

"""simple docstring"""




import functools
from typing import Any

def 						A							(  snake_case__		,					snake_case__     ):




			'''simple docstring'''


			if not isinstance(snake_case__		,					snake_case__     ) or len(snake_case__     ) == 0:
						raise ValueError("""the string should be not empty string"""     )

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

			# Build trie
			SCREAMING_SNAKE_CASE__										=   {}
			SCREAMING_SNAKE_CASE__										=   """WORD_KEEPER"""

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

									SCREAMING_SNAKE_CASE__										=   trie_node[c]

						SCREAMING_SNAKE_CASE__										=   True

			SCREAMING_SNAKE_CASE__										=   len(snake_case__     )

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

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

									if trie_node is None:
												return False

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

						return False

			return is_breakable(0     )


if __name__ == "__main__":
						import doctest

						doctest.testmod()





"""simple docstring"""




import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu





@require_cpu
class  lowerCamelCase    (unittest.TestCase      ):

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     str:
						SCREAMING_SNAKE_CASE__										=   torch.nn.Linear(1_0     ,					1_0							)
						SCREAMING_SNAKE_CASE__										=   torch.optim.SGD(model.parameters()     ,					0.1							)
						SCREAMING_SNAKE_CASE__										=   Accelerator()
						SCREAMING_SNAKE_CASE__										=   accelerator.prepare(__UpperCAmelCase							)
						try:
									pickle.loads(pickle.dumps(__UpperCAmelCase							)							)
						except Exception as e:
									self.fail(F"""Accelerated optimizer pickling failed with {e}"""							)
						AcceleratorState._reset_state()


