






'''simple docstring'''






import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class a_    (    UpperCamelCase_							):
   _snake_case        =							(KDPMaDiscreteScheduler,)
   _snake_case        =							10







   def     SCREAMING_SNAKE_CASE__					(self					,   **__a)			->      List[str]:
      """simple docstring"""






      __snake_case				:      Any     							= {
          'num_train_timesteps': 1_1_0_0,
          'beta_start': 0.0_001,
          'beta_end': 0.02,
          'beta_schedule': 'linear',
      }

      config.update(**__a)
      return config







   def     SCREAMING_SNAKE_CASE__					(self)			->      List[str]:
      """simple docstring"""






      for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
         self.check_over_configs(num_train_timesteps=__a)







   def     SCREAMING_SNAKE_CASE__					(self)			->      List[str]:
      """simple docstring"""






      for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]					,   [0.0_002, 0.002, 0.02]):
         self.check_over_configs(beta_start=__a					,   beta_end=__a)







   def     SCREAMING_SNAKE_CASE__					(self)			->      Tuple:
      """simple docstring"""






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







   def     SCREAMING_SNAKE_CASE__					(self)			->      Optional[int]:
      """simple docstring"""






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







   def     SCREAMING_SNAKE_CASE__					(self)			->      List[str]:
      """simple docstring"""






      __snake_case				:      List[Any]     							= self.scheduler_classes[0]
      __snake_case				:      Optional[int]     							= self.get_scheduler_config(prediction_type='v_prediction')
      __snake_case				:      str     							= scheduler_class(**__a)

      scheduler.set_timesteps(self.num_inference_steps)

      __snake_case				:      str     							= self.dummy_model()
      __snake_case				:      Any     							= self.dummy_sample_deter * scheduler.init_noise_sigma
      __snake_case				:      Dict     							= sample.to(__a)

      for i, t in enumerate(scheduler.timesteps):
         __snake_case				:      Any     							= scheduler.scale_model_input(__a					,   __a)

         __snake_case				:      str     							= model(__a					,   __a)

         __snake_case				:      int     							= scheduler.step(__a					,   __a					,   __a)
         __snake_case				:      Tuple     							= output.prev_sample

      __snake_case				:      Optional[Any]     							= torch.sum(torch.abs(__a))
      __snake_case				:      List[str]     							= torch.mean(torch.abs(__a))

      if torch_device in ["cpu", "mps"]:
         assert abs(result_sum.item() - 4.6_934E-07) < 1E-2
         assert abs(result_mean.item() - 6.1_112E-10) < 1E-3
      else:
         # CUDA
         assert abs(result_sum.item() - 4.693_428_650_170_972E-07) < 1E-2
         assert abs(result_mean.item() - 0.0_002) < 1E-3







   def     SCREAMING_SNAKE_CASE__					(self)			->      List[Any]:
      """simple docstring"""






      if torch_device == "mps":
         return
      __snake_case				:      str     							= self.scheduler_classes[0]
      __snake_case				:      int     							= self.get_scheduler_config()
      __snake_case				:      Optional[int]     							= scheduler_class(**__a)

      scheduler.set_timesteps(self.num_inference_steps)

      __snake_case				:      List[Any]     							= self.dummy_model()
      __snake_case				:      Optional[int]     							= self.dummy_sample_deter * scheduler.init_noise_sigma
      __snake_case				:      Tuple     							= sample.to(__a)

      for i, t in enumerate(scheduler.timesteps):
         __snake_case				:      Tuple     							= scheduler.scale_model_input(__a					,   __a)

         __snake_case				:      Dict     							= model(__a					,   __a)

         __snake_case				:      Dict     							= scheduler.step(__a					,   __a					,   __a)
         __snake_case				:      Optional[int]     							= output.prev_sample

      __snake_case				:      str     							= torch.sum(torch.abs(__a))
      __snake_case				:      Tuple     							= torch.mean(torch.abs(__a))

      if torch_device in ["cpu", "mps"]:
         assert abs(result_sum.item() - 20.4_125) < 1E-2
         assert abs(result_mean.item() - 0.0_266) < 1E-3
      else:
         # CUDA
         assert abs(result_sum.item() - 20.4_125) < 1E-2
         assert abs(result_mean.item() - 0.0_266) < 1E-3







   def     SCREAMING_SNAKE_CASE__					(self)			->      Optional[Any]:
      """simple docstring"""






      if torch_device == "mps":
         return
      __snake_case				:      Dict     							= self.scheduler_classes[0]
      __snake_case				:      str     							= self.get_scheduler_config()
      __snake_case				:      Dict     							= scheduler_class(**__a)

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

      __snake_case				:      str     							= self.dummy_model()
      __snake_case				:      Tuple     							= self.dummy_sample_deter.to(__a) * scheduler.init_noise_sigma

      for t in scheduler.timesteps:
         __snake_case				:      Optional[Any]     							= scheduler.scale_model_input(__a					,   __a)

         __snake_case				:      str     							= model(__a					,   __a)

         __snake_case				:      str     							= scheduler.step(__a					,   __a					,   __a)
         __snake_case				:      Optional[Any]     							= output.prev_sample

      __snake_case				:      int     							= torch.sum(torch.abs(__a))
      __snake_case				:      Dict     							= torch.mean(torch.abs(__a))

      if str(__a).startswith('cpu'):
         # The following sum varies between 148 and 156 on mps. Why?
         assert abs(result_sum.item() - 20.4_125) < 1E-2
         assert abs(result_mean.item() - 0.0_266) < 1E-3
      else:
         # CUDA
         assert abs(result_sum.item() - 20.4_125) < 1E-2
         assert abs(result_mean.item() - 0.0_266) < 1E-3









'''simple docstring'''






from typing import TYPE_CHECKING

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


__A    	=   {
    '''configuration_biogpt''': ['''BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BioGptConfig'''],
    '''tokenization_biogpt''': ['''BioGptTokenizer'''],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __A    	=   [
          '''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

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