import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest







class __snake_case				(						lowerCamelCase_		):
   lowerCAmelCase_														=							(DDIMParallelScheduler,)
   lowerCAmelCase_														=							(("eta", 0.0), ("num_inference_steps", 50))




   def 	__a						(    self			:				List[str]       ,	**_lowercase			:				Dict      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      {
         """num_train_timesteps""": 10_00,
         """beta_start""": 0.00_01,
         """beta_end""": 0.02,
         """beta_schedule""": """linear""",
         """clip_sample""": True,
     }

     config.update(**_lowercase      )
     return config




   def 	__a						(    self			:				Union[str, Any]       ,	**_lowercase			:				Tuple      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.scheduler_classes[0]
     SCREAMING_SNAKE_CASE__										=      self.get_scheduler_config(**_lowercase      )
     SCREAMING_SNAKE_CASE__										=      scheduler_class(**_lowercase      )

     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      10, 0.0

     SCREAMING_SNAKE_CASE__										=      self.dummy_model()
     SCREAMING_SNAKE_CASE__										=      self.dummy_sample_deter

     scheduler.set_timesteps(_lowercase      )

     for t in scheduler.timesteps:
       SCREAMING_SNAKE_CASE__										=      model(_lowercase       ,	_lowercase      )
       SCREAMING_SNAKE_CASE__										=      scheduler.step(_lowercase       ,	_lowercase       ,	_lowercase       ,	_lowercase      ).prev_sample

     return sample




   def 	__a						(    self			:				Union[str, Any]      ):



     """simple docstring"""






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




   def 	__a						(    self			:				List[Any]      ):



     """simple docstring"""






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

     SCREAMING_SNAKE_CASE__										=      self.scheduler_classes[0]
     SCREAMING_SNAKE_CASE__										=      self.get_scheduler_config(steps_offset=1      )
     SCREAMING_SNAKE_CASE__										=      scheduler_class(**_lowercase      )
     scheduler.set_timesteps(5      )
     assert torch.equal(scheduler.timesteps       ,	torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]      )      )




   def 	__a						(    self			:				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 	__a						(    self			:				Union[str, Any]      ):



     """simple docstring"""






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




   def 	__a						(    self			:				Optional[Any]      ):



     """simple docstring"""






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




   def 	__a						(    self			:				Dict      ):



     """simple docstring"""






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




   def 	__a						(    self			:				str      ):



     """simple docstring"""






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




   def 	__a						(    self			:				List[str]      ):



     """simple docstring"""






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




   def 	__a						(    self			:				Union[str, Any]      ):



     """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 	__a						(    self			:				Tuple      ):



     """simple docstring"""






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




   def 	__a						(    self			:				List[Any]      ):



     """simple docstring"""






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




   def 	__a						(    self			:				Any      ):



     """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 	__a						(    self			:				Any      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.scheduler_classes[0]
     SCREAMING_SNAKE_CASE__										=      self.get_scheduler_config()
     SCREAMING_SNAKE_CASE__										=      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(4_20       ,	4_00      ) - 0.1_47_71      )      ) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(9_80       ,	9_60      ) - 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(4_87       ,	4_86      ) - 0.0_09_79      )      ) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(9_99       ,	9_98      ) - 0.02      )      ) < 1E-5




   def 	__a						(    self			:				Any      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.scheduler_classes[0]
     SCREAMING_SNAKE_CASE__										=      self.get_scheduler_config()
     SCREAMING_SNAKE_CASE__										=      scheduler_class(**_lowercase      )

     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      10, 0.0
     scheduler.set_timesteps(_lowercase      )

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

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

     SCREAMING_SNAKE_CASE__										=      model(samples.flatten(0       ,	1      )       ,	timesteps.flatten(0       ,	1      )      )
     SCREAMING_SNAKE_CASE__										=      scheduler.batch_step_no_noise(_lowercase       ,	timesteps.flatten(0       ,	1      )       ,	samples.flatten(0       ,	1      )       ,	_lowercase      )

     SCREAMING_SNAKE_CASE__										=      torch.sum(torch.abs(_lowercase      )      )
     SCREAMING_SNAKE_CASE__										=      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 	__a						(    self			:				Dict      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.full_loop()

     SCREAMING_SNAKE_CASE__										=      torch.sum(torch.abs(_lowercase      )      )
     SCREAMING_SNAKE_CASE__										=      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 	__a						(    self			:				Optional[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.full_loop(prediction_type="""v_prediction"""      )

     SCREAMING_SNAKE_CASE__										=      torch.sum(torch.abs(_lowercase      )      )
     SCREAMING_SNAKE_CASE__										=      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 	__a						(    self			:				Union[str, Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.full_loop(set_alpha_to_one=_lowercase       ,	beta_start=0.01      )
     SCREAMING_SNAKE_CASE__										=      torch.sum(torch.abs(_lowercase      )      )
     SCREAMING_SNAKE_CASE__										=      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 	__a						(    self			:				List[str]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.full_loop(set_alpha_to_one=_lowercase       ,	beta_start=0.01      )
     SCREAMING_SNAKE_CASE__										=      torch.sum(torch.abs(_lowercase      )      )
     SCREAMING_SNAKE_CASE__										=      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

import warnings

from ..trainer import Trainer
from ..utils import logging


__lowerCamelCase			: List[Any]							   =		logging.get_logger(__name__)







class __snake_case				(						lowerCamelCase_		):




   def __init__(    self			:				Tuple       ,	_lowercase			:				Optional[int]=None       ,	**_lowercase			:				List[Any]      ):



     """simple docstring"""






     warnings.warn(
         """`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` """
         """instead."""       ,	_lowercase       ,	)
     super().__init__(args=_lowercase       ,	**_lowercase      )
