


'''simple docstring'''






import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase






def 		_A			(lowerCAmelCase__					:int			,  lowerCAmelCase__					:Any			,  lowerCAmelCase__					:Optional[Any]			,  lowerCAmelCase__					:Dict						)				->					Optional[int]:




     '''simple docstring'''



     if isinstance(lowerCAmelCase__			,  lowerCAmelCase__						):
          _a      =							np.full((len(lowerCAmelCase__						), sequence_length, 2)			,  lowerCAmelCase__						)
     else:
          _a      =							np.full((len(lowerCAmelCase__						), sequence_length)			,  lowerCAmelCase__						)

     for i, tensor in enumerate(lowerCAmelCase__						):
          if padding_side == "right":
               if isinstance(lowerCAmelCase__			,  lowerCAmelCase__						):
                    _a      =							tensor[:sequence_length]
               else:
                    _a      =							tensor[:sequence_length]
          else:
               if isinstance(lowerCAmelCase__			,  lowerCAmelCase__						):
                    _a      =							tensor[:sequence_length]
               else:
                    _a      =							tensor[:sequence_length]

     return out_tensor.tolist()






def 		_A			(lowerCAmelCase__					:Any						)				->					Union[str, Any]:




     '''simple docstring'''



     _a      =							ord(lowerCAmelCase__						)
     if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 1_23 and cp <= 1_26):
          return True
     _a      =							unicodedata.category(lowerCAmelCase__						)
     if cat.startswith('P'						):
          return True
     return False






@dataclass
class 	a       (       _SCREAMING_SNAKE_CASE    ):
 _lowerCAmelCase	   =		42
 _lowerCAmelCase	   =		True
 _lowerCAmelCase	   =		None
 _lowerCAmelCase	   =		None
 _lowerCAmelCase	   =		-1_0_0
 _lowerCAmelCase	   =		"pt"
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							Any:
      import torch

      _a      =							'label' if 'label' in features[0].keys() else 'labels'
      _a      =							[feature[label_name] for feature in features] if label_name in features[0].keys() else None
      _a      =							self.tokenizer.pad(
          __magic_name__ ,			padding=self.padding ,			max_length=self.max_length ,			pad_to_multiple_of=self.pad_to_multiple_of ,			return_tensors='pt' if labels is None else None ,			)

      if labels is None:
           return batch

      _a      =							torch.tensor(batch['entity_ids']  ).shape[1]
      _a      =							self.tokenizer.padding_side
      if padding_side == "right":
           _a      =							[
               list(__magic_name__  ) + [self.label_pad_token_id] * (sequence_length - len(__magic_name__  )) for label in labels
           ]
      else:
           _a      =							[
               [self.label_pad_token_id] * (sequence_length - len(__magic_name__  )) + list(__magic_name__  ) for label in labels
           ]

      _a      =							[feature['ner_tags'] for feature in features]
      _a      =							padding_tensor(__magic_name__ ,			-1 ,			__magic_name__ ,			__magic_name__  )
      _a      =							[feature['original_entity_spans'] for feature in features]
      _a      =							padding_tensor(__magic_name__ ,			(-1, -1) ,			__magic_name__ ,			__magic_name__  )
      _a      =							{k: torch.tensor(__magic_name__ ,			dtype=torch.intaa  ) for k, v in batch.items()}

      return batch





'''simple docstring'''






from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

a_   :      Optional[Any]				    =      TypeVar("T")






def 		_A			(lowerCAmelCase__					:int						)				->					int:




     '''simple docstring'''



     return (position - 1) // 2






def 		_A			(lowerCAmelCase__					:int						)				->					int:




     '''simple docstring'''



     return (2 * position) + 1






def 		_A			(lowerCAmelCase__					:int						)				->					int:




