





"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase_     :						Dict						=					{
    'configuration_time_series_transformer': [
        'TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TimeSeriesTransformerConfig',
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_     :						Union[str, Any]						=					[
       'TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TimeSeriesTransformerForPrediction',
       'TimeSeriesTransformerModel',
       'TimeSeriesTransformerPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_time_series_transformer import (
       TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       TimeSeriesTransformerConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_time_series_transformer import (
          TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TimeSeriesTransformerForPrediction,
          TimeSeriesTransformerModel,
          TimeSeriesTransformerPreTrainedModel,
      )

else:
   import sys

   lowerCamelCase_     :						Union[str, Any]						=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






"""simple docstring"""




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  _UpperCAmelCase  (   UpperCAmelCase__			,   UpperCAmelCase__		):



      '''simple docstring'''

      @register_to_config
      def __init__(     self					,      snake_case_ = 7_6_8					,      ):

        """simple docstring"""

        super().__init__()

        A_  :       Optional[int]										=  nn.Parameter(torch.zeros(1					,      snake_case_  )  )
        A_  :       Optional[int]										=  nn.Parameter(torch.ones(1					,      snake_case_  )  )

      def 					lowerCamelCase_			(     self					,      snake_case_ = None					,      snake_case_ = None					,      ):

        """simple docstring"""

        A_  :       str										=  nn.Parameter(self.mean.to(snake_case_  ).to(snake_case_  )  )
        A_  :       Optional[int]										=  nn.Parameter(self.std.to(snake_case_  ).to(snake_case_  )  )
        return self

      def 					lowerCamelCase_			(     self					,      snake_case_  ):

        """simple docstring"""

        A_  :       Tuple										=  (embeds - self.mean) * 1.0 / self.std
        return embeds

      def 					lowerCamelCase_			(     self					,      snake_case_  ):

        """simple docstring"""

        A_  :       List[str]										=  (embeds * self.std) + self.mean
        return embeds