

import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest






class 		__magic_name__ (				snake_case							):
     UpperCamelCase_     :List[Any]									=	(DDIMScheduler,)
     UpperCamelCase_     :Any									=	(("""eta""", 0.0), ("""num_inference_steps""", 5_0))






     def      UpperCAmelCase_							(							self						,		**_lowercase     )->			Optional[Any]:
         UpperCamelCase_          =		{
             "num_train_timesteps": 1_000,
             "beta_start": 0.0_001,
             "beta_end": 0.02,
             "beta_schedule": "linear",
             "clip_sample": True,
         }

         config.update(**_lowercase     )
         return config






     def      UpperCAmelCase_							(							self						,		**_lowercase     )->			Optional[Any]:
         UpperCamelCase_          =		self.scheduler_classes[0]
         UpperCamelCase_          =		self.get_scheduler_config(**_lowercase     )
         UpperCamelCase_          =		scheduler_class(**_lowercase     )

         UpperCamelCase_   ,    UpperCamelCase_          =		10, 0.0

         UpperCamelCase_          =		self.dummy_model()
         UpperCamelCase_          =		self.dummy_sample_deter

         scheduler.set_timesteps(_lowercase     )

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

         return sample






     def      UpperCAmelCase_							(							self     )->			int:
         for timesteps in [100, 500, 1_000]:
             self.check_over_configs(num_train_timesteps=_lowercase     )






     def      UpperCAmelCase_							(							self     )->			str:
         for steps_offset in [0, 1]:
             self.check_over_configs(steps_offset=_lowercase     )

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






     def      UpperCAmelCase_							(							self     )->			Dict:
         for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]						,		[0.002, 0.02, 0.2, 2]     ):
             self.check_over_configs(beta_start=_lowercase						,		beta_end=_lowercase     )






     def      UpperCAmelCase_							(							self     )->			int:
         for schedule in ["linear", "squaredcos_cap_v2"]:
             self.check_over_configs(beta_schedule=_lowercase     )






     def      UpperCAmelCase_							(							self     )->			Tuple:
         for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=_lowercase     )






     def      UpperCAmelCase_							(							self     )->			Dict:
         for clip_sample in [True, False]:
             self.check_over_configs(clip_sample=_lowercase     )






     def      UpperCAmelCase_							(							self     )->			int:
         for timestep_spacing in ["trailing", "leading"]:
             self.check_over_configs(timestep_spacing=_lowercase     )






     def      UpperCAmelCase_							(							self     )->			str:
         for rescale_betas_zero_snr in [True, False]:
             self.check_over_configs(rescale_betas_zero_snr=_lowercase     )






     def      UpperCAmelCase_							(							self     )->			Any:
         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      UpperCAmelCase_							(							self     )->			Any:
         for t in [1, 10, 49]:
             self.check_over_forward(time_step=_lowercase     )






     def      UpperCAmelCase_							(							self     )->			Tuple:
         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      UpperCAmelCase_							(							self     )->			List[Any]:
         for t, eta in zip([1, 10, 49]						,		[0.0, 0.5, 1.0]     ):
             self.check_over_forward(time_step=_lowercase						,		eta=_lowercase     )






     def      UpperCAmelCase_							(							self     )->			Optional[Any]:
         UpperCamelCase_          =		self.scheduler_classes[0]
         UpperCamelCase_          =		self.get_scheduler_config()
         UpperCamelCase_          =		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     ) - 0.14_771     )     ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(980						,		960     ) - 0.32_460     )     ) < 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     ) - 0.00_979     )     ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(999						,		998     ) - 0.02     )     ) < 1e-5






     def      UpperCAmelCase_							(							self     )->			Tuple:
         UpperCamelCase_          =		self.full_loop()

         UpperCamelCase_          =		torch.sum(torch.abs(_lowercase     )     )
         UpperCamelCase_          =		torch.mean(torch.abs(_lowercase     )     )

         assert abs(result_sum.item() - 172.0_067     ) < 1e-2
         assert abs(result_mean.item() - 0.223_967     ) < 1e-3






     def      UpperCAmelCase_							(							self     )->			Any:
         UpperCamelCase_          =		self.full_loop(prediction_type="v_prediction"     )

         UpperCamelCase_          =		torch.sum(torch.abs(_lowercase     )     )
         UpperCamelCase_          =		torch.mean(torch.abs(_lowercase     )     )

         assert abs(result_sum.item() - 52.5_302     ) < 1e-2
         assert abs(result_mean.item() - 0.0_684     ) < 1e-3






     def      UpperCAmelCase_							(							self     )->			str:
         # We specify different beta, so that the first alpha is 0.99
         UpperCamelCase_          =		self.full_loop(set_alpha_to_one=_lowercase						,		beta_start=0.01     )
         UpperCamelCase_          =		torch.sum(torch.abs(_lowercase     )     )
         UpperCamelCase_          =		torch.mean(torch.abs(_lowercase     )     )

         assert abs(result_sum.item() - 149.8_295     ) < 1e-2
         assert abs(result_mean.item() - 0.1_951     ) < 1e-3






     def      UpperCAmelCase_							(							self     )->			List[str]:
         # We specify different beta, so that the first alpha is 0.99
         UpperCamelCase_          =		self.full_loop(set_alpha_to_one=_lowercase						,		beta_start=0.01     )
         UpperCamelCase_          =		torch.sum(torch.abs(_lowercase     )     )
         UpperCamelCase_          =		torch.mean(torch.abs(_lowercase     )     )

         assert abs(result_sum.item() - 149.0_784     ) < 1e-2
         assert abs(result_mean.item() - 0.1_941     ) < 1e-3



from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE						:int      =			{"""configuration_yoso""": ["""YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """YosoConfig"""]}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE						:List[Any]      =			[
     """YOSO_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """YosoForMaskedLM""",
     """YosoForMultipleChoice""",
     """YosoForQuestionAnswering""",
     """YosoForSequenceClassification""",
     """YosoForTokenClassification""",
     """YosoLayer""",
     """YosoModel""",
     """YosoPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_yoso import YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP, YosoConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_yoso import (
      YOSO_PRETRAINED_MODEL_ARCHIVE_LIST,
      YosoForMaskedLM,
      YosoForMultipleChoice,
      YosoForQuestionAnswering,
      YosoForSequenceClassification,
      YosoForTokenClassification,
      YosoLayer,
      YosoModel,
      YosoPreTrainedModel,
  )


else:
 import sys

 SCREAMING_SNAKE_CASE						:str      =			_LazyModule(__name__, globals()["""__file__"""], _import_structure)
