code: 
  '''simple docstring'''
  
  import tempfile
  import unittest
  
  import numpy as np
  import torch
  
  from diffusers import ScoreSdeVeScheduler
  
  
  class   a_  (unittest.TestCase    ):
         # TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
         __lowerCAmelCase    :				Union[str, Any]    =      (ScoreSdeVeScheduler,)
         __lowerCAmelCase    :				Dict    =      ()
  
  
  
  
         @property
         def 							__UpperCamelCase       (     self  ):
           _lowerCAmelCase       :  Any   				=					4
           _lowerCAmelCase       :  Any   				=					3
           _lowerCAmelCase       :  Optional[int]   				=					8
           _lowerCAmelCase       :  Optional[Any]   				=					8
  
           _lowerCAmelCase       :  Optional[Any]   				=					torch.rand((batch_size, num_channels, height, width)  )
  
           return sample
  
  
  
  
         @property
         def 							__UpperCamelCase       (     self  ):
           _lowerCAmelCase       :  int   				=					4
           _lowerCAmelCase       :  Dict   				=					3
           _lowerCAmelCase       :  str   				=					8
           _lowerCAmelCase       :  Any   				=					8
  
           _lowerCAmelCase       :  Tuple   				=					batch_size * num_channels * height * width
           _lowerCAmelCase       :  Union[str, Any]   				=					torch.arange(snake_case_  )
           _lowerCAmelCase       :  Dict   				=					sample.reshape(snake_case_   ,   snake_case_   ,   snake_case_   ,   snake_case_  )
           _lowerCAmelCase       :  Optional[Any]   				=					sample / num_elems
           _lowerCAmelCase       :  Optional[int]   				=					sample.permute(3   ,   0   ,   1   ,   2  )
  
           return sample
  
  
  
  
         def 							__UpperCamelCase       (     self  ):
  
  
  
  
  
  
  
           def model(snake_case_   ,   snake_case_   ,   *snake_case_  ):
             return sample * t / (t + 1)
  
           return model
  
  
  
  
         def 							__UpperCamelCase       (     self   ,   **snake_case_  ):
           _lowerCAmelCase       :  str   				=					{
               "num_train_timesteps": 2_0_0_0,
               "snr": 0.15,
               "sigma_min": 0.01,
               "sigma_max": 1_3_4_8,
               "sampling_eps": 1E-5,
           }
  
           config.update(**snake_case_  )
           return config
  
  
  
  
         def 							__UpperCamelCase       (     self   ,   snake_case_=0   ,   **snake_case_  ):
           _lowerCAmelCase       :  int   				=					dict(self.forward_default_kwargs  )
  
           for scheduler_class in self.scheduler_classes:
             _lowerCAmelCase       :  Any   				=					self.dummy_sample
             _lowerCAmelCase       :  Union[str, Any]   				=					0.1 * sample
  
             _lowerCAmelCase       :  Union[str, Any]   				=					self.get_scheduler_config(**snake_case_  )
             _lowerCAmelCase       :  str   				=					scheduler_class(**snake_case_  )
  
             with tempfile.TemporaryDirectory() as tmpdirname:
               scheduler.save_config(snake_case_  )
               _lowerCAmelCase       :  int   				=					scheduler_class.from_pretrained(snake_case_  )
  
             _lowerCAmelCase       :  List[Any]   				=					scheduler.step_pred(
                 snake_case_   ,   snake_case_   ,   snake_case_   ,   generator=torch.manual_seed(0  )   ,   **snake_case_  ).prev_sample
             _lowerCAmelCase       :  Dict   				=					new_scheduler.step_pred(
                 snake_case_   ,   snake_case_   ,   snake_case_   ,   generator=torch.manual_seed(0  )   ,   **snake_case_  ).prev_sample
  
             assert torch.sum(torch.abs(output - new_output  )  ) < 1E-5, "Scheduler outputs are not identical"
  
             _lowerCAmelCase       :  List[Any]   				=					scheduler.step_correct(snake_case_   ,   snake_case_   ,   generator=torch.manual_seed(0  )   ,   **snake_case_  ).prev_sample
             _lowerCAmelCase       :  Optional[Any]   				=					new_scheduler.step_correct(
                 snake_case_   ,   snake_case_   ,   generator=torch.manual_seed(0  )   ,   **snake_case_  ).prev_sample
  
             assert torch.sum(torch.abs(output - new_output  )  ) < 1E-5, "Scheduler correction are not identical"
  
