



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


__snake_case		:str 						=	logging.getLogger(__name__)





def 	__snake_case      (     _UpperCAmelCase							):
    random.seed(_UpperCAmelCase							)
    np.random.seed(_UpperCAmelCase							)
    torch.manual_seed(_UpperCAmelCase							)
    torch.cuda.manual_seed_all(_UpperCAmelCase							)





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





def 	__snake_case      (     _UpperCAmelCase="gpt2"							):
    __a					=							GPTaLMHeadModel.from_pretrained(_UpperCAmelCase  ,					output_hidden_states=_UpperCAmelCase							)
    torch.save(model.state_dict()  ,					model_name + '''local.pt'''							)
    return model





def 	__snake_case      (     _UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase							):
    __a					=							copy.deepcopy(_UpperCAmelCase							)
    model.to(_UpperCAmelCase							)

    __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(_UpperCAmelCase  ,					lr=5E-5  ,					eps=1E-8							)
    __a					=							get_linear_schedule_with_warmup(_UpperCAmelCase  ,					0  ,					_UpperCAmelCase							)
    torch.cuda.empty_cache()
    return model, lm_optimizer, lm_scheduler





def 	__snake_case      (     _UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase							):
    __a					=							np.array(_UpperCAmelCase							).mean()
    __a					=							np.array(_UpperCAmelCase							).std()
    __a					=							(real_perps - avg) / std
    __a					=							list(zip(_UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase							)							)
    joblib.dump(_UpperCAmelCase  ,					_UpperCAmelCase							)





def 	__snake_case      (     _UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase="dev.jbl"  ,					_UpperCAmelCase=recopy_gpta  ,					):
    __a					=							[]
    __a					=							[]
    __a					=							[]

    # Initialize the transformer model
    __a					=							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					=							recopy_model(_UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase							)

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

        # Periodically save the stored (X, IG(X)) pairs
        if step % 1000 == 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					=							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 	__snake_case      (     _UpperCAmelCase  ,					_UpperCAmelCase="data/tokenized_stories_train_wikitext103.jbl"  ,					_UpperCAmelCase=100  ,					_UpperCAmelCase=1026  ,					_UpperCAmelCase=True							):
    __a					=							joblib.load(_UpperCAmelCase							)
    print('''data loaded'''							)
    __a					=							[]
    if trim:
        for i, example in enumerate(_UpperCAmelCase							):
            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(_UpperCAmelCase							) >= number:
                break
        __a					=							[]
        for j in range(i + 1  ,					len(_UpperCAmelCase							)							):
            if len(data[j][0]							) > min_len:
                train_data.append(data[j]							)
    else:
        __a					=							data[0:number]
        __a					=							data[number:]

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





def 	__snake_case      (     _UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase=50  ,					_UpperCAmelCase="secondary_learner.pt"							):
    __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(_UpperCAmelCase  ,					shuffle=_UpperCAmelCase  ,					batch_size=_UpperCAmelCase							)
    __a					=							DataLoader(_UpperCAmelCase  ,					shuffle=_UpperCAmelCase  ,					batch_size=_UpperCAmelCase							)

    # secondary learner model set up
    __a					=							nn.MSELoss()
    __a					=							nn.MSELoss(reduction='''sum'''							)
    secondary_learner.to(_UpperCAmelCase							)
    __a					=							torch.optim.Adam(secondary_learner.parameters()  ,					lr=0.0_00_01							)
    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(_UpperCAmelCase							)							):
        __a					=							0.0
        secondary_learner.train()
        for step, batch in enumerate(_UpperCAmelCase							):
            __a					=							batch[0].to(_UpperCAmelCase							)
            __a					=							batch[1].to(_UpperCAmelCase							)
            __a					=							secondary_learner(_UpperCAmelCase							)
            q_optimizer.zero_grad()
            __a					=							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					=							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(_UpperCAmelCase							):
                    __a					=							batcha[0].to(_UpperCAmelCase							)
                    __a					=							batcha[1].to(_UpperCAmelCase							)
                    __a					=							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					=							q_lossa

            secondary_learner.train()
    return secondary_learner

class        _A			(      nn.Module       ):



       def __init__(						self				:	Optional[Any]	,						__SCREAMING_SNAKE_CASE				:	List[Any]):


