




'''simple docstring'''





import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class 	lowerCAmelCase_(    __UpperCamelCase					):
  '''simple docstring'''




  __lowercase							:				List[str]           =				(DDPMParallelScheduler,)




  def 							UpperCAmelCase_     (   self  ,**__UpperCAmelCase				)   ->		int:
      lowerCAmelCase__			:  str											= {
          'num_train_timesteps': 1000,
          'beta_start': 0.0_0_0_1,
          'beta_end': 0.0_2,
          'beta_schedule': 'linear',
          'variance_type': 'fixed_small',
          'clip_sample': True,
      }

      config.update(**__UpperCAmelCase				)
      return config




  def 							UpperCAmelCase_     (   self				)   ->		Dict:
      for timesteps in [1, 5, 100, 1000]:
          self.check_over_configs(num_train_timesteps=__UpperCAmelCase				)




  def 							UpperCAmelCase_     (   self				)   ->		Tuple:
      for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]  ,[0.0_0_2, 0.0_2, 0.2, 2]				):
          self.check_over_configs(beta_start=__UpperCAmelCase  ,beta_end=__UpperCAmelCase				)




  def 							UpperCAmelCase_     (   self				)   ->		Dict:
      for schedule in ["linear", "squaredcos_cap_v2"]:
          self.check_over_configs(beta_schedule=__UpperCAmelCase				)




  def 							UpperCAmelCase_     (   self				)   ->		List[Any]:
      for variance in ["fixed_small", "fixed_large", "other"]:
          self.check_over_configs(variance_type=__UpperCAmelCase				)




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




  def 							UpperCAmelCase_     (   self				)   ->		str:
      self.check_over_configs(thresholding=__UpperCAmelCase				)
      for threshold in [0.5, 1.0, 2.0]:
          for prediction_type in ["epsilon", "sample", "v_prediction"]:
              self.check_over_configs(
                  thresholding=__UpperCAmelCase  ,prediction_type=__UpperCAmelCase  ,sample_max_value=__UpperCAmelCase  ,)




  def 							UpperCAmelCase_     (   self				)   ->		Optional[int]:
      for prediction_type in ["epsilon", "sample", "v_prediction"]:
          self.check_over_configs(prediction_type=__UpperCAmelCase				)




  def 							UpperCAmelCase_     (   self				)   ->		Union[str, Any]:
      for t in [0, 500, 999]:
          self.check_over_forward(time_step=__UpperCAmelCase				)




  def 							UpperCAmelCase_     (   self				)   ->		Optional[Any]:
      lowerCAmelCase__			:  List[str]											= self.scheduler_classes[0]
      lowerCAmelCase__			:  Any											= self.get_scheduler_config()
      lowerCAmelCase__			:  Any											= scheduler_class(**__UpperCAmelCase				)

      assert torch.sum(torch.abs(scheduler._get_variance(0				) - 0.0				)				) < 1E-5
      assert torch.sum(torch.abs(scheduler._get_variance(487				) - 0.0_0_9_7_9				)				) < 1E-5
      assert torch.sum(torch.abs(scheduler._get_variance(999				) - 0.0_2				)				) < 1E-5




  def 							UpperCAmelCase_     (   self				)   ->		Tuple:
      lowerCAmelCase__			:  Tuple											= self.scheduler_classes[0]
      lowerCAmelCase__			:  Optional[Any]											= self.get_scheduler_config()
      lowerCAmelCase__			:  str											= scheduler_class(**__UpperCAmelCase				)

      lowerCAmelCase__			:  Optional[int]											= len(__UpperCAmelCase				)

      lowerCAmelCase__			:  List[str]											= self.dummy_model()
      lowerCAmelCase__			:  Optional[int]											= self.dummy_sample_deter
      lowerCAmelCase__			:  int											= self.dummy_sample_deter + 0.1
      lowerCAmelCase__			:  Union[str, Any]											= self.dummy_sample_deter - 0.1

      lowerCAmelCase__			:  str											= samplea.shape[0]
      lowerCAmelCase__			:  int											= torch.stack([samplea, samplea, samplea]  ,dim=0				)
      lowerCAmelCase__			:  Optional[Any]											= torch.arange(__UpperCAmelCase				)[0:3, None].repeat(1  ,__UpperCAmelCase				)

