






import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest







class 				UpperCAmelCase_  (		__lowerCamelCase		):
     __lowerCamelCase      				=  (DEISMultistepScheduler,)
     __lowerCamelCase      				=  (('num_inference_steps', 25),)




     def   __UpperCAmelCase  (				self	,						**_lowerCAmelCase		):
          UpperCAmelCase__      :  Optional[Any]				      =			{
              """num_train_timesteps""": 1000,
              """beta_start""": 0.0_0_0_1,
              """beta_end""": 0.0_2,
              """beta_schedule""": """linear""",
              """solver_order""": 2,
          }

          config.update(**_lowerCAmelCase		)
          return config




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase=0	,						**_lowerCAmelCase		):
          UpperCAmelCase__      :  str				      =			dict(self.forward_default_kwargs		)
          UpperCAmelCase__      :  int				      =			kwargs.pop("""num_inference_steps"""	,						_lowerCAmelCase		)
          UpperCAmelCase__      :  Optional[int]				      =			self.dummy_sample
          UpperCAmelCase__      :  List[str]				      =			0.1 * sample
          UpperCAmelCase__      :  Union[str, Any]				      =			[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

          for scheduler_class in self.scheduler_classes:
               UpperCAmelCase__      :  List[Any]				      =			self.get_scheduler_config(**_lowerCAmelCase		)
               UpperCAmelCase__      :  List[Any]				      =			scheduler_class(**_lowerCAmelCase		)
               scheduler.set_timesteps(_lowerCAmelCase		)
               # copy over dummy past residuals
               UpperCAmelCase__      :  Optional[Any]				      =			dummy_past_residuals[: scheduler.config.solver_order]

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

               UpperCAmelCase__      ,		UpperCAmelCase__      :  Optional[int]				      =			sample, sample
               for t in range(_lowerCAmelCase	,						time_step + scheduler.config.solver_order + 1		):
                    UpperCAmelCase__      :  str				      =			scheduler.step(_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						**_lowerCAmelCase		).prev_sample
                    UpperCAmelCase__      :  int				      =			new_scheduler.step(_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						**_lowerCAmelCase		).prev_sample

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




     def   __UpperCAmelCase  (				self		):
          pass




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase=0	,						**_lowerCAmelCase		):
          UpperCAmelCase__      :  int				      =			dict(self.forward_default_kwargs		)
          UpperCAmelCase__      :  str				      =			kwargs.pop("""num_inference_steps"""	,						_lowerCAmelCase		)
          UpperCAmelCase__      :  int				      =			self.dummy_sample
          UpperCAmelCase__      :  Tuple				      =			0.1 * sample
          UpperCAmelCase__      :  int				      =			[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

          for scheduler_class in self.scheduler_classes:
               UpperCAmelCase__      :  Union[str, Any]				      =			self.get_scheduler_config()
               UpperCAmelCase__      :  Union[str, Any]				      =			scheduler_class(**_lowerCAmelCase		)
               scheduler.set_timesteps(_lowerCAmelCase		)

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

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

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

               UpperCAmelCase__      :  Dict				      =			scheduler.step(_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						**_lowerCAmelCase		).prev_sample
               UpperCAmelCase__      :  int				      =			new_scheduler.step(_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						**_lowerCAmelCase		).prev_sample

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




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase=None	,						**_lowerCAmelCase		):
          if scheduler is None:
               UpperCAmelCase__      :  int				      =			self.scheduler_classes[0]
               UpperCAmelCase__      :  Optional[Any]				      =			self.get_scheduler_config(**_lowerCAmelCase		)
               UpperCAmelCase__      :  Union[str, Any]				      =			scheduler_class(**_lowerCAmelCase		)

          UpperCAmelCase__      :  Tuple				      =			self.scheduler_classes[0]
          UpperCAmelCase__      :  Any				      =			self.get_scheduler_config(**_lowerCAmelCase		)
          UpperCAmelCase__      :  Dict				      =			scheduler_class(**_lowerCAmelCase		)

          UpperCAmelCase__      :  Dict				      =			10
          UpperCAmelCase__      :  int				      =			self.dummy_model()
          UpperCAmelCase__      :  str				      =			self.dummy_sample_deter
          scheduler.set_timesteps(_lowerCAmelCase		)

          for i, t in enumerate(scheduler.timesteps		):
               UpperCAmelCase__      :  Optional[Any]				      =			model(_lowerCAmelCase	,						_lowerCAmelCase		)
               UpperCAmelCase__      :  Any				      =			scheduler.step(_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase		).prev_sample

          return sample




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  Any				      =			dict(self.forward_default_kwargs		)

