



from __future__ import annotations

from typing import Any






def 					a(   *A :					list[list[int]] )	-> list[list[int]]:
					"""simple docstring"""





					if all(_check_not_integer(A ) for m in matrix_s ):
										for i in matrix_s[1:]:
															_verify_matrix_sizes(matrix_s[0]				,  A )
										return [[sum(A ) for t in zip(*A )] for m in zip(*A )]
					raise TypeError("Expected a matrix, got int/list instead" )






def 					a(   A :					list[list[int]]				,  A :					list[list[int]] )	-> list[list[int]]:
					"""simple docstring"""





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






def 					a(   A :					list[list[int]]				,  A :					int | float )	-> list[list[float]]:
					"""simple docstring"""





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






def 					a(   A :					list[list[int]]				,  A :					list[list[int]] )	-> list[list[int]]:
					"""simple docstring"""





					if _check_not_integer(A ) and _check_not_integer(A ):
										a  ,       a        =     _verify_matrix_sizes(A				,  A )

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






def 					a(   A :					int )	-> list[list[int]]:
					"""simple docstring"""





					a        =     int(A )
					return [[int(row == column ) for column in range(A )] for row in range(A )]






def 					a(   A :					list[list[int]]				,  A :					bool = True )	-> list[list[int]] | map[list[int]]:
					"""simple docstring"""





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






def 					a(   A :					list[list[int]]				,  A :					int				,  A :					int )	-> list[list[int]]:
					"""simple docstring"""





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






def 					a(   A :					list[list[int]] )	-> Any:
					"""simple docstring"""





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

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






def 					a(   A :					list[list[int]] )	-> list[list[float]] | None:
					"""simple docstring"""





					a        =     determinant(A )
					if det == 0:
										return None

					a        =     [
					    [determinant(minor(A				,  A				,  A ) ) for j in range(len(A ) )]
					    for i in range(len(A ) )
					]

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






def 					a(   A :					list[list[int]] )	-> bool:
					"""simple docstring"""





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






def 					a(   A :					list[list[int]] )	-> tuple[int, int]:
					"""simple docstring"""





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






def 					a(   A :					list[list[int]]				,  A :					list[list[int]] )	-> tuple[tuple[int, int], tuple[int, int]]:
					"""simple docstring"""





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






def 					a(   )	-> None:
					"""simple docstring"""





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


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				main()





import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class 			_lowercase							(     lowerCAmelCase			):





	"""simple docstring"""
	__A					=							(EulerAncestralDiscreteScheduler,)
	__A					=							10




	def   UpperCamelCase_ (self	, **lowerCamelCase_	):
						"""simple docstring"""

						a        =     {
						    "num_train_timesteps": 1100,
						    "beta_start": 0.0001,
						    "beta_end": 0.02,
						    "beta_schedule": "linear",
						}

						config.update(**lowerCamelCase_	)
						return config




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						for timesteps in [10, 50, 100, 1000]:
											self.check_over_configs(num_train_timesteps=lowerCamelCase_	)




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]	, [0.0002, 0.002, 0.02]	):
											self.check_over_configs(beta_start=lowerCamelCase_	, beta_end=lowerCamelCase_	)




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

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




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

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




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

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

						scheduler.set_timesteps(self.num_inference_steps	)

						a        =     torch.manual_seed(0	)

						a        =     self.dummy_model()
						a        =     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
						a        =     sample.to(lowerCamelCase_	)

						for i, t in enumerate(scheduler.timesteps	):
											a        =     scheduler.scale_model_input(lowerCamelCase_	, lowerCamelCase_	)

											a        =     model(lowerCamelCase_	, lowerCamelCase_	)

											a        =     scheduler.step(lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, generator=lowerCamelCase_	)
											a        =     output.prev_sample

						a        =     torch.sum(torch.abs(lowerCamelCase_	)	)
						a        =     torch.mean(torch.abs(lowerCamelCase_	)	)

						assert abs(result_sum.item() - 152.3192	) < 1E-2
						assert abs(result_mean.item() - 0.1983	) < 1E-3




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     self.scheduler_classes[0]
						a        =     self.get_scheduler_config(prediction_type="v_prediction"	)
						a        =     scheduler_class(**lowerCamelCase_	)

						scheduler.set_timesteps(self.num_inference_steps	)

						a        =     torch.manual_seed(0	)

						a        =     self.dummy_model()
						a        =     self.dummy_sample_deter * scheduler.init_noise_sigma
						a        =     sample.to(lowerCamelCase_	)

						for i, t in enumerate(scheduler.timesteps	):
											a        =     scheduler.scale_model_input(lowerCamelCase_	, lowerCamelCase_	)

											a        =     model(lowerCamelCase_	, lowerCamelCase_	)

											a        =     scheduler.step(lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, generator=lowerCamelCase_	)
											a        =     output.prev_sample

						a        =     torch.sum(torch.abs(lowerCamelCase_	)	)
						a        =     torch.mean(torch.abs(lowerCamelCase_	)	)

						assert abs(result_sum.item() - 108.4439	) < 1E-2
						assert abs(result_mean.item() - 0.1412	) < 1E-3




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

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

						scheduler.set_timesteps(self.num_inference_steps	, device=lowerCamelCase_	)
						a        =     torch.manual_seed(0	)

						a        =     self.dummy_model()
						a        =     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
						a        =     sample.to(lowerCamelCase_	)

						for t in scheduler.timesteps:
											a        =     scheduler.scale_model_input(lowerCamelCase_	, lowerCamelCase_	)

											a        =     model(lowerCamelCase_	, lowerCamelCase_	)

											a        =     scheduler.step(lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, generator=lowerCamelCase_	)
											a        =     output.prev_sample

						a        =     torch.sum(torch.abs(lowerCamelCase_	)	)
						a        =     torch.mean(torch.abs(lowerCamelCase_	)	)

						assert abs(result_sum.item() - 152.3192	) < 1E-2
						assert abs(result_mean.item() - 0.1983	) < 1E-3
