






from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

a 			=							TypeVar('T')
a 			=							TypeVar('U')
class 							__snake_case(       Generic[T, U]   ):



 def __init__(						self : List[str]			,		UpperCAmelCase__ : T | None			,		UpperCAmelCase__ : U | None						):

    """simple docstring"""






    lowercase__    :						Any       =			key
    lowercase__    :						List[Any]       =			val
    lowercase__    :						int       =			0
    lowercase__    :						DoubleLinkedListNode[T, U] | None       =			None
    lowercase__    :						DoubleLinkedListNode[T, U] | None       =			None







 def __repr__(						self : Tuple						):

    """simple docstring"""






    return "Node: key: {}, val: {}, freq: {}, has next: {}, has prev: {}".format(
        self.key			,		self.val			,		self.freq			,		self.next is not None			,		self.prev is not None						)
class 							__snake_case(       Generic[T, U]   ):



 def __init__(						self : str						):

    """simple docstring"""






    lowercase__    :						DoubleLinkedListNode[T, U]       =			DoubleLinkedListNode(UpperCAmelCase__			,		UpperCAmelCase__						)
    lowercase__    :						DoubleLinkedListNode[T, U]       =			DoubleLinkedListNode(UpperCAmelCase__			,		UpperCAmelCase__						)
    lowercase__  ,       lowercase__    :						int       =			self.rear, self.head



 def __repr__(						self : Optional[Any]						):

    """simple docstring"""






    lowercase__    :						int       =			["""DoubleLinkedList"""]
    lowercase__    :						int       =			self.head
    while node.next is not None:
       rep.append(str(UpperCAmelCase__						)						)
       lowercase__    :						int       =			node.next
    rep.append(str(self.rear						)						)
    return ",\n    ".join(UpperCAmelCase__						)



 def     UpperCAmelCase     (						self : List[str]			,		UpperCAmelCase__ : DoubleLinkedListNode[T, U]						):

    """simple docstring"""






    lowercase__    :						Any       =			self.rear.prev

    # All nodes other than self.head are guaranteed to have non-None previous
    assert previous is not None

    lowercase__    :						List[str]       =			node
    lowercase__    :						Optional[int]       =			previous
    lowercase__    :						Any       =			node
    lowercase__    :						Union[str, Any]       =			self.rear
    node.freq += 1
    self._position_node(UpperCAmelCase__						)



 def     UpperCAmelCase     (						self : List[Any]			,		UpperCAmelCase__ : DoubleLinkedListNode[T, U]						):

    """simple docstring"""






    while node.prev is not None and node.prev.freq > node.freq:
       # swap node with previous node
       lowercase__    :						Dict       =			node.prev

       lowercase__    :						List[Any]       =			previous_node.prev
       lowercase__    :						int       =			node.prev
       lowercase__    :						str       =			previous_node
       lowercase__    :						List[str]       =			node







 def     UpperCAmelCase     (						self : int			,		UpperCAmelCase__ : DoubleLinkedListNode[T, U]						):

    """simple docstring"""






    if node.prev is None or node.next is None:
       return None

    lowercase__    :						Any       =			node.next
    lowercase__    :						List[Any]       =			node.prev
    lowercase__    :						Optional[int]       =			None
    lowercase__    :						Union[str, Any]       =			None
    return node
class 							__snake_case(       Generic[T, U]   ):
 lowercase			:							dict[Callable[[T], U], LFUCache[T, U]]		   =		{}



 def __init__(						self : List[str]			,		UpperCAmelCase__ : int						):

    """simple docstring"""






    lowercase__    :						DoubleLinkedList[T, U]       =			DoubleLinkedList()
    lowercase__    :						int       =			capacity
    lowercase__    :						List[str]       =			0
    lowercase__    :						List[Any]       =			0
    lowercase__    :						Dict       =			0
    lowercase__    :						dict[T, DoubleLinkedListNode[T, U]]       =			{}



 def __repr__(						self : str						):

    """simple docstring"""






    return (
        f'''CacheInfo(hits={self.hits}, misses={self.miss}, '''
        f'''capacity={self.capacity}, current_size={self.num_keys})'''
    )



 def __contains__(						self : str			,		UpperCAmelCase__ : T						):

    """simple docstring"""






    return key in self.cache



 def     UpperCAmelCase     (						self : Tuple			,		UpperCAmelCase__ : T						):

