
'''simple docstring'''
from __future__ import annotations

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    list[list[int]]     			=						[]
    snake_case_ :    list[int]     			=						[]
    snake_case_ :    Dict     			=						0
    snake_case_ :    List[str]     			=						sum(__SCREAMING_SNAKE_CASE       )
    create_state_space_tree(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )
    return result

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		):
    """simple docstring"""






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


print(*result)






'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a_											=						{
    "configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
    "tokenization_biogpt": ["BioGptTokenizer"],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_											=						[
      "BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "BioGptForCausalLM",
      "BioGptForTokenClassification",
      "BioGptForSequenceClassification",
      "BioGptModel",
      "BioGptPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
  from .tokenization_biogpt import BioGptTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_biogpt import (
        BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        BioGptForCausalLM,
        BioGptForSequenceClassification,
        BioGptForTokenClassification,
        BioGptModel,
        BioGptPreTrainedModel,
    )


else:
  import sys

  a_											=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




