import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest





@require_torchsde
class 		_lowercase						(					snake_case_       ):
 lowercase				     =   (DPMSolverSDEScheduler,)
 lowercase				     =   1_0

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[Any]   ,      **snake_case	:							List[Any]			)   ->						Any:

  """simple docstring"""





  UpperCamelCase_					:						Optional[int] =					{
      'num_train_timesteps': 1_1_0_0,
      'beta_start': 0.0001,
      'beta_end': 0.02,
      'beta_schedule': 'linear',
      'noise_sampler_seed': 0,
  }

  config.update(**snake_case			)
  return config

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Dict			)   ->						Dict:

  """simple docstring"""





  for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
   self.check_over_configs(num_train_timesteps=snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							str			)   ->						str:

  """simple docstring"""





  for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]   ,      [0.0002, 0.002, 0.02]			):
   self.check_over_configs(beta_start=snake_case   ,      beta_end=snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Dict			)   ->						Optional[int]:

  """simple docstring"""





  for schedule in ["linear", "scaled_linear"]:
   self.check_over_configs(beta_schedule=snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[int]			)   ->						Any:

  """simple docstring"""





  for prediction_type in ["epsilon", "v_prediction"]:
   self.check_over_configs(prediction_type=snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[Any]			)   ->						List[str]:

  """simple docstring"""





  UpperCamelCase_					:						Tuple =					self.scheduler_classes[0]
  UpperCamelCase_					:						Union[str, Any] =					self.get_scheduler_config()
  UpperCamelCase_					:						Union[str, Any] =					scheduler_class(**snake_case			)

  scheduler.set_timesteps(self.num_inference_steps			)

  UpperCamelCase_					:						str =					self.dummy_model()
  UpperCamelCase_					:						Optional[Any] =					self.dummy_sample_deter * scheduler.init_noise_sigma
  UpperCamelCase_					:						Tuple =					sample.to(snake_case			)

  for i, t in enumerate(scheduler.timesteps			):
   UpperCamelCase_					:						Tuple =					scheduler.scale_model_input(snake_case   ,      snake_case			)

   UpperCamelCase_					:						Dict =					model(snake_case   ,      snake_case			)

   UpperCamelCase_					:						int =					scheduler.step(snake_case   ,      snake_case   ,      snake_case			)
   UpperCamelCase_					:						List[str] =					output.prev_sample

  UpperCamelCase_					:						Optional[int] =					torch.sum(torch.abs(snake_case			)			)
  UpperCamelCase_					:						List[str] =					torch.mean(torch.abs(snake_case			)			)

  if torch_device in ["mps"]:
   assert abs(result_sum.item() - 167.47821044921875			) < 1e-2
   assert abs(result_mean.item() - 0.2178705964565277			) < 1e-3
  elif torch_device in ["cuda"]:
   assert abs(result_sum.item() - 171.59352111816406			) < 1e-2
   assert abs(result_mean.item() - 0.22342906892299652			) < 1e-3
  else:
   assert abs(result_sum.item() - 162.52383422851562			) < 1e-2
   assert abs(result_mean.item() - 0.211619570851326			) < 1e-3

 def 						SCREAMING_SNAKE_CASE__   ( self	:							str			)   ->						Any:

  """simple docstring"""





  UpperCamelCase_					:						Optional[Any] =					self.scheduler_classes[0]
  UpperCamelCase_					:						Tuple =					self.get_scheduler_config(prediction_type='v_prediction'			)
  UpperCamelCase_					:						str =					scheduler_class(**snake_case			)

  scheduler.set_timesteps(self.num_inference_steps			)

  UpperCamelCase_					:						Optional[int] =					self.dummy_model()
  UpperCamelCase_					:						Optional[int] =					self.dummy_sample_deter * scheduler.init_noise_sigma
  UpperCamelCase_					:						Union[str, Any] =					sample.to(snake_case			)

  for i, t in enumerate(scheduler.timesteps			):
   UpperCamelCase_					:						Any =					scheduler.scale_model_input(snake_case   ,      snake_case			)

   UpperCamelCase_					:						str =					model(snake_case   ,      snake_case			)

   UpperCamelCase_					:						List[Any] =					scheduler.step(snake_case   ,      snake_case   ,      snake_case			)
   UpperCamelCase_					:						Optional[int] =					output.prev_sample

  UpperCamelCase_					:						List[str] =					torch.sum(torch.abs(snake_case			)			)
  UpperCamelCase_					:						Tuple =					torch.mean(torch.abs(snake_case			)			)

  if torch_device in ["mps"]:
   assert abs(result_sum.item() - 124.77149200439453			) < 1e-2
   assert abs(result_mean.item() - 0.16226289014816284			) < 1e-3
  elif torch_device in ["cuda"]:
   assert abs(result_sum.item() - 128.1663360595703			) < 1e-2
   assert abs(result_mean.item() - 0.16688326001167297			) < 1e-3
  else:
   assert abs(result_sum.item() - 119.8487548828125			) < 1e-2
   assert abs(result_mean.item() - 0.1560530662536621			) < 1e-3

