





'''simple docstring'''


from __future__ import annotations


def 	__a(SCREAMING_SNAKE_CASE_  :					str		,       SCREAMING_SNAKE_CASE_  :					list[str] | None = None    ):
    _lowerCAmelCase				=      word_bank or []
    # create a table
    _lowerCAmelCase				=      len(SCREAMING_SNAKE_CASE_    ) + 1

    _lowerCAmelCase				=      []
    for _ in range(SCREAMING_SNAKE_CASE_    ):
        table.append([]    )
    # seed value
    _lowerCAmelCase				=      [[]]  # because empty string has empty combination

    # iterate through the indices
    for i in range(SCREAMING_SNAKE_CASE_    ):
        # condition
        if table[i] != []:
            for word in word_bank:
                # slice condition
                if target[i : i + len(SCREAMING_SNAKE_CASE_    )] == word:
                    _lowerCAmelCase				=      [
                        [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(SCREAMING_SNAKE_CASE_    )] += new_combinations

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

    return table[len(SCREAMING_SNAKE_CASE_    )]


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"],
           )
       )









'''simple docstring'''


from ...configuration_utils import PretrainedConfig

class    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							List[str]         =					"bert-generation"




 def __init__(	self			,     _lowerCAmelCase=50358			,     _lowerCAmelCase=1024			,     _lowerCAmelCase=24			,     _lowerCAmelCase=16			,     _lowerCAmelCase=4096			,     _lowerCAmelCase="gelu"			,     _lowerCAmelCase=0.1			,     _lowerCAmelCase=0.1			,     _lowerCAmelCase=512			,     _lowerCAmelCase=0.02			,     _lowerCAmelCase=1E-12			,     _lowerCAmelCase=0			,     _lowerCAmelCase=2			,     _lowerCAmelCase=1			,     _lowerCAmelCase="absolute"			,     _lowerCAmelCase=True			,     **_lowerCAmelCase			,     )    ->							Tuple:
     super().__init__(pad_token_id=_lowerCAmelCase			,     bos_token_id=_lowerCAmelCase			,     eos_token_id=_lowerCAmelCase			,     **_lowerCAmelCase		)

     _lowerCAmelCase				=      vocab_size
     _lowerCAmelCase				=      hidden_size
     _lowerCAmelCase				=      num_hidden_layers
     _lowerCAmelCase				=      num_attention_heads
     _lowerCAmelCase				=      hidden_act
     _lowerCAmelCase				=      intermediate_size
     _lowerCAmelCase				=      hidden_dropout_prob
     _lowerCAmelCase				=      attention_probs_dropout_prob
     _lowerCAmelCase				=      max_position_embeddings
     _lowerCAmelCase				=      initializer_range
     _lowerCAmelCase				=      layer_norm_eps
     _lowerCAmelCase				=      position_embedding_type
     _lowerCAmelCase				=      use_cache


