





"""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_				=    logging.getLogger(__name__)







def 	__lowercase  (     snake_case_						:						Tuple		)     ->Optional[Any]:



    '''simple docstring'''







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







def 	__lowercase  (     snake_case_						:						List[str]       ,snake_case_						:						int       ,snake_case_						:						List[str]		)     ->List[str]:



    '''simple docstring'''







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







def 	__lowercase  (     snake_case_						:						Union[str, Any]="gpt2"		)     ->List[Any]:



    '''simple docstring'''







    __A					:		Tuple						=  GPTaLMHeadModel.from_pretrained(snake_case_       ,output_hidden_states=snake_case_		)
    torch.save(model.state_dict()       ,model_name + '''local.pt'''		)
    return model







def 	__lowercase  (     snake_case_						:						Optional[Any]       ,snake_case_						:						Tuple       ,snake_case_						:						str		)     ->List[Any]:



    '''simple docstring'''







    __A					:		List[Any]						=  copy.deepcopy(snake_case_		)
    model.to(snake_case_		)

    __A					:		Dict						=  ['''bias''', '''LayerNorm.weight''']
    __A					:		Any						=  [
        {
            '''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					:		Optional[int]						=  AdamW(snake_case_       ,lr=5e-5       ,eps=1e-8		)
    __A					:		int						=  get_linear_schedule_with_warmup(snake_case_       ,0       ,snake_case_		)
    torch.cuda.empty_cache()
    return model, lm_optimizer, lm_scheduler







def 	__lowercase  (     snake_case_						:						List[Any]       ,snake_case_						:						List[str]       ,snake_case_						:						str       ,snake_case_						:						Any		)     ->List[str]:



    '''simple docstring'''







    __A					:		List[Any]						=  np.array(snake_case_		).mean()
    __A					:		Dict						=  np.array(snake_case_		).std()
    __A					:		Tuple						=  (real_perps - avg) / std
    __A					:		Tuple						=  list(zip(snake_case_       ,snake_case_       ,snake_case_		)		)
    joblib.dump(snake_case_       ,snake_case_		)







def 	__lowercase  (     snake_case_						:						List[Any]       ,snake_case_						:						Dict       ,snake_case_						:						Dict       ,snake_case_						:						Union[str, Any]       ,snake_case_						:						Optional[int]       ,snake_case_						:						Union[str, Any]       ,snake_case_						:						Tuple       ,snake_case_						:						Dict="dev.jbl"       ,snake_case_						:						str=recopy_gpta       ,)     ->List[Any]:



    '''simple docstring'''







    __A					:		Union[str, Any]						=  []
    __A					:		List[str]						=  []
    __A					:		str						=  []

    # Initialize the transformer model
    __A					:		Any						=  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					:		Optional[int]						=  recopy_model(snake_case_       ,snake_case_       ,snake_case_		)

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

        # Periodically save the stored (X, IG(X)) pairs
        if step % 1000 == 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					:		Union[str, Any]						=  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 	__lowercase  (     snake_case_						:						Union[str, Any]       ,snake_case_						:						int="data/tokenized_stories_train_wikitext103.jbl"       ,snake_case_						:						List[str]=100       ,snake_case_						:						List[Any]=1026       ,snake_case_						:						Optional[Any]=True		)     ->Any:



    '''simple docstring'''







    __A					:		Dict						=  joblib.load(snake_case_		)
    print('''data loaded'''		)
    __A					:		List[Any]						=  []
    if trim:
        for i, example in enumerate(snake_case_		):
            if len(example[0]		) > min_len:
                __A					:		Union[str, Any]						=  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					:		str						=  []
        for j in range(i + 1       ,len(snake_case_		)		):
            if len(data[j][0]		) > min_len:
                train_data.append(data[j]		)
    else:
        __A					:		str						=  data[0:number]
        __A					:		Union[str, Any]						=  data[number:]

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







def 	__lowercase  (     snake_case_						:						List[str]       ,snake_case_						:						Optional[Any]       ,snake_case_						:						int       ,snake_case_						:						Tuple       ,snake_case_						:						Dict=50       ,snake_case_						:						Optional[Any]="secondary_learner.pt"		)     ->str:



    '''simple docstring'''







