




from typing import TYPE_CHECKING

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


__a  :		List[str]			=    {
    'configuration_deberta_v2': ['DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaV2Config', 'DebertaV2OnnxConfig'],
    'tokenization_deberta_v2': ['DebertaV2Tokenizer'],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  :		List[str]			=    ['DebertaV2TokenizerFast']

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  :		Optional[int]			=    [
						    'TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TFDebertaV2ForMaskedLM',
						    'TFDebertaV2ForQuestionAnswering',
						    'TFDebertaV2ForSequenceClassification',
						    'TFDebertaV2ForTokenClassification',
						    'TFDebertaV2Model',
						    'TFDebertaV2PreTrainedModel',
						]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  :		str			=    [
						    'DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'DebertaV2ForMaskedLM',
						    'DebertaV2ForMultipleChoice',
						    'DebertaV2ForQuestionAnswering',
						    'DebertaV2ForSequenceClassification',
						    'DebertaV2ForTokenClassification',
						    'DebertaV2Model',
						    'DebertaV2PreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_deberta_va import (
						    DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    DebertaVaConfig,
						    DebertaVaOnnxConfig,
						)
						from .tokenization_deberta_va import DebertaVaTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_deberta_va import (
												    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFDebertaVaForMaskedLM,
												    TFDebertaVaForQuestionAnswering,
												    TFDebertaVaForSequenceClassification,
												    TFDebertaVaForTokenClassification,
												    TFDebertaVaModel,
												    TFDebertaVaPreTrainedModel,
												)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_deberta_va import (
												    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DebertaVaForMaskedLM,
												    DebertaVaForMultipleChoice,
												    DebertaVaForQuestionAnswering,
												    DebertaVaForSequenceClassification,
												    DebertaVaForTokenClassification,
												    DebertaVaModel,
												    DebertaVaPreTrainedModel,
												)

else:
						import sys

						__a  :		Optional[int]			=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)







"""simple docstring"""


import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class 						snake_case  (  UpperCAmelCase       ):
    __magic_name__         =     (LMSDiscreteScheduler,)
    __magic_name__         =     10





    def 				lowerCamelCase__      (							self	:					Any				,		**A	:					Dict			):


      '''simple docstring'''




      a     :			Optional[Any]				=   {
          'num_train_timesteps': 1_1_0_0,
          'beta_start': 0.00_01,
          'beta_end': 0.02,
          'beta_schedule': 'linear',
      }

      config.update(**A			)
      return config





    def 				lowerCamelCase__      (							self	:					Optional[int]			):


      '''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 				lowerCamelCase__      (							self	:					List[str]			):


      '''simple docstring'''




      for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]				,		[0.00_02, 0.0_02, 0.02]			):
        self.check_over_configs(beta_start=A				,		beta_end=A			)





    def 				lowerCamelCase__      (							self	:					List[Any]			):


      '''simple docstring'''




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





    def 				lowerCamelCase__      (							self	:					Any			):


      '''simple docstring'''




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





    def 				lowerCamelCase__      (							self	:					Dict			):


      '''simple docstring'''




      for t in [0, 5_0_0, 8_0_0]:
        self.check_over_forward(time_step=A			)





    def 				lowerCamelCase__      (							self	:					Any			):


      '''simple docstring'''




      a     :			Any				=   self.scheduler_classes[0]
      a     :			Dict				=   self.get_scheduler_config()
      a     :			str				=   scheduler_class(**A			)

      scheduler.set_timesteps(self.num_inference_steps			)

      a     :			List[Any]				=   self.dummy_model()
      a     :			str				=   self.dummy_sample_deter * scheduler.init_noise_sigma

      for i, t in enumerate(scheduler.timesteps			):
        a     :			int				=   scheduler.scale_model_input(A				,		A			)

        a     :			int				=   model(A				,		A			)

        a     :			Optional[int]				=   scheduler.step(A				,		A				,		A			)
        a     :			Union[str, Any]				=   output.prev_sample

      a     :			Any				=   torch.sum(torch.abs(A			)			)
      a     :			List[str]				=   torch.mean(torch.abs(A			)			)

      assert abs(result_sum.item() - 10_06.3_88			) < 1E-2
      assert abs(result_mean.item() - 1.31			) < 1E-3





    def 				lowerCamelCase__      (							self	:					Optional[int]			):


      '''simple docstring'''




      a     :			List[str]				=   self.scheduler_classes[0]
      a     :			Any				=   self.get_scheduler_config(prediction_type='v_prediction'			)
      a     :			str				=   scheduler_class(**A			)

      scheduler.set_timesteps(self.num_inference_steps			)

      a     :			Tuple				=   self.dummy_model()
      a     :			Tuple				=   self.dummy_sample_deter * scheduler.init_noise_sigma

      for i, t in enumerate(scheduler.timesteps			):
        a     :			Any				=   scheduler.scale_model_input(A				,		A			)

        a     :			List[str]				=   model(A				,		A			)

        a     :			Optional[int]				=   scheduler.step(A				,		A				,		A			)
        a     :			Tuple				=   output.prev_sample

      a     :			Optional[Any]				=   torch.sum(torch.abs(A			)			)
      a     :			Optional[int]				=   torch.mean(torch.abs(A			)			)

      assert abs(result_sum.item() - 0.00_17			) < 1E-2
      assert abs(result_mean.item() - 2.2676E-06			) < 1E-3





    def 				lowerCamelCase__      (							self	:					Tuple			):


      '''simple docstring'''




      a     :			Optional[Any]				=   self.scheduler_classes[0]
      a     :			Union[str, Any]				=   self.get_scheduler_config()
      a     :			Union[str, Any]				=   scheduler_class(**A			)

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

      a     :			Optional[Any]				=   self.dummy_model()
      a     :			Dict				=   self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
      a     :			Union[str, Any]				=   sample.to(A			)

      for i, t in enumerate(scheduler.timesteps			):
        a     :			Optional[int]				=   scheduler.scale_model_input(A				,		A			)

        a     :			List[str]				=   model(A				,		A			)

        a     :			Optional[Any]				=   scheduler.step(A				,		A				,		A			)
        a     :			str				=   output.prev_sample

      a     :			Dict				=   torch.sum(torch.abs(A			)			)
      a     :			Tuple				=   torch.mean(torch.abs(A			)			)

      assert abs(result_sum.item() - 10_06.3_88			) < 1E-2
      assert abs(result_mean.item() - 1.31			) < 1E-3





    def 				lowerCamelCase__      (							self	:					Dict			):


      '''simple docstring'''




      a     :			Tuple				=   self.scheduler_classes[0]
      a     :			Dict				=   self.get_scheduler_config()
      a     :			Optional[Any]				=   scheduler_class(**A				,		use_karras_sigmas=A			)

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

      a     :			Any				=   self.dummy_model()
      a     :			Union[str, Any]				=   self.dummy_sample_deter.to(A			) * scheduler.init_noise_sigma
      a     :			Any				=   sample.to(A			)

      for t in scheduler.timesteps:
        a     :			int				=   scheduler.scale_model_input(A				,		A			)

        a     :			Tuple				=   model(A				,		A			)

        a     :			List[Any]				=   scheduler.step(A				,		A				,		A			)
        a     :			int				=   output.prev_sample

      a     :			Tuple				=   torch.sum(torch.abs(A			)			)
      a     :			Any				=   torch.mean(torch.abs(A			)			)

      assert abs(result_sum.item() - 38_12.99_27			) < 2E-2
      assert abs(result_mean.item() - 4.96_48			) < 1E-3




