

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


_UpperCamelCase		    =						logging.getLogger(__name__)



def 			SCREAMING_SNAKE_CASE       (   lowercase__      ) ->	Optional[Any]:
 random.seed(__A      )
 np.random.seed(__A      )
 torch.manual_seed(__A      )
 torch.cuda.manual_seed_all(__A      )



def 			SCREAMING_SNAKE_CASE       (   lowercase__				,				lowercase__				,				lowercase__      ) ->	List[Any]:
 model.eval()
 lowerCAmelCase__      :							Dict    =							next(model.parameters()      ).device
 lowerCAmelCase__      :							List[str]    =							1
 lowerCAmelCase__      :							Any    =							torch.zeros((eval_batch_size, context_len)				,				dtype=torch.long				,				device=__A      )
 lowerCAmelCase__      :							Optional[Any]    =							DataLoader(__A				,				shuffle=__A				,				batch_size=__A      )
 lowerCAmelCase__      :							str    =							torch.zeros(1				,				device=__A      )
 lowerCAmelCase__      :							Any    =							0
 for batch in eval_dataloader:
  batch.to(__A      )
  # pad
  context.zero_()
  for i in range(__A      ):
   lowerCAmelCase__      :							Optional[int]    =							batch[i]
  lowerCAmelCase__      :							Any    =							model(__A				,				labels=__A      )
  eval_loss += outputs[0].sum().item()
  nb_eval_examples += batch.size(0      )
 lowerCAmelCase__      :							Tuple    =							eval_loss / nb_eval_examples
 lowerCAmelCase__      :							Tuple    =							torch.exp(__A      )
 model.train()
 return perplexity



def 			SCREAMING_SNAKE_CASE       (   lowercase__="gpt2"      ) ->	Any:
 lowerCAmelCase__      :							List[str]    =							GPTaLMHeadModel.from_pretrained(__A				,				output_hidden_states=__A      )
 torch.save(model.state_dict()				,				model_name + "local.pt"      )
 return model



def 			SCREAMING_SNAKE_CASE       (   lowercase__				,				lowercase__				,				lowercase__      ) ->	Tuple:
 lowerCAmelCase__      :							Dict    =							copy.deepcopy(__A      )
 model.to(__A      )

 lowerCAmelCase__      :							Tuple    =							["bias", "LayerNorm.weight"]
 lowerCAmelCase__      :							Any    =							[
     {
         "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},
 ]
 lowerCAmelCase__      :							Dict    =							AdamW(__A				,				lr=5E-5				,				eps=1E-8      )
 lowerCAmelCase__      :							Tuple    =							get_linear_schedule_with_warmup(__A				,				0				,				__A      )
 torch.cuda.empty_cache()
 return model, lm_optimizer, lm_scheduler



def 			SCREAMING_SNAKE_CASE       (   lowercase__				,				lowercase__				,				lowercase__				,				lowercase__      ) ->	Tuple:
 lowerCAmelCase__      :							List[str]    =							np.array(__A      ).mean()
 lowerCAmelCase__      :							List[Any]    =							np.array(__A      ).std()
 lowerCAmelCase__      :							Tuple    =							(real_perps - avg) / std
 lowerCAmelCase__      :							Dict    =							list(zip(__A				,				__A				,				__A      )      )
 joblib.dump(__A				,				__A      )



def 			SCREAMING_SNAKE_CASE       (   lowercase__				,				lowercase__				,				lowercase__				,				lowercase__				,				lowercase__				,				lowercase__				,				lowercase__				,				lowercase__="dev.jbl"				,				lowercase__=recopy_gpta				,				) ->	List[str]:
 lowerCAmelCase__      :							Dict    =							[]
 lowerCAmelCase__      :							Union[str, Any]    =							[]
 lowerCAmelCase__      :							List[Any]    =							[]

 # Initialize the transformer model
 lowerCAmelCase__      :							Optional[int]    =							copy.deepcopy(__A      )
 orig_model.to(device="cpu"      )
 torch.cuda.empty_cache()

