






import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def       snake_case_		(     ):
       raise RuntimeError("""CUDA out of memory."""      )

class 		lowerCAmelCase     ( nn.Module			):

     '''simple docstring'''

     def __init__(  self				:	Optional[Any]							)			->  List[str]:


            """simple docstring"""

            super().__init__()
            __lowercase      :							Optional[Any]				=     nn.Linear(3		,     4							)
            __lowercase      :							Tuple				=     nn.BatchNormad(4							)
            __lowercase      :							Optional[Any]				=     nn.Linear(4		,     5							)



     def       lowerCAmelCase				(  self				:	Union[str, Any]		,     __a				:	Optional[int]							)			->  List[Any]:


            """simple docstring"""

            return self.lineara(self.batchnorm(self.lineara(_SCREAMING_SNAKE_CASE							)							)							)







class 		lowerCAmelCase     ( unittest.TestCase			):

     '''simple docstring'''

     def       lowerCAmelCase				(  self				:	int							)			->  Tuple:


            """simple docstring"""

            __lowercase      :							Dict				=     []

            @find_executable_batch_size(starting_batch_size=128							)
            def mock_training_loop_function(__a				:	Tuple							):
                   nonlocal batch_sizes
                   batch_sizes.append(_SCREAMING_SNAKE_CASE							)
                   if batch_size != 8:
                          raise_fake_out_of_memory()

            mock_training_loop_function()
            self.assertListEqual(_SCREAMING_SNAKE_CASE		,     [128, 64, 32, 16, 8]							)



     def       lowerCAmelCase				(  self				:	Dict							)			->  List[str]:


            """simple docstring"""

            __lowercase      :							Union[str, Any]				=     []

            @find_executable_batch_size(starting_batch_size=128							)
            def mock_training_loop_function(__a				:	Union[str, Any]		,     __a				:	Optional[int]							):
                   nonlocal batch_sizes
                   batch_sizes.append(_SCREAMING_SNAKE_CASE							)
                   if batch_size != 8:
                          raise_fake_out_of_memory()
                   return batch_size, arga

            __lowercase			,			__lowercase      :							Optional[int]				=     mock_training_loop_function("""hello"""							)
            self.assertListEqual(_SCREAMING_SNAKE_CASE		,     [128, 64, 32, 16, 8]							)
            self.assertListEqual([bs, arga]		,     [8, """hello"""]							)



     def       lowerCAmelCase				(  self				:	List[Any]							)			->  Dict:


            """simple docstring"""

            @find_executable_batch_size(starting_batch_size=0							)
            def mock_training_loop_function(__a				:	str							):
                   pass

            with self.assertRaises(_SCREAMING_SNAKE_CASE							) as cm:
                   mock_training_loop_function()
                   self.assertIn("""No executable batch size found, reached zero."""		,     cm.exception.args[0]							)



     def       lowerCAmelCase				(  self				:	Optional[int]							)			->  Union[str, Any]:


            """simple docstring"""

            @find_executable_batch_size(starting_batch_size=16							)
            def mock_training_loop_function(__a				:	Any							):
                   if batch_size > 0:
                          raise_fake_out_of_memory()
                   pass

            with self.assertRaises(_SCREAMING_SNAKE_CASE							) as cm:
                   mock_training_loop_function()
                   self.assertIn("""No executable batch size found, reached zero."""		,     cm.exception.args[0]							)



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


            """simple docstring"""

            @find_executable_batch_size(starting_batch_size=128							)
            def mock_training_loop_function(__a				:	Optional[int]		,     __a				:	str		,     __a				:	Tuple							):
                   if batch_size != 8:
                          raise raise_fake_out_of_memory()

            with self.assertRaises(_SCREAMING_SNAKE_CASE							) as cm:
                   mock_training_loop_function(128		,     """hello"""		,     """world"""							)
                   self.assertIn("""Batch size was passed into `f`"""		,     cm.exception.args[0]							)
                   self.assertIn("""`f(arg1='hello', arg2='world')"""		,     cm.exception.args[0]							)



     def       lowerCAmelCase				(  self				:	List[str]							)			->  Tuple:


            """simple docstring"""

            @find_executable_batch_size(starting_batch_size=16							)
            def mock_training_loop_function(__a				:	Optional[int]							):
                   raise ValueError("""Oops, we had an error!"""							)

            with self.assertRaises(_SCREAMING_SNAKE_CASE							) as cm:
                   mock_training_loop_function()
                   self.assertIn("""Oops, we had an error!"""		,     cm.exception.args[0]							)



     @require_cuda
     def       lowerCAmelCase				(  self				:	Any							)			->  Union[str, Any]:


            """simple docstring"""

            __lowercase      :							List[str]				=     torch.cuda.memory_allocated()
            __lowercase      :							List[str]				=     ModelForTest()
            model.cuda()
            self.assertGreater(torch.cuda.memory_allocated()		,     _SCREAMING_SNAKE_CASE							)
            __lowercase      :							Union[str, Any]				=     release_memory(_SCREAMING_SNAKE_CASE							)
            self.assertEqual(torch.cuda.memory_allocated()		,     _SCREAMING_SNAKE_CASE							)







from __future__ import annotations
def       snake_case_		(     lowerCAmelCase_       :						str       ,       lowerCAmelCase_       :						list[str] | None = None      ):
							__lowercase      :							Tuple				=     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      :							Dict				=     [[]]  # 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'''],
	    )
	)