






"""simple docstring"""





import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class 						_a  (		lowercase_       ):





   '''simple docstring'''






   UpperCamelCase__     	=							(EulerAncestralDiscreteScheduler,)
   UpperCamelCase__     	=							10





   def __lowercase							(       self					, **UpperCAmelCase_)			->   Optional[Any]:




          '''simple docstring'''




          lowercase__:							Optional[int]         =     {
              "num_train_timesteps": 1_100,
              "beta_start": 0.00_01,
              "beta_end": 0.02,
              "beta_schedule": "linear",
          }

          config.update(**UpperCAmelCase_)
          return config





   def __lowercase							(       self)			->   List[str]:




          '''simple docstring'''




          for timesteps in [10, 50, 100, 1_000]:
                 self.check_over_configs(num_train_timesteps=UpperCAmelCase_)





   def __lowercase							(       self)			->   Optional[Any]:




          '''simple docstring'''




          for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]					, [0.00_02, 0.0_02, 0.02]):
                 self.check_over_configs(beta_start=UpperCAmelCase_					, beta_end=UpperCAmelCase_)





   def __lowercase							(       self)			->   Dict:




          '''simple docstring'''




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





   def __lowercase							(       self)			->   Optional[Any]:




          '''simple docstring'''




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





   def __lowercase							(       self)			->   List[Any]:




          '''simple docstring'''




          lowercase__:							Optional[Any]         =     self.scheduler_classes[0]
          lowercase__:							Dict         =     self.get_scheduler_config()
          lowercase__:							int         =     scheduler_class(**UpperCAmelCase_)

          scheduler.set_timesteps(self.num_inference_steps)

          lowercase__:							Union[str, Any]         =     torch.manual_seed(0)

          lowercase__:							Tuple         =     self.dummy_model()
          lowercase__:							Any         =     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
          lowercase__:							str         =     sample.to(UpperCAmelCase_)

          for i, t in enumerate(scheduler.timesteps):
                 lowercase__:							Optional[Any]         =     scheduler.scale_model_input(UpperCAmelCase_					, UpperCAmelCase_)

                 lowercase__:							str         =     model(UpperCAmelCase_					, UpperCAmelCase_)

                 lowercase__:							int         =     scheduler.step(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, generator=UpperCAmelCase_)
                 lowercase__:							Optional[Any]         =     output.prev_sample

          lowercase__:							Optional[int]         =     torch.sum(torch.abs(UpperCAmelCase_))
          lowercase__:							Any         =     torch.mean(torch.abs(UpperCAmelCase_))

          assert abs(result_sum.item() - 1_52.31_92) < 1E-2
          assert abs(result_mean.item() - 0.19_83) < 1E-3





   def __lowercase							(       self)			->   int:




          '''simple docstring'''




          lowercase__:							Any         =     self.scheduler_classes[0]
          lowercase__:							Union[str, Any]         =     self.get_scheduler_config(prediction_type="v_prediction")
          lowercase__:							str         =     scheduler_class(**UpperCAmelCase_)

          scheduler.set_timesteps(self.num_inference_steps)

          lowercase__:							List[str]         =     torch.manual_seed(0)

          lowercase__:							Any         =     self.dummy_model()
          lowercase__:							Tuple         =     self.dummy_sample_deter * scheduler.init_noise_sigma
          lowercase__:							List[Any]         =     sample.to(UpperCAmelCase_)

          for i, t in enumerate(scheduler.timesteps):
                 lowercase__:							int         =     scheduler.scale_model_input(UpperCAmelCase_					, UpperCAmelCase_)

                 lowercase__:							Optional[int]         =     model(UpperCAmelCase_					, UpperCAmelCase_)

                 lowercase__:							Any         =     scheduler.step(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, generator=UpperCAmelCase_)
                 lowercase__:							Optional[Any]         =     output.prev_sample

          lowercase__:							int         =     torch.sum(torch.abs(UpperCAmelCase_))
          lowercase__:							Dict         =     torch.mean(torch.abs(UpperCAmelCase_))

          assert abs(result_sum.item() - 1_08.44_39) < 1E-2
          assert abs(result_mean.item() - 0.14_12) < 1E-3





   def __lowercase							(       self)			->   Union[str, Any]:




          '''simple docstring'''




          lowercase__:							Optional[Any]         =     self.scheduler_classes[0]
          lowercase__:							int         =     self.get_scheduler_config()
          lowercase__:							str         =     scheduler_class(**UpperCAmelCase_)

          scheduler.set_timesteps(self.num_inference_steps					, device=UpperCAmelCase_)
          lowercase__:							Optional[Any]         =     torch.manual_seed(0)

          lowercase__:							Dict         =     self.dummy_model()
          lowercase__:							int         =     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
          lowercase__:							Union[str, Any]         =     sample.to(UpperCAmelCase_)

          for t in scheduler.timesteps:
                 lowercase__:							int         =     scheduler.scale_model_input(UpperCAmelCase_					, UpperCAmelCase_)

                 lowercase__:							int         =     model(UpperCAmelCase_					, UpperCAmelCase_)

                 lowercase__:							Optional[Any]         =     scheduler.step(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, generator=UpperCAmelCase_)
                 lowercase__:							Dict         =     output.prev_sample

          lowercase__:							str         =     torch.sum(torch.abs(UpperCAmelCase_))
          lowercase__:							Optional[int]         =     torch.mean(torch.abs(UpperCAmelCase_))

          assert abs(result_sum.item() - 1_52.31_92) < 1E-2
          assert abs(result_mean.item() - 0.19_83) < 1E-3













'''simple docstring'''







from typing import TYPE_CHECKING

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


snake_case_  :							Any            =	{
    "configuration_flava": [
        "FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "FlavaConfig",
        "FlavaImageCodebookConfig",
        "FlavaImageConfig",
        "FlavaMultimodalConfig",
        "FlavaTextConfig",
    ],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_  :							List[str]            =	["FlavaFeatureExtractor"]
					snake_case_  :							int            =	["FlavaImageProcessor"]
					snake_case_  :							Tuple            =	["FlavaProcessor"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_  :							Optional[int]            =	[
					    "FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "FlavaForPreTraining",
					    "FlavaImageCodebook",
					    "FlavaImageModel",
					    "FlavaModel",
					    "FlavaMultimodalModel",
					    "FlavaPreTrainedModel",
					    "FlavaTextModel",
					]

if TYPE_CHECKING:
					from .configuration_flava import (
					    FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    FlavaConfig,
					    FlavaImageCodebookConfig,
					    FlavaImageConfig,
					    FlavaMultimodalConfig,
					    FlavaTextConfig,
					)

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_flava import FlavaFeatureExtractor
										from .image_processing_flava import FlavaImageProcessor
										from .processing_flava import FlavaProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flava import (
										    FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    FlavaForPreTraining,
										    FlavaImageCodebook,
										    FlavaImageModel,
										    FlavaModel,
										    FlavaMultimodalModel,
										    FlavaPreTrainedModel,
										    FlavaTextModel,
										)

else:
					import sys

					snake_case_  :							Any            =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





