code: 
  
  
  
  '''simple docstring'''
  
  
  
  import copy
  import logging
  import random
  
  import joblib
  import numpy as np
  import torch
  import torch.nn as nn
  from torch.utils.data import DataLoader
  from tqdm import tqdm
  
  from transformers import AdamW, GPTaLMHeadModel, get_linear_schedule_with_warmup
  
  
  a__						:			Tuple										=			logging.getLogger(__name__)
  
  
  
  
  
  
  
  def _lowercase   (  __A       ):
  
  
  
   '''simple docstring'''
  
  
  
   random.seed(__A       )
   np.random.seed(__A       )
   torch.manual_seed(__A       )
   torch.cuda.manual_seed_all(__A       )
  
  
  
  
  
  
  
  def _lowercase   (  __A							,__A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   model.eval()
   __UpperCamelCase				      =  next(model.parameters()       ).device
   __UpperCamelCase				      =  1
   __UpperCamelCase				      =  torch.zeros((eval_batch_size, context_len)							,dtype=torch.long							,device=__A       )
   __UpperCamelCase				      =  DataLoader(__A							,shuffle=__A							,batch_size=__A       )
   __UpperCamelCase				      =  torch.zeros(1							,device=__A       )
   __UpperCamelCase				      =  0
   for batch in eval_dataloader:
    batch.to(__A       )
    # pad
    context.zero_()
    for i in range(__A       ):
     __UpperCamelCase				      =  batch[i]
    __UpperCamelCase				      =  model(__A							,labels=__A       )
    eval_loss += outputs[0].sum().item()
    nb_eval_examples += batch.size(0       )
   __UpperCamelCase				      =  eval_loss / nb_eval_examples
   __UpperCamelCase				      =  torch.exp(__A       )
   model.train()
   return perplexity
  
  
  
  
  
  
  
  def _lowercase   (  __A="gpt2"       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  GPTaLMHeadModel.from_pretrained(__A							,output_hidden_states=__A       )
   torch.save(model.state_dict()							,model_name + """local.pt"""       )
   return model
  
  
  
  
  
  
  
  def _lowercase   (  __A							,__A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  copy.deepcopy(__A       )
   model.to(__A       )
  
   __UpperCamelCase				      =  ["""bias""", """LayerNorm.weight"""]
   __UpperCamelCase				      =  [
       {
           """params""": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay       )],
           """weight_decay""": 0.0,
       },
       {"""params""": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay       )], """weight_decay""": 0.0},
   ]
   __UpperCamelCase				      =  AdamW(__A							,lr=5E-5							,eps=1E-8       )
   __UpperCamelCase				      =  get_linear_schedule_with_warmup(__A							,0							,__A       )
   torch.cuda.empty_cache()
   return model, lm_optimizer, lm_scheduler
  
  
  
  
  
  
  
  def _lowercase   (  __A							,__A							,__A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  np.array(__A       ).mean()
   __UpperCamelCase				      =  np.array(__A       ).std()
   __UpperCamelCase				      =  (real_perps - avg) / std
   __UpperCamelCase				      =  list(zip(__A							,__A							,__A       )       )
   joblib.dump(__A							,__A       )
  
  
  
  
  
  
  
  def _lowercase   (  __A							,__A							,__A							,__A							,__A							,__A							,__A							,__A="dev.jbl"							,__A=recopy_gpta							,):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  []
   __UpperCamelCase				      =  []
   __UpperCamelCase				      =  []
  
   # Initialize the transformer model
   __UpperCamelCase				      =  copy.deepcopy(__A       )
   orig_model.to(device="""cpu"""       )
   torch.cuda.empty_cache()
  
   # Compute perplexity of initial transformer model for comparison
   model.train()
   __UpperCamelCase      ,		__UpperCamelCase      ,		__UpperCamelCase				      =  recopy_model(__A							,__A							,__A       )
  
   for step in tqdm(range(__A       )       ):
    __UpperCamelCase				      =  torch.zeros((1, context_len)							,dtype=torch.long							,device=__A       )
    __UpperCamelCase				      =  random.choice(__A       )
    __UpperCamelCase				      =  random.randint(0							,len(story[0]       ) - context_len - 1       )
    __UpperCamelCase				      =  story[0][start : start + context_len]
    lm_optimizer.zero_grad()
    __UpperCamelCase				      =  model(__A							,labels=__A       )
    __UpperCamelCase				      =  outputs[0]
    __UpperCamelCase				      =  compute_perplexity(__A							,__A							,__A       )
    model.train()
    lm_loss.backward()
    # Do LM backprop
    torch.nn.utils.clip_grad_norm_(model.parameters()							,3.0       )
    lm_optimizer.step()
    lm_scheduler.step()  # Update learning rate schedule
  
