
'''simple docstring'''
def        A_     (						__SCREAMING_SNAKE_CASE						:  list )				->       list:





  """simple docstring"""

  if len(__SCREAMING_SNAKE_CASE ) < 2:
    return collection

  def circle_sort_util(__SCREAMING_SNAKE_CASE						:  list			,     __SCREAMING_SNAKE_CASE						:  int			,     __SCREAMING_SNAKE_CASE						:  int ) -> bool:
    __A		:     Optional[int]												=		False

    if low == high:
      return swapped

    __A		:     List[Any]												=		low
    __A		:     Optional[int]												=		high

    while left < right:
      if collection[left] > collection[right]:
        __A							,					__A		:     str												=		(
            collection[right],
            collection[left],
        )
        __A		:     Optional[Any]												=		True

      left += 1
      right -= 1

    if left == right and collection[left] > collection[right + 1]:
      __A							,					__A		:     Any												=		(
          collection[right + 1],
          collection[left],
      )

      __A		:     Union[str, Any]												=		True

    __A		:     Optional[int]												=		low + int((high - low) / 2 )
    __A		:     int												=		circle_sort_util(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
    __A		:     Any												=		circle_sort_util(__SCREAMING_SNAKE_CASE			,     mid + 1			,     __SCREAMING_SNAKE_CASE )

    return swapped or left_swap or right_swap

  __A		:     Any												=		True

  while is_not_sorted is True:
    __A		:     List[str]												=		circle_sort_util(__SCREAMING_SNAKE_CASE			,     0			,     len(__SCREAMING_SNAKE_CASE ) - 1 )

  return collection


if __name__ == "__main__":
     A__   :		Union[str, Any] 		=input('Enter numbers separated by a comma:\n').strip()
     A__   :		Any 		=[int(item) for item in user_input.split(',')]
     print(circle_sort(unsorted))





'''simple docstring'''
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

A__   :		Any 		=logging.get_logger(__name__)







@add_end_docstrings(_SCREAMING_SNAKE_CASE			)
class  __A				( _SCREAMING_SNAKE_CASE			):
 def __init__(   self			:    Union[str, Any]				,   *lowerCamelCase			:    int				,   **lowerCamelCase			:    Optional[int]       ):



   """simple docstring"""





   super().__init__(*lowerCamelCase				,   **lowerCamelCase       )
   self.check_model_type(lowerCamelCase       )
 def 		lowercase_(   self			:    Any				,   lowerCamelCase			:    Dict=None				,   lowerCamelCase			:    Union[str, Any]=None				,   lowerCamelCase			:    int=None				,   **lowerCamelCase			:    int       ):



   """simple docstring"""





   __A							,					__A		:     Tuple												=		{}, {}
   if padding is not None:
     __A		:     Any												=		padding
   if truncation is not None:
     __A		:     Optional[Any]												=		truncation
   if top_k is not None:
     __A		:     List[str]												=		top_k
   return preprocess_params, {}, postprocess_params
 def __call__(   self			:    Any				,   lowerCamelCase			:    Union["Image.Image", str]				,   lowerCamelCase			:    str = None				,   **lowerCamelCase			:    Tuple       ):



   """simple docstring"""





   if isinstance(lowerCamelCase				,   (Image.Image, str)       ) and isinstance(lowerCamelCase				,   lowerCamelCase       ):
     __A		:     Tuple												=		{"""image""": image, """question""": question}
   else:
     __A		:     List[Any]												=		image
   __A		:     Any												=		super().__call__(lowerCamelCase				,   **lowerCamelCase       )
   return results
 def 		lowercase_(   self			:    int				,   lowerCamelCase			:    Optional[int]				,   lowerCamelCase			:    Dict=False				,   lowerCamelCase			:    str=False       ):



   """simple docstring"""





   __A		:     List[str]												=		load_image(inputs["""image"""]       )
   __A		:     Optional[Any]												=		self.tokenizer(
       inputs["""question"""]				,   return_tensors=self.framework				,   padding=lowerCamelCase				,   truncation=lowerCamelCase       )
   __A		:     Union[str, Any]												=		self.image_processor(images=lowerCamelCase				,   return_tensors=self.framework       )
   model_inputs.update(lowerCamelCase       )
   return model_inputs
 def 		lowercase_(   self			:    List[str]				,   lowerCamelCase			:    Tuple       ):



   """simple docstring"""





   __A		:     List[str]												=		self.model(**lowerCamelCase       )
   return model_outputs






 def 		lowercase_(   self			:    str				,   lowerCamelCase			:    Optional[Any]				,   lowerCamelCase			:    List[Any]=5       ):



   """simple docstring"""





   if top_k > self.model.config.num_labels:
     __A		:     str												=		self.model.config.num_labels

   if self.framework == "pt":
     __A		:     Optional[Any]												=		model_outputs.logits.sigmoid()[0]
     __A							,					__A		:     int												=		probs.topk(lowerCamelCase       )
   else:
     raise ValueError(f"Unsupported framework: {self.framework}"       )

   __A		:     Optional[Any]												=		scores.tolist()
   __A		:     Dict												=		ids.tolist()
   return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(lowerCamelCase				,   lowerCamelCase       )]



