






"""simple docstring"""






import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest




class 							lowerCAmelCase__				(			UpperCAmelCase__ ):






      '''simple docstring'''

      __UpperCamelCase	 =  (DDIMParallelScheduler,)
      __UpperCamelCase	 =  (("eta", 0.0), ("num_inference_steps", 5_0))
      def _SCREAMING_SNAKE_CASE (						self     :  Union[str, Any]						,	**lowercase_     :  Optional[int]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Any    						=		{
              '''num_train_timesteps''': 1000,
              '''beta_start''': 0.00_01,
              '''beta_end''': 0.02,
              '''beta_schedule''': '''linear''',
              '''clip_sample''': True,
          }

          config.update(**lowercase_)
          return config
      def _SCREAMING_SNAKE_CASE (						self     :  str						,	**lowercase_     :  Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Tuple    						=		self.scheduler_classes[0]
          SCREAMING_SNAKE_CASE_      :  str    						=		self.get_scheduler_config(**lowercase_)
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		scheduler_class(**lowercase_)

          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  int    						=		10, 0.0

          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		self.dummy_model()
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		self.dummy_sample_deter

          scheduler.set_timesteps(lowercase_)

          for t in scheduler.timesteps:
              SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		model(lowercase_						,	lowercase_)
              SCREAMING_SNAKE_CASE_      :  Any    						=		scheduler.step(lowercase_						,	lowercase_						,	lowercase_						,	lowercase_).prev_sample

          return sample
      def _SCREAMING_SNAKE_CASE (						self     :  str):




          '''simple docstring'''


          for timesteps in [100, 500, 1000]:
              self.check_over_configs(num_train_timesteps=lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]):




          '''simple docstring'''


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

          SCREAMING_SNAKE_CASE_      :  Tuple    						=		self.scheduler_classes[0]
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		self.get_scheduler_config(steps_offset=1)
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		scheduler_class(**lowercase_)
          scheduler.set_timesteps(5)
          assert torch.equal(scheduler.timesteps						,	torch.LongTensor([801, 601, 401, 201, 1]))
      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]):




          '''simple docstring'''


          for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]						,	[0.0_02, 0.02, 0.2, 2]):
              self.check_over_configs(beta_start=lowercase_						,	beta_end=lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Dict):




          '''simple docstring'''


          for schedule in ["linear", "squaredcos_cap_v2"]:
              self.check_over_configs(beta_schedule=lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  str):




          '''simple docstring'''


          for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  int):




          '''simple docstring'''


          for clip_sample in [True, False]:
              self.check_over_configs(clip_sample=lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]):




          '''simple docstring'''


          for timestep_spacing in ["trailing", "leading"]:
              self.check_over_configs(timestep_spacing=lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          for rescale_betas_zero_snr in [True, False]:
              self.check_over_configs(rescale_betas_zero_snr=lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]):




          '''simple docstring'''


          self.check_over_configs(thresholding=lowercase_)
          for threshold in [0.5, 1.0, 2.0]:
              for prediction_type in ["epsilon", "v_prediction"]:
                  self.check_over_configs(
                      thresholding=lowercase_						,	prediction_type=lowercase_						,	sample_max_value=lowercase_						,	)
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]):




          '''simple docstring'''


          for t in [1, 10, 49]:
              self.check_over_forward(time_step=lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  str):




          '''simple docstring'''


          for t, num_inference_steps in zip([1, 10, 50]						,	[10, 50, 500]):
              self.check_over_forward(time_step=lowercase_						,	num_inference_steps=lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          for t, eta in zip([1, 10, 49]						,	[0.0, 0.5, 1.0]):
              self.check_over_forward(time_step=lowercase_						,	eta=lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  str    						=		self.scheduler_classes[0]
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		self.get_scheduler_config()
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		scheduler_class(**lowercase_)

          assert torch.sum(torch.abs(scheduler._get_variance(0						,	0) - 0.0)) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(420						,	400) - 0.1_47_71)) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(980						,	960) - 0.3_24_60)) < 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) - 0.0_09_79)) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(999						,	998) - 0.02)) < 1e-5
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]):




          '''simple docstring'''


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

          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Dict    						=		10, 0.0
          scheduler.set_timesteps(lowercase_)

          SCREAMING_SNAKE_CASE_      :  Any    						=		self.dummy_model()
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		self.dummy_sample_deter
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		self.dummy_sample_deter + 0.1
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		self.dummy_sample_deter - 0.1

          SCREAMING_SNAKE_CASE_      :  Dict    						=		samplea.shape[0]
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		torch.stack([samplea, samplea, samplea]						,	dim=0)
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		torch.arange(lowercase_)[0:3, None].repeat(1						,	lowercase_)

          SCREAMING_SNAKE_CASE_      :  str    						=		model(samples.flatten(0						,	1)						,	timesteps.flatten(0						,	1))
          SCREAMING_SNAKE_CASE_      :  Any    						=		scheduler.batch_step_no_noise(lowercase_						,	timesteps.flatten(0						,	1)						,	samples.flatten(0						,	1)						,	lowercase_)

          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		torch.sum(torch.abs(lowercase_))
          SCREAMING_SNAKE_CASE_      :  int    						=		torch.mean(torch.abs(lowercase_))

          assert abs(result_sum.item() - 11_47.79_04) < 1e-2
          assert abs(result_mean.item() - 0.49_82) < 1e-3
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  str    						=		self.full_loop()

          SCREAMING_SNAKE_CASE_      :  Dict    						=		torch.sum(torch.abs(lowercase_))
          SCREAMING_SNAKE_CASE_      :  int    						=		torch.mean(torch.abs(lowercase_))

          assert abs(result_sum.item() - 1_72.00_67) < 1e-2
          assert abs(result_mean.item() - 0.22_39_67) < 1e-3
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  str    						=		self.full_loop(prediction_type='''v_prediction''')

          SCREAMING_SNAKE_CASE_      :  Dict    						=		torch.sum(torch.abs(lowercase_))
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		torch.mean(torch.abs(lowercase_))

          assert abs(result_sum.item() - 52.53_02) < 1e-2
          assert abs(result_mean.item() - 0.06_84) < 1e-3
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]):




          '''simple docstring'''


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

          assert abs(result_sum.item() - 1_49.82_95) < 1e-2
          assert abs(result_mean.item() - 0.19_51) < 1e-3



      def _SCREAMING_SNAKE_CASE (						self     :  str):




          '''simple docstring'''


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

          assert abs(result_sum.item() - 1_49.07_84) < 1e-2
          assert abs(result_mean.item() - 0.19_41) < 1e-3










"""simple docstring"""






import random
from typing import Any



def 					_A			(__a							) ->       list[Any]:
    """simple docstring"""






    for _ in range(len(__a							)							):
        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		random.randint(0    ,				len(__a							) - 1							)
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		random.randint(0    ,				len(__a							) - 1							)
        SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		data[b], data[a]
    return data


if __name__ == "__main__":
  UpperCAmelCase_   : Dict   			= [0, 1, 2, 3, 4, 5, 6, 7]
  UpperCAmelCase_   : Dict   			= ["""python""", """says""", """hello""", """!"""]
  print("""Fisher-Yates Shuffle:""")
  print("""List""", integers, strings)
  print("""FY Shuffle""", fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))


