_lowerCamelCase  ) else (content, {})
          return cls(**_lowerCamelCase  )
  
  
  
  
  _A						=     {
      """gelu""": GELUActivation,
      """gelu_10""": (ClippedGELUActivation, {"""min""": -10, """max""": 10}),
      """gelu_fast""": FastGELUActivation,
      """gelu_new""": NewGELUActivation,
      """gelu_python""": (GELUActivation, {"""use_gelu_python""": True}),
      """gelu_pytorch_tanh""": PytorchGELUTanh,
      """gelu_accurate""": AccurateGELUActivation,
      """laplace""": LaplaceActivation,
      """linear""": LinearActivation,
      """mish""": MishActivation,
      """quick_gelu""": QuickGELUActivation,
      """relu""": nn.ReLU,
      """relu2""": ReLUSquaredActivation,
      """relu6""": nn.ReLUa,
      """sigmoid""": nn.Sigmoid,
      """silu""": SiLUActivation,
      """swish""": SiLUActivation,
      """tanh""": nn.Tanh,
  }
  _A						=     ClassInstantier(ACTaCLS)
  
  
  
  
  def 				a__	(	lowerCAmelCase						)  ->   Union[str, Any]:
        if activation_string in ACTaFN:
              return ACTaFN[activation_string]
        else:
              raise KeyError(F"""function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()						)}"""						)
  
  
  # For backwards compatibility with: from activations import gelu_python
  _A						=     get_activation("""gelu_python""")
  _A						=     get_activation("""gelu_new""")
  _A						=     get_activation("""gelu""")
  _A						=     get_activation("""gelu_fast""")
  _A						=     get_activation("""quick_gelu""")
  _A						=     get_activation("""silu""")
  _A						=     get_activation("""mish""")
  
  
  
  
  
  _A						=     get_activation("""linear""")
  
  
style_context: 
  
  """simple docstring"""
  import tempfile
  import unittest
  
  import numpy as np
  import torch
  
  from diffusers import ScoreSdeVeScheduler
  
  class 			lowerCamelCase  (					unittest.TestCase      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    SCREAMING_SNAKE_CASE							=     (ScoreSdeVeScheduler,)
    SCREAMING_SNAKE_CASE							=     ()
  
  
  
    @property
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       List[Any]						=						4
          UpperCAmelCase__ :       Tuple						=						3
          UpperCAmelCase__ :       Optional[Any]						=						8
          UpperCAmelCase__ :       Dict						=						8
  
          UpperCAmelCase__ :       List[Any]						=						torch.rand((batch_size, num_channels, height, width)  )
  
          return sample
  
  
  
    @property
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Tuple						=						4
          UpperCAmelCase__ :       Optional[Any]						=						3
          UpperCAmelCase__ :       List[str]						=						8
          UpperCAmelCase__ :       Dict						=						8
  
          UpperCAmelCase__ :       List[str]						=						batch_size * num_channels * height * width
          UpperCAmelCase__ :       Any						=						torch.arange(_lowerCamelCase  )
          UpperCAmelCase__ :       Any						=						sample.reshape(_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase  )
          UpperCAmelCase__ :       Dict						=						sample / num_elems
          UpperCAmelCase__ :       Optional[int]						=						sample.permute(3       ,					0       ,					1       ,					2  )
  
          return sample
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          def model(_lowerCamelCase       ,					_lowerCamelCase       ,					*_lowerCamelCase  ):
                return sample * t / (t + 1)
  
          return model
  
  
  
    def    _a			(self       ,					**_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       List[str]						=						{
              """num_train_timesteps""": 2000,
              """snr""": 0.15,
              """sigma_min""": 0.01,
              """sigma_max""": 1348,
              """sampling_eps""": 1e-5,
          }
  
          config.update(**_lowerCamelCase  )
          return config
  
  
  
    def    _a			(self       ,					_lowerCamelCase=0       ,					**_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       str						=						dict(self.forward_default_kwargs  )
  
          for scheduler_class in self.scheduler_classes:
                UpperCAmelCase__ :       int						=						self.dummy_sample
                UpperCAmelCase__ :       Tuple						=						0.1 * sample
  
                UpperCAmelCase__ :       int						=						self.get_scheduler_config(**_lowerCamelCase  )
                UpperCAmelCase__ :       Dict						=						scheduler_class(**_lowerCamelCase  )
  
                with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(_lowerCamelCase  )
                      UpperCAmelCase__ :       Union[str, Any]						=						scheduler_class.from_pretrained(_lowerCamelCase  )
  
                UpperCAmelCase__ :       Tuple						=						scheduler.step_pred(
                    _lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
                UpperCAmelCase__ :       Optional[int]						=						new_scheduler.step_pred(
                    _lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
  
                assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"
  
                UpperCAmelCase__ :       Optional[int]						=						scheduler.step_correct(_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
                UpperCAmelCase__ :       Union[str, Any]						=						new_scheduler.step_correct(
                    _lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
  
                assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler correction are not identical"
  
