

import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class  UpperCamelCase_      (	_snake_case      ):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__					:			Optional[int]					    =   (LMSDiscreteScheduler,)
  SCREAMING_SNAKE_CASE__					:			str					    =   10



  def   _UpperCamelCase     (						self     :					List[str]  ,	**A_     :					int						)       ->			Dict:
      SCREAMING_SNAKE_CASE__         =    {
          "num_train_timesteps": 1100,
          "beta_start": 0.0_001,
          "beta_end": 0.02,
          "beta_schedule": "linear",
      }

      config.update(**A_						)
      return config



  def   _UpperCamelCase     (						self     :					List[Any]						)       ->			Optional[Any]:
      for timesteps in [10, 50, 100, 1000]:
          self.check_over_configs(num_train_timesteps=A_						)



  def   _UpperCamelCase     (						self     :					Optional[int]						)       ->			int:
      for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]  ,	[0.0_002, 0.002, 0.02]						):
          self.check_over_configs(beta_start=A_  ,	beta_end=A_						)



  def   _UpperCamelCase     (						self     :					Tuple						)       ->			Tuple:
      for schedule in ["linear", "scaled_linear"]:
          self.check_over_configs(beta_schedule=A_						)



  def   _UpperCamelCase     (						self     :					Dict						)       ->			List[str]:
      for prediction_type in ["epsilon", "v_prediction"]:
          self.check_over_configs(prediction_type=A_						)



  def   _UpperCamelCase     (						self     :					Optional[int]						)       ->			Dict:
      for t in [0, 500, 800]:
          self.check_over_forward(time_step=A_						)



  def   _UpperCamelCase     (						self     :					Any						)       ->			Union[str, Any]:
      SCREAMING_SNAKE_CASE__         =    self.scheduler_classes[0]
      SCREAMING_SNAKE_CASE__         =    self.get_scheduler_config()
      SCREAMING_SNAKE_CASE__         =    scheduler_class(**A_						)

      scheduler.set_timesteps(self.num_inference_steps						)

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

      for i, t in enumerate(scheduler.timesteps						):
          SCREAMING_SNAKE_CASE__         =    scheduler.scale_model_input(A_  ,	A_						)

          SCREAMING_SNAKE_CASE__         =    model(A_  ,	A_						)

          SCREAMING_SNAKE_CASE__         =    scheduler.step(A_  ,	A_  ,	A_						)
          SCREAMING_SNAKE_CASE__         =    output.prev_sample

      SCREAMING_SNAKE_CASE__         =    torch.sum(torch.abs(A_						)						)
      SCREAMING_SNAKE_CASE__         =    torch.mean(torch.abs(A_						)						)

      assert abs(result_sum.item() - 1_006.388						) < 1E-2
      assert abs(result_mean.item() - 1.31						) < 1E-3



  def   _UpperCamelCase     (						self     :					Optional[int]						)       ->			int:
      SCREAMING_SNAKE_CASE__         =    self.scheduler_classes[0]
      SCREAMING_SNAKE_CASE__         =    self.get_scheduler_config(prediction_type="v_prediction"						)
      SCREAMING_SNAKE_CASE__         =    scheduler_class(**A_						)

      scheduler.set_timesteps(self.num_inference_steps						)

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

      for i, t in enumerate(scheduler.timesteps						):
          SCREAMING_SNAKE_CASE__         =    scheduler.scale_model_input(A_  ,	A_						)

          SCREAMING_SNAKE_CASE__         =    model(A_  ,	A_						)

          SCREAMING_SNAKE_CASE__         =    scheduler.step(A_  ,	A_  ,	A_						)
          SCREAMING_SNAKE_CASE__         =    output.prev_sample

      SCREAMING_SNAKE_CASE__         =    torch.sum(torch.abs(A_						)						)
      SCREAMING_SNAKE_CASE__         =    torch.mean(torch.abs(A_						)						)

      assert abs(result_sum.item() - 0.0_017						) < 1E-2
      assert abs(result_mean.item() - 2.2_6_7_6E-0_6						) < 1E-3



  def   _UpperCamelCase     (						self     :					Any						)       ->			Tuple:
      SCREAMING_SNAKE_CASE__         =    self.scheduler_classes[0]
      SCREAMING_SNAKE_CASE__         =    self.get_scheduler_config()
      SCREAMING_SNAKE_CASE__         =    scheduler_class(**A_						)

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

      SCREAMING_SNAKE_CASE__         =    self.dummy_model()
      SCREAMING_SNAKE_CASE__         =    self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
      SCREAMING_SNAKE_CASE__         =    sample.to(A_						)

      for i, t in enumerate(scheduler.timesteps						):
          SCREAMING_SNAKE_CASE__         =    scheduler.scale_model_input(A_  ,	A_						)

