





'''simple docstring'''


from __future__ import annotations

from typing import Any






class     lowerCAmelCase__ :

       '''simple docstring'''

       def __init__( self					:				Any       ,     a__					:				int       ,     a__					:				int       ,     a__					:				float = 0						):
              UpperCAmelCase,					UpperCAmelCase  =						row, column
              UpperCAmelCase  =						[[default_value for c in range(a__						)] for r in range(a__						)]

       def __str__( self					:				int						):
              UpperCAmelCase  =						f"Matrix consist of {self.row} rows and {self.column} columns\n"

              # Make string identifier
              UpperCAmelCase  =						0
              for row_vector in self.array:
                     for obj in row_vector:
                            UpperCAmelCase  =						max(a__       ,     len(str(a__						)						)						)
              UpperCAmelCase  =						f"%{max_element_length}s"

              # Make string and return
              def single_line(a__					:				list[float]						) -> str:
                     nonlocal string_format_identifier
                     UpperCAmelCase  =						'''['''
                     line += ", ".join(string_format_identifier % (obj,) for obj in row_vector						)
                     line += "]"
                     return line

              s += "\n".join(single_line(a__						) for row_vector in self.array						)
              return s

       def __repr__( self					:				Optional[Any]						):
              return str(self						)

       def 			__snake_case      ( self					:				Dict       ,     a__					:				tuple[int, int]						):
              if not (isinstance(a__       ,     (list, tuple)						) and len(a__						) == 2):
                     return False
              elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
                     return False
              else:
                     return True

       def __getitem__( self					:				Optional[int]       ,     a__					:				tuple[int, int]						):
              assert self.validate_indicies(a__						)
              return self.array[loc[0]][loc[1]]

       def __setitem__( self					:				Dict       ,     a__					:				tuple[int, int]       ,     a__					:				float						):
              assert self.validate_indicies(a__						)
              UpperCAmelCase  =						value

       def __add__( self					:				int       ,     a__					:				Matrix						):
              assert isinstance(a__       ,     a__						)
              assert self.row == another.row and self.column == another.column

              # Add
              UpperCAmelCase  =						Matrix(self.row       ,     self.column						)
              for r in range(self.row						):
                     for c in range(self.column						):
                            UpperCAmelCase  =						self[r, c] + another[r, c]
              return result

       def __neg__( self					:				List[Any]						):
              UpperCAmelCase  =						Matrix(self.row       ,     self.column						)
              for r in range(self.row						):
                     for c in range(self.column						):
                            UpperCAmelCase  =						-self[r, c]
              return result

       def __sub__( self					:				List[str]       ,     a__					:				Matrix						):
              return self + (-another)

       def __mul__( self					:				Any       ,     a__					:				int | float | Matrix						):

              if isinstance(a__       ,     (int, float)						):  # Scalar multiplication
                     UpperCAmelCase  =						Matrix(self.row       ,     self.column						)
                     for r in range(self.row						):
                            for c in range(self.column						):
                                   UpperCAmelCase  =						self[r, c] * another
                     return result
              elif isinstance(a__       ,     a__						):  # Matrix multiplication
                     assert self.column == another.row
                     UpperCAmelCase  =						Matrix(self.row       ,     another.column						)
                     for r in range(self.row						):
                            for c in range(another.column						):
                                   for i in range(self.column						):
                                          result[r, c] += self[r, i] * another[i, c]
                     return result
              else:
                     UpperCAmelCase  =						f"Unsupported type given for another ({type(a__						)})"
                     raise TypeError(a__						)

       def 			__snake_case      ( self					:				Tuple						):
              UpperCAmelCase  =						Matrix(self.column       ,     self.row						)
              for r in range(self.row						):
                     for c in range(self.column						):
                            UpperCAmelCase  =						self[r, c]
              return result



       def 			__snake_case      ( self					:				Optional[Any]       ,     a__					:				Matrix       ,     a__					:				Matrix						):
              assert isinstance(a__       ,     a__						) and isinstance(a__       ,     a__						)
              assert self.row == self.column == u.row == v.row  # u, v should be column vector
              assert u.column == v.column == 1  # u, v should be column vector

