





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case 				=       {
    """configuration_deberta""": ["""DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DebertaConfig""", """DebertaOnnxConfig"""],
    """tokenization_deberta""": ["""DebertaTokenizer"""],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case 				=       ["""DebertaTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case 				=       [
          """DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """DebertaForMaskedLM""",
          """DebertaForQuestionAnswering""",
          """DebertaForSequenceClassification""",
          """DebertaForTokenClassification""",
          """DebertaModel""",
          """DebertaPreTrainedModel""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case 				=       [
          """TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFDebertaForMaskedLM""",
          """TFDebertaForQuestionAnswering""",
          """TFDebertaForSequenceClassification""",
          """TFDebertaForTokenClassification""",
          """TFDebertaModel""",
          """TFDebertaPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
      from .tokenization_deberta import DebertaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_deberta_fast import DebertaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_deberta import (
                DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                DebertaForMaskedLM,
                DebertaForQuestionAnswering,
                DebertaForSequenceClassification,
                DebertaForTokenClassification,
                DebertaModel,
                DebertaPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_deberta import (
                TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFDebertaForMaskedLM,
                TFDebertaForQuestionAnswering,
                TFDebertaForSequenceClassification,
                TFDebertaForTokenClassification,
                TFDebertaModel,
                TFDebertaPreTrainedModel,
            )


else:
      import sys

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






import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest





class 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : int      =  (DEISMultistepScheduler,)
       SCREAMING_SNAKE_CASE_      : List[Any]      =  (('''num_inference_steps''', 2_5),)







       def    __UpperCAmelCase (    self	:	List[Any]						,**__A	:	int  )						->					List[Any]:
         _lowercase							= {
             'num_train_timesteps': 1000,
             'beta_start': 0.0001,
             'beta_end': 0.02,
             'beta_schedule': 'linear',
             'solver_order': 2,
         }

         config.update(**__A  )
         return config







       def    __UpperCAmelCase (    self	:	Dict						,__A	:	str=0						,**__A	:	Optional[int]  )						->					Tuple:
         _lowercase							= dict(self.forward_default_kwargs  )
         _lowercase							= kwargs.pop('num_inference_steps'						,__A  )
         _lowercase							= self.dummy_sample
         _lowercase							= 0.1 * sample
         _lowercase							= [residual + 0.2, residual + 0.15, residual + 0.10]

         for scheduler_class in self.scheduler_classes:
           _lowercase							= self.get_scheduler_config(**__A  )
           _lowercase							= scheduler_class(**__A  )
           scheduler.set_timesteps(__A  )
           # copy over dummy past residuals
           _lowercase							= dummy_past_residuals[: scheduler.config.solver_order]

           with tempfile.TemporaryDirectory() as tmpdirname:
             scheduler.save_config(__A  )
             _lowercase							= scheduler_class.from_pretrained(__A  )
             new_scheduler.set_timesteps(__A  )
             # copy over dummy past residuals
             _lowercase							= dummy_past_residuals[: new_scheduler.config.solver_order]

           _lowercase						,		_lowercase							= sample, sample
           for t in range(__A						,time_step + scheduler.config.solver_order + 1  ):
             _lowercase							= scheduler.step(__A						,__A						,__A						,**__A  ).prev_sample
             _lowercase							= new_scheduler.step(__A						,__A						,__A						,**__A  ).prev_sample

             assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"







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







       def    __UpperCAmelCase (    self	:	int						,__A	:	Dict=0						,**__A	:	Optional[Any]  )						->					Dict:
         _lowercase							= dict(self.forward_default_kwargs  )
         _lowercase							= kwargs.pop('num_inference_steps'						,__A  )
         _lowercase							= self.dummy_sample
         _lowercase							= 0.1 * sample
         _lowercase							= [residual + 0.2, residual + 0.15, residual + 0.10]

         for scheduler_class in self.scheduler_classes:
           _lowercase							= self.get_scheduler_config()
           _lowercase							= scheduler_class(**__A  )
           scheduler.set_timesteps(__A  )

           # copy over dummy past residuals (must be after setting timesteps)
           _lowercase							= dummy_past_residuals[: scheduler.config.solver_order]

           with tempfile.TemporaryDirectory() as tmpdirname:
             scheduler.save_config(__A  )
             _lowercase							= scheduler_class.from_pretrained(__A  )
             # copy over dummy past residuals
             new_scheduler.set_timesteps(__A  )

             # copy over dummy past residual (must be after setting timesteps)
             _lowercase							= dummy_past_residuals[: new_scheduler.config.solver_order]

           _lowercase							= scheduler.step(__A						,__A						,__A						,**__A  ).prev_sample
           _lowercase							= new_scheduler.step(__A						,__A						,__A						,**__A  ).prev_sample

           assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"







       def    __UpperCAmelCase (    self	:	Tuple						,__A	:	Tuple=None						,**__A	:	Optional[Any]  )						->					Tuple:
         if scheduler is None:
           _lowercase							= self.scheduler_classes[0]
           _lowercase							= self.get_scheduler_config(**__A  )
           _lowercase							= scheduler_class(**__A  )

         _lowercase							= self.scheduler_classes[0]
         _lowercase							= self.get_scheduler_config(**__A  )
         _lowercase							= scheduler_class(**__A  )

