






import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest





class 					A__     (     __snake_case ):

     '''simple docstring'''






     snake_case__					=					(IPNDMScheduler,)
     snake_case__					=					(("""num_inference_steps""", 50),)







     def   _SCREAMING_SNAKE_CASE     ( self      :    Any   ,						**_SCREAMING_SNAKE_CASE      :    str ):






           """simple docstring"""




           UpperCamelCase					       =  {'num_train_timesteps': 1000}
           config.update(**_SCREAMING_SNAKE_CASE )
           return config







     def   _SCREAMING_SNAKE_CASE     ( self      :    Union[str, Any]   ,						_SCREAMING_SNAKE_CASE      :    Any=0   ,						**_SCREAMING_SNAKE_CASE      :    int ):






           """simple docstring"""




           UpperCamelCase					       =  dict(self.forward_default_kwargs )
           UpperCamelCase					       =  kwargs.pop('num_inference_steps'   ,						_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  self.dummy_sample
           UpperCamelCase					       =  0.1 * sample
           UpperCamelCase					       =  [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

           for scheduler_class in self.scheduler_classes:
                 UpperCamelCase					       =  self.get_scheduler_config(**_SCREAMING_SNAKE_CASE )
                 UpperCamelCase					       =  scheduler_class(**_SCREAMING_SNAKE_CASE )
                 scheduler.set_timesteps(_SCREAMING_SNAKE_CASE )
                 # copy over dummy past residuals
                 UpperCamelCase					       =  dummy_past_residuals[:]

                 if time_step is None:
                       UpperCamelCase					       =  scheduler.timesteps[len(scheduler.timesteps ) // 2]

                 with tempfile.TemporaryDirectory() as tmpdirname:
                       scheduler.save_config(_SCREAMING_SNAKE_CASE )
                       UpperCamelCase					       =  scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE )
                       new_scheduler.set_timesteps(_SCREAMING_SNAKE_CASE )
                       # copy over dummy past residuals
                       UpperCamelCase					       =  dummy_past_residuals[:]

                 UpperCamelCase					       =  scheduler.step(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						**_SCREAMING_SNAKE_CASE ).prev_sample
                 UpperCamelCase					       =  new_scheduler.step(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						**_SCREAMING_SNAKE_CASE ).prev_sample

                 assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"

                 UpperCamelCase					       =  scheduler.step(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						**_SCREAMING_SNAKE_CASE ).prev_sample
                 UpperCamelCase					       =  new_scheduler.step(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						**_SCREAMING_SNAKE_CASE ).prev_sample

                 assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[Any] ):






           """simple docstring"""




           pass







     def   _SCREAMING_SNAKE_CASE     ( self      :    Dict   ,						_SCREAMING_SNAKE_CASE      :    List[str]=0   ,						**_SCREAMING_SNAKE_CASE      :    Any ):






           """simple docstring"""




           UpperCamelCase					       =  dict(self.forward_default_kwargs )
           UpperCamelCase					       =  kwargs.pop('num_inference_steps'   ,						_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  self.dummy_sample
           UpperCamelCase					       =  0.1 * sample
           UpperCamelCase					       =  [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

           for scheduler_class in self.scheduler_classes:
                 UpperCamelCase					       =  self.get_scheduler_config()
                 UpperCamelCase					       =  scheduler_class(**_SCREAMING_SNAKE_CASE )
                 scheduler.set_timesteps(_SCREAMING_SNAKE_CASE )

                 # copy over dummy past residuals (must be after setting timesteps)
                 UpperCamelCase					       =  dummy_past_residuals[:]

                 if time_step is None:
                       UpperCamelCase					       =  scheduler.timesteps[len(scheduler.timesteps ) // 2]

                 with tempfile.TemporaryDirectory() as tmpdirname:
                       scheduler.save_config(_SCREAMING_SNAKE_CASE )
                       UpperCamelCase					       =  scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE )
                       # copy over dummy past residuals
                       new_scheduler.set_timesteps(_SCREAMING_SNAKE_CASE )

                       # copy over dummy past residual (must be after setting timesteps)
                       UpperCamelCase					       =  dummy_past_residuals[:]

