




from __future__ import annotations
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase = None				):
  lowerCAmelCase__										=      word_bank or []
  # create a table
  lowerCAmelCase__										=      len(__lowerCAmelCase				) + 1

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

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

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

  return table[len(__lowerCAmelCase				)]


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"""],
          )
      )







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__magic_name__  :  Optional[int]					=       {
    """configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
    """tokenization_biogpt""": ["""BioGptTokenizer"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __magic_name__  :  int					=       [
          """BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """BioGptForCausalLM""",
          """BioGptForTokenClassification""",
          """BioGptForSequenceClassification""",
          """BioGptModel""",
          """BioGptPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
      from .tokenization_biogpt import BioGptTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_biogpt import (
                BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
                BioGptForCausalLM,
                BioGptForSequenceClassification,
                BioGptForTokenClassification,
                BioGptModel,
                BioGptPreTrainedModel,
            )


else:
      import sys

      __magic_name__  :  int					=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

