
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowercase											=    {
    """configuration_blenderbot_small""": [
        """BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BlenderbotSmallConfig""",
        """BlenderbotSmallOnnxConfig""",
    ],
    """tokenization_blenderbot_small""": ["""BlenderbotSmallTokenizer"""],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowercase											=    ["""BlenderbotSmallTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowercase											=    [
     """BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """BlenderbotSmallForCausalLM""",
     """BlenderbotSmallForConditionalGeneration""",
     """BlenderbotSmallModel""",
     """BlenderbotSmallPreTrainedModel""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowercase											=    [
     """TFBlenderbotSmallForConditionalGeneration""",
     """TFBlenderbotSmallModel""",
     """TFBlenderbotSmallPreTrainedModel""",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowercase											=    [
     """FlaxBlenderbotSmallForConditionalGeneration""",
     """FlaxBlenderbotSmallModel""",
     """FlaxBlenderbotSmallPreTrainedModel""",
 ]

if TYPE_CHECKING:
 from .configuration_blenderbot_small import (
     BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
     BlenderbotSmallConfig,
     BlenderbotSmallOnnxConfig,
 )
 from .tokenization_blenderbot_small import BlenderbotSmallTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_blenderbot_small_fast import BlenderbotSmallTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_blenderbot_small import (
      BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST,
      BlenderbotSmallForCausalLM,
      BlenderbotSmallForConditionalGeneration,
      BlenderbotSmallModel,
      BlenderbotSmallPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_blenderbot_small import (
      TFBlenderbotSmallForConditionalGeneration,
      TFBlenderbotSmallModel,
      TFBlenderbotSmallPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_blenderbot_small import (
      FlaxBlenderbotSmallForConditionalGeneration,
      FlaxBlenderbotSmallModel,
      FlaxBlenderbotSmallPreTrainedModel,
  )

else:
 import sys

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


from collections.abc import Sequence
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Sequence[int] | None = None				)			->						int:
       if nums is None or not nums:
              raise ValueError('''Input sequence should not be empty'''				)

       SCREAMING_SNAKE_CASE_							:				Tuple		=nums[0]
       for i in range(1			,							len(UpperCAmelCase_				)				):
              SCREAMING_SNAKE_CASE_							:				Any		=nums[i]
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=max(UpperCAmelCase_			,							ans + num			,							UpperCAmelCase_				)

       return ans


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 # Try on a sample input from the user
 _lowercase											=    int(input("""Enter number of elements : """).strip())
 _lowercase											=    list(map(int, input("""\nEnter the numbers : """).strip().split()))[:n]
 print(max_subsequence_sum(array))
