
"""simple docstring"""


import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest







class  lowerCAmelCase_ (					_lowercase			):



						'''simple docstring'''


						_lowerCamelCase:			Union[str, Any]					=			(IPNDMScheduler,)
						_lowerCamelCase:			Any					=			(('''num_inference_steps''', 50),)


						def   _SCREAMING_SNAKE_CASE					(       self   :					Dict     ,**A_   :					Any					)   ->      str:
							A        =      {'num_train_timesteps': 1000}
							config.update(**A_					)
							return config


						def   _SCREAMING_SNAKE_CASE					(       self   :					Optional[int]     ,A_   :					Dict=0     ,**A_   :					Any					)   ->      Tuple:
							A        =      dict(self.forward_default_kwargs					)
							A        =      kwargs.pop('num_inference_steps'     ,A_					)
							A        =      self.dummy_sample
							A        =      0.1 * sample
							A        =      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

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

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

								A        =      scheduler.step(A_     ,A_     ,A_     ,**A_					).prev_sample
								A        =      new_scheduler.step(A_     ,A_     ,A_     ,**A_					).prev_sample

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

								A        =      scheduler.step(A_     ,A_     ,A_     ,**A_					).prev_sample
								A        =      new_scheduler.step(A_     ,A_     ,A_     ,**A_					).prev_sample

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


						def   _SCREAMING_SNAKE_CASE					(       self   :					Optional[int]					)   ->      Optional[int]:
							pass


						def   _SCREAMING_SNAKE_CASE					(       self   :					Optional[int]     ,A_   :					Optional[int]=0     ,**A_   :					Union[str, Any]					)   ->      Any:
							A        =      dict(self.forward_default_kwargs					)
							A        =      kwargs.pop('num_inference_steps'     ,A_					)
							A        =      self.dummy_sample
							A        =      0.1 * sample
							A        =      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

							for scheduler_class in self.scheduler_classes:
								A        =      self.get_scheduler_config()
								A        =      scheduler_class(**A_					)
								scheduler.set_timesteps(A_					)

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

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

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

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

								A        =      scheduler.step(A_     ,A_     ,A_     ,**A_					).prev_sample
								A        =      new_scheduler.step(A_     ,A_     ,A_     ,**A_					).prev_sample

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

								A        =      scheduler.step(A_     ,A_     ,A_     ,**A_					).prev_sample
								A        =      new_scheduler.step(A_     ,A_     ,A_     ,**A_					).prev_sample

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


						def   _SCREAMING_SNAKE_CASE					(       self   :					Tuple     ,**A_   :					Any					)   ->      List[Any]:
							A        =      self.scheduler_classes[0]
							A        =      self.get_scheduler_config(**A_					)
							A        =      scheduler_class(**A_					)

							A        =      10
							A        =      self.dummy_model()
							A        =      self.dummy_sample_deter
							scheduler.set_timesteps(A_					)

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

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

							return sample


						def   _SCREAMING_SNAKE_CASE					(       self   :					Tuple					)   ->      Any:
							A        =      dict(self.forward_default_kwargs					)

							A        =      kwargs.pop('num_inference_steps'     ,A_					)

							for scheduler_class in self.scheduler_classes:
								A        =      self.get_scheduler_config()
								A        =      scheduler_class(**A_					)

								A        =      self.dummy_sample
								A        =      0.1 * sample

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

								# copy over dummy past residuals (must be done after set_timesteps)
								A        =      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
								A        =      dummy_past_residuals[:]

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

								A        =      scheduler.step(A_     ,A_     ,A_     ,**A_					).prev_sample
								A        =      scheduler.step(A_     ,A_     ,A_     ,**A_					).prev_sample

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

								A        =      scheduler.step(A_     ,A_     ,A_     ,**A_					).prev_sample
								A        =      scheduler.step(A_     ,A_     ,A_     ,**A_					).prev_sample

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


						def   _SCREAMING_SNAKE_CASE					(       self   :					int					)   ->      Union[str, Any]:
							for timesteps in [100, 1000]:
								self.check_over_configs(num_train_timesteps=A_     ,time_step=A_					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					Optional[Any]					)   ->      Any:
							for t, num_inference_steps in zip([1, 5, 10]     ,[10, 50, 100]					):
								self.check_over_forward(num_inference_steps=A_     ,time_step=A_					)




						def   _SCREAMING_SNAKE_CASE					(       self   :					Tuple					)   ->      Any:
							A        =      self.full_loop()
							A        =      torch.mean(torch.abs(A_					)					)

							assert abs(result_mean.item() - 254_0529					) < 10
from __future__ import annotations





class      A							:


   def __init__(self     :						Union[str, Any]       ,  __UpperCAmelCase     :						list[list[int]]		)						->				List[str]:
         """simple docstring"""



