
from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


__lowerCAmelCase			:     List[Any]           =   {'configuration_gpt_neox': ['GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoXConfig']}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCAmelCase			:     List[Any]           =   ['GPTNeoXTokenizerFast']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCAmelCase			:     Optional[int]           =   [
        'GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST',
        'GPTNeoXForCausalLM',
        'GPTNeoXForQuestionAnswering',
        'GPTNeoXForSequenceClassification',
        'GPTNeoXForTokenClassification',
        'GPTNeoXLayer',
        'GPTNeoXModel',
        'GPTNeoXPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_gpt_neox import (
            GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
            GPTNeoXForCausalLM,
            GPTNeoXForQuestionAnswering,
            GPTNeoXForSequenceClassification,
            GPTNeoXForTokenClassification,
            GPTNeoXLayer,
            GPTNeoXModel,
            GPTNeoXPreTrainedModel,
        )


else:
    import sys

    __lowerCAmelCase			:     Union[str, Any]           =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)








def    __lowercase   (      _SCREAMING_SNAKE_CASE = 10	) ->   str:


      '''simple docstring'''


      if not isinstance(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) or n < 0:
            raise ValueError("""Invalid input"""	)
      SCREAMING_SNAKE_CASE		=						10**n
      SCREAMING_SNAKE_CASE		=						2_84_33 * (pow(2 ,						7_83_04_57 ,						_SCREAMING_SNAKE_CASE	)) + 1
      return str(number % modulus	)


if __name__ == "__main__":
      from doctest import testmod

      testmod()
      print(F'''{solution(1_0) = }''')


