




import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class     A(		UpperCamelCase  ):



 '''simple docstring'''


 UpperCamelCase		  =		(EulerDiscreteScheduler,)
 UpperCamelCase		  =		10

 def 							a__					(							self   :    Dict		,				**A_   :    int    )  ->      Optional[Any]:

       """simple docstring"""






       lowerCamelCase_							=       {
           'num_train_timesteps': 1100,
           'beta_start': 0.0001,
           'beta_end': 0.02,
           'beta_schedule': 'linear',
       }

       config.update(**A_    )
       return config

 def 							a__					(							self   :    Tuple    )  ->      Dict:

       """simple docstring"""






       for timesteps in [10, 50, 100, 1000]:
             self.check_over_configs(num_train_timesteps=A_    )

 def 							a__					(							self   :    Optional[int]    )  ->      Dict:

       """simple docstring"""






       for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]		,				[0.0002, 0.002, 0.02]    ):
             self.check_over_configs(beta_start=A_		,				beta_end=A_    )

 def 							a__					(							self   :    Tuple    )  ->      Optional[int]:

       """simple docstring"""






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

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

       """simple docstring"""






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

 def 							a__					(							self   :    List[Any]    )  ->      List[str]:

       """simple docstring"""






       lowerCamelCase_							=       self.scheduler_classes[0]
       lowerCamelCase_							=       self.get_scheduler_config()
       lowerCamelCase_							=       scheduler_class(**A_    )

       scheduler.set_timesteps(self.num_inference_steps    )

       lowerCamelCase_							=       torch.manual_seed(0    )

       lowerCamelCase_							=       self.dummy_model()
       lowerCamelCase_							=       self.dummy_sample_deter * scheduler.init_noise_sigma
       lowerCamelCase_							=       sample.to(A_    )

       for i, t in enumerate(scheduler.timesteps    ):
             lowerCamelCase_							=       scheduler.scale_model_input(A_		,				A_    )

             lowerCamelCase_							=       model(A_		,				A_    )

             lowerCamelCase_							=       scheduler.step(A_		,				A_		,				A_		,				generator=A_    )
             lowerCamelCase_							=       output.prev_sample

       lowerCamelCase_							=       torch.sum(torch.abs(A_    )    )
       lowerCamelCase_							=       torch.mean(torch.abs(A_    )    )

       assert abs(result_sum.item() - 10.0807    ) < 1E-2
       assert abs(result_mean.item() - 0.0131    ) < 1E-3

 def 							a__					(							self   :    Dict    )  ->      Dict:

       """simple docstring"""






       lowerCamelCase_							=       self.scheduler_classes[0]
       lowerCamelCase_							=       self.get_scheduler_config(prediction_type='v_prediction'    )
       lowerCamelCase_							=       scheduler_class(**A_    )

       scheduler.set_timesteps(self.num_inference_steps    )

       lowerCamelCase_							=       torch.manual_seed(0    )

       lowerCamelCase_							=       self.dummy_model()
       lowerCamelCase_							=       self.dummy_sample_deter * scheduler.init_noise_sigma
       lowerCamelCase_							=       sample.to(A_    )

       for i, t in enumerate(scheduler.timesteps    ):
             lowerCamelCase_							=       scheduler.scale_model_input(A_		,				A_    )

             lowerCamelCase_							=       model(A_		,				A_    )

             lowerCamelCase_							=       scheduler.step(A_		,				A_		,				A_		,				generator=A_    )
             lowerCamelCase_							=       output.prev_sample

       lowerCamelCase_							=       torch.sum(torch.abs(A_    )    )
       lowerCamelCase_							=       torch.mean(torch.abs(A_    )    )

       assert abs(result_sum.item() - 0.0002    ) < 1E-2
       assert abs(result_mean.item() - 2.2_676E-06    ) < 1E-3

 def 							a__					(							self   :    Any    )  ->      Any:

       """simple docstring"""






       lowerCamelCase_							=       self.scheduler_classes[0]
       lowerCamelCase_							=       self.get_scheduler_config()
       lowerCamelCase_							=       scheduler_class(**A_    )

       scheduler.set_timesteps(self.num_inference_steps		,				device=A_    )

       lowerCamelCase_							=       torch.manual_seed(0    )

       lowerCamelCase_							=       self.dummy_model()
       lowerCamelCase_							=       self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
       lowerCamelCase_							=       sample.to(A_    )

       for t in scheduler.timesteps:
             lowerCamelCase_							=       scheduler.scale_model_input(A_		,				A_    )

             lowerCamelCase_							=       model(A_		,				A_    )

             lowerCamelCase_							=       scheduler.step(A_		,				A_		,				A_		,				generator=A_    )
             lowerCamelCase_							=       output.prev_sample

       lowerCamelCase_							=       torch.sum(torch.abs(A_    )    )
       lowerCamelCase_							=       torch.mean(torch.abs(A_    )    )

       assert abs(result_sum.item() - 10.0807    ) < 1E-2
       assert abs(result_mean.item() - 0.0131    ) < 1E-3





 def 							a__					(							self   :    Optional[Any]    )  ->      Tuple:

       """simple docstring"""






       lowerCamelCase_							=       self.scheduler_classes[0]
       lowerCamelCase_							=       self.get_scheduler_config()
       lowerCamelCase_							=       scheduler_class(**A_		,				use_karras_sigmas=A_    )

       scheduler.set_timesteps(self.num_inference_steps		,				device=A_    )

       lowerCamelCase_							=       torch.manual_seed(0    )

       lowerCamelCase_							=       self.dummy_model()
       lowerCamelCase_							=       self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
       lowerCamelCase_							=       sample.to(A_    )

       for t in scheduler.timesteps:
             lowerCamelCase_							=       scheduler.scale_model_input(A_		,				A_    )

             lowerCamelCase_							=       model(A_		,				A_    )

             lowerCamelCase_							=       scheduler.step(A_		,				A_		,				A_		,				generator=A_    )
             lowerCamelCase_							=       output.prev_sample

       lowerCamelCase_							=       torch.sum(torch.abs(A_    )    )
       lowerCamelCase_							=       torch.mean(torch.abs(A_    )    )

       assert abs(result_sum.item() - 124.52299499511719    ) < 1E-2
       assert abs(result_mean.item() - 0.16213932633399963    ) < 1E-3









from typing import TYPE_CHECKING

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


__magic_name__  :  Optional[int]					=       {
    """configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
    """tokenization_biogpt""": ["""BioGptTokenizer"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __magic_name__  :  int					=       [
          """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

      __magic_name__  :  int					=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

