





'''simple docstring'''






import math
def 							_lowerCamelCase(  UpperCamelCase__    : int		)		->  bool:
    assert isinstance(lowerCamelCase__    ,       lowerCamelCase__		) and (
        number >= 0
    ), "'number' must been an int and positive"

    if 1 < number < 4:
        # 2 and 3 are primes
        return True
    elif number < 2 or not number % 2:
        # Negatives, 0, 1 and all even numbers are not primes
        return False

    A       :       List[str]							=						range(3    ,       int(math.sqrt(lowerCamelCase__		) + 1		)    ,       2		)
    return not any(not number % i for i in odd_numbers		)





def 							_lowerCamelCase(  UpperCamelCase__    : Optional[Any]    ,       UpperCamelCase__    : str=1    ,       **UpperCamelCase__    : Union[str, Any]		)		->  Optional[int]:
    A       :       str							=						factor * value
    A       :       Optional[int]							=						value

    while not is_prime(lowerCamelCase__		):
        value += 1 if not ("desc" in kwargs and kwargs["desc"] is True) else -1

    if value == first_value_val:
        return next_prime(value + 1    ,       **lowerCamelCase__		)
    return value







'''simple docstring'''






from typing import TYPE_CHECKING

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


snake_case_     =	{
    """configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
    """tokenization_biogpt""": ["""BioGptTokenizer"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case_     =	[
         """BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """BioGptForCausalLM""",
         """BioGptForTokenClassification""",
         """BioGptForSequenceClassification""",
         """BioGptModel""",
         """BioGptPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
     from .tokenization_biogpt import BioGptTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_biogpt import (
              BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
              BioGptForCausalLM,
              BioGptForSequenceClassification,
              BioGptForTokenClassification,
              BioGptModel,
              BioGptPreTrainedModel,
          )


else:
     import sys

     snake_case_     =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
