





"""simple docstring"""





import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest
class 		SCREAMING_SNAKE_CASE_							(    snake_case__   ):
				"""simple docstring"""
				__snake_case   :       Tuple	 =		(DEISMultistepScheduler,)
				__snake_case   :       Dict	 =		(("""num_inference_steps""", 25),)




				def 					__lowercase  (     self  :str						,    **__lowercase  :Tuple			):
											__lowerCamelCase      :      Tuple         ={
											    '''num_train_timesteps''': 1000,
											    '''beta_start''': 0.0001,
											    '''beta_end''': 0.02,
											    '''beta_schedule''': '''linear''',
											    '''solver_order''': 2,
											}

											config.update(**__lowercase			)
											return config




				def 					__lowercase  (     self  :Tuple						,    __lowercase  :int=0						,    **__lowercase  :Tuple			):
											__lowerCamelCase      :      List[Any]         =dict(self.forward_default_kwargs			)
											__lowerCamelCase      :      Tuple         =kwargs.pop('''num_inference_steps'''						,    __lowercase			)
											__lowerCamelCase      :      Union[str, Any]         =self.dummy_sample
											__lowerCamelCase      :      Optional[Any]         =0.1 * sample
											__lowerCamelCase      :      Dict         =[residual + 0.2, residual + 0.15, residual + 0.10]

											for scheduler_class in self.scheduler_classes:
																		__lowerCamelCase      :      Optional[int]         =self.get_scheduler_config(**__lowercase			)
																		__lowerCamelCase      :      Tuple         =scheduler_class(**__lowercase			)
																		scheduler.set_timesteps(__lowercase			)
																		# copy over dummy past residuals
																		__lowerCamelCase      :      Union[str, Any]         =dummy_past_residuals[: scheduler.config.solver_order]

																		with tempfile.TemporaryDirectory() as tmpdirname:
																									scheduler.save_config(__lowercase			)
																									__lowerCamelCase      :      Union[str, Any]         =scheduler_class.from_pretrained(__lowercase			)
																									new_scheduler.set_timesteps(__lowercase			)
																									# copy over dummy past residuals
																									__lowerCamelCase      :      Union[str, Any]         =dummy_past_residuals[: new_scheduler.config.solver_order]

																		__lowerCamelCase    ,						__lowerCamelCase      :      Dict         =sample, sample
																		for t in range(__lowercase						,    time_step + scheduler.config.solver_order + 1			):
																									__lowerCamelCase      :      Dict         =scheduler.step(__lowercase						,    __lowercase						,    __lowercase						,    **__lowercase			).prev_sample
																									__lowerCamelCase      :      int         =new_scheduler.step(__lowercase						,    __lowercase						,    __lowercase						,    **__lowercase			).prev_sample

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




				def 					__lowercase  (     self  :Optional[int]			):
											pass




				def 					__lowercase  (     self  :List[str]						,    __lowercase  :Union[str, Any]=0						,    **__lowercase  :Dict			):
											__lowerCamelCase      :      int         =dict(self.forward_default_kwargs			)
											__lowerCamelCase      :      str         =kwargs.pop('''num_inference_steps'''						,    __lowercase			)
											__lowerCamelCase      :      str         =self.dummy_sample
											__lowerCamelCase      :      str         =0.1 * sample
											__lowerCamelCase      :      List[Any]         =[residual + 0.2, residual + 0.15, residual + 0.10]

											for scheduler_class in self.scheduler_classes:
																		__lowerCamelCase      :      Any         =self.get_scheduler_config()
																		__lowerCamelCase      :      str         =scheduler_class(**__lowercase			)
																		scheduler.set_timesteps(__lowercase			)

																		# copy over dummy past residuals (must be after setting timesteps)
																		__lowerCamelCase      :      List[str]         =dummy_past_residuals[: scheduler.config.solver_order]

																		with tempfile.TemporaryDirectory() as tmpdirname:
																									scheduler.save_config(__lowercase			)
																									__lowerCamelCase      :      Optional[Any]         =scheduler_class.from_pretrained(__lowercase			)
																									# copy over dummy past residuals
																									new_scheduler.set_timesteps(__lowercase			)

