code: 
  
  '''simple docstring'''
  
  
  
  
  
  import argparse
  
  import torch
  from saprl.hub import distilhubert
  
  from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging
  
  
  logging.set_verbosity_info()
  lowerCamelCase     =      logging.get_logger(__name__)
  
  lowerCamelCase     =      {
      '''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__       (    _A   :				Any			,   _A   :				Tuple			,   _A   :				Optional[int]			,   _A   :				List[Any]			,   _A   :				Union[str, Any]							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    for attribute in key.split('''.'''							):
      a__						=getattr(_A			,   _A							)
  
    if weight_type is not None:
      a__						=getattr(_A			,   _A							).shape
    else:
      a__						=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":
      a__						=value
    elif weight_type == "weight_g":
      a__						=value
    elif weight_type == "weight_v":
      a__						=value
    elif weight_type == "bias":
      a__						=value
    else:
      a__						=value
  
    logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""							)
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				Dict			,   _A   :				Optional[int]							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						=[]
    a__						=fairseq_model.state_dict()
  
    a__						=hf_model.feature_extractor
  
    for name, value in fairseq_dict.items():
      a__						=False
      if "conv_layers" in name:
        load_conv_layer(
            _A			,   _A			,   _A			,   _A			,   hf_model.config.feat_extract_norm == '''group'''			,   )
        a__						=True
      else:
        for key, mapped_key in MAPPING.items():
          a__						=mapped_key
  
          if key in name:
            a__						=True
            if "*" in mapped_key:
              a__						=name.split(_A							)[0].split('''.'''							)[-2]
              a__						=mapped_key.replace('''*'''			,   _A							)
            if "weight_g" in name:
              a__						='''weight_g'''
            elif "weight_v" in name:
              a__						='''weight_v'''
            elif "weight" in name:
              a__						='''weight'''
            elif "bias" in name:
              a__						='''bias'''
            else:
              a__						=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     UpperCAmelCase__       (    _A   :				Optional[int]			,   _A   :				str			,   _A   :				Optional[Any]			,   _A   :				Dict			,   _A   :				List[Any]							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						=full_name.split('''conv_layers.'''							)[-1]
    a__						=name.split('''.'''							)
    a__						=int(items[0]							)
    a__						=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."""
        )
        a__						=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."""
        )
        a__						=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."
        )
        a__						=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."""
        )
        a__						=value
        logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""							)
    else:
      unused_weights.append(_A							)
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				str							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						=HubertConfig()
    a__						=model.config
  
    a__						=fs_config.activation_dropout
    a__						=False
    a__						=fs_config.attention_dropout
    a__						=False
    a__						=eval(fs_config.extractor_conv_feature_layers							)
    a__						=[x[0] for x in conv_layers]
    a__						=[x[1] for x in conv_layers]
    a__						=[x[2] for x in conv_layers]
    a__						='''gelu'''
    a__						='''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
    a__						=False
    a__						=0.0
    a__						=0.0
    a__						=fs_config.activation_fn
    a__						=fs_config.dropout
    a__						=fs_config.encoder_embed_dim
    a__						=0.02
    a__						=fs_config.encoder_ffn_embed_dim
    a__						=1E-5
    a__						=0.0
    a__						=fs_config.encoder_attention_heads
    a__						=fs_config.conv_pos_groups
    a__						=fs_config.conv_pos
    a__						=len(_A							)
    a__						=fs_config.encoder_layers
  
    return config
  
  
  
  
  @torch.no_grad()
  def     UpperCAmelCase__       (    _A   :				Tuple			,   _A   :				List[Any]=None							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						=distilhubert().model.model
  
    if config_path is not None:
      a__						=HubertConfig.from_pretrained(_A							)
    else:
      a__						=convert_config(_A							)
    a__						=model.eval()
  
    a__						=WavaVecaFeatureExtractor(
        feature_size=1			,   sampling_rate=1_60_00			,   padding_value=0			,   do_normalize=_A			,   return_attention_mask=_A			,   )
    a__						=HubertModel(_A							)
  
    recursively_load_weights(_A			,   _A							)
  
    feature_extractor.save_pretrained(_A							)
    hf_model.save_pretrained(_A							)
  
  
  if __name__ == "__main__":
         lowerCamelCase     =      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''')
         lowerCamelCase     =      parser.parse_args()
         convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)
  
style_context: 
  
  import os
  import random
  from pathlib import Path
  from typing import List
  
  import numpy as np
  import torch
  from torch.cuda.amp import GradScaler
  
  from .utils import (
      MODEL_NAME,
      OPTIMIZER_NAME,
      RNG_STATE_NAME,
      SCALER_NAME,
      SCHEDULER_NAME,
      get_pretty_name,
      is_tpu_available,
      is_xpu_available,
      save,
  )
  
  
  if is_tpu_available(check_device=False):
         import torch_xla.core.xla_model as xm
  