    __A					:		Union[str, Any]						=  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					:		Dict						=  train_dataset[:512]
    __A					:		Optional[Any]						=  train_dataset[512:]
    __A					:		Union[str, Any]						=  DataLoader(snake_case_       ,shuffle=snake_case_       ,batch_size=snake_case_		)
    __A					:		Union[str, Any]						=  DataLoader(snake_case_       ,shuffle=snake_case_       ,batch_size=snake_case_		)

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

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

            secondary_learner.train()
    return secondary_learner






class 	__snake_case      (	nn.Module  ):





 """simple docstring"""


 def __init__(					self      ,		__lowerCamelCase						):



     '''simple docstring'''




     super(__lowerCamelCase      ,		self						).__init__()
     __A					:		List[str]						=  model.transformer.wte
     __A					:		Any						=  copy.deepcopy(model.transformer.wte.weight						)
     __A					:		Tuple						=  nn.Convad(self.embeddings.weight.size(1						)      ,		256      ,		3      ,		padding=1						)
     __A					:		List[str]						=  nn.Sequential(nn.Linear(256      ,		32						)      ,		nn.Dropout(p=0.1						)      ,		nn.Linear(32      ,		32						)      ,		nn.Linear(32      ,		1						)						)

 def       UpperCamelCase__(					self      ,		__lowerCamelCase						):



     '''simple docstring'''




     __A					:		Optional[Any]						=  torch.max(self.conv(self.embeddings(__lowerCamelCase						).squeeze(1						).transpose(1      ,		2						)						)      ,		2						)[0]
     __A					:		int						=  self.fc(__lowerCamelCase						)
     return qs.squeeze(1						)







 @classmethod
 def       UpperCamelCase__(					cls      ,		__lowerCamelCase      ,		__lowerCamelCase						):



     '''simple docstring'''




     __A					:		Tuple						=  cls(__lowerCamelCase						)  # this calls __init__
     __A					:		Optional[int]						=  torch.load(__lowerCamelCase						)
     secondary_learner.load_state_dict(__lowerCamelCase						)
     __A					:		Optional[Any]						=  model.transformer.wte
     __A					:		Union[str, Any]						=  copy.deepcopy(model.transformer.wte.weight						)
     return secondary_learner











"""simple docstring"""



import unittest
from functools import partial

import torch

from accelerate import Accelerator, debug_launcher
from accelerate.state import AcceleratorState, GradientState
from accelerate.test_utils import require_cpu, require_huggingface_suite
from accelerate.utils import GradientAccumulationPlugin







def 	__lowercase  (     snake_case_						:						Dict=2       ,snake_case_						:						str=True       ,snake_case_						:						Tuple=False		)     ->Any:



    '''simple docstring'''







    __A					:		List[str]						=  Accelerator(step_scheduler_with_optimizer=snake_case_       ,split_batches=snake_case_		)
    __A					:		Dict						=  torch.nn.Linear(2       ,4		)
    __A					:		Tuple						=  torch.optim.AdamW(model.parameters()       ,lr=1.0		)
    __A					:		int						=  torch.optim.lr_scheduler.OneCycleLR(snake_case_       ,max_lr=0.01       ,steps_per_epoch=2       ,epochs=1		)
    __A				, __A				, __A					:		Optional[int]						=  accelerator.prepare(snake_case_       ,snake_case_       ,snake_case_		)

    # Optimizer has stepped
    scheduler.step()
    if step_scheduler_with_optimizer or (num_processes == 1):
        assert (
            scheduler.scheduler.last_epoch == num_processes
        ), F"""Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})"""
    else:
        assert (
            scheduler.scheduler.last_epoch != num_processes
        ), F"""Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})"""







def 	__lowercase  (     snake_case_						:						str=2       ,snake_case_						:						Dict=True       ,snake_case_						:						Tuple=False		)     ->List[Any]:



    '''simple docstring'''







    __A					:		int						=  Accelerator(step_scheduler_with_optimizer=snake_case_       ,split_batches=snake_case_		)
    __A					:		Any						=  torch.nn.Linear(2       ,4		)
    __A					:		Optional[int]						=  torch.optim.AdamW(model.parameters()       ,lr=1.0		)
    __A					:		Tuple						=  torch.optim.lr_scheduler.LambdaLR(snake_case_       ,lr_lambda=lambda snake_case_		: 1 - n / 10		)
    __A				, __A				, __A					:		Tuple						=  accelerator.prepare(snake_case_       ,snake_case_       ,snake_case_		)