																									# copy over dummy past residual (must be after setting timesteps)
																									__lowerCamelCase      :      List[str]         =dummy_past_residuals[: new_scheduler.config.solver_order]

																		__lowerCamelCase      :      Optional[Any]         =scheduler.step(__lowercase						,    __lowercase						,    __lowercase						,    **__lowercase			).prev_sample
																		__lowerCamelCase      :      Union[str, Any]         =new_scheduler.step(__lowercase						,    __lowercase						,    __lowercase						,    **__lowercase			).prev_sample

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




				def 					__lowercase  (     self  :Tuple						,    __lowercase  :Any=None						,    **__lowercase  :Any			):
											if scheduler is None:
																		__lowerCamelCase      :      List[Any]         =self.scheduler_classes[0]
																		__lowerCamelCase      :      Optional[Any]         =self.get_scheduler_config(**__lowercase			)
																		__lowerCamelCase      :      Union[str, Any]         =scheduler_class(**__lowercase			)

											__lowerCamelCase      :      int         =self.scheduler_classes[0]
											__lowerCamelCase      :      Dict         =self.get_scheduler_config(**__lowercase			)
											__lowerCamelCase      :      List[str]         =scheduler_class(**__lowercase			)

											__lowerCamelCase      :      List[Any]         =10
											__lowerCamelCase      :      int         =self.dummy_model()
											__lowerCamelCase      :      Optional[int]         =self.dummy_sample_deter
											scheduler.set_timesteps(__lowercase			)

											for i, t in enumerate(scheduler.timesteps			):
																		__lowerCamelCase      :      List[str]         =model(__lowercase						,    __lowercase			)
																		__lowerCamelCase      :      List[str]         =scheduler.step(__lowercase						,    __lowercase						,    __lowercase			).prev_sample

											return sample




				def 					__lowercase  (     self  :List[Any]			):
											__lowerCamelCase      :      Tuple         =dict(self.forward_default_kwargs			)

											__lowerCamelCase      :      Union[str, Any]         =kwargs.pop('''num_inference_steps'''						,    __lowercase			)

											for scheduler_class in self.scheduler_classes:
																		__lowerCamelCase      :      Any         =self.get_scheduler_config()
																		__lowerCamelCase      :      Any         =scheduler_class(**__lowercase			)

																		__lowerCamelCase      :      int         =self.dummy_sample
																		__lowerCamelCase      :      Union[str, Any]         =0.1 * sample

																		if num_inference_steps is not None and hasattr(__lowercase						,    '''set_timesteps'''			):
																									scheduler.set_timesteps(__lowercase			)
																		elif num_inference_steps is not None and not hasattr(__lowercase						,    '''set_timesteps'''			):
																									__lowerCamelCase      :      Optional[int]         =num_inference_steps

																		# copy over dummy past residuals (must be done after set_timesteps)
																		__lowerCamelCase      :      Optional[Any]         =[residual + 0.2, residual + 0.15, residual + 0.10]
																		__lowerCamelCase      :      List[Any]         =dummy_past_residuals[: scheduler.config.solver_order]

																		__lowerCamelCase      :      List[Any]         =scheduler.timesteps[5]
																		__lowerCamelCase      :      Dict         =scheduler.timesteps[6]

																		__lowerCamelCase      :      List[Any]         =scheduler.step(__lowercase						,    __lowercase						,    __lowercase						,    **__lowercase			).prev_sample
																		__lowerCamelCase      :      List[Any]         =scheduler.step(__lowercase						,    __lowercase						,    __lowercase						,    **__lowercase			).prev_sample

