
"""simple docstring"""





from typing import TYPE_CHECKING

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


_a	 =							{
    'configuration_open_llama': ['OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OpenLlamaConfig'],
}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _a	 =							['LlamaTokenizer']

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _a	 =							['LlamaTokenizerFast']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _a	 =							[
        'OpenLlamaForCausalLM',
        'OpenLlamaModel',
        'OpenLlamaPreTrainedModel',
        'OpenLlamaForSequenceClassification',
    ]


if TYPE_CHECKING:
    from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from transformers import LlamaTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from transformers import LlamaTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_open_llama import (
            OpenLlamaForCausalLM,
            OpenLlamaForSequenceClassification,
            OpenLlamaModel,
            OpenLlamaPreTrainedModel,
        )


else:
    import sys

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




"""simple docstring"""





def 			__a  (	__lowerCamelCase = 1000						):
   UpperCAmelCase_      :      List[Any]      =					0
   UpperCAmelCase_      :      List[str]      =					(n - 1) // 3
   total += ((terms) * (6 + (terms - 1) * 3)) // 2  # total of an A.P.
   UpperCAmelCase_      :      Any      =					(n - 1) // 5
   total += ((terms) * (10 + (terms - 1) * 5)) // 2
   UpperCAmelCase_      :      Optional[int]      =					(n - 1) // 15
   total -= ((terms) * (30 + (terms - 1) * 15)) // 2
   return total


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


