





import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest

class      lowerCamelCase	(  SCREAMING_SNAKE_CASE     ):
       _UpperCamelCase    		=      (DDIMScheduler,)
       _UpperCamelCase    		=      (("""eta""", 0.0), ("""num_inference_steps""", 50))

       def 				UpperCAmelCase				(			self	,						**a__					):






        '''simple docstring'''






        snake_case   : str	  =      {
            '''num_train_timesteps''': 10_00,
            '''beta_start''': 0.0001,
            '''beta_end''': 0.02,
            '''beta_schedule''': '''linear''',
            '''clip_sample''': True,
        }

        config.update(**a__					)
        return config

       def 				UpperCAmelCase				(			self	,						**a__					):






        '''simple docstring'''






        snake_case   : Optional[Any]	  =      self.scheduler_classes[0]
        snake_case   : Optional[Any]	  =      self.get_scheduler_config(**a__					)
        snake_case   : List[str]	  =      scheduler_class(**a__					)

        snake_case   : Optional[Any]	  =      10, 0.0

        snake_case   : Tuple	  =      self.dummy_model()
        snake_case   : Optional[Any]	  =      self.dummy_sample_deter

        scheduler.set_timesteps(a__					)

        for t in scheduler.timesteps:
         snake_case   : Optional[Any]	  =      model(a__	,						a__					)
         snake_case   : Dict	  =      scheduler.step(a__	,						a__	,						a__	,						a__					).prev_sample

        return sample

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        for timesteps in [1_00, 5_00, 10_00]:
         self.check_over_configs(num_train_timesteps=a__					)

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        for steps_offset in [0, 1]:
         self.check_over_configs(steps_offset=a__					)

        snake_case   : Tuple	  =      self.scheduler_classes[0]
        snake_case   : List[str]	  =      self.get_scheduler_config(steps_offset=1					)
        snake_case   : List[Any]	  =      scheduler_class(**a__					)
        scheduler.set_timesteps(5					)
        assert torch.equal(scheduler.timesteps	,						torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]					)					)

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]	,						[0.002, 0.02, 0.2, 2]					):
         self.check_over_configs(beta_start=a__	,						beta_end=a__					)

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        for schedule in ["linear", "squaredcos_cap_v2"]:
         self.check_over_configs(beta_schedule=a__					)

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






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

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






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

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        for timestep_spacing in ["trailing", "leading"]:
         self.check_over_configs(timestep_spacing=a__					)

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        for rescale_betas_zero_snr in [True, False]:
         self.check_over_configs(rescale_betas_zero_snr=a__					)

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        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 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        for t in [1, 10, 49]:
         self.check_over_forward(time_step=a__					)

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        for t, num_inference_steps in zip([1, 10, 50]	,						[10, 50, 5_00]					):
         self.check_over_forward(time_step=a__	,						num_inference_steps=a__					)

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        for t, eta in zip([1, 10, 49]	,						[0.0, 0.5, 1.0]					):
         self.check_over_forward(time_step=a__	,						eta=a__					)

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : int	  =      self.scheduler_classes[0]
        snake_case   : Tuple	  =      self.get_scheduler_config()
        snake_case   : Optional[Any]	  =      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(4_20	,						4_00					) - 0.1_4771					)					) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(9_80	,						9_60					) - 0.3_2460					)					) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(0	,						0					) - 0.0					)					) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(4_87	,						4_86					) - 0.0_0979					)					) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(9_99	,						9_98					) - 0.02					)					) < 1e-5

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : str	  =      self.full_loop()

        snake_case   : str	  =      torch.sum(torch.abs(a__					)					)
        snake_case   : str	  =      torch.mean(torch.abs(a__					)					)

        assert abs(result_sum.item() - 172.0067					) < 1e-2
        assert abs(result_mean.item() - 0.22_3967					) < 1e-3

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : Any	  =      self.full_loop(prediction_type='''v_prediction'''					)

        snake_case   : Optional[Any]	  =      torch.sum(torch.abs(a__					)					)
        snake_case   : str	  =      torch.mean(torch.abs(a__					)					)

        assert abs(result_sum.item() - 52.5302					) < 1e-2
        assert abs(result_mean.item() - 0.0684					) < 1e-3

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : Optional[int]	  =      self.full_loop(set_alpha_to_one=a__	,						beta_start=0.01					)
        snake_case   : Any	  =      torch.sum(torch.abs(a__					)					)
        snake_case   : Optional[Any]	  =      torch.mean(torch.abs(a__					)					)

        assert abs(result_sum.item() - 149.8295					) < 1e-2
        assert abs(result_mean.item() - 0.1951					) < 1e-3



       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : List[str]	  =      self.full_loop(set_alpha_to_one=a__	,						beta_start=0.01					)
        snake_case   : int	  =      torch.sum(torch.abs(a__					)					)
        snake_case   : List[Any]	  =      torch.mean(torch.abs(a__					)					)

        assert abs(result_sum.item() - 149.0784					) < 1e-2
        assert abs(result_mean.item() - 0.1941					) < 1e-3










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a				:							str					=			{'configuration_dinat': ['DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DinatConfig']}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a				:							Union[str, Any]					=			[
      '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

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