 # Compute perplexity of initial transformer model for comparison
 model.train()
 lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__      :							Dict    =							recopy_model(__A				,				__A				,				__A      )

 for step in tqdm(range(__A      )      ):
  lowerCAmelCase__      :							Optional[Any]    =							torch.zeros((1, context_len)				,				dtype=torch.long				,				device=__A      )
  lowerCAmelCase__      :							str    =							random.choice(__A      )
  lowerCAmelCase__      :							List[str]    =							random.randint(0				,				len(story[0]      ) - context_len - 1      )
  lowerCAmelCase__      :							Dict    =							story[0][start : start + context_len]
  lm_optimizer.zero_grad()
  lowerCAmelCase__      :							int    =							model(__A				,				labels=__A      )
  lowerCAmelCase__      :							Any    =							outputs[0]
  lowerCAmelCase__      :							Dict    =							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
  lowerCAmelCase__      :							Optional[int]    =							compute_perplexity(__A				,				__A				,				__A      )

  # Periodically save the stored (X, IG(X)) pairs
  if step % 1_0_0_0 == 0 and step > 1:
   intermittent_save(__A				,				__A				,				__A				,				__A      )

  # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
  lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__      :							Dict    =							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 			SCREAMING_SNAKE_CASE       (   lowercase__				,				lowercase__="data/tokenized_stories_train_wikitext103.jbl"				,				lowercase__=1_0_0				,				lowercase__=1_0_2_6				,				lowercase__=True      ) ->	Union[str, Any]:
 lowerCAmelCase__      :							str    =							joblib.load(__A      )
 print("data loaded"      )
 lowerCAmelCase__      :							Tuple    =							[]
 if trim:
  for i, example in enumerate(__A      ):
   if len(example[0]      ) > min_len:
    lowerCAmelCase__      :							Optional[Any]    =							random.randint(0				,				len(example[0]      ) - context_len - 1      )
    objective_set.append(example[0, start : start + context_len]      )
   if len(__A      ) >= number:
    break
  lowerCAmelCase__      :							List[Any]    =							[]
  for j in range(i + 1				,				len(__A      )      ):
   if len(data[j][0]      ) > min_len:
    train_data.append(data[j]      )
 else:
  lowerCAmelCase__      :							List[str]    =							data[0:number]
  lowerCAmelCase__      :							str    =							data[number:]

 joblib.dump(__A				,				"objective_set.jbl"      )
 print("objective set saved"      )
 return train_data, objective_set



def 			SCREAMING_SNAKE_CASE       (   lowercase__				,				lowercase__				,				lowercase__				,				lowercase__				,				lowercase__=5_0				,				lowercase__="secondary_learner.pt"      ) ->	Optional[Any]:
 lowerCAmelCase__      :							Dict    =							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
 lowerCAmelCase__      :							Dict    =							train_dataset[:5_1_2]
 lowerCAmelCase__      :							int    =							train_dataset[5_1_2:]
 lowerCAmelCase__      :							int    =							DataLoader(__A				,				shuffle=__A				,				batch_size=__A      )
 lowerCAmelCase__      :							str    =							DataLoader(__A				,				shuffle=__A				,				batch_size=__A      )

 # secondary learner model set up
 lowerCAmelCase__      :							List[str]    =							nn.MSELoss()
 lowerCAmelCase__      :							Dict    =							nn.MSELoss(reduction="sum"      )
 secondary_learner.to(__A      )
 lowerCAmelCase__      :							List[str]    =							torch.optim.Adam(secondary_learner.parameters()				,				lr=0.00_001      )
 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
 lowerCAmelCase__      :							Optional[Any]    =							float("inf"      )
 # Iterate through batches until we've used max_steps batches
 for epoch in range(int(__A      )      ):
  lowerCAmelCase__      :							Optional[int]    =							0.0
  secondary_learner.train()
  for step, batch in enumerate(__A      ):
   lowerCAmelCase__      :							str    =							batch[0].to(__A      )
   lowerCAmelCase__      :							int    =							batch[1].to(__A      )
   lowerCAmelCase__      :							Tuple    =							secondary_learner(__A      )
   q_optimizer.zero_grad()
   lowerCAmelCase__      :							Any    =							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      )):
    lowerCAmelCase__      :							Optional[int]    =							tr_q_loss / (step + 1)

