



import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
   import torch



@require_torch
@require_optimum
@slow
class     _UpperCAmelCase			(       unittest.TestCase     ):




     def 		snake_case_		(				self):
         A__		 =					'''hf-internal-testing/tiny-random-t5'''
         A__		 =					AutoTokenizer.from_pretrained(a__)
         A__		 =					AutoModelForSeqaSeqLM.from_pretrained(a__)

         A__		 =					tokenizer('''This is me''' ,						return_tensors='''pt''')

         A__		 =					model.to_bettertransformer()

         self.assertTrue(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()))

         A__		 =					model.generate(**a__)

         A__		 =					model.reverse_bettertransformer()

         self.assertFalse(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()))

         with tempfile.TemporaryDirectory() as tmpdirname:
             model.save_pretrained(a__)

             A__		 =					AutoModelForSeqaSeqLM.from_pretrained(a__)

             self.assertFalse(
                 any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()))

             A__		 =					model_reloaded.generate(**a__)
             self.assertTrue(torch.allclose(a__ ,						a__))




     def 		snake_case_		(				self):
         A__		 =					'''hf-internal-testing/tiny-random-t5'''
         A__		 =					AutoModelForSeqaSeqLM.from_pretrained(a__)

         A__		 =					model.to_bettertransformer()

         with tempfile.TemporaryDirectory() as tmpdirname:
             with self.assertRaises(a__):
                 model.save_pretrained(a__)

             A__		 =					model.reverse_bettertransformer()
             model.save_pretrained(a__)








from __future__ import annotations



def 		lowerCAmelCase__    (					UpperCamelCase_				:       str ,       UpperCamelCase_				:       list[str] | None = None	)->  list[list[str]]:
    A__		 =					word_bank or []
    # create a table
    A__		 =					len(UpperCamelCase_	) + 1

    A__		 =					[]
    for _ in range(UpperCamelCase_	):
        table.append([]	)
    # seed value
    A__		 =					[[]]  # 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:
                    A__		 =					[
                        [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"],
       )
   )
