'''simple docstring'''





SCREAMING_SNAKE_CASE_:	dict[str, float]    ={
    "km/h": 1.0,
    "m/s": 3.6,
    "mph": 1.609344,
    "knot": 1.852,
}

SCREAMING_SNAKE_CASE_:	dict[str, float]    ={
    "km/h": 1.0,
    "m/s": 0.277777778,
    "mph": 0.621371192,
    "knot": 0.539956803,
}





def       lowerCAmelCase_							(   snake_case_			:					float   ,       snake_case_			:					str   ,       snake_case_			:					str     )       -> float:


     '''simple docstring'''




     if unit_to not in speed_chart or unit_from not in speed_chart_inverse:
          UpperCAmelCase_			       =  (
              f"""Incorrect 'from_type' or 'to_type' value: {unit_from!r}, {unit_to!r}\n"""
              f"""Valid values are: {", ".join(snake_case_     )}"""
          )
          raise ValueError(snake_case_     )
     return round(speed * speed_chart[unit_from] * speed_chart_inverse[unit_to]   ,       3     )


if __name__ == "__main__":
    import doctest

    doctest.testmod()








"""simple docstring"""


from typing import TYPE_CHECKING

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


__snake_case    					=					{
    'configuration_informer': [
        'INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'InformerConfig',
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __snake_case    					=					[
       '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

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