  from .logging import get_logger
  from .state import PartialState
  
  
  lowerCamelCase     =      get_logger(__name__)
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				str			,   _A   :				List[dict]			,   _A   :				list			,   _A   :				list			,   _A   :				int			,   _A   :				GradScaler = None			,   ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    for i, state in enumerate(_A							):
      a__						=F"""{MODEL_NAME}.bin""" if i == 0 else F"""{MODEL_NAME}_{i}.bin"""
      a__						=os.path.join(_A			,   _A							)
      save(_A			,   _A							)
      logger.info(F"""Model weights saved in {output_model_file}"""							)
    # Optimizer states
    for i, opt in enumerate(_A							):
      a__						=opt.state_dict()
      a__						=F"""{OPTIMIZER_NAME}.bin""" if i == 0 else F"""{OPTIMIZER_NAME}_{i}.bin"""
      a__						=os.path.join(_A			,   _A							)
      save(_A			,   _A							)
      logger.info(F"""Optimizer state saved in {output_optimizer_file}"""							)
    # Scheduler states
    for i, scheduler in enumerate(_A							):
      a__						=scheduler.state_dict()
      a__						=F"""{SCHEDULER_NAME}.bin""" if i == 0 else F"""{SCHEDULER_NAME}_{i}.bin"""
      a__						=os.path.join(_A			,   _A							)
      save(_A			,   _A							)
      logger.info(F"""Scheduler state saved in {output_scheduler_file}"""							)
    # GradScaler state
    if scaler is not None:
      a__						=scaler.state_dict()
      a__						=os.path.join(_A			,   _A							)
      torch.save(_A			,   _A							)
      logger.info(F"""Gradient scaler state saved in {output_scaler_file}"""							)
    # Random number generator states
    a__						={}
    a__						=F"""{RNG_STATE_NAME}_{process_index}.pkl"""
    a__						=random.getstate()
    a__						=np.random.get_state()
    a__						=torch.get_rng_state()
    if is_xpu_available():
      a__						=torch.xpu.get_rng_state_all()
    else:
      a__						=torch.cuda.get_rng_state_all()
    if is_tpu_available():
      a__						=xm.get_rng_state()
    a__						=os.path.join(_A			,   _A							)
    torch.save(_A			,   _A							)
    logger.info(F"""Random states saved in {output_states_file}"""							)
    return output_dir
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				Union[str, Any]			,   _A   :				int			,   _A   :				int			,   _A   :				Union[str, Any]			,   _A   :				Tuple			,   _A   :				Optional[int]=None			,   _A   :				List[Any]=None			,   **_A   :				Dict			,   ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    if map_location not in [None, "cpu", "on_device"]:
      raise TypeError(
          '''Unsupported optimizer map location passed, please choose one of `None`, `\'cpu\'`, or `\'on_device\'`'''							)
    if map_location is None:
      a__						='''cpu'''
    elif map_location == "on_device":
      a__						=PartialState().device
    # Model states
    for i, model in enumerate(_A							):
      a__						=F"""{MODEL_NAME}.bin""" if i == 0 else F"""{MODEL_NAME}_{i}.bin"""
      a__						=os.path.join(_A			,   _A							)
      models[i].load_state_dict(torch.load(_A			,   map_location=_A							)			,   **_A							)
    logger.info('''All model weights loaded successfully'''							)
  
    # Optimizer states
    for i, opt in enumerate(_A							):
      a__						=F"""{OPTIMIZER_NAME}.bin""" if i == 0 else F"""{OPTIMIZER_NAME}_{i}.bin"""
      a__						=os.path.join(_A			,   _A							)
      a__						=torch.load(_A			,   map_location=_A							)
      optimizers[i].load_state_dict(_A							)
    logger.info('''All optimizer states loaded successfully'''							)
  
    # Scheduler states
    for i, scheduler in enumerate(_A							):
      a__						=F"""{SCHEDULER_NAME}.bin""" if i == 0 else F"""{SCHEDULER_NAME}_{i}.bin"""
      a__						=os.path.join(_A			,   _A							)
      scheduler.load_state_dict(torch.load(_A							)							)
    logger.info('''All scheduler states loaded successfully'''							)
  
    # GradScaler state
    if scaler is not None:
      a__						=os.path.join(_A			,   _A							)
      scaler.load_state_dict(torch.load(_A							)							)
      logger.info('''GradScaler state loaded successfully'''							)
  
    # Random states
    try:
      a__						=torch.load(os.path.join(_A			,   F"""{RNG_STATE_NAME}_{process_index}.pkl"""							)							)
      random.setstate(states['''random_state''']							)
      np.random.set_state(states['''numpy_random_seed''']							)
      torch.set_rng_state(states['''torch_manual_seed''']							)
      if is_xpu_available():
        torch.xpu.set_rng_state_all(states['''torch_xpu_manual_seed''']							)
      else:
        torch.cuda.set_rng_state_all(states['''torch_cuda_manual_seed''']							)
      if is_tpu_available():
        xm.set_rng_state(states['''xm_seed''']							)
      logger.info('''All random states loaded successfully'''							)
    except Exception:
      logger.info('''Could not load random states'''							)
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				Optional[Any]			,   _A   :				Any			,   _A   :				int = 0							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						=Path(_A							) / F"""custom_checkpoint_{index}.pkl"""
    logger.info(F"""Saving the state of {get_pretty_name(_A							)} to {save_location}"""							)
    torch.save(obj.state_dict()			,   _A							)
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				int			,   _A   :				Any			,   _A   :				int = 0							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						=F"""{path}/custom_checkpoint_{index}.pkl"""
    logger.info(F"""Loading the state of {get_pretty_name(_A							)} from {load_location}"""							)
    obj.load_state_dict(torch.load(_A			,   map_location='''cpu'''							)							)
  