

'''simple docstring'''

from __future__ import annotations

from typing import Any

def 			__UpperCamelCase							(		*lowercase__  :					Optional[Any]	):






     '''simple docstring'''





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

def 			__UpperCamelCase							(		lowercase__  :					Dict,   lowercase__  :					List[Any]	):






     '''simple docstring'''





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

def 			__UpperCamelCase							(		lowercase__  :					List[str],   lowercase__  :					Union[str, Any]	):






     '''simple docstring'''





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

def 			__UpperCamelCase							(		lowercase__  :					Union[str, Any],   lowercase__  :					Dict	):






     '''simple docstring'''





     if _check_not_integer(__UpperCamelCase	) and _check_not_integer(__UpperCamelCase	):
          __lowercase						,       __lowercase     						=_verify_matrix_sizes(__UpperCamelCase,   __UpperCamelCase	)

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

def 			__UpperCamelCase							(		lowercase__  :					Dict	):






     '''simple docstring'''





     __lowercase     						=int(__UpperCamelCase	)
     return [[int(row == column	) for column in range(__UpperCamelCase	)] for row in range(__UpperCamelCase	)]

def 			__UpperCamelCase							(		lowercase__  :					List[Any],   lowercase__  :					Dict = True	):






     '''simple docstring'''





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

def 			__UpperCamelCase							(		lowercase__  :					Union[str, Any],   lowercase__  :					Optional[int],   lowercase__  :					List[Any]	):






     '''simple docstring'''





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

def 			__UpperCamelCase							(		lowercase__  :					int	):






     '''simple docstring'''





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

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

def 			__UpperCamelCase							(		lowercase__  :					Tuple	):






     '''simple docstring'''





     __lowercase     						=determinant(__UpperCamelCase	)
     if det == 0:
          return None

     __lowercase     						=[
         [determinant(minor(__UpperCamelCase,   __UpperCamelCase,   __UpperCamelCase	)	) for j in range(len(__UpperCamelCase	)	)]
         for i in range(len(__UpperCamelCase	)	)
     ]

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

def 			__UpperCamelCase							(		lowercase__  :					Dict	):






     '''simple docstring'''





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

def 			__UpperCamelCase							(		lowercase__  :					Tuple	):






     '''simple docstring'''





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

def 			__UpperCamelCase							(		lowercase__  :					List[str],   lowercase__  :					Dict	):






     '''simple docstring'''





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

def 			__UpperCamelCase							(		):






     '''simple docstring'''





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


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       main()





'''simple docstring'''

import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest


class 					lowerCAmelCase       ( A				):
       lowerCAmelCase_				=	(DDIMScheduler,)
       lowerCAmelCase_				=	(("eta", 0.0), ("num_inference_steps", 5_0))







       def      snake_case				(							self     :	List[Any]  , **__lowercase     :	Optional[Any] ):







            """simple docstring"""







            __lowercase     						={
                'num_train_timesteps': 1000,
                'beta_start': 0.0_0_0_1,
                'beta_end': 0.0_2,
                'beta_schedule': 'linear',
                'clip_sample': True,
            }

            config.update(**__lowercase )
            return config







       def      snake_case				(							self     :	Optional[Any]  , **__lowercase     :	str ):







            """simple docstring"""







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

            __lowercase						,       __lowercase     						=10, 0.0

            __lowercase     						=self.dummy_model()
            __lowercase     						=self.dummy_sample_deter

            scheduler.set_timesteps(__lowercase )

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

            return sample







       def      snake_case				(							self     :	List[Any] ):







            """simple docstring"""







            for timesteps in [100, 500, 1000]:
                 self.check_over_configs(num_train_timesteps=__lowercase )







       def      snake_case				(							self     :	List[Any] ):







            """simple docstring"""







            for steps_offset in [0, 1]:
                 self.check_over_configs(steps_offset=__lowercase )

