'''simple docstring'''






def    _SCREAMING_SNAKE_CASE		(			):

     for a in range(3_0_0							):
          for b in range(a + 1      ,		4_0_0							):
               for c in range(b + 1      ,		5_0_0							):
                    if (a + b + c) == 1_0_0_0 and (a**2) + (b**2) == (c**2):
                         return a * b * c

     return -1





def    _SCREAMING_SNAKE_CASE		(			):

     for a in range(3_0_0							):
          for b in range(4_0_0							):
               _A								=     1_0_0_0 - a - b
               if a < b < c and (a**2) + (b**2) == (c**2):
                    return a * b * c

     return -1





def    _SCREAMING_SNAKE_CASE		(			):
     import timeit

     print(
         timeit.timeit('solution()'      ,		setup='from __main__ import solution'      ,		number=1_0_0_0							)							)
     print(
         timeit.timeit(
             'solution_fast()'      ,		setup='from __main__ import solution_fast'      ,		number=1_0_0_0							)							)


if __name__ == "__main__":
  print(F'''{solution() = }''')




'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCAmelCase						:    Dict     					=					{
    '''configuration_decision_transformer''': [
        '''DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''DecisionTransformerConfig''',
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase						:    Union[str, Any]     					=					[
       '''DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''DecisionTransformerGPT2Model''',
       '''DecisionTransformerGPT2PreTrainedModel''',
       '''DecisionTransformerModel''',
       '''DecisionTransformerPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_decision_transformer import (
       DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       DecisionTransformerConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_decision_transformer import (
          DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          DecisionTransformerGPTaModel,
          DecisionTransformerGPTaPreTrainedModel,
          DecisionTransformerModel,
          DecisionTransformerPreTrainedModel,
      )


else:
   import sys

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



