




import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest
class 							__lowercase   (			a__    ):
     _lowerCAmelCase		       =						(DDIMScheduler,)
     _lowerCAmelCase		       =						(("eta", 0.0), ("num_inference_steps", 50))



     def 				__magic_name__		(  self   :							Union[str, Any]      ,		**lowercase__   :							Union[str, Any]       ):
       a_		    =  {
           '''num_train_timesteps''': 1_0_0_0,
           '''beta_start''': 0.0001,
           '''beta_end''': 0.02,
           '''beta_schedule''': '''linear''',
           '''clip_sample''': True,
       }

       config.update(**lowercase__       )
       return config



     def 				__magic_name__		(  self   :							Optional[int]      ,		**lowercase__   :							Any       ):
       a_		    =  self.scheduler_classes[0]
       a_		    =  self.get_scheduler_config(**lowercase__       )
       a_		    =  scheduler_class(**lowercase__       )

       a_    , a_		    =  1_0, 0.0

       a_		    =  self.dummy_model()
       a_		    =  self.dummy_sample_deter

       scheduler.set_timesteps(lowercase__       )

       for t in scheduler.timesteps:
         a_		    =  model(lowercase__      ,		lowercase__       )
         a_		    =  scheduler.step(lowercase__      ,		lowercase__      ,		lowercase__      ,		lowercase__       ).prev_sample

       return sample



     def 				__magic_name__		(  self   :							Any       ):
       for timesteps in [1_0_0, 5_0_0, 1_0_0_0]:
         self.check_over_configs(num_train_timesteps=lowercase__       )



     def 				__magic_name__		(  self   :							Optional[int]       ):
       for steps_offset in [0, 1]:
         self.check_over_configs(steps_offset=lowercase__       )

       a_		    =  self.scheduler_classes[0]
       a_		    =  self.get_scheduler_config(steps_offset=1       )
       a_		    =  scheduler_class(**lowercase__       )
       scheduler.set_timesteps(5       )
       assert torch.equal(scheduler.timesteps      ,		torch.LongTensor([8_0_1, 6_0_1, 4_0_1, 2_0_1, 1]       )       )



     def 				__magic_name__		(  self   :							Optional[Any]       ):
       for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]      ,		[0.002, 0.02, 0.2, 2]       ):
         self.check_over_configs(beta_start=lowercase__      ,		beta_end=lowercase__       )



     def 				__magic_name__		(  self   :							List[Any]       ):
       for schedule in ["linear", "squaredcos_cap_v2"]:
         self.check_over_configs(beta_schedule=lowercase__       )



     def 				__magic_name__		(  self   :							List[str]       ):
       for prediction_type in ["epsilon", "v_prediction"]:
         self.check_over_configs(prediction_type=lowercase__       )



     def 				__magic_name__		(  self   :							int       ):
       for clip_sample in [True, False]:
         self.check_over_configs(clip_sample=lowercase__       )



     def 				__magic_name__		(  self   :							Optional[int]       ):
       for timestep_spacing in ["trailing", "leading"]:
         self.check_over_configs(timestep_spacing=lowercase__       )



     def 				__magic_name__		(  self   :							Dict       ):
       for rescale_betas_zero_snr in [True, False]:
         self.check_over_configs(rescale_betas_zero_snr=lowercase__       )



     def 				__magic_name__		(  self   :							Optional[Any]       ):
       self.check_over_configs(thresholding=lowercase__       )
       for threshold in [0.5, 1.0, 2.0]:
         for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(
               thresholding=lowercase__      ,		prediction_type=lowercase__      ,		sample_max_value=lowercase__      ,		)



     def 				__magic_name__		(  self   :							List[Any]       ):
       for t in [1, 1_0, 4_9]:
         self.check_over_forward(time_step=lowercase__       )



