
'''simple docstring'''







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			Optional[Any]:
	_snake_case     		=	len(__A			)
	_snake_case     		=	sum(__A			)

	_snake_case     		=	[[False for x in range(s + 1			)] for y in range(n + 1			)]

	for i in range(1    ,       n + 1			):
		_snake_case     		=	True

	for i in range(1    ,       s + 1			):
		_snake_case     		=	False

	for i in range(1    ,       n + 1			):
		for j in range(1    ,       s + 1			):
			_snake_case     		=	dp[i][j - 1]

			if arr[i - 1] <= j:
				_snake_case     		=	dp[i][j] or dp[i - 1][j - arr[i - 1]]

	for j in range(int(s / 2			)    ,       -1    ,       -1			):
		if dp[n][j] is True:
			_snake_case     		=	s - 2 * j
			break

	return diff



'''simple docstring'''







from typing import TYPE_CHECKING

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


lowercase					:							str   =      {
    "configuration_nezha": ["NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP", "NezhaConfig"],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase					:							Any   =      [
					    "NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "NezhaForNextSentencePrediction",
					    "NezhaForMaskedLM",
					    "NezhaForPreTraining",
					    "NezhaForMultipleChoice",
					    "NezhaForQuestionAnswering",
					    "NezhaForSequenceClassification",
					    "NezhaForTokenClassification",
					    "NezhaModel",
					    "NezhaPreTrainedModel",
					]


if TYPE_CHECKING:
					from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_nezha import (
										    NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    NezhaForMaskedLM,
										    NezhaForMultipleChoice,
										    NezhaForNextSentencePrediction,
										    NezhaForPreTraining,
										    NezhaForQuestionAnswering,
										    NezhaForSequenceClassification,
										    NezhaForTokenClassification,
										    NezhaModel,
										    NezhaPreTrainedModel,
										)


else:
					import sys

					lowercase					:							Union[str, Any]   =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