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




				def 					__lowercase  (     self  :str			):
											# make sure that iterating over schedulers with same config names gives same results
											# for defaults
											__lowerCamelCase      :      str         =DEISMultistepScheduler(**self.get_scheduler_config()			)
											__lowerCamelCase      :      Union[str, Any]         =self.full_loop(scheduler=__lowercase			)
											__lowerCamelCase      :      str         =torch.mean(torch.abs(__lowercase			)			)

											assert abs(result_mean.item() - 0.23916			) < 1e-3

											__lowerCamelCase      :      List[Any]         =DPMSolverSinglestepScheduler.from_config(scheduler.config			)
											__lowerCamelCase      :      Any         =DPMSolverMultistepScheduler.from_config(scheduler.config			)
											__lowerCamelCase      :      str         =UniPCMultistepScheduler.from_config(scheduler.config			)
											__lowerCamelCase      :      Union[str, Any]         =DEISMultistepScheduler.from_config(scheduler.config			)

											__lowerCamelCase      :      Dict         =self.full_loop(scheduler=__lowercase			)
											__lowerCamelCase      :      List[str]         =torch.mean(torch.abs(__lowercase			)			)

											assert abs(result_mean.item() - 0.23916			) < 1e-3




				def 					__lowercase  (     self  :Optional[int]			):
											for timesteps in [25, 50, 100, 999, 1000]:
																		self.check_over_configs(num_train_timesteps=__lowercase			)




				def 					__lowercase  (     self  :Tuple			):
											self.check_over_configs(thresholding=__lowercase			)
											for order in [1, 2, 3]:
																		for solver_type in ["logrho"]:
																									for threshold in [0.5, 1.0, 2.0]:
																																for prediction_type in ["epsilon", "sample"]:
																																							self.check_over_configs(
																																							    thresholding=__lowercase						,    prediction_type=__lowercase						,    sample_max_value=__lowercase						,    algorithm_type='''deis'''						,    solver_order=__lowercase						,    solver_type=__lowercase						,    )




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




				def 					__lowercase  (     self  :List[str]			):
											for algorithm_type in ["deis"]:
																		for solver_type in ["logrho"]:
																									for order in [1, 2, 3]:
																																for prediction_type in ["epsilon", "sample"]:
																																							self.check_over_configs(
																																							    solver_order=__lowercase						,    solver_type=__lowercase						,    prediction_type=__lowercase						,    algorithm_type=__lowercase						,    )
																																							__lowerCamelCase      :      Optional[Any]         =self.full_loop(
																																							    solver_order=__lowercase						,    solver_type=__lowercase						,    prediction_type=__lowercase						,    algorithm_type=__lowercase						,    )
																																							assert not torch.isnan(__lowercase			).any(), "Samples have nan numbers"




				def 					__lowercase  (     self  :Optional[int]			):
											self.check_over_configs(lower_order_final=__lowercase			)
											self.check_over_configs(lower_order_final=__lowercase			)




				def 					__lowercase  (     self  :Optional[Any]			):
											for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
																		self.check_over_forward(num_inference_steps=__lowercase						,    time_step=0			)




				def 					__lowercase  (     self  :List[Any]			):
											__lowerCamelCase      :      str         =self.full_loop()
											__lowerCamelCase      :      List[Any]         =torch.mean(torch.abs(__lowercase			)			)

											assert abs(result_mean.item() - 0.23916			) < 1e-3




				def 					__lowercase  (     self  :Optional[int]			):
											__lowerCamelCase      :      str         =self.full_loop(prediction_type='''v_prediction'''			)
											__lowerCamelCase      :      Tuple         =torch.mean(torch.abs(__lowercase			)			)

											assert abs(result_mean.item() - 0.091			) < 1e-3




				def 					__lowercase  (     self  :Any			):
											__lowerCamelCase      :      Dict         =self.scheduler_classes[0]
											__lowerCamelCase      :      int         =self.get_scheduler_config(thresholding=__lowercase						,    dynamic_thresholding_ratio=0			)
											__lowerCamelCase      :      Tuple         =scheduler_class(**__lowercase			)

