'''simple docstring'''




import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest





class 					__magic_name__		(					_UpperCAmelCase):
			UpperCamelCase__       = (DDIMParallelScheduler,)
			UpperCamelCase__       = (('''eta''', 0.0), ('''num_inference_steps''', 50))







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Any       ,    **lowercase_       :						str					):
										lowercase_      :    Tuple       		= {
										    """num_train_timesteps""": 1000,
										    """beta_start""": 0.00_01,
										    """beta_end""": 0.02,
										    """beta_schedule""": """linear""",
										    """clip_sample""": True,
										}

										config.update(**lowercase_					)
										return config







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Dict       ,    **lowercase_       :						List[str]					):
										lowercase_      :    Optional[int]       		= self.scheduler_classes[0]
										lowercase_      :    List[Any]       		= self.get_scheduler_config(**lowercase_					)
										lowercase_      :    Any       		= scheduler_class(**lowercase_					)

										lowercase_		,		lowercase_      :    Optional[int]       		= 10, 0.0

										lowercase_      :    Tuple       		= self.dummy_model()
										lowercase_      :    Optional[int]       		= self.dummy_sample_deter

										scheduler.set_timesteps(lowercase_					)

										for t in scheduler.timesteps:
																	lowercase_      :    Any       		= model(lowercase_       ,    lowercase_					)
																	lowercase_      :    Optional[int]       		= scheduler.step(lowercase_       ,    lowercase_       ,    lowercase_       ,    lowercase_					).prev_sample

										return sample







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple					):
										for timesteps in [100, 500, 1000]:
																	self.check_over_configs(num_train_timesteps=lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Any					):
										for steps_offset in [0, 1]:
																	self.check_over_configs(steps_offset=lowercase_					)

										lowercase_      :    Dict       		= self.scheduler_classes[0]
										lowercase_      :    Union[str, Any]       		= self.get_scheduler_config(steps_offset=1					)
										lowercase_      :    int       		= scheduler_class(**lowercase_					)
										scheduler.set_timesteps(5					)
										assert torch.equal(scheduler.timesteps       ,    torch.LongTensor([801, 601, 401, 201, 1]					)					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Any					):
										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=lowercase_       ,    beta_end=lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple					):
										for schedule in ["linear", "squaredcos_cap_v2"]:
																	self.check_over_configs(beta_schedule=lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]					):
										for prediction_type in ["epsilon", "v_prediction"]:
																	self.check_over_configs(prediction_type=lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]					):
										for clip_sample in [True, False]:
																	self.check_over_configs(clip_sample=lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[Any]					):
										for timestep_spacing in ["trailing", "leading"]:
																	self.check_over_configs(timestep_spacing=lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Union[str, Any]					):
										for rescale_betas_zero_snr in [True, False]:
																	self.check_over_configs(rescale_betas_zero_snr=lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Dict					):
										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 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]					):
										for t in [1, 10, 49]:
																	self.check_over_forward(time_step=lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple					):
										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 			SCREAMING_SNAKE_CASE_  (			self       :						Union[str, Any]					):
										for t, eta in zip([1, 10, 49]       ,    [0.0, 0.5, 1.0]					):
																	self.check_over_forward(time_step=lowercase_       ,    eta=lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Dict					):
										lowercase_      :    Dict       		= self.scheduler_classes[0]
										lowercase_      :    Dict       		= self.get_scheduler_config()
										lowercase_      :    Optional[Any]       		= 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_47_71					)					) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(980       ,    960					) - 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(487       ,    486					) - 0.0_09_79					)					) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(999       ,    998					) - 0.02					)					) < 1E-5







			def 			SCREAMING_SNAKE_CASE_  (			self       :						int					):
										lowercase_      :    List[Any]       		= self.scheduler_classes[0]
										lowercase_      :    List[Any]       		= self.get_scheduler_config()
										lowercase_      :    str       		= scheduler_class(**lowercase_					)

										lowercase_		,		lowercase_      :    Dict       		= 10, 0.0
										scheduler.set_timesteps(lowercase_					)

										lowercase_      :    Union[str, Any]       		= self.dummy_model()
										lowercase_      :    int       		= self.dummy_sample_deter
										lowercase_      :    List[Any]       		= self.dummy_sample_deter + 0.1
										lowercase_      :    Optional[Any]       		= self.dummy_sample_deter - 0.1

										lowercase_      :    int       		= samplea.shape[0]
										lowercase_      :    Optional[int]       		= torch.stack([samplea, samplea, samplea]       ,    dim=0					)
										lowercase_      :    Dict       		= torch.arange(lowercase_					)[0:3, None].repeat(1       ,    lowercase_					)

										lowercase_      :    Any       		= model(samples.flatten(0       ,    1					)       ,    timesteps.flatten(0       ,    1					)					)
										lowercase_      :    Any       		= scheduler.batch_step_no_noise(lowercase_       ,    timesteps.flatten(0       ,    1					)       ,    samples.flatten(0       ,    1					)       ,    lowercase_					)

										lowercase_      :    Optional[int]       		= torch.sum(torch.abs(lowercase_					)					)
										lowercase_      :    Tuple       		= torch.mean(torch.abs(lowercase_					)					)

										assert abs(result_sum.item() - 11_47.79_04					) < 1E-2
										assert abs(result_mean.item() - 0.49_82					) < 1E-3







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[Any]					):
										lowercase_      :    str       		= self.full_loop()

										lowercase_      :    List[Any]       		= torch.sum(torch.abs(lowercase_					)					)
										lowercase_      :    Union[str, Any]       		= torch.mean(torch.abs(lowercase_					)					)

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







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Union[str, Any]					):
										lowercase_      :    Dict       		= self.full_loop(prediction_type="""v_prediction"""					)

										lowercase_      :    Union[str, Any]       		= torch.sum(torch.abs(lowercase_					)					)
										lowercase_      :    List[str]       		= torch.mean(torch.abs(lowercase_					)					)

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







			def 			SCREAMING_SNAKE_CASE_  (			self       :						int					):
										# We specify different beta, so that the first alpha is 0.99
										lowercase_      :    Tuple       		= self.full_loop(set_alpha_to_one=lowercase_       ,    beta_start=0.01					)
										lowercase_      :    Tuple       		= torch.sum(torch.abs(lowercase_					)					)
										lowercase_      :    Optional[Any]       		= torch.mean(torch.abs(lowercase_					)					)

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







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]					):
										# We specify different beta, so that the first alpha is 0.99
										lowercase_      :    Optional[Any]       		= self.full_loop(set_alpha_to_one=lowercase_       ,    beta_start=0.01					)
										lowercase_      :    Dict       		= torch.sum(torch.abs(lowercase_					)					)
										lowercase_      :    Dict       		= torch.mean(torch.abs(lowercase_					)					)

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






'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_lowercase		:				Optional[Any]	       =		{
    "configuration_swiftformer": [
        "SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SwiftFormerConfig",
        "SwiftFormerOnnxConfig",
    ]
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase		:				Any	       =		[
						    "SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "SwiftFormerForImageClassification",
						    "SwiftFormerModel",
						    "SwiftFormerPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_swiftformer import (
						    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    SwiftFormerConfig,
						    SwiftFormerOnnxConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_swiftformer import (
												    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    SwiftFormerForImageClassification,
												    SwiftFormerModel,
												    SwiftFormerPreTrainedModel,
												)

else:
						import sys

						_lowercase		:				Optional[int]	       =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





