'''simple docstring'''




import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class 		__A       (__magic_name__   ):
       snake_case       :int				    =					(DDIMParallelScheduler,)
       snake_case       :Tuple				    =					(("eta", 0.0), ("num_inference_steps", 50))






       def 			_snake_case						(      self				,     **UpperCamelCase_      ):
         __UpperCAmelCase  :				Dict       				=		{
             "num_train_timesteps": 10_00,
             "beta_start": 0.0_0_0_1,
             "beta_end": 0.0_2,
             "beta_schedule": "linear",
             "clip_sample": True,
         }

         config.update(**UpperCamelCase_      )
         return config






       def 			_snake_case						(      self				,     **UpperCamelCase_      ):
         __UpperCAmelCase  :				Union[str, Any]       				=		self.scheduler_classes[0]
         __UpperCAmelCase  :				Optional[Any]       				=		self.get_scheduler_config(**UpperCamelCase_      )
         __UpperCAmelCase  :				int       				=		scheduler_class(**UpperCamelCase_      )

         __UpperCAmelCase  :				Any       				=		10, 0.0

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

         scheduler.set_timesteps(UpperCamelCase_      )

         for t in scheduler.timesteps:
           __UpperCAmelCase  :				Tuple       				=		model(UpperCamelCase_				,     UpperCamelCase_      )
           __UpperCAmelCase  :				Optional[Any]       				=		scheduler.step(UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_      ).prev_sample

         return sample






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






       def 			_snake_case						(      self      ):
         for steps_offset in [0, 1]:
           self.check_over_configs(steps_offset=UpperCamelCase_      )

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






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






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






       def 			_snake_case						(      self      ):
         for clip_sample in [True, False]:
           self.check_over_configs(clip_sample=UpperCamelCase_      )






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






       def 			_snake_case						(      self      ):
         for rescale_betas_zero_snr in [True, False]:
           self.check_over_configs(rescale_betas_zero_snr=UpperCamelCase_      )






       def 			_snake_case						(      self      ):
         self.check_over_configs(thresholding=UpperCamelCase_      )
         for threshold in [0.5, 1.0, 2.0]:
           for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(
                 thresholding=UpperCamelCase_				,     prediction_type=UpperCamelCase_				,     sample_max_value=UpperCamelCase_				,     )






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






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






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






       def 			_snake_case						(      self      ):
         __UpperCAmelCase  :				List[Any]       				=		self.scheduler_classes[0]
         __UpperCAmelCase  :				Dict       				=		self.get_scheduler_config()
         __UpperCAmelCase  :				Optional[Any]       				=		scheduler_class(**UpperCamelCase_      )

         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_4_7_7_1      )      ) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(9_80				,     9_60      ) - 0.3_2_4_6_0      )      ) < 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_0_9_7_9      )      ) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(9_99				,     9_98      ) - 0.0_2      )      ) < 1E-5






       def 			_snake_case						(      self      ):
         __UpperCAmelCase  :				Union[str, Any]       				=		self.scheduler_classes[0]
         __UpperCAmelCase  :				Optional[int]       				=		self.get_scheduler_config()
         __UpperCAmelCase  :				List[str]       				=		scheduler_class(**UpperCamelCase_      )

         __UpperCAmelCase  :				int       				=		10, 0.0
         scheduler.set_timesteps(UpperCamelCase_      )

         __UpperCAmelCase  :				Any       				=		self.dummy_model()
         __UpperCAmelCase  :				str       				=		self.dummy_sample_deter
         __UpperCAmelCase  :				Optional[Any]       				=		self.dummy_sample_deter + 0.1
         __UpperCAmelCase  :				List[str]       				=		self.dummy_sample_deter - 0.1

         __UpperCAmelCase  :				str       				=		samplea.shape[0]
         __UpperCAmelCase  :				Tuple       				=		torch.stack([samplea, samplea, samplea]				,     dim=0      )
         __UpperCAmelCase  :				List[Any]       				=		torch.arange(UpperCamelCase_      )[0:3, None].repeat(1				,     UpperCamelCase_      )

         __UpperCAmelCase  :				List[str]       				=		model(samples.flatten(0				,     1      )				,     timesteps.flatten(0				,     1      )      )
         __UpperCAmelCase  :				Tuple       				=		scheduler.batch_step_no_noise(UpperCamelCase_				,     timesteps.flatten(0				,     1      )				,     samples.flatten(0				,     1      )				,     UpperCamelCase_      )