              # Calculate
              UpperCAmelCase  =						v.transpose()
              UpperCAmelCase  =						(v_t * self * u)[0, 0] + 1
              if numerator_factor == 0:
                     return None  # It's not invertable
              return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":
    def       __snake_case		( )							->     None:

           """simple docstring"""


           UpperCAmelCase  =						Matrix(3			,	3			,	0 )
           for i in range(3 ):
                  UpperCAmelCase  =						1
           print(f"a^(-1) is {ainv}" )
           # u, v
           UpperCAmelCase  =						Matrix(3			,	1			,	0 )
           UpperCAmelCase,					UpperCAmelCase,					UpperCAmelCase  =						1, 2, -3
           UpperCAmelCase  =						Matrix(3			,	1			,	0 )
           UpperCAmelCase,					UpperCAmelCase,					UpperCAmelCase  =						4, -2, 5
           print(f"u is {u}" )
           print(f"v is {v}" )
           print(f"uv^T is {u * v.transpose()}" )
           # Sherman Morrison
           print(f"(a + uv^T)^(-1) is {ainv.sherman_morrison(SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_ )}" )
    def       __snake_case		( )							->     None:

           """simple docstring"""


           import doctest

           doctest.testmod()

    testa()









'''simple docstring'''


import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				Any								=       (DDPMParallelScheduler,)




 def 				_snake_case (						self      ,  **_lowerCAmelCase		)     ->				int:
     _lowerCAmelCase						      =   {
         "num_train_timesteps": 1000,
         "beta_start": 0.0001,
         "beta_end": 0.02,
         "beta_schedule": "linear",
         "variance_type": "fixed_small",
         "clip_sample": True,
     }

     config.update(**_lowerCAmelCase		)
     return config




 def 				_snake_case (						self		)     ->				List[Any]:
     for timesteps in [1, 5, 100, 1000]:
         self.check_over_configs(num_train_timesteps=_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				List[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=_lowerCAmelCase      ,  beta_end=_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Any:
     for schedule in ["linear", "squaredcos_cap_v2"]:
         self.check_over_configs(beta_schedule=_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Optional[Any]:
     for variance in ["fixed_small", "fixed_large", "other"]:
         self.check_over_configs(variance_type=_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Optional[int]:
     for clip_sample in [True, False]:
         self.check_over_configs(clip_sample=_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				List[str]:
     self.check_over_configs(thresholding=_lowerCAmelCase		)
     for threshold in [0.5, 1.0, 2.0]:
         for prediction_type in ["epsilon", "sample", "v_prediction"]:
             self.check_over_configs(
                 thresholding=_lowerCAmelCase      ,  prediction_type=_lowerCAmelCase      ,  sample_max_value=_lowerCAmelCase      ,  )




 def 				_snake_case (						self		)     ->				int:
     for prediction_type in ["epsilon", "sample", "v_prediction"]:
         self.check_over_configs(prediction_type=_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Dict:
     for t in [0, 500, 999]:
         self.check_over_forward(time_step=_lowerCAmelCase		)




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

     assert torch.sum(torch.abs(scheduler._get_variance(0		) - 0.0		)		) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(487		) - 0.00979		)		) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(999		) - 0.02		)		) < 1E-5




 def 				_snake_case (						self		)     ->				Tuple:
     _lowerCAmelCase						      =   self.scheduler_classes[0]
     _lowerCAmelCase						      =   self.get_scheduler_config()
     _lowerCAmelCase						      =   scheduler_class(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   len(_lowerCAmelCase		)

     _lowerCAmelCase						      =   self.dummy_model()
     _lowerCAmelCase						      =   self.dummy_sample_deter
     _lowerCAmelCase						      =   self.dummy_sample_deter + 0.1
     _lowerCAmelCase						      =   self.dummy_sample_deter - 0.1

