
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


a__				:      str    		=     logging.getLogger(__name__)







def 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



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







def 				UpperCAmelCase_(    a__ ,						a__ ,						a__   ):



    """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=a__   )
    SCREAMING_SNAKE_CASE				:				int				      = DataLoader(a__ ,						shuffle=a__ ,						batch_size=a__   )
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = torch.zeros(1 ,						device=a__   )
    SCREAMING_SNAKE_CASE				:				int				      = 0
    for batch in eval_dataloader:
        batch.to(a__   )
        # pad
        context.zero_()
        for i in range(a__   ):
            SCREAMING_SNAKE_CASE				:				Any				      = batch[i]
        SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = model(a__ ,						labels=a__   )
        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(a__   )
    model.train()
    return perplexity







def 				UpperCAmelCase_(    a__="gpt2"   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				List[Any]				      = GPTaLMHeadModel.from_pretrained(a__ ,						output_hidden_states=a__   )
    torch.save(model.state_dict() ,						model_name + '''local.pt'''   )
    return model







def 				UpperCAmelCase_(    a__ ,						a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				List[Any]				      = copy.deepcopy(a__   )
    model.to(a__   )

    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(a__ ,						lr=5e-5 ,						eps=1e-8   )
    SCREAMING_SNAKE_CASE				:				List[str]				      = get_linear_schedule_with_warmup(a__ ,						0 ,						a__   )
    torch.cuda.empty_cache()
    return model, lm_optimizer, lm_scheduler







def 				UpperCAmelCase_(    a__ ,						a__ ,						a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Dict				      = np.array(a__   ).mean()
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = np.array(a__   ).std()
    SCREAMING_SNAKE_CASE				:				str				      = (real_perps - avg) / std
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = list(zip(a__ ,						a__ ,						a__   )   )
    joblib.dump(a__ ,						a__   )







def 				UpperCAmelCase_(    a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__="dev.jbl" ,						a__=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(a__   )
    orig_model.to(device='''cpu'''   )
    torch.cuda.empty_cache()

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

    for step in tqdm(range(a__   )   ):
        SCREAMING_SNAKE_CASE				:				str				      = torch.zeros((1, context_len) ,						dtype=torch.long ,						device=a__   )
        SCREAMING_SNAKE_CASE				:				Any				      = random.choice(a__   )
        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(a__ ,						labels=a__   )
        SCREAMING_SNAKE_CASE				:				int				      = outputs[0]
        SCREAMING_SNAKE_CASE				:				str				      = compute_perplexity(a__ ,						a__ ,						a__   )
        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(a__ ,						a__ ,						a__   )

        # Periodically save the stored (X, IG(X)) pairs
        if step % 1_000 == 0 and step > 1:
            intermittent_save(a__ ,						a__ ,						a__ ,						a__   )

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

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

    intermittent_save(a__ ,						a__ ,						a__ ,						a__   )







def 				UpperCAmelCase_(    a__ ,						a__="data/tokenized_stories_train_wikitext103.jbl" ,						a__=100 ,						a__=1_026 ,						a__=True   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Dict				      = joblib.load(a__   )
    print('''data loaded'''   )
    SCREAMING_SNAKE_CASE				:				Optional[int]				      = []
    if trim:
        for i, example in enumerate(a__   ):
            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(a__   ) >= number:
                break
        SCREAMING_SNAKE_CASE				:				Dict				      = []
        for j in range(i + 1 ,						len(a__   )   ):
            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(a__ ,						'''objective_set.jbl'''   )
    print('''objective set saved'''   )
    return train_data, objective_set







def 				UpperCAmelCase_(    a__ ,						a__ ,						a__ ,						a__ ,						a__=50 ,						a__="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[:512]
    SCREAMING_SNAKE_CASE				:				List[str]				      = train_dataset[512:]
    SCREAMING_SNAKE_CASE				:				int				      = DataLoader(a__ ,						shuffle=a__ ,						batch_size=a__   )
    SCREAMING_SNAKE_CASE				:				Any				      = DataLoader(a__ ,						shuffle=a__ ,						batch_size=a__   )

