

def       a__					(		UpperCAmelCase							:      int = 1_000      )  ->						int:
  return sum(e for e in range(3    ,			UpperCAmelCase      ) if e % 3 == 0 or e % 5 == 0      )


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








from typing import TYPE_CHECKING

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


_lowerCamelCase    :       str        =							{"configuration_xlnet": ["XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLNetConfig"]}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _lowerCamelCase    :       List[Any]        =							["XLNetTokenizer"]

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _lowerCamelCase    :       Optional[Any]        =							["XLNetTokenizerFast"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _lowerCamelCase    :       Dict        =							[
         "XLNET_PRETRAINED_MODEL_ARCHIVE_LIST",
         "XLNetForMultipleChoice",
         "XLNetForQuestionAnswering",
         "XLNetForQuestionAnsweringSimple",
         "XLNetForSequenceClassification",
         "XLNetForTokenClassification",
         "XLNetLMHeadModel",
         "XLNetModel",
         "XLNetPreTrainedModel",
         "load_tf_weights_in_xlnet",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _lowerCamelCase    :       Union[str, Any]        =							[
         "TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFXLNetForMultipleChoice",
         "TFXLNetForQuestionAnsweringSimple",
         "TFXLNetForSequenceClassification",
         "TFXLNetForTokenClassification",
         "TFXLNetLMHeadModel",
         "TFXLNetMainLayer",
         "TFXLNetModel",
         "TFXLNetPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_xlnet import XLNetTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_xlnet_fast import XLNetTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_xlnet import (
              XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              XLNetForMultipleChoice,
              XLNetForQuestionAnswering,
              XLNetForQuestionAnsweringSimple,
              XLNetForSequenceClassification,
              XLNetForTokenClassification,
              XLNetLMHeadModel,
              XLNetModel,
              XLNetPreTrainedModel,
              load_tf_weights_in_xlnet,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_xlnet import (
              TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFXLNetForMultipleChoice,
              TFXLNetForQuestionAnsweringSimple,
              TFXLNetForSequenceClassification,
              TFXLNetForTokenClassification,
              TFXLNetLMHeadModel,
              TFXLNetMainLayer,
              TFXLNetModel,
              TFXLNetPreTrainedModel,
          )

else:
     import sys

     _lowerCamelCase    :       Optional[Any]        =							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