         UpperCAmelCase__								=  TypeError(
             "Matrices must be formed from a list of zero or more lists containing at "
             "least one and the same number of values, each of which must be of type "
             "int or float."		)
         if len(__UpperCAmelCase		) != 0:
               UpperCAmelCase__								=  len(rows[0]		)
               if cols == 0:
                     raise error
               for row in rows:
                     if len(__UpperCAmelCase		) != cols:
                           raise error
                     for value in row:
                           if not isinstance(__UpperCAmelCase       ,  (int, float)		):
                                 raise error
               UpperCAmelCase__								=  rows
         else:
               UpperCAmelCase__								=  []


   def 			lowercase_      (self     :						Any		)						->				list[list[int]]:
         """simple docstring"""



         return [[row[i] for row in self.rows] for i in range(len(self.rows[0]		)		)]


   @property
   def 			lowercase_      (self     :						Any		)						->				int:
         """simple docstring"""



         return len(self.rows		)


   @property
   def 			lowercase_      (self     :						Union[str, Any]		)						->				int:
         """simple docstring"""



         return len(self.rows[0]		)


   @property
   def 			lowercase_      (self     :						List[Any]		)						->				tuple[int, int]:
         """simple docstring"""



         return (self.num_rows, self.num_columns)


   @property
   def 			lowercase_      (self     :						Tuple		)						->				bool:
         """simple docstring"""



         return self.order[0] == self.order[1]


   def 			lowercase_      (self     :						Any		)						->				Matrix:
         """simple docstring"""



         UpperCAmelCase__								=  [
             [0 if column_num != row_num else 1 for column_num in range(self.num_rows		)]
             for row_num in range(self.num_rows		)
         ]
         return Matrix(__UpperCAmelCase		)


   def 			lowercase_      (self     :						int		)						->				int:
         """simple docstring"""



         if not self.is_square:
               return 0
         if self.order == (0, 0):
               return 1
         if self.order == (1, 1):
               return int(self.rows[0][0]		)
         if self.order == (2, 2):
               return int(
                   (self.rows[0][0] * self.rows[1][1])
                   - (self.rows[0][1] * self.rows[1][0])		)
         else:
               return sum(
                   self.rows[0][column] * self.cofactors().rows[0][column]
                   for column in range(self.num_columns		)		)


   def 			lowercase_      (self     :						Tuple		)						->				bool:
         """simple docstring"""



         return bool(self.determinant()		)


   def 			lowercase_      (self     :						Dict       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						int		)						->				int:
         """simple docstring"""



         UpperCAmelCase__								=  [
             [
                 self.rows[other_row][other_column]
                 for other_column in range(self.num_columns		)
                 if other_column != column
             ]
             for other_row in range(self.num_rows		)
             if other_row != row
         ]
         return Matrix(__UpperCAmelCase		).determinant()


   def 			lowercase_      (self     :						int       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						int		)						->				int:
         """simple docstring"""



         if (row + column) % 2 == 0:
               return self.get_minor(__UpperCAmelCase       ,  __UpperCAmelCase		)
         return -1 * self.get_minor(__UpperCAmelCase       ,  __UpperCAmelCase		)


   def 			lowercase_      (self     :						Union[str, Any]		)						->				Matrix:
         """simple docstring"""



         return Matrix(
             [
                 [self.get_minor(__UpperCAmelCase       ,  __UpperCAmelCase		) for column in range(self.num_columns		)]
                 for row in range(self.num_rows		)
             ]		)


   def 			lowercase_      (self     :						List[str]		)						->				Matrix:
         """simple docstring"""



         return Matrix(
             [
                 [
                     self.minors().rows[row][column]
                     if (row + column) % 2 == 0
                     else self.minors().rows[row][column] * -1
                     for column in range(self.minors().num_columns		)
                 ]
                 for row in range(self.minors().num_rows		)
             ]		)


   def 			lowercase_      (self     :						Optional[Any]		)						->				Matrix:
         """simple docstring"""



         UpperCAmelCase__								=  [
             [self.cofactors().rows[column][row] for column in range(self.num_columns		)]
             for row in range(self.num_rows		)
         ]
         return Matrix(__UpperCAmelCase		)


   def 			lowercase_      (self     :						List[Any]		)						->				Matrix:
         """simple docstring"""



         UpperCAmelCase__								=  self.determinant()
         if not determinant:
               raise TypeError("Only matrices with a non-zero determinant have an inverse"		)
         return self.adjugate() * (1 / determinant)


   def __repr__(self     :						Dict		)						->				str:
         """simple docstring"""



         return str(self.rows		)


   def __str__(self     :						Optional[Any]		)						->				str:
         """simple docstring"""



         if self.num_rows == 0:
               return "[]"
         if self.num_rows == 1:
               return "[[" + ". ".join(str(self.rows[0]		)		) + "]]"
         return (
             "["
             + "\n ".join(
                 [
                     "[" + ". ".join([str(__UpperCAmelCase		) for value in row]		) + ".]"
                     for row in self.rows
                 ]		)
             + "]"
         )


   def 			lowercase_      (self     :						Optional[int]       ,  __UpperCAmelCase     :						list[int]       ,  __UpperCAmelCase     :						int | None = None		)						->				None:
         """simple docstring"""