											__lowerCamelCase      :      Dict         =10
											__lowerCamelCase      :      List[str]         =self.dummy_model()
											__lowerCamelCase      :      str         =self.dummy_sample_deter.half()
											scheduler.set_timesteps(__lowercase			)

											for i, t in enumerate(scheduler.timesteps			):
																		__lowerCamelCase      :      Dict         =model(__lowercase						,    __lowercase			)
																		__lowerCamelCase      :      str         =scheduler.step(__lowercase						,    __lowercase						,    __lowercase			).prev_sample

											assert sample.dtype == torch.floataa











"""simple docstring"""





import unittest

from .lib import (
    Matrix,
    Vector,
    axpy,
    square_zero_matrix,
    unit_basis_vector,
    zero_vector,
)
class 		SCREAMING_SNAKE_CASE_							(    unittest.TestCase   ):
				"""simple docstring"""




				def 					__lowercase  (     self  :Tuple			):
											__lowerCamelCase      :      Any         =Vector([1, 2, 3]			)
											self.assertEqual(x.component(0			)						,    1			)
											self.assertEqual(x.component(2			)						,    3			)
											__lowerCamelCase      :      Any         =Vector()




				def 					__lowercase  (     self  :Dict			):
											__lowerCamelCase      :      Tuple         =Vector([0, 0, 0, 0, 0, 1]			)
											self.assertEqual(str(__lowercase			)						,    '''(0,0,0,0,0,1)'''			)




				def 					__lowercase  (     self  :Dict			):
											__lowerCamelCase      :      int         =Vector([1, 2, 3, 4]			)
											self.assertEqual(len(__lowercase			)						,    4			)




				def 					__lowercase  (     self  :Dict			):
											__lowerCamelCase      :      Optional[Any]         =Vector([1, 2]			)
											__lowerCamelCase      :      Dict         =Vector([1, 2, 3, 4, 5]			)
											__lowerCamelCase      :      List[Any]         =Vector([0, 0, 0, 0, 0, 0, 0, 0, 0, 0]			)
											__lowerCamelCase      :      int         =Vector([1, -1, 1, -1, 2, -3, 4, -5]			)
											self.assertAlmostEqual(x.euclidean_length()						,    2.236						,    3			)
											self.assertAlmostEqual(y.euclidean_length()						,    7.416						,    3			)
											self.assertEqual(z.euclidean_length()						,    0			)
											self.assertAlmostEqual(w.euclidean_length()						,    7.616						,    3			)




				def 					__lowercase  (     self  :Optional[int]			):
											__lowerCamelCase      :      Tuple         =Vector([1, 2, 3]			)
											__lowerCamelCase      :      Any         =Vector([1, 1, 1]			)
											self.assertEqual((x + y).component(0			)						,    2			)
											self.assertEqual((x + y).component(1			)						,    3			)
											self.assertEqual((x + y).component(2			)						,    4			)




				def 					__lowercase  (     self  :str			):
											__lowerCamelCase      :      Union[str, Any]         =Vector([1, 2, 3]			)
											__lowerCamelCase      :      int         =Vector([1, 1, 1]			)
											self.assertEqual((x - y).component(0			)						,    0			)
											self.assertEqual((x - y).component(1			)						,    1			)
											self.assertEqual((x - y).component(2			)						,    2			)




				def 					__lowercase  (     self  :int			):
											__lowerCamelCase      :      List[Any]         =Vector([1, 2, 3]			)
											__lowerCamelCase      :      List[Any]         =Vector([2, -1, 4]			)  # for test of dot product
											__lowerCamelCase      :      Any         =Vector([1, -2, -1]			)
											self.assertEqual(str(x * 3.0			)						,    '''(3.0,6.0,9.0)'''			)
											self.assertEqual((a * b)						,    0			)




				def 					__lowercase  (     self  :List[Any]			):
											self.assertEqual(str(zero_vector(10			)			).count('''0'''			)						,    10			)




				def 					__lowercase  (     self  :Union[str, Any]			):
											self.assertEqual(str(unit_basis_vector(3						,    1			)			)						,    '''(0,1,0)'''			)




				def 					__lowercase  (     self  :List[Any]			):
											__lowerCamelCase      :      Any         =Vector([1, 2, 3]			)
											__lowerCamelCase      :      Optional[int]         =Vector([1, 0, 1]			)
											self.assertEqual(str(axpy(2						,    __lowercase						,    __lowercase			)			)						,    '''(3,4,7)'''			)




