

from __future__ import annotations

def       a__					(		UpperCAmelCase							:      list[int]    ,			UpperCAmelCase							:      int      )  ->						list[list[int]]:
  UpperCAmelCase	:							list[list[int]]						=			[]
  UpperCAmelCase	:							list[int]						=			[]
  UpperCAmelCase	:							Any						=			0
  UpperCAmelCase	:							str						=			sum(UpperCAmelCase      )
  create_state_space_tree(UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase      )
  return result

def       a__					(		UpperCAmelCase							:      list[int]    ,			UpperCAmelCase							:      int    ,			UpperCAmelCase							:      int    ,			UpperCAmelCase							:      list[int]    ,			UpperCAmelCase							:      list[list[int]]    ,			UpperCAmelCase							:      int    ,			)  ->						None:
  if sum(UpperCAmelCase      ) > max_sum or (remaining_nums_sum + sum(UpperCAmelCase      )) < max_sum:
    return
  if sum(UpperCAmelCase      ) == max_sum:
    result.append(UpperCAmelCase      )
    return
  for index in range(UpperCAmelCase    ,			len(UpperCAmelCase      )      ):
    create_state_space_tree(
        UpperCAmelCase    ,			UpperCAmelCase    ,			index + 1    ,			[*path, nums[index]]    ,			UpperCAmelCase    ,			remaining_nums_sum - nums[index]    ,			)
_lowerCamelCase    :       Any        =							[3, 3_4, 4, 1_2, 5, 2]
_lowerCamelCase    :       List[Any]        =							9
_lowerCamelCase    :       Any        =							generate_sum_of_subsets_soln(nums, max_sum)






print(*result)








from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
     from PIL import Image

     from ..image_utils import load_image

if is_torch_available():
     from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

_lowerCamelCase    :       Dict        =							logging.get_logger(__name__)





@add_end_docstrings(lowerCamelCase__						)
class  __UpperCAmelCase					(	lowerCamelCase__						):



   def __init__(							self							:					Optional[Any],		*__A							:					Tuple,		**__A							:					Tuple						):
     super().__init__(*__A,		**__A						)
     self.check_model_type(__A						)



   def 	__magic_name__   (							self							:					Union[str, Any],		__A							:					int=None,		__A							:					Tuple=None,		__A							:					Any=None,		**__A							:					Optional[int]						):
     UpperCAmelCase			,						UpperCAmelCase	:							List[Any]						=			{}, {}
     if padding is not None:
       UpperCAmelCase	:							Optional[int]						=			padding
     if truncation is not None:
       UpperCAmelCase	:							Optional[int]						=			truncation
     if top_k is not None:
       UpperCAmelCase	:							Tuple						=			top_k
     return preprocess_params, {}, postprocess_params



   def __call__(							self							:					Union[str, Any],		__A							:					Union["Image.Image", str],		__A							:					str = None,		**__A							:					Optional[int]						):
     if isinstance(__A,		(Image.Image, str)						) and isinstance(__A,		__A						):
       UpperCAmelCase	:							int						=			{'''image''': image, '''question''': question}
     else:
       UpperCAmelCase	:							str						=			image
     UpperCAmelCase	:							Union[str, Any]						=			super().__call__(__A,		**__A						)
     return results



   def 	__magic_name__   (							self							:					List[str],		__A							:					Union[str, Any],		__A							:					Tuple=False,		__A							:					List[Any]=False						):
     UpperCAmelCase	:							int						=			load_image(inputs['''image''']						)
     UpperCAmelCase	:							List[str]						=			self.tokenizer(
         inputs['''question'''],		return_tensors=self.framework,		padding=__A,		truncation=__A						)
     UpperCAmelCase	:							Union[str, Any]						=			self.image_processor(images=__A,		return_tensors=self.framework						)
     model_inputs.update(__A						)
     return model_inputs



   def 	__magic_name__   (							self							:					Optional[Any],		__A							:					List[Any]						):
     UpperCAmelCase	:							Optional[int]						=			self.model(**__A						)
     return model_outputs





   def 	__magic_name__   (							self							:					Any,		__A							:					List[str],		__A							:					Union[str, Any]=5						):
     if top_k > self.model.config.num_labels:
       UpperCAmelCase	:							Any						=			self.model.config.num_labels

     if self.framework == "pt":
       UpperCAmelCase	:							Any						=			model_outputs.logits.sigmoid()[0]
       UpperCAmelCase			,						UpperCAmelCase	:							Union[str, Any]						=			probs.topk(__A						)
     else:
       raise ValueError(F'''Unsupported framework: {self.framework}'''						)

     UpperCAmelCase	:							str						=			scores.tolist()
     UpperCAmelCase	:							Tuple						=			ids.tolist()
     return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(__A,		__A						)]





