



'''simple docstring'''





import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       str          =       (DDIMParallelScheduler,)
 __lowerCamelCase    :       Union[str, Any]          =       (('eta', 0.0), ('num_inference_steps', 50))



 def 					a__  (self ,   **A       )    ->     Dict:


      """simple docstring"""



      _a      =  {
          '''num_train_timesteps''': 1_000,
          '''beta_start''': 0.0001,
          '''beta_end''': 0.02,
          '''beta_schedule''': '''linear''',
          '''clip_sample''': True,
      }

      config.update(**A       )
      return config



 def 					a__  (self ,   **A       )    ->     List[Any]:


      """simple docstring"""



      _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 					a__  (self       )    ->     Any:


      """simple docstring"""



      for timesteps in [100, 500, 1_000]:
           self.check_over_configs(num_train_timesteps=A       )



 def 					a__  (self       )    ->     int:


      """simple docstring"""



      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 					a__  (self       )    ->     List[str]:


      """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 					a__  (self       )    ->     str:


      """simple docstring"""



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



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


      """simple docstring"""



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



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


      """simple docstring"""



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



 def 					a__  (self       )    ->     Any:


      """simple docstring"""



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



 def 					a__  (self       )    ->     Dict:


      """simple docstring"""



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



 def 					a__  (self       )    ->     Dict:


      """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 					a__  (self       )    ->     List[str]:


      """simple docstring"""



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



 def 					a__  (self       )    ->     int:


      """simple docstring"""



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


      """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 					a__  (self       )    ->     Union[str, Any]:


      """simple docstring"""



      _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       ) - 0.14771       )       ) < 1E-5
      assert torch.sum(torch.abs(scheduler._get_variance(980 ,   960       ) - 0.32460       )       ) < 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       ) - 0.00979       )       ) < 1E-5
      assert torch.sum(torch.abs(scheduler._get_variance(999 ,   998       ) - 0.02       )       ) < 1E-5



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


      """simple docstring"""



      _a      =  self.scheduler_classes[0]
      _a      =  self.get_scheduler_config()
      _a      =  scheduler_class(**A       )

      _a  ,    _a      =  10, 0.0
      scheduler.set_timesteps(A       )

      _a      =  self.dummy_model()
      _a      =  self.dummy_sample_deter
      _a      =  self.dummy_sample_deter + 0.1
      _a      =  self.dummy_sample_deter - 0.1

      _a      =  samplea.shape[0]
      _a      =  torch.stack([samplea, samplea, samplea] ,   dim=0       )
      _a      =  torch.arange(A       )[0:3, None].repeat(1 ,   A       )

      _a      =  model(samples.flatten(0 ,   1       ) ,   timesteps.flatten(0 ,   1       )       )
      _a      =  scheduler.batch_step_no_noise(A ,   timesteps.flatten(0 ,   1       ) ,   samples.flatten(0 ,   1       ) ,   A       )

      _a      =  torch.sum(torch.abs(A       )       )
      _a      =  torch.mean(torch.abs(A       )       )

      assert abs(result_sum.item() - 1147.7904       ) < 1E-2
      assert abs(result_mean.item() - 0.4982       ) < 1E-3



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


      """simple docstring"""



      _a      =  self.full_loop()

      _a      =  torch.sum(torch.abs(A       )       )
      _a      =  torch.mean(torch.abs(A       )       )

      assert abs(result_sum.item() - 172.0067       ) < 1E-2
      assert abs(result_mean.item() - 0.223967       ) < 1E-3



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


      """simple docstring"""



      _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() - 52.5302       ) < 1E-2
      assert abs(result_mean.item() - 0.0684       ) < 1E-3



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


      """simple docstring"""



      _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() - 149.8295       ) < 1E-2
      assert abs(result_mean.item() - 0.1951       ) < 1E-3



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


      """simple docstring"""



      _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() - 149.0784       ) < 1E-2
      assert abs(result_mean.item() - 0.1941       ) < 1E-3








'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_		       =  logging.get_logger(__name__)

lowercase_		       =  {
    "vinvino02/glpn-kitti": "https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json",
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       str          =       'glpn'



 def __init__(self ,   A=3 ,   A=4 ,   A=[2, 2, 2, 2] ,   A=[8, 4, 2, 1] ,   A=[32, 64, 160, 256] ,   A=[7, 3, 3, 3] ,   A=[4, 2, 2, 2] ,   A=[1, 2, 5, 8] ,   A=[4, 4, 4, 4] ,   A="gelu" ,   A=0.0 ,   A=0.0 ,   A=0.02 ,   A=0.1 ,   A=1E-6 ,   A=64 ,   A=10 ,   A=-1 ,   **A ,   )    ->     Any:


      """simple docstring"""



      super().__init__(**A       )

      _a      =  num_channels
      _a      =  num_encoder_blocks
      _a      =  depths
      _a      =  sr_ratios
      _a      =  hidden_sizes
      _a      =  patch_sizes
      _a      =  strides
      _a      =  mlp_ratios
      _a      =  num_attention_heads
      _a      =  hidden_act
      _a      =  hidden_dropout_prob
      _a      =  attention_probs_dropout_prob
      _a      =  initializer_range
      _a      =  drop_path_rate
      _a      =  layer_norm_eps
      _a      =  decoder_hidden_size
      _a      =  max_depth
      _a      =  head_in_index



