



"""simple docstring"""







from __future__ import annotations



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase = None	)							->      list[list[str]]:
		lowercase__   :    List[str]				=			word_bank or []
		# create a table
		lowercase__   :    int				=			len(__lowerCamelCase	) + 1

		lowercase__   :    list[list[list[str]]]				=			[]
		for _ in range(__lowerCamelCase	):
				table.append([]	)
		# seed value
		lowercase__   :    Union[str, Any]				=			[[]]  # 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:
										lowercase__   :    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(__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'],
							    )
							)





"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A:   List[str]								=		{
    "configuration_clipseg": [
        "CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "CLIPSegConfig",
        "CLIPSegTextConfig",
        "CLIPSegVisionConfig",
    ],
    "processing_clipseg": ["CLIPSegProcessor"],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   Optional[int]								=		[
          "CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST",
          "CLIPSegModel",
          "CLIPSegPreTrainedModel",
          "CLIPSegTextModel",
          "CLIPSegVisionModel",
          "CLIPSegForImageSegmentation",
      ]

if TYPE_CHECKING:
      from .configuration_clipseg import (
          CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
          CLIPSegConfig,
          CLIPSegTextConfig,
          CLIPSegVisionConfig,
      )
      from .processing_clipseg import CLIPSegProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_clipseg import (
                CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
                CLIPSegForImageSegmentation,
                CLIPSegModel,
                CLIPSegPreTrainedModel,
                CLIPSegTextModel,
                CLIPSegVisionModel,
            )

else:
      import sys

      A:   Tuple								=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
