


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 							UpperCAmelCase_   (				__snake_case		)	->     Optional[int]:







 """simple docstring"""


 random.seed(__snake_case		)
 np.random.seed(__snake_case		)
 torch.manual_seed(__snake_case		)
 torch.cuda.manual_seed_all(__snake_case		)
def 							UpperCAmelCase_   (				__snake_case	,		__snake_case	,		__snake_case		)	->     List[str]:







 """simple docstring"""


 model.eval()
 _lowercase				 =next(model.parameters()		).device
 _lowercase				 =1
 _lowercase				 =torch.zeros((eval_batch_size, context_len)	,		dtype=torch.long	,		device=__snake_case		)
 _lowercase				 =DataLoader(__snake_case	,		shuffle=__snake_case	,		batch_size=__snake_case		)
 _lowercase				 =torch.zeros(1	,		device=__snake_case		)
 _lowercase				 =0
 for batch in eval_dataloader:
  batch.to(__snake_case		)
  # pad
  context.zero_()
  for i in range(__snake_case		):
   _lowercase				 =batch[i]
  _lowercase				 =model(__snake_case	,		labels=__snake_case		)
  eval_loss += outputs[0].sum().item()
  nb_eval_examples += batch.size(0		)
 _lowercase				 =eval_loss / nb_eval_examples
 _lowercase				 =torch.exp(__snake_case		)
 model.train()
 return perplexity
def 							UpperCAmelCase_   (				__snake_case="gpt2"		)	->     List[str]:







 """simple docstring"""


 _lowercase				 =GPTaLMHeadModel.from_pretrained(__snake_case	,		output_hidden_states=__snake_case		)
 torch.save(model.state_dict()	,		model_name + '''local.pt'''		)
 return model
def 							UpperCAmelCase_   (				__snake_case	,		__snake_case	,		__snake_case		)	->     Any:







 """simple docstring"""


 _lowercase				 =copy.deepcopy(__snake_case		)
 model.to(__snake_case		)

 _lowercase				 =['''bias''', '''LayerNorm.weight''']
 _lowercase				 =[
     {
         '''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},
 ]
 _lowercase				 =AdamW(__snake_case	,		lr=5e-5	,		eps=1e-8		)
 _lowercase				 =get_linear_schedule_with_warmup(__snake_case	,		0	,		__snake_case		)
 torch.cuda.empty_cache()
 return model, lm_optimizer, lm_scheduler
def 							UpperCAmelCase_   (				__snake_case	,		__snake_case	,		__snake_case	,		__snake_case		)	->     Any:







 """simple docstring"""


 _lowercase				 =np.array(__snake_case		).mean()
 _lowercase				 =np.array(__snake_case		).std()
 _lowercase				 =(real_perps - avg) / std
 _lowercase				 =list(zip(__snake_case	,		__snake_case	,		__snake_case		)		)
 joblib.dump(__snake_case	,		__snake_case		)
def 							UpperCAmelCase_   (				__snake_case	,		__snake_case	,		__snake_case	,		__snake_case	,		__snake_case	,		__snake_case	,		__snake_case	,		__snake_case="dev.jbl"	,		__snake_case=recopy_gpta	,		)	->     List[Any]:







 """simple docstring"""


 _lowercase				 =[]
 _lowercase				 =[]
 _lowercase				 =[]

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

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

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

  # Periodically save the stored (X, IG(X)) pairs
  if step % 1000 == 0 and step > 1:
   intermittent_save(__snake_case	,		__snake_case	,		__snake_case	,		__snake_case		)

  # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
  _lowercase ,       _lowercase ,       _lowercase				 =recopy_model(__snake_case	,		__snake_case	,		__snake_case		)

  past_perps.append(past_perp.item()		)
  real_perps.append(orig_perp - real_perp.item()		)
  contexts.append(np.array(context.cpu()		)		)

 intermittent_save(__snake_case	,		__snake_case	,		__snake_case	,		__snake_case		)
def 							UpperCAmelCase_   (				__snake_case	,		__snake_case="data/tokenized_stories_train_wikitext103.jbl"	,		__snake_case=100	,		__snake_case=1026	,		__snake_case=True		)	->     Tuple:







 """simple docstring"""


 _lowercase				 =joblib.load(__snake_case		)
 print('''data loaded'''		)
 _lowercase				 =[]
 if trim:
  for i, example in enumerate(__snake_case		):
   if len(example[0]		) > min_len:
    _lowercase				 =random.randint(0	,		len(example[0]		) - context_len - 1		)
    objective_set.append(example[0, start : start + context_len]		)
   if len(__snake_case		) >= number:
    break
  _lowercase				 =[]
  for j in range(i + 1	,		len(__snake_case		)		):
   if len(data[j][0]		) > min_len:
    train_data.append(data[j]		)
 else:
  _lowercase				 =data[0:number]
  _lowercase				 =data[number:]

 joblib.dump(__snake_case	,		'''objective_set.jbl'''		)
 print('''objective set saved'''		)
 return train_data, objective_set
def 							UpperCAmelCase_   (				__snake_case	,		__snake_case	,		__snake_case	,		__snake_case	,		__snake_case=50	,		__snake_case="secondary_learner.pt"		)	->     Union[str, Any]:







 """simple docstring"""


 _lowercase				 =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
 _lowercase				 =train_dataset[:512]
 _lowercase				 =train_dataset[512:]
 _lowercase				 =DataLoader(__snake_case	,		shuffle=__snake_case	,		batch_size=__snake_case		)
 _lowercase				 =DataLoader(__snake_case	,		shuffle=__snake_case	,		batch_size=__snake_case		)

