






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCamelCase    :						List[str]		     =  {"""configuration_reformer""": ["""REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ReformerConfig"""]}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase    :						Union[str, Any]		     =  ["""ReformerTokenizer"""]

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase    :						int		     =  ["""ReformerTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase    :						Optional[Any]		     =  [
							    """REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """ReformerAttention""",
							    """ReformerForMaskedLM""",
							    """ReformerForQuestionAnswering""",
							    """ReformerForSequenceClassification""",
							    """ReformerLayer""",
							    """ReformerModel""",
							    """ReformerModelWithLMHead""",
							    """ReformerPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_reformer import ReformerTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_reformer_fast import ReformerTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_reformer import (
														    REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ReformerAttention,
														    ReformerForMaskedLM,
														    ReformerForQuestionAnswering,
														    ReformerForSequenceClassification,
														    ReformerLayer,
														    ReformerModel,
														    ReformerModelWithLMHead,
														    ReformerPreTrainedModel,
														)

else:
							import sys

							__UpperCamelCase    :						str		     =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)











def lowerCamelCase__    (							)      ->				int:
   return [
       a * b * (1000 - a - b)
       for a in range(1       ,      999       )
       for b in range(snake_case_       ,      999       )
       if (a * a + b * b == (1000 - a - b) ** 2)
   ][0]


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

