



"""simple docstring"""





import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest




class       lowercase__      (   A__     ):
							'''simple docstring'''







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






							def 							lowerCamelCase_ (	self						,							**snake_case    )      ->				Optional[int]:
									_UpperCAmelCase							=		{
									    'num_train_timesteps': 1000,
									    'beta_start': 0.0001,
									    'beta_end': 0.02,
									    'beta_schedule': 'linear',
									    'clip_sample': True,
									}

									config.update(**_lowerCamelCase    )
									return config






							def 							lowerCamelCase_ (	self						,							**snake_case    )      ->				List[Any]:
									_UpperCAmelCase							=		self.scheduler_classes[0]
									_UpperCAmelCase							=		self.get_scheduler_config(**_lowerCamelCase    )
									_UpperCAmelCase							=		scheduler_class(**_lowerCamelCase    )

									_UpperCAmelCase							=		10, 0.0

									_UpperCAmelCase							=		self.dummy_model()
									_UpperCAmelCase							=		self.dummy_sample_deter

									scheduler.set_timesteps(_lowerCamelCase    )

									for t in scheduler.timesteps:
											_UpperCAmelCase							=		model(_lowerCamelCase						,							_lowerCamelCase    )
											_UpperCAmelCase							=		scheduler.step(_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase    ).prev_sample

									return sample






							def 							lowerCamelCase_ (	self    )      ->				Tuple:



									for timesteps in [100, 500, 1000]:
											self.check_over_configs(num_train_timesteps=_lowerCamelCase    )






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



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

									_UpperCAmelCase							=		self.scheduler_classes[0]
									_UpperCAmelCase							=		self.get_scheduler_config(steps_offset=1    )
									_UpperCAmelCase							=		scheduler_class(**_lowerCamelCase    )
									scheduler.set_timesteps(5    )
									assert torch.equal(scheduler.timesteps						,							torch.LongTensor([801, 601, 401, 201, 1]    )    )






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



									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=_lowerCamelCase						,							beta_end=_lowerCamelCase    )






							def 							lowerCamelCase_ (	self    )      ->				str:



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






							def 							lowerCamelCase_ (	self    )      ->				Tuple:



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






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



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






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



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






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



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






							def 							lowerCamelCase_ (	self    )      ->				List[Any]:
									self.check_over_configs(thresholding=_lowerCamelCase    )
									for threshold in [0.5, 1.0, 2.0]:
											for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(
													    thresholding=_lowerCamelCase						,							prediction_type=_lowerCamelCase						,							sample_max_value=_lowerCamelCase						,							)






							def 							lowerCamelCase_ (	self    )      ->				Tuple:



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






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



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






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



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






							def 							lowerCamelCase_ (	self    )      ->				Tuple:
									_UpperCAmelCase							=		self.scheduler_classes[0]
									_UpperCAmelCase							=		self.get_scheduler_config()
									_UpperCAmelCase							=		scheduler_class(**_lowerCamelCase    )

									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 							lowerCamelCase_ (	self    )      ->				Optional[int]:
									_UpperCAmelCase							=		self.scheduler_classes[0]
									_UpperCAmelCase							=		self.get_scheduler_config()
									_UpperCAmelCase							=		scheduler_class(**_lowerCamelCase    )

									_UpperCAmelCase							=		10, 0.0
									scheduler.set_timesteps(_lowerCamelCase    )

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

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

									_UpperCAmelCase							=		model(samples.flatten(0						,							1    )						,							timesteps.flatten(0						,							1    )    )
									_UpperCAmelCase							=		scheduler.batch_step_no_noise(_lowerCamelCase						,							timesteps.flatten(0						,							1    )						,							samples.flatten(0						,							1    )						,							_lowerCamelCase    )

									_UpperCAmelCase							=		torch.sum(torch.abs(_lowerCamelCase    )    )
									_UpperCAmelCase							=		torch.mean(torch.abs(_lowerCamelCase    )    )

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






							def 							lowerCamelCase_ (	self    )      ->				Optional[Any]:
									_UpperCAmelCase							=		self.full_loop()

									_UpperCAmelCase							=		torch.sum(torch.abs(_lowerCamelCase    )    )
									_UpperCAmelCase							=		torch.mean(torch.abs(_lowerCamelCase    )    )

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






							def 							lowerCamelCase_ (	self    )      ->				Any:
									_UpperCAmelCase							=		self.full_loop(prediction_type='v_prediction'    )

									_UpperCAmelCase							=		torch.sum(torch.abs(_lowerCamelCase    )    )
									_UpperCAmelCase							=		torch.mean(torch.abs(_lowerCamelCase    )    )

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






							def 							lowerCamelCase_ (	self    )      ->				List[str]:
									_UpperCAmelCase							=		self.full_loop(set_alpha_to_one=_lowerCamelCase						,							beta_start=0.01    )
									_UpperCAmelCase							=		torch.sum(torch.abs(_lowerCamelCase    )    )
									_UpperCAmelCase							=		torch.mean(torch.abs(_lowerCamelCase    )    )

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






							def 							lowerCamelCase_ (	self    )      ->				Any:
									_UpperCAmelCase							=		self.full_loop(set_alpha_to_one=_lowerCamelCase						,							beta_start=0.01    )
									_UpperCAmelCase							=		torch.sum(torch.abs(_lowerCamelCase    )    )
									_UpperCAmelCase							=		torch.mean(torch.abs(_lowerCamelCase    )    )

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




"""simple docstring"""






from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__SCREAMING_SNAKE_CASE							=     {
    'configuration_mctct': ['MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MCTCTConfig'],
    'feature_extraction_mctct': ['MCTCTFeatureExtractor'],
    'processing_mctct': ['MCTCTProcessor'],
}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __SCREAMING_SNAKE_CASE							=     [
     'MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'MCTCTForCTC',
     'MCTCTModel',
     'MCTCTPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
 from .feature_extraction_mctct import MCTCTFeatureExtractor
 from .processing_mctct import MCTCTProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
 import sys

 __SCREAMING_SNAKE_CASE							=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
