





from __future__ import annotations


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase )			->     int:
    if len(_UpperCAmelCase ) < k or k < 0:
        raise ValueError('Invalid Input' )
    lowerCamelCase__    :      Optional[int]					 =		sum(array[:k] )
    for i in range(len(_UpperCAmelCase ) - k ):
        lowerCamelCase__    :      str					 =		current_sum - array[i] + array[i + k]
        lowerCamelCase__    :      List[str]					 =		max(_UpperCAmelCase						,	_UpperCAmelCase )
    return max_sum


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

    testmod()
    _UpperCAmelCase				:       List[Any]	   =				[randint(-10_00, 10_00) for i in range(1_00)]
    _UpperCAmelCase				:       Optional[int]	   =				randint(0, 1_10)
    print(F"""The maximum sum of {k} consecutive elements is {max_sum_in_array(array,k)}""")












from typing import TYPE_CHECKING

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


_snake_case  :    List[Any]						    =  {
    '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  :    Optional[int]						    =  [
         '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  :    List[str]						    =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

