






"""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


_UpperCamelCase :    str      =			logging.getLogger(__name__)




def 					a			(  UpperCamelCase_				:					Optional[Any]		)	->		Optional[Any]:
 random.seed(UpperCamelCase_		)
 np.random.seed(UpperCamelCase_		)
 torch.manual_seed(UpperCamelCase_		)
 torch.cuda.manual_seed_all(UpperCamelCase_		)




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




def 					a			(  UpperCamelCase_				:					str="gpt2"		)	->		Optional[Any]:
 __A						:							Union[str, Any]      =						GPTaLMHeadModel.from_pretrained(UpperCamelCase_  ,						output_hidden_states=UpperCamelCase_		)
 torch.save(model.state_dict()  ,						model_name + 'local.pt'		)
 return model




def 					a			(  UpperCamelCase_				:					Dict  ,						UpperCamelCase_				:					Any  ,						UpperCamelCase_				:					List[Any]		)	->		Tuple:
 __A						:							Tuple      =						copy.deepcopy(UpperCamelCase_		)
 model.to(UpperCamelCase_		)

 __A						:							int      =						['bias', 'LayerNorm.weight']
 __A						:							List[str]      =						[
     {
         '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						:							Any      =						AdamW(UpperCamelCase_  ,						lr=5E-5  ,						eps=1E-8		)
 __A						:							Optional[int]      =						get_linear_schedule_with_warmup(UpperCamelCase_  ,						0  ,						UpperCamelCase_		)
 torch.cuda.empty_cache()
 return model, lm_optimizer, lm_scheduler




def 					a			(  UpperCamelCase_				:					List[Any]  ,						UpperCamelCase_				:					Any  ,						UpperCamelCase_				:					str  ,						UpperCamelCase_				:					Any		)	->		Optional[Any]:
 __A						:							int      =						np.array(UpperCamelCase_		).mean()
 __A						:							Union[str, Any]      =						np.array(UpperCamelCase_		).std()
 __A						:							Tuple      =						(real_perps - avg) / std
 __A						:							Union[str, Any]      =						list(zip(UpperCamelCase_  ,						UpperCamelCase_  ,						UpperCamelCase_		)		)
 joblib.dump(UpperCamelCase_  ,						UpperCamelCase_		)




def 					a			(  UpperCamelCase_				:					Optional[Any]  ,						UpperCamelCase_				:					Tuple  ,						UpperCamelCase_				:					List[Any]  ,						UpperCamelCase_				:					Dict  ,						UpperCamelCase_				:					List[Any]  ,						UpperCamelCase_				:					Optional[Any]  ,						UpperCamelCase_				:					Optional[Any]  ,						UpperCamelCase_				:					Any="dev.jbl"  ,						UpperCamelCase_				:					Dict=recopy_gpta  ,						)	->		List[Any]:
 __A						:							Optional[int]      =						[]
 __A						:							Any      =						[]
 __A						:							List[str]      =						[]

 # Initialize the transformer model
 __A						:							Tuple      =						copy.deepcopy(UpperCamelCase_		)
 orig_model.to(device='cpu'		)
 torch.cuda.empty_cache()

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

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

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

  # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
  __A     ,       __A     ,       __A						:							Optional[int]      =						recopy_model(UpperCamelCase_  ,						UpperCamelCase_  ,						UpperCamelCase_		)

