





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      lowerCAmelCase							(      __UpperCamelCase,						__UpperCamelCase    ):
 @register_to_config
 def __init__(   self						:   Any    ,  UpperCAmelCase						:   Union[str, Any] = 768    ,  )    ->			int:
     super().__init__()

     lowerCamelCase__					:   List[str]      				=	nn.Parameter(torch.zeros(1    ,  UpperCamelCase_			)			)
     lowerCamelCase__					:   Dict      				=	nn.Parameter(torch.ones(1    ,  UpperCamelCase_			)			)
 def   A_			(   self						:   int    ,  UpperCAmelCase						:   Optional[Any] = None    ,  UpperCAmelCase						:   int = None    ,  )    ->			Any:
     lowerCamelCase__					:   int      				=	nn.Parameter(self.mean.to(UpperCamelCase_			).to(UpperCamelCase_			)			)
     lowerCamelCase__					:   Optional[int]      				=	nn.Parameter(self.std.to(UpperCamelCase_			).to(UpperCamelCase_			)			)
     return self
 def   A_			(   self						:   int    ,  UpperCAmelCase						:   Union[str, Any]			)    ->			Tuple:
     lowerCamelCase__					:   List[Any]      				=	(embeds - self.mean) * 1.0 / self.std
     return embeds







 def   A_			(   self						:   Optional[int]    ,  UpperCAmelCase						:   int			)    ->			List[Any]:
     lowerCamelCase__					:   Optional[Any]      				=	(embeds * self.std) + self.mean
     return embeds












from typing import TYPE_CHECKING

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


_UpperCAmelCase       : List[str]			     =       {
    """configuration_autoformer""": [
        """AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """AutoformerConfig""",
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCAmelCase       : List[Any]			     =       [
        """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

    _UpperCAmelCase       : Dict			     =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





