




'''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


lowercase__								=						logging.getLogger(__name__)


def 					UpperCamelCase						(	snake_case ):




   '''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 ):




   '''simple docstring'''

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


def 					UpperCamelCase						(	snake_case="gpt2" ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					List[Any]							    =			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 ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					List[Any]							    =			copy.deepcopy(snake_case )
   model.to(snake_case )

   SCREAMING_SNAKE_CASE__      :					int							    =			['''bias''', '''LayerNorm.weight''']
   SCREAMING_SNAKE_CASE__      :					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},
   ]
   SCREAMING_SNAKE_CASE__      :					List[Any]							    =			AdamW(snake_case						,  lr=5e-5						,  eps=1e-8 )
   SCREAMING_SNAKE_CASE__      :					List[str]							    =			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 ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					Dict							    =			np.array(snake_case ).mean()
   SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			np.array(snake_case ).std()
   SCREAMING_SNAKE_CASE__      :					str							    =			(real_perps - avg) / std
   SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			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						,  ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					List[Any]							    =			[]
   SCREAMING_SNAKE_CASE__      :					Dict							    =			[]
   SCREAMING_SNAKE_CASE__      :					Tuple							    =			[]

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

   # Compute perplexity of initial transformer model for comparison
   model.train()
   SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					List[str]							    =			recopy_model(snake_case						,  snake_case						,  snake_case )

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

      # Periodically save the stored (X, IG(X)) pairs
      if step % 10_00 == 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
      SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			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=1_00						,  snake_case=10_26						,  snake_case=True ):




   '''simple docstring'''

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




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			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
   SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			train_dataset[:5_12]
   SCREAMING_SNAKE_CASE__      :					List[str]							    =			train_dataset[5_12:]
   SCREAMING_SNAKE_CASE__      :					int							    =			DataLoader(snake_case						,  shuffle=snake_case						,  batch_size=snake_case )
   SCREAMING_SNAKE_CASE__      :					Any							    =			DataLoader(snake_case						,  shuffle=snake_case						,  batch_size=snake_case )

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

            secondary_learner.eval()
            SCREAMING_SNAKE_CASE__      :					Dict							    =			0.0
            SCREAMING_SNAKE_CASE__      :					Dict							    =			0.0
            SCREAMING_SNAKE_CASE__      :					Tuple							    =			[]
            SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			[]
            # Compute performance of the secondary learner after this batch
            for stepa, batcha in enumerate(snake_case ):
               SCREAMING_SNAKE_CASE__      :					int							    =			batcha[0].to(snake_case )
               SCREAMING_SNAKE_CASE__      :					Any							    =			batcha[1].to(snake_case )
               SCREAMING_SNAKE_CASE__      :					Any							    =			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 )
               SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			q_lossa

         secondary_learner.train()
   return secondary_learner







class     lowercase  ( nn.Module):






      def __init__(self			,	a__       )							-> Tuple:
         super(a__			,	self       ).__init__()
         SCREAMING_SNAKE_CASE__      :					str							    =			model.transformer.wte
         SCREAMING_SNAKE_CASE__      :					str							    =			copy.deepcopy(model.transformer.wte.weight       )
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			nn.Convad(self.embeddings.weight.size(1       )			,	2_5_6			,	3			,	padding=1       )
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			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			,	a__       )							-> Dict:
         SCREAMING_SNAKE_CASE__      :					Dict							    =			torch.max(self.conv(self.embeddings(a__       ).squeeze(1       ).transpose(1			,	2       )       )			,	2       )[0]
         SCREAMING_SNAKE_CASE__      :					Dict							    =			self.fc(a__       )
         return qs.squeeze(1       )






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







'''simple docstring'''





