



'''simple docstring'''






import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase:      Dict		 =		logging.get_logger(__name__)

lowerCAmelCase:      str		 =		{
    'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/config.json',
    'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/config.json',
}

class 							a__(      __SCREAMING_SNAKE_CASE			):
				lowercase__				=					"""xlnet"""
				lowercase__				=					["""mems"""]
				lowercase__				=					{
				    """n_token""": """vocab_size""",  # Backward compatibility
				    """hidden_size""": """d_model""",
				    """num_attention_heads""": """n_head""",
				    """num_hidden_layers""": """n_layer""",
				}







				def __init__(   self				:						List[str]		,    __snake_case				:						List[str]=3_20_00		,    __snake_case				:						List[Any]=10_24		,    __snake_case				:						List[str]=24		,    __snake_case				:						Union[str, Any]=16		,    __snake_case				:						Tuple=40_96		,    __snake_case				:						Any="gelu"		,    __snake_case				:						Dict=True		,    __snake_case				:						Tuple="bi"		,    __snake_case				:						Tuple=0.02		,    __snake_case				:						Dict=1e-1_2		,    __snake_case				:						Union[str, Any]=0.1		,    __snake_case				:						str=5_12		,    __snake_case				:						int=None		,    __snake_case				:						Optional[int]=True		,    __snake_case				:						Optional[int]=False		,    __snake_case				:						Optional[Any]=False		,    __snake_case				:						Any=-1		,    __snake_case				:						Any=False		,    __snake_case				:						Optional[int]="last"		,    __snake_case				:						int=True		,    __snake_case				:						Any="tanh"		,    __snake_case				:						Any=0.1		,    __snake_case				:						int=5		,    __snake_case				:						str=5		,    __snake_case				:						Optional[int]=5		,    __snake_case				:						Dict=1		,    __snake_case				:						Optional[int]=2		,    **__snake_case				:						Union[str, Any]		,    ):
						a		:      Optional[int]       =  vocab_size
						a		:      Any       =  d_model
						a		:      Union[str, Any]       =  n_layer
						a		:      Union[str, Any]       =  n_head
						if d_model % n_head != 0:
								raise ValueError(F"""'d_model % n_head' ({d_model % n_head}) should be equal to 0"""			)
						if "d_head" in kwargs:
								if kwargs["d_head"] != d_model // n_head:
										raise ValueError(
										    F"""`d_head` ({kwargs["d_head"]}) should be equal to `d_model // n_head` ({d_model // n_head})"""			)
						a		:      str       =  d_model // n_head
						a		:      int       =  ff_activation
						a		:      Dict       =  d_inner
						a		:      int       =  untie_r
						a		:      Optional[int]       =  attn_type

						a		:      Optional[Any]       =  initializer_range
						a		:      Dict       =  layer_norm_eps

						a		:      Union[str, Any]       =  dropout
						a		:      Union[str, Any]       =  mem_len
						a		:      Optional[int]       =  reuse_len
						a		:      List[str]       =  bi_data
						a		:      Any       =  clamp_len
						a		:      Dict       =  same_length

						a		:      Optional[Any]       =  summary_type
						a		:      Union[str, Any]       =  summary_use_proj
						a		:      Optional[Any]       =  summary_activation
						a		:      Optional[Any]       =  summary_last_dropout
						a		:      List[str]       =  start_n_top
						a		:      int       =  end_n_top

						a		:      Any       =  bos_token_id
						a		:      Optional[Any]       =  pad_token_id
						a		:      Union[str, Any]       =  eos_token_id

						if "use_cache" in kwargs:
								warnings.warn(
								    'The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`'
								    ' instead.'		,    __snake_case		,    )
								a		:      Optional[int]       =  kwargs['''use_cache''']

						a		:      Optional[int]       =  use_mems_eval
						a		:      Optional[int]       =  use_mems_train
						super().__init__(pad_token_id=__snake_case		,    bos_token_id=__snake_case		,    eos_token_id=__snake_case		,    **__snake_case			)







				@property
				def     lowercase_ (   self				:						Dict			):
						logger.info(F"""The model {self.model_type} is one of the few models that has no sequence length limit."""			)
						return -1







				@max_position_embeddings.setter
				def     lowercase_ (   self				:						Optional[Any]		,    __snake_case				:						int			):
						# Message copied from Transformer-XL documentation
						raise NotImplementedError(
						    F"""The model {self.model_type} is one of the few models that has no sequence length limit."""			)




