
"""simple docstring"""




import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest







class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							(DDIMParallelScheduler,)
     _A								=							(('eta', 0.0), ('num_inference_steps', 50))



     def  lowerCAmelCase__       (      self ,			**__UpperCamelCase  )->					Optional[int]:
          UpperCAmelCase__							: List[Any] 							=						{
              "num_train_timesteps": 10_00,
              "beta_start": 0.0001,
              "beta_end": 0.02,
              "beta_schedule": "linear",
              "clip_sample": True,
          }

          config.update(**__UpperCamelCase  )
          return config



     def  lowerCAmelCase__       (      self ,			**__UpperCamelCase  )->					int:
          UpperCAmelCase__							: Optional[Any] 							=						self.scheduler_classes[0]
          UpperCAmelCase__							: Union[str, Any] 							=						self.get_scheduler_config(**__UpperCamelCase  )
          UpperCAmelCase__							: List[str] 							=						scheduler_class(**__UpperCamelCase  )

          UpperCAmelCase__							: Optional[int] 							=						10, 0.0

          UpperCAmelCase__							: Optional[int] 							=						self.dummy_model()
          UpperCAmelCase__							: int 							=						self.dummy_sample_deter

          scheduler.set_timesteps(__UpperCamelCase  )

          for t in scheduler.timesteps:
               UpperCAmelCase__							: List[Any] 							=						model(__UpperCamelCase ,			__UpperCamelCase  )
               UpperCAmelCase__							: Any 							=						scheduler.step(__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  ).prev_sample

          return sample



     def  lowerCAmelCase__       (      self  )->					str:
          for timesteps in [1_00, 5_00, 10_00]:
               self.check_over_configs(num_train_timesteps=__UpperCamelCase  )



     def  lowerCAmelCase__       (      self  )->					int:
          for steps_offset in [0, 1]:
               self.check_over_configs(steps_offset=__UpperCamelCase  )

          UpperCAmelCase__							: List[str] 							=						self.scheduler_classes[0]
          UpperCAmelCase__							: int 							=						self.get_scheduler_config(steps_offset=1  )
          UpperCAmelCase__							: Optional[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  lowerCAmelCase__       (      self  )->					List[Any]:
          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=__UpperCamelCase ,			beta_end=__UpperCamelCase  )



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



     def  lowerCAmelCase__       (      self  )->					Dict:
          for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(prediction_type=__UpperCamelCase  )



     def  lowerCAmelCase__       (      self  )->					List[Any]:
          for clip_sample in [True, False]:
               self.check_over_configs(clip_sample=__UpperCamelCase  )



     def  lowerCAmelCase__       (      self  )->					Any:
          for timestep_spacing in ["trailing", "leading"]:
               self.check_over_configs(timestep_spacing=__UpperCamelCase  )



     def  lowerCAmelCase__       (      self  )->					Optional[int]:
          for rescale_betas_zero_snr in [True, False]:
               self.check_over_configs(rescale_betas_zero_snr=__UpperCamelCase  )



     def  lowerCAmelCase__       (      self  )->					Tuple:
          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  lowerCAmelCase__       (      self  )->					Optional[Any]:
          for t in [1, 10, 49]:
               self.check_over_forward(time_step=__UpperCamelCase  )



     def  lowerCAmelCase__       (      self  )->					Any:
          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  lowerCAmelCase__       (      self  )->					Optional[Any]:
          for t, eta in zip([1, 10, 49] ,			[0.0, 0.5, 1.0]  ):
               self.check_over_forward(time_step=__UpperCamelCase ,			eta=__UpperCamelCase  )



     def  lowerCAmelCase__       (      self  )->					Union[str, Any]:
          UpperCAmelCase__							: Optional[int] 							=						self.scheduler_classes[0]
          UpperCAmelCase__							: Dict 							=						self.get_scheduler_config()
          UpperCAmelCase__							: List[str] 							=						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_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  lowerCAmelCase__       (      self  )->					Tuple:
          UpperCAmelCase__							: Optional[Any] 							=						self.scheduler_classes[0]
          UpperCAmelCase__							: List[str] 							=						self.get_scheduler_config()
          UpperCAmelCase__							: str 							=						scheduler_class(**__UpperCamelCase  )