      lowerCAmelCase__			:  Dict											= model(samples.flatten(0  ,1				)  ,timesteps.flatten(0  ,1				)				)
      lowerCAmelCase__			:  Optional[Any]											= scheduler.batch_step_no_noise(__UpperCAmelCase  ,timesteps.flatten(0  ,1				)  ,samples.flatten(0  ,1				)				)

      lowerCAmelCase__			:  int											= torch.sum(torch.abs(__UpperCAmelCase				)				)
      lowerCAmelCase__			:  Union[str, Any]											= torch.mean(torch.abs(__UpperCAmelCase				)				)

      assert abs(result_sum.item() - 1153.1833				) < 1E-2
      assert abs(result_mean.item() - 0.5_0_0_5				) < 1E-3




  def 							UpperCAmelCase_     (   self				)   ->		Optional[int]:
      lowerCAmelCase__			:  int											= self.scheduler_classes[0]
      lowerCAmelCase__			:  str											= self.get_scheduler_config()
      lowerCAmelCase__			:  str											= scheduler_class(**__UpperCAmelCase				)

      lowerCAmelCase__			:  int											= len(__UpperCAmelCase				)

      lowerCAmelCase__			:  List[Any]											= self.dummy_model()
      lowerCAmelCase__			:  List[str]											= self.dummy_sample_deter
      lowerCAmelCase__			:  Union[str, Any]											= torch.manual_seed(0				)

      for t in reversed(range(__UpperCAmelCase				)				):
          # 1. predict noise residual
          lowerCAmelCase__			:  int											= model(__UpperCAmelCase  ,__UpperCAmelCase				)

          # 2. predict previous mean of sample x_t-1
          lowerCAmelCase__			:  Union[str, Any]											= scheduler.step(__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,generator=__UpperCAmelCase				).prev_sample

          lowerCAmelCase__			:  Any											= pred_prev_sample

      lowerCAmelCase__			:  int											= torch.sum(torch.abs(__UpperCAmelCase				)				)
      lowerCAmelCase__			:  Dict											= torch.mean(torch.abs(__UpperCAmelCase				)				)

      assert abs(result_sum.item() - 258.9606				) < 1E-2
      assert abs(result_mean.item() - 0.3_3_7_2				) < 1E-3




  def 							UpperCAmelCase_     (   self				)   ->		List[str]:
      lowerCAmelCase__			:  Tuple											= self.scheduler_classes[0]
      lowerCAmelCase__			:  Tuple											= self.get_scheduler_config(prediction_type="""v_prediction"""				)
      lowerCAmelCase__			:  Any											= scheduler_class(**__UpperCAmelCase				)

      lowerCAmelCase__			:  List[str]											= len(__UpperCAmelCase				)

      lowerCAmelCase__			:  Tuple											= self.dummy_model()
      lowerCAmelCase__			:  Optional[Any]											= self.dummy_sample_deter
      lowerCAmelCase__			:  List[Any]											= torch.manual_seed(0				)

      for t in reversed(range(__UpperCAmelCase				)				):
          # 1. predict noise residual
          lowerCAmelCase__			:  List[Any]											= model(__UpperCAmelCase  ,__UpperCAmelCase				)

          # 2. predict previous mean of sample x_t-1
          lowerCAmelCase__			:  List[Any]											= scheduler.step(__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,generator=__UpperCAmelCase				).prev_sample

          lowerCAmelCase__			:  Tuple											= pred_prev_sample

      lowerCAmelCase__			:  int											= torch.sum(torch.abs(__UpperCAmelCase				)				)
      lowerCAmelCase__			:  List[str]											= torch.mean(torch.abs(__UpperCAmelCase				)				)

      assert abs(result_sum.item() - 202.0296				) < 1E-2
      assert abs(result_mean.item() - 0.2_6_3_1				) < 1E-3




  def 							UpperCAmelCase_     (   self				)   ->		Any:
      lowerCAmelCase__			:  Any											= self.scheduler_classes[0]
      lowerCAmelCase__			:  List[Any]											= self.get_scheduler_config()
      lowerCAmelCase__			:  Optional[int]											= scheduler_class(**__UpperCAmelCase				)

      lowerCAmelCase__			:  Tuple											= [100, 87, 50, 1, 0]

      scheduler.set_timesteps(timesteps=__UpperCAmelCase				)