    # Compute perplexity after back-propagating on the selected context
    __UpperCamelCase				      =  compute_perplexity(__A							,__A							,__A       )
  
    # Periodically save the stored (X, IG(X)) pairs
    if step % 1_000 == 0 and step > 1:
     intermittent_save(__A							,__A							,__A							,__A       )
  
    # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
    __UpperCamelCase      ,		__UpperCamelCase      ,		__UpperCamelCase				      =  recopy_model(__A							,__A							,__A       )
  
    past_perps.append(past_perp.item()       )
    real_perps.append(orig_perp - real_perp.item()       )
    contexts.append(np.array(context.cpu()       )       )
  
   intermittent_save(__A							,__A							,__A							,__A       )
  
  
  
  
  
  
  
  def _lowercase   (  __A							,__A="data/tokenized_stories_train_wikitext103.jbl"							,__A=100							,__A=1_026							,__A=True       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  joblib.load(__A       )
   print("""data loaded"""       )
   __UpperCamelCase				      =  []
   if trim:
    for i, example in enumerate(__A       ):
     if len(example[0]       ) > min_len:
      __UpperCamelCase				      =  random.randint(0							,len(example[0]       ) - context_len - 1       )
      objective_set.append(example[0, start : start + context_len]       )
     if len(__A       ) >= number:
      break
    __UpperCamelCase				      =  []
    for j in range(i + 1							,len(__A       )       ):
     if len(data[j][0]       ) > min_len:
      train_data.append(data[j]       )
   else:
    __UpperCamelCase				      =  data[0:number]
    __UpperCamelCase				      =  data[number:]
  
   joblib.dump(__A							,"""objective_set.jbl"""       )
   print("""objective set saved"""       )
   return train_data, objective_set
  
  
  
  
  
  
  
  def _lowercase   (  __A							,__A							,__A							,__A							,__A=50							,__A="secondary_learner.pt"       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""       )
   # We will use the first 512 pairs from our dataset as a test set for
   # our secondary learner and the rest to train
   __UpperCamelCase				      =  train_dataset[:512]
   __UpperCamelCase				      =  train_dataset[512:]
   __UpperCamelCase				      =  DataLoader(__A							,shuffle=__A							,batch_size=__A       )
   __UpperCamelCase				      =  DataLoader(__A							,shuffle=__A							,batch_size=__A       )
  
   # secondary learner model set up
   __UpperCamelCase				      =  nn.MSELoss()
   __UpperCamelCase				      =  nn.MSELoss(reduction="""sum"""       )
   secondary_learner.to(__A       )
   __UpperCamelCase				      =  torch.optim.Adam(secondary_learner.parameters()							,lr=0.0_0001       )
   secondary_learner.train()
  
   # TODO in original code this is written as number of actual batches seen
   # not number of items seen but other places it is number of items instead.
   # improve consistency! changed this to epochs for clarity
   __UpperCamelCase				      =  float("""inf"""       )
   # Iterate through batches until we've used max_steps batches
   for epoch in range(int(__A       )       ):
    __UpperCamelCase				      =  0.0
    secondary_learner.train()
    for step, batch in enumerate(__A       ):
     __UpperCamelCase				      =  batch[0].to(__A       )
     __UpperCamelCase				      =  batch[1].to(__A       )
     __UpperCamelCase				      =  secondary_learner(__A       )
     q_optimizer.zero_grad()
     __UpperCamelCase				      =  loss(__A							,real_q.float()       )
     q_loss.backward()
     q_optimizer.step()
     tr_q_loss += q_loss.item()
  
