


import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

UpperCAmelCase__            =							{
    '''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     UpperCAmelCase_ (							__snake_case					,						__snake_case					,						__snake_case					,						__snake_case					,						__snake_case						)					->      Tuple:







 """simple docstring"""


 for attribute in key.split('''.'''						):
  _lowercase											=getattr(__snake_case					,						__snake_case						)

 if weight_type is not None:
  _lowercase											=getattr(__snake_case					,						__snake_case						).shape
 else:
  _lowercase											=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											=value
 elif weight_type == "weight_g":
  _lowercase											=value
 elif weight_type == "weight_v":
  _lowercase											=value
 elif weight_type == "bias":
  _lowercase											=value
 else:
  _lowercase											=value

 logger.info(F"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."						)
def     UpperCAmelCase_ (							__snake_case					,						__snake_case						)					->      Tuple:







 """simple docstring"""


 _lowercase											=[]
 _lowercase											=fairseq_model.state_dict()

 _lowercase											=hf_model.feature_extractor

 for name, value in fairseq_dict.items():
  _lowercase											=False
  if "conv_layers" in name:
   load_conv_layer(
       __snake_case					,						__snake_case					,						__snake_case					,						__snake_case					,						hf_model.config.feat_extract_norm == '''group'''					,						)
   _lowercase											=True
  else:
   for key, mapped_key in MAPPING.items():
    _lowercase											=mapped_key

    if key in name:
     _lowercase											=True
     if "*" in mapped_key:
      _lowercase											=name.split(__snake_case						)[0].split('''.'''						)[-2]
      _lowercase											=mapped_key.replace('''*'''					,						__snake_case						)
     if "weight_g" in name:
      _lowercase											='''weight_g'''
     elif "weight_v" in name:
      _lowercase											='''weight_v'''
     elif "weight" in name:
      _lowercase											='''weight'''
     elif "bias" in name:
      _lowercase											='''bias'''
     else:
      _lowercase											=None
     set_recursively(__snake_case					,						__snake_case					,						__snake_case					,						__snake_case					,						__snake_case						)
    continue
  if not is_used:
   unused_weights.append(__snake_case						)

 logger.warning(F"Unused weights: {unused_weights}"						)
def     UpperCAmelCase_ (							__snake_case					,						__snake_case					,						__snake_case					,						__snake_case					,						__snake_case						)					->      Dict:







 """simple docstring"""


 _lowercase											=full_name.split('''conv_layers.'''						)[-1]
 _lowercase											=name.split('''.'''						)
 _lowercase											=int(items[0]						)
 _lowercase											=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											=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											=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											=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											=value
   logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."						)
 else:
  unused_weights.append(__snake_case						)
def     UpperCAmelCase_ (							__snake_case						)					->      Optional[Any]:







 """simple docstring"""


 _lowercase											=HubertConfig()
 _lowercase											=model.config

 _lowercase											=fs_config.activation_dropout
 _lowercase											=False
 _lowercase											=fs_config.attention_dropout
 _lowercase											=False
 _lowercase											=eval(fs_config.extractor_conv_feature_layers						)
 _lowercase											=[x[0] for x in conv_layers]
 _lowercase											=[x[1] for x in conv_layers]
 _lowercase											=[x[2] for x in conv_layers]
 _lowercase											='''gelu'''
 _lowercase											='''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
 _lowercase											=False
 _lowercase											=0.0
 _lowercase											=0.0
 _lowercase											=fs_config.activation_fn
 _lowercase											=fs_config.dropout
 _lowercase											=fs_config.encoder_embed_dim
 _lowercase											=0.02
 _lowercase											=fs_config.encoder_ffn_embed_dim
 _lowercase											=1e-5
 _lowercase											=0.0
 _lowercase											=fs_config.encoder_attention_heads
 _lowercase											=fs_config.conv_pos_groups
 _lowercase											=fs_config.conv_pos
 _lowercase											=len(__snake_case						)
 _lowercase											=fs_config.encoder_layers

 return config
@torch.no_grad()
def     UpperCAmelCase_ (							__snake_case					,						__snake_case=None						)					->      Any:







 """simple docstring"""


 _lowercase											=distilhubert().model.model

 if config_path is not None:
  _lowercase											=HubertConfig.from_pretrained(__snake_case						)
 else:
  _lowercase											=convert_config(__snake_case						)
 _lowercase											=model.eval()

