
"""simple docstring"""

from __future__ import annotations
class      _UpperCAmelCase							:




      def __init__(							self   ,       lowercase_							)						->					List[Any]:
          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(lowercase_							) != 0:
              UpperCAmelCase									=					len(rows[0]							)
              if cols == 0:
                  raise error
              for row in rows:
                  if len(lowercase_							) != cols:
                      raise error
                  for value in row:
                      if not isinstance(lowercase_   ,       (int, float)							):
                          raise error
              UpperCAmelCase									=					rows
          else:
              UpperCAmelCase									=					[]




      def     a_      (							self							)						->					list[list[int]]:
          return [[row[i] for row in self.rows] for i in range(len(self.rows[0]							)							)]




      @property
      def     a_      (							self							)						->					int:
          return len(self.rows							)




      @property
      def     a_      (							self							)						->					int:
          return len(self.rows[0]							)




      @property
      def     a_      (							self							)						->					tuple[int, int]:
          return (self.num_rows, self.num_columns)




      @property
      def     a_      (							self							)						->					bool:
          return self.order[0] == self.order[1]




      def     a_      (							self							)						->					Matrix:
          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(lowercase_							)




      def     a_      (							self							)						->					int:
          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     a_      (							self							)						->					bool:
          return bool(self.determinant()							)




      def     a_      (							self   ,       lowercase_   ,       lowercase_							)						->					int:
          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(lowercase_							).determinant()




      def     a_      (							self   ,       lowercase_   ,       lowercase_							)						->					int:
          if (row + column) % 2 == 0:
              return self.get_minor(lowercase_   ,       lowercase_							)
          return -1 * self.get_minor(lowercase_   ,       lowercase_							)




      def     a_      (							self							)						->					Matrix:
          return Matrix(
              [
                  [self.get_minor(lowercase_   ,       lowercase_							) for column in range(self.num_columns							)]
                  for row in range(self.num_rows							)
              ]							)




      def     a_      (							self							)						->					Matrix:
          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     a_      (							self							)						->					Matrix:
          UpperCAmelCase									=					[
              [self.cofactors().rows[column][row] for column in range(self.num_columns							)]
              for row in range(self.num_rows							)
          ]
          return Matrix(lowercase_							)




      def     a_      (							self							)						->					Matrix:
          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							)						->					str:
          return str(self.rows							)




      def __str__(							self							)						->					str:
          if self.num_rows == 0:
              return "[]"
          if self.num_rows == 1:
              return "[[" + ". ".join(str(self.rows[0]							)							) + "]]"
          return (
              "["
              + "\n ".join(
                  [
                      '[' + '. '.join([str(lowercase_							) for value in row]							) + '.]'
                      for row in self.rows
                  ]							)
              + "]"
          )




      def     a_      (							self   ,       lowercase_   ,       lowercase_ = None							)						->					None:
          UpperCAmelCase									=					TypeError('Row must be a list containing all ints and/or floats'							)
          if not isinstance(lowercase_   ,       lowercase_							):
              raise type_error
          for value in row:
              if not isinstance(lowercase_   ,       (int, float)							):
                  raise type_error
          if len(lowercase_							) != 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(lowercase_							)
          else:
              UpperCAmelCase									=					self.rows[0:position] + [row] + self.rows[position:]




      def     a_      (							self   ,       lowercase_   ,       lowercase_ = None							)						->					None:
          UpperCAmelCase									=					TypeError(
              'Column must be a list containing all ints and/or floats'							)
          if not isinstance(lowercase_   ,       lowercase_							):
              raise type_error
          for value in column:
              if not isinstance(lowercase_   ,       (int, float)							):
                  raise type_error
          if len(lowercase_							) != 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   ,       lowercase_							)						->					bool:
          if not isinstance(lowercase_   ,       lowercase_							):
              return NotImplemented
          return self.rows == other.rows




      def __ne__(							self   ,       lowercase_							)						->					bool:
          return not self == other




      def __neg__(							self							)						->					Matrix:
          return self * -1




      def __add__(							self   ,       lowercase_							)						->					Matrix:
          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   ,       lowercase_							)						->					Matrix:
          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   ,       lowercase_							)						->					Matrix:
          if isinstance(lowercase_   ,       (int, float)							):
              return Matrix(
                  [[int(element * other							) for element in row] for row in self.rows]							)
          elif isinstance(lowercase_   ,       lowercase_							):
              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(lowercase_   ,       lowercase_							) 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   ,       lowercase_							)						->					Matrix:
          if not isinstance(lowercase_   ,       lowercase_							):
              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     a_      (							cls   ,       lowercase_   ,       lowercase_							)						->					int:
          return sum(row[i] * column[i] for i in range(len(lowercase_							)							)							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()





