






'''simple docstring'''
from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

a_					=	TypeVar('T')





def 	_a(					UpperCamelCase__			:  int							):




      '''simple docstring'''






      return (position - 1) // 2





def 	_a(					UpperCamelCase__			:  int							):




      '''simple docstring'''






      return (2 * position) + 1





def 	_a(					UpperCamelCase__			:  int							):




      '''simple docstring'''






      return (2 * position) + 2






class        __SCREAMING_SNAKE_CASE (     Generic[T]			):




 def __init__( self		:							List[str] )		->  None:
       SCREAMING_SNAKE_CASE__       :			list[tuple[T, int]]													=[]
       SCREAMING_SNAKE_CASE__       :			dict[T, int]													={}
       SCREAMING_SNAKE_CASE__       :			int													=0




 def __len__( self		:							List[Any] )		->  int:
       return self.elements




 def __repr__( self		:							Union[str, Any] )		->  str:
       return str(self.heap )




 def        __magic_name__		( self		:							Optional[Any] )		->  bool:
       # Check if the priority queue is empty
       return self.elements == 0




 def        __magic_name__		( self		:							List[str]   , __lowercase		:							T   , __lowercase		:							int )		->  None:
       # Add an element with given priority to the queue
       self.heap.append((elem, weight) )
       SCREAMING_SNAKE_CASE__       :			Dict													=self.elements
       self.elements += 1
       self._bubble_up(__lowercase )




 def        __magic_name__		( self		:							Optional[Any] )		->  T:
       # Remove and return the element with lowest weight (highest priority)
       if self.elements > 1:
             self._swap_nodes(0   , self.elements - 1 )
       SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			Optional[int]													=self.heap.pop()
       del self.position_map[elem]
       self.elements -= 1
       if self.elements > 0:
             SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			Optional[int]													=self.heap[0]
             self._bubble_down(__lowercase )
       return elem




 def        __magic_name__		( self		:							int   , __lowercase		:							T   , __lowercase		:							int )		->  None:
       # Update the weight of the given key
       SCREAMING_SNAKE_CASE__       :			Dict													=self.position_map[elem]
       SCREAMING_SNAKE_CASE__       :			List[Any]													=(elem, weight)
       if position > 0:
             SCREAMING_SNAKE_CASE__       :			int													=get_parent_position(__lowercase )
             SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			Optional[Any]													=self.heap[parent_position]
             if parent_weight > weight:
                   self._bubble_up(__lowercase )
             else:
                   self._bubble_down(__lowercase )
       else:
             self._bubble_down(__lowercase )




 def        __magic_name__		( self		:							Tuple   , __lowercase		:							T )		->  None:
       # Place a node at the proper position (upward movement) [to be used internally
       # only]
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=self.position_map[elem]
       if curr_pos == 0:
             return None
       SCREAMING_SNAKE_CASE__       :			int													=get_parent_position(__lowercase )
       SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			Dict													=self.heap[curr_pos]
       SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			Any													=self.heap[parent_position]
       if parent_weight > weight:
             self._swap_nodes(__lowercase   , __lowercase )
             return self._bubble_up(__lowercase )
       return None




 def        __magic_name__		( self		:							str   , __lowercase		:							T )		->  None:
       # Place a node at the proper position (downward movement) [to be used
       # internally only]
       SCREAMING_SNAKE_CASE__       :			Any													=self.position_map[elem]
       SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			str													=self.heap[curr_pos]
       SCREAMING_SNAKE_CASE__       :			Any													=get_child_left_position(__lowercase )
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=get_child_right_position(__lowercase )
       if child_left_position < self.elements and child_right_position < self.elements:
             SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			int													=self.heap[child_left_position]
             SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			List[Any]													=self.heap[child_right_position]
             if child_right_weight < child_left_weight and child_right_weight < weight:
                   self._swap_nodes(__lowercase   , __lowercase )
                   return self._bubble_down(__lowercase )
       if child_left_position < self.elements:
             SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			List[Any]													=self.heap[child_left_position]
             if child_left_weight < weight:
                   self._swap_nodes(__lowercase   , __lowercase )
                   return self._bubble_down(__lowercase )
       else:
             return None
       if child_right_position < self.elements:
             SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			Tuple													=self.heap[child_right_position]
             if child_right_weight < weight:
                   self._swap_nodes(__lowercase   , __lowercase )
                   return self._bubble_down(__lowercase )
       return None