          UpperCAmelCase__      :  List[Any]				      =			kwargs.pop("""num_inference_steps"""	,						_lowerCAmelCase		)

          for scheduler_class in self.scheduler_classes:
               UpperCAmelCase__      :  Optional[int]				      =			self.get_scheduler_config()
               UpperCAmelCase__      :  Optional[int]				      =			scheduler_class(**_lowerCAmelCase		)

               UpperCAmelCase__      :  List[str]				      =			self.dummy_sample
               UpperCAmelCase__      :  int				      =			0.1 * sample

               if num_inference_steps is not None and hasattr(_lowerCAmelCase	,						"""set_timesteps"""		):
                    scheduler.set_timesteps(_lowerCAmelCase		)
               elif num_inference_steps is not None and not hasattr(_lowerCAmelCase	,						"""set_timesteps"""		):
                    UpperCAmelCase__      :  Any				      =			num_inference_steps

               # copy over dummy past residuals (must be done after set_timesteps)
               UpperCAmelCase__      :  Any				      =			[residual + 0.2, residual + 0.1_5, residual + 0.1_0]
               UpperCAmelCase__      :  List[str]				      =			dummy_past_residuals[: scheduler.config.solver_order]

               UpperCAmelCase__      :  List[Any]				      =			scheduler.timesteps[5]
               UpperCAmelCase__      :  Union[str, Any]				      =			scheduler.timesteps[6]

               UpperCAmelCase__      :  List[str]				      =			scheduler.step(_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						**_lowerCAmelCase		).prev_sample
               UpperCAmelCase__      :  List[str]				      =			scheduler.step(_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						**_lowerCAmelCase		).prev_sample

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




     def   __UpperCAmelCase  (				self		):
          # make sure that iterating over schedulers with same config names gives same results
          # for defaults
          UpperCAmelCase__      :  Tuple				      =			DEISMultistepScheduler(**self.get_scheduler_config()		)
          UpperCAmelCase__      :  Dict				      =			self.full_loop(scheduler=_lowerCAmelCase		)
          UpperCAmelCase__      :  Dict				      =			torch.mean(torch.abs(_lowerCAmelCase		)		)

          assert abs(result_mean.item() - 0.2_3_9_1_6		) < 1e-3

          UpperCAmelCase__      :  Any				      =			DPMSolverSinglestepScheduler.from_config(scheduler.config		)
          UpperCAmelCase__      :  Any				      =			DPMSolverMultistepScheduler.from_config(scheduler.config		)
          UpperCAmelCase__      :  Optional[Any]				      =			UniPCMultistepScheduler.from_config(scheduler.config		)
          UpperCAmelCase__      :  Union[str, Any]				      =			DEISMultistepScheduler.from_config(scheduler.config		)

          UpperCAmelCase__      :  str				      =			self.full_loop(scheduler=_lowerCAmelCase		)
          UpperCAmelCase__      :  Union[str, Any]				      =			torch.mean(torch.abs(_lowerCAmelCase		)		)

          assert abs(result_mean.item() - 0.2_3_9_1_6		) < 1e-3




     def   __UpperCAmelCase  (				self		):
          for timesteps in [25, 50, 100, 999, 1000]:
               self.check_over_configs(num_train_timesteps=_lowerCAmelCase		)




     def   __UpperCAmelCase  (				self		):
          self.check_over_configs(thresholding=_lowerCAmelCase		)
          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=_lowerCAmelCase	,						prediction_type=_lowerCAmelCase	,						sample_max_value=_lowerCAmelCase	,						algorithm_type="""deis"""	,						solver_order=_lowerCAmelCase	,						solver_type=_lowerCAmelCase	,						)




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




     def   __UpperCAmelCase  (				self		):
          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=_lowerCAmelCase	,						solver_type=_lowerCAmelCase	,						prediction_type=_lowerCAmelCase	,						algorithm_type=_lowerCAmelCase	,						)
                              UpperCAmelCase__      :  Any				      =			self.full_loop(
                                  solver_order=_lowerCAmelCase	,						solver_type=_lowerCAmelCase	,						prediction_type=_lowerCAmelCase	,						algorithm_type=_lowerCAmelCase	,						)
                              assert not torch.isnan(_lowerCAmelCase		).any(), "Samples have nan numbers"




     def   __UpperCAmelCase  (				self		):
          self.check_over_configs(lower_order_final=_lowerCAmelCase		)
          self.check_over_configs(lower_order_final=_lowerCAmelCase		)




