





import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest


class 			lowerCamelCase						(		lowerCAmelCase_  ):
  UpperCAmelCase		:       Dict										=     (DDIMParallelScheduler,)
  UpperCAmelCase		:       Tuple										=     (('eta', 0.0), ('num_inference_steps', 5_0))







  def      snake_case_							(     self						:   Optional[Any]  ,					**__snake_case						:   Tuple       )    ->		Tuple:
        _a							:  Union[str, Any]				= {
            'num_train_timesteps': 1000,
            'beta_start': 0.0_001,
            'beta_end': 0.02,
            'beta_schedule': 'linear',
            'clip_sample': True,
        }

        config.update(**__lowerCamelCase       )
        return config







  def      snake_case_							(     self						:   List[Any]  ,					**__snake_case						:   int       )    ->		List[str]:
        _a							:  Any				= self.scheduler_classes[0]
        _a							:  int				= self.get_scheduler_config(**__lowerCamelCase       )
        _a							:  Dict				= scheduler_class(**__lowerCamelCase       )

        _a							:  Optional[Any]				= 10, 0.0

        _a							:  List[str]				= self.dummy_model()
        _a							:  Tuple				= self.dummy_sample_deter

        scheduler.set_timesteps(__lowerCamelCase       )

        for t in scheduler.timesteps:
              _a							:  List[str]				= model(__lowerCamelCase  ,					__lowerCamelCase       )
              _a							:  Dict				= scheduler.step(__lowerCamelCase  ,					__lowerCamelCase  ,					__lowerCamelCase  ,					__lowerCamelCase       ).prev_sample

        return sample







  def      snake_case_							(     self						:   List[str]       )    ->		Optional[int]:
        for timesteps in [100, 500, 1000]:
              self.check_over_configs(num_train_timesteps=__lowerCamelCase       )







  def      snake_case_							(     self						:   Any       )    ->		Optional[Any]:
        for steps_offset in [0, 1]:
              self.check_over_configs(steps_offset=__lowerCamelCase       )

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







  def      snake_case_							(     self						:   Any       )    ->		Tuple:
        for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]  ,					[0.002, 0.02, 0.2, 2]       ):
              self.check_over_configs(beta_start=__lowerCamelCase  ,					beta_end=__lowerCamelCase       )







  def      snake_case_							(     self						:   Tuple       )    ->		Dict:
        for schedule in ["linear", "squaredcos_cap_v2"]:
              self.check_over_configs(beta_schedule=__lowerCamelCase       )







  def      snake_case_							(     self						:   Dict       )    ->		Optional[Any]:
        for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=__lowerCamelCase       )







  def      snake_case_							(     self						:   List[Any]       )    ->		List[Any]:
        for clip_sample in [True, False]:
              self.check_over_configs(clip_sample=__lowerCamelCase       )







  def      snake_case_							(     self						:   List[str]       )    ->		List[Any]:
        for timestep_spacing in ["trailing", "leading"]:
              self.check_over_configs(timestep_spacing=__lowerCamelCase       )







  def      snake_case_							(     self						:   Union[str, Any]       )    ->		List[str]:
        for rescale_betas_zero_snr in [True, False]:
              self.check_over_configs(rescale_betas_zero_snr=__lowerCamelCase       )







  def      snake_case_							(     self						:   List[str]       )    ->		Union[str, Any]:
        self.check_over_configs(thresholding=__lowerCamelCase       )
        for threshold in [0.5, 1.0, 2.0]:
              for prediction_type in ["epsilon", "v_prediction"]:
                    self.check_over_configs(
                        thresholding=__lowerCamelCase  ,					prediction_type=__lowerCamelCase  ,					sample_max_value=__lowerCamelCase  ,					)







  def      snake_case_							(     self						:   Any       )    ->		List[Any]:
        for t in [1, 10, 49]:
              self.check_over_forward(time_step=__lowerCamelCase       )







  def      snake_case_							(     self						:   Optional[Any]       )    ->		Dict:
        for t, num_inference_steps in zip([1, 10, 50]  ,					[10, 50, 500]       ):
              self.check_over_forward(time_step=__lowerCamelCase  ,					num_inference_steps=__lowerCamelCase       )