  
  
    def    _a			(self       ,					_lowerCamelCase=0       ,					**_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[int]						=						dict(self.forward_default_kwargs  )
          kwargs.update(_lowerCamelCase  )
  
          for scheduler_class in self.scheduler_classes:
                UpperCAmelCase__ :       List[str]						=						self.dummy_sample
                UpperCAmelCase__ :       List[str]						=						0.1 * sample
  
                UpperCAmelCase__ :       Optional[Any]						=						self.get_scheduler_config()
                UpperCAmelCase__ :       Optional[Any]						=						scheduler_class(**_lowerCamelCase  )
  
                with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(_lowerCamelCase  )
                      UpperCAmelCase__ :       Union[str, Any]						=						scheduler_class.from_pretrained(_lowerCamelCase  )
  
                UpperCAmelCase__ :       Optional[int]						=						scheduler.step_pred(
                    _lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
                UpperCAmelCase__ :       Any						=						new_scheduler.step_pred(
                    _lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
  
                assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"
  
                UpperCAmelCase__ :       Optional[Any]						=						scheduler.step_correct(_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
                UpperCAmelCase__ :       Dict						=						new_scheduler.step_correct(
                    _lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
  
                assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler correction are not identical"
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          for timesteps in [10, 100, 1000]:
                self.check_over_configs(num_train_timesteps=_lowerCamelCase  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          for sigma_min, sigma_max in zip([0.0_001, 0.001, 0.01]       ,					[1, 100, 1000]  ):
                self.check_over_configs(sigma_min=_lowerCamelCase       ,					sigma_max=_lowerCamelCase  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          for t in [0.1, 0.5, 0.75]:
                self.check_over_forward(time_step=_lowerCamelCase  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[Any]						=						dict(self.forward_default_kwargs  )
  
          UpperCAmelCase__ :       int						=						self.scheduler_classes[0]
          UpperCAmelCase__ :       int						=						self.get_scheduler_config()
          UpperCAmelCase__ :       Dict						=						scheduler_class(**_lowerCamelCase  )
  
          UpperCAmelCase__ :       List[str]						=						3
  
          UpperCAmelCase__ :       List[Any]						=						self.dummy_model()
          UpperCAmelCase__ :       int						=						self.dummy_sample_deter
  
          scheduler.set_sigmas(_lowerCamelCase  )
          scheduler.set_timesteps(_lowerCamelCase  )
          UpperCAmelCase__ :       Tuple						=						torch.manual_seed(0  )
  
          for i, t in enumerate(scheduler.timesteps  ):
                UpperCAmelCase__ :       Tuple						=						scheduler.sigmas[i]
  
                for _ in range(scheduler.config.correct_steps  ):
                      with torch.no_grad():
                            UpperCAmelCase__ :       Optional[int]						=						model(_lowerCamelCase       ,					_lowerCamelCase  )
                      UpperCAmelCase__ :       List[str]						=						scheduler.step_correct(_lowerCamelCase       ,					_lowerCamelCase       ,					generator=_lowerCamelCase       ,					**_lowerCamelCase  ).prev_sample
  
                with torch.no_grad():
                      UpperCAmelCase__ :       Tuple						=						model(_lowerCamelCase       ,					_lowerCamelCase  )
  
                UpperCAmelCase__ :       str						=						scheduler.step_pred(_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					generator=_lowerCamelCase       ,					**_lowerCamelCase  )
                UpperCAmelCase__				,		UpperCAmelCase__ :       List[str]						=						output.prev_sample, output.prev_sample_mean
  
          UpperCAmelCase__ :       int						=						torch.sum(torch.abs(_lowerCamelCase  )  )
          UpperCAmelCase__ :       str						=						torch.mean(torch.abs(_lowerCamelCase  )  )
  
          assert np.isclose(result_sum.item()       ,					14_372_758_528.0  )
          assert np.isclose(result_mean.item()       ,					18_714_530.0  )
  
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[int]						=						dict(self.forward_default_kwargs  )
  
          UpperCAmelCase__ :       Union[str, Any]						=						kwargs.pop("""num_inference_steps"""       ,					_lowerCamelCase  )
  
          for scheduler_class in self.scheduler_classes:
                UpperCAmelCase__ :       Dict						=						self.get_scheduler_config()
                UpperCAmelCase__ :       List[Any]						=						scheduler_class(**_lowerCamelCase  )
  
                UpperCAmelCase__ :       str						=						self.dummy_sample
                UpperCAmelCase__ :       Tuple						=						0.1 * sample
  
                if num_inference_steps is not None and hasattr(_lowerCamelCase       ,					"""set_timesteps"""  ):
                      scheduler.set_timesteps(_lowerCamelCase  )
                elif num_inference_steps is not None and not hasattr(_lowerCamelCase       ,					"""set_timesteps"""  ):
                      UpperCAmelCase__ :       Dict						=						num_inference_steps
  
                UpperCAmelCase__ :       str						=						scheduler.step_pred(_lowerCamelCase       ,					0       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
                UpperCAmelCase__ :       int						=						scheduler.step_pred(_lowerCamelCase       ,					1       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
  
                self.assertEqual(output_a.shape       ,					sample.shape  )
                self.assertEqual(output_a.shape       ,					output_a.shape  )
  
  