



import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest







class 				a_    (						_snake_case							):
						UpperCamelCase__ :		Union[str, Any]										=(DDIMScheduler,)
						UpperCamelCase__ :		Union[str, Any]										=(("eta", 0.0), ("num_inference_steps", 50))

						def       __a			(       self      :Optional[int]  ,		**_lowercase      :Optional[int])						->   str:
												UpperCAmelCase_					=	{
												    '''num_train_timesteps''': 1000,
												    '''beta_start''': 0.0_001,
												    '''beta_end''': 0.02,
												    '''beta_schedule''': '''linear''',
												    '''clip_sample''': True,
												}

												config.update(**_lowercase)
												return config

						def       __a			(       self      :List[Any]  ,		**_lowercase      :Tuple)						->   Optional[Any]:
												UpperCAmelCase_					=	self.scheduler_classes[0]
												UpperCAmelCase_					=	self.get_scheduler_config(**_lowercase)
												UpperCAmelCase_					=	scheduler_class(**_lowercase)

												UpperCAmelCase_				, UpperCAmelCase_					=	10, 0.0

												UpperCAmelCase_					=	self.dummy_model()
												UpperCAmelCase_					=	self.dummy_sample_deter

												scheduler.set_timesteps(_lowercase)

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

												return sample

						def       __a			(       self      :Optional[Any])						->   int:
												for timesteps in [100, 500, 1000]:
																		self.check_over_configs(num_train_timesteps=_lowercase)

						def       __a			(       self      :List[Any])						->   Tuple:
												for steps_offset in [0, 1]:
																		self.check_over_configs(steps_offset=_lowercase)

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

						def       __a			(       self      :int)						->   List[str]:
												for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]  ,		[0.002, 0.02, 0.2, 2]):
																		self.check_over_configs(beta_start=_lowercase  ,		beta_end=_lowercase)

						def       __a			(       self      :List[Any])						->   Optional[int]:
												for schedule in ["linear", "squaredcos_cap_v2"]:
																		self.check_over_configs(beta_schedule=_lowercase)

						def       __a			(       self      :Union[str, Any])						->   Optional[Any]:
												for prediction_type in ["epsilon", "v_prediction"]:
																		self.check_over_configs(prediction_type=_lowercase)

						def       __a			(       self      :Optional[int])						->   str:
												for clip_sample in [True, False]:
																		self.check_over_configs(clip_sample=_lowercase)

						def       __a			(       self      :str)						->   List[Any]:
												for timestep_spacing in ["trailing", "leading"]:
																		self.check_over_configs(timestep_spacing=_lowercase)

						def       __a			(       self      :Union[str, Any])						->   Optional[int]:
												for rescale_betas_zero_snr in [True, False]:
																		self.check_over_configs(rescale_betas_zero_snr=_lowercase)

						def       __a			(       self      :Dict)						->   List[str]:
												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       __a			(       self      :Optional[Any])						->   Optional[int]:
												for t in [1, 10, 49]:
																		self.check_over_forward(time_step=_lowercase)

						def       __a			(       self      :Dict)						->   Union[str, Any]:
												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       __a			(       self      :Tuple)						->   Optional[int]:
												for t, eta in zip([1, 10, 49]  ,		[0.0, 0.5, 1.0]):
																		self.check_over_forward(time_step=_lowercase  ,		eta=_lowercase)

						def       __a			(       self      :str)						->   str:
												UpperCAmelCase_					=	self.scheduler_classes[0]
												UpperCAmelCase_					=	self.get_scheduler_config()
												UpperCAmelCase_					=	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) - 0.14_771)) < 1E-5
												assert torch.sum(torch.abs(scheduler._get_variance(980  ,		960) - 0.32_460)) < 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) - 0.00_979)) < 1E-5
												assert torch.sum(torch.abs(scheduler._get_variance(999  ,		998) - 0.02)) < 1E-5

						def       __a			(       self      :List[str])						->   Dict:
												UpperCAmelCase_					=	self.full_loop()

												UpperCAmelCase_					=	torch.sum(torch.abs(_lowercase))
												UpperCAmelCase_					=	torch.mean(torch.abs(_lowercase))

												assert abs(result_sum.item() - 172.0_067) < 1E-2
												assert abs(result_mean.item() - 0.223_967) < 1E-3

						def       __a			(       self      :List[str])						->   int:
												UpperCAmelCase_					=	self.full_loop(prediction_type='''v_prediction''')

												UpperCAmelCase_					=	torch.sum(torch.abs(_lowercase))
												UpperCAmelCase_					=	torch.mean(torch.abs(_lowercase))

												assert abs(result_sum.item() - 52.5_302) < 1E-2
												assert abs(result_mean.item() - 0.0_684) < 1E-3

						def       __a			(       self      :str)						->   List[Any]:
												# We specify different beta, so that the first alpha is 0.99
												UpperCAmelCase_					=	self.full_loop(set_alpha_to_one=_lowercase  ,		beta_start=0.01)
												UpperCAmelCase_					=	torch.sum(torch.abs(_lowercase))
												UpperCAmelCase_					=	torch.mean(torch.abs(_lowercase))

												assert abs(result_sum.item() - 149.8_295) < 1E-2
												assert abs(result_mean.item() - 0.1_951) < 1E-3




						def       __a			(       self      :Union[str, Any])						->   str:
												# We specify different beta, so that the first alpha is 0.99
												UpperCAmelCase_					=	self.full_loop(set_alpha_to_one=_lowercase  ,		beta_start=0.01)
												UpperCAmelCase_					=	torch.sum(torch.abs(_lowercase))
												UpperCAmelCase_					=	torch.mean(torch.abs(_lowercase))

												assert abs(result_sum.item() - 149.0_784) < 1E-2
												assert abs(result_mean.item() - 0.1_941) < 1E-3










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCamelCase_      			=  {
    "configuration_mobilenet_v1": [
        "MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileNetV1Config",
        "MobileNetV1OnnxConfig",
    ],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_      			=  ["MobileNetV1FeatureExtractor"]
							UpperCamelCase_      			=  ["MobileNetV1ImageProcessor"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_      			=  [
							    "MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "MobileNetV1ForImageClassification",
							    "MobileNetV1Model",
							    "MobileNetV1PreTrainedModel",
							    "load_tf_weights_in_mobilenet_v1",
							]


if TYPE_CHECKING:
							from .configuration_mobilenet_va import (
							    MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    MobileNetVaConfig,
							    MobileNetVaOnnxConfig,
							)

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
														from .image_processing_mobilenet_va import MobileNetVaImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mobilenet_va import (
														    MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MobileNetVaForImageClassification,
														    MobileNetVaModel,
														    MobileNetVaPreTrainedModel,
														    load_tf_weights_in_mobilenet_va,
														)


else:
							import sys

							UpperCamelCase_      			=  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