     '''simple docstring'''



     return (2 * position) + 2




class 	a       (       Generic[T]    ):
 def __init__(	self  )   ->							None:
      _a      =							[]
      _a      =							{}
      _a      =							0
 def __len__(	self  )   ->							int:
      return self.elements
 def __repr__(	self  )   ->							str:
      return str(self.heap  )
 def 							__UpperCAmelCase						(	self  )   ->							bool:
      # Check if the priority queue is empty
      return self.elements == 0
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__  )   ->							None:
      # Add an element with given priority to the queue
      self.heap.append((elem, weight)  )
      _a      =							self.elements
      self.elements += 1
      self._bubble_up(__magic_name__  )
 def 							__UpperCAmelCase						(	self  )   ->							T:
      # Remove and return the element with lowest weight (highest priority)
      if self.elements > 1:
           self._swap_nodes(0 ,			self.elements - 1  )
      _a ,				_a      =							self.heap.pop()
      del self.position_map[elem]
      self.elements -= 1
      if self.elements > 0:
           _a ,				_a      =							self.heap[0]
           self._bubble_down(__magic_name__  )
      return elem
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__  )   ->							None:
      # Update the weight of the given key
      _a      =							self.position_map[elem]
      _a      =							(elem, weight)
      if position > 0:
           _a      =							get_parent_position(__magic_name__  )
           _a ,				_a      =							self.heap[parent_position]
           if parent_weight > weight:
                self._bubble_up(__magic_name__  )
           else:
                self._bubble_down(__magic_name__  )
      else:
           self._bubble_down(__magic_name__  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							None:
      # Place a node at the proper position (upward movement) [to be used internally
      # only]
      _a      =							self.position_map[elem]
      if curr_pos == 0:
           return None
      _a      =							get_parent_position(__magic_name__  )
      _a ,				_a      =							self.heap[curr_pos]
      _a ,				_a      =							self.heap[parent_position]
      if parent_weight > weight:
           self._swap_nodes(__magic_name__ ,			__magic_name__  )
           return self._bubble_up(__magic_name__  )
      return None
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							None:
      # Place a node at the proper position (downward movement) [to be used
      # internally only]
      _a      =							self.position_map[elem]
      _a ,				_a      =							self.heap[curr_pos]
      _a      =							get_child_left_position(__magic_name__  )
      _a      =							get_child_right_position(__magic_name__  )
      if child_left_position < self.elements and child_right_position < self.elements:
           _a ,				_a      =							self.heap[child_left_position]
           _a ,				_a      =							self.heap[child_right_position]
           if child_right_weight < child_left_weight and child_right_weight < weight:
                self._swap_nodes(__magic_name__ ,			__magic_name__  )
                return self._bubble_down(__magic_name__  )
      if child_left_position < self.elements:
           _a ,				_a      =							self.heap[child_left_position]
           if child_left_weight < weight:
                self._swap_nodes(__magic_name__ ,			__magic_name__  )
                return self._bubble_down(__magic_name__  )
      else:
           return None
      if child_right_position < self.elements:
           _a ,				_a      =							self.heap[child_right_position]
           if child_right_weight < weight:
                self._swap_nodes(__magic_name__ ,			__magic_name__  )
                return self._bubble_down(__magic_name__  )
      return None




 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__  )   ->							None:
      # Swap the nodes at the given positions
      _a      =							self.heap[nodea_pos][0]
      _a      =							self.heap[nodea_pos][0]
      _a ,				_a      =							(
          self.heap[nodea_pos],
          self.heap[nodea_pos],
      )
      _a      =							nodea_pos
      _a      =							nodea_pos





class 	a       (       Generic[T]    ):
 def __init__(	self  )   ->							None:
      _a      =							{}
      _a      =							0
 def __repr__(	self  )   ->							str:
      return str(self.connections  )
 def __len__(	self  )   ->							int:
      return self.nodes
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							None:
      # Add a node in the graph if it is not in the graph
      if node not in self.connections:
           _a      =							{}
           self.nodes += 1




 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							None:
      # Add an edge between 2 nodes in the graph
      self.add_node(__magic_name__  )
      self.add_node(__magic_name__  )
      _a      =							weight
      _a      =							weight






def 		_A			(lowerCAmelCase__					:GraphUndirectedWeighted[T]			,  )				->					tuple[dict[T, int], dict[T, T | None]]:




     '''simple docstring'''



     _a      =							{node: maxsize for node in graph.connections}
     _a      =							{node: None for node in graph.connections}

     _a      =							MinPriorityQueue()
     for node, weight in dist.items():
          priority_queue.push(lowerCAmelCase__			,  lowerCAmelCase__						)

     if priority_queue.is_empty():
          return dist, parent

     # initialization
     _a      =							priority_queue.extract_min()
     _a      =							0
     for neighbour in graph.connections[node]:
          if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
               _a      =							dist[node] + graph.connections[node][neighbour]
               priority_queue.update_key(lowerCAmelCase__			,  dist[neighbour]						)
               _a      =							node

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