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




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  Union[str, Any]				      =			self.full_loop()
          UpperCAmelCase__      :  Optional[int]				      =			torch.mean(torch.abs(_lowerCAmelCase		)		)

          assert abs(result_mean.item() - 0.2_3_9_1_6		) < 1e-3




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  str				      =			self.full_loop(prediction_type="""v_prediction"""		)
          UpperCAmelCase__      :  Optional[int]				      =			torch.mean(torch.abs(_lowerCAmelCase		)		)

          assert abs(result_mean.item() - 0.0_9_1		) < 1e-3




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  Union[str, Any]				      =			self.scheduler_classes[0]
          UpperCAmelCase__      :  Any				      =			self.get_scheduler_config(thresholding=_lowerCAmelCase	,						dynamic_thresholding_ratio=0		)
          UpperCAmelCase__      :  List[Any]				      =			scheduler_class(**_lowerCAmelCase		)

          UpperCAmelCase__      :  int				      =			10
          UpperCAmelCase__      :  str				      =			self.dummy_model()
          UpperCAmelCase__      :  Tuple				      =			self.dummy_sample_deter.half()
          scheduler.set_timesteps(_lowerCAmelCase		)

          for i, t in enumerate(scheduler.timesteps		):
               UpperCAmelCase__      :  Tuple				      =			model(_lowerCAmelCase	,						_lowerCAmelCase		)
               UpperCAmelCase__      :  Optional[Any]				      =			scheduler.step(_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase		).prev_sample

          assert sample.dtype == torch.floataa










'''simple docstring'''







from typing import TYPE_CHECKING

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


lowerCAmelCase     :  List[str]											=    {
    """configuration_electra""": ["""ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ElectraConfig""", """ElectraOnnxConfig"""],
    """tokenization_electra""": ["""ElectraTokenizer"""],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase     :  Optional[Any]											=    ["""ElectraTokenizerFast"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase     :  str											=    [
           """ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """ElectraForCausalLM""",
           """ElectraForMaskedLM""",
           """ElectraForMultipleChoice""",
           """ElectraForPreTraining""",
           """ElectraForQuestionAnswering""",
           """ElectraForSequenceClassification""",
           """ElectraForTokenClassification""",
           """ElectraModel""",
           """ElectraPreTrainedModel""",
           """load_tf_weights_in_electra""",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase     :  Tuple											=    [
           """TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """TFElectraForMaskedLM""",
           """TFElectraForMultipleChoice""",
           """TFElectraForPreTraining""",
           """TFElectraForQuestionAnswering""",
           """TFElectraForSequenceClassification""",
           """TFElectraForTokenClassification""",
           """TFElectraModel""",
           """TFElectraPreTrainedModel""",
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase     :  Union[str, Any]											=    [
           """FlaxElectraForCausalLM""",
           """FlaxElectraForMaskedLM""",
           """FlaxElectraForMultipleChoice""",
           """FlaxElectraForPreTraining""",
           """FlaxElectraForQuestionAnswering""",
           """FlaxElectraForSequenceClassification""",
           """FlaxElectraForTokenClassification""",
           """FlaxElectraModel""",
           """FlaxElectraPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
       from .tokenization_electra import ElectraTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_electra_fast import ElectraTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_electra import (
                  ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  ElectraForCausalLM,
                  ElectraForMaskedLM,
                  ElectraForMultipleChoice,
                  ElectraForPreTraining,
                  ElectraForQuestionAnswering,
                  ElectraForSequenceClassification,
                  ElectraForTokenClassification,
                  ElectraModel,
                  ElectraPreTrainedModel,
                  load_tf_weights_in_electra,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_electra import (
                  TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFElectraForMaskedLM,
                  TFElectraForMultipleChoice,
                  TFElectraForPreTraining,
                  TFElectraForQuestionAnswering,
                  TFElectraForSequenceClassification,
                  TFElectraForTokenClassification,
                  TFElectraModel,
                  TFElectraPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_electra import (
                  FlaxElectraForCausalLM,
                  FlaxElectraForMaskedLM,
                  FlaxElectraForMultipleChoice,
                  FlaxElectraForPreTraining,
                  FlaxElectraForQuestionAnswering,
                  FlaxElectraForSequenceClassification,
                  FlaxElectraForTokenClassification,
                  FlaxElectraModel,
                  FlaxElectraPreTrainedModel,
              )

else:
       import sys

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