     # model trains fairly quickly so we won't wait for a full epoch
     # eval is triggered at eval_freq and end of epochs
     if (step % eval_freq == 0 and step > 0) or ((step + 1) == len(__A       )):
      __UpperCamelCase				      =  tr_q_loss / (step + 1)
  
      secondary_learner.eval()
      __UpperCamelCase				      =  0.0
      __UpperCamelCase				      =  0.0
      __UpperCamelCase				      =  []
      __UpperCamelCase				      =  []
      # Compute performance of the secondary learner after this batch
      for stepa, batcha in enumerate(__A       ):
       __UpperCamelCase				      =  batcha[0].to(__A       )
       __UpperCamelCase				      =  batcha[1].to(__A       )
       __UpperCamelCase				      =  secondary_learner(__A       )
       q_lossa += test_loss(__A							,__A       ).item()
       sum_qa += torch.sum(__A       ).item()
       for ei, i in enumerate(predicted_qa.cpu().detach().numpy()       ):
        predicted.append(i.item()       )
       for ei, i in enumerate(real_qa.cpu().detach().numpy()       ):
        actual.append(i.item()       )
  
      q_lossa /= len(__A       )
      print(
          """Epoch: """							,__A							,"""step: """							,__A							,"""Avg. q:"""							,sum_qa / len(__A       )							,"""Train Loss: """							,__A							,"""Test Loss: """							,__A							,)
      if q_lossa < best_test_loss:
       joblib.dump((predicted, actual)							,"""pred_vs_actual.jbl"""       )
       torch.save(secondary_learner.state_dict()							,__A       )
       __UpperCamelCase				      =  q_lossa
  
     secondary_learner.train()
   return secondary_learner
  
  
  
  
  
  
  class        UpperCAmelCase__  (    nn.Module):
  
  
  
   def __init__(  self		,	lowercase			)  ->							List[Any]:
    super(lowercase		,	self			).__init__()
    __UpperCamelCase				      =  model.transformer.wte
    __UpperCamelCase				      =  copy.deepcopy(model.transformer.wte.weight			)
    __UpperCamelCase				      =  nn.Convad(self.embeddings.weight.size(1			)		,	2_5_6		,	3		,	padding=1			)
    __UpperCamelCase				      =  nn.Sequential(nn.Linear(2_5_6		,	3_2			)		,	nn.Dropout(p=0.1			)		,	nn.Linear(3_2		,	3_2			)		,	nn.Linear(3_2		,	1			)			)
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase			)  ->							Union[str, Any]:
    __UpperCamelCase				      =  torch.max(self.conv(self.embeddings(lowercase			).squeeze(1			).transpose(1		,	2			)			)		,	2			)[0]
    __UpperCamelCase				      =  self.fc(lowercase			)
    return qs.squeeze(1			)
  
  
  
  
   @classmethod
   def    __lowerCamelCase				(  cls		,	lowercase		,	lowercase			)  ->							Tuple:
    __UpperCamelCase				      =  cls(lowercase			)  # this calls __init__
    __UpperCamelCase				      =  torch.load(lowercase			)
    secondary_learner.load_state_dict(lowercase			)
    __UpperCamelCase				      =  model.transformer.wte
    __UpperCamelCase				      =  copy.deepcopy(model.transformer.wte.weight			)
    return secondary_learner
  
  
  
  
  
  
style_context: 
  
  
  
  '''simple docstring'''
  
  
  
  import argparse
  
  import torch
  from saprl.hub import distilhubert
  
  from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging
  
  
  logging.set_verbosity_info()
  a__						:			str										=			logging.get_logger(__name__)
  
  a__						:			Optional[int]										=			{
      '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 _lowercase   (  __A							,__A							,__A							,__A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   for attribute in key.split("""."""       ):
    __UpperCamelCase				      =  getattr(__A							,__A       )
  
   if weight_type is not None:
    __UpperCamelCase				      =  getattr(__A							,__A       ).shape
   else:
    __UpperCamelCase				      =  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":
    __UpperCamelCase				      =  value
   elif weight_type == "weight_g":
    __UpperCamelCase				      =  value
   elif weight_type == "weight_v":
    __UpperCamelCase				      =  value
   elif weight_type == "bias":
    __UpperCamelCase				      =  value
   else:
    __UpperCamelCase				      =  value
  
   logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."       )
  
  
  
  
  
  
  
  def _lowercase   (  __A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  []
   __UpperCamelCase				      =  fairseq_model.state_dict()
  
