

from __future__ import annotations


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


 SCREAMING_SNAKE_CASE								=			[]
 SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			input_list[low:mid], input_list[mid : high + 1]
 while left and right:
  result.append((left if left[0] <= right[0] else right).pop(0	)	)
 SCREAMING_SNAKE_CASE								=			result + left + right
 return input_list


def        __lowerCAmelCase    (       _UpperCamelCase		:  list	)		->							list:
 '''simple docstring'''


 if len(_UpperCamelCase	) <= 1:
  return input_list
 SCREAMING_SNAKE_CASE								=			list(_UpperCamelCase	)

 # iteration for two-way merging
 SCREAMING_SNAKE_CASE								=			2
 while p <= len(_UpperCamelCase	):
  # getting low, high and middle value for merge-sort of single list
  for i in range(0		,							len(_UpperCamelCase	)		,							_UpperCamelCase	):
   SCREAMING_SNAKE_CASE								=			i
   SCREAMING_SNAKE_CASE								=			i + p - 1
   SCREAMING_SNAKE_CASE								=			(low + high + 1) // 2
   SCREAMING_SNAKE_CASE								=			merge(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase	)
  # final merge of last two parts
  if p * 2 >= len(_UpperCamelCase	):
   SCREAMING_SNAKE_CASE								=			i
   SCREAMING_SNAKE_CASE								=			merge(_UpperCamelCase		,							0		,							_UpperCamelCase		,							len(_UpperCamelCase	) - 1	)
   break
  p *= 2

 return input_list


if __name__ == "__main__":
      a_    :						Dict    		=      input("Enter numbers separated by a comma:\n").strip()
      if user_input == "":
            a_    :						str    		=      []
      else:
            a_    :						str    		=      [int(item.strip()) for item in user_input.split(",")]
      print(iter_merge_sort(unsorted))




# 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]

