



'''simple docstring'''


from typing import TYPE_CHECKING

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


_lowerCamelCase      :			Any       =      {
    "configuration_resnet": ["RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "ResNetConfig", "ResNetOnnxConfig"]
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowerCamelCase      :			Union[str, Any]       =      [
       "RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
       "ResNetForImageClassification",
       "ResNetModel",
       "ResNetPreTrainedModel",
       "ResNetBackbone",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowerCamelCase      :			Optional[int]       =      [
       "TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TFResNetForImageClassification",
       "TFResNetModel",
       "TFResNetPreTrainedModel",
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowerCamelCase      :			Dict       =      [
       "FlaxResNetForImageClassification",
       "FlaxResNetModel",
       "FlaxResNetPreTrainedModel",
   ]

if TYPE_CHECKING:
   from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_resnet import (
          RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
          ResNetBackbone,
          ResNetForImageClassification,
          ResNetModel,
          ResNetPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_resnet import (
          TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFResNetForImageClassification,
          TFResNetModel,
          TFResNetPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
   import sys

   _lowerCamelCase      :			Optional[int]       =      _LazyModule(__name__, globals()["__file__"], _import_structure)



from collections.abc import Sequence


def 				__A(lowerCAmelCase = None			)		->					int:

		"""simple docstring"""






		if nums is None or not nums:
				raise ValueError("""Input sequence should not be empty"""			)

		_UpperCamelCase         =						nums[0]
		for i in range(1    ,	len(lowerCAmelCase			)			):
				_UpperCamelCase         =						nums[i]
				_UpperCamelCase         =						max(lowerCAmelCase    ,	ans + num    ,	lowerCAmelCase			)

		return ans


if __name__ == "__main__":
		import doctest

		doctest.testmod()

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