   __UpperCamelCase				      =  hf_model.feature_extractor
  
   for name, value in fairseq_dict.items():
    __UpperCamelCase				      =  False
    if "conv_layers" in name:
     load_conv_layer(
         __A							,__A							,__A							,__A							,hf_model.config.feat_extract_norm == """group"""							,)
     __UpperCamelCase				      =  True
    else:
     for key, mapped_key in MAPPING.items():
      __UpperCamelCase				      =  mapped_key
  
      if key in name:
       __UpperCamelCase				      =  True
       if "*" in mapped_key:
        __UpperCamelCase				      =  name.split(__A       )[0].split("""."""       )[-2]
        __UpperCamelCase				      =  mapped_key.replace("""*"""							,__A       )
       if "weight_g" in name:
        __UpperCamelCase				      =  """weight_g"""
       elif "weight_v" in name:
        __UpperCamelCase				      =  """weight_v"""
       elif "weight" in name:
        __UpperCamelCase				      =  """weight"""
       elif "bias" in name:
        __UpperCamelCase				      =  """bias"""
       else:
        __UpperCamelCase				      =  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 _lowercase   (  __A							,__A							,__A							,__A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  full_name.split("""conv_layers."""       )[-1]
   __UpperCamelCase				      =  name.split("""."""       )
   __UpperCamelCase				      =  int(items[0]       )
   __UpperCamelCase				      =  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."
     )
     __UpperCamelCase				      =  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."
     )
     __UpperCamelCase				      =  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."
     )
     __UpperCamelCase				      =  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."
     )
     __UpperCamelCase				      =  value
     logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."       )
   else:
    unused_weights.append(__A       )
  
  
  
  
  
  
  
  def _lowercase   (  __A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  HubertConfig()
   __UpperCamelCase				      =  model.config
  
   __UpperCamelCase				      =  fs_config.activation_dropout
   __UpperCamelCase				      =  False
   __UpperCamelCase				      =  fs_config.attention_dropout
   __UpperCamelCase				      =  False
   __UpperCamelCase				      =  eval(fs_config.extractor_conv_feature_layers       )
   __UpperCamelCase				      =  [x[0] for x in conv_layers]
   __UpperCamelCase				      =  [x[1] for x in conv_layers]
   __UpperCamelCase				      =  [x[2] for x in conv_layers]
   __UpperCamelCase				      =  """gelu"""
   __UpperCamelCase				      =  """layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
   __UpperCamelCase				      =  False
   __UpperCamelCase				      =  0.0
   __UpperCamelCase				      =  0.0
   __UpperCamelCase				      =  fs_config.activation_fn
   __UpperCamelCase				      =  fs_config.dropout
   __UpperCamelCase				      =  fs_config.encoder_embed_dim
   __UpperCamelCase				      =  0.02
   __UpperCamelCase				      =  fs_config.encoder_ffn_embed_dim
   __UpperCamelCase				      =  1E-5
   __UpperCamelCase				      =  0.0
   __UpperCamelCase				      =  fs_config.encoder_attention_heads
   __UpperCamelCase				      =  fs_config.conv_pos_groups
   __UpperCamelCase				      =  fs_config.conv_pos
   __UpperCamelCase				      =  len(__A       )
   __UpperCamelCase				      =  fs_config.encoder_layers
  
   return config
  
  
  
  
  
  
  
  @torch.no_grad()
  def _lowercase   (  __A							,__A=None       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  distilhubert().model.model
  
   if config_path is not None:
    __UpperCamelCase				      =  HubertConfig.from_pretrained(__A       )
   else:
    __UpperCamelCase				      =  convert_config(__A       )
   __UpperCamelCase				      =  model.eval()
  
   __UpperCamelCase				      =  WavaVecaFeatureExtractor(
       feature_size=1							,sampling_rate=16_000							,padding_value=0							,do_normalize=__A							,return_attention_mask=__A							,)
   __UpperCamelCase				      =  HubertModel(__A       )
  
   recursively_load_weights(__A							,__A       )
  
   feature_extractor.save_pretrained(__A       )
   hf_model.save_pretrained(__A       )
  
  
  if __name__ == "__main__":
   a__						:			List[str]										=			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__						:			Optional[Any]										=			parser.parse_args()
   convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)
  
  
  
  
  
  