




'''simple docstring'''


from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

UpperCAmelCase_ 		=      TypeVar("T")


def 						SCREAMING_SNAKE_CASE       (      a_	:							int			):
  return (position - 1) // 2


def 						SCREAMING_SNAKE_CASE       (      a_	:							int			):
  return (2 * position) + 1


def 						SCREAMING_SNAKE_CASE       (      a_	:							int			):
  return (2 * position) + 2





class    __lowercase						(  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      ,							UpperCamelCase      ,							UpperCamelCase      )				->    None:
     # Add an element with given priority to the queue
     self.heap.append((elem, weight)      )
     __a			=					self.elements
     self.elements += 1
     self._bubble_up(UpperCamelCase      )







   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(UpperCamelCase      )
     return elem







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      )				->    None:
     # Update the weight of the given key
     __a			=					self.position_map[elem]
     __a			=					(elem, weight)
     if position > 0:
       __a			=					get_parent_position(UpperCamelCase      )
       __a	,       __a			=					self.heap[parent_position]
       if parent_weight > weight:
         self._bubble_up(UpperCamelCase      )
       else:
         self._bubble_down(UpperCamelCase      )
     else:
       self._bubble_down(UpperCamelCase      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    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(UpperCamelCase      )
     __a	,       __a			=					self.heap[curr_pos]
     __a	,       __a			=					self.heap[parent_position]
     if parent_weight > weight:
       self._swap_nodes(UpperCamelCase      ,							UpperCamelCase      )
       return self._bubble_up(UpperCamelCase      )
     return None







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    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(UpperCamelCase      )
     __a			=					get_child_right_position(UpperCamelCase      )
     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(UpperCamelCase      ,							UpperCamelCase      )
         return self._bubble_down(UpperCamelCase      )
     if child_left_position < self.elements:
       __a	,       __a			=					self.heap[child_left_position]
       if child_left_weight < weight:
         self._swap_nodes(UpperCamelCase      ,							UpperCamelCase      )
         return self._bubble_down(UpperCamelCase      )
     else:
       return None
     if child_right_position < self.elements:
       __a	,       __a			=					self.heap[child_right_position]
       if child_right_weight < weight:
         self._swap_nodes(UpperCamelCase      ,							UpperCamelCase      )
         return self._bubble_down(UpperCamelCase      )
     return None







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      )				->    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    __lowercase						(  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      ,							UpperCamelCase      )				->    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      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      )				->    None:
     # Add an edge between 2 nodes in the graph
     self.add_node(UpperCamelCase      )
     self.add_node(UpperCamelCase      )
     __a			=					weight
     __a			=					weight


def 						SCREAMING_SNAKE_CASE       (      a_	:							GraphUndirectedWeighted[T]				,					):
  __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(a_				,					a_			)

  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(a_				,					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(a_				,					dist[neighbour]			)
        __a			=					node
  return dist, parent









'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase_ 		=      logging.get_logger(__name__)

UpperCAmelCase_ 		=      {
    "distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/config.json",
    "distilbert-base-uncased-distilled-squad": (
        "https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json"
    ),
    "distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/config.json",
    "distilbert-base-cased-distilled-squad": (
        "https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json"
    ),
    "distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json",
    "distilbert-base-multilingual-cased": (
        "https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json"
    ),
    "distilbert-base-uncased-finetuned-sst-2-english": (
        "https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json"
    ),
}





class    __lowercase						(  __magic_name__ ):
   _a							=      """distilbert"""
   _a							=      {
       """hidden_size""": """dim""",
       """num_attention_heads""": """n_heads""",
       """num_hidden_layers""": """n_layers""",
   }







   def __init__(	self      ,							UpperCamelCase=3_0522      ,							UpperCamelCase=512      ,							UpperCamelCase=False      ,							UpperCamelCase=6      ,							UpperCamelCase=12      ,							UpperCamelCase=768      ,							UpperCamelCase=4 * 768      ,							UpperCamelCase=0.1      ,							UpperCamelCase=0.1      ,							UpperCamelCase="gelu"      ,							UpperCamelCase=0.02      ,							UpperCamelCase=0.1      ,							UpperCamelCase=0.2      ,							UpperCamelCase=0      ,							**UpperCamelCase      ,							)				->    int:
     __a			=					vocab_size
     __a			=					max_position_embeddings
     __a			=					sinusoidal_pos_embds
     __a			=					n_layers
     __a			=					n_heads
     __a			=					dim
     __a			=					hidden_dim
     __a			=					dropout
     __a			=					attention_dropout
     __a			=					activation
     __a			=					initializer_range
     __a			=					qa_dropout
     __a			=					seq_classif_dropout
     super().__init__(**UpperCamelCase      ,							pad_token_id=UpperCamelCase      )





class    __lowercase						(  __magic_name__ ):







   @property
   def 		UpperCamelCase__			(	self      )				->    Mapping[str, Mapping[int, str]]:
     if self.task == "multiple-choice":
       __a			=					{0: 'batch', 1: 'choice', 2: 'sequence'}
     else:
       __a			=					{0: 'batch', 1: 'sequence'}
     return OrderedDict(
         [
             ('input_ids', dynamic_axis),
             ('attention_mask', dynamic_axis),
         ]      )