            __lowercase     						=self.scheduler_classes[0]
            __lowercase     						=self.get_scheduler_config(steps_offset=1 )
            __lowercase     						=scheduler_class(**__lowercase )
            scheduler.set_timesteps(5 )
            assert torch.equal(scheduler.timesteps  , torch.LongTensor([801, 601, 401, 201, 1] ) )







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







            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      snake_case				(							self     :	str ):







            """simple docstring"""







            for schedule in ["linear", "squaredcos_cap_v2"]:
                 self.check_over_configs(beta_schedule=__lowercase )







       def      snake_case				(							self     :	str ):







            """simple docstring"""







            for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=__lowercase )







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







            for clip_sample in [True, False]:
                 self.check_over_configs(clip_sample=__lowercase )







       def      snake_case				(							self     :	Dict ):







            """simple docstring"""







            for timestep_spacing in ["trailing", "leading"]:
                 self.check_over_configs(timestep_spacing=__lowercase )







       def      snake_case				(							self     :	Tuple ):







            """simple docstring"""







            for rescale_betas_zero_snr in [True, False]:
                 self.check_over_configs(rescale_betas_zero_snr=__lowercase )







       def      snake_case				(							self     :	int ):







            """simple docstring"""







            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      snake_case				(							self     :	Dict ):







            """simple docstring"""







            for t in [1, 10, 49]:
                 self.check_over_forward(time_step=__lowercase )







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







            for t, num_inference_steps in zip([1, 10, 50]  , [10, 50, 500] ):
                 self.check_over_forward(time_step=__lowercase  , num_inference_steps=__lowercase )







       def      snake_case				(							self     :	Tuple ):







            """simple docstring"""







            for t, eta in zip([1, 10, 49]  , [0.0, 0.5, 1.0] ):
                 self.check_over_forward(time_step=__lowercase  , eta=__lowercase )







       def      snake_case				(							self     :	Optional[int] ):







            """simple docstring"""







            __lowercase     						=self.scheduler_classes[0]
            __lowercase     						=self.get_scheduler_config()
            __lowercase     						=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(420  , 400 ) - 0.1_4_7_7_1 ) ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(980  , 960 ) - 0.3_2_4_6_0 ) ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(0  , 0 ) - 0.0 ) ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(487  , 486 ) - 0.0_0_9_7_9 ) ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(999  , 998 ) - 0.0_2 ) ) < 1E-5







       def      snake_case				(							self     :	Tuple ):







            """simple docstring"""







            __lowercase     						=self.full_loop()

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

            assert abs(result_sum.item() - 1_7_2.0_0_6_7 ) < 1E-2
            assert abs(result_mean.item() - 0.2_2_3_9_6_7 ) < 1E-3







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







            __lowercase     						=self.full_loop(prediction_type='v_prediction' )

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

            assert abs(result_sum.item() - 5_2.5_3_0_2 ) < 1E-2
            assert abs(result_mean.item() - 0.0_6_8_4 ) < 1E-3







       def      snake_case				(							self     :	Optional[Any] ):







            """simple docstring"""







            __lowercase     						=self.full_loop(set_alpha_to_one=__lowercase  , beta_start=0.0_1 )
            __lowercase     						=torch.sum(torch.abs(__lowercase ) )
            __lowercase     						=torch.mean(torch.abs(__lowercase ) )

            assert abs(result_sum.item() - 1_4_9.8_2_9_5 ) < 1E-2
            assert abs(result_mean.item() - 0.1_9_5_1 ) < 1E-3







       def      snake_case				(							self     :	Dict ):







            """simple docstring"""







            __lowercase     						=self.full_loop(set_alpha_to_one=__lowercase  , beta_start=0.0_1 )
            __lowercase     						=torch.sum(torch.abs(__lowercase ) )
            __lowercase     						=torch.mean(torch.abs(__lowercase ) )

            assert abs(result_sum.item() - 1_4_9.0_7_8_4 ) < 1E-2
            assert abs(result_mean.item() - 0.1_9_4_1 ) < 1E-3