  
  
  
         def 							__UpperCamelCase       (     self   ,   snake_case_=0   ,   **snake_case_  ):
           _lowerCAmelCase       :  List[Any]   				=					dict(self.forward_default_kwargs  )
           kwargs.update(snake_case_  )
  
           for scheduler_class in self.scheduler_classes:
             _lowerCAmelCase       :  Tuple   				=					self.dummy_sample
             _lowerCAmelCase       :  Optional[int]   				=					0.1 * sample
  
             _lowerCAmelCase       :  Union[str, Any]   				=					self.get_scheduler_config()
             _lowerCAmelCase       :  Optional[int]   				=					scheduler_class(**snake_case_  )
  
             with tempfile.TemporaryDirectory() as tmpdirname:
               scheduler.save_config(snake_case_  )
               _lowerCAmelCase       :  Optional[int]   				=					scheduler_class.from_pretrained(snake_case_  )
  
             _lowerCAmelCase       :  Any   				=					scheduler.step_pred(
                 snake_case_   ,   snake_case_   ,   snake_case_   ,   generator=torch.manual_seed(0  )   ,   **snake_case_  ).prev_sample
             _lowerCAmelCase       :  Optional[Any]   				=					new_scheduler.step_pred(
                 snake_case_   ,   snake_case_   ,   snake_case_   ,   generator=torch.manual_seed(0  )   ,   **snake_case_  ).prev_sample
  
             assert torch.sum(torch.abs(output - new_output  )  ) < 1E-5, "Scheduler outputs are not identical"
  
             _lowerCAmelCase       :  Any   				=					scheduler.step_correct(snake_case_   ,   snake_case_   ,   generator=torch.manual_seed(0  )   ,   **snake_case_  ).prev_sample
             _lowerCAmelCase       :  Optional[int]   				=					new_scheduler.step_correct(
                 snake_case_   ,   snake_case_   ,   generator=torch.manual_seed(0  )   ,   **snake_case_  ).prev_sample
  
             assert torch.sum(torch.abs(output - new_output  )  ) < 1E-5, "Scheduler correction are not identical"
  
  
  
  
         def 							__UpperCamelCase       (     self  ):
  
  
  
  
  
  
  
           for timesteps in [1_0, 1_0_0, 1_0_0_0]:
             self.check_over_configs(num_train_timesteps=snake_case_  )
  
  
  
  
         def 							__UpperCamelCase       (     self  ):
  
  
  
  
  
  
  
           for sigma_min, sigma_max in zip([0.0001, 0.001, 0.01]   ,   [1, 1_0_0, 1_0_0_0]  ):
             self.check_over_configs(sigma_min=snake_case_   ,   sigma_max=snake_case_  )
  
  
  
  
         def 							__UpperCamelCase       (     self  ):
  
  
  
  
  
  
  
           for t in [0.1, 0.5, 0.75]:
             self.check_over_forward(time_step=snake_case_  )
  
  
  
  
         def 							__UpperCamelCase       (     self  ):
           _lowerCAmelCase       :  List[str]   				=					dict(self.forward_default_kwargs  )
  
           _lowerCAmelCase       :  Optional[Any]   				=					self.scheduler_classes[0]
           _lowerCAmelCase       :  Any   				=					self.get_scheduler_config()
           _lowerCAmelCase       :  Any   				=					scheduler_class(**snake_case_  )
  