          UpperCAmelCase__							: Dict 							=						10, 0.0
          scheduler.set_timesteps(__UpperCamelCase  )

          UpperCAmelCase__							: str 							=						self.dummy_model()
          UpperCAmelCase__							: Tuple 							=						self.dummy_sample_deter
          UpperCAmelCase__							: Dict 							=						self.dummy_sample_deter + 0.1
          UpperCAmelCase__							: Optional[Any] 							=						self.dummy_sample_deter - 0.1

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

          UpperCAmelCase__							: List[Any] 							=						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__							: Tuple 							=						torch.sum(torch.abs(__UpperCamelCase  )  )
          UpperCAmelCase__							: Union[str, Any] 							=						torch.mean(torch.abs(__UpperCamelCase  )  )

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



     def  lowerCAmelCase__       (      self  )->					Dict:
          UpperCAmelCase__							: Tuple 							=						self.full_loop()

          UpperCAmelCase__							: Tuple 							=						torch.sum(torch.abs(__UpperCamelCase  )  )
          UpperCAmelCase__							: Any 							=						torch.mean(torch.abs(__UpperCamelCase  )  )

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



     def  lowerCAmelCase__       (      self  )->					Dict:
          UpperCAmelCase__							: str 							=						self.full_loop(prediction_type="v_prediction"  )

          UpperCAmelCase__							: List[str] 							=						torch.sum(torch.abs(__UpperCamelCase  )  )
          UpperCAmelCase__							: List[Any] 							=						torch.mean(torch.abs(__UpperCamelCase  )  )

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



     def  lowerCAmelCase__       (      self  )->					List[Any]:
          # We specify different beta, so that the first alpha is 0.99
          UpperCAmelCase__							: Tuple 							=						self.full_loop(set_alpha_to_one=__UpperCamelCase ,			beta_start=0.01  )
          UpperCAmelCase__							: Union[str, Any] 							=						torch.sum(torch.abs(__UpperCamelCase  )  )
          UpperCAmelCase__							: Dict 							=						torch.mean(torch.abs(__UpperCamelCase  )  )

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



     def  lowerCAmelCase__       (      self  )->					Tuple:
          # We specify different beta, so that the first alpha is 0.99
          UpperCAmelCase__							: str 							=						self.full_loop(set_alpha_to_one=__UpperCamelCase ,			beta_start=0.01  )
          UpperCAmelCase__							: Optional[int] 							=						torch.sum(torch.abs(__UpperCamelCase  )  )
          UpperCAmelCase__							: int 							=						torch.mean(torch.abs(__UpperCamelCase  )  )

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





"""simple docstring"""




import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin







class   _lowercase						(       unittest.TestCase	,   lowerCAmelCase_				):


     '''simple docstring'''





     def  lowerCAmelCase__       (      self  )->					Dict:
          UpperCAmelCase__							: Optional[Any] 							=						load_tool("text-classification"  )
          self.tool.setup()
          UpperCAmelCase__							: List[str] 							=						load_tool("text-classification" ,			remote=__UpperCamelCase  )



     def  lowerCAmelCase__       (      self  )->					Union[str, Any]:
          UpperCAmelCase__							: Dict 							=						self.tool("That's quite cool" ,			["positive", "negative"]  )
          self.assertEqual(__UpperCamelCase ,			"positive"  )



     def  lowerCAmelCase__       (      self  )->					str:
          UpperCAmelCase__							: List[Any] 							=						self.remote_tool("That's quite cool" ,			["positive", "negative"]  )
          self.assertEqual(__UpperCamelCase ,			"positive"  )



     def  lowerCAmelCase__       (      self  )->					Optional[int]:
          UpperCAmelCase__							: Any 							=						self.tool(text="That's quite cool" ,			labels=["positive", "negative"]  )
          self.assertEqual(__UpperCamelCase ,			"positive"  )



     def  lowerCAmelCase__       (      self  )->					str:
          UpperCAmelCase__							: str 							=						self.remote_tool(text="That's quite cool" ,			labels=["positive", "negative"]  )
          self.assertEqual(__UpperCamelCase ,			"positive"  )



