'''simple docstring'''

def lowerCamelCase__						(       ):






 for a in range(300       ):
  for b in range(a + 1       ,			400       ):
   for c in range(b + 1       ,			500       ):
    if (a + b + c) == 1_000 and (a**2) + (b**2) == (c**2):
     return a * b * c

 return -1



def lowerCamelCase__						(       ):






 for a in range(300       ):
  for b in range(400       ):
   UpperCAmelCase_				       =					1_000 - a - b
   if a < b < c and (a**2) + (b**2) == (c**2):
    return a * b * c

 return -1



def lowerCamelCase__						(       ):
 import timeit

 print(
     timeit.timeit("solution()"       ,			setup="from __main__ import solution"       ,			number=1_000       )       )
 print(
     timeit.timeit(
         "solution_fast()"       ,			setup="from __main__ import solution_fast"       ,			number=1_000       )       )


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

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__snake_case       :   Optional[int]  		=    {
    '''configuration_gptsan_japanese''': ['''GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTSanJapaneseConfig'''],
    '''tokenization_gptsan_japanese''': ['''GPTSanJapaneseTokenizer'''],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __snake_case       :   List[Any]  		=    [
          '''GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''GPTSanJapaneseForConditionalGeneration''',
          '''GPTSanJapaneseModel''',
          '''GPTSanJapanesePreTrainedModel''',
      ]
      __snake_case       :   Optional[int]  		=    [
          '''GPTSanJapaneseTokenizer''',
      ]


if TYPE_CHECKING:
      from .configuration_gptsan_japanese import GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTSanJapaneseConfig
      from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_gptsan_japanese import (
                GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
                GPTSanJapaneseForConditionalGeneration,
                GPTSanJapaneseModel,
                GPTSanJapanesePreTrainedModel,
            )
            from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer


else:
      import sys

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