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
  
  
  SCREAMING_SNAKE_CASE       = logging.getLogger(__name__)
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_		)		->	str:
  
   '''simple docstring'''
  
  
  
  
  
   random.seed(lowercase_		)
   np.random.seed(lowercase_		)
   torch.manual_seed(lowercase_		)
   torch.cuda.manual_seed_all(lowercase_		)
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_		)		->	Optional[int]:
  
   '''simple docstring'''
  
  
  
  
  
   model.eval()
   A__ =       next(model.parameters()		).device
   A__ =       1
   A__ =       torch.zeros((eval_batch_size, context_len)    ,				dtype=torch.long    ,				device=lowercase_		)
   A__ =       DataLoader(lowercase_    ,				shuffle=lowercase_    ,				batch_size=lowercase_		)
   A__ =       torch.zeros(1    ,				device=lowercase_		)
   A__ =       0
   for batch in eval_dataloader:
    batch.to(lowercase_		)
    # pad
    context.zero_()
    for i in range(lowercase_		):
     A__ =       batch[i]
    A__ =       model(lowercase_    ,				labels=lowercase_		)
    eval_loss += outputs[0].sum().item()
    nb_eval_examples += batch.size(0		)
   A__ =       eval_loss / nb_eval_examples
   A__ =       torch.exp(lowercase_		)
   model.train()
   return perplexity
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_="gpt2"		)		->	str:
  
   '''simple docstring'''
  
  
  
  
  
   A__ =       GPTaLMHeadModel.from_pretrained(lowercase_    ,				output_hidden_states=lowercase_		)
   torch.save(model.state_dict()    ,				model_name + "local.pt"		)
   return model
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_		)		->	str:
  
   '''simple docstring'''
  
  
  
  
  
   A__ =       copy.deepcopy(lowercase_		)
   model.to(lowercase_		)
  
   A__ =       ["bias", "LayerNorm.weight"]
   A__ =       [
       {
           "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},
   ]
   A__ =       AdamW(lowercase_    ,				lr=5E-5    ,				eps=1E-8		)
   A__ =       get_linear_schedule_with_warmup(lowercase_    ,				0    ,				lowercase_		)
   torch.cuda.empty_cache()
   return model, lm_optimizer, lm_scheduler
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_		)		->	List[str]:
  
   '''simple docstring'''
  
  
  
  
  
   A__ =       np.array(lowercase_		).mean()
   A__ =       np.array(lowercase_		).std()
   A__ =       (real_perps - avg) / std
   A__ =       list(zip(lowercase_    ,				lowercase_    ,				lowercase_		)		)
   joblib.dump(lowercase_    ,				lowercase_		)
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_="dev.jbl"    ,				lowercase_=recopy_gpta    ,				)		->	Dict:
  
   '''simple docstring'''
  
  
  
  
  
   A__ =       []
   A__ =       []
   A__ =       []
  
   # Initialize the transformer model
   A__ =       copy.deepcopy(lowercase_		)
   orig_model.to(device="cpu"		)
   torch.cuda.empty_cache()
  
   # Compute perplexity of initial transformer model for comparison
   model.train()
   A__,   A__,   A__ =       recopy_model(lowercase_    ,				lowercase_    ,				lowercase_		)
  
   for step in tqdm(range(lowercase_		)		):
    A__ =       torch.zeros((1, context_len)    ,				dtype=torch.long    ,				device=lowercase_		)
    A__ =       random.choice(lowercase_		)
    A__ =       random.randint(0    ,				len(story[0]		) - context_len - 1		)
    A__ =       story[0][start : start + context_len]
    lm_optimizer.zero_grad()
    A__ =       model(lowercase_    ,				labels=lowercase_		)
    A__ =       outputs[0]
    A__ =       compute_perplexity(lowercase_    ,				lowercase_    ,				lowercase_		)
    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
    A__ =       compute_perplexity(lowercase_    ,				lowercase_    ,				lowercase_		)
  
    # Periodically save the stored (X, IG(X)) pairs
    if step % 10_00 == 0 and step > 1:
     intermittent_save(lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_		)
  
    # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
    A__,   A__,   A__ =       recopy_model(lowercase_    ,				lowercase_    ,				lowercase_		)
  
    past_perps.append(past_perp.item()		)
    real_perps.append(orig_perp - real_perp.item()		)
    contexts.append(np.array(context.cpu()		)		)
  
   intermittent_save(lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_		)
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_="data/tokenized_stories_train_wikitext103.jbl"    ,				lowercase_=1_00    ,				lowercase_=10_26    ,				lowercase_=True		)		->	Optional[int]:
  
   '''simple docstring'''
  
  
  
  
  
   A__ =       joblib.load(lowercase_		)
   print("data loaded"		)
   A__ =       []
   if trim:
    for i, example in enumerate(lowercase_		):
     if len(example[0]		) > min_len:
      A__ =       random.randint(0    ,				len(example[0]		) - context_len - 1		)
      objective_set.append(example[0, start : start + context_len]		)
     if len(lowercase_		) >= number:
      break
    A__ =       []
    for j in range(i + 1    ,				len(lowercase_		)		):
     if len(data[j][0]		) > min_len:
      train_data.append(data[j]		)
   else:
    A__ =       data[0:number]
    A__ =       data[number:]
  
   joblib.dump(lowercase_    ,				"objective_set.jbl"		)
   print("objective set saved"		)
   return train_data, objective_set
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_=50    ,				lowercase_="secondary_learner.pt"		)		->	List[Any]:
  
   '''simple docstring'''
  
  
  
  
  
   A__ =       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
   A__ =       train_dataset[:5_12]
   A__ =       train_dataset[5_12:]
   A__ =       DataLoader(lowercase_    ,				shuffle=lowercase_    ,				batch_size=lowercase_		)
   A__ =       DataLoader(lowercase_    ,				shuffle=lowercase_    ,				batch_size=lowercase_		)
  
   # secondary learner model set up
   A__ =       nn.MSELoss()
   A__ =       nn.MSELoss(reduction="sum"		)
   secondary_learner.to(lowercase_		)
   A__ =       torch.optim.Adam(secondary_learner.parameters()    ,				lr=0.0_0_0_0_1		)
   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
   A__ =       float("inf"		)
   # Iterate through batches until we've used max_steps batches
   for epoch in range(int(lowercase_		)		):
    A__ =       0.0
    secondary_learner.train()
    for step, batch in enumerate(lowercase_		):
     A__ =       batch[0].to(lowercase_		)
     A__ =       batch[1].to(lowercase_		)
     A__ =       secondary_learner(lowercase_		)
     q_optimizer.zero_grad()
     A__ =       loss(lowercase_    ,				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(lowercase_		)):
      A__ =       tr_q_loss / (step + 1)
  
      secondary_learner.eval()
      A__ =       0.0
      A__ =       0.0
      A__ =       []
      A__ =       []
      # Compute performance of the secondary learner after this batch
      for stepa, batcha in enumerate(lowercase_		):
       A__ =       batcha[0].to(lowercase_		)
       A__ =       batcha[1].to(lowercase_		)
       A__ =       secondary_learner(lowercase_		)
       q_lossa += test_loss(lowercase_    ,				lowercase_		).item()
       sum_qa += torch.sum(lowercase_		).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(lowercase_		)
      print(
          "Epoch: "    ,				lowercase_    ,				"step: "    ,				lowercase_    ,				"Avg. q:"    ,				sum_qa / len(lowercase_		)    ,				"Train Loss: "    ,				lowercase_    ,				"Test Loss: "    ,				lowercase_    ,				)
      if q_lossa < best_test_loss:
       joblib.dump((predicted, actual)    ,				"pred_vs_actual.jbl"		)
       torch.save(secondary_learner.state_dict()    ,				lowercase_		)
       A__ =       q_lossa
  
     secondary_learner.train()
   return secondary_learner
  
  
  
  
  
  
  class 			UpperCAmelCase_						(		nn.Module							):
  
  
  
  
      def __init__(					self		: Optional[Any]			,				snake_case_		: Dict   )   ->							Any:
  
  
  
  
       '''simple docstring'''
  
       super(snake_case_			,				self   ).__init__()
       A__ =       model.transformer.wte
       A__ =       copy.deepcopy(model.transformer.wte.weight   )
       A__ =       nn.Convad(self.embeddings.weight.size(1   )			,				256			,				3			,				padding=1   )
       A__ =       nn.Sequential(nn.Linear(256			,				32   )			,				nn.Dropout(p=0.1   )			,				nn.Linear(32			,				32   )			,				nn.Linear(32			,				1   )   )
  
