





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_       (  _snake_case  :Union[str, Any]							)     ->    Union[str, Any]:
   random.seed(_snake_case							)
   np.random.seed(_snake_case							)
   torch.manual_seed(_snake_case							)
   torch.cuda.manual_seed_all(_snake_case							)

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

def   SCREAMING_SNAKE_CASE_       (  _snake_case  :List[str]="gpt2"							)     ->    List[str]:
   _A						     =					GPTaLMHeadModel.from_pretrained(_snake_case  ,      output_hidden_states=_snake_case							)
   torch.save(model.state_dict()  ,      model_name + '''local.pt'''							)
   return model

def   SCREAMING_SNAKE_CASE_       (  _snake_case  :Optional[Any]  ,      _snake_case  :List[str]  ,      _snake_case  :int							)     ->    Any:
   _A						     =					copy.deepcopy(_snake_case							)
   model.to(_snake_case							)

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

def   SCREAMING_SNAKE_CASE_       (  _snake_case  :int  ,      _snake_case  :List[str]  ,      _snake_case  :Tuple  ,      _snake_case  :Dict							)     ->    List[str]:
   _A						     =					np.array(_snake_case							).mean()
   _A						     =					np.array(_snake_case							).std()
   _A						     =					(real_perps - avg) / std
   _A						     =					list(zip(_snake_case  ,      _snake_case  ,      _snake_case							)							)
   joblib.dump(_snake_case  ,      _snake_case							)

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

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

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

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

      # Periodically save the stored (X, IG(X)) pairs
      if step % 1_000 == 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
      _A     ,						_A     ,						_A						     =					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   SCREAMING_SNAKE_CASE_       (  _snake_case  :List[Any]  ,      _snake_case  :int="data/tokenized_stories_train_wikitext103.jbl"  ,      _snake_case  :str=100  ,      _snake_case  :List[str]=1_026  ,      _snake_case  :int=True							)     ->    Optional[int]:
   _A						     =					joblib.load(_snake_case							)
   print('''data loaded'''							)
   _A						     =					[]
   if trim:
      for i, example in enumerate(_snake_case							):
         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(_snake_case							) >= number:
            break
      _A						     =					[]
      for j in range(i + 1  ,      len(_snake_case							)							):
         if len(data[j][0]							) > min_len:
            train_data.append(data[j]							)
   else:
      _A						     =					data[0:number]
      _A						     =					data[number:]

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

def   SCREAMING_SNAKE_CASE_       (  _snake_case  :str  ,      _snake_case  :str  ,      _snake_case  :Tuple  ,      _snake_case  :Union[str, Any]  ,      _snake_case  :List[str]=50  ,      _snake_case  :Dict="secondary_learner.pt"							)     ->    Any:
   _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[:512]
   _A						     =					train_dataset[512:]
   _A						     =					DataLoader(_snake_case  ,      shuffle=_snake_case  ,      batch_size=_snake_case							)
   _A						     =					DataLoader(_snake_case  ,      shuffle=_snake_case  ,      batch_size=_snake_case							)

   # secondary learner model set up
   _A						     =					nn.MSELoss()
   _A						     =					nn.MSELoss(reduction='''sum'''							)
   secondary_learner.to(_snake_case							)
   _A						     =					torch.optim.Adam(secondary_learner.parameters()  ,      lr=0.0_0001							)
   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(_snake_case							)							):
      _A						     =					0.0
      secondary_learner.train()
      for step, batch in enumerate(_snake_case							):
         _A						     =					batch[0].to(_snake_case							)
         _A						     =					batch[1].to(_snake_case							)
         _A						     =					secondary_learner(_snake_case							)
         q_optimizer.zero_grad()
         _A						     =					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							)):
            _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(_snake_case							):
               _A						     =					batcha[0].to(_snake_case							)
               _A						     =					batcha[1].to(_snake_case							)
               _A						     =					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							)
               _A						     =					q_lossa

         secondary_learner.train()
   return secondary_learner


class   lowerCamelCase__  (					nn.Module):



