



'''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










'''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)





