



'''simple docstring'''






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


lowerCAmelCase:      Any		 =		logging.get_logger(__name__)

lowerCAmelCase:      List[Any]		 =		{
    'speechbrain/m-ctc-t-large': 'https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json',
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}

class 							a__(      lowerCamelCase__			):
				lowercase__				=					"""mctct"""







				def __init__(   self				:						List[Any]		,    __snake_case				:						Union[str, Any]=80_65		,    __snake_case				:						Tuple=15_36		,    __snake_case				:						Optional[Any]=36		,    __snake_case				:						str=61_44		,    __snake_case				:						List[Any]=4		,    __snake_case				:						Union[str, Any]=3_84		,    __snake_case				:						Union[str, Any]=9_20		,    __snake_case				:						Union[str, Any]=1e-5		,    __snake_case				:						int=0.3		,    __snake_case				:						Any="relu"		,    __snake_case				:						Optional[Any]=0.02		,    __snake_case				:						List[str]=0.3		,    __snake_case				:						Any=0.3		,    __snake_case				:						str=1		,    __snake_case				:						Union[str, Any]=0		,    __snake_case				:						Optional[int]=2		,    __snake_case				:						Any=1		,    __snake_case				:						List[Any]=0.3		,    __snake_case				:						Any=1		,    __snake_case				:						Dict=(7,)		,    __snake_case				:						Union[str, Any]=(3,)		,    __snake_case				:						Optional[Any]=80		,    __snake_case				:						int=1		,    __snake_case				:						Optional[int]=None		,    __snake_case				:						str="sum"		,    __snake_case				:						str=False		,    **__snake_case				:						Tuple		,    ):
						super().__init__(**__snake_case		,    pad_token_id=__snake_case		,    bos_token_id=__snake_case		,    eos_token_id=__snake_case			)
						a		:      Any       =  vocab_size
						a		:      Dict       =  hidden_size
						a		:      Any       =  num_hidden_layers
						a		:      List[Any]       =  intermediate_size
						a		:      Optional[int]       =  num_attention_heads
						a		:      Tuple       =  attention_head_dim
						a		:      Optional[int]       =  max_position_embeddings
						a		:      Any       =  layer_norm_eps
						a		:      List[Any]       =  layerdrop
						a		:      Union[str, Any]       =  hidden_act
						a		:      Dict       =  initializer_range
						a		:      Dict       =  hidden_dropout_prob
						a		:      Tuple       =  attention_probs_dropout_prob
						a		:      Union[str, Any]       =  pad_token_id
						a		:      List[Any]       =  bos_token_id
						a		:      List[str]       =  eos_token_id
						a		:      Dict       =  conv_glu_dim
						a		:      List[str]       =  conv_dropout
						a		:      List[Any]       =  num_conv_layers
						a		:      Optional[int]       =  input_feat_per_channel
						a		:      List[Any]       =  input_channels
						a		:      List[Any]       =  conv_channels
						a		:      Optional[Any]       =  ctc_loss_reduction
						a		:      List[Any]       =  ctc_zero_infinity

						# prevents config testing fail with exporting to json
						a		:      Optional[int]       =  list(__snake_case			)
						a		:      List[str]       =  list(__snake_case			)

						if len(self.conv_kernel			) != self.num_conv_layers:
								raise ValueError(
								    'Configuration for convolutional module is incorrect. '
								    'It is required that `len(config.conv_kernel)` == `config.num_conv_layers` '
								    F"""but is `len(config.conv_kernel) = {len(self.conv_kernel			)}`, """
								    F"""`config.num_conv_layers = {self.num_conv_layers}`."""			)




'''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