'''simple docstring'''





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 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   Tuple							=						["image_processor", "tokenizer"]
    snake_case__							:   Optional[int]							=						"ViltImageProcessor"
    snake_case__							:   str							=						("BertTokenizer", "BertTokenizerFast")






    def __init__(       self    ,							lowercase__=None    ,							lowercase__=None    ,							**lowercase__ )							-> int:
         SCREAMING_SNAKE_CASE					:			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.'    ,							lowercase__    ,							)
              SCREAMING_SNAKE_CASE					:			Optional[int]    	=       kwargs.pop('feature_extractor' )

         SCREAMING_SNAKE_CASE					:			str    	=       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__(lowercase__    ,							lowercase__ )
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.image_processor






    def __call__(       self    ,							lowercase__    ,							lowercase__ = None    ,							lowercase__ = True    ,							lowercase__ = False    ,							lowercase__ = None    ,							lowercase__ = None    ,							lowercase__ = 0    ,							lowercase__ = None    ,							lowercase__ = None    ,							lowercase__ = None    ,							lowercase__ = False    ,							lowercase__ = False    ,							lowercase__ = False    ,							lowercase__ = False    ,							lowercase__ = True    ,							lowercase__ = None    ,							**lowercase__    ,							)							-> BatchEncoding:
         SCREAMING_SNAKE_CASE					:			int    	=       self.tokenizer(
             text=lowercase__    ,							add_special_tokens=lowercase__    ,							padding=lowercase__    ,							truncation=lowercase__    ,							max_length=lowercase__    ,							stride=lowercase__    ,							pad_to_multiple_of=lowercase__    ,							return_token_type_ids=lowercase__    ,							return_attention_mask=lowercase__    ,							return_overflowing_tokens=lowercase__    ,							return_special_tokens_mask=lowercase__    ,							return_offsets_mapping=lowercase__    ,							return_length=lowercase__    ,							verbose=lowercase__    ,							return_tensors=lowercase__    ,							**lowercase__    ,							)
         # add pixel_values + pixel_mask
         SCREAMING_SNAKE_CASE					:			Dict    	=       self.image_processor(lowercase__    ,							return_tensors=lowercase__ )
         encoding.update(lowercase__ )

         return encoding






    def       _UpperCamelCase    (       self    ,							*lowercase__    ,							**lowercase__ )							-> Any:
         return self.tokenizer.batch_decode(*lowercase__    ,							**lowercase__ )






    def       _UpperCamelCase    (       self    ,							*lowercase__    ,							**lowercase__ )							-> List[Any]:
         return self.tokenizer.decode(*lowercase__    ,							**lowercase__ )






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






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






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

'''simple docstring'''





from __future__ import annotations


def     __lowerCAmelCase						(       a_       ,      a_ = None		)      ->	list[list[str]]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       word_bank or []
     # create a table
     SCREAMING_SNAKE_CASE					:			int    	=       len(a_		) + 1

     SCREAMING_SNAKE_CASE					:			list[list[list[str]]]    	=       []
     for _ in range(a_		):
          table.append([]		)
     # seed value
     SCREAMING_SNAKE_CASE					:			List[Any]    	=       [[]]  # because empty string has empty combination

     # iterate through the indices
     for i in range(a_		):
          # condition
          if table[i] != []:
               for word in word_bank:
                    # slice condition
                    if target[i : i + len(a_		)] == word:
                         SCREAMING_SNAKE_CASE					:			list[list[str]]    	=       [
                             [word, *way] for way in table[i]
                         ]
                         # adds the word to every combination the current position holds
                         # now,push that combination to the table[i+len(word)]
                         table[i + len(a_		)] += new_combinations

    # combinations are in reverse order so reverse for better output
     for combination in table[len(a_		)]:
          combination.reverse()

     return table[len(a_		)]


if __name__ == "__main__":
 print(all_construct("""jwajalapa""", ["""jwa""", """j""", """w""", """a""", """la""", """lapa"""]))
 print(all_construct("""rajamati""", ["""s""", """raj""", """amat""", """raja""", """ma""", """i""", """t"""]))
 print(
     all_construct(
         """hexagonosaurus""",
         ["""h""", """ex""", """hex""", """ag""", """ago""", """ru""", """auru""", """rus""", """go""", """no""", """o""", """s"""],
     )
 )