     def 				__magic_name__		(  self   :							List[Any]       ):
       for t, num_inference_steps in zip([1, 1_0, 5_0]      ,		[1_0, 5_0, 5_0_0]       ):
         self.check_over_forward(time_step=lowercase__      ,		num_inference_steps=lowercase__       )



     def 				__magic_name__		(  self   :							str       ):
       for t, eta in zip([1, 1_0, 4_9]      ,		[0.0, 0.5, 1.0]       ):
         self.check_over_forward(time_step=lowercase__      ,		eta=lowercase__       )



     def 				__magic_name__		(  self   :							str       ):
       a_		    =  self.scheduler_classes[0]
       a_		    =  self.get_scheduler_config()
       a_		    =  scheduler_class(**lowercase__       )

       assert torch.sum(torch.abs(scheduler._get_variance(0      ,		0       ) - 0.0       )       ) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(4_2_0      ,		4_0_0       ) - 0.1_4771       )       ) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(9_8_0      ,		9_6_0       ) - 0.3_2460       )       ) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(0      ,		0       ) - 0.0       )       ) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(4_8_7      ,		4_8_6       ) - 0.0_0979       )       ) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(9_9_9      ,		9_9_8       ) - 0.02       )       ) < 1e-5



     def 				__magic_name__		(  self   :							List[Any]       ):
       a_		    =  self.full_loop()

       a_		    =  torch.sum(torch.abs(lowercase__       )       )
       a_		    =  torch.mean(torch.abs(lowercase__       )       )

       assert abs(result_sum.item() - 172.0067       ) < 1e-2
       assert abs(result_mean.item() - 0.22_3967       ) < 1e-3



     def 				__magic_name__		(  self   :							Union[str, Any]       ):
       a_		    =  self.full_loop(prediction_type='''v_prediction'''       )

       a_		    =  torch.sum(torch.abs(lowercase__       )       )
       a_		    =  torch.mean(torch.abs(lowercase__       )       )

       assert abs(result_sum.item() - 52.5302       ) < 1e-2
       assert abs(result_mean.item() - 0.0684       ) < 1e-3



     def 				__magic_name__		(  self   :							Tuple       ):
       # We specify different beta, so that the first alpha is 0.99
       a_		    =  self.full_loop(set_alpha_to_one=lowercase__      ,		beta_start=0.01       )
       a_		    =  torch.sum(torch.abs(lowercase__       )       )
       a_		    =  torch.mean(torch.abs(lowercase__       )       )

       assert abs(result_sum.item() - 149.8295       ) < 1e-2
       assert abs(result_mean.item() - 0.1951       ) < 1e-3



     def 				__magic_name__		(  self   :							Union[str, Any]       ):
       # We specify different beta, so that the first alpha is 0.99
       a_		    =  self.full_loop(set_alpha_to_one=lowercase__      ,		beta_start=0.01       )
       a_		    =  torch.sum(torch.abs(lowercase__       )       )
       a_		    =  torch.mean(torch.abs(lowercase__       )       )

       assert abs(result_sum.item() - 149.0784       ) < 1e-2
       assert abs(result_mean.item() - 0.1941       ) < 1e-3






from __future__ import annotations

from typing import Any






def 				UpperCAmelCase__  (							*_A			):






  """simple docstring"""


  if all(_check_not_integer(_A			) for m in matrix_s			):
    for i in matrix_s[1:]:
      _verify_matrix_sizes(matrix_s[0]      ,      _A			)
    return [[sum(_A			) for t in zip(*_A			)] for m in zip(*_A			)]
  raise TypeError('''Expected a matrix, got int/list instead'''			)






def 				UpperCAmelCase__  (							_A      ,      _A			):






  """simple docstring"""


  if (
      _check_not_integer(_A			)
      and _check_not_integer(_A			)
      and _verify_matrix_sizes(_A      ,      _A			)
  ):
    return [[i - j for i, j in zip(*_A			)] for m in zip(_A      ,      _A			)]
  raise TypeError('''Expected a matrix, got int/list instead'''			)






def 				UpperCAmelCase__  (							_A      ,      _A			):






