






'''simple docstring'''






import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




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







   def     SCREAMING_SNAKE_CASE__					(self					,   **__a)			->      Any:
      """simple docstring"""






      __snake_case				:      Dict     							= {
          '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)			->      Optional[Any]:
      """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)			->      Union[str, Any]:
      """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)			->      Union[str, Any]:
      """simple docstring"""






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







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






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







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






      __snake_case				:      Optional[Any]     							= self.scheduler_classes[0]
      __snake_case				:      Optional[Any]     							= self.get_scheduler_config()
      __snake_case				:      Any     							= scheduler_class(**__a)

      scheduler.set_timesteps(self.num_inference_steps)

      __snake_case				:      Union[str, Any]     							= torch.manual_seed(0)

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

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

         __snake_case				:      Union[str, Any]     							= model(__a					,   __a)

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

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

      assert abs(result_sum.item() - 10.0_807) < 1E-2
      assert abs(result_mean.item() - 0.0_131) < 1E-3







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






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

      scheduler.set_timesteps(self.num_inference_steps)

      __snake_case				:      Tuple     							= torch.manual_seed(0)

      __snake_case				:      List[str]     							= self.dummy_model()
      __snake_case				:      Tuple     							= self.dummy_sample_deter * scheduler.init_noise_sigma
      __snake_case				:      Dict     							= sample.to(__a)

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

         __snake_case				:      Union[str, Any]     							= model(__a					,   __a)

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

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

      assert abs(result_sum.item() - 0.0_002) < 1E-2
      assert abs(result_mean.item() - 2.2_676E-06) < 1E-3







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






      __snake_case				:      List[str]     							= self.scheduler_classes[0]
      __snake_case				:      Dict     							= self.get_scheduler_config()
      __snake_case				:      str     							= scheduler_class(**__a)

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

      __snake_case				:      List[str]     							= torch.manual_seed(0)

      __snake_case				:      Union[str, Any]     							= self.dummy_model()
      __snake_case				:      Optional[int]     							= self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
      __snake_case				:      Any     							= sample.to(__a)

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

         __snake_case				:      Optional[int]     							= model(__a					,   __a)

         __snake_case				:      List[str]     							= scheduler.step(__a					,   __a					,   __a					,   generator=__a)
         __snake_case				:      int     							= output.prev_sample

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

      assert abs(result_sum.item() - 10.0_807) < 1E-2
      assert abs(result_mean.item() - 0.0_131) < 1E-3







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






      __snake_case				:      Tuple     							= self.scheduler_classes[0]
      __snake_case				:      Tuple     							= self.get_scheduler_config()
      __snake_case				:      str     							= scheduler_class(**__a					,   use_karras_sigmas=__a)

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

      __snake_case				:      str     							= torch.manual_seed(0)

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

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

         __snake_case				:      Tuple     							= model(__a					,   __a)

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

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

      assert abs(result_sum.item() - 124.52_299_499_511_719) < 1E-2
      assert abs(result_mean.item() - 0.16_213_932_633_399_963) < 1E-3







'''simple docstring'''






from typing import TYPE_CHECKING

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


__A    	=   {
    '''configuration_distilbert''': [
        '''DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''DistilBertConfig''',
        '''DistilBertOnnxConfig''',
    ],
    '''tokenization_distilbert''': ['''DistilBertTokenizer'''],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __A    	=   ['''DistilBertTokenizerFast''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __A    	=   [
          '''DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''DistilBertForMaskedLM''',
          '''DistilBertForMultipleChoice''',
          '''DistilBertForQuestionAnswering''',
          '''DistilBertForSequenceClassification''',
          '''DistilBertForTokenClassification''',
          '''DistilBertModel''',
          '''DistilBertPreTrainedModel''',
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __A    	=   [
          '''TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''TFDistilBertForMaskedLM''',
          '''TFDistilBertForMultipleChoice''',
          '''TFDistilBertForQuestionAnswering''',
          '''TFDistilBertForSequenceClassification''',
          '''TFDistilBertForTokenClassification''',
          '''TFDistilBertMainLayer''',
          '''TFDistilBertModel''',
          '''TFDistilBertPreTrainedModel''',
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __A    	=   [
          '''FlaxDistilBertForMaskedLM''',
          '''FlaxDistilBertForMultipleChoice''',
          '''FlaxDistilBertForQuestionAnswering''',
          '''FlaxDistilBertForSequenceClassification''',
          '''FlaxDistilBertForTokenClassification''',
          '''FlaxDistilBertModel''',
          '''FlaxDistilBertPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_distilbert import (
          DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
          DistilBertConfig,
          DistilBertOnnxConfig,
      )
      from .tokenization_distilbert import DistilBertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_distilbert_fast import DistilBertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_distilbert import (
                DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                DistilBertForMaskedLM,
                DistilBertForMultipleChoice,
                DistilBertForQuestionAnswering,
                DistilBertForSequenceClassification,
                DistilBertForTokenClassification,
                DistilBertModel,
                DistilBertPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_distilbert import (
                TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFDistilBertForMaskedLM,
                TFDistilBertForMultipleChoice,
                TFDistilBertForQuestionAnswering,
                TFDistilBertForSequenceClassification,
                TFDistilBertForTokenClassification,
                TFDistilBertMainLayer,
                TFDistilBertModel,
                TFDistilBertPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_distilbert import (
                FlaxDistilBertForMaskedLM,
                FlaxDistilBertForMultipleChoice,
                FlaxDistilBertForQuestionAnswering,
                FlaxDistilBertForSequenceClassification,
                FlaxDistilBertForTokenClassification,
                FlaxDistilBertModel,
                FlaxDistilBertPreTrainedModel,
            )

else:
      import sys

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