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

 intermittent_save(UpperCamelCase_  ,						UpperCamelCase_  ,						UpperCamelCase_  ,						UpperCamelCase_		)




def 					a			(  UpperCamelCase_				:					List[Any]  ,						UpperCamelCase_				:					Dict="data/tokenized_stories_train_wikitext103.jbl"  ,						UpperCamelCase_				:					Optional[int]=1_0_0  ,						UpperCamelCase_				:					int=1_0_2_6  ,						UpperCamelCase_				:					Dict=True		)	->		Optional[Any]:
 __A						:							Optional[Any]      =						joblib.load(UpperCamelCase_		)
 print('data loaded'		)
 __A						:							str      =						[]
 if trim:
  for i, example in enumerate(UpperCamelCase_		):
   if len(example[0]		) > min_len:
    __A						:							Dict      =						random.randint(0  ,						len(example[0]		) - context_len - 1		)
    objective_set.append(example[0, start : start + context_len]		)
   if len(UpperCamelCase_		) >= number:
    break
  __A						:							Tuple      =						[]
  for j in range(i + 1  ,						len(UpperCamelCase_		)		):
   if len(data[j][0]		) > min_len:
    train_data.append(data[j]		)
 else:
  __A						:							Any      =						data[0:number]
  __A						:							List[Any]      =						data[number:]

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




def 					a			(  UpperCamelCase_				:					Union[str, Any]  ,						UpperCamelCase_				:					Dict  ,						UpperCamelCase_				:					int  ,						UpperCamelCase_				:					str  ,						UpperCamelCase_				:					Dict=5_0  ,						UpperCamelCase_				:					Tuple="secondary_learner.pt"		)	->		Any:
 __A						:							List[str]      =						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						:							List[str]      =						train_dataset[:5_1_2]
 __A						:							Optional[Any]      =						train_dataset[5_1_2:]
 __A						:							int      =						DataLoader(UpperCamelCase_  ,						shuffle=UpperCamelCase_  ,						batch_size=UpperCamelCase_		)
 __A						:							Dict      =						DataLoader(UpperCamelCase_  ,						shuffle=UpperCamelCase_  ,						batch_size=UpperCamelCase_		)

 # secondary learner model set up
 __A						:							List[Any]      =						nn.MSELoss()
 __A						:							List[Any]      =						nn.MSELoss(reduction='sum'		)
 secondary_learner.to(UpperCamelCase_		)
 __A						:							List[Any]      =						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
 __A						:							Dict      =						float('inf'		)
 # Iterate through batches until we've used max_steps batches
 for epoch in range(int(UpperCamelCase_		)		):
  __A						:							List[Any]      =						0.0
  secondary_learner.train()
  for step, batch in enumerate(UpperCamelCase_		):
   __A						:							List[Any]      =						batch[0].to(UpperCamelCase_		)
   __A						:							List[str]      =						batch[1].to(UpperCamelCase_		)
   __A						:							Union[str, Any]      =						secondary_learner(UpperCamelCase_		)
   q_optimizer.zero_grad()
   __A						:							List[Any]      =						loss(UpperCamelCase_  ,						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(UpperCamelCase_		)):
    __A						:							int      =						tr_q_loss / (step + 1)

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

   secondary_learner.train()
 return secondary_learner






class __a ( nn.Module					):



  def __init__(self	:		str					,      lowercase_	:		List[str] )		->				List[str]:
   super(lowercase_					,      self ).__init__()
   __A						:							Tuple      =						model.transformer.wte
   __A						:							str      =						copy.deepcopy(model.transformer.wte.weight )
   __A						:							List[Any]      =						nn.Convad(self.embeddings.weight.size(1 )					,      256					,      3					,      padding=1 )
   __A						:							Dict      =						nn.Sequential(nn.Linear(256					,      32 )					,      nn.Dropout(p=0.1 )					,      nn.Linear(32					,      32 )					,      nn.Linear(32					,      1 ) )



  def     A_					(self	:		Tuple					,      lowercase_	:		Tuple )		->				Tuple:
   __A						:							str      =						torch.max(self.conv(self.embeddings(lowercase_ ).squeeze(1 ).transpose(1					,      2 ) )					,      2 )[0]
   __A						:							List[Any]      =						self.fc(lowercase_ )
   return qs.squeeze(1 )