  """simple docstring"""


  return [[x * n for x in row] for row in matrix]






def 				UpperCAmelCase__  (							_A      ,      _A			):






  """simple docstring"""


  if _check_not_integer(_A			) and _check_not_integer(_A			):
    a_    , a_		    =  _verify_matrix_sizes(_A      ,      _A			)

  if cols[0] != rows[1]:
    a_		    =  (
        '''Cannot multiply matrix of dimensions '''
        f"({rows[0]},{cols[0]}) and ({rows[1]},{cols[1]})"
    )
    raise ValueError(_A			)
  return [
      [sum(m * n for m, n in zip(_A      ,      _A			)			) for j in zip(*_A			)] for i in matrix_a
  ]






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


  a_		    =  int(_A			)
  return [[int(row == column			) for column in range(_A			)] for row in range(_A			)]






def 				UpperCAmelCase__  (							_A      ,      _A = True			):






  """simple docstring"""


  if _check_not_integer(_A			):
    if return_map:
      return map(_A      ,      zip(*_A			)			)
    else:
      return list(map(_A      ,      zip(*_A			)			)			)
  raise TypeError('''Expected a matrix, got int/list instead'''			)






def 				UpperCAmelCase__  (							_A      ,      _A      ,      _A			):






  """simple docstring"""


  a_		    =  matrix[:row] + matrix[row + 1 :]
  return [row[:column] + row[column + 1 :] for row in minor]






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


  if len(_A			) == 1:
    return matrix[0][0]

  return sum(
      x * determinant(minor(_A      ,      0      ,      _A			)			) * (-1) ** i
      for i, x in enumerate(matrix[0]			)			)






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


  a_		    =  determinant(_A			)
  if det == 0:
    return None

  a_		    =  [
      [determinant(minor(_A      ,      _A      ,      _A			)			) for j in range(len(_A			)			)]
      for i in range(len(_A			)			)
  ]

  a_		    =  [
      [x * (-1) ** (row + col) for col, x in enumerate(matrix_minor[row]			)]
      for row in range(len(_A			)			)
  ]
  a_		    =  list(transpose(_A			)			)
  return scalar_multiply(_A      ,      1 / det			)






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


  return not isinstance(_A      ,      _A			) and not isinstance(matrix[0]      ,      _A			)






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


  return len(_A			), len(matrix[0]			)






def 				UpperCAmelCase__  (							_A      ,      _A			):






  """simple docstring"""


  a_		    =  _shape(_A			) + _shape(_A			)
  if shape[0] != shape[3] or shape[1] != shape[2]:
    a_		    =  (
        '''operands could not be broadcast together with shape '''
        f"({shape[0], shape[1]}), ({shape[2], shape[3]})"
    )
    raise ValueError(_A			)
  return (shape[0], shape[2]), (shape[1], shape[3])






def 				UpperCAmelCase__  (							):






  """simple docstring"""


  a_		    =  [[12, 10], [3, 9]]
  a_		    =  [[3, 4], [7, 4]]
  a_		    =  [[11, 12, 13, 14], [21, 22, 23, 24], [31, 32, 33, 34], [41, 42, 43, 44]]
  a_		    =  [[3, 0, 2], [2, 0, -2], [0, 1, 1]]
  print(f"Add Operation, {add(_A      ,      _A			) = } \n"			)
  print(
      f"Multiply Operation, {multiply(_A      ,      _A			) = } \n"      ,      )
  print(f"Identity: {identity(5			)}\n"			)
  print(f"Minor of {matrix_c} = {minor(_A      ,      1      ,      2			)} \n"			)
  print(f"Determinant of {matrix_b} = {determinant(_A			)} \n"			)
  print(f"Inverse of {matrix_d} = {inverse(_A			)}\n"			)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()