                 UpperCamelCase					       =  scheduler.step(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						**_SCREAMING_SNAKE_CASE ).prev_sample
                 UpperCamelCase					       =  new_scheduler.step(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						**_SCREAMING_SNAKE_CASE ).prev_sample

                 assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"

                 UpperCamelCase					       =  scheduler.step(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						**_SCREAMING_SNAKE_CASE ).prev_sample
                 UpperCamelCase					       =  new_scheduler.step(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						**_SCREAMING_SNAKE_CASE ).prev_sample

                 assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"







     def   _SCREAMING_SNAKE_CASE     ( self      :    Dict   ,						**_SCREAMING_SNAKE_CASE      :    Optional[Any] ):






           """simple docstring"""




           UpperCamelCase					       =  self.scheduler_classes[0]
           UpperCamelCase					       =  self.get_scheduler_config(**_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  scheduler_class(**_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  10
           UpperCamelCase					       =  self.dummy_model()
           UpperCamelCase					       =  self.dummy_sample_deter
           scheduler.set_timesteps(_SCREAMING_SNAKE_CASE )

           for i, t in enumerate(scheduler.timesteps ):
                 UpperCamelCase					       =  model(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )
                 UpperCamelCase					       =  scheduler.step(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE ).prev_sample

           for i, t in enumerate(scheduler.timesteps ):
                 UpperCamelCase					       =  model(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )
                 UpperCamelCase					       =  scheduler.step(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE ).prev_sample

           return sample







     def   _SCREAMING_SNAKE_CASE     ( self      :    Tuple ):






           """simple docstring"""




           UpperCamelCase					       =  dict(self.forward_default_kwargs )

           UpperCamelCase					       =  kwargs.pop('num_inference_steps'   ,						_SCREAMING_SNAKE_CASE )

           for scheduler_class in self.scheduler_classes:
                 UpperCamelCase					       =  self.get_scheduler_config()
                 UpperCamelCase					       =  scheduler_class(**_SCREAMING_SNAKE_CASE )

                 UpperCamelCase					       =  self.dummy_sample
                 UpperCamelCase					       =  0.1 * sample

                 if num_inference_steps is not None and hasattr(_SCREAMING_SNAKE_CASE   ,						'set_timesteps' ):
                       scheduler.set_timesteps(_SCREAMING_SNAKE_CASE )
                 elif num_inference_steps is not None and not hasattr(_SCREAMING_SNAKE_CASE   ,						'set_timesteps' ):
                       UpperCamelCase					       =  num_inference_steps

                 # copy over dummy past residuals (must be done after set_timesteps)
                 UpperCamelCase					       =  [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
                 UpperCamelCase					       =  dummy_past_residuals[:]

                 UpperCamelCase					       =  scheduler.timesteps[5]
                 UpperCamelCase					       =  scheduler.timesteps[6]

                 UpperCamelCase					       =  scheduler.step(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						**_SCREAMING_SNAKE_CASE ).prev_sample
                 UpperCamelCase					       =  scheduler.step(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						**_SCREAMING_SNAKE_CASE ).prev_sample

                 self.assertEqual(output_a.shape   ,						sample.shape )
                 self.assertEqual(output_a.shape   ,						output_a.shape )

                 UpperCamelCase					       =  scheduler.step(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						**_SCREAMING_SNAKE_CASE ).prev_sample
                 UpperCamelCase					       =  scheduler.step(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						**_SCREAMING_SNAKE_CASE ).prev_sample

                 self.assertEqual(output_a.shape   ,						sample.shape )
                 self.assertEqual(output_a.shape   ,						output_a.shape )







     def   _SCREAMING_SNAKE_CASE     ( self      :    List[Any] ):






           """simple docstring"""




           for timesteps in [100, 1000]:
                 self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE   ,						time_step=_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    int ):






           """simple docstring"""




           for t, num_inference_steps in zip([1, 5, 10]   ,						[10, 50, 100] ):
                 self.check_over_forward(num_inference_steps=_SCREAMING_SNAKE_CASE   ,						time_step=_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    List[str] ):