 def        __magic_name__		( self		:							Union[str, Any]   , __lowercase		:							int   , __lowercase		:							int )		->  None:
       # Swap the nodes at the given positions
       SCREAMING_SNAKE_CASE__       :			List[Any]													=self.heap[nodea_pos][0]
       SCREAMING_SNAKE_CASE__       :			Tuple													=self.heap[nodea_pos][0]
       SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			List[Any]													=(
           self.heap[nodea_pos],
           self.heap[nodea_pos],
       )
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=nodea_pos
       SCREAMING_SNAKE_CASE__       :			List[str]													=nodea_pos






class        __SCREAMING_SNAKE_CASE (     Generic[T]			):




 def __init__( self		:							Any )		->  None:
       SCREAMING_SNAKE_CASE__       :			dict[T, dict[T, int]]													={}
       SCREAMING_SNAKE_CASE__       :			int													=0




 def __repr__( self		:							Dict )		->  str:
       return str(self.connections )




 def __len__( self		:							Optional[Any] )		->  int:
       return self.nodes




 def        __magic_name__		( self		:							str   , __lowercase		:							T )		->  None:
       # Add a node in the graph if it is not in the graph
       if node not in self.connections:
             SCREAMING_SNAKE_CASE__       :			str													={}
             self.nodes += 1




 def        __magic_name__		( self		:							Union[str, Any]   , __lowercase		:							T   , __lowercase		:							T   , __lowercase		:							int )		->  None:
       # Add an edge between 2 nodes in the graph
       self.add_node(__lowercase )
       self.add_node(__lowercase )
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=weight
       SCREAMING_SNAKE_CASE__       :			int													=weight





def 	_a(					UpperCamelCase__			:  GraphUndirectedWeighted[T],		):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			dict[T, int]													={node: maxsize for node in graph.connections}
      SCREAMING_SNAKE_CASE__       :			dict[T, T | None]													={node: None for node in graph.connections}

      SCREAMING_SNAKE_CASE__       :			MinPriorityQueue[T]													=MinPriorityQueue()
      for node, weight in dist.items():
            priority_queue.push(UpperCamelCase__,		UpperCamelCase__							)

      if priority_queue.is_empty():
            return dist, parent

      # initialization
      SCREAMING_SNAKE_CASE__       :			Optional[int]													=priority_queue.extract_min()
      SCREAMING_SNAKE_CASE__       :			List[Any]													=0
      for neighbour in graph.connections[node]:
            if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
                  SCREAMING_SNAKE_CASE__       :			Dict													=dist[node] + graph.connections[node][neighbour]
                  priority_queue.update_key(UpperCamelCase__,		dist[neighbour]							)
                  SCREAMING_SNAKE_CASE__       :			Optional[Any]													=node

    # running prim's algorithm
      while not priority_queue.is_empty():
            SCREAMING_SNAKE_CASE__       :			Tuple													=priority_queue.extract_min()
            for neighbour in graph.connections[node]:
                  if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
                        SCREAMING_SNAKE_CASE__       :			Optional[Any]													=dist[node] + graph.connections[node][neighbour]
                        priority_queue.update_key(UpperCamelCase__,		dist[neighbour]							)
                        SCREAMING_SNAKE_CASE__       :			int													=node
      return dist, parent







'''simple docstring'''
# 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        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):
 snake_case_												=							"""facebook/bart-large-mnli"""
 snake_case_												=							(
     """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."""
 )
 snake_case_												=							"""text_classifier"""
 snake_case_												=							AutoTokenizer
 snake_case_												=							AutoModelForSequenceClassification

 snake_case_												=							["""text""", ["""text"""]]
 snake_case_												=							["""text"""]




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




 def        __magic_name__		( self		:							str   , __lowercase		:							Any   , __lowercase		:							Optional[Any] )		->  Optional[Any]:
       SCREAMING_SNAKE_CASE__       :			Dict													=labels
       return self.pre_processor(
           [text] * len(__lowercase )   , [F"This example is {label}" for label in labels]   , return_tensors='''pt'''   , padding='''max_length'''   , )




 def        __magic_name__		( self		:							List[Any]   , __lowercase		:							str )		->  List[Any]:
       SCREAMING_SNAKE_CASE__       :			int													=outputs.logits
       SCREAMING_SNAKE_CASE__       :			Any													=torch.argmax(logits[:, 2] ).item()
       return self._labels[label_id]