    """simple docstring"""






    if key in self.cache:
       self.hits += 1
       lowercase__    :						DoubleLinkedListNode[T, U]       =			self.cache[key]
       lowercase__    :						Any       =			self.list.remove(self.cache[key]						)
       assert node == value_node

       # node is guaranteed not None because it is in self.cache
       assert node is not None
       self.list.add(UpperCAmelCase__						)
       return node.val
    self.miss += 1
    return None



 def     UpperCAmelCase     (						self : Optional[int]			,		UpperCAmelCase__ : T			,		UpperCAmelCase__ : U						):

    """simple docstring"""






    if key not in self.cache:
       if self.num_keys >= self.capacity:
          # delete first node when over capacity
          lowercase__    :						str       =			self.list.head.next

          # guaranteed to have a non-None first node when num_keys > 0
          # explain to type checker via assertions
          assert first_node is not None
          assert first_node.key is not None
          assert self.list.remove(UpperCAmelCase__						) is not None
          # first_node guaranteed to be in list

          del self.cache[first_node.key]
          self.num_keys -= 1
       lowercase__    :						List[Any]       =			DoubleLinkedListNode(UpperCAmelCase__			,		UpperCAmelCase__						)
       self.list.add(self.cache[key]						)
       self.num_keys += 1

    else:
       lowercase__    :						List[str]       =			self.list.remove(self.cache[key]						)
       assert node is not None  # node guaranteed to be in list
       lowercase__    :						Optional[int]       =			value
       self.list.add(UpperCAmelCase__						)







 @classmethod
 def     UpperCAmelCase     (						cls : type[LFUCache[T, U]]			,		UpperCAmelCase__ : int = 128						):

    """simple docstring"""






    def cache_decorator_inner(UpperCAmelCase__ : Callable[[T], U]						) -> Callable[..., U]:
       def cache_decorator_wrapper(*UpperCAmelCase__ : T						) -> U:
          if func not in cls.decorator_function_to_instance_map:
             lowercase__    :						Optional[Any]       =			LFUCache(UpperCAmelCase__						)

          lowercase__    :						List[Any]       =			cls.decorator_function_to_instance_map[func].get(args[0]						)
          if result is None:
             lowercase__    :						List[Any]       =			func(*UpperCAmelCase__						)
             cls.decorator_function_to_instance_map[func].put(args[0]			,		UpperCAmelCase__						)
          return result

       def cache_info() -> LFUCache[T, U]:
          return cls.decorator_function_to_instance_map[func]

       setattr(UpperCAmelCase__			,		"""cache_info"""			,		UpperCAmelCase__						)  # noqa: B010

       return cache_decorator_wrapper

    return cache_decorator_inner


if __name__ == "__main__":
 import doctest

 doctest.testmod()










import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
a 			=							logging.get_logger(__name__)

a 			=							{
    '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',
    'mask_emb': 'masked_spec_embed',
}
def    a__    (	A		,					A		,					A		,					A		,					A       ):
   for attribute in key.split("""."""       ):
      lowercase__    :						Dict       =			getattr(A		,					A       )

   if weight_type is not None:
      lowercase__    :						List[Any]       =			getattr(A		,					A       ).shape
   else:
      lowercase__    :						Tuple       =			hf_pointer.shape

   assert hf_shape == value.shape, (
       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":
      lowercase__    :						Dict       =			value
   elif weight_type == "weight_g":
      lowercase__    :						Any       =			value
   elif weight_type == "weight_v":
      lowercase__    :						Dict       =			value
   elif weight_type == "bias":
      lowercase__    :						Any       =			value
   else:
      lowercase__    :						Tuple       =			value

   logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''       )
def    a__    (	A		,					A       ):
   lowercase__    :						str       =			[]
   lowercase__    :						int       =			fairseq_model.state_dict()

   lowercase__    :						Tuple       =			hf_model.feature_extractor

   for name, value in fairseq_dict.items():
      lowercase__    :						Optional[int]       =			False
      if "conv_layers" in name:
         load_conv_layer(
             A		,					A		,					A		,					A		,					hf_model.config.feat_extract_norm == """group"""		,					)
         lowercase__    :						Optional[int]       =			True
      else:
         for key, mapped_key in MAPPING.items():
            lowercase__    :						Dict       =			mapped_key

