


"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


lowerCamelCase_  :  List[Any]     		=    {
    """configuration_xlm_prophetnet""": ["""XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMProphetNetConfig"""],
}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_  :  Any     		=    ["""XLMProphetNetTokenizer"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_  :  str     		=    [
           """XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """XLMProphetNetDecoder""",
           """XLMProphetNetEncoder""",
           """XLMProphetNetForCausalLM""",
           """XLMProphetNetForConditionalGeneration""",
           """XLMProphetNetModel""",
           """XLMProphetNetPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_xlm_prophetnet import (
                  XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                  XLMProphetNetDecoder,
                  XLMProphetNetEncoder,
                  XLMProphetNetForCausalLM,
                  XLMProphetNetForConditionalGeneration,
                  XLMProphetNetModel,
                  XLMProphetNetPreTrainedModel,
              )

else:
       import sys

       lowerCamelCase_  :  Tuple     		=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


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


lowerCamelCase 	=   logging.getLogger(__name__)




def      UpperCAmelCase__							(     _A :					Optional[Any]				):





  '''simple docstring'''







  random.seed(_A				)
  np.random.seed(_A				)
  torch.manual_seed(_A				)
  torch.cuda.manual_seed_all(_A				)




def      UpperCAmelCase__							(     _A :					Optional[Any]      , _A :					Tuple      , _A :					Union[str, Any]				):





  '''simple docstring'''







  model.eval()
  a__    =next(model.parameters()				).device
  a__    =1
  a__    =torch.zeros((eval_batch_size, context_len)      , dtype=torch.long      , device=_A				)
  a__    =DataLoader(_A      , shuffle=_A      , batch_size=_A				)
  a__    =torch.zeros(1      , device=_A				)
  a__    =0
  for batch in eval_dataloader:
    batch.to(_A				)
    # pad
    context.zero_()
    for i in range(_A				):
      a__    =batch[i]
    a__    =model(_A      , labels=_A				)
    eval_loss += outputs[0].sum().item()
    nb_eval_examples += batch.size(0				)
  a__    =eval_loss / nb_eval_examples
  a__    =torch.exp(_A				)
  model.train()
  return perplexity




def      UpperCAmelCase__							(     _A :					Dict="gpt2"				):





  '''simple docstring'''







  a__    =GPTaLMHeadModel.from_pretrained(_A      , output_hidden_states=_A				)
  torch.save(model.state_dict()      , model_name + '''local.pt'''				)
  return model




def      UpperCAmelCase__							(     _A :					Dict      , _A :					Tuple      , _A :					Dict				):





  '''simple docstring'''







  a__    =copy.deepcopy(_A				)
  model.to(_A				)

  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(_A      , lr=5E-5      , eps=1E-8				)
  a__    =get_linear_schedule_with_warmup(_A      , 0      , _A				)
  torch.cuda.empty_cache()
  return model, lm_optimizer, lm_scheduler




def      UpperCAmelCase__							(     _A :					List[str]      , _A :					Optional[Any]      , _A :					Tuple      , _A :					List[str]				):





  '''simple docstring'''







  a__    =np.array(_A				).mean()
  a__    =np.array(_A				).std()
  a__    =(real_perps - avg) / std
  a__    =list(zip(_A      , _A      , _A				)				)
  joblib.dump(_A      , _A				)




def      UpperCAmelCase__							(     _A :					Union[str, Any]      , _A :					Dict      , _A :					Optional[Any]      , _A :					Union[str, Any]      , _A :					Dict      , _A :					str      , _A :					List[Any]      , _A :					Optional[Any]="dev.jbl"      , _A :					Optional[int]=recopy_gpta      , ):





  '''simple docstring'''







  a__    =[]
  a__    =[]
  a__    =[]

  # Initialize the transformer model
  a__    =copy.deepcopy(_A				)
  orig_model.to(device='''cpu'''				)
  torch.cuda.empty_cache()

  # Compute perplexity of initial transformer model for comparison
  model.train()
  a__, a__, a__    =recopy_model(_A      , _A      , _A				)

  for step in tqdm(range(_A				)				):
    a__    =torch.zeros((1, context_len)      , dtype=torch.long      , device=_A				)
    a__    =random.choice(_A				)
    a__    =random.randint(0      , len(story[0]				) - context_len - 1				)
    a__    =story[0][start : start + context_len]
    lm_optimizer.zero_grad()
    a__    =model(_A      , labels=_A				)
    a__    =outputs[0]
    a__    =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
    a__    =compute_perplexity(_A      , _A      , _A				)

    # Periodically save the stored (X, IG(X)) pairs
    if step % 10_00 == 0 and step > 1:
      intermittent_save(_A      , _A      , _A      , _A				)

    # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
    a__, a__, a__    =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      UpperCAmelCase__							(     _A :					Any      , _A :					Optional[int]="data/tokenized_stories_train_wikitext103.jbl"      , _A :					Any=1_00      , _A :					Dict=10_26      , _A :					Any=True				):





  '''simple docstring'''







  a__    =joblib.load(_A				)
  print('''data loaded'''				)
  a__    =[]
  if trim:
    for i, example in enumerate(_A				):
      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(_A				) >= number:
        break
    a__    =[]
    for j in range(i + 1      , len(_A				)				):
      if len(data[j][0]				) > min_len:
        train_data.append(data[j]				)
  else:
    a__    =data[0:number]
    a__    =data[number:]

  joblib.dump(_A      , '''objective_set.jbl'''				)
  print('''objective set saved'''				)
  return train_data, objective_set




def      UpperCAmelCase__							(     _A :					str      , _A :					Dict      , _A :					Union[str, Any]      , _A :					Dict      , _A :					Dict=50      , _A :					int="secondary_learner.pt"				):





  '''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(_A      , shuffle=_A      , batch_size=_A				)
  a__    =DataLoader(_A      , shuffle=_A      , batch_size=_A				)

  # secondary learner model set up
  a__    =nn.MSELoss()
  a__    =nn.MSELoss(reduction='''sum'''				)
  secondary_learner.to(_A				)
  a__    =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
  a__    =float('''inf'''				)
  # Iterate through batches until we've used max_steps batches
  for epoch in range(int(_A				)				):
    a__    =0.0
    secondary_learner.train()
    for step, batch in enumerate(_A				):
      a__    =batch[0].to(_A				)
      a__    =batch[1].to(_A				)
      a__    =secondary_learner(_A				)
      q_optimizer.zero_grad()
      a__    =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				)):
        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(_A				):
          a__    =batcha[0].to(_A				)
          a__    =batcha[1].to(_A				)
          a__    =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				)
          a__    =q_lossa

      secondary_learner.train()
  return secondary_learner







class 		__magic_name__     (				nn.Module				):

   '''simple docstring'''




   def __init__(							self,       lowercase_			)				->       str:






     """simple docstring"""






     super(lowercase_,       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 					_UpperCAmelCase					(							self,       lowercase_			)				->       List[str]:






     """simple docstring"""






     a__    =torch.max(self.conv(self.embeddings(lowercase_			).squeeze(1			).transpose(1,       2			)			),       2			)[0]
     a__    =self.fc(lowercase_			)
     return qs.squeeze(1			)



   @classmethod
   def 					_UpperCAmelCase					(							cls,       lowercase_,       lowercase_			)				->       Union[str, Any]:






     """simple docstring"""






     a__    =cls(lowercase_			)  # this calls __init__
     a__    =torch.load(lowercase_			)
     secondary_learner.load_state_dict(lowercase_			)
     a__    =model.transformer.wte
     a__    =copy.deepcopy(model.transformer.wte.weight			)
     return secondary_learner