'''simple docstring'''






from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

lowerCAmelCase:      List[str]		 =		TypeVar('T')
def    lowerCamelCase__ (		_A       ):
  return (position - 1) // 2
def    lowerCamelCase__ (		_A       ):
  return (2 * position) + 1
def    lowerCamelCase__ (		_A       ):
  return (2 * position) + 2

class 							a__(      Generic[T]			):







    def __init__(   self				:						Optional[Any]			):
      a		:      list[tuple[T, int]]       =  []
      a		:      dict[T, int]       =  {}
      a		:      int       =  0







    def __len__(   self				:						Union[str, Any]			):
      return self.elements







    def __repr__(   self				:						Any			):
      return str(self.heap			)







    def     lowercase_ (   self				:						Optional[int]			):
      # Check if the priority queue is empty
      return self.elements == 0







    def     lowercase_ (   self				:						Union[str, Any]		,    __snake_case				:						T		,    __snake_case				:						int			):
      # Add an element with given priority to the queue
      self.heap.append((elem, weight)			)
      a		:      int       =  self.elements
      self.elements += 1
      self._bubble_up(__snake_case			)







    def     lowercase_ (   self				:						Union[str, Any]			):
      # Remove and return the element with lowest weight (highest priority)
      if self.elements > 1:
        self._swap_nodes(0		,    self.elements - 1			)
      a     ,			a		:      str       =  self.heap.pop()
      del self.position_map[elem]
      self.elements -= 1
      if self.elements > 0:
        a     ,			a		:      List[Any]       =  self.heap[0]
        self._bubble_down(__snake_case			)
      return elem







    def     lowercase_ (   self				:						List[str]		,    __snake_case				:						T		,    __snake_case				:						int			):
      # Update the weight of the given key
      a		:      Any       =  self.position_map[elem]
      a		:      Union[str, Any]       =  (elem, weight)
      if position > 0:
        a		:      Optional[Any]       =  get_parent_position(__snake_case			)
        a     ,			a		:      Union[str, Any]       =  self.heap[parent_position]
        if parent_weight > weight:
          self._bubble_up(__snake_case			)
        else:
          self._bubble_down(__snake_case			)
      else:
        self._bubble_down(__snake_case			)







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







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







    def     lowercase_ (   self				:						List[Any]		,    __snake_case				:						int		,    __snake_case				:						int			):
      # Swap the nodes at the given positions
      a		:      Optional[int]       =  self.heap[nodea_pos][0]
      a		:      int       =  self.heap[nodea_pos][0]
      a     ,			a		:      List[str]       =  (
          self.heap[nodea_pos],
          self.heap[nodea_pos],
      )
      a		:      Tuple       =  nodea_pos
      a		:      Any       =  nodea_pos

class 							a__(      Generic[T]			):







    def __init__(   self				:						Optional[int]			):
      a		:      dict[T, dict[T, int]]       =  {}
      a		:      int       =  0







    def __repr__(   self				:						List[str]			):
      return str(self.connections			)







    def __len__(   self				:						str			):
      return self.nodes







    def     lowercase_ (   self				:						int		,    __snake_case				:						T			):
      # Add a node in the graph if it is not in the graph
      if node not in self.connections:
        a		:      Optional[int]       =  {}
        self.nodes += 1







    def     lowercase_ (   self				:						Dict		,    __snake_case				:						T		,    __snake_case				:						T		,    __snake_case				:						int			):
      # Add an edge between 2 nodes in the graph
      self.add_node(__snake_case			)
      self.add_node(__snake_case			)
      a		:      Any       =  weight
      a		:      Dict       =  weight
def    lowerCamelCase__ (		_A				,    ):
  a		:      dict[T, int]       =  {node: maxsize for node in graph.connections}
  a		:      dict[T, T | None]       =  {node: None for node in graph.connections}

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

  if priority_queue.is_empty():
    return dist, parent

  # initialization
  a		:      int       =  priority_queue.extract_min()
  a		:      Dict       =  0
  for neighbour in graph.connections[node]:
    if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
      a		:      Tuple       =  dist[node] + graph.connections[node][neighbour]
      priority_queue.update_key(_A				,    dist[neighbour]       )
      a		:      Dict       =  node

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