            if key in name:
               lowercase__    :						int       =			True
               if "*" in mapped_key:
                  lowercase__    :						Any       =			name.split(A       )[0].split("""."""       )[-2]
                  lowercase__    :						int       =			mapped_key.replace("""*"""		,					A       )
               if "weight_g" in name:
                  lowercase__    :						Dict       =			"""weight_g"""
               elif "weight_v" in name:
                  lowercase__    :						Dict       =			"""weight_v"""
               elif "weight" in name:
                  lowercase__    :						Union[str, Any]       =			"""weight"""
               elif "bias" in name:
                  lowercase__    :						Optional[int]       =			"""bias"""
               else:
                  lowercase__    :						List[str]       =			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    a__    (	A		,					A		,					A		,					A		,					A       ):
   lowercase__    :						Optional[int]       =			full_name.split("""conv_layers."""       )[-1]
   lowercase__    :						int       =			name.split("""."""       )
   lowercase__    :						Optional[Any]       =			int(items[0]       )
   lowercase__    :						Optional[int]       =			int(items[1]       )

   if type_id == 0:
      if "bias" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
             F'''{full_name} has size {value.shape}, but'''
             F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
         )
         lowercase__    :						int       =			value
         logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''       )
      elif "weight" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
             F'''{full_name} has size {value.shape}, but'''
             F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
         )
         lowercase__    :						str       =			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:
         assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
             F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
             " found."
         )
         lowercase__    :						Any       =			value
         logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''       )
      elif "weight" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
             F'''{full_name} has size {value.shape}, but'''
             F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
         )
         lowercase__    :						int       =			value
         logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''       )
   else:
      unused_weights.append(A       )
def    a__    (	A       ):
   lowercase__    :						Optional[Any]       =			HubertConfig()
   lowercase__    :						List[str]       =			model.config

   lowercase__    :						int       =			fs_config.activation_dropout
   lowercase__    :						Tuple       =			False
   lowercase__    :						Optional[Any]       =			fs_config.attention_dropout
   lowercase__    :						str       =			False
   lowercase__    :						List[Any]       =			eval(fs_config.extractor_conv_feature_layers       )
   lowercase__    :						Union[str, Any]       =			[x[0] for x in conv_layers]
   lowercase__    :						str       =			[x[1] for x in conv_layers]
   lowercase__    :						str       =			[x[2] for x in conv_layers]
   lowercase__    :						Any       =			"""gelu"""
   lowercase__    :						Optional[int]       =			"""layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
   lowercase__    :						Optional[int]       =			False
   lowercase__    :						Tuple       =			0.0
   lowercase__    :						Optional[Any]       =			0.0
   lowercase__    :						Optional[Any]       =			fs_config.activation_fn
   lowercase__    :						Optional[Any]       =			fs_config.dropout
   lowercase__    :						Any       =			fs_config.encoder_embed_dim
   lowercase__    :						List[Any]       =			0.0_2
   lowercase__    :						List[Any]       =			fs_config.encoder_ffn_embed_dim
   lowercase__    :						List[Any]       =			1e-5
   lowercase__    :						Dict       =			0.0
   lowercase__    :						List[Any]       =			fs_config.encoder_attention_heads
   lowercase__    :						List[str]       =			fs_config.conv_pos_groups
   lowercase__    :						List[Any]       =			fs_config.conv_pos
   lowercase__    :						Union[str, Any]       =			len(A       )
   lowercase__    :						List[str]       =			fs_config.encoder_layers

   return config
@torch.no_grad()
def    a__    (	A		,					A=None       ):
   lowercase__    :						List[str]       =			distilhubert().model.model

   if config_path is not None:
      lowercase__    :						List[str]       =			HubertConfig.from_pretrained(A       )
   else:
      lowercase__    :						List[str]       =			convert_config(A       )
   lowercase__    :						List[Any]       =			model.eval()

   lowercase__    :						Optional[Any]       =			WavaVecaFeatureExtractor(
       feature_size=1		,					sampling_rate=16_000		,					padding_value=0		,					do_normalize=A		,					return_attention_mask=A		,					)
   lowercase__    :						Union[str, Any]       =			HubertModel(A       )

   recursively_load_weights(A		,					A       )

   feature_extractor.save_pretrained(A       )
   hf_model.save_pretrained(A       )


if __name__ == "__main__":
 a 			=							argparse.ArgumentParser()
 parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
 parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
 a 			=							parser.parse_args()
 convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)


