



from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

__snake_case     :Union[str, Any]			       =TypeVar('T')






def  lowerCamelCase_	(      lowerCAmelCase__	:	int				)       ->							int:

							'''simple docstring'''






							return (position - 1) // 2






def  lowerCamelCase_	(      lowerCAmelCase__	:	int				)       ->							int:

							'''simple docstring'''






							return (2 * position) + 1






def  lowerCamelCase_	(      lowerCAmelCase__	:	int				)       ->							int:

							'''simple docstring'''






							return (2 * position) + 2



class        lowerCAmelCase__					(  Generic[T]       ):


				def __init__(							self      :  Any       )		->     None:
											A      						=    []
											A      						=    {}
											A      						=    0


				def __len__(							self      :  str       )		->     int:
											return self.elements


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


				def __UpperCamelCase						(							self      :  Tuple       )		->     bool:
											# Check if the priority queue is empty
											return self.elements == 0


				def __UpperCamelCase						(							self      :  Optional[int]						, __UpperCamelCase      :  T						, __UpperCamelCase      :  int       )		->     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      :  Dict       )		->     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      :  Any						, __UpperCamelCase      :  T						, __UpperCamelCase      :  int       )		->     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      :  Optional[int]						, __UpperCamelCase      :  T       )		->     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      :  Tuple						, __UpperCamelCase      :  T       )		->     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      :  List[str]						, __UpperCamelCase      :  int						, __UpperCamelCase      :  int       )		->     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        lowerCAmelCase__					(  Generic[T]       ):


				def __init__(							self      :  List[str]       )		->     None:
											A      						=    {}
											A      						=    0


				def __repr__(							self      :  Optional[int]       )		->     str:
											return str(self.connections       )


				def __len__(							self      :  Union[str, Any]       )		->     int:
											return self.nodes


				def __UpperCamelCase						(							self      :  Optional[int]						, __UpperCamelCase      :  T       )		->     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      :  Optional[int]						, __UpperCamelCase      :  T						, __UpperCamelCase      :  T						, __UpperCamelCase      :  int       )		->     None:
											# Add an edge between 2 nodes in the graph
											self.add_node(__UpperCamelCase       )
											self.add_node(__UpperCamelCase       )
											A      						=    weight
											A      						=    weight






def  lowerCamelCase_	(      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




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


__snake_case     :Optional[Any]			       =logging.get_logger(__name__)

__snake_case     :Tuple			       ={
    'transfo-xl-wt103': 'https://huggingface.co/transfo-xl-wt103/resolve/main/config.json',
}



class        lowerCAmelCase__					(  _lowerCamelCase       ):
				A_   :     Any								=     'transfo-xl'
				A_   :     Dict								=     ['mems']
				A_   :     int								=     {
				    'n_token': 'vocab_size',
				    'hidden_size': 'd_model',
				    'num_attention_heads': 'n_head',
				    'num_hidden_layers': 'n_layer',
				}


				def __init__(							self      :  Optional[Any]						, __UpperCamelCase      :  str=267_735						, __UpperCamelCase      :  int=[20_000, 40_000, 200_000]						, __UpperCamelCase      :  List[str]=1_024						, __UpperCamelCase      :  Union[str, Any]=1_024						, __UpperCamelCase      :  Optional[Any]=16						, __UpperCamelCase      :  Dict=64						, __UpperCamelCase      :  Optional[Any]=4_096						, __UpperCamelCase      :  int=4						, __UpperCamelCase      :  List[str]=False						, __UpperCamelCase      :  Optional[int]=18						, __UpperCamelCase      :  int=1_600						, __UpperCamelCase      :  Dict=1_000						, __UpperCamelCase      :  Optional[Any]=True						, __UpperCamelCase      :  Optional[Any]=True						, __UpperCamelCase      :  Optional[Any]=0						, __UpperCamelCase      :  Any=-1						, __UpperCamelCase      :  Any=True						, __UpperCamelCase      :  List[Any]=0.1						, __UpperCamelCase      :  str=0.0						, __UpperCamelCase      :  Dict=True						, __UpperCamelCase      :  int="normal"						, __UpperCamelCase      :  List[Any]=0.0_1						, __UpperCamelCase      :  str=0.0_1						, __UpperCamelCase      :  Any=0.0_2						, __UpperCamelCase      :  str=1e-5						, __UpperCamelCase      :  Optional[int]=0						, **__UpperCamelCase      :  Any						, )		->     int:
											A      						=    vocab_size
											A      						=    []
											self.cutoffs.extend(__UpperCamelCase       )
											if proj_share_all_but_first:
																		A      						=    [False] + [True] * len(self.cutoffs       )
											else:
																		A      						=    [False] + [False] * len(self.cutoffs       )
											A      						=    d_model
											A      						=    d_embed
											A      						=    d_head
											A      						=    d_inner
											A      						=    div_val
											A      						=    pre_lnorm
											A      						=    n_layer
											A      						=    n_head
											A      						=    mem_len
											A      						=    same_length
											A      						=    attn_type
											A      						=    clamp_len
											A      						=    sample_softmax
											A      						=    adaptive
											A      						=    dropout
											A      						=    dropatt
											A      						=    untie_r
											A      						=    init
											A      						=    init_range
											A      						=    proj_init_std
											A      						=    init_std
											A      						=    layer_norm_epsilon
											super().__init__(eos_token_id=__UpperCamelCase						, **__UpperCamelCase       )


				@property
				def __UpperCamelCase						(							self      :  Any       )		->     Optional[int]:
											# Message copied from Transformer-XL documentation
											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 __UpperCamelCase						(							self      :  List[Any]						, __UpperCamelCase      :  List[Any]       )		->     List[Any]:
											# 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.'''       )