 _lowercase											=WavaVecaFeatureExtractor(
     feature_size=1					,						sampling_rate=16000					,						padding_value=0					,						do_normalize=__snake_case					,						return_attention_mask=__snake_case					,						)
 _lowercase											=HubertModel(__snake_case						)

 recursively_load_weights(__snake_case					,						__snake_case						)

 feature_extractor.save_pretrained(__snake_case						)
 hf_model.save_pretrained(__snake_case						)


if __name__ == "__main__":
  UpperCAmelCase__            =							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''')
  UpperCAmelCase__            =							parser.parse_args()
  convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)





from __future__ import annotations

UpperCAmelCase__            =							[2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]







class 				lowerCamelCase__       :





     def __init__(self		,					UpperCAmelCase	)					->       None:
      _lowercase											=-1
      _lowercase											=-1
      _lowercase											=[-1] * length
      _lowercase											=None
      _lowercase											=None





     def __repr__(self	)					->       str:
      return f"Node(min_value={self.minn} max_value={self.maxx})"



def     UpperCAmelCase_ (							__snake_case						)					->      Node | None:







 """simple docstring"""


 _lowercase											=Node(len(__snake_case						)						)
 _lowercase				,   _lowercase											=min(__snake_case						), max(__snake_case						)
 # Leaf node case where the node contains only one unique value
 if root.minn == root.maxx:
  return root
 _lowercase											=(root.minn + root.maxx) // 2

 _lowercase											=[]
 _lowercase											=[]

 for index, num in enumerate(__snake_case						):
  if num <= pivot:
   left_arr.append(__snake_case						)
  else:
   right_arr.append(__snake_case						)
  _lowercase											=len(__snake_case						)
 _lowercase											=build_tree(__snake_case						)
 _lowercase											=build_tree(__snake_case						)
 return root
def     UpperCAmelCase_ (							__snake_case					,						__snake_case					,						__snake_case						)					->      int:







 """simple docstring"""


 if index < 0 or node is None:
  return 0
 # Leaf node cases
 if node.minn == node.maxx:
  return index + 1 if node.minn == num else 0
 _lowercase											=(node.minn + node.maxx) // 2
 if num <= pivot:
  # go the left subtree and map index to the left subtree
  return rank_till_index(node.left					,						__snake_case					,						node.map_left[index] - 1						)
 else:
  # go to the right subtree and map index to the right subtree
  return rank_till_index(node.right					,						__snake_case					,						index - node.map_left[index]						)
def     UpperCAmelCase_ (							__snake_case					,						__snake_case					,						__snake_case					,						__snake_case						)					->      int:







 """simple docstring"""


 if start > end:
  return 0
 _lowercase											=rank_till_index(__snake_case					,						__snake_case					,						__snake_case						)
 _lowercase											=rank_till_index(__snake_case					,						__snake_case					,						start - 1						)
 return rank_till_end - rank_before_start
def     UpperCAmelCase_ (							__snake_case					,						__snake_case					,						__snake_case					,						__snake_case						)					->      int:







 """simple docstring"""


 if index > (end - start) or start > end or node is None:
  return -1
 # Leaf node case
 if node.minn == node.maxx:
  return node.minn
 # Number of elements in the left subtree in interval [start, end]
 _lowercase											=node.map_left[end] - (
     node.map_left[start - 1] if start else 0
 )
 if num_elements_in_left_tree > index:
  return quantile(
      node.left					,						__snake_case					,						(node.map_left[start - 1] if start else 0)					,						node.map_left[end] - 1					,						)
 else:
  return quantile(
      node.right					,						index - num_elements_in_left_tree					,						start - (node.map_left[start - 1] if start else 0)					,						end - node.map_left[end]					,						)
def     UpperCAmelCase_ (							__snake_case					,						__snake_case					,						__snake_case					,						__snake_case					,						__snake_case						)					->      int:







 """simple docstring"""


 if (
     start > end
     or node is None
     or start_num > end_num
     or node.minn > end_num
     or node.maxx < start_num
 ):
  return 0
 if start_num <= node.minn and node.maxx <= end_num:
  return end - start + 1
 _lowercase											=range_counting(
     node.left					,						(node.map_left[start - 1] if start else 0)					,						node.map_left[end] - 1					,						__snake_case					,						__snake_case					,						)
 _lowercase											=range_counting(
     node.right					,						start - (node.map_left[start - 1] if start else 0)					,						end - node.map_left[end]					,						__snake_case					,						__snake_case					,						)
 return left + right


if __name__ == "__main__":
  import doctest

  doctest.testmod()

