


from __future__ import annotations







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


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

   testmod()
   lowerCamelCase__							:						List[Any]							     =   [randint(-1_0_0_0, 1_0_0_0) for i in range(1_0_0)]
   lowerCamelCase__							:						List[Any]							     =   randint(0, 1_1_0)
   print(F"""The maximum sum of {k} consecutive elements is {max_sum_in_array(array,k)}""")





import os
import sys

lowerCamelCase__							:						Optional[int]							     =   os.path.join(os.path.dirname(__file__), """src""")
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


lowerCamelCase__							:						Optional[int]							     =   [
    """torch""",
    """numpy""",
    """tokenizers""",
    """filelock""",
    """requests""",
    """tqdm""",
    """regex""",
    """sentencepiece""",
    """sacremoses""",
    """importlib_metadata""",
    """huggingface_hub""",
]







@add_start_docstrings(AutoConfig.__doc__				)
def SCREAMING_SNAKE_CASE	(			*__lowerCAmelCase							,   **__lowerCAmelCase				)    ->				Any:
  return AutoConfig.from_pretrained(*__lowerCAmelCase							,   **__lowerCAmelCase				)







@add_start_docstrings(AutoTokenizer.__doc__				)
def SCREAMING_SNAKE_CASE	(			*__lowerCAmelCase							,   **__lowerCAmelCase				)    ->				List[str]:
  return AutoTokenizer.from_pretrained(*__lowerCAmelCase							,   **__lowerCAmelCase				)







@add_start_docstrings(AutoModel.__doc__				)
def SCREAMING_SNAKE_CASE	(			*__lowerCAmelCase							,   **__lowerCAmelCase				)    ->				Tuple:
  return AutoModel.from_pretrained(*__lowerCAmelCase							,   **__lowerCAmelCase				)







@add_start_docstrings(AutoModelForCausalLM.__doc__				)
def SCREAMING_SNAKE_CASE	(			*__lowerCAmelCase							,   **__lowerCAmelCase				)    ->				Union[str, Any]:
  return AutoModelForCausalLM.from_pretrained(*__lowerCAmelCase							,   **__lowerCAmelCase				)







@add_start_docstrings(AutoModelForMaskedLM.__doc__				)
def SCREAMING_SNAKE_CASE	(			*__lowerCAmelCase							,   **__lowerCAmelCase				)    ->				List[Any]:
  return AutoModelForMaskedLM.from_pretrained(*__lowerCAmelCase							,   **__lowerCAmelCase				)







@add_start_docstrings(AutoModelForSequenceClassification.__doc__				)
def SCREAMING_SNAKE_CASE	(			*__lowerCAmelCase							,   **__lowerCAmelCase				)    ->				List[str]:
  return AutoModelForSequenceClassification.from_pretrained(*__lowerCAmelCase							,   **__lowerCAmelCase				)







@add_start_docstrings(AutoModelForQuestionAnswering.__doc__				)
def SCREAMING_SNAKE_CASE	(			*__lowerCAmelCase							,   **__lowerCAmelCase				)    ->				Union[str, Any]:
  return AutoModelForQuestionAnswering.from_pretrained(*__lowerCAmelCase							,   **__lowerCAmelCase				)

