






from __future__ import annotations






def    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE = None				)     ->    list[list[str]]:
							_lowercase							:   Union[str, Any] 							=   word_bank or []
							# create a table
							_lowercase							:   int 							=   len(SCREAMING_SNAKE_CASE				) + 1

							_lowercase							:   list[list[list[str]]] 							=   []
							for _ in range(SCREAMING_SNAKE_CASE				):
														table.append([]				)
							# seed value
							_lowercase							:   int 							=   [[]]  # 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:
																																			_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(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'''





import json
import logging
import os
import sys
from time import time
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, require_torch_tpu


logging.basicConfig(level=logging.DEBUG)

A__    :   List[str]           =					logging.getLogger()





def   a_   (		_UpperCAmelCase    :      int				)    ->  Optional[int]:
      __snake_case :   List[str]													=    {}
      __snake_case :   List[str]													=    os.path.join(_UpperCAmelCase ,'all_results.json'				)
      if os.path.exists(_UpperCAmelCase				):
            with open(_UpperCAmelCase ,'r'				) as f:
                  __snake_case :   Dict													=    json.load(_UpperCAmelCase				)
      else:
            raise ValueError(f'''can\'t find {path}'''				)
      return results


A__    :   Dict           =					logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





@require_torch_tpu
class  snake_case__						(    SCREAMING_SNAKE_CASE_				):
 def A_   (   self						:							Optional[Any]  )      ->   List[str]:







       '''simple docstring'''
       import xla_spawn

       __snake_case :   int													=    self.get_auto_remove_tmp_dir()
       __snake_case :   Tuple													=    f'''
            ./examples/pytorch/text-classification/run_glue.py
            --num_cores=8
            ./examples/pytorch/text-classification/run_glue.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --do_train
            --do_eval
            --debug tpu_metrics_debug
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --max_steps=10
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            '''.split()

       with patch.object(__a      ,     'argv'      ,     __a  ):
             __snake_case :   Tuple													=    time()
             xla_spawn.main()
             __snake_case :   Optional[int]													=    time()

             __snake_case :   List[Any]													=    get_results(__a  )
             self.assertGreaterEqual(result['eval_accuracy']      ,     0.7_5  )

             # Assert that the script takes less than 500 seconds to make sure it doesn't hang.
             self.assertLess(end - start      ,     500  )





 def A_   (   self						:							List[str]  )      ->   Optional[int]:







       '''simple docstring'''
       import xla_spawn

       __snake_case :   Optional[Any]													=    '\n            ./tests/test_trainer_tpu.py\n            --num_cores=8\n            ./tests/test_trainer_tpu.py\n            '.split()
       with patch.object(__a      ,     'argv'      ,     __a  ):
             xla_spawn.main()