           """simple docstring"""




           UpperCamelCase					       =  self.full_loop()
           UpperCamelCase					       =  torch.mean(torch.abs(_SCREAMING_SNAKE_CASE ) )

           assert abs(result_mean.item() - 254_0529 ) < 10











from __future__ import annotations

import math
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase) ->			list:
      """simple docstring"""

      if len(_UpperCamelCase) != 2 or len(a[0]) != 2 or len(_UpperCamelCase) != 2 or len(b[0]) != 2:
            raise Exception('Matrices are not 2x2')
      UpperCamelCase					       =  [
          [a[0][0] * b[0][0] + a[0][1] * b[1][0], a[0][0] * b[0][1] + a[0][1] * b[1][1]],
          [a[1][0] * b[0][0] + a[1][1] * b[1][0], a[1][0] * b[0][1] + a[1][1] * b[1][1]],
      ]
      return new_matrix
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase) ->			Union[str, Any]:
      """simple docstring"""

      return [
          [matrix_a[row][col] + matrix_b[row][col] for col in range(len(matrix_a[row]))]
          for row in range(len(_UpperCamelCase))
      ]
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase) ->			Optional[int]:
      """simple docstring"""

      return [
          [matrix_a[row][col] - matrix_b[row][col] for col in range(len(matrix_a[row]))]
          for row in range(len(_UpperCamelCase))
      ]
def 		lowercase__  (				_UpperCamelCase) ->			tuple[list, list, list, list]:
      """simple docstring"""

      if len(_UpperCamelCase) % 2 != 0 or len(a[0]) % 2 != 0:
            raise Exception('Odd matrices are not supported!')

      UpperCamelCase					       =  len(_UpperCamelCase)
      UpperCamelCase					       =  matrix_length // 2

      UpperCamelCase					       =  [[a[i][j] for j in range(_UpperCamelCase       ,							_UpperCamelCase)] for i in range(_UpperCamelCase)]
      UpperCamelCase					       =  [
          [a[i][j] for j in range(_UpperCamelCase       ,							_UpperCamelCase)] for i in range(_UpperCamelCase       ,							_UpperCamelCase)
      ]

      UpperCamelCase					       =  [[a[i][j] for j in range(_UpperCamelCase)] for i in range(_UpperCamelCase)]
      UpperCamelCase					       =  [[a[i][j] for j in range(_UpperCamelCase)] for i in range(_UpperCamelCase       ,							_UpperCamelCase)]

      return top_left, top_right, bot_left, bot_right
def 		lowercase__  (				_UpperCamelCase) ->			tuple[int, int]:
      """simple docstring"""

      return len(_UpperCamelCase), len(matrix[0])
def 		lowercase__  (				_UpperCamelCase) ->			None:
      """simple docstring"""

      print('\n'.join(str(_UpperCamelCase) for line in matrix))
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase) ->			list:
      """simple docstring"""

      if matrix_dimensions(_UpperCamelCase) == (2, 2):
            return default_matrix_multiplication(_UpperCamelCase       ,							_UpperCamelCase)

      UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase					       =  split_matrix(_UpperCamelCase)
      UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase					       =  split_matrix(_UpperCamelCase)

      UpperCamelCase					       =  actual_strassen(_UpperCamelCase       ,							matrix_subtraction(_UpperCamelCase       ,							_UpperCamelCase))
      UpperCamelCase					       =  actual_strassen(matrix_addition(_UpperCamelCase       ,							_UpperCamelCase)       ,							_UpperCamelCase)
      UpperCamelCase					       =  actual_strassen(matrix_addition(_UpperCamelCase       ,							_UpperCamelCase)       ,							_UpperCamelCase)
      UpperCamelCase					       =  actual_strassen(_UpperCamelCase       ,							matrix_subtraction(_UpperCamelCase       ,							_UpperCamelCase))
      UpperCamelCase					       =  actual_strassen(matrix_addition(_UpperCamelCase       ,							_UpperCamelCase)       ,							matrix_addition(_UpperCamelCase       ,							_UpperCamelCase))
      UpperCamelCase					       =  actual_strassen(matrix_subtraction(_UpperCamelCase       ,							_UpperCamelCase)       ,							matrix_addition(_UpperCamelCase       ,							_UpperCamelCase))
      UpperCamelCase					       =  actual_strassen(matrix_subtraction(_UpperCamelCase       ,							_UpperCamelCase)       ,							matrix_addition(_UpperCamelCase       ,							_UpperCamelCase))