  def      snake_case_							(     self						:   List[str]       )    ->		Optional[int]:
        for t, eta in zip([1, 10, 49]  ,					[0.0, 0.5, 1.0]       ):
              self.check_over_forward(time_step=__lowerCamelCase  ,					eta=__lowerCamelCase       )







  def      snake_case_							(     self						:   Tuple       )    ->		Optional[Any]:
        _a							:  List[Any]				= self.scheduler_classes[0]
        _a							:  Tuple				= self.get_scheduler_config()
        _a							:  List[Any]				= scheduler_class(**__lowerCamelCase       )

        assert torch.sum(torch.abs(scheduler._get_variance(0  ,					0       ) - 0.0       )       ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(420  ,					400       ) - 0.14_771       )       ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(980  ,					960       ) - 0.32_460       )       ) < 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       ) - 0.00_979       )       ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(999  ,					998       ) - 0.02       )       ) < 1E-5







  def      snake_case_							(     self						:   Optional[Any]       )    ->		Tuple:
        _a							:  Optional[int]				= self.scheduler_classes[0]
        _a							:  List[Any]				= self.get_scheduler_config()
        _a							:  Any				= scheduler_class(**__lowerCamelCase       )

        _a							:  Union[str, Any]				= 10, 0.0
        scheduler.set_timesteps(__lowerCamelCase       )

        _a							:  Any				= self.dummy_model()
        _a							:  str				= self.dummy_sample_deter
        _a							:  Union[str, Any]				= self.dummy_sample_deter + 0.1
        _a							:  str				= self.dummy_sample_deter - 0.1

        _a							:  Optional[int]				= samplea.shape[0]
        _a							:  Dict				= torch.stack([samplea, samplea, samplea]  ,					dim=0       )
        _a							:  Optional[Any]				= torch.arange(__lowerCamelCase       )[0:3, None].repeat(1  ,					__lowerCamelCase       )

        _a							:  Optional[int]				= model(samples.flatten(0  ,					1       )  ,					timesteps.flatten(0  ,					1       )       )
        _a							:  int				= scheduler.batch_step_no_noise(__lowerCamelCase  ,					timesteps.flatten(0  ,					1       )  ,					samples.flatten(0  ,					1       )  ,					__lowerCamelCase       )

        _a							:  Optional[Any]				= torch.sum(torch.abs(__lowerCamelCase       )       )
        _a							:  Optional[Any]				= torch.mean(torch.abs(__lowerCamelCase       )       )

        assert abs(result_sum.item() - 1147.7904       ) < 1E-2
        assert abs(result_mean.item() - 0.4_982       ) < 1E-3







  def      snake_case_							(     self						:   Union[str, Any]       )    ->		Optional[Any]:
        _a							:  Optional[int]				= self.full_loop()

        _a							:  Any				= torch.sum(torch.abs(__lowerCamelCase       )       )
        _a							:  Dict				= torch.mean(torch.abs(__lowerCamelCase       )       )

        assert abs(result_sum.item() - 172.0067       ) < 1E-2
        assert abs(result_mean.item() - 0.223_967       ) < 1E-3







  def      snake_case_							(     self						:   Any       )    ->		Optional[int]:
        _a							:  str				= self.full_loop(prediction_type='''v_prediction'''       )

        _a							:  List[Any]				= torch.sum(torch.abs(__lowerCamelCase       )       )
        _a							:  Optional[Any]				= torch.mean(torch.abs(__lowerCamelCase       )       )

        assert abs(result_sum.item() - 52.5302       ) < 1E-2
        assert abs(result_mean.item() - 0.0_684       ) < 1E-3







  def      snake_case_							(     self						:   int       )    ->		Optional[int]:
        # We specify different beta, so that the first alpha is 0.99
        _a							:  Optional[Any]				= self.full_loop(set_alpha_to_one=__lowerCamelCase  ,					beta_start=0.01       )
        _a							:  Optional[Any]				= torch.sum(torch.abs(__lowerCamelCase       )       )
        _a							:  Dict				= torch.mean(torch.abs(__lowerCamelCase       )       )