# This Python program implements an optimal binary search tree (abbreviated BST)
# building dynamic programming algorithm that delivers O(n^2) performance.
#
# The goal of the optimal BST problem is to build a low-cost BST for a
# given set of nodes, each with its own key and frequency. The frequency
# of the node is defined as how many time the node is being searched.
# The search cost of binary search tree is given by this formula:
#
# cost(1, n) = sum{i = 1 to n}((depth(node_i) + 1) * node_i_freq)
#
# where n is number of nodes in the BST. The characteristic of low-cost
# BSTs is having a faster overall search time than other implementations.
# The reason for their fast search time is that the nodes with high
# frequencies will be placed near the root of the tree while the nodes
# with low frequencies will be placed near the leaves of the tree thus
# reducing search time in the most frequent instances.
import sys
from random import randint






class     lowercase  :






      def __init__(self			,	a__			,	a__       )							-> Optional[int]:
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			key
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			freq






      def __str__(self       )							-> Dict:
         return f"""Node(key={self.key}, freq={self.freq})"""


def 					UpperCamelCase						(	snake_case						,  snake_case						,  snake_case						,  snake_case						,  snake_case						,  snake_case ):




   '''simple docstring'''

   if i > j or i < 0 or j > len(snake_case ) - 1:
      return

   SCREAMING_SNAKE_CASE__      :					Tuple							    =			root[i][j]
   if parent == -1:  # root does not have a parent
      print(f"""{key[node]} is the root of the binary search tree.""" )
   elif is_left:
      print(f"""{key[node]} is the left child of key {parent}.""" )
   else:
      print(f"""{key[node]} is the right child of key {parent}.""" )

   print_binary_search_tree(snake_case						,  snake_case						,  snake_case						,  node - 1						,  key[node]						,  snake_case )
   print_binary_search_tree(snake_case						,  snake_case						,  node + 1						,  snake_case						,  key[node]						,  snake_case )


def 					UpperCamelCase						(	snake_case ):




   '''simple docstring'''

   nodes.sort(key=lambda snake_case : node.key )

   SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			len(snake_case )

   SCREAMING_SNAKE_CASE__      :					List[str]							    =			[nodes[i].key for i in range(snake_case )]
   SCREAMING_SNAKE_CASE__      :					int							    =			[nodes[i].freq for i in range(snake_case )]

   # This 2D array stores the overall tree cost (which's as minimized as possible);
   # for a single key, cost is equal to frequency of the key.
   SCREAMING_SNAKE_CASE__      :					Dict							    =			[[freqs[i] if i == j else 0 for j in range(snake_case )] for i in range(snake_case )]
   # sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes
   # array
   SCREAMING_SNAKE_CASE__      :					str							    =			[[freqs[i] if i == j else 0 for j in range(snake_case )] for i in range(snake_case )]
   # stores tree roots that will be used later for constructing binary search tree
   SCREAMING_SNAKE_CASE__      :					int							    =			[[i if i == j else 0 for j in range(snake_case )] for i in range(snake_case )]

   for interval_length in range(2						,  n + 1 ):
      for i in range(n - interval_length + 1 ):
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			i + interval_length - 1

         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			sys.maxsize  # set the value to "infinity"
         SCREAMING_SNAKE_CASE__      :					List[str]							    =			total[i][j - 1] + freqs[j]

         # Apply Knuth's optimization
         # Loop without optimization: for r in range(i, j + 1):
         for r in range(root[i][j - 1]						,  root[i + 1][j] + 1 ):  # r is a temporal root
            SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			dp[i][r - 1] if r != i else 0  # optimal cost for left subtree
            SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			dp[r + 1][j] if r != j else 0  # optimal cost for right subtree
            SCREAMING_SNAKE_CASE__      :					List[str]							    =			left + total[i][j] + right

            if dp[i][j] > cost:
               SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			cost
               SCREAMING_SNAKE_CASE__      :					str							    =			r

   print('''Binary search tree nodes:''' )
   for node in nodes:
      print(snake_case )

   print(f"""\nThe cost of optimal BST for given tree nodes is {dp[0][n - 1]}.""" )
   print_binary_search_tree(snake_case						,  snake_case						,  0						,  n - 1						,  -1						,  snake_case )


def 					UpperCamelCase						(	):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			[Node(snake_case						,  randint(1						,  50 ) ) for i in range(10						,  0						,  -1 )]
   find_optimal_binary_search_tree(snake_case )


if __name__ == "__main__":
       main()

