





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


__lowercase													=			logging.getLogger(__name__)


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		Union[str, Any]):



  '''simple docstring'''






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


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		Optional[int]     ,							__lowercase:		int     ,							__lowercase:		Optional[int]):



  '''simple docstring'''






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


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		List[Any]="gpt2"):



  '''simple docstring'''






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


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		str     ,							__lowercase:		Tuple     ,							__lowercase:		str):



  '''simple docstring'''






  __lowerCAmelCase			=				copy.deepcopy(__lowercase)
  model.to(__lowercase)

  __lowerCAmelCase			=				['''bias''', '''LayerNorm.weight''']
  __lowerCAmelCase			=				[
      {
          '''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			=				AdamW(__lowercase     ,							lr=5E-5     ,							eps=1E-8)
  __lowerCAmelCase			=				get_linear_schedule_with_warmup(__lowercase     ,							0     ,							__lowercase)
  torch.cuda.empty_cache()
  return model, lm_optimizer, lm_scheduler


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		Union[str, Any]     ,							__lowercase:		Tuple     ,							__lowercase:		Tuple     ,							__lowercase:		Any):



  '''simple docstring'''






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


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		Any     ,							__lowercase:		List[str]     ,							__lowercase:		Dict     ,							__lowercase:		Any     ,							__lowercase:		Optional[int]     ,							__lowercase:		Optional[int]     ,							__lowercase:		Any     ,							__lowercase:		Dict="dev.jbl"     ,							__lowercase:		List[str]=recopy_gpta     ,							):



  '''simple docstring'''






  __lowerCAmelCase			=				[]
  __lowerCAmelCase			=				[]
  __lowerCAmelCase			=				[]

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

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

  for step in tqdm(range(__lowercase)):
    __lowerCAmelCase			=				torch.zeros((1, context_len)     ,							dtype=torch.long     ,							device=__lowercase)
    __lowerCAmelCase			=				random.choice(__lowercase)
    __lowerCAmelCase			=				random.randint(0     ,							len(story[0]) - context_len - 1)
    __lowerCAmelCase			=				story[0][start : start + context_len]
    lm_optimizer.zero_grad()
    __lowerCAmelCase			=				model(__lowercase     ,							labels=__lowercase)
    __lowerCAmelCase			=				outputs[0]
    __lowerCAmelCase			=				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
    __lowerCAmelCase			=				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
    __lowerCAmelCase	,						__lowerCAmelCase	,						__lowerCAmelCase			=				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:		int     ,							__lowercase:		Any="data/tokenized_stories_train_wikitext103.jbl"     ,							__lowercase:		Dict=1_00     ,							__lowercase:		Optional[int]=10_26     ,							__lowercase:		int=True):



  '''simple docstring'''






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

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


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		str     ,							__lowercase:		Dict     ,							__lowercase:		List[Any]     ,							__lowercase:		int     ,							__lowercase:		Union[str, Any]=50     ,							__lowercase:		Any="secondary_learner.pt"):



  '''simple docstring'''






  __lowerCAmelCase			=				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			=				train_dataset[:5_12]
  __lowerCAmelCase			=				train_dataset[5_12:]
  __lowerCAmelCase			=				DataLoader(__lowercase     ,							shuffle=__lowercase     ,							batch_size=__lowercase)
  __lowerCAmelCase			=				DataLoader(__lowercase     ,							shuffle=__lowercase     ,							batch_size=__lowercase)

  # secondary learner model set up
  __lowerCAmelCase			=				nn.MSELoss()
  __lowerCAmelCase			=				nn.MSELoss(reduction='''sum''')
  secondary_learner.to(__lowercase)
  __lowerCAmelCase			=				torch.optim.Adam(secondary_learner.parameters()     ,							lr=0.00001)
  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			=				float('''inf''')
  # Iterate through batches until we've used max_steps batches
  for epoch in range(int(__lowercase)):
    __lowerCAmelCase			=				0.0
    secondary_learner.train()
    for step, batch in enumerate(__lowercase):
      __lowerCAmelCase			=				batch[0].to(__lowercase)
      __lowerCAmelCase			=				batch[1].to(__lowercase)
      __lowerCAmelCase			=				secondary_learner(__lowercase)
      q_optimizer.zero_grad()
      __lowerCAmelCase			=				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)):
        __lowerCAmelCase			=				tr_q_loss / (step + 1)

        secondary_learner.eval()
        __lowerCAmelCase			=				0.0
        __lowerCAmelCase			=				0.0
        __lowerCAmelCase			=				[]
        __lowerCAmelCase			=				[]
        # Compute performance of the secondary learner after this batch
        for stepa, batcha in enumerate(__lowercase):
          __lowerCAmelCase			=				batcha[0].to(__lowercase)
          __lowerCAmelCase			=				batcha[1].to(__lowercase)
          __lowerCAmelCase			=				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)
          __lowerCAmelCase			=				q_lossa

      secondary_learner.train()
  return secondary_learner




class   __snake_case				(			nn.Module   ):






    '''simple docstring'''





    def __init__(   self						:		Union[str, Any]			,						snake_case						:		Union[str, Any]							):

      """simple docstring"""





      super(snake_case			,						self							).__init__()
      __lowerCAmelCase			=				model.transformer.wte
      __lowerCAmelCase			=				copy.deepcopy(model.transformer.wte.weight							)
      __lowerCAmelCase			=				nn.Convad(self.embeddings.weight.size(1							)			,						256			,						3			,						padding=1							)
      __lowerCAmelCase			=				nn.Sequential(nn.Linear(256			,						32							)			,						nn.Dropout(p=0.1							)			,						nn.Linear(32			,						32							)			,						nn.Linear(32			,						1							)							)





    def 	A		(   self						:		List[Any]			,						snake_case						:		Optional[Any]							):

      """simple docstring"""





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





    @classmethod
    def 	A		(   cls						:		Optional[Any]			,						snake_case						:		Optional[Any]			,						snake_case						:		List[Any]							):

      """simple docstring"""





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







import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_multi_gpu,
    require_single_gpu,
    test_sync,
)
from accelerate.utils import patch_environment

class   __snake_case				(			unittest.TestCase   ):






    '''simple docstring'''





    def 	A		(   self						:		int							):

      """simple docstring"""





      __lowerCAmelCase			=				inspect.getfile(accelerate.test_utils							)
      __lowerCAmelCase			=				os.path.sep.join(mod_file.split(os.path.sep							)[:-1] + ['''scripts''', '''test_sync.py''']							)





    @require_cpu
    def 	A		(   self						:		str							):

      """simple docstring"""





      debug_launcher(test_sync.main			,						num_processes=1							)





    @require_cpu
    def 	A		(   self						:		List[str]							):

      """simple docstring"""





      debug_launcher(test_sync.main							)





    @require_single_gpu
    def 	A		(   self						:		Optional[Any]							):

      """simple docstring"""





      test_sync.main()





    @require_multi_gpu
    def 	A		(   self						:		Union[str, Any]							):

      """simple docstring"""





      print(f'''Found {torch.cuda.device_count()} devices.'''							)
      __lowerCAmelCase			=				['''torchrun''', f'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
      with patch_environment(omp_num_threads=1							):
        execute_subprocess_async(snake_case			,						env=os.environ.copy()							)
