





import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_ ):
       _UpperCAmelCase				:  Optional[int]			   =   ["image_processor", "tokenizer"]
       _UpperCAmelCase				:  Union[str, Any]			   =   "ViltImageProcessor"
       _UpperCAmelCase				:  Tuple			   =   ("BertTokenizer", "BertTokenizerFast")







       def __init__(				self					:     Optional[int]		, A					:     Optional[Any]=None		, A					:     Tuple=None		, **A					:     Optional[Any]      )       ->str:
          lowerCamelCase__       :						List[str]        =							None
          if "feature_extractor" in kwargs:
             warnings.warn(
                 '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                 ''' instead.'''		, A		, )
             lowerCamelCase__       :						List[Any]        =							kwargs.pop('''feature_extractor'''      )

          lowerCamelCase__       :						Tuple        =							image_processor if image_processor is not None else feature_extractor
          if image_processor is None:
             raise ValueError('''You need to specify an `image_processor`.'''      )
          if tokenizer is None:
             raise ValueError('''You need to specify a `tokenizer`.'''      )

          super().__init__(A		, A      )
          lowerCamelCase__       :						Any        =							self.image_processor







       def __call__(				self					:     str		, A					:     Dict		, A					:     Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None		, A					:     bool = True		, A					:     Union[bool, str, PaddingStrategy] = False		, A					:     Union[bool, str, TruncationStrategy] = None		, A					:     Optional[int] = None		, A					:     int = 0		, A					:     Optional[int] = None		, A					:     Optional[bool] = None		, A					:     Optional[bool] = None		, A					:     bool = False		, A					:     bool = False		, A					:     bool = False		, A					:     bool = False		, A					:     bool = True		, A					:     Optional[Union[str, TensorType]] = None		, **A					:     int		, )       ->BatchEncoding:
          lowerCamelCase__       :						Tuple        =							self.tokenizer(
              text=A		, add_special_tokens=A		, padding=A		, truncation=A		, max_length=A		, stride=A		, pad_to_multiple_of=A		, return_token_type_ids=A		, return_attention_mask=A		, return_overflowing_tokens=A		, return_special_tokens_mask=A		, return_offsets_mapping=A		, return_length=A		, verbose=A		, return_tensors=A		, **A		, )
          # add pixel_values + pixel_mask
          lowerCamelCase__       :						Union[str, Any]        =							self.image_processor(A		, return_tensors=A      )
          encoding.update(A      )

          return encoding







       def 						__lowerCamelCase   (				self					:     Union[str, Any]		, *A					:     Any		, **A					:     Any      )       ->str:
          return self.tokenizer.batch_decode(*A		, **A      )







       def 						__lowerCamelCase   (				self					:     List[str]		, *A					:     Optional[int]		, **A					:     Dict      )       ->Tuple:
          return self.tokenizer.decode(*A		, **A      )







       @property
       def 						__lowerCamelCase   (				self					:     List[Any]      )       ->Union[str, Any]:
          lowerCamelCase__       :						Any        =							self.tokenizer.model_input_names
          lowerCamelCase__       :						List[Any]        =							self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )







       @property
       def 						__lowerCamelCase   (				self					:     Tuple      )       ->int:
          warnings.warn(
              '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''		, A		, )
          return self.image_processor_class







       @property
       def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->Any:
          warnings.warn(
              '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''		, A		, )
          return self.image_processor










from __future__ import annotations

_A :   List[str]  			=  '#'


class        __SCREAMING_SNAKE_CASE      :







       def __init__(				self					:     List[Any]      )       ->None:
          lowerCamelCase__       :						dict        =							{}







       def 						__lowerCamelCase   (				self					:     Union[str, Any]		, A					:     str      )       ->None:
          lowerCamelCase__       :						Any        =							self._trie
          for char in text:
             if char not in trie:
                lowerCamelCase__       :						Any        =							{}
             lowerCamelCase__       :						Any        =							trie[char]
          lowerCamelCase__       :						List[str]        =							True







       def 						__lowerCamelCase   (				self					:     List[Any]		, A					:     str      )       ->tuple | list:
          lowerCamelCase__       :						Dict        =							self._trie
          for char in prefix:
             if char in trie:
                lowerCamelCase__       :						List[Any]        =							trie[char]
             else:
                return []
          return self._elements(A      )







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


_A :   str  			=  Trie()
_A :   List[Any]  			=  ('depart', 'detergent', 'daring', 'dog', 'deer', 'deal')
for word in words:
       trie.insert_word(word)



def 	_a						(   UpperCAmelCase					)    ->    tuple:







   """simple docstring"""
   lowerCamelCase__       :						Optional[int]        =							trie.find_word(UpperCAmelCase					)
   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()



