

import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest







class     a__    (				a_    ):







		"""simple docstring"""




		UpperCAmelCase__ :	str         =(DDIMScheduler,)
		UpperCAmelCase__ :	Optional[int]         =(("""eta""", 0.0), ("""num_inference_steps""", 5_0))


		def 	_lowercase (     self      :	Optional[int]			,			**UpperCAmelCase__      :	List[str]						)   ->Optional[Any]:
								"""simple docstring"""







								SCREAMING_SNAKE_CASE	: List[str]					       =				{
								    """num_train_timesteps""": 1_0_0_0,
								    """beta_start""": 0.00_01,
								    """beta_end""": 0.02,
								    """beta_schedule""": """linear""",
								    """clip_sample""": True,
								}

								config.update(**snake_case__						)
								return config


		def 	_lowercase (     self      :	List[Any]			,			**UpperCAmelCase__      :	Union[str, Any]						)   ->int:
								"""simple docstring"""







								SCREAMING_SNAKE_CASE	: List[Any]					       =				self.scheduler_classes[0]
								SCREAMING_SNAKE_CASE	: List[str]					       =				self.get_scheduler_config(**snake_case__						)
								SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				scheduler_class(**snake_case__						)

								SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: Any					       =				1_0, 0.0

								SCREAMING_SNAKE_CASE	: int					       =				self.dummy_model()
								SCREAMING_SNAKE_CASE	: List[Any]					       =				self.dummy_sample_deter

								scheduler.set_timesteps(snake_case__						)

								for t in scheduler.timesteps:
														SCREAMING_SNAKE_CASE	: Optional[Any]					       =				model(snake_case__			,			snake_case__						)
														SCREAMING_SNAKE_CASE	: List[Any]					       =				scheduler.step(snake_case__			,			snake_case__			,			snake_case__			,			snake_case__						).prev_sample

								return sample


		def 	_lowercase (     self      :	Optional[Any]						)   ->int:
								"""simple docstring"""







								for timesteps in [1_0_0, 5_0_0, 1_0_0_0]:
														self.check_over_configs(num_train_timesteps=snake_case__						)


		def 	_lowercase (     self      :	Dict						)   ->Tuple:
								"""simple docstring"""







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

								SCREAMING_SNAKE_CASE	: Tuple					       =				self.scheduler_classes[0]
								SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				self.get_scheduler_config(steps_offset=1						)
								SCREAMING_SNAKE_CASE	: Dict					       =				scheduler_class(**snake_case__						)
								scheduler.set_timesteps(5						)
								assert torch.equal(scheduler.timesteps			,			torch.LongTensor([8_0_1, 6_0_1, 4_0_1, 2_0_1, 1]						)						)


		def 	_lowercase (     self      :	List[str]						)   ->List[str]:
								"""simple docstring"""







								for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]			,			[0.0_02, 0.02, 0.2, 2]						):
														self.check_over_configs(beta_start=snake_case__			,			beta_end=snake_case__						)


		def 	_lowercase (     self      :	Union[str, Any]						)   ->Union[str, Any]:
								"""simple docstring"""







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


		def 	_lowercase (     self      :	Optional[Any]						)   ->Union[str, Any]:
								"""simple docstring"""







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


		def 	_lowercase (     self      :	int						)   ->List[Any]:
								"""simple docstring"""







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


		def 	_lowercase (     self      :	List[Any]						)   ->List[str]:
								"""simple docstring"""







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


		def 	_lowercase (     self      :	str						)   ->Union[str, Any]:
								"""simple docstring"""







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


		def 	_lowercase (     self      :	List[Any]						)   ->Union[str, Any]:
								"""simple docstring"""







								self.check_over_configs(thresholding=snake_case__						)
								for threshold in [0.5, 1.0, 2.0]:
														for prediction_type in ["epsilon", "v_prediction"]:
																				self.check_over_configs(
																				    thresholding=snake_case__			,			prediction_type=snake_case__			,			sample_max_value=snake_case__			,			)


		def 	_lowercase (     self      :	int						)   ->int:
								"""simple docstring"""







								for t in [1, 1_0, 4_9]:
														self.check_over_forward(time_step=snake_case__						)


		def 	_lowercase (     self      :	Optional[Any]						)   ->Dict:
								"""simple docstring"""







								for t, num_inference_steps in zip([1, 1_0, 5_0]			,			[1_0, 5_0, 5_0_0]						):
														self.check_over_forward(time_step=snake_case__			,			num_inference_steps=snake_case__						)


		def 	_lowercase (     self      :	List[Any]						)   ->Union[str, Any]:
								"""simple docstring"""







								for t, eta in zip([1, 1_0, 4_9]			,			[0.0, 0.5, 1.0]						):
														self.check_over_forward(time_step=snake_case__			,			eta=snake_case__						)


		def 	_lowercase (     self      :	Optional[int]						)   ->Union[str, Any]:
								"""simple docstring"""