        assert abs(result_sum.item() - 149.8295       ) < 1E-2
        assert abs(result_mean.item() - 0.1_951       ) < 1E-3







  def      snake_case_							(     self						:   int       )    ->		List[str]:
        # We specify different beta, so that the first alpha is 0.99
        _a							:  Optional[Any]				= self.full_loop(set_alpha_to_one=__lowerCamelCase  ,					beta_start=0.01       )
        _a							:  Tuple				= torch.sum(torch.abs(__lowerCamelCase       )       )
        _a							:  Dict				= torch.mean(torch.abs(__lowerCamelCase       )       )

        assert abs(result_sum.item() - 149.0784       ) < 1E-2
        assert abs(result_mean.item() - 0.1_941       ) < 1E-3











'''simple docstring'''






from typing import TYPE_CHECKING

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


A_			      =		{
    "configuration_roberta_prelayernorm": [
        "ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "RobertaPreLayerNormConfig",
        "RobertaPreLayerNormOnnxConfig",
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A_			      =		[
          "ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
          "RobertaPreLayerNormForCausalLM",
          "RobertaPreLayerNormForMaskedLM",
          "RobertaPreLayerNormForMultipleChoice",
          "RobertaPreLayerNormForQuestionAnswering",
          "RobertaPreLayerNormForSequenceClassification",
          "RobertaPreLayerNormForTokenClassification",
          "RobertaPreLayerNormModel",
          "RobertaPreLayerNormPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A_			      =		[
          "TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFRobertaPreLayerNormForCausalLM",
          "TFRobertaPreLayerNormForMaskedLM",
          "TFRobertaPreLayerNormForMultipleChoice",
          "TFRobertaPreLayerNormForQuestionAnswering",
          "TFRobertaPreLayerNormForSequenceClassification",
          "TFRobertaPreLayerNormForTokenClassification",
          "TFRobertaPreLayerNormMainLayer",
          "TFRobertaPreLayerNormModel",
          "TFRobertaPreLayerNormPreTrainedModel",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A_			      =		[
          "FlaxRobertaPreLayerNormForCausalLM",
          "FlaxRobertaPreLayerNormForMaskedLM",
          "FlaxRobertaPreLayerNormForMultipleChoice",
          "FlaxRobertaPreLayerNormForQuestionAnswering",
          "FlaxRobertaPreLayerNormForSequenceClassification",
          "FlaxRobertaPreLayerNormForTokenClassification",
          "FlaxRobertaPreLayerNormModel",
          "FlaxRobertaPreLayerNormPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_roberta_prelayernorm import (
          ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
          RobertaPreLayerNormConfig,
          RobertaPreLayerNormOnnxConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roberta_prelayernorm import (
                ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
                RobertaPreLayerNormForCausalLM,
                RobertaPreLayerNormForMaskedLM,
                RobertaPreLayerNormForMultipleChoice,
                RobertaPreLayerNormForQuestionAnswering,
                RobertaPreLayerNormForSequenceClassification,
                RobertaPreLayerNormForTokenClassification,
                RobertaPreLayerNormModel,
                RobertaPreLayerNormPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roberta_prelayernorm import (
                TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRobertaPreLayerNormForCausalLM,
                TFRobertaPreLayerNormForMaskedLM,
                TFRobertaPreLayerNormForMultipleChoice,
                TFRobertaPreLayerNormForQuestionAnswering,
                TFRobertaPreLayerNormForSequenceClassification,
                TFRobertaPreLayerNormForTokenClassification,
                TFRobertaPreLayerNormMainLayer,
                TFRobertaPreLayerNormModel,
                TFRobertaPreLayerNormPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roberta_prelayernorm import (
                FlaxRobertaPreLayerNormForCausalLM,
                FlaxRobertaPreLayerNormForMaskedLM,
                FlaxRobertaPreLayerNormForMultipleChoice,
                FlaxRobertaPreLayerNormForQuestionAnswering,
                FlaxRobertaPreLayerNormForSequenceClassification,
                FlaxRobertaPreLayerNormForTokenClassification,
                FlaxRobertaPreLayerNormModel,
                FlaxRobertaPreLayerNormPreTrainedModel,
            )

else:
      import sys

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