from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__magic_name__								={
    '''configuration_autoformer''': [
        '''AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''AutoformerConfig''',
    ],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__								=[
         '''AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''AutoformerForPrediction''',
         '''AutoformerModel''',
         '''AutoformerPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_autoformer import (
         AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
         AutoformerConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_autoformer import (
              AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              AutoformerForPrediction,
              AutoformerModel,
              AutoformerPreTrainedModel,
          )

else:
     import sys

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







from typing import Optional, Union

import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin



class     _A   (    __UpperCamelCase	, __UpperCamelCase							):






  @register_to_config
  def __init__(self   ,				SCREAMING_SNAKE_CASE_ = 768   ,				)		->      Tuple:





       '''simple docstring'''





       super().__init__()

       UpperCamelCase__										=      nn.Parameter(torch.zeros(1   ,				SCREAMING_SNAKE_CASE_	)	)
       UpperCamelCase__										=      nn.Parameter(torch.ones(1   ,				SCREAMING_SNAKE_CASE_	)	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = None   ,				)		->      Tuple:





       '''simple docstring'''





       UpperCamelCase__										=      nn.Parameter(self.mean.to(SCREAMING_SNAKE_CASE_	).to(SCREAMING_SNAKE_CASE_	)	)
       UpperCamelCase__										=      nn.Parameter(self.std.to(SCREAMING_SNAKE_CASE_	).to(SCREAMING_SNAKE_CASE_	)	)
       return self






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      int:





       '''simple docstring'''





       UpperCamelCase__										=      (embeds - self.mean) * 1.0 / self.std
       return embeds






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      int:





       '''simple docstring'''





       UpperCamelCase__										=      (embeds * self.std) + self.mean
       return embeds



