__lowerCamelCase    :				int							     =	{
    0: '''0''',
    1: '''1''',
    2: '''2''',
    3: '''3''',
    4: '''4''',
    5: '''5''',
    6: '''6''',
    7: '''7''',
    8: '''8''',
    9: '''9''',
    10: '''a''',
    11: '''b''',
    12: '''c''',
    13: '''d''',
    14: '''e''',
    15: '''f''',
}



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: float			)							->      str:







      """simple docstring"""





      assert type(__UpperCamelCase			) in (int, float) and decimal == int(__UpperCamelCase			)
      SCREAMING_SNAKE_CASE__    							=   int(__UpperCamelCase			)
      SCREAMING_SNAKE_CASE__    							=   """"""
      SCREAMING_SNAKE_CASE__    							=   False
      if decimal < 0:
            SCREAMING_SNAKE_CASE__    							=   True
            decimal *= -1
      while decimal > 0:
            SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   divmod(__UpperCamelCase				,  16			)
            SCREAMING_SNAKE_CASE__    							=   values[remainder] + hexadecimal
      SCREAMING_SNAKE_CASE__    							=   """0x""" + hexadecimal
      if negative:
            SCREAMING_SNAKE_CASE__    							=   """-""" + hexadecimal
      return hexadecimal


if __name__ == "__main__":
   import doctest

   doctest.testmod()

from typing import TYPE_CHECKING

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


__lowerCamelCase    :				List[Any]							     =	{
    '''configuration_informer''': [
        '''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''InformerConfig''',
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase    :				Union[str, Any]							     =	[
       '''INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''InformerForPrediction''',
       '''InformerModel''',
       '''InformerPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_informer import (
          INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          InformerForPrediction,
          InformerModel,
          InformerPreTrainedModel,
      )

else:
   import sys

   __lowerCamelCase    :				Dict							     =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