				def 					__lowercase  (     self  :Dict			):
											__lowerCamelCase      :      List[Any]         =Vector([1, 0, 0, 0, 0, 0]			)
											__lowerCamelCase      :      Optional[int]         =x.copy()
											self.assertEqual(str(__lowercase			)						,    str(__lowercase			)			)




				def 					__lowercase  (     self  :int			):
											__lowerCamelCase      :      str         =Vector([1, 0, 0]			)
											x.change_component(0						,    0			)
											x.change_component(1						,    1			)
											self.assertEqual(str(__lowercase			)						,    '''(0,1,0)'''			)




				def 					__lowercase  (     self  :int			):
											__lowerCamelCase      :      Any         =Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]						,    3						,    3			)
											self.assertEqual('''|1,2,3|\n|2,4,5|\n|6,7,8|\n'''						,    str(__lowercase			)			)




				def 					__lowercase  (     self  :int			):
											__lowerCamelCase      :      Tuple         =Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]						,    3						,    3			)
											__lowerCamelCase      :      List[Any]         =[[-3, -14, -10], [-5, -10, -5], [-2, -1, 0]]
											for x in range(a.height()			):
																		for y in range(a.width()			):
																									self.assertEqual(minors[x][y]						,    a.minor(__lowercase						,    __lowercase			)			)




				def 					__lowercase  (     self  :Optional[int]			):
											__lowerCamelCase      :      Optional[Any]         =Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]						,    3						,    3			)
											__lowerCamelCase      :      Tuple         =[[-3, 14, -10], [5, -10, 5], [-2, 1, 0]]
											for x in range(a.height()			):
																		for y in range(a.width()			):
																									self.assertEqual(cofactors[x][y]						,    a.cofactor(__lowercase						,    __lowercase			)			)




				def 					__lowercase  (     self  :Tuple			):
											__lowerCamelCase      :      Tuple         =Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]						,    3						,    3			)
											self.assertEqual(-5						,    a.determinant()			)




				def 					__lowercase  (     self  :int			):
											__lowerCamelCase      :      Union[str, Any]         =Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]]						,    3						,    3			)
											__lowerCamelCase      :      Tuple         =Vector([1, 2, 3]			)
											self.assertEqual('''(14,32,50)'''						,    str(a * x			)			)
											self.assertEqual('''|2,4,6|\n|8,10,12|\n|14,16,18|\n'''						,    str(a * 2			)			)




				def 					__lowercase  (     self  :Optional[Any]			):
											__lowerCamelCase      :      Optional[int]         =Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]						,    3						,    3			)
											a.change_component(0						,    2						,    5			)
											self.assertEqual('''|1,2,5|\n|2,4,5|\n|6,7,8|\n'''						,    str(__lowercase			)			)




				def 					__lowercase  (     self  :str			):
											__lowerCamelCase      :      str         =Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]						,    3						,    3			)
											self.assertEqual(7						,    a.component(2						,    1			)						,    0.01			)




				def 					__lowercase  (     self  :Optional[int]			):
											__lowerCamelCase      :      List[str]         =Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]						,    3						,    3			)
											__lowerCamelCase      :      List[str]         =Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]]						,    3						,    3			)
											self.assertEqual('''|2,4,10|\n|4,8,10|\n|12,14,18|\n'''						,    str(a + b			)			)




				def 					__lowercase  (     self  :Union[str, Any]			):
											__lowerCamelCase      :      int         =Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]						,    3						,    3			)
											__lowerCamelCase      :      Optional[int]         =Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]]						,    3						,    3			)
											self.assertEqual('''|0,0,-4|\n|0,0,0|\n|0,0,-2|\n'''						,    str(a - b			)			)




				def 					__lowercase  (     self  :Any			):
											self.assertEqual(
											    '''|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n'''						,    str(square_zero_matrix(5			)			)						,    )


if __name__ == "__main__":
							unittest.main()




