





from typing import TYPE_CHECKING

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


_UpperCAmelCase							:			List[str]        =							{
    """configuration_distilbert""": [
        """DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """DistilBertConfig""",
        """DistilBertOnnxConfig""",
    ],
    """tokenization_distilbert""": ["""DistilBertTokenizer"""],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCAmelCase							:			Any        =							["""DistilBertTokenizerFast"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCAmelCase							:			Dict        =							[
        """DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """DistilBertForMaskedLM""",
        """DistilBertForMultipleChoice""",
        """DistilBertForQuestionAnswering""",
        """DistilBertForSequenceClassification""",
        """DistilBertForTokenClassification""",
        """DistilBertModel""",
        """DistilBertPreTrainedModel""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCAmelCase							:			Dict        =							[
        """TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFDistilBertForMaskedLM""",
        """TFDistilBertForMultipleChoice""",
        """TFDistilBertForQuestionAnswering""",
        """TFDistilBertForSequenceClassification""",
        """TFDistilBertForTokenClassification""",
        """TFDistilBertMainLayer""",
        """TFDistilBertModel""",
        """TFDistilBertPreTrainedModel""",
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCAmelCase							:			str        =							[
        """FlaxDistilBertForMaskedLM""",
        """FlaxDistilBertForMultipleChoice""",
        """FlaxDistilBertForQuestionAnswering""",
        """FlaxDistilBertForSequenceClassification""",
        """FlaxDistilBertForTokenClassification""",
        """FlaxDistilBertModel""",
        """FlaxDistilBertPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_distilbert import (
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
        DistilBertConfig,
        DistilBertOnnxConfig,
    )
    from .tokenization_distilbert import DistilBertTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_distilbert_fast import DistilBertTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_distilbert import (
            DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            DistilBertForMaskedLM,
            DistilBertForMultipleChoice,
            DistilBertForQuestionAnswering,
            DistilBertForSequenceClassification,
            DistilBertForTokenClassification,
            DistilBertModel,
            DistilBertPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_distilbert import (
            TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFDistilBertForMaskedLM,
            TFDistilBertForMultipleChoice,
            TFDistilBertForQuestionAnswering,
            TFDistilBertForSequenceClassification,
            TFDistilBertForTokenClassification,
            TFDistilBertMainLayer,
            TFDistilBertModel,
            TFDistilBertPreTrainedModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_distilbert import (
            FlaxDistilBertForMaskedLM,
            FlaxDistilBertForMultipleChoice,
            FlaxDistilBertForQuestionAnswering,
            FlaxDistilBertForSequenceClassification,
            FlaxDistilBertForTokenClassification,
            FlaxDistilBertModel,
            FlaxDistilBertPreTrainedModel,
        )

else:
    import sys

    _UpperCAmelCase							:			List[str]        =							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)












from __future__ import annotations

_UpperCAmelCase							:			Tuple        =							"""#"""

class   lowerCAmelCase				:
 def __init__( self    : Optional[Any]      )     ->      None:
     lowerCamelCase__       : dict   					=						{}
 def 	A_      ( self    : Optional[int]     ,	UpperCAmelCase    : str      )     ->      None:
     lowerCamelCase__       : int   					=						self._trie
     for char in text:
         if char not in trie:
             lowerCamelCase__       : Tuple   					=						{}
         lowerCamelCase__       : Dict   					=						trie[char]
     lowerCamelCase__       : Optional[int]   					=						True
 def 	A_      ( self    : Optional[int]     ,	UpperCAmelCase    : str      )     ->      tuple | list:
     lowerCamelCase__       : Tuple   					=						self._trie
     for char in prefix:
         if char in trie:
             lowerCamelCase__       : List[Any]   					=						trie[char]
         else:
             return []
     return self._elements(UpperCAmelCase      )







 def 	A_      ( self    : List[str]     ,	UpperCAmelCase    : dict      )     ->      tuple:
     lowerCamelCase__       : Optional[Any]   					=						[]
     for c, v in d.items():
         lowerCamelCase__       : Optional[Any]   					=						[' '] if c == END else [(c + s) for s in self._elements(UpperCAmelCase      )]
         result.extend(UpperCAmelCase      )
     return tuple(UpperCAmelCase      )


_UpperCAmelCase							:			str        =							Trie()
_UpperCAmelCase							:			Tuple        =							("""depart""", """detergent""", """daring""", """dog""", """deer""", """deal""")
for word in words:
    trie.insert_word(word)


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	tuple:
    lowerCamelCase__       : str   					=						trie.find_word(_UpperCAmelCase					)
    return tuple(string + word for word in suffixes					)


def 			SCREAMING_SNAKE_CASE (			)     ->	None:
    print(autocomplete_using_trie('de'					)					)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()