     _lowerCAmelCase						      =   samplea.shape[0]
     _lowerCAmelCase						      =   torch.stack([samplea, samplea, samplea]      ,  dim=0		)
     _lowerCAmelCase						      =   torch.arange(_lowerCAmelCase		)[0:3, None].repeat(1      ,  _lowerCAmelCase		)

     _lowerCAmelCase						      =   model(samples.flatten(0      ,  1		)      ,  timesteps.flatten(0      ,  1		)		)
     _lowerCAmelCase						      =   scheduler.batch_step_no_noise(_lowerCAmelCase      ,  timesteps.flatten(0      ,  1		)      ,  samples.flatten(0      ,  1		)		)

     _lowerCAmelCase						      =   torch.sum(torch.abs(_lowerCAmelCase		)		)
     _lowerCAmelCase						      =   torch.mean(torch.abs(_lowerCAmelCase		)		)

     assert abs(result_sum.item() - 1153.1833		) < 1E-2
     assert abs(result_mean.item() - 0.5005		) < 1E-3




 def 				_snake_case (						self		)     ->				Dict:
     _lowerCAmelCase						      =   self.scheduler_classes[0]
     _lowerCAmelCase						      =   self.get_scheduler_config()
     _lowerCAmelCase						      =   scheduler_class(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   len(_lowerCAmelCase		)

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

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

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

         _lowerCAmelCase						      =   pred_prev_sample

     _lowerCAmelCase						      =   torch.sum(torch.abs(_lowerCAmelCase		)		)
     _lowerCAmelCase						      =   torch.mean(torch.abs(_lowerCAmelCase		)		)

     assert abs(result_sum.item() - 258.9606		) < 1E-2
     assert abs(result_mean.item() - 0.3372		) < 1E-3




 def 				_snake_case (						self		)     ->				Optional[Any]:
     _lowerCAmelCase						      =   self.scheduler_classes[0]
     _lowerCAmelCase						      =   self.get_scheduler_config(prediction_type="v_prediction"		)
     _lowerCAmelCase						      =   scheduler_class(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   len(_lowerCAmelCase		)

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

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

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

         _lowerCAmelCase						      =   pred_prev_sample

     _lowerCAmelCase						      =   torch.sum(torch.abs(_lowerCAmelCase		)		)
     _lowerCAmelCase						      =   torch.mean(torch.abs(_lowerCAmelCase		)		)

     assert abs(result_sum.item() - 202.0296		) < 1E-2
     assert abs(result_mean.item() - 0.2631		) < 1E-3




 def 				_snake_case (						self		)     ->				Dict:
     _lowerCAmelCase						      =   self.scheduler_classes[0]
     _lowerCAmelCase						      =   self.get_scheduler_config()
     _lowerCAmelCase						      =   scheduler_class(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   [100, 87, 50, 1, 0]

     scheduler.set_timesteps(timesteps=_lowerCAmelCase		)

     _lowerCAmelCase						      =   scheduler.timesteps

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

         _lowerCAmelCase						      =   scheduler.previous_timestep(_lowerCAmelCase		)
         _lowerCAmelCase						      =   prev_t.item()

         self.assertEqual(_lowerCAmelCase      ,  _lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Any:
     _lowerCAmelCase						      =   self.scheduler_classes[0]
     _lowerCAmelCase						      =   self.get_scheduler_config()
     _lowerCAmelCase						      =   scheduler_class(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   [100, 87, 50, 51, 0]

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




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

     _lowerCAmelCase						      =   [100, 87, 50, 1, 0]
     _lowerCAmelCase						      =   len(_lowerCAmelCase		)

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





 def 				_snake_case (						self		)     ->				Optional[int]:
     _lowerCAmelCase						      =   self.scheduler_classes[0]
     _lowerCAmelCase						      =   self.get_scheduler_config()
     _lowerCAmelCase						      =   scheduler_class(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   [scheduler.config.num_train_timesteps]

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


