





import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class 				_lowerCAmelCase			(	UpperCAmelCase_							):


  '''simple docstring'''
  a_							:				Optional[int]					    =(EulerAncestralDiscreteScheduler,)
  a_							:				int					    =10







  def UpperCamelCase_       (					self					:    str					, **UpperCamelCase					:    Optional[Any]							):


    '''simple docstring'''





    _snake_case      : Union[str, Any]														=		{
        'num_train_timesteps': 11_00,
        'beta_start': 0.00_01,
        'beta_end': 0.02,
        'beta_schedule': 'linear',
    }

    config.update(**UpperCamelCase							)
    return config







  def UpperCamelCase_       (					self					:    List[str]							):


    '''simple docstring'''





    for timesteps in [10, 50, 1_00, 10_00]:
      self.check_over_configs(num_train_timesteps=UpperCamelCase							)







  def UpperCamelCase_       (					self					:    Tuple							):


    '''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=UpperCamelCase					, beta_end=UpperCamelCase							)







  def UpperCamelCase_       (					self					:    str							):


    '''simple docstring'''





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







  def UpperCamelCase_       (					self					:    Tuple							):


    '''simple docstring'''





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







  def UpperCamelCase_       (					self					:    Union[str, Any]							):


    '''simple docstring'''





    _snake_case      : List[str]														=		self.scheduler_classes[0]
    _snake_case      : Optional[int]														=		self.get_scheduler_config()
    _snake_case      : List[Any]														=		scheduler_class(**UpperCamelCase							)

    scheduler.set_timesteps(self.num_inference_steps							)

    _snake_case      : Optional[int]														=		torch.manual_seed(0							)

    _snake_case      : Any														=		self.dummy_model()
    _snake_case      : Union[str, Any]														=		self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
    _snake_case      : Dict														=		sample.to(UpperCamelCase							)

    for i, t in enumerate(scheduler.timesteps							):
      _snake_case      : Optional[Any]														=		scheduler.scale_model_input(UpperCamelCase					, UpperCamelCase							)

      _snake_case      : Optional[int]														=		model(UpperCamelCase					, UpperCamelCase							)

      _snake_case      : Any														=		scheduler.step(UpperCamelCase					, UpperCamelCase					, UpperCamelCase					, generator=UpperCamelCase							)
      _snake_case      : Dict														=		output.prev_sample

    _snake_case      : Dict														=		torch.sum(torch.abs(UpperCamelCase							)							)
    _snake_case      : List[str]														=		torch.mean(torch.abs(UpperCamelCase							)							)

    assert abs(result_sum.item() - 1_52.31_92							) < 1e-2
    assert abs(result_mean.item() - 0.19_83							) < 1e-3







  def UpperCamelCase_       (					self					:    Union[str, Any]							):


    '''simple docstring'''





    _snake_case      : List[Any]														=		self.scheduler_classes[0]
    _snake_case      : List[Any]														=		self.get_scheduler_config(prediction_type='v_prediction'							)
    _snake_case      : Tuple														=		scheduler_class(**UpperCamelCase							)

    scheduler.set_timesteps(self.num_inference_steps							)

    _snake_case      : Optional[Any]														=		torch.manual_seed(0							)

    _snake_case      : Dict														=		self.dummy_model()
    _snake_case      : Tuple														=		self.dummy_sample_deter * scheduler.init_noise_sigma
    _snake_case      : str														=		sample.to(UpperCamelCase							)

    for i, t in enumerate(scheduler.timesteps							):
      _snake_case      : Any														=		scheduler.scale_model_input(UpperCamelCase					, UpperCamelCase							)

      _snake_case      : List[str]														=		model(UpperCamelCase					, UpperCamelCase							)

      _snake_case      : Union[str, Any]														=		scheduler.step(UpperCamelCase					, UpperCamelCase					, UpperCamelCase					, generator=UpperCamelCase							)
      _snake_case      : Tuple														=		output.prev_sample

    _snake_case      : List[Any]														=		torch.sum(torch.abs(UpperCamelCase							)							)
    _snake_case      : Dict														=		torch.mean(torch.abs(UpperCamelCase							)							)

    assert abs(result_sum.item() - 1_08.44_39							) < 1e-2
    assert abs(result_mean.item() - 0.14_12							) < 1e-3







  def UpperCamelCase_       (					self					:    Union[str, Any]							):


    '''simple docstring'''





    _snake_case      : Tuple														=		self.scheduler_classes[0]
    _snake_case      : List[str]														=		self.get_scheduler_config()
    _snake_case      : str														=		scheduler_class(**UpperCamelCase							)

    scheduler.set_timesteps(self.num_inference_steps					, device=UpperCamelCase							)
    _snake_case      : Tuple														=		torch.manual_seed(0							)

    _snake_case      : Dict														=		self.dummy_model()
    _snake_case      : Optional[Any]														=		self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
    _snake_case      : Optional[int]														=		sample.to(UpperCamelCase							)

    for t in scheduler.timesteps:
      _snake_case      : Tuple														=		scheduler.scale_model_input(UpperCamelCase					, UpperCamelCase							)

      _snake_case      : List[str]														=		model(UpperCamelCase					, UpperCamelCase							)

      _snake_case      : str														=		scheduler.step(UpperCamelCase					, UpperCamelCase					, UpperCamelCase					, generator=UpperCamelCase							)
      _snake_case      : str														=		output.prev_sample

    _snake_case      : Union[str, Any]														=		torch.sum(torch.abs(UpperCamelCase							)							)
    _snake_case      : Dict														=		torch.mean(torch.abs(UpperCamelCase							)							)

    assert abs(result_sum.item() - 1_52.31_92							) < 1e-2
    assert abs(result_mean.item() - 0.19_83							) < 1e-3











def 			lowerCamelCase_		(					lowerCAmelCase:		str			,							lowerCAmelCase:		str )->						int:
  # The longer word should come first
  if len(lowerCAmelCase ) < len(lowerCAmelCase ):
    return levenshtein_distance(lowerCAmelCase			,							lowerCAmelCase )

  if len(lowerCAmelCase ) == 0:
    return len(lowerCAmelCase )

  _snake_case      : Dict														=		list(range(len(lowerCAmelCase ) + 1 ) )

  for i, ca in enumerate(lowerCAmelCase ):
    _snake_case      : Optional[int]														=		[i + 1]

    for j, ca in enumerate(lowerCAmelCase ):
      # Calculate insertions, deletions and substitutions
      _snake_case      : Optional[Any]														=		previous_row[j + 1] + 1
      _snake_case      : Optional[int]														=		current_row[j] + 1
      _snake_case      : Union[str, Any]														=		previous_row[j] + (ca != ca)

      # Get the minimum to append to the current row
      current_row.append(min(lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase ) )

    # Store the previous row
    _snake_case      : str														=		current_row

  # Returns the last element (distance)
  return previous_row[-1]


if __name__ == "__main__":
      lowerCAmelCase_        =			input("""Enter the first word:\n""").strip()
      lowerCAmelCase_        =			input("""Enter the second word:\n""").strip()

      lowerCAmelCase_        =			levenshtein_distance(first_word, second_word)
      print(F"""Levenshtein distance between {first_word} and {second_word} is {result}""")