         UpperCAmelCase__								=  TypeError("Row must be a list containing all ints and/or floats"		)
         if not isinstance(__UpperCAmelCase       ,  __UpperCAmelCase		):
               raise type_error
         for value in row:
               if not isinstance(__UpperCAmelCase       ,  (int, float)		):
                     raise type_error
         if len(__UpperCAmelCase		) != self.num_columns:
               raise ValueError(
                   "Row must be equal in length to the other rows in the matrix"		)
         if position is None:
               self.rows.append(__UpperCAmelCase		)
         else:
               UpperCAmelCase__								=  self.rows[0:position] + [row] + self.rows[position:]


   def 			lowercase_      (self     :						Union[str, Any]       ,  __UpperCAmelCase     :						list[int]       ,  __UpperCAmelCase     :						int | None = None		)						->				None:
         """simple docstring"""



         UpperCAmelCase__								=  TypeError(
             "Column must be a list containing all ints and/or floats"		)
         if not isinstance(__UpperCAmelCase       ,  __UpperCAmelCase		):
               raise type_error
         for value in column:
               if not isinstance(__UpperCAmelCase       ,  (int, float)		):
                     raise type_error
         if len(__UpperCAmelCase		) != self.num_rows:
               raise ValueError(
                   "Column must be equal in length to the other columns in the matrix"		)
         if position is None:
               UpperCAmelCase__								=  [self.rows[i] + [column[i]] for i in range(self.num_rows		)]
         else:
               UpperCAmelCase__								=  [
                   self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
                   for i in range(self.num_rows		)
               ]


   def __eq__(self     :						Any       ,  __UpperCAmelCase     :						object		)						->				bool:
         """simple docstring"""



         if not isinstance(__UpperCAmelCase       ,  __UpperCAmelCase		):
               return NotImplemented
         return self.rows == other.rows


   def __ne__(self     :						int       ,  __UpperCAmelCase     :						object		)						->				bool:
         """simple docstring"""



         return not self == other


   def __neg__(self     :						Dict		)						->				Matrix:
         """simple docstring"""



         return self * -1


   def __add__(self     :						Dict       ,  __UpperCAmelCase     :						Matrix		)						->				Matrix:
         """simple docstring"""



         if self.order != other.order:
               raise ValueError("Addition requires matrices of the same order"		)
         return Matrix(
             [
                 [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns		)]
                 for i in range(self.num_rows		)
             ]		)


   def __sub__(self     :						Optional[Any]       ,  __UpperCAmelCase     :						Matrix		)						->				Matrix:
         """simple docstring"""



         if self.order != other.order:
               raise ValueError("Subtraction requires matrices of the same order"		)
         return Matrix(
             [
                 [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns		)]
                 for i in range(self.num_rows		)
             ]		)


   def __mul__(self     :						Tuple       ,  __UpperCAmelCase     :						Matrix | int | float		)						->				Matrix:
         """simple docstring"""



         if isinstance(__UpperCAmelCase       ,  (int, float)		):
               return Matrix(
                   [[int(element * other		) for element in row] for row in self.rows]		)
         elif isinstance(__UpperCAmelCase       ,  __UpperCAmelCase		):
               if self.num_columns != other.num_rows:
                     raise ValueError(
                         "The number of columns in the first matrix must "
                         "be equal to the number of rows in the second"		)
               return Matrix(
                   [
                       [Matrix.dot_product(__UpperCAmelCase       ,  __UpperCAmelCase		) for column in other.columns()]
                       for row in self.rows
                   ]		)
         else:
               raise TypeError(
                   "A Matrix can only be multiplied by an int, float, or another matrix"		)


   def __pow__(self     :						List[Any]       ,  __UpperCAmelCase     :						int		)						->				Matrix:
         """simple docstring"""



         if not isinstance(__UpperCAmelCase       ,  __UpperCAmelCase		):
               raise TypeError("A Matrix can only be raised to the power of an int"		)
         if not self.is_square:
               raise ValueError("Only square matrices can be raised to a power"		)
         if other == 0:
               return self.identity()
         if other < 0:
               if self.is_invertable():
                     return self.inverse() ** (-other)
               raise ValueError(
                   "Only invertable matrices can be raised to a negative power"		)
         UpperCAmelCase__								=  self
         for _ in range(other - 1		):
               result *= self
         return result





   @classmethod
   def 			lowercase_      (cls     :						Dict       ,  __UpperCAmelCase     :						list[int]       ,  __UpperCAmelCase     :						list[int]		)						->				int:
         """simple docstring"""



         return sum(row[i] * column[i] for i in range(len(__UpperCAmelCase		)		)		)


if __name__ == "__main__":
      import doctest

      doctest.testmod()