 # secondary learner model set up
 _lowercase				 =nn.MSELoss()
 _lowercase				 =nn.MSELoss(reduction='''sum'''		)
 secondary_learner.to(__snake_case		)
 _lowercase				 =torch.optim.Adam(secondary_learner.parameters()	,		lr=0.0_00_01		)
 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
 _lowercase				 =float('''inf'''		)
 # Iterate through batches until we've used max_steps batches
 for epoch in range(int(__snake_case		)		):
  _lowercase				 =0.0
  secondary_learner.train()
  for step, batch in enumerate(__snake_case		):
   _lowercase				 =batch[0].to(__snake_case		)
   _lowercase				 =batch[1].to(__snake_case		)
   _lowercase				 =secondary_learner(__snake_case		)
   q_optimizer.zero_grad()
   _lowercase				 =loss(__snake_case	,		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(__snake_case		)):
    _lowercase				 =tr_q_loss / (step + 1)

    secondary_learner.eval()
    _lowercase				 =0.0
    _lowercase				 =0.0
    _lowercase				 =[]
    _lowercase				 =[]
    # Compute performance of the secondary learner after this batch
    for stepa, batcha in enumerate(__snake_case		):
     _lowercase				 =batcha[0].to(__snake_case		)
     _lowercase				 =batcha[1].to(__snake_case		)
     _lowercase				 =secondary_learner(__snake_case		)
     q_lossa += test_loss(__snake_case	,		__snake_case		).item()
     sum_qa += torch.sum(__snake_case		).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(__snake_case		)
    print(
        '''Epoch: '''	,		__snake_case	,		'''step: '''	,		__snake_case	,		'''Avg. q:'''	,		sum_qa / len(__snake_case		)	,		'''Train Loss: '''	,		__snake_case	,		'''Test Loss: '''	,		__snake_case	,		)
    if q_lossa < best_test_loss:
     joblib.dump((predicted, actual)	,		'''pred_vs_actual.jbl'''		)
     torch.save(secondary_learner.state_dict()	,		__snake_case		)
     _lowercase				 =q_lossa

   secondary_learner.train()
 return secondary_learner







class  lowerCamelCase__  (			nn.Module):





     def __init__(self						,	UpperCAmelCase			)	->  int:
      super(UpperCAmelCase						,	self			).__init__()
      _lowercase				 =model.transformer.wte
      _lowercase				 =copy.deepcopy(model.transformer.wte.weight			)
      _lowercase				 =nn.Convad(self.embeddings.weight.size(1			)						,	2_5_6						,	3						,	padding=1			)
      _lowercase				 =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 	__A   (self						,	UpperCAmelCase			)	->  Optional[int]:
      _lowercase				 =torch.max(self.conv(self.embeddings(UpperCAmelCase			).squeeze(1			).transpose(1						,	2			)			)						,	2			)[0]
      _lowercase				 =self.fc(UpperCAmelCase			)
      return qs.squeeze(1			)





     @classmethod
     def 	__A   (cls						,	UpperCAmelCase						,	UpperCAmelCase			)	->  Optional[Any]:
      _lowercase				 =cls(UpperCAmelCase			)  # this calls __init__
      _lowercase				 =torch.load(UpperCAmelCase			)
      secondary_learner.load_state_dict(UpperCAmelCase			)
      _lowercase				 =model.transformer.wte
      _lowercase				 =copy.deepcopy(model.transformer.wte.weight			)
      return secondary_learner





from typing import TYPE_CHECKING

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


UpperCAmelCase__        =				{
    '''configuration_flaubert''': ['''FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FlaubertConfig''', '''FlaubertOnnxConfig'''],
    '''tokenization_flaubert''': ['''FlaubertTokenizer'''],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__        =				[
      '''FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''FlaubertForMultipleChoice''',
      '''FlaubertForQuestionAnswering''',
      '''FlaubertForQuestionAnsweringSimple''',
      '''FlaubertForSequenceClassification''',
      '''FlaubertForTokenClassification''',
      '''FlaubertModel''',
      '''FlaubertWithLMHeadModel''',
      '''FlaubertPreTrainedModel''',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__        =				[
      '''TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TFFlaubertForMultipleChoice''',
      '''TFFlaubertForQuestionAnsweringSimple''',
      '''TFFlaubertForSequenceClassification''',
      '''TFFlaubertForTokenClassification''',
      '''TFFlaubertModel''',
      '''TFFlaubertPreTrainedModel''',
      '''TFFlaubertWithLMHeadModel''',
  ]


if TYPE_CHECKING:
  from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
  from .tokenization_flaubert import FlaubertTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flaubert import (
        FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        FlaubertForMultipleChoice,
        FlaubertForQuestionAnswering,
        FlaubertForQuestionAnsweringSimple,
        FlaubertForSequenceClassification,
        FlaubertForTokenClassification,
        FlaubertModel,
        FlaubertPreTrainedModel,
        FlaubertWithLMHeadModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_flaubert import (
        TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFFlaubertForMultipleChoice,
        TFFlaubertForQuestionAnsweringSimple,
        TFFlaubertForSequenceClassification,
        TFFlaubertForTokenClassification,
        TFFlaubertModel,
        TFFlaubertPreTrainedModel,
        TFFlaubertWithLMHeadModel,
    )

else:
  import sys

  UpperCAmelCase__        =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