 """simple docstring"""

 def __init__(						self				:				Dict ,	__lowerCAmelCase				:				List[Any]			)      ->  Any:
    super(__lowerCAmelCase ,	self			).__init__()
    _A						     =					model.transformer.wte
    _A						     =					copy.deepcopy(model.transformer.wte.weight			)
    _A						     =					nn.Convad(self.embeddings.weight.size(1			) ,	2_56 ,	3 ,	padding=1			)
    _A						     =					nn.Sequential(nn.Linear(2_56 ,	32			) ,	nn.Dropout(p=0.1			) ,	nn.Linear(32 ,	32			) ,	nn.Linear(32 ,	1			)			)

 def 						snake_case_			(						self				:				int ,	__lowerCAmelCase				:				int			)      ->  Tuple:
    _A						     =					torch.max(self.conv(self.embeddings(__lowerCAmelCase			).squeeze(1			).transpose(1 ,	2			)			) ,	2			)[0]
    _A						     =					self.fc(__lowerCAmelCase			)
    return qs.squeeze(1			)

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








import argparse
import logging
import os
import sys
import time

import tensorflow as tf
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
from transformers.utils import is_sagemaker_dp_enabled


if os.environ.get("""SDP_ENABLED""") or is_sagemaker_dp_enabled():
   UpperCAmelCase_		   =				True
   UpperCAmelCase_		   =				"""p3dn.24xlarge"""
   import smdistributed.dataparallel.tensorflow as sdp
else:
   UpperCAmelCase_		   =				False

def   SCREAMING_SNAKE_CASE_       (  _snake_case  :Optional[Any]  ,      _snake_case  :Union[str, Any]  ,      _snake_case  :List[str]  ,      _snake_case  :int  ,      _snake_case  :Any  ,      _snake_case  :List[Any]  ,      _snake_case  :Optional[int]=None							)     ->    Union[str, Any]:
   _A						     =					tqdm(_snake_case							)
   for i, batch in enumerate(_snake_case							):
      with tf.GradientTape() as tape:
         _A     ,						_A						     =					batch
         _A						     =					model(_snake_case							)
         _A						     =					loss(_snake_case  ,      outputs.logits							)

      if SDP_ENABLED:
         _A						     =					sdp.DistributedGradientTape(_snake_case  ,      sparse_as_dense=_snake_case							)

      _A						     =					tape.gradient(_snake_case  ,      model.trainable_variables							)
      opt.apply_gradients(zip(_snake_case  ,      model.trainable_variables							)							)

      pbar.set_description(F'''Loss: {loss_value:.4f}'''							)

      if SDP_ENABLED and i == 0:
         sdp.broadcast_variables(model.variables  ,      root_rank=0							)
         sdp.broadcast_variables(opt.variables()  ,      root_rank=0							)

      if max_steps and i >= max_steps:
         break

   _A						     =					{'''loss''': loss_value.numpy()}
   return train_results

def   SCREAMING_SNAKE_CASE_       (  _snake_case  :List[Any]  ,      _snake_case  :Optional[Any]  ,      _snake_case  :List[str]							)     ->    Dict:
   # Load dataset
   _A     ,						_A						     =					load_dataset('''imdb'''  ,      split=['''train''', '''test''']							)

   # Preprocess train dataset
   _A						     =					train_dataset.map(
       lambda _snake_case							: tokenizer(e['''text''']  ,      truncation=_snake_case  ,      padding='''max_length'''							)  ,      batched=_snake_case							)
   train_dataset.set_format(type='''tensorflow'''  ,      columns=['''input_ids''', '''attention_mask''', '''label''']							)

   _A						     =					{
       x: train_dataset[x].to_tensor(default_value=0  ,      shape=[None, tokenizer.model_max_length]							)
       for x in ['''input_ids''', '''attention_mask''']
   }
   _A						     =					tf.data.Dataset.from_tensor_slices((train_features, train_dataset['''label'''])							)

   # Preprocess test dataset
   _A						     =					test_dataset.map(
       lambda _snake_case							: tokenizer(e['''text''']  ,      truncation=_snake_case  ,      padding='''max_length'''							)  ,      batched=_snake_case							)
   test_dataset.set_format(type='''tensorflow'''  ,      columns=['''input_ids''', '''attention_mask''', '''label''']							)

   _A						     =					{
       x: test_dataset[x].to_tensor(default_value=0  ,      shape=[None, tokenizer.model_max_length]							)
       for x in ['''input_ids''', '''attention_mask''']
   }
   _A						     =					tf.data.Dataset.from_tensor_slices((test_features, test_dataset['''label'''])							)

   if SDP_ENABLED:
      _A						     =					tf_train_dataset.shard(sdp.size()  ,      sdp.rank()							)
      _A						     =					tf_test_dataset.shard(sdp.size()  ,      sdp.rank()							)
   _A						     =					tf_train_dataset.batch(_snake_case  ,      drop_remainder=_snake_case							)
   _A						     =					tf_test_dataset.batch(_snake_case  ,      drop_remainder=_snake_case							)

   return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
   UpperCAmelCase_		   =				argparse.ArgumentParser()