    secondary_learner.eval()
    lowerCAmelCase__      :							Optional[int]    =							0.0
    lowerCAmelCase__      :							Tuple    =							0.0
    lowerCAmelCase__      :							List[Any]    =							[]
    lowerCAmelCase__      :							Any    =							[]
    # Compute performance of the secondary learner after this batch
    for stepa, batcha in enumerate(__A      ):
     lowerCAmelCase__      :							Tuple    =							batcha[0].to(__A      )
     lowerCAmelCase__      :							Optional[Any]    =							batcha[1].to(__A      )
     lowerCAmelCase__      :							Dict    =							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      )
     lowerCAmelCase__      :							Tuple    =							q_lossa

   secondary_learner.train()
 return secondary_learner






class  __a				(	nn.Module					):





       """simple docstring"""


       def __init__(      self ,				snake_case      ):





        """simple docstring"""

        super(snake_case ,				self      ).__init__()
        lowerCAmelCase__      :							Union[str, Any]    =							model.transformer.wte
        lowerCAmelCase__      :							List[Any]    =							copy.deepcopy(model.transformer.wte.weight      )
        lowerCAmelCase__      :							Optional[Any]    =							nn.Convad(self.embeddings.weight.size(1      ) ,				256 ,				3 ,				padding=1      )
        lowerCAmelCase__      :							Union[str, Any]    =							nn.Sequential(nn.Linear(256 ,				32      ) ,				nn.Dropout(p=0.1      ) ,				nn.Linear(32 ,				32      ) ,				nn.Linear(32 ,				1      )      )






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							int    =							torch.max(self.conv(self.embeddings(snake_case      ).squeeze(1      ).transpose(1 ,				2      )      ) ,				2      )[0]
        lowerCAmelCase__      :							str    =							self.fc(snake_case      )
        return qs.squeeze(1      )






       @classmethod
       def  SCREAMING_SNAKE_CASE_  (      cls ,				snake_case ,				snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							Optional[int]    =							cls(snake_case      )  # this calls __init__
        lowerCAmelCase__      :							List[str]    =							torch.load(snake_case      )
        secondary_learner.load_state_dict(snake_case      )
        lowerCAmelCase__      :							List[str]    =							model.transformer.wte
        lowerCAmelCase__      :							str    =							copy.deepcopy(model.transformer.wte.weight      )
        return secondary_learner









"""simple docstring"""

from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


_UpperCamelCase		    =						logging.get_logger(__name__)






class  __a				(	__magic_name__					):





       """simple docstring"""


       __UpperCamelCase				:			Tuple          =	['input_values', 'attention_mask']






       def __init__(      self ,				snake_case=1 ,				snake_case=16_000 ,				snake_case=0.0 ,				snake_case=False ,				snake_case=True ,				**snake_case ,				):





        """simple docstring"""

        super().__init__(feature_size=snake_case ,				sampling_rate=snake_case ,				padding_value=snake_case ,				**snake_case      )
        lowerCAmelCase__      :							Dict    =							return_attention_mask
        lowerCAmelCase__      :							List[str]    =							do_normalize






       @staticmethod
       def  SCREAMING_SNAKE_CASE_  (      snake_case ,				snake_case ,				snake_case = 0.0      ):





