






"""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


A__       :			str=						logging.getLogger(__name__)







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE       )       ->						Tuple:
    """simple docstring"""

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







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )       ->						Tuple:
    """simple docstring"""

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







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE="gpt2"       )       ->						List[Any]:
    """simple docstring"""

    UpperCamelCase__								=     GPTaLMHeadModel.from_pretrained(SCREAMING_SNAKE_CASE			,						output_hidden_states=SCREAMING_SNAKE_CASE       )
    torch.save(model.state_dict()			,						model_name + 'local.pt'       )
    return model







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )       ->						str:
    """simple docstring"""

    UpperCamelCase__								=     copy.deepcopy(SCREAMING_SNAKE_CASE       )
    model.to(SCREAMING_SNAKE_CASE       )

    UpperCamelCase__								=     ['bias', 'LayerNorm.weight']
    UpperCamelCase__								=     [
        {
            '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},
    ]
    UpperCamelCase__								=     AdamW(SCREAMING_SNAKE_CASE			,						lr=5E-5			,						eps=1E-8       )
    UpperCamelCase__								=     get_linear_schedule_with_warmup(SCREAMING_SNAKE_CASE			,						0			,						SCREAMING_SNAKE_CASE       )
    torch.cuda.empty_cache()
    return model, lm_optimizer, lm_scheduler







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )       ->						Optional[Any]:
    """simple docstring"""

    UpperCamelCase__								=     np.array(SCREAMING_SNAKE_CASE       ).mean()
    UpperCamelCase__								=     np.array(SCREAMING_SNAKE_CASE       ).std()
    UpperCamelCase__								=     (real_perps - avg) / std
    UpperCamelCase__								=     list(zip(SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )       )
    joblib.dump(SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE="dev.jbl"			,						SCREAMING_SNAKE_CASE=recopy_gpta			,						)       ->						str:
    """simple docstring"""

    UpperCamelCase__								=     []
    UpperCamelCase__								=     []
    UpperCamelCase__								=     []

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

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

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

        # Periodically save the stored (X, IG(X)) pairs
        if step % 10_00 == 0 and step > 1:
            intermittent_save(SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )

        # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
        UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__								=     recopy_model(SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )

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

    intermittent_save(SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE="data/tokenized_stories_train_wikitext103.jbl"			,						SCREAMING_SNAKE_CASE=1_00			,						SCREAMING_SNAKE_CASE=10_26			,						SCREAMING_SNAKE_CASE=True       )       ->						int:
    """simple docstring"""

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

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







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE=50			,						SCREAMING_SNAKE_CASE="secondary_learner.pt"       )       ->						List[Any]:
    """simple docstring"""

    UpperCamelCase__								=     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
    UpperCamelCase__								=     train_dataset[:5_12]
    UpperCamelCase__								=     train_dataset[5_12:]
    UpperCamelCase__								=     DataLoader(SCREAMING_SNAKE_CASE			,						shuffle=SCREAMING_SNAKE_CASE			,						batch_size=SCREAMING_SNAKE_CASE       )
    UpperCamelCase__								=     DataLoader(SCREAMING_SNAKE_CASE			,						shuffle=SCREAMING_SNAKE_CASE			,						batch_size=SCREAMING_SNAKE_CASE       )

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

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

            secondary_learner.train()
    return secondary_learner





class  __lowerCamelCase     (	nn.Module	):

   def __init__(       self			,					snake_case_   )		->							int:
       super(snake_case_			,					self   ).__init__()
       UpperCamelCase__								=     model.transformer.wte
       UpperCamelCase__								=     copy.deepcopy(model.transformer.wte.weight   )
       UpperCamelCase__								=     nn.Convad(self.embeddings.weight.size(1   )			,					256			,					3			,					padding=1   )
       UpperCamelCase__								=     nn.Sequential(nn.Linear(256			,					32   )			,					nn.Dropout(p=0.1   )			,					nn.Linear(32			,					32   )			,					nn.Linear(32			,					1   )   )

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







   @classmethod
   def     SCREAMING_SNAKE_CASE__			(       cls			,					snake_case_			,					snake_case_   )		->							int:
       UpperCamelCase__								=     cls(snake_case_   )  # this calls __init__
       UpperCamelCase__								=     torch.load(snake_case_   )
       secondary_learner.load_state_dict(snake_case_   )
       UpperCamelCase__								=     model.transformer.wte
       UpperCamelCase__								=     copy.deepcopy(model.transformer.wte.weight   )
       return secondary_learner












"""simple docstring"""





import torch

from transformers import PreTrainedModel

from .custom_configuration import CustomConfig, NoSuperInitConfig
class  __lowerCamelCase     (	_a	):
   a   :					List[Any]			    =CustomConfig

   def __init__(       self			,					snake_case_   )		->							str:
       super().__init__(snake_case_   )
       UpperCamelCase__								=     torch.nn.Linear(config.hidden_size			,					config.hidden_size   )

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_   )		->							Optional[int]:
       return self.linear(snake_case_   )







   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_   )		->							Union[str, Any]:
       pass







class  __lowerCamelCase     (	_a	):
   a   :					Optional[int]			    =NoSuperInitConfig

   def __init__(       self			,					snake_case_   )		->							Optional[int]:
       super().__init__(snake_case_   )
       UpperCamelCase__								=     torch.nn.Linear(config.attribute			,					config.attribute   )

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_   )		->							int:
       return self.linear(snake_case_   )







   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_   )		->							Union[str, Any]:
       pass