								SCREAMING_SNAKE_CASE	: List[Any]					       =				self.scheduler_classes[0]
								SCREAMING_SNAKE_CASE	: Optional[int]					       =				self.get_scheduler_config()
								SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				scheduler_class(**snake_case__						)

								assert torch.sum(torch.abs(scheduler._get_variance(0			,			0						) - 0.0						)						) < 1e-5
								assert torch.sum(torch.abs(scheduler._get_variance(4_2_0			,			4_0_0						) - 0.1_47_71						)						) < 1e-5
								assert torch.sum(torch.abs(scheduler._get_variance(9_8_0			,			9_6_0						) - 0.3_24_60						)						) < 1e-5
								assert torch.sum(torch.abs(scheduler._get_variance(0			,			0						) - 0.0						)						) < 1e-5
								assert torch.sum(torch.abs(scheduler._get_variance(4_8_7			,			4_8_6						) - 0.0_09_79						)						) < 1e-5
								assert torch.sum(torch.abs(scheduler._get_variance(9_9_9			,			9_9_8						) - 0.02						)						) < 1e-5


		def 	_lowercase (     self      :	str						)   ->List[str]:
								"""simple docstring"""







								SCREAMING_SNAKE_CASE	: int					       =				self.full_loop()

								SCREAMING_SNAKE_CASE	: List[str]					       =				torch.sum(torch.abs(snake_case__						)						)
								SCREAMING_SNAKE_CASE	: List[str]					       =				torch.mean(torch.abs(snake_case__						)						)

								assert abs(result_sum.item() - 1_72.00_67						) < 1e-2
								assert abs(result_mean.item() - 0.22_39_67						) < 1e-3


		def 	_lowercase (     self      :	Union[str, Any]						)   ->Union[str, Any]:
								"""simple docstring"""







								SCREAMING_SNAKE_CASE	: Any					       =				self.full_loop(prediction_type="""v_prediction"""						)

								SCREAMING_SNAKE_CASE	: List[Any]					       =				torch.sum(torch.abs(snake_case__						)						)
								SCREAMING_SNAKE_CASE	: Any					       =				torch.mean(torch.abs(snake_case__						)						)

								assert abs(result_sum.item() - 52.53_02						) < 1e-2
								assert abs(result_mean.item() - 0.06_84						) < 1e-3


		def 	_lowercase (     self      :	List[Any]						)   ->List[Any]:
								"""simple docstring"""







								SCREAMING_SNAKE_CASE	: Optional[int]					       =				self.full_loop(set_alpha_to_one=snake_case__			,			beta_start=0.01						)
								SCREAMING_SNAKE_CASE	: str					       =				torch.sum(torch.abs(snake_case__						)						)
								SCREAMING_SNAKE_CASE	: int					       =				torch.mean(torch.abs(snake_case__						)						)

								assert abs(result_sum.item() - 1_49.82_95						) < 1e-2
								assert abs(result_mean.item() - 0.19_51						) < 1e-3




		def 	_lowercase (     self      :	Optional[Any]						)   ->str:
								"""simple docstring"""







								SCREAMING_SNAKE_CASE	: str					       =				self.full_loop(set_alpha_to_one=snake_case__			,			beta_start=0.01						)
								SCREAMING_SNAKE_CASE	: Tuple					       =				torch.sum(torch.abs(snake_case__						)						)
								SCREAMING_SNAKE_CASE	: List[Any]					       =				torch.mean(torch.abs(snake_case__						)						)

								assert abs(result_sum.item() - 1_49.07_84						) < 1e-2
								assert abs(result_mean.item() - 0.19_41						) < 1e-3







UpperCAmelCase__	:       Union[str, Any]			      =      """
# Installation de Transformers
! pip install transformers datasets
# Pour installer à partir du code source au lieu de la dernière version, commentez la commande ci-dessus et décommentez la suivante.
# ! pip install git+https://github.com/huggingface/transformers.git
"""

UpperCAmelCase__	:       List[Any]			      =      [{"""type""": """code""", """content""": INSTALL_CONTENT}]
UpperCAmelCase__	:       List[Any]			      =      {
    """{processor_class}""": """FakeProcessorClass""",
    """{model_class}""": """FakeModelClass""",
    """{object_class}""": """FakeObjectClass""",
}




