



'''simple docstring'''






import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest



class 							lowercase       (  UpperCamelCase_				):


 """simple docstring"""


 UpperCAmelCase							=  (UnCLIPScheduler,)







 def _snake_case (							self  ,**a_							)		->   Optional[Any]:
        _UpperCAmelCase  :						Optional[Any]			     = {
            '''num_train_timesteps''': 1_000,
            '''variance_type''': '''fixed_small_log''',
            '''clip_sample''': True,
            '''clip_sample_range''': 1.0,
            '''prediction_type''': '''epsilon''',
        }

        config.update(**UpperCamelCase__							)
        return config







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





        for timesteps in [1, 5, 100, 1_000]:
               self.check_over_configs(num_train_timesteps=UpperCamelCase__							)







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





        for variance in ["fixed_small_log", "learned_range"]:
               self.check_over_configs(variance_type=UpperCamelCase__							)







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





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







 def _snake_case (							self							)		->   str:





        for clip_sample_range in [1, 5, 10, 20]:
               self.check_over_configs(clip_sample_range=UpperCamelCase__							)







 def _snake_case (							self							)		->   Optional[int]:





        for prediction_type in ["epsilon", "sample"]:
               self.check_over_configs(prediction_type=UpperCamelCase__							)







 def _snake_case (							self							)		->   str:





        for time_step in [0, 500, 999]:
               for prev_timestep in [None, 5, 100, 250, 500, 750]:
                      if prev_timestep is not None and prev_timestep >= time_step:
                             continue

                      self.check_over_forward(time_step=UpperCamelCase__  ,prev_timestep=UpperCamelCase__							)







 def _snake_case (							self							)		->   str:
        _UpperCAmelCase  :						Union[str, Any]			     = self.scheduler_classes[0]
        _UpperCAmelCase  :						Tuple			     = self.get_scheduler_config(variance_type="""fixed_small_log"""							)
        _UpperCAmelCase  :						int			     = scheduler_class(**UpperCamelCase__							)

        assert torch.sum(torch.abs(scheduler._get_variance(0							) - 1.0_0_0_0E-1_0							)							) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(487							) - 0.054_9625							)							) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(999							) - 0.999_4987							)							) < 1E-5







 def _snake_case (							self							)		->   Any:
        _UpperCAmelCase  :						Tuple			     = self.scheduler_classes[0]
        _UpperCAmelCase  :						Any			     = self.get_scheduler_config(variance_type="""learned_range"""							)
        _UpperCAmelCase  :						Union[str, Any]			     = scheduler_class(**UpperCamelCase__							)

        _UpperCAmelCase  :						Tuple			     = 0.5

        assert scheduler._get_variance(1  ,predicted_variance=UpperCamelCase__							) - -10.171_2790 < 1E-5
        assert scheduler._get_variance(487  ,predicted_variance=UpperCamelCase__							) - -5.799_8052 < 1E-5
        assert scheduler._get_variance(999  ,predicted_variance=UpperCamelCase__							) - -0.001_0011 < 1E-5







 def _snake_case (							self							)		->   Optional[int]:
        _UpperCAmelCase  :						Dict			     = self.scheduler_classes[0]
        _UpperCAmelCase  :						int			     = self.get_scheduler_config()
        _UpperCAmelCase  :						Tuple			     = scheduler_class(**UpperCamelCase__							)

        _UpperCAmelCase  :						int			     = scheduler.timesteps

        _UpperCAmelCase  :						Optional[int]			     = self.dummy_model()
        _UpperCAmelCase  :						str			     = self.dummy_sample_deter
        _UpperCAmelCase  :						Any			     = torch.manual_seed(0							)

        for i, t in enumerate(UpperCamelCase__							):
               # 1. predict noise residual
               _UpperCAmelCase  :						List[Any]			     = model(UpperCamelCase__  ,UpperCamelCase__							)

               # 2. predict previous mean of sample x_t-1
               _UpperCAmelCase  :						int			     = scheduler.step(UpperCamelCase__  ,UpperCamelCase__  ,UpperCamelCase__  ,generator=UpperCamelCase__							).prev_sample

               _UpperCAmelCase  :						int			     = pred_prev_sample

        _UpperCAmelCase  :						Optional[Any]			     = torch.sum(torch.abs(UpperCamelCase__							)							)
        _UpperCAmelCase  :						List[Any]			     = torch.mean(torch.abs(UpperCamelCase__							)							)

        assert abs(result_sum.item() - 252.268_2495							) < 1E-2
        assert abs(result_mean.item() - 0.328_4743							) < 1E-3







 def _snake_case (							self							)		->   Any:
        _UpperCAmelCase  :						Union[str, Any]			     = self.scheduler_classes[0]
        _UpperCAmelCase  :						Any			     = self.get_scheduler_config()
        _UpperCAmelCase  :						List[str]			     = scheduler_class(**UpperCamelCase__							)

        scheduler.set_timesteps(25							)

        _UpperCAmelCase  :						Optional[Any]			     = scheduler.timesteps

        _UpperCAmelCase  :						Any			     = self.dummy_model()
        _UpperCAmelCase  :						Optional[Any]			     = self.dummy_sample_deter
        _UpperCAmelCase  :						Any			     = torch.manual_seed(0							)

        for i, t in enumerate(UpperCamelCase__							):
               # 1. predict noise residual
               _UpperCAmelCase  :						List[Any]			     = model(UpperCamelCase__  ,UpperCamelCase__							)

               if i + 1 == timesteps.shape[0]:
                      _UpperCAmelCase  :						int			     = None
               else:
                      _UpperCAmelCase  :						List[Any]			     = timesteps[i + 1]

               # 2. predict previous mean of sample x_t-1
               _UpperCAmelCase  :						Optional[int]			     = scheduler.step(
                   UpperCamelCase__  ,UpperCamelCase__  ,UpperCamelCase__  ,prev_timestep=UpperCamelCase__  ,generator=UpperCamelCase__							).prev_sample

               _UpperCAmelCase  :						Tuple			     = pred_prev_sample

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

        assert abs(result_sum.item() - 258.204_4983							) < 1E-2
        assert abs(result_mean.item() - 0.336_2038							) < 1E-3







 def _snake_case (							self							)		->   List[str]:
        pass







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




from typing import TYPE_CHECKING

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


__UpperCamelCase		:       Tuple									=     {
    'configuration_swiftformer': [
        'SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SwiftFormerConfig',
        'SwiftFormerOnnxConfig',
    ]
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       Optional[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

					__UpperCamelCase		:       List[Any]									=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





