





def 				_UpperCAmelCase			(    A     ):




   '''simple docstring'''

   if len(A     ) < 2:
      return collection

   def circle_sort_util(A				,							A				,							A     ) -> bool:
      UpperCAmelCase__						=False

      if low == high:
         return swapped

      UpperCAmelCase__						=low
      UpperCAmelCase__						=high

      while left < right:
         if collection[left] > collection[right]:
            UpperCAmelCase__			,							UpperCAmelCase__						=(
                collection[right],
                collection[left],
            )
            UpperCAmelCase__						=True

         left += 1
         right -= 1

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

         UpperCAmelCase__						=True

      UpperCAmelCase__						=low + int((high - low) / 2     )
      UpperCAmelCase__						=circle_sort_util(A				,							A				,							A     )
      UpperCAmelCase__						=circle_sort_util(A				,							mid + 1				,							A     )

      return swapped or left_swap or right_swap

   UpperCAmelCase__						=True

   while is_not_sorted is True:
      UpperCAmelCase__						=circle_sort_util(A				,							0				,							len(A     ) - 1     )

   return collection


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










import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
    from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

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






def 				_UpperCAmelCase			(    A     ):




   '''simple docstring'''

   return 1.0 / (1.0 + np.exp(-_outputs     ))






def 				_UpperCAmelCase			(    A     ):




   '''simple docstring'''

   UpperCAmelCase__						=np.max(_outputs				,							axis=-1				,							keepdims=A     )
   UpperCAmelCase__						=np.exp(_outputs - maxes     )
   return shifted_exp / shifted_exp.sum(axis=-1				,							keepdims=A     )

class     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      'sigmoid'
 __UpperCamelCase       =      'softmax'
 __UpperCamelCase       =      'none'

@add_end_docstrings(
    a,		R'\n        return_all_scores (`bool`, *optional*, defaults to `False`):\n            Whether to return all prediction scores or just the one of the predicted class.\n        function_to_apply (`str`, *optional*, defaults to `"default"`):\n            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:\n\n            - `"default"`: if the model has a single label, will apply the sigmoid function on the output. If the model\n              has several labels, will apply the softmax function on the output.\n            - `"sigmoid"`: Applies the sigmoid function on the output.\n            - `"softmax"`: Applies the softmax function on the output.\n            - `"none"`: Does not apply any function on the output.\n    ',		)
class     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      False
 __UpperCamelCase       =      ClassificationFunction.NONE

 def __init__(  self,		**A_		)	->       Optional[int]:
    super().__init__(**A_		)

    self.check_model_type(
        TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
        if self.framework == "tf"
        else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING		)

 def  __UpperCAmelCase						(  self,		A_=None,		A_=None,		A_="",		**A_		)	->       Optional[int]:
    # Using "" as default argument because we're going to use `top_k=None` in user code to declare
    # "No top_k"
    UpperCAmelCase__						=tokenizer_kwargs

    UpperCAmelCase__						={}
    if hasattr(self.model.config,		"return_all_scores"		) and return_all_scores is None:
       UpperCAmelCase__						=self.model.config.return_all_scores

    if isinstance(A_,		A_		) or top_k is None:
       UpperCAmelCase__						=top_k
       UpperCAmelCase__						=False
    elif return_all_scores is not None:
       warnings.warn(
           "`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of"
           " `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.",		A_,		)
       if return_all_scores:
          UpperCAmelCase__						=None
       else:
          UpperCAmelCase__						=1

    if isinstance(A_,		A_		):
       UpperCAmelCase__						=ClassificationFunction[function_to_apply.upper()]

    if function_to_apply is not None:
       UpperCAmelCase__						=function_to_apply
    return preprocess_params, {}, postprocess_params

 def __call__(  self,		*A_,		**A_		)	->       str:
    UpperCAmelCase__						=super().__call__(*A_,		**A_		)
    # TODO try and retrieve it in a nicer way from _sanitize_parameters.
    UpperCAmelCase__						="top_k" not in kwargs
    if isinstance(args[0],		A_		) and _legacy:
       # This pipeline is odd, and return a list when single item is run
       return [result]
    else:
       return result

 def  __UpperCAmelCase						(  self,		A_,		**A_		)	->       Dict[str, GenericTensor]:
    UpperCAmelCase__						=self.framework
    if isinstance(A_,		A_		):
       return self.tokenizer(**A_,		return_tensors=A_,		**A_		)
    elif isinstance(A_,		A_		) and len(A_		) == 1 and isinstance(inputs[0],		A_		) and len(inputs[0]		) == 2:
       # It used to be valid to use a list of list of list for text pairs, keeping this path for BC
       return self.tokenizer(
           text=inputs[0][0],		text_pair=inputs[0][1],		return_tensors=A_,		**A_		)
    elif isinstance(A_,		A_		):
       # This is likely an invalid usage of the pipeline attempting to pass text pairs.
       raise ValueError(
           "The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a"
           " dictionary `{\"text\": \"My text\", \"text_pair\": \"My pair\"}` in order to send a text pair."		)
    return self.tokenizer(A_,		return_tensors=A_,		**A_		)

 def  __UpperCAmelCase						(  self,		A_		)	->       Dict:
    return self.model(**A_		)






 def  __UpperCAmelCase						(  self,		A_,		A_=None,		A_=1,		A_=True		)	->       Any:
    # `_legacy` is used to determine if we're running the naked pipeline and in backward
    # compatibility mode, or if running the pipeline with `pipeline(..., top_k=1)` we're running
    # the more natural result containing the list.
    # Default value before `set_parameters`
    if function_to_apply is None:
       if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
          UpperCAmelCase__						=ClassificationFunction.SIGMOID
       elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
          UpperCAmelCase__						=ClassificationFunction.SOFTMAX
       elif hasattr(self.model.config,		"function_to_apply"		) and function_to_apply is None:
          UpperCAmelCase__						=self.model.config.function_to_apply
       else:
          UpperCAmelCase__						=ClassificationFunction.NONE

    UpperCAmelCase__						=model_outputs["logits"][0]
    UpperCAmelCase__						=outputs.numpy()

    if function_to_apply == ClassificationFunction.SIGMOID:
       UpperCAmelCase__						=sigmoid(A_		)
    elif function_to_apply == ClassificationFunction.SOFTMAX:
       UpperCAmelCase__						=softmax(A_		)
    elif function_to_apply == ClassificationFunction.NONE:
       UpperCAmelCase__						=outputs
    else:
       raise ValueError(f"""Unrecognized `function_to_apply` argument: {function_to_apply}"""		)

    if top_k == 1 and _legacy:
       return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

    UpperCAmelCase__						=[
        {"label": self.model.config.idalabel[i], "score": score.item()} for i, score in enumerate(A_		)
    ]
    if not _legacy:
       dict_scores.sort(key=lambda A_		: x["score"],		reverse=A_		)
       if top_k is not None:
          UpperCAmelCase__						=dict_scores[:top_k]
    return dict_scores



