

import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class 					lowercase		( _UpperCamelCase      ):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE      							=       (LMSDiscreteScheduler,)
 __SCREAMING_SNAKE_CASE      							=       10




 def 						UpperCamelCase__							(self , **__a				)				->      List[str]:






  """simple docstring"""





  UpperCAmelCase__           =       {
      'num_train_timesteps': 1100,
      'beta_start': 0.00_01,
      'beta_end': 0.02,
      'beta_schedule': 'linear',
  }

  config.update(**__a				)
  return config




 def 						UpperCamelCase__							(self				)				->      Optional[Any]:






  """simple docstring"""





  for timesteps in [10, 50, 100, 1000]:
   self.check_over_configs(num_train_timesteps=__a				)




 def 						UpperCamelCase__							(self				)				->      Dict:






  """simple docstring"""





  for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01] , [0.00_02, 0.0_02, 0.02]				):
   self.check_over_configs(beta_start=__a , beta_end=__a				)




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






  """simple docstring"""





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




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






  """simple docstring"""





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




 def 						UpperCamelCase__							(self				)				->      str:






  """simple docstring"""





  for t in [0, 500, 800]:
   self.check_over_forward(time_step=__a				)




 def 						UpperCamelCase__							(self				)				->      Optional[Any]:






  """simple docstring"""





  UpperCAmelCase__           =       self.scheduler_classes[0]
  UpperCAmelCase__           =       self.get_scheduler_config()
  UpperCAmelCase__           =       scheduler_class(**__a				)

  scheduler.set_timesteps(self.num_inference_steps				)

  UpperCAmelCase__           =       self.dummy_model()
  UpperCAmelCase__           =       self.dummy_sample_deter * scheduler.init_noise_sigma

  for i, t in enumerate(scheduler.timesteps				):
   UpperCAmelCase__           =       scheduler.scale_model_input(__a , __a				)

   UpperCAmelCase__           =       model(__a , __a				)

   UpperCAmelCase__           =       scheduler.step(__a , __a , __a				)
   UpperCAmelCase__           =       output.prev_sample

  UpperCAmelCase__           =       torch.sum(torch.abs(__a				)				)
  UpperCAmelCase__           =       torch.mean(torch.abs(__a				)				)

  assert abs(result_sum.item() - 1006.388				) < 1E-2
  assert abs(result_mean.item() - 1.31				) < 1E-3




 def 						UpperCamelCase__							(self				)				->      str:






  """simple docstring"""





  UpperCAmelCase__           =       self.scheduler_classes[0]
  UpperCAmelCase__           =       self.get_scheduler_config(prediction_type='v_prediction'				)
  UpperCAmelCase__           =       scheduler_class(**__a				)

  scheduler.set_timesteps(self.num_inference_steps				)

  UpperCAmelCase__           =       self.dummy_model()
  UpperCAmelCase__           =       self.dummy_sample_deter * scheduler.init_noise_sigma

  for i, t in enumerate(scheduler.timesteps				):
   UpperCAmelCase__           =       scheduler.scale_model_input(__a , __a				)

   UpperCAmelCase__           =       model(__a , __a				)

   UpperCAmelCase__           =       scheduler.step(__a , __a , __a				)
   UpperCAmelCase__           =       output.prev_sample

  UpperCAmelCase__           =       torch.sum(torch.abs(__a				)				)
  UpperCAmelCase__           =       torch.mean(torch.abs(__a				)				)

  assert abs(result_sum.item() - 0.00_17				) < 1E-2
  assert abs(result_mean.item() - 2.2_6_7_6E-0_6				) < 1E-3




 def 						UpperCamelCase__							(self				)				->      int:






  """simple docstring"""





  UpperCAmelCase__           =       self.scheduler_classes[0]
  UpperCAmelCase__           =       self.get_scheduler_config()
  UpperCAmelCase__           =       scheduler_class(**__a				)

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

  UpperCAmelCase__           =       self.dummy_model()
  UpperCAmelCase__           =       self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
  UpperCAmelCase__           =       sample.to(__a				)

  for i, t in enumerate(scheduler.timesteps				):
   UpperCAmelCase__           =       scheduler.scale_model_input(__a , __a				)

   UpperCAmelCase__           =       model(__a , __a				)

   UpperCAmelCase__           =       scheduler.step(__a , __a , __a				)
   UpperCAmelCase__           =       output.prev_sample

  UpperCAmelCase__           =       torch.sum(torch.abs(__a				)				)
  UpperCAmelCase__           =       torch.mean(torch.abs(__a				)				)

  assert abs(result_sum.item() - 1006.388				) < 1E-2
  assert abs(result_mean.item() - 1.31				) < 1E-3




 def 						UpperCamelCase__							(self				)				->      Tuple:






  """simple docstring"""





  UpperCAmelCase__           =       self.scheduler_classes[0]
  UpperCAmelCase__           =       self.get_scheduler_config()
  UpperCAmelCase__           =       scheduler_class(**__a , use_karras_sigmas=__a				)

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

  UpperCAmelCase__           =       self.dummy_model()
  UpperCAmelCase__           =       self.dummy_sample_deter.to(__a				) * scheduler.init_noise_sigma
  UpperCAmelCase__           =       sample.to(__a				)

  for t in scheduler.timesteps:
   UpperCAmelCase__           =       scheduler.scale_model_input(__a , __a				)

   UpperCAmelCase__           =       model(__a , __a				)

   UpperCAmelCase__           =       scheduler.step(__a , __a , __a				)
   UpperCAmelCase__           =       output.prev_sample

  UpperCAmelCase__           =       torch.sum(torch.abs(__a				)				)
  UpperCAmelCase__           =       torch.mean(torch.abs(__a				)				)

  assert abs(result_sum.item() - 3812.9927				) < 2E-2
  assert abs(result_mean.item() - 4.96_48				) < 1E-3








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCamelCase     =    {
    '''configuration_fsmt''': ['''FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FSMTConfig'''],
    '''tokenization_fsmt''': ['''FSMTTokenizer'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _UpperCamelCase     =    ['''FSMTForConditionalGeneration''', '''FSMTModel''', '''PretrainedFSMTModel''']


if TYPE_CHECKING:
 from .configuration_fsmt import FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP, FSMTConfig
 from .tokenization_fsmt import FSMTTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_fsmt import FSMTForConditionalGeneration, FSMTModel, PretrainedFSMTModel

else:
 import sys

 _UpperCamelCase     =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