    # Optimizer has stepped
    __A					:		List[Any]						=  False
    scheduler.step()
    __A					:		List[Any]						=  1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 10
    assert (
        scheduler.get_last_lr()[0] == expected_lr
    ), F"""Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"""

    # Optimizer has not stepped
    __A					:		List[str]						=  True
    scheduler.step()
    if not step_scheduler_with_optimizer:
        __A					:		List[Any]						=  1 - 2 / 10
    assert (
        scheduler.get_last_lr()[0] == expected_lr
    ), F"""Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"""







def 	__lowercase  (     snake_case_						:						int = 2		)     ->str:



    '''simple docstring'''







    from transformers import get_linear_schedule_with_warmup

    __A					:		Any						=  [1, 2, 4]
    for num_steps in steps:
        __A					:		Tuple						=  GradientAccumulationPlugin(num_steps=snake_case_       ,adjust_scheduler=num_steps > 1		)
        __A					:		Optional[Any]						=  Accelerator(gradient_accumulation_plugin=snake_case_		)
        __A					:		Optional[int]						=  torch.nn.Linear(2       ,4		)
        __A					:		Any						=  torch.optim.AdamW(model.parameters()       ,lr=10.0		)
        __A					:		Dict						=  get_linear_schedule_with_warmup(optimizer=snake_case_       ,num_warmup_steps=0       ,num_training_steps=20		)

        __A				, __A				, __A					:		Union[str, Any]						=  accelerator.prepare(snake_case_       ,snake_case_       ,snake_case_		)

        for i in range(10 * num_steps		):
            with accelerator.accumulate(snake_case_		):
                optimizer.step()
                scheduler.step()

            if i == (10 * num_steps - 2):
                assert (
                    scheduler.get_last_lr()[0] != 0
                ), F"""Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}"""
        assert (
            scheduler.get_last_lr()[0] == 0
        ), F"""Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}"""
        GradientState._reset_state()






@require_cpu
class 	__snake_case      (	unittest.TestCase  ):





 """simple docstring"""


 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     debug_launcher(partial(__lowerCamelCase      ,		num_processes=1						)      ,		num_processes=1						)
     debug_launcher(partial(__lowerCamelCase      ,		num_processes=1      ,		split_batches=__lowerCamelCase						)      ,		num_processes=1						)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     debug_launcher(partial(__lowerCamelCase      ,		num_processes=1						)      ,		num_processes=1						)
     debug_launcher(partial(__lowerCamelCase      ,		num_processes=1      ,		split_batches=__lowerCamelCase						)      ,		num_processes=1						)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     debug_launcher(partial(__lowerCamelCase      ,		num_processes=1      ,		step_scheduler_with_optimizer=__lowerCamelCase						)      ,		num_processes=1						)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     debug_launcher(partial(__lowerCamelCase      ,		num_processes=1      ,		step_scheduler_with_optimizer=__lowerCamelCase						)      ,		num_processes=1						)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     AcceleratorState._reset_state(__lowerCamelCase						)
     debug_launcher(__lowerCamelCase						)
     debug_launcher(partial(__lowerCamelCase      ,		num_processes=1      ,		split_batches=__lowerCamelCase						)      ,		num_processes=1						)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     AcceleratorState._reset_state(__lowerCamelCase						)
     debug_launcher(__lowerCamelCase						)
     debug_launcher(partial(__lowerCamelCase      ,		num_processes=1      ,		split_batches=__lowerCamelCase						)      ,		num_processes=1						)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     AcceleratorState._reset_state(__lowerCamelCase						)
     debug_launcher(partial(__lowerCamelCase      ,		step_scheduler_with_optimizer=__lowerCamelCase						)						)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     AcceleratorState._reset_state(__lowerCamelCase						)
     debug_launcher(partial(__lowerCamelCase      ,		step_scheduler_with_optimizer=__lowerCamelCase						)						)







 @require_huggingface_suite
 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     AcceleratorState._reset_state(__lowerCamelCase						)
     debug_launcher(partial(__lowerCamelCase      ,		num_processes=1						)						)
     debug_launcher(__lowerCamelCase						)