      lowerCAmelCase__			:  Union[str, Any]											= scheduler.timesteps

      for i, timestep in enumerate(__UpperCAmelCase				):
          if i == len(__UpperCAmelCase				) - 1:
              lowerCAmelCase__			:  Dict											= -1
          else:
              lowerCAmelCase__			:  List[str]											= timesteps[i + 1]

          lowerCAmelCase__			:  Any											= scheduler.previous_timestep(__UpperCAmelCase				)
          lowerCAmelCase__			:  Union[str, Any]											= prev_t.item()

          self.assertEqual(__UpperCAmelCase  ,__UpperCAmelCase				)




  def 							UpperCAmelCase_     (   self				)   ->		int:
      lowerCAmelCase__			:  int											= self.scheduler_classes[0]
      lowerCAmelCase__			:  int											= self.get_scheduler_config()
      lowerCAmelCase__			:  str											= scheduler_class(**__UpperCAmelCase				)

      lowerCAmelCase__			:  List[Any]											= [100, 87, 50, 51, 0]

      with self.assertRaises(__UpperCAmelCase  ,msg="""`custom_timesteps` must be in descending order."""				):
          scheduler.set_timesteps(timesteps=__UpperCAmelCase				)




  def 							UpperCAmelCase_     (   self				)   ->		Any:
      lowerCAmelCase__			:  Any											= self.scheduler_classes[0]
      lowerCAmelCase__			:  Union[str, Any]											= self.get_scheduler_config()
      lowerCAmelCase__			:  List[str]											= scheduler_class(**__UpperCAmelCase				)

      lowerCAmelCase__			:  Dict											= [100, 87, 50, 1, 0]
      lowerCAmelCase__			:  List[str]											= len(__UpperCAmelCase				)

      with self.assertRaises(__UpperCAmelCase  ,msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`."""				):
          scheduler.set_timesteps(num_inference_steps=__UpperCAmelCase  ,timesteps=__UpperCAmelCase				)






  def 							UpperCAmelCase_     (   self				)   ->		int:
      lowerCAmelCase__			:  Optional[Any]											= self.scheduler_classes[0]
      lowerCAmelCase__			:  List[str]											= self.get_scheduler_config()
      lowerCAmelCase__			:  int											= scheduler_class(**__UpperCAmelCase				)

      lowerCAmelCase__			:  List[Any]											= [scheduler.config.num_train_timesteps]

      with self.assertRaises(
          __UpperCAmelCase  ,msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""  ,):
          scheduler.set_timesteps(timesteps=__UpperCAmelCase				)







'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_lowerCAmelCase										=     logging.get_logger(__name__)

_lowerCAmelCase										=     {
    '''facebook/convnextv2-tiny-1k-224''': '''https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json''',
}






class 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  __lowercase							:				Tuple           =				'''convnextv2'''




  def __init__(   self  ,__UpperCAmelCase=3  ,__UpperCAmelCase=4  ,__UpperCAmelCase=4  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,__UpperCAmelCase="gelu"  ,__UpperCAmelCase=0.0_2  ,__UpperCAmelCase=1E-12  ,__UpperCAmelCase=0.0  ,__UpperCAmelCase=224  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,**__UpperCAmelCase  ,)   ->		Union[str, Any]:
      super().__init__(**__UpperCAmelCase				)

      lowerCAmelCase__			:  int											= num_channels
      lowerCAmelCase__			:  List[Any]											= patch_size
      lowerCAmelCase__			:  Union[str, Any]											= num_stages
      lowerCAmelCase__			:  Tuple											= [96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
      lowerCAmelCase__			:  str											= [3, 3, 9, 3] if depths is None else depths
      lowerCAmelCase__			:  Optional[Any]											= hidden_act
      lowerCAmelCase__			:  str											= initializer_range
      lowerCAmelCase__			:  List[str]											= layer_norm_eps
      lowerCAmelCase__			:  Dict											= drop_path_rate
      lowerCAmelCase__			:  int											= image_size
      lowerCAmelCase__			:  int											= ["""stem"""] + [F"""stage{idx}""" for idx in range(1  ,len(self.depths				) + 1				)]
      lowerCAmelCase__				,       lowerCAmelCase__			:  Tuple											= get_aligned_output_features_output_indices(
          out_features=__UpperCAmelCase  ,out_indices=__UpperCAmelCase  ,stage_names=self.stage_names				)


