"""simple docstring"""


from __future__ import annotations







def 					__a   (		A    ,				A      ):






     '''simple docstring'''



     lowercase__				 =							[]
     lowercase__				 =							[]
     lowercase__				 =							0
     lowercase__				 =							sum(A      )
     create_state_space_tree(A    ,				A    ,				A    ,				A    ,				A    ,				A      )
     return result







def 					__a   (		A    ,				A    ,				A    ,				A    ,				A    ,				A    ,				):






     '''simple docstring'''



     if sum(A      ) > max_sum or (remaining_nums_sum + sum(A      )) < max_sum:
          return
     if sum(A      ) == max_sum:
          result.append(A      )
          return
     for index in range(A    ,				len(A      )      ):
          create_state_space_tree(
              A    ,				A    ,				index + 1    ,				[*path, nums[index]]    ,				A    ,				remaining_nums_sum - nums[index]    ,				)
lowerCAmelCase_:					List[Any]     =					[3, 3_4, 4, 1_2, 5, 2]
lowerCAmelCase_:					str     =					9
lowerCAmelCase_:					Union[str, Any]     =					generate_sum_of_subsets_soln(nums, max_sum)




print(*result)





"""simple docstring"""


import os
import sys

lowerCAmelCase_:					Any     =					os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


lowerCAmelCase_:					Union[str, Any]     =					[
    "torch",
    "numpy",
    "tokenizers",
    "filelock",
    "requests",
    "tqdm",
    "regex",
    "sentencepiece",
    "sacremoses",
    "importlib_metadata",
    "huggingface_hub",
]







@add_start_docstrings(AutoConfig.__doc__      )
def 					__a   (		*A    ,				**A      ):






     '''simple docstring'''



     return AutoConfig.from_pretrained(*A    ,				**A      )







@add_start_docstrings(AutoTokenizer.__doc__      )
def 					__a   (		*A    ,				**A      ):






     '''simple docstring'''



     return AutoTokenizer.from_pretrained(*A    ,				**A      )







@add_start_docstrings(AutoModel.__doc__      )
def 					__a   (		*A    ,				**A      ):






     '''simple docstring'''



     return AutoModel.from_pretrained(*A    ,				**A      )







@add_start_docstrings(AutoModelForCausalLM.__doc__      )
def 					__a   (		*A    ,				**A      ):






     '''simple docstring'''



     return AutoModelForCausalLM.from_pretrained(*A    ,				**A      )







@add_start_docstrings(AutoModelForMaskedLM.__doc__      )
def 					__a   (		*A    ,				**A      ):






     '''simple docstring'''



     return AutoModelForMaskedLM.from_pretrained(*A    ,				**A      )







@add_start_docstrings(AutoModelForSequenceClassification.__doc__      )
def 					__a   (		*A    ,				**A      ):






     '''simple docstring'''



     return AutoModelForSequenceClassification.from_pretrained(*A    ,				**A      )







@add_start_docstrings(AutoModelForQuestionAnswering.__doc__      )
def 					__a   (		*A    ,				**A      ):






     '''simple docstring'''



     return AutoModelForQuestionAnswering.from_pretrained(*A    ,				**A      )




