
'''simple docstring'''







import functools
from typing import Any







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A			) ->			bool:

	# Validation
	if not isinstance(__A    ,       __A			) or len(__A			) == 0:
		raise ValueError('the string should be not empty string'			)

	if not isinstance(__A    ,       __A			) or not all(
	    isinstance(__A    ,       __A			) and len(__A			) > 0 for item in words			):
		raise ValueError('the words should be a list of non-empty strings'			)

	# Build trie
	_snake_case     		=	{}
	_snake_case     		=	'WORD_KEEPER'

	for word in words:
		_snake_case     		=	trie
		for c in word:
			if c not in trie_node:
				_snake_case     		=	{}

			_snake_case     		=	trie_node[c]

		_snake_case     		=	True

	_snake_case     		=	len(__A			)

	# Dynamic programming method
	@functools.cache
	def is_breakable(__A			) -> bool:
		if index == len_string:
			return True

		_snake_case     		=	trie
		for i in range(__A    ,       __A			):
			_snake_case     		=	trie_node.get(string[i]    ,       __A			)

			if trie_node is None:
				return False

			if trie_node.get(__A    ,       __A			) and is_breakable(i + 1			):
				return True

		return False

	return is_breakable(0			)


if __name__ == "__main__":
					import doctest

					doctest.testmod()



'''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__)

