





from __future__ import annotations






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list[int]   )      ->   int:
 if not nums:
  return 0
 __lowerCAmelCase					:	str      =       nums[0]
 __lowerCAmelCase					:	List[str]      =       0
 for num in nums[1:]:
  __lowerCAmelCase ,					__lowerCAmelCase					:	Union[str, Any]      =       (
      max_excluding + num,
      max(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ),
  )
 return max(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
      import doctest

      doctest.testmod()






from typing import TYPE_CHECKING

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


_UpperCAmelCase             =							{
    'configuration_gpt_bigcode': ['GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTBigCodeConfig'],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _UpperCAmelCase             =							[
          'GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST',
          'GPTBigCodeForSequenceClassification',
          'GPTBigCodeForTokenClassification',
          'GPTBigCodeForCausalLM',
          'GPTBigCodeModel',
          'GPTBigCodePreTrainedModel',
      ]

if TYPE_CHECKING:
      from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_gpt_bigcode import (
                GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
                GPTBigCodeForCausalLM,
                GPTBigCodeForSequenceClassification,
                GPTBigCodeForTokenClassification,
                GPTBigCodeModel,
                GPTBigCodePreTrainedModel,
            )


else:
      import sys

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