

import random


def        __lowerCAmelCase    (       _UpperCamelCase		:  List[Any]		,							_UpperCamelCase		:  Optional[int]		,							_UpperCamelCase		:  str	)		->							Any:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			a[left_index]
 SCREAMING_SNAKE_CASE								=			left_index + 1
 for j in range(left_index + 1		,							_UpperCamelCase	):
  if a[j] < pivot:
   SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			a[i], a[j]
   i += 1
 SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			a[i - 1], a[left_index]
 return i - 1


def        __lowerCAmelCase    (       _UpperCamelCase		:  Any		,							_UpperCamelCase		:  int		,							_UpperCamelCase		:  str	)		->							str:
 '''simple docstring'''


 if left < right:
  SCREAMING_SNAKE_CASE								=			random.randint(_UpperCamelCase		,							right - 1	)
  SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			(
      a[left],
      a[pivot],
  )  # switches the pivot with the left most bound
  SCREAMING_SNAKE_CASE								=			partition(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase	)
  quick_sort_random(
      _UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase	)  # recursive quicksort to the left of the pivot point
  quick_sort_random(
      _UpperCamelCase		,							pivot_index + 1		,							_UpperCamelCase	)  # recursive quicksort to the right of the pivot point


def        __lowerCAmelCase    (       )		->							List[Any]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			input('Enter numbers separated by a comma:\n'	).strip()
 SCREAMING_SNAKE_CASE								=			[int(_UpperCamelCase	) for item in user_input.split(','	)]

 quick_sort_random(_UpperCamelCase		,							0		,							len(_UpperCamelCase	)	)

 print(_UpperCamelCase	)


if __name__ == "__main__":
      main()




# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool




class      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):
    __UpperCamelCase         ="facebook/bart-large-mnli"
    __UpperCamelCase         =(
        "This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which "
        "should be the text to classify, and `labels`, which should be the list of labels to use for classification. "
        "It returns the most likely label in the list of provided `labels` for the input text."
    )
    __UpperCamelCase         ="text_classifier"
    __UpperCamelCase         =AutoTokenizer
    __UpperCamelCase         =AutoModelForSequenceClassification

    __UpperCamelCase         =["text", ["text"]]
    __UpperCamelCase         =["text"]

    def UpperCamelCase			(			self :      Optional[Any]    ):

     """simple docstring"""






     super().setup()
     SCREAMING_SNAKE_CASE								=			self.model.config
     SCREAMING_SNAKE_CASE								=			-1
     for idx, label in config.idalabel.items():
      if label.lower().startswith('entail'    ):
       SCREAMING_SNAKE_CASE								=			int(snake_case__    )
     if self.entailment_id == -1:
      raise ValueError('Could not determine the entailment ID from the model config, please pass it at init.'    )

    def UpperCamelCase			(			self :      Optional[Any]							,    snake_case__ :      List[str]							,    snake_case__ :      Dict    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			labels
     return self.pre_processor(
         [text] * len(snake_case__    )							,    [F"""This example is {label}""" for label in labels]							,    return_tensors='pt'							,    padding='max_length'							,    )




    def UpperCamelCase			(			self :      Dict							,    snake_case__ :      Dict    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			outputs.logits
     SCREAMING_SNAKE_CASE								=			torch.argmax(logits[:, 2]    ).item()
     return self._labels[label_id]

