






import functools
from typing import Any







def     a_   (__A				,       __A      )				->						bool:



					"""simple docstring"""

					# 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
					__a					:						dict[str, Any]      =					{}
					__a					:						Optional[Any]      =					"WORD_KEEPER"

					for word in words:
										__a					:						Optional[int]      =					trie
										for c in word:
															if c not in trie_node:
																				__a					:						Optional[Any]      =					{}

															__a					:						Union[str, Any]      =					trie_node[c]

										__a					:						Optional[Any]      =					True

					__a					:						Any      =					len(__A      )

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

										__a					:						List[Any]      =					trie
										for i in range(__A				,       __A      ):
															__a					:						Any      =					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()













import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
			import optax
			from flax.training.common_utils import onehot

			from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
			from transformers.models.ta.modeling_flax_ta import shift_tokens_right




@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 							snake_case_       (    unittest.TestCase      ):

						"""simple docstring"""



						@slow
						def        UpperCAmelCase__					(self:					Optional[int]   )    ->     List[str]:




											'''simple docstring'''



											__a					:						Optional[Any]      =					FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small"   )
											__a					:						Dict      =					AutoTokenizer.from_pretrained("google/mt5-small"   )

											__a					:						Any      =					tokenizer("Hello there" ,					return_tensors="np"   ).input_ids
											__a					:						Union[str, Any]      =					tokenizer("Hi I am" ,					return_tensors="np"   ).input_ids

											__a					:						Union[str, Any]      =					shift_tokens_right(__UpperCAmelCase ,					model.config.pad_token_id ,					model.config.decoder_start_token_id   )

											__a					:						Tuple      =					model(__UpperCAmelCase ,					decoder_input_ids=__UpperCAmelCase   ).logits
											__a					:						str      =					optax.softmax_cross_entropy(__UpperCAmelCase ,					onehot(__UpperCAmelCase ,					logits.shape[-1]   )   ).mean()

											__a					:						List[str]      =					-(labels.shape[-1] * loss.item())

											__a					:						Optional[int]      =					-84.91_27
											self.assertTrue(abs(mtf_score - EXPECTED_SCORE   ) < 1E-4   )





