from __future__ import annotations

def    UpperCamelCase						(	_A,   _A			):






						"""simple docstring"""







						__magic_name__      :       list[list[int]]       						= []
						__magic_name__      :       list[int]       						= []
						__magic_name__      :       Dict       						= 0
						__magic_name__      :       Any       						= sum(_lowercase			)
						create_state_space_tree(_lowercase,   _lowercase,   _lowercase,   _lowercase,   _lowercase,   _lowercase			)
						return result

def    UpperCamelCase						(	_A,   _A,   _A,   _A,   _A,   _A,   ):






						"""simple docstring"""







						if sum(_lowercase			) > max_sum or (remaining_nums_sum + sum(_lowercase			)) < max_sum:
												return
						if sum(_lowercase			) == max_sum:
												result.append(_lowercase			)
												return
						for index in range(_lowercase,   len(_lowercase			)			):
												create_state_space_tree(
												    _lowercase,   _lowercase,   index + 1,   [*path, nums[index]],   _lowercase,   remaining_nums_sum - nums[index],   )
__magic_name__:					Optional[Any]							=     [3, 34, 4, 12, 5, 2]
__magic_name__:					Optional[Any]							=     9
__magic_name__:					Optional[int]							=     generate_sum_of_subsets_soln(nums, max_sum)

print(*result)








import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
		import optax
		from flax.training.common_utils import onehot

		from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
		from transformers.models.ta.modeling_flax_ta import shift_tokens_right





@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 	snake_case__ (							unittest.TestCase				):
	@slow
	def       __magic_name__    (    self			)							->				Optional[Any]:
							__magic_name__      :       Any       						= FlaxMTaForConditionalGeneration.from_pretrained("""google/mt5-small"""			)
							__magic_name__      :       Dict       						= AutoTokenizer.from_pretrained("""google/mt5-small"""			)

							__magic_name__      :       Tuple       						= tokenizer("""Hello there"""      ,       return_tensors="""np"""			).input_ids
							__magic_name__      :       List[Any]       						= tokenizer("""Hi I am"""      ,       return_tensors="""np"""			).input_ids

							__magic_name__      :       Any       						= shift_tokens_right(lowerCAmelCase__      ,       model.config.pad_token_id      ,       model.config.decoder_start_token_id			)

							__magic_name__      :       List[Any]       						= model(lowerCAmelCase__      ,       decoder_input_ids=lowerCAmelCase__			).logits
							__magic_name__      :       Tuple       						= optax.softmax_cross_entropy(lowerCAmelCase__      ,       onehot(lowerCAmelCase__      ,       logits.shape[-1]			)			).mean()

							__magic_name__      :       List[Any]       						= -(labels.shape[-1] * loss.item())

							__magic_name__      :       List[Any]       						= -8_4.9_1_2_7
							self.assertTrue(abs(mtf_score - EXPECTED_SCORE			) < 1e-4			)