           '''simple docstring'''

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



       def 						_lowerCamelCase     (						self				:	List[str]	,						__SCREAMING_SNAKE_CASE				:	Tuple):


           '''simple docstring'''

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





       @classmethod
       def 						_lowerCamelCase     (						cls				:	List[str]	,						__SCREAMING_SNAKE_CASE				:	List[Any]	,						__SCREAMING_SNAKE_CASE				:	Union[str, Any]):


           '''simple docstring'''

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










from __future__ import annotations

from typing import Generic, TypeVar

__snake_case		:str 						=	TypeVar('''T''')

class        _A			(      __UpperCAmelCase       ):
       pass


class        _A			(      __UpperCAmelCase       ):
       pass


class        _A			(      Generic[T]       ):



       def __init__(						self				:	List[Any]	,						__SCREAMING_SNAKE_CASE				:	int = 10):


           '''simple docstring'''

           __a					=							[]
           __a					=							limit



       def __bool__(						self				:	List[Any]):


           '''simple docstring'''

           return bool(self.stack)



       def __str__(						self				:	Any):


           '''simple docstring'''

           return str(self.stack)



       def 						_lowerCamelCase     (						self				:	List[Any]	,						__SCREAMING_SNAKE_CASE				:	T):


           '''simple docstring'''

           if len(self.stack) >= self.limit:
               raise StackOverflowError
           self.stack.append(__SCREAMING_SNAKE_CASE)



       def 						_lowerCamelCase     (						self				:	str):


           '''simple docstring'''

           if not self.stack:
               raise StackUnderflowError
           return self.stack.pop()



       def 						_lowerCamelCase     (						self				:	Optional[Any]):


           '''simple docstring'''

           if not self.stack:
               raise StackUnderflowError
           return self.stack[-1]



       def 						_lowerCamelCase     (						self				:	Union[str, Any]):


           '''simple docstring'''

           return not bool(self.stack)



       def 						_lowerCamelCase     (						self				:	Tuple):


           '''simple docstring'''

           return self.size() == self.limit



       def 						_lowerCamelCase     (						self				:	Union[str, Any]):


           '''simple docstring'''

           return len(self.stack)





       def __contains__(						self				:	int	,						__SCREAMING_SNAKE_CASE				:	T):


           '''simple docstring'''

           return item in self.stack





def 	__snake_case      (     ):
    __a					=							Stack(10							)
    assert bool(_UpperCAmelCase							) is False
    assert stack.is_empty() is True
    assert stack.is_full() is False
    assert str(_UpperCAmelCase							) == "[]"

    try:
        __a					=							stack.pop()
        raise AssertionError  # This should not happen
    except StackUnderflowError:
        assert True  # This should happen

    try:
        __a					=							stack.peek()
        raise AssertionError  # This should not happen
    except StackUnderflowError:
        assert True  # This should happen

    for i in range(10							):
        assert stack.size() == i
        stack.push(_UpperCAmelCase							)

    assert bool(_UpperCAmelCase							)
    assert not stack.is_empty()
    assert stack.is_full()
    assert str(_UpperCAmelCase							) == str(list(range(10							)							)							)
    assert stack.pop() == 9
    assert stack.peek() == 8

    stack.push(100							)
    assert str(_UpperCAmelCase							) == str([0, 1, 2, 3, 4, 5, 6, 7, 8, 100]							)

    try:
        stack.push(200							)
        raise AssertionError  # This should not happen
    except StackOverflowError:
        assert True  # This should happen

    assert not stack.is_empty()
    assert stack.size() == 10

    assert 5 in stack
    assert 55 not in stack


if __name__ == "__main__":
 test_stack()





