


"""simple docstring"""






import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest





class      lowerCamelCase__   (     A_				):







					"""simple docstring"""


					__a	       =							(DDIMParallelScheduler,)
					__a	       =							(("eta", 0.0), ("num_inference_steps", 50))






					def lowerCamelCase__							(       self    : Tuple ,       **UpperCamelCase    : Optional[int]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Any      							=					{
												    "num_train_timesteps": 1_000,
												    "beta_start": 0.0001,
												    "beta_end": 0.02,
												    "beta_schedule": "linear",
												    "clip_sample": True,
												}

												config.update(**snake_case__  )
												return config






					def lowerCamelCase__							(       self    : Dict ,       **UpperCamelCase    : Optional[int]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[Any]      							=					self.scheduler_classes[0]
												__UpperCAmelCase   :						Any      							=					self.get_scheduler_config(**snake_case__  )
												__UpperCAmelCase   :						Any      							=					scheduler_class(**snake_case__  )

												__UpperCAmelCase   :						Union[str, Any]      							=					10, 0.0

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

												scheduler.set_timesteps(snake_case__  )

												for t in scheduler.timesteps:
																			__UpperCAmelCase   :						Optional[int]      							=					model(snake_case__ ,       snake_case__  )
																			__UpperCAmelCase   :						List[str]      							=					scheduler.step(snake_case__ ,       snake_case__ ,       snake_case__ ,       snake_case__  ).prev_sample

												return sample






					def lowerCamelCase__							(       self    : Union[str, Any]  ):
												'''simple docstring'''






												for timesteps in [100, 500, 1_000]:
																			self.check_over_configs(num_train_timesteps=snake_case__  )






					def lowerCamelCase__							(       self    : str  ):
												'''simple docstring'''






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

												__UpperCAmelCase   :						Optional[int]      							=					self.scheduler_classes[0]
												__UpperCAmelCase   :						Optional[int]      							=					self.get_scheduler_config(steps_offset=1  )
												__UpperCAmelCase   :						Union[str, Any]      							=					scheduler_class(**snake_case__  )
												scheduler.set_timesteps(5  )
												assert torch.equal(scheduler.timesteps ,       torch.LongTensor([801, 601, 401, 201, 1]  )  )






					def lowerCamelCase__							(       self    : int  ):
												'''simple docstring'''






												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=snake_case__ ,       beta_end=snake_case__  )






					def lowerCamelCase__							(       self    : Union[str, Any]  ):
												'''simple docstring'''






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






					def lowerCamelCase__							(       self    : str  ):
												'''simple docstring'''






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






					def lowerCamelCase__							(       self    : Optional[Any]  ):
												'''simple docstring'''






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






					def lowerCamelCase__							(       self    : List[Any]  ):
												'''simple docstring'''






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






					def lowerCamelCase__							(       self    : Optional[int]  ):
												'''simple docstring'''






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






					def lowerCamelCase__							(       self    : Optional[Any]  ):
												'''simple docstring'''






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






					def lowerCamelCase__							(       self    : Any  ):
												'''simple docstring'''






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






					def lowerCamelCase__							(       self    : Optional[int]  ):
												'''simple docstring'''






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






					def lowerCamelCase__							(       self    : List[str]  ):
												'''simple docstring'''






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






					def lowerCamelCase__							(       self    : Optional[Any]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Dict      							=					self.scheduler_classes[0]
												__UpperCAmelCase   :						Tuple      							=					self.get_scheduler_config()
												__UpperCAmelCase   :						Dict      							=					scheduler_class(**snake_case__  )

												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]  ):
												'''simple docstring'''






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

												__UpperCAmelCase   :						Any      							=					10, 0.0
												scheduler.set_timesteps(snake_case__  )

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

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

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

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

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






					def lowerCamelCase__							(       self    : Optional[Any]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[Any]      							=					self.full_loop()

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

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






					def lowerCamelCase__							(       self    : str  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Dict      							=					self.full_loop(prediction_type="""v_prediction"""  )

												__UpperCAmelCase   :						int      							=					torch.sum(torch.abs(snake_case__  )  )
												__UpperCAmelCase   :						Optional[int]      							=					torch.mean(torch.abs(snake_case__  )  )

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






					def lowerCamelCase__							(       self    : Any  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Dict      							=					self.full_loop(set_alpha_to_one=snake_case__ ,       beta_start=0.01  )
												__UpperCAmelCase   :						str      							=					torch.sum(torch.abs(snake_case__  )  )
												__UpperCAmelCase   :						Optional[Any]      							=					torch.mean(torch.abs(snake_case__  )  )

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






					def lowerCamelCase__							(       self    : int  ):
												'''simple docstring'''






												__UpperCAmelCase   :						int      							=					self.full_loop(set_alpha_to_one=snake_case__ ,       beta_start=0.01  )
												__UpperCAmelCase   :						Tuple      							=					torch.sum(torch.abs(snake_case__  )  )
												__UpperCAmelCase   :						List[Any]      							=					torch.mean(torch.abs(snake_case__  )  )

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


from typing import TYPE_CHECKING

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


__lowerCamelCase							=       {
    """configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
    """tokenization_biogpt""": ["""BioGptTokenizer"""],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase							=       [
       """BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """BioGptForCausalLM""",
       """BioGptForTokenClassification""",
       """BioGptForSequenceClassification""",
       """BioGptModel""",
       """BioGptPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
   from .tokenization_biogpt import BioGptTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_biogpt import (
          BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          BioGptForCausalLM,
          BioGptForSequenceClassification,
          BioGptForTokenClassification,
          BioGptModel,
          BioGptPreTrainedModel,
      )


else:
   import sys

   __lowerCamelCase							=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