    # secondary learner model set up
    SCREAMING_SNAKE_CASE				:				List[Any]				      = nn.MSELoss()
    SCREAMING_SNAKE_CASE				:				Any				      = nn.MSELoss(reduction='''sum'''   )
    secondary_learner.to(a__   )
    SCREAMING_SNAKE_CASE				:				Tuple				      = torch.optim.Adam(secondary_learner.parameters() ,						lr=0.00_001   )
    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(a__   )   ):
        SCREAMING_SNAKE_CASE				:				Optional[int]				      = 0.0
        secondary_learner.train()
        for step, batch in enumerate(a__   ):
            SCREAMING_SNAKE_CASE				:				List[str]				      = batch[0].to(a__   )
            SCREAMING_SNAKE_CASE				:				List[str]				      = batch[1].to(a__   )
            SCREAMING_SNAKE_CASE				:				Dict				      = secondary_learner(a__   )
            q_optimizer.zero_grad()
            SCREAMING_SNAKE_CASE				:				Optional[Any]				      = loss(a__ ,						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(a__   )):
                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(a__   ):
                    SCREAMING_SNAKE_CASE				:				int				      = batcha[0].to(a__   )
                    SCREAMING_SNAKE_CASE				:				Any				      = batcha[1].to(a__   )
                    SCREAMING_SNAKE_CASE				:				Any				      = secondary_learner(a__   )
                    q_lossa += test_loss(a__ ,						a__   ).item()
                    sum_qa += torch.sum(a__   ).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(a__   )
                print(
                    '''Epoch: ''' ,						a__ ,						'''step: ''' ,						a__ ,						'''Avg. q:''' ,						sum_qa / len(a__   ) ,						'''Train Loss: ''' ,						a__ ,						'''Test Loss: ''' ,						a__ ,						)
                if q_lossa < best_test_loss:
                    joblib.dump((predicted, actual) ,						'''pred_vs_actual.jbl'''   )
                    torch.save(secondary_learner.state_dict() ,						a__   )
                    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = q_lossa

            secondary_learner.train()
    return secondary_learner






class     a_       (			nn.Module							):



       """simple docstring"""






       def __init__(		self				,       _lowerCamelCase   )					->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   )				,       256				,       3				,       padding=1   )
           SCREAMING_SNAKE_CASE				:				List[Any]				      = nn.Sequential(nn.Linear(256				,       32   )				,       nn.Dropout(p=0.1   )				,       nn.Linear(32				,       32   )				,       nn.Linear(32				,       1   )   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase   )					->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 	__lowerCAmelCase     (		cls				,       _lowerCamelCase				,       _lowerCamelCase   )					->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'''





import math
from decimal import Decimal, getcontext


def 					UpperCamelCase						(	snake_case = 4_00_00_00 ):




   '''simple docstring'''

   try:
      SCREAMING_SNAKE_CASE__      :					List[Any]							    =			int(snake_case )
   except (TypeError, ValueError):
      raise TypeError('''Parameter n must be int or castable to int.''' )
   if n <= 0:
      raise ValueError('''Parameter n must be greater than or equal to one.''' )
   SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			1_00
   SCREAMING_SNAKE_CASE__      :					int							    =			(Decimal(5 ) ** Decimal(0.5 ) + 1) / Decimal(2 )

   SCREAMING_SNAKE_CASE__      :					int							    =			(math.floor(math.log(n * (phi + 2)						,  snake_case ) - 1 ) // 3) * 3 + 2
   SCREAMING_SNAKE_CASE__      :					int							    =			Decimal(round(phi ** Decimal(index + 1 ) ) ) / (phi + 2)
   SCREAMING_SNAKE_CASE__      :					Any							    =			num // 2
   return int(snake_case )


if __name__ == "__main__":
       print(F"{solution() = }")