        """simple docstring"""

        if attention_mask is not None:
         lowerCAmelCase__      :							List[str]    =							np.array(snake_case ,				np.intaa      )
         lowerCAmelCase__      :							str    =							[]

         for vector, length in zip(snake_case ,				attention_mask.sum(-1      )      ):
          lowerCAmelCase__      :							Any    =							(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7      )
          if length < normed_slice.shape[0]:
           lowerCAmelCase__      :							List[Any]    =							padding_value

          normed_input_values.append(snake_case      )
        else:
         lowerCAmelCase__      :							List[Any]    =							[(x - x.mean()) / np.sqrt(x.var() + 1e-7      ) for x in input_values]

        return normed_input_values






       def __call__(      self ,				snake_case ,				snake_case = False ,				snake_case = None ,				snake_case = False ,				snake_case = None ,				snake_case = None ,				snake_case = None ,				snake_case = None ,				**snake_case ,				):





        """simple docstring"""

        if sampling_rate is not None:
         if sampling_rate != self.sampling_rate:
          raise ValueError(
              F"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
              F""" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"""
              F""" {self.sampling_rate} and not {sampling_rate}."""      )
        else:
         logger.warning(
             "It is strongly recommended to pass the ``sampling_rate`` argument to this function. "
             "Failing to do so can result in silent errors that might be hard to debug."      )

        lowerCAmelCase__      :							List[str]    =							isinstance(snake_case ,				np.ndarray      ) and len(raw_speech.shape      ) > 1
        if is_batched_numpy and len(raw_speech.shape      ) > 2:
         raise ValueError(F"""Only mono-channel audio is supported for input to {self}"""      )
        lowerCAmelCase__      :							Tuple    =							is_batched_numpy or (
            isinstance(snake_case ,				(list, tuple)      ) and (isinstance(raw_speech[0] ,				(np.ndarray, tuple, list)      ))
        )

        # always return batch
        if not is_batched:
         lowerCAmelCase__      :							Tuple    =							[raw_speech]

        # convert into correct format for padding
        lowerCAmelCase__      :							Tuple    =							BatchFeature({"input_values": raw_speech}      )

        lowerCAmelCase__      :							Any    =							self.pad(
            snake_case ,				padding=snake_case ,				max_length=snake_case ,				truncation=snake_case ,				pad_to_multiple_of=snake_case ,				return_attention_mask=snake_case ,				)

        # convert input values to correct format
        lowerCAmelCase__      :							List[Any]    =							padded_inputs["input_values"]
        if not isinstance(input_values[0] ,				np.ndarray      ):
         lowerCAmelCase__      :							Any    =							[np.asarray(snake_case ,				dtype=np.floataa      ) for array in input_values]
        elif (
            not isinstance(snake_case ,				np.ndarray      )
            and isinstance(input_values[0] ,				np.ndarray      )
            and input_values[0].dtype is np.dtype(np.floataa      )
        ):
         lowerCAmelCase__      :							str    =							[array.astype(np.floataa      ) for array in input_values]
        elif isinstance(snake_case ,				np.ndarray      ) and input_values.dtype is np.dtype(np.floataa      ):
         lowerCAmelCase__      :							List[Any]    =							input_values.astype(np.floataa      )

        # convert attention_mask to correct format
        lowerCAmelCase__      :							int    =							padded_inputs.get("attention_mask"      )
        if attention_mask is not None:
         lowerCAmelCase__      :							Tuple    =							[np.asarray(snake_case ,				dtype=np.intaa      ) for array in attention_mask]

        # zero-mean and unit-variance normalization
        if self.do_normalize:
         lowerCAmelCase__      :							List[Any]    =							(
             attention_mask
             if self._get_padding_strategies(snake_case ,				max_length=snake_case      ) is not PaddingStrategy.DO_NOT_PAD
             else None
         )
         lowerCAmelCase__      :							Union[str, Any]    =							self.zero_mean_unit_var_norm(
             padded_inputs["input_values"] ,				attention_mask=snake_case ,				padding_value=self.padding_value      )

        if return_tensors is not None:
         lowerCAmelCase__      :							Optional[Any]    =							padded_inputs.convert_to_tensors(snake_case      )

        return padded_inputs