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]			)   ->						Tuple:

  """simple docstring"""





  UpperCamelCase_					:						Tuple =					self.scheduler_classes[0]
  UpperCamelCase_					:						int =					self.get_scheduler_config()
  UpperCamelCase_					:						Dict =					scheduler_class(**snake_case			)

  scheduler.set_timesteps(self.num_inference_steps   ,      device=snake_case			)

  UpperCamelCase_					:						str =					self.dummy_model()
  UpperCamelCase_					:						Union[str, Any] =					self.dummy_sample_deter.to(snake_case			) * scheduler.init_noise_sigma

  for t in scheduler.timesteps:
   UpperCamelCase_					:						str =					scheduler.scale_model_input(snake_case   ,      snake_case			)

   UpperCamelCase_					:						List[Any] =					model(snake_case   ,      snake_case			)

   UpperCamelCase_					:						Any =					scheduler.step(snake_case   ,      snake_case   ,      snake_case			)
   UpperCamelCase_					:						Optional[Any] =					output.prev_sample

  UpperCamelCase_					:						str =					torch.sum(torch.abs(snake_case			)			)
  UpperCamelCase_					:						List[str] =					torch.mean(torch.abs(snake_case			)			)

  if torch_device in ["mps"]:
   assert abs(result_sum.item() - 167.46957397460938			) < 1e-2
   assert abs(result_mean.item() - 0.21805934607982635			) < 1e-3
  elif torch_device in ["cuda"]:
   assert abs(result_sum.item() - 171.59353637695312			) < 1e-2
   assert abs(result_mean.item() - 0.22342908382415771			) < 1e-3
  else:
   assert abs(result_sum.item() - 162.52383422851562			) < 1e-2
   assert abs(result_mean.item() - 0.211619570851326			) < 1e-3


 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any			)   ->						str:

  """simple docstring"""





  UpperCamelCase_					:						List[str] =					self.scheduler_classes[0]
  UpperCamelCase_					:						Union[str, Any] =					self.get_scheduler_config()
  UpperCamelCase_					:						Tuple =					scheduler_class(**snake_case   ,      use_karras_sigmas=snake_case			)

  scheduler.set_timesteps(self.num_inference_steps   ,      device=snake_case			)

  UpperCamelCase_					:						Any =					self.dummy_model()
  UpperCamelCase_					:						Any =					self.dummy_sample_deter.to(snake_case			) * scheduler.init_noise_sigma
  UpperCamelCase_					:						str =					sample.to(snake_case			)

  for t in scheduler.timesteps:
   UpperCamelCase_					:						str =					scheduler.scale_model_input(snake_case   ,      snake_case			)

   UpperCamelCase_					:						List[Any] =					model(snake_case   ,      snake_case			)

   UpperCamelCase_					:						Any =					scheduler.step(snake_case   ,      snake_case   ,      snake_case			)
   UpperCamelCase_					:						int =					output.prev_sample

  UpperCamelCase_					:						str =					torch.sum(torch.abs(snake_case			)			)
  UpperCamelCase_					:						Optional[int] =					torch.mean(torch.abs(snake_case			)			)

  if torch_device in ["mps"]:
   assert abs(result_sum.item() - 176.66974135742188			) < 1e-2
   assert abs(result_mean.item() - 0.23003872730981811			) < 1e-2
  elif torch_device in ["cuda"]:
   assert abs(result_sum.item() - 177.63653564453125			) < 1e-2
   assert abs(result_mean.item() - 0.23003872730981811			) < 1e-2
  else:
   assert abs(result_sum.item() - 170.3135223388672			) < 1e-2
   assert abs(result_mean.item() - 0.23003872730981811			) < 1e-2




import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer





def    __lowercase     (						lowerCamelCase					:     str  ,  lowerCamelCase					:     str  ,  **lowerCamelCase					:     List[Any]	):
 UpperCamelCase_					:						Union[str, Any] =					AutoConfig.from_pretrained(lowerCamelCase  ,  **lowerCamelCase	)
 UpperCamelCase_					:						str =					AutoModelForSeqaSeqLM.from_config(lowerCamelCase	)
 model.save_pretrained(lowerCamelCase	)
 AutoTokenizer.from_pretrained(lowerCamelCase	).save_pretrained(lowerCamelCase	)
 return model


if __name__ == "__main__":
   fire.Fire(save_randomly_initialized_version)