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













"""simple docstring"""


import argparse
import logging

import datasets
from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers, processors
from tokenizers.models import Unigram
from tokenizers.trainers import UnigramTrainer

from transformers import AlbertTokenizerFast


_UpperCamelCase :    List[str]      =			logging.getLogger(__name__)




def 					a			(  )	->		Union[str, Any]:
 __A						:							int      =						argparse.ArgumentParser(description='Train a unigram tokenizer on the wikitext dataset.'		)
 parser.add_argument(
     '--dataset_name'  ,						type=UpperCamelCase_  ,						default='wikitext'  ,						help='Name of the training. Explore datasets at: hf.co/datasets.'  ,						)
 parser.add_argument(
     '--dataset_config'  ,						type=UpperCamelCase_  ,						default='wikitext-103-raw-v1'  ,						help='Configuration name of the dataset.'		)
 parser.add_argument(
     '--batch_size'  ,						type=UpperCamelCase_  ,						default=1_0_0_0  ,						help='Batch size during training.'  ,						)
 parser.add_argument(
     '--vocab_size'  ,						type=UpperCamelCase_  ,						default=1_0_0_4_8  ,						help='Size of the desired vocabulary.'  ,						)
 parser.add_argument(
     '--limit'  ,						default=UpperCamelCase_  ,						type=UpperCamelCase_  ,						help='Limit the number of shards (used for debugging).'  ,						)
 parser.add_argument(
     '--export_to_hub'  ,						action='store_true'  ,						)

 __A						:							List[Any]      =						parser.parse_args()
 return args




def 					a			(  UpperCamelCase_				:					Optional[Any]		)	->		Optional[Any]:
 __A						:							Union[str, Any]      =						datasets.load_dataset(args.dataset_name  ,						args.dataset_config  ,						split='train'		)

 if args.limit is not None:
  __A						:							Tuple      =						min(len(UpperCamelCase_		)  ,						args.limit		)
  __A						:							Optional[int]      =						dataset.select(range(UpperCamelCase_		)		)
  logger.info(F"Limiting the dataset to {args.limit} entries."		)

 def batch_iterator():
  for i in range(0  ,						len(UpperCamelCase_		)  ,						args.batch_size		):
   yield dataset[i : i + args.batch_size]["text"]

    # Prepare the tokenizer.
 __A						:							Any      =						Tokenizer(Unigram()		)
 __A						:							List[Any]      =						normalizers.Sequence([normalizers.Replace('``'  ,						'"'		), normalizers.Replace('\'\''  ,						'"'		)]		)
 __A						:							Optional[int]      =						pre_tokenizers.Metaspace()

 # Prepare the trainer.
 __A						:							int      =						UnigramTrainer(
     unk_token='<unk>'  ,						special_tokens=['[CLS]', '[SEP]', '<unk>', '<pad>', '[MASK]']  ,						vocab_size=args.vocab_size  ,						)

 logger.info('Training the tokenizer.'		)
 tokenizer.train_from_iterator(batch_iterator()  ,						trainer=UpperCamelCase_		)
 logger.info('Tokenizer training complete!'		)

 __A						:							Union[str, Any]      =						tokenizer.token_to_id('[CLS]'		)
 __A						:							str      =						tokenizer.token_to_id('[SEP]'		)
 __A						:							Tuple      =						processors.TemplateProcessing(
     single='[CLS]:0 $A:0 [SEP]:0'  ,						pair='[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1'  ,						special_tokens=[
         ('[CLS]', cls_token_id),
         ('[SEP]', sep_token_id),
     ]  ,						)
 __A						:							List[str]      =						decoders.Metaspace()

 if args.export_to_hub:
  logger.info('Exporting the trained tokenzier to Hub.'		)
  __A						:							Union[str, Any]      =						AlbertTokenizerFast(tokenizer_object=UpperCamelCase_		)
  new_tokenizer.push_to_hub('unigram-tokenizer-dataset'		)


if __name__ == "__main__":
   _UpperCamelCase :    Optional[Any]      =			parse_args()
   main(args)