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

         assert abs(result_sum.item() - 11_47.79_04      ) < 1E-2
         assert abs(result_mean.item() - 0.4_9_8_2      ) < 1E-3






       def 			_snake_case						(      self      ):
         __UpperCAmelCase  :				str       				=		self.full_loop()

         __UpperCAmelCase  :				Optional[Any]       				=		torch.sum(torch.abs(UpperCamelCase_      )      )
         __UpperCAmelCase  :				List[Any]       				=		torch.mean(torch.abs(UpperCamelCase_      )      )

         assert abs(result_sum.item() - 1_72.00_67      ) < 1E-2
         assert abs(result_mean.item() - 0.2_2_3_9_6_7      ) < 1E-3






       def 			_snake_case						(      self      ):
         __UpperCAmelCase  :				List[Any]       				=		self.full_loop(prediction_type="v_prediction"      )

         __UpperCAmelCase  :				Any       				=		torch.sum(torch.abs(UpperCamelCase_      )      )
         __UpperCAmelCase  :				List[Any]       				=		torch.mean(torch.abs(UpperCamelCase_      )      )

         assert abs(result_sum.item() - 52.53_02      ) < 1E-2
         assert abs(result_mean.item() - 0.0_6_8_4      ) < 1E-3






       def 			_snake_case						(      self      ):
         # We specify different beta, so that the first alpha is 0.99
         __UpperCAmelCase  :				List[Any]       				=		self.full_loop(set_alpha_to_one=UpperCamelCase_				,     beta_start=0.0_1      )
         __UpperCAmelCase  :				List[str]       				=		torch.sum(torch.abs(UpperCamelCase_      )      )
         __UpperCAmelCase  :				int       				=		torch.mean(torch.abs(UpperCamelCase_      )      )

         assert abs(result_sum.item() - 1_49.82_95      ) < 1E-2
         assert abs(result_mean.item() - 0.1_9_5_1      ) < 1E-3







       def 			_snake_case						(      self      ):
         # We specify different beta, so that the first alpha is 0.99
         __UpperCAmelCase  :				Optional[int]       				=		self.full_loop(set_alpha_to_one=UpperCamelCase_				,     beta_start=0.0_1      )
         __UpperCAmelCase  :				Tuple       				=		torch.sum(torch.abs(UpperCamelCase_      )      )
         __UpperCAmelCase  :				Dict       				=		torch.mean(torch.abs(UpperCamelCase_      )      )

         assert abs(result_sum.item() - 1_49.07_84      ) < 1E-2
         assert abs(result_mean.item() - 0.1_9_4_1      ) < 1E-3







'''simple docstring'''




from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments

def _lowercase	(      )					->       Dict:





       """simple docstring"""


       __UpperCAmelCase  :				str       				=		HfArgumentParser(lowerCamelCase__						)
       __UpperCAmelCase  :				Optional[Any]       				=		parser.parse_args_into_dataclasses()[0]
       __UpperCAmelCase  :				Any       				=		TensorFlowBenchmark(args=lowerCamelCase__						)
       try:
              __UpperCAmelCase  :				List[Any]       				=		parser.parse_args_into_dataclasses()[0]
       except ValueError as e:
              __UpperCAmelCase  :				str       				=		"Arg --no_{0} is no longer used, please use --no-{0} instead."
              __UpperCAmelCase  :				Tuple       				=		" ".join(str(lowerCamelCase__						).split(" "						)[:-1]						)
              __UpperCAmelCase  :				Any       				=		""
              __UpperCAmelCase  :				List[Any]       				=		eval(str(lowerCamelCase__						).split(" "						)[-1]						)
              __UpperCAmelCase  :				Optional[int]       				=		[]
              for arg in depreciated_args:
                     # arg[2:] removes '--'
                     if arg[2:] in TensorFlowBenchmark.deprecated_args:
                            # arg[5:] removes '--no_'
                            full_error_msg += arg_error_msg.format(arg[5:]						)
                     else:
                            wrong_args.append(lowerCamelCase__						)
              if len(lowerCamelCase__						) > 0:
                     __UpperCAmelCase  :				Union[str, Any]       				=		full_error_msg + begin_error_msg + str(lowerCamelCase__						)
              raise ValueError(lowerCamelCase__						)
       benchmark.run()


if __name__ == "__main__":
   main()