          SCREAMING_SNAKE_CASE__         =    model(A_  ,	A_						)

          SCREAMING_SNAKE_CASE__         =    scheduler.step(A_  ,	A_  ,	A_						)
          SCREAMING_SNAKE_CASE__         =    output.prev_sample

      SCREAMING_SNAKE_CASE__         =    torch.sum(torch.abs(A_						)						)
      SCREAMING_SNAKE_CASE__         =    torch.mean(torch.abs(A_						)						)

      assert abs(result_sum.item() - 1_006.388						) < 1E-2
      assert abs(result_mean.item() - 1.31						) < 1E-3







  def   _UpperCamelCase     (						self     :					int						)       ->			List[Any]:
      SCREAMING_SNAKE_CASE__         =    self.scheduler_classes[0]
      SCREAMING_SNAKE_CASE__         =    self.get_scheduler_config()
      SCREAMING_SNAKE_CASE__         =    scheduler_class(**A_  ,	use_karras_sigmas=A_						)

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

      SCREAMING_SNAKE_CASE__         =    self.dummy_model()
      SCREAMING_SNAKE_CASE__         =    self.dummy_sample_deter.to(A_						) * scheduler.init_noise_sigma
      SCREAMING_SNAKE_CASE__         =    sample.to(A_						)

      for t in scheduler.timesteps:
          SCREAMING_SNAKE_CASE__         =    scheduler.scale_model_input(A_  ,	A_						)

          SCREAMING_SNAKE_CASE__         =    model(A_  ,	A_						)

          SCREAMING_SNAKE_CASE__         =    scheduler.step(A_  ,	A_  ,	A_						)
          SCREAMING_SNAKE_CASE__         =    output.prev_sample

      SCREAMING_SNAKE_CASE__         =    torch.sum(torch.abs(A_						)						)
      SCREAMING_SNAKE_CASE__         =    torch.mean(torch.abs(A_						)						)

      assert abs(result_sum.item() - 3_812.9_927						) < 2E-2
      assert abs(result_mean.item() - 4.9_648						) < 1E-3





def 				__a							(       __A			,							__A			,							__A			,							__A						):
    return len_board * len_board_column * row + column





def 				__a							(       __A			,							__A			,							__A			,							__A			,							__A			,							__A			,							):

    if board[row][column] != word[word_index]:
        return False

    if word_index == len(__A						) - 1:
        return True

    SCREAMING_SNAKE_CASE__         =    [(0, 1), (0, -1), (-1, 0), (1, 0)]
    SCREAMING_SNAKE_CASE__         =    len(__A						)
    SCREAMING_SNAKE_CASE__         =    len(board[0]						)
    for direction in traverts_directions:
        SCREAMING_SNAKE_CASE__         =    row + direction[0]
        SCREAMING_SNAKE_CASE__         =    column + direction[1]
        if not (0 <= next_i < len_board and 0 <= next_j < len_board_column):
            continue

        SCREAMING_SNAKE_CASE__         =    get_point_key(__A			,							__A			,							__A			,							__A						)
        if key in visited_points_set:
            continue

        visited_points_set.add(__A						)
        if exits_word(__A			,							__A			,							__A			,							__A			,							word_index + 1			,							__A						):
            return True

        visited_points_set.remove(__A						)

    return False





def 				__a							(       __A			,							__A						):
    SCREAMING_SNAKE_CASE__         =    (
        "The board should be a non empty matrix of single chars strings."
    )

    SCREAMING_SNAKE_CASE__         =    len(__A						)
    if not isinstance(__A			,							__A						) or len(__A						) == 0:
        raise ValueError(__A						)

    for row in board:
        if not isinstance(__A			,							__A						) or len(__A						) == 0:
            raise ValueError(__A						)

        for item in row:
            if not isinstance(__A			,							__A						) or len(__A						) != 1:
                raise ValueError(__A						)

    # Validate word
    if not isinstance(__A			,							__A						) or len(__A						) == 0:
        raise ValueError(
            "The word parameter should be a string of length greater than 0."						)

    SCREAMING_SNAKE_CASE__         =    len(board[0]						)
    for i in range(__A						):
        for j in range(__A						):
            if exits_word(
                __A			,							__A			,							__A			,							__A			,							0			,							{get_point_key(__A			,							__A			,							__A			,							__A						)}						):
                return True

    return False


if __name__ == "__main__":
  import doctest

  doctest.testmod()


