



'''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		:      str       =  self.heap.pop()
      del self.position_map[elem]
      self.elements -= 1
      if self.elements > 0:
        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		:      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		:      Tuple       =  self.heap[curr_pos]
      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		:      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		:      int       =  self.heap[child_left_position]
        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		:      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		:      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		:      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




'''simple docstring'''






import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
lowerCAmelCase:      Optional[int]		 =		logging.get_logger(__name__)

lowerCAmelCase:      Union[str, Any]		 =		{
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'encoder.layer_norm_for_extract': 'layer_norm_for_extract',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'lm_head',
    'label_embs_concat': 'label_embeddings_concat',
    'mask_emb': 'masked_spec_embed',
    'spk_proj': 'speaker_proj',
}
lowerCAmelCase:      int		 =		[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'label_embeddings_concat',
    'speaker_proj',
    'layer_norm_for_extract',
]
def    lowerCamelCase__ (		_A				,    _A				,    _A				,    _A				,    _A       ):
  for attribute in key.split('.'       ):
    a		:      Tuple       =  getattr(_A				,    _A       )

  if weight_type is not None:
    a		:      Union[str, Any]       =  getattr(_A				,    _A       ).shape
  else:
    a		:      Optional[Any]       =  hf_pointer.shape

  if hf_shape != value.shape:
    raise ValueError(
        f"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
        f""" {value.shape} for {full_name}"""       )

  if weight_type == "weight":
    a		:      str       =  value
  elif weight_type == "weight_g":
    a		:      Optional[int]       =  value
  elif weight_type == "weight_v":
    a		:      List[Any]       =  value
  elif weight_type == "bias":
    a		:      Any       =  value
  else:
    a		:      Optional[Any]       =  value

  logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""       )
def    lowerCamelCase__ (		_A				,    _A       ):
  a		:      Optional[int]       =  []
  a		:      Tuple       =  fairseq_model.state_dict()

  a		:      List[Any]       =  hf_model.unispeech_sat.feature_extractor

  for name, value in fairseq_dict.items():
    a		:      Optional[Any]       =  False
    if "conv_layers" in name:
      load_conv_layer(
          _A				,    _A				,    _A				,    _A				,    hf_model.config.feat_extract_norm == 'group'				,    )
      a		:      int       =  True
    else:
      for key, mapped_key in MAPPING.items():
        a		:      Any       =  'unispeech_sat.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
        if key in name or key.split('w2v_model.'       )[-1] == name.split('.'       )[0]:
          if "layer_norm_for_extract" in name and (".".join(name.split('.'       )[:-1]       ) != key):
            # special case since naming is very similar
            continue
          a		:      str       =  True
          if "*" in mapped_key:
            a		:      Union[str, Any]       =  name.split(_A       )[0].split('.'       )[-2]
            a		:      List[str]       =  mapped_key.replace('*'				,    _A       )
          if "weight_g" in name:
            a		:      Optional[int]       =  'weight_g'
          elif "weight_v" in name:
            a		:      Any       =  'weight_v'
          elif "bias" in name:
            a		:      str       =  'bias'
          elif "weight" in name:
            # TODO: don't match quantizer.weight_proj
            a		:      List[Any]       =  'weight'
          else:
            a		:      Any       =  None
          set_recursively(_A				,    _A				,    _A				,    _A				,    _A       )
        continue
    if not is_used:
      unused_weights.append(_A       )

  logger.warning(f"""Unused weights: {unused_weights}"""       )
def    lowerCamelCase__ (		_A				,    _A				,    _A				,    _A				,    _A       ):
  a		:      List[str]       =  full_name.split('conv_layers.'       )[-1]
  a		:      Any       =  name.split('.'       )
  a		:      Optional[Any]       =  int(items[0]       )
  a		:      Union[str, Any]       =  int(items[1]       )

  if type_id == 0:
    if "bias" in name:
      if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
        raise ValueError(
            f"""{full_name} has size {value.shape}, but"""
            f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""       )
      a		:      Optional[int]       =  value
      logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""       )
    elif "weight" in name:
      if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
        raise ValueError(
            f"""{full_name} has size {value.shape}, but"""
            f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""       )
      a		:      Optional[int]       =  value
      logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""       )
  elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
    if "bias" in name:
      if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
        raise ValueError(
            f"""{full_name} has size {value.shape}, but"""
            f""" {feature_extractor[layer_id].layer_norm.bias.data.shape} was found."""       )
      a		:      Union[str, Any]       =  value
      logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""       )
    elif "weight" in name:
      if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
        raise ValueError(
            f"""{full_name} has size {value.shape}, but"""
            f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""       )
      a		:      Union[str, Any]       =  value
      logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""       )
  else:
    unused_weights.append(_A       )
@torch.no_grad()
def    lowerCamelCase__ (		_A				,    _A				,    _A=None				,    _A=None				,    _A=True       ):
  if config_path is not None:
    a		:      str       =  UniSpeechSatConfig.from_pretrained(_A       )
  else:
    a		:      Dict       =  UniSpeechSatConfig()

  a		:      Any       =  ''

  if is_finetuned:
    a		:      Tuple       =  UniSpeechSatForCTC(_A       )
  else:
    a		:      List[str]       =  UniSpeechSatForPreTraining(_A       )

  a     ,			a     ,			a		:      str       =  fairseq.checkpoint_utils.load_model_ensemble_and_task(
      [checkpoint_path]				,    arg_overrides={'data': '/'.join(dict_path.split('/'       )[:-1]       )}       )
  a		:      str       =  model[0].eval()

  recursively_load_weights(_A				,    _A       )

  hf_wavavec.save_pretrained(_A       )


if __name__ == "__main__":
     lowerCAmelCase:      Tuple		 =		argparse.ArgumentParser()
     parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
     parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
     parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
     parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
     parser.add_argument(
         '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
     )
     lowerCAmelCase:      Optional[int]		 =		parser.parse_args()
     convert_unispeech_sat_checkpoint(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
     )