      UpperCamelCase					       =  matrix_addition(matrix_subtraction(matrix_addition(_UpperCamelCase       ,							_UpperCamelCase)       ,							_UpperCamelCase)       ,							_UpperCamelCase)
      UpperCamelCase					       =  matrix_addition(_UpperCamelCase       ,							_UpperCamelCase)
      UpperCamelCase					       =  matrix_addition(_UpperCamelCase       ,							_UpperCamelCase)
      UpperCamelCase					       =  matrix_subtraction(matrix_subtraction(matrix_addition(_UpperCamelCase       ,							_UpperCamelCase)       ,							_UpperCamelCase)       ,							_UpperCamelCase)

      # construct the new matrix from our 4 quadrants
      UpperCamelCase					       =  []
      for i in range(len(_UpperCamelCase)):
            new_matrix.append(top_left[i] + top_right[i])
      for i in range(len(_UpperCamelCase)):
            new_matrix.append(bot_left[i] + bot_right[i])
      return new_matrix
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase) ->			list:
      """simple docstring"""

      if matrix_dimensions(_UpperCamelCase)[1] != matrix_dimensions(_UpperCamelCase)[0]:
            UpperCamelCase					       =  (
                'Unable to multiply these matrices, please check the dimensions.\n'
                F'Matrix A: {matrixa}\n'
                F'Matrix B: {matrixa}'
            )
            raise Exception(_UpperCamelCase)
      UpperCamelCase					       =  matrix_dimensions(_UpperCamelCase)
      UpperCamelCase					       =  matrix_dimensions(_UpperCamelCase)

      if dimensiona[0] == dimensiona[1] and dimensiona[0] == dimensiona[1]:
            return [matrixa, matrixa]

      UpperCamelCase					       =  max(*_UpperCamelCase       ,							*_UpperCamelCase)
      UpperCamelCase					       =  int(math.pow(2       ,							math.ceil(math.loga(_UpperCamelCase))))
      UpperCamelCase					       =  matrixa
      UpperCamelCase					       =  matrixa

      # Adding zeros to the matrices so that the arrays dimensions are the same and also
      # power of 2
      for i in range(0       ,							_UpperCamelCase):
            if i < dimensiona[0]:
                  for _ in range(dimensiona[1]       ,							_UpperCamelCase):
                        new_matrixa[i].append(0)
            else:
                  new_matrixa.append([0] * maxim)
            if i < dimensiona[0]:
                  for _ in range(dimensiona[1]       ,							_UpperCamelCase):
                        new_matrixa[i].append(0)
            else:
                  new_matrixa.append([0] * maxim)

      UpperCamelCase					       =  actual_strassen(_UpperCamelCase       ,							_UpperCamelCase)

      # Removing the additional zeros
      for i in range(0       ,							_UpperCamelCase):
            if i < dimensiona[0]:
                  for _ in range(dimensiona[1]       ,							_UpperCamelCase):
                        final_matrix[i].pop()
            else:
                  final_matrix.pop()
      return final_matrix


if __name__ == "__main__":
       __magic_name__   :  Optional[Any]        =							[
           [2, 3, 4, 5],
           [6, 4, 3, 1],
           [2, 3, 6, 7],
           [3, 1, 2, 4],
           [2, 3, 4, 5],
           [6, 4, 3, 1],
           [2, 3, 6, 7],
           [3, 1, 2, 4],
           [2, 3, 4, 5],
           [6, 2, 3, 1],
       ]
       __magic_name__   :  List[str]        =							[[0, 2, 1, 1], [16, 2, 3, 3], [2, 2, 7, 7], [13, 11, 22, 4]]
       print(strassen(matrixa, matrixa))