"""simple docstring"""

import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest
class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE   :							str							      =	(DDPMScheduler,)




      def     a_      (							self   ,       **lowercase_							)						->					Union[str, Any]:
          UpperCAmelCase									=					{
              'num_train_timesteps': 1_0_0_0,
              'beta_start': 0.0_0_0_1,
              'beta_end': 0.0_2,
              'beta_schedule': 'linear',
              'variance_type': 'fixed_small',
              'clip_sample': True,
          }

          config.update(**lowercase_							)
          return config




      def     a_      (							self							)						->					int:
          for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
              self.check_over_configs(num_train_timesteps=lowercase_							)




      def     a_      (							self							)						->					Optional[int]:
          for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]   ,       [0.0_0_2, 0.0_2, 0.2, 2]							):
              self.check_over_configs(beta_start=lowercase_   ,       beta_end=lowercase_							)




      def     a_      (							self							)						->					Dict:
          for schedule in ["linear", "squaredcos_cap_v2"]:
              self.check_over_configs(beta_schedule=lowercase_							)




      def     a_      (							self							)						->					Union[str, Any]:
          for variance in ["fixed_small", "fixed_large", "other"]:
              self.check_over_configs(variance_type=lowercase_							)




      def     a_      (							self							)						->					Optional[Any]:
          for clip_sample in [True, False]:
              self.check_over_configs(clip_sample=lowercase_							)




      def     a_      (							self							)						->					Tuple:
          self.check_over_configs(thresholding=lowercase_							)
          for threshold in [0.5, 1.0, 2.0]:
              for prediction_type in ["epsilon", "sample", "v_prediction"]:
                  self.check_over_configs(
                      thresholding=lowercase_   ,       prediction_type=lowercase_   ,       sample_max_value=lowercase_   ,       )




      def     a_      (							self							)						->					Any:
          for prediction_type in ["epsilon", "sample", "v_prediction"]:
              self.check_over_configs(prediction_type=lowercase_							)




      def     a_      (							self							)						->					Union[str, Any]:
          for t in [0, 5_0_0, 9_9_9]:
              self.check_over_forward(time_step=lowercase_							)




      def     a_      (							self							)						->					Optional[Any]:
          UpperCAmelCase									=					self.scheduler_classes[0]
          UpperCAmelCase									=					self.get_scheduler_config()
          UpperCAmelCase									=					scheduler_class(**lowercase_							)

          assert torch.sum(torch.abs(scheduler._get_variance(0							) - 0.0							)							) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(4_8_7							) - 0.0_0_9_7_9							)							) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(9_9_9							) - 0.0_2							)							) < 1E-5




      def     a_      (							self							)						->					str:
          UpperCAmelCase									=					self.scheduler_classes[0]
          UpperCAmelCase									=					self.get_scheduler_config()
          UpperCAmelCase									=					scheduler_class(**lowercase_							)

          UpperCAmelCase									=					len(lowercase_							)

          UpperCAmelCase									=					self.dummy_model()
          UpperCAmelCase									=					self.dummy_sample_deter
          UpperCAmelCase									=					torch.manual_seed(0							)

          for t in reversed(range(lowercase_							)							):
              # 1. predict noise residual
              UpperCAmelCase									=					model(lowercase_   ,       lowercase_							)

              # 2. predict previous mean of sample x_t-1
              UpperCAmelCase									=					scheduler.step(lowercase_   ,       lowercase_   ,       lowercase_   ,       generator=lowercase_							).prev_sample

              # if t > 0:
              #     noise = self.dummy_sample_deter
              #     variance = scheduler.get_variance(t) ** (0.5) * noise
              #
              # sample = pred_prev_sample + variance
              UpperCAmelCase									=					pred_prev_sample