         _lowercase							= 10
         _lowercase							= self.dummy_model()
         _lowercase							= self.dummy_sample_deter
         scheduler.set_timesteps(__A  )

         for i, t in enumerate(scheduler.timesteps  ):
           _lowercase							= model(__A						,__A  )
           _lowercase							= scheduler.step(__A						,__A						,__A  ).prev_sample

         return sample







       def    __UpperCAmelCase (    self	:	Optional[Any]  )						->					Any:
         _lowercase							= dict(self.forward_default_kwargs  )

         _lowercase							= kwargs.pop('num_inference_steps'						,__A  )

         for scheduler_class in self.scheduler_classes:
           _lowercase							= self.get_scheduler_config()
           _lowercase							= scheduler_class(**__A  )

           _lowercase							= self.dummy_sample
           _lowercase							= 0.1 * sample

           if num_inference_steps is not None and hasattr(__A						,'set_timesteps'  ):
             scheduler.set_timesteps(__A  )
           elif num_inference_steps is not None and not hasattr(__A						,'set_timesteps'  ):
             _lowercase							= num_inference_steps

           # copy over dummy past residuals (must be done after set_timesteps)
           _lowercase							= [residual + 0.2, residual + 0.15, residual + 0.10]
           _lowercase							= dummy_past_residuals[: scheduler.config.solver_order]

           _lowercase							= scheduler.timesteps[5]
           _lowercase							= scheduler.timesteps[6]

           _lowercase							= scheduler.step(__A						,__A						,__A						,**__A  ).prev_sample
           _lowercase							= scheduler.step(__A						,__A						,__A						,**__A  ).prev_sample

           self.assertEqual(output_a.shape						,sample.shape  )
           self.assertEqual(output_a.shape						,output_a.shape  )







       def    __UpperCAmelCase (    self	:	int  )						->					Optional[Any]:
         # make sure that iterating over schedulers with same config names gives same results
         # for defaults
         _lowercase							= DEISMultistepScheduler(**self.get_scheduler_config()  )
         _lowercase							= self.full_loop(scheduler=__A  )
         _lowercase							= torch.mean(torch.abs(__A  )  )

         assert abs(result_mean.item() - 0.23916  ) < 1e-3

         _lowercase							= DPMSolverSinglestepScheduler.from_config(scheduler.config  )
         _lowercase							= DPMSolverMultistepScheduler.from_config(scheduler.config  )
         _lowercase							= UniPCMultistepScheduler.from_config(scheduler.config  )
         _lowercase							= DEISMultistepScheduler.from_config(scheduler.config  )

         _lowercase							= self.full_loop(scheduler=__A  )
         _lowercase							= torch.mean(torch.abs(__A  )  )

         assert abs(result_mean.item() - 0.23916  ) < 1e-3







       def    __UpperCAmelCase (    self	:	Optional[int]  )						->					Optional[int]:
         for timesteps in [25, 50, 100, 999, 1000]:
           self.check_over_configs(num_train_timesteps=__A  )







       def    __UpperCAmelCase (    self	:	int  )						->					str:
         self.check_over_configs(thresholding=__A  )
         for order in [1, 2, 3]:
           for solver_type in ["logrho"]:
             for threshold in [0.5, 1.0, 2.0]:
               for prediction_type in ["epsilon", "sample"]:
                 self.check_over_configs(
                     thresholding=__A						,prediction_type=__A						,sample_max_value=__A						,algorithm_type='deis'						,solver_order=__A						,solver_type=__A						,)







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







       def    __UpperCAmelCase (    self	:	Dict  )						->					Optional[int]:
         for algorithm_type in ["deis"]:
           for solver_type in ["logrho"]:
             for order in [1, 2, 3]:
               for prediction_type in ["epsilon", "sample"]:
                 self.check_over_configs(
                     solver_order=__A						,solver_type=__A						,prediction_type=__A						,algorithm_type=__A						,)
                 _lowercase							= self.full_loop(
                     solver_order=__A						,solver_type=__A						,prediction_type=__A						,algorithm_type=__A						,)
                 assert not torch.isnan(__A  ).any(), "Samples have nan numbers"







       def    __UpperCAmelCase (    self	:	Dict  )						->					int:
         self.check_over_configs(lower_order_final=__A  )
         self.check_over_configs(lower_order_final=__A  )







       def    __UpperCAmelCase (    self	:	str  )						->					Tuple:
         for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
           self.check_over_forward(num_inference_steps=__A						,time_step=0  )







       def    __UpperCAmelCase (    self	:	Tuple  )						->					Any:
         _lowercase							= self.full_loop()
         _lowercase							= torch.mean(torch.abs(__A  )  )

         assert abs(result_mean.item() - 0.23916  ) < 1e-3







       def    __UpperCAmelCase (    self	:	Any  )						->					List[str]:
         _lowercase							= self.full_loop(prediction_type='v_prediction'  )
         _lowercase							= torch.mean(torch.abs(__A  )  )

         assert abs(result_mean.item() - 0.091  ) < 1e-3







       def    __UpperCAmelCase (    self	:	Optional[int]  )						->					List[Any]:
         _lowercase							= self.scheduler_classes[0]
         _lowercase							= self.get_scheduler_config(thresholding=__A						,dynamic_thresholding_ratio=0  )
         _lowercase							= scheduler_class(**__A  )

         _lowercase							= 10
         _lowercase							= self.dummy_model()
         _lowercase							= self.dummy_sample_deter.half()
         scheduler.set_timesteps(__A  )

         for i, t in enumerate(scheduler.timesteps  ):
           _lowercase							= model(__A						,__A  )
           _lowercase							= scheduler.step(__A						,__A						,__A  ).prev_sample

         assert sample.dtype == torch.floataa