




"""simple docstring"""



from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE    =	{
    "configuration_layoutlmv3": [
        "LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "LayoutLMv3Config",
        "LayoutLMv3OnnxConfig",
    ],
    "processing_layoutlmv3": ["LayoutLMv3Processor"],
    "tokenization_layoutlmv3": ["LayoutLMv3Tokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE    =	["LayoutLMv3TokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE    =	[
          "LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST",
          "LayoutLMv3ForQuestionAnswering",
          "LayoutLMv3ForSequenceClassification",
          "LayoutLMv3ForTokenClassification",
          "LayoutLMv3Model",
          "LayoutLMv3PreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE    =	[
          "TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFLayoutLMv3ForQuestionAnswering",
          "TFLayoutLMv3ForSequenceClassification",
          "TFLayoutLMv3ForTokenClassification",
          "TFLayoutLMv3Model",
          "TFLayoutLMv3PreTrainedModel",
      ]

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE    =	["LayoutLMv3FeatureExtractor"]
      SCREAMING_SNAKE_CASE    =	["LayoutLMv3ImageProcessor"]


if TYPE_CHECKING:
      from .configuration_layoutlmva import (
          LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
          LayoutLMvaConfig,
          LayoutLMvaOnnxConfig,
      )
      from .processing_layoutlmva import LayoutLMvaProcessor
      from .tokenization_layoutlmva import LayoutLMvaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_layoutlmva import (
                LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
                LayoutLMvaForQuestionAnswering,
                LayoutLMvaForSequenceClassification,
                LayoutLMvaForTokenClassification,
                LayoutLMvaModel,
                LayoutLMvaPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_layoutlmva import (
                TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFLayoutLMvaForQuestionAnswering,
                TFLayoutLMvaForSequenceClassification,
                TFLayoutLMvaForTokenClassification,
                TFLayoutLMvaModel,
                TFLayoutLMvaPreTrainedModel,
            )

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
            from .image_processing_layoutlmva import LayoutLMvaImageProcessor

else:
      import sys

      SCREAMING_SNAKE_CASE    =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)











"""simple docstring"""



from __future__ import annotations

SCREAMING_SNAKE_CASE    =	"#"




class      UpperCAmelCase_  :




    def __init__(   self						:	Dict				)				->							None:




     '''simple docstring'''

     A__	=    {}




    def 							__magic_name__							(   self						:	Optional[Any]     ,			snake_case_						:	str				)				->							None:




     '''simple docstring'''

     A__	=    self._trie
     for char in text:
      if char not in trie:
       A__	=    {}
      A__	=    trie[char]
     A__	=    True




    def 							__magic_name__							(   self						:	List[Any]     ,			snake_case_						:	str				)				->							tuple | list:




     '''simple docstring'''

     A__	=    self._trie
     for char in prefix:
      if char in trie:
       A__	=    trie[char]
      else:
       return []
     return self._elements(snake_case_				)




    def 							__magic_name__							(   self						:	Union[str, Any]     ,			snake_case_						:	dict				)				->							tuple:




     '''simple docstring'''

     A__	=    []
     for c, v in d.items():
      A__	=    [" "] if c == END else [(c + s) for s in self._elements(snake_case_				)]
      result.extend(snake_case_				)
     return tuple(snake_case_				)






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







def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		tuple:
 A__	=    trie.find_word(lowercase_						)
 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()