          UpperCAmelCase									=					torch.sum(torch.abs(lowercase_							)							)
          UpperCAmelCase									=					torch.mean(torch.abs(lowercase_							)							)

          assert abs(result_sum.item() - 2_5_8.9_6_0_6							) < 1E-2
          assert abs(result_mean.item() - 0.3_3_7_2							) < 1E-3




      def     a_      (							self							)						->					Union[str, Any]:
          UpperCAmelCase									=					self.scheduler_classes[0]
          UpperCAmelCase									=					self.get_scheduler_config(prediction_type='v_prediction'							)
          UpperCAmelCase									=					scheduler_class(**lowercase_							)

          UpperCAmelCase									=					len(lowercase_							)

          UpperCAmelCase									=					self.dummy_model()
          UpperCAmelCase									=					self.dummy_sample_deter
          UpperCAmelCase									=					torch.manual_seed(0							)

          for t in reversed(range(lowercase_							)							):
              # 1. predict noise residual
              UpperCAmelCase									=					model(lowercase_   ,       lowercase_							)

              # 2. predict previous mean of sample x_t-1
              UpperCAmelCase									=					scheduler.step(lowercase_   ,       lowercase_   ,       lowercase_   ,       generator=lowercase_							).prev_sample

              # if t > 0:
              #     noise = self.dummy_sample_deter
              #     variance = scheduler.get_variance(t) ** (0.5) * noise
              #
              # sample = pred_prev_sample + variance
              UpperCAmelCase									=					pred_prev_sample

          UpperCAmelCase									=					torch.sum(torch.abs(lowercase_							)							)
          UpperCAmelCase									=					torch.mean(torch.abs(lowercase_							)							)

          assert abs(result_sum.item() - 2_0_2.0_2_9_6							) < 1E-2
          assert abs(result_mean.item() - 0.2_6_3_1							) < 1E-3




      def     a_      (							self							)						->					Optional[Any]:
          UpperCAmelCase									=					self.scheduler_classes[0]
          UpperCAmelCase									=					self.get_scheduler_config()
          UpperCAmelCase									=					scheduler_class(**lowercase_							)

          UpperCAmelCase									=					[1_0_0, 8_7, 5_0, 1, 0]

          scheduler.set_timesteps(timesteps=lowercase_							)

          UpperCAmelCase									=					scheduler.timesteps

          for i, timestep in enumerate(lowercase_							):
              if i == len(lowercase_							) - 1:
                  UpperCAmelCase									=					-1
              else:
                  UpperCAmelCase									=					timesteps[i + 1]

              UpperCAmelCase									=					scheduler.previous_timestep(lowercase_							)
              UpperCAmelCase									=					prev_t.item()

              self.assertEqual(lowercase_   ,       lowercase_							)




      def     a_      (							self							)						->					Optional[Any]:
          UpperCAmelCase									=					self.scheduler_classes[0]
          UpperCAmelCase									=					self.get_scheduler_config()
          UpperCAmelCase									=					scheduler_class(**lowercase_							)

          UpperCAmelCase									=					[1_0_0, 8_7, 5_0, 5_1, 0]

          with self.assertRaises(lowercase_   ,       msg='`custom_timesteps` must be in descending order.'							):
              scheduler.set_timesteps(timesteps=lowercase_							)




      def     a_      (							self							)						->					List[Any]:
          UpperCAmelCase									=					self.scheduler_classes[0]
          UpperCAmelCase									=					self.get_scheduler_config()
          UpperCAmelCase									=					scheduler_class(**lowercase_							)

          UpperCAmelCase									=					[1_0_0, 8_7, 5_0, 1, 0]
          UpperCAmelCase									=					len(lowercase_							)

          with self.assertRaises(lowercase_   ,       msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.'							):
              scheduler.set_timesteps(num_inference_steps=lowercase_   ,       timesteps=lowercase_							)




      def     a_      (							self							)						->					str:
          UpperCAmelCase									=					self.scheduler_classes[0]
          UpperCAmelCase									=					self.get_scheduler_config()
          UpperCAmelCase									=					scheduler_class(**lowercase_							)

          UpperCAmelCase									=					[scheduler.config.num_train_timesteps]

          with self.assertRaises(
              lowercase_   ,       msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'   ,       ):
              scheduler.set_timesteps(timesteps=lowercase_							)



