






from __future__ import annotations

__UpperCamelCase					:		Tuple	  =						"""#"""


class   __SCREAMING_SNAKE_CASE:
							def __init__(     self:      Optional[Any]						)		->		None:
											snake_case__									=     {}
							def lowerCAmelCase_ (     self:      Any       ,	UpperCamelCase:      str						)		->		None:
											snake_case__									=     self._trie
											for char in text:
															if char not in trie:
																			snake_case__									=     {}
															snake_case__									=     trie[char]
											snake_case__									=     True
							def lowerCAmelCase_ (     self:      int       ,	UpperCamelCase:      str						)		->		tuple | list:
											snake_case__									=     self._trie
											for char in prefix:
															if char in trie:
																			snake_case__									=     trie[char]
															else:
																			return []
											return self._elements(UpperCamelCase						)


							def lowerCAmelCase_ (     self:      Union[str, Any]       ,	UpperCamelCase:      dict						)		->		tuple:
											snake_case__									=     []
											for c, v in d.items():
															snake_case__									=     [' '] if c == END else [(c + s) for s in self._elements(UpperCamelCase						)]
															result.extend(UpperCamelCase						)
											return tuple(UpperCamelCase						)


__UpperCamelCase					:		Optional[int]	  =						Trie()
__UpperCamelCase					:		Dict	  =						("""depart""", """detergent""", """daring""", """dog""", """deer""", """deal""")
for word in words:
							trie.insert_word(word)
def      a_ (      _A  )			->			tuple:







				"""simple docstring"""
				snake_case__									=     trie.find_word(_A  )
				return tuple(string + word for word in suffixes  )
def      a_ (      )			->			None:







				"""simple docstring"""
				print(autocomplete_using_trie('de'  )  )


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()












from typing import TYPE_CHECKING

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


__UpperCamelCase					:		int	  =						{"""configuration_xglm""": ["""XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XGLMConfig"""]}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		Dict	  =						["""XGLMTokenizer"""]

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		Optional[int]	  =						["""XGLMTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		List[str]	  =						[
							    """XGLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """XGLMForCausalLM""",
							    """XGLMModel""",
							    """XGLMPreTrainedModel""",
							]


try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		Any	  =						[
							    """FlaxXGLMForCausalLM""",
							    """FlaxXGLMModel""",
							    """FlaxXGLMPreTrainedModel""",
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		str	  =						[
							    """TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFXGLMForCausalLM""",
							    """TFXGLMModel""",
							    """TFXGLMPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm import XGLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm_fast import XGLMTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_xglm import (
														    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFXGLMForCausalLM,
														    TFXGLMModel,
														    TFXGLMPreTrainedModel,
														)


else:
							import sys

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




