"""simple docstring"""



from __future__ import annotations



def 					_lowerCamelCase(					a ,  a							):
       __a     				=  []
       __a     				=  []
       __a     				=  0
       __a     				=  sum(a							)
       create_state_space_tree(a ,  a ,  a ,  a ,  a ,  a							)
       return result



def 					_lowerCamelCase(					a ,  a ,  a ,  a ,  a ,  a ,  ):
       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] ,  )
SCREAMING_SNAKE_CASE__:List[Any]			=	[3, 34, 4, 12, 5, 2]
SCREAMING_SNAKE_CASE__:str			=	9
SCREAMING_SNAKE_CASE__:Optional[int]			=	generate_sum_of_subsets_soln(nums, max_sum)




print(*result)

"""simple docstring"""



from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
   from PIL import Image

   from ..image_utils import load_image

if is_tf_available():
   import tensorflow as tf

   from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
   from ..tf_utils import stable_softmax

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

SCREAMING_SNAKE_CASE__:Optional[Any]			=	logging.get_logger(__name__)






@add_end_docstrings(snake_case_   )
class 				snake_case__     (   snake_case_   ):


 def __init__(				self  ,						*lowerCamelCase  ,						**lowerCamelCase		):
        super().__init__(*lowerCamelCase  ,						**lowerCamelCase		)
        requires_backends(self  ,						"vision"		)
        self.check_model_type(
            TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
            if self.framework == "tf"
            else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING		)


 def 		a__ (				self  ,						lowerCamelCase=None		):
        __a     				=  {}
        if top_k is not None:
               __a     				=  top_k
        return {}, {}, postprocess_params


 def __call__(				self  ,						lowerCamelCase  ,						**lowerCamelCase		):
        return super().__call__(lowerCamelCase  ,						**lowerCamelCase		)


 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  load_image(lowerCamelCase		)
        __a     				=  self.image_processor(images=lowerCamelCase  ,						return_tensors=self.framework		)
        return model_inputs


 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  self.model(**lowerCamelCase		)
        return model_outputs






 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase=5		):
        if top_k > self.model.config.num_labels:
               __a     				=  self.model.config.num_labels

        if self.framework == "pt":
               __a     				=  model_outputs.logits.softmax(-1		)[0]
               __a				, __a     				=  probs.topk(lowerCamelCase		)
        elif self.framework == "tf":
               __a     				=  stable_softmax(model_outputs.logits  ,						axis=-1		)[0]
               __a     				=  tf.math.top_k(lowerCamelCase  ,						k=lowerCamelCase		)
               __a				, __a     				=  topk.values.numpy(), topk.indices.numpy()
        else:
               raise ValueError(F"Unsupported framework: {self.framework}"		)

        __a     				=  scores.tolist()
        __a     				=  ids.tolist()
        return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowerCamelCase  ,						lowerCamelCase		)]