           _lowerCAmelCase       :  List[str]   				=					3
  
           _lowerCAmelCase       :  List[str]   				=					self.dummy_model()
           _lowerCAmelCase       :  Tuple   				=					self.dummy_sample_deter
  
           scheduler.set_sigmas(snake_case_  )
           scheduler.set_timesteps(snake_case_  )
           _lowerCAmelCase       :  Union[str, Any]   				=					torch.manual_seed(0  )
  
           for i, t in enumerate(scheduler.timesteps  ):
             _lowerCAmelCase       :  Optional[int]   				=					scheduler.sigmas[i]
  
             for _ in range(scheduler.config.correct_steps  ):
               with torch.no_grad():
                 _lowerCAmelCase       :  Dict   				=					model(snake_case_   ,   snake_case_  )
               _lowerCAmelCase       :  Optional[Any]   				=					scheduler.step_correct(snake_case_   ,   snake_case_   ,   generator=snake_case_   ,   **snake_case_  ).prev_sample
  
             with torch.no_grad():
               _lowerCAmelCase       :  Tuple   				=					model(snake_case_   ,   snake_case_  )
  
             _lowerCAmelCase       :  int   				=					scheduler.step_pred(snake_case_   ,   snake_case_   ,   snake_case_   ,   generator=snake_case_   ,   **snake_case_  )
             _lowerCAmelCase       :  List[str]   				=					output.prev_sample, output.prev_sample_mean
  
           _lowerCAmelCase       :  Any   				=					torch.sum(torch.abs(snake_case_  )  )
           _lowerCAmelCase       :  List[str]   				=					torch.mean(torch.abs(snake_case_  )  )
  
           assert np.isclose(result_sum.item()   ,   1_4_3_7_2_7_5_8_5_2_8.0  )
           assert np.isclose(result_mean.item()   ,   1_8_7_1_4_5_3_0.0  )
  
  
  
  
  
  
  
         def 							__UpperCamelCase       (     self  ):
           _lowerCAmelCase       :  Union[str, Any]   				=					dict(self.forward_default_kwargs  )
  
           _lowerCAmelCase       :  List[Any]   				=					kwargs.pop("""num_inference_steps"""   ,   snake_case_  )
  
           for scheduler_class in self.scheduler_classes:
             _lowerCAmelCase       :  int   				=					self.get_scheduler_config()
             _lowerCAmelCase       :  Optional[int]   				=					scheduler_class(**snake_case_  )
  
             _lowerCAmelCase       :  Any   				=					self.dummy_sample
             _lowerCAmelCase       :  Any   				=					0.1 * sample
  
             if num_inference_steps is not None and hasattr(snake_case_   ,   """set_timesteps"""  ):
               scheduler.set_timesteps(snake_case_  )
             elif num_inference_steps is not None and not hasattr(snake_case_   ,   """set_timesteps"""  ):
               _lowerCAmelCase       :  Tuple   				=					num_inference_steps
  
             _lowerCAmelCase       :  List[Any]   				=					scheduler.step_pred(snake_case_   ,   0   ,   snake_case_   ,   generator=torch.manual_seed(0  )   ,   **snake_case_  ).prev_sample
             _lowerCAmelCase       :  Dict   				=					scheduler.step_pred(snake_case_   ,   1   ,   snake_case_   ,   generator=torch.manual_seed(0  )   ,   **snake_case_  ).prev_sample
  
             self.assertEqual(output_a.shape   ,   sample.shape  )
             self.assertEqual(output_a.shape   ,   output_a.shape  )
  
  
  
style_context: 
  '''simple docstring'''
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_tf_available,
      is_torch_available,
  )
  
  
  UpperCamelCase_     			=       {
      """configuration_gptsan_japanese""": ["""GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTSanJapaneseConfig"""],
      """tokenization_gptsan_japanese""": ["""GPTSanJapaneseTokenizer"""],
  }
  
  try:
       if not is_torch_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       UpperCamelCase_     			=       [
           """GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """GPTSanJapaneseForConditionalGeneration""",
           """GPTSanJapaneseModel""",
           """GPTSanJapanesePreTrainedModel""",
       ]
       UpperCamelCase_     			=       [
           """GPTSanJapaneseTokenizer""",
       ]
  
  
  if TYPE_CHECKING:
       from .configuration_gptsan_japanese import GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTSanJapaneseConfig
       from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer
  
       try:
            if not is_torch_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from .modeling_gptsan_japanese import (
                GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
                GPTSanJapaneseForConditionalGeneration,
                GPTSanJapaneseModel,
                GPTSanJapanesePreTrainedModel,
            )
            from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer
  
  
  else:
       import sys
  
       UpperCamelCase_     			=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  
  