


"""simple docstring"""







import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest






class    __A       ( _SCREAMING_SNAKE_CASE    ):






 """simple docstring"""




 __lowerCAmelCase				      =						(DDIMScheduler,)
 __lowerCAmelCase				      =						(("eta", 0.0), ("num_inference_steps", 50))


 def    SCREAMING_SNAKE_CASE							(	self				,					**__A		)      ->  Optional[Any]:
     a					={
         '''num_train_timesteps''': 1000,
         '''beta_start''': 0.0_001,
         '''beta_end''': 0.02,
         '''beta_schedule''': '''linear''',
         '''clip_sample''': True,
     }

     config.update(**__A		)
     return config


 def    SCREAMING_SNAKE_CASE							(	self				,					**__A		)      ->  List[str]:
     a					=self.scheduler_classes[0]
     a					=self.get_scheduler_config(**__A		)
     a					=scheduler_class(**__A		)

     a	,   a					=10, 0.0

     a					=self.dummy_model()
     a					=self.dummy_sample_deter

     scheduler.set_timesteps(__A		)

     for t in scheduler.timesteps:
         a					=model(__A				,					__A		)
         a					=scheduler.step(__A				,					__A				,					__A				,					__A		).prev_sample

     return sample


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


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

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


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  List[Any]:
     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=__A				,					beta_end=__A		)


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  Tuple:
     for schedule in ["linear", "squaredcos_cap_v2"]:
         self.check_over_configs(beta_schedule=__A		)


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  Optional[Any]:
     for prediction_type in ["epsilon", "v_prediction"]:
         self.check_over_configs(prediction_type=__A		)


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  Optional[Any]:
     for clip_sample in [True, False]:
         self.check_over_configs(clip_sample=__A		)


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  str:
     for timestep_spacing in ["trailing", "leading"]:
         self.check_over_configs(timestep_spacing=__A		)


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  int:
     for rescale_betas_zero_snr in [True, False]:
         self.check_over_configs(rescale_betas_zero_snr=__A		)


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  Optional[Any]:
     self.check_over_configs(thresholding=__A		)
     for threshold in [0.5, 1.0, 2.0]:
         for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(
                 thresholding=__A				,					prediction_type=__A				,					sample_max_value=__A				,					)


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  Dict:
     for t in [1, 10, 49]:
         self.check_over_forward(time_step=__A		)


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  str:
     for t, num_inference_steps in zip([1, 10, 50]				,					[10, 50, 500]		):
         self.check_over_forward(time_step=__A				,					num_inference_steps=__A		)


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  Optional[int]:
     for t, eta in zip([1, 10, 49]				,					[0.0, 0.5, 1.0]		):
         self.check_over_forward(time_step=__A				,					eta=__A		)


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  List[str]:
     a					=self.scheduler_classes[0]
     a					=self.get_scheduler_config()
     a					=scheduler_class(**__A		)

     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    SCREAMING_SNAKE_CASE							(	self		)      ->  int:
     a					=self.full_loop()

     a					=torch.sum(torch.abs(__A		)		)
     a					=torch.mean(torch.abs(__A		)		)

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


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  Tuple:
     a					=self.full_loop(prediction_type='''v_prediction'''		)

     a					=torch.sum(torch.abs(__A		)		)
     a					=torch.mean(torch.abs(__A		)		)

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


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  Optional[int]:
     # We specify different beta, so that the first alpha is 0.99
     a					=self.full_loop(set_alpha_to_one=__A				,					beta_start=0.01		)
     a					=torch.sum(torch.abs(__A		)		)
     a					=torch.mean(torch.abs(__A		)		)

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





 def    SCREAMING_SNAKE_CASE							(	self		)      ->  Any:
     # We specify different beta, so that the first alpha is 0.99
     a					=self.full_loop(set_alpha_to_one=__A				,					beta_start=0.01		)
     a					=torch.sum(torch.abs(__A		)		)
     a					=torch.mean(torch.abs(__A		)		)

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



"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase_     :     Any    		=						{"""configuration_dinat""": ["""DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DinatConfig"""]}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_     :     List[str]    		=						[
           """DINAT_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """DinatForImageClassification""",
           """DinatModel""",
           """DinatPreTrainedModel""",
           """DinatBackbone""",
       ]

if TYPE_CHECKING:
       from .configuration_dinat import DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP, DinatConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_dinat import (
                  DINAT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DinatBackbone,
                  DinatForImageClassification,
                  DinatModel,
                  DinatPreTrainedModel,
              )

else:
       import sys

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