






from __future__ import annotations





def 		a__			(		_UpperCamelCase			: str ,_UpperCamelCase			: list[str] | None = None							):
   __lowerCamelCase													=    word_bank or []
   # create a table
   __lowerCamelCase													=    len(_UpperCamelCase							) + 1

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

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

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

   return table[len(_UpperCamelCase							)]


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"""],
      )
  )











import torch

from diffusers import StableDiffusionPipeline


a_			=					"""path-to-your-trained-model"""
a_			=					StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("""cuda""")

a_			=					"""A photo of sks dog in a bucket"""
a_			=					pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save("""dog-bucket.png""")