  
  
  
      def 							__magic_name__							(					self		: List[Any]			,				snake_case_		: List[Any]   )   ->							Dict:
  
  
  
  
       '''simple docstring'''
  
       A__ =       torch.max(self.conv(self.embeddings(snake_case_   ).squeeze(1   ).transpose(1			,				2   )   )			,				2   )[0]
       A__ =       self.fc(snake_case_   )
       return qs.squeeze(1   )
  
  
  
  
      @classmethod
      def 							__magic_name__							(					cls		: int			,				snake_case_		: List[Any]			,				snake_case_		: Union[str, Any]   )   ->							List[Any]:
  
  
  
  
       '''simple docstring'''
  
       A__ =       cls(snake_case_   )  # this calls __init__
       A__ =       torch.load(snake_case_   )
       secondary_learner.load_state_dict(snake_case_   )
       A__ =       model.transformer.wte
       A__ =       copy.deepcopy(model.transformer.wte.weight   )
       return secondary_learner
  
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  import argparse
  
  import torch
  
  from transformers import (
      WavaVecaFeatureExtractor,
      WavLMConfig,
      WavLMForAudioFrameClassification,
      WavLMForSequenceClassification,
      WavLMForXVector,
      logging,
  )
  
  
  logging.set_verbosity_info()
  SCREAMING_SNAKE_CASE       = logging.get_logger(__name__)
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_		)		->	int:
   A__ =       WavLMForSequenceClassification.from_pretrained(lowercase_    ,				config=lowercase_		)
   A__ =       downstream_dict["projector.weight"]
   A__ =       downstream_dict["projector.bias"]
   A__ =       downstream_dict["model.post_net.linear.weight"]
   A__ =       downstream_dict["model.post_net.linear.bias"]
   return model
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_		)		->	Optional[Any]:
   A__ =       WavLMForAudioFrameClassification.from_pretrained(lowercase_    ,				config=lowercase_		)
   A__ =       downstream_dict["model.linear.weight"]
   A__ =       downstream_dict["model.linear.bias"]
   return model
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_		)		->	int:
   A__ =       WavLMForXVector.from_pretrained(lowercase_    ,				config=lowercase_		)
   A__ =       downstream_dict["connector.weight"]
   A__ =       downstream_dict["connector.bias"]
   for i, kernel_size in enumerate(hf_config.tdnn_kernel		):
    A__ =       downstream_dict[
        f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
    ]
    A__ =       downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]
  
   A__ =       downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
   A__ =       downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
   A__ =       downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
   A__ =       downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
   A__ =       downstream_dict["objective.W"]
   return model
  
  
  
  
  
  
  
  @torch.no_grad()
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_		)		->	Union[str, Any]:
   A__ =       torch.load(lowercase_    ,				map_location="cpu"		)
  
   A__ =       checkpoint["Downstream"]
  
   A__ =       WavLMConfig.from_pretrained(lowercase_		)
   A__ =       WavaVecaFeatureExtractor.from_pretrained(
       lowercase_    ,				return_attention_mask=lowercase_    ,				do_normalize=lowercase_		)
  
   A__ =       hf_config.architectures[0]
   if arch.endswith("ForSequenceClassification"		):
    A__ =       convert_classification(lowercase_    ,				lowercase_    ,				lowercase_		)
   elif arch.endswith("ForAudioFrameClassification"		):
    A__ =       convert_diarization(lowercase_    ,				lowercase_    ,				lowercase_		)
   elif arch.endswith("ForXVector"		):
    A__ =       convert_xvector(lowercase_    ,				lowercase_    ,				lowercase_		)
   else:
    raise NotImplementedError(f"""S3PRL weights conversion is not supported for {arch}"""		)
  
   if hf_config.use_weighted_layer_sum:
    A__ =       checkpoint["Featurizer"]["weights"]
  
   hf_feature_extractor.save_pretrained(lowercase_		)
   hf_model.save_pretrained(lowercase_		)
  
  
  if __name__ == "__main__":
        SCREAMING_SNAKE_CASE       = argparse.ArgumentParser()
        parser.add_argument(
            "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
        )
        parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
        parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
        parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
        SCREAMING_SNAKE_CASE       = parser.parse_args()
        convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
  
  
  
  
  
  