   # Hyperparameters sent by the client are passed as command-line arguments to the script.
   parser.add_argument("""--epochs""", type=int, default=3)
   parser.add_argument("""--per_device_train_batch_size""", type=int, default=1_6)
   parser.add_argument("""--per_device_eval_batch_size""", type=int, default=8)
   parser.add_argument("""--model_name_or_path""", type=str)
   parser.add_argument("""--learning_rate""", type=str, default=5E-5)
   parser.add_argument("""--do_train""", type=bool, default=True)
   parser.add_argument("""--do_eval""", type=bool, default=True)
   parser.add_argument("""--output_dir""", type=str)
   parser.add_argument("""--max_steps""", type=int, default=None)

   # Data, model, and output directories
   parser.add_argument("""--output_data_dir""", type=str, default=os.environ["""SM_OUTPUT_DATA_DIR"""])
   parser.add_argument("""--model_dir""", type=str, default=os.environ["""SM_MODEL_DIR"""])
   parser.add_argument("""--n_gpus""", type=str, default=os.environ["""SM_NUM_GPUS"""])

   UpperCAmelCase_   ,UpperCAmelCase_		   =				parser.parse_known_args()

   # Set up logging
   UpperCAmelCase_		   =				logging.getLogger(__name__)

   logging.basicConfig(
       level=logging.getLevelName("""INFO"""),
       handlers=[logging.StreamHandler(sys.stdout)],
       format="""%(asctime)s - %(name)s - %(levelname)s - %(message)s""",
   )

   if SDP_ENABLED:
      sdp.init()

      UpperCAmelCase_		   =				tf.config.experimental.list_physical_devices("""GPU""")
      for gpu in gpus:
         tf.config.experimental.set_memory_growth(gpu, True)
      if gpus:
         tf.config.experimental.set_visible_devices(gpus[sdp.local_rank()], """GPU""")

    # Load model and tokenizer
   UpperCAmelCase_		   =				TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
   UpperCAmelCase_		   =				AutoTokenizer.from_pretrained(args.model_name_or_path)

   # get datasets
   UpperCAmelCase_   ,UpperCAmelCase_		   =				get_datasets(
       tokenizer=tokenizer,
       train_batch_size=args.per_device_train_batch_size,
       eval_batch_size=args.per_device_eval_batch_size,
   )

   # fine optimizer and loss
   UpperCAmelCase_		   =				tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
   UpperCAmelCase_		   =				tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
   UpperCAmelCase_		   =				[tf.keras.metrics.SparseCategoricalAccuracy()]
   model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

   # Training
   if args.do_train:
      # train_results = model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.train_batch_size)
      UpperCAmelCase_		   =				time.time()
      UpperCAmelCase_		   =				fit(
          model,
          loss,
          optimizer,
          tf_train_dataset,
          args.epochs,
          args.per_device_train_batch_size,
          max_steps=args.max_steps,
      )
      UpperCAmelCase_		   =				time.time() - start_train_time
      logger.info("""*** Train ***""")
      logger.info(f'train_runtime = {end_train_time}')

      UpperCAmelCase_		   =				os.path.join(args.output_dir, """train_results.txt""")

      if not SDP_ENABLED or sdp.rank() == 0:
         with open(output_eval_file, """w""") as writer:
            logger.info("""***** Train results *****""")
            logger.info(train_results)
            for key, value in train_results.items():
               logger.info(f'  {key} = {value}')
               writer.write(f'{key} = {value}\n')

    # Evaluation
   if args.do_eval and (not SDP_ENABLED or sdp.rank() == 0):
      UpperCAmelCase_		   =				model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
      logger.info("""*** Evaluate ***""")

      UpperCAmelCase_		   =				os.path.join(args.output_dir, """eval_results.txt""")

      with open(output_eval_file, """w""") as writer:
         logger.info("""***** Eval results *****""")
         logger.info(result)
         for key, value in result.items():
            logger.info(f'  {key} = {value}')
            writer.write(f'{key} = {value}\n')

    # Save result
   if SDP_ENABLED:
      if sdp.rank() == 0:
         model.save_pretrained(args.output_dir)
         tokenizer.save_pretrained(args.output_dir)
   else:
      model.save_pretrained(args.output_dir)
      tokenizer.save_pretrained(args.output_dir)



