
"""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


lowerCamelCase__     =						logging.getLogger(__name__)
def        __lowerCAmelCase							(_UpperCamelCase						):
	random.seed(_UpperCamelCase						)
	np.random.seed(_UpperCamelCase						)
	torch.manual_seed(_UpperCamelCase						)
	torch.cuda.manual_seed_all(_UpperCamelCase						)
def        __lowerCAmelCase							(_UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase						):
	model.eval()
	__lowerCAmelCase     :		Any		     = next(model.parameters()						).device
	__lowerCAmelCase     :		List[str]		     = 1
	__lowerCAmelCase     :		List[str]		     = torch.zeros((eval_batch_size, context_len)			,    dtype=torch.long			,    device=_UpperCamelCase						)
	__lowerCAmelCase     :		Dict		     = DataLoader(_UpperCamelCase			,    shuffle=_UpperCamelCase			,    batch_size=_UpperCamelCase						)
	__lowerCAmelCase     :		Optional[int]		     = torch.zeros(1			,    device=_UpperCamelCase						)
	__lowerCAmelCase     :		List[str]		     = 0
	for batch in eval_dataloader:
		batch.to(_UpperCamelCase						)
		# pad
		context.zero_()
		for i in range(_UpperCamelCase						):
			__lowerCAmelCase     :		List[str]		     = batch[i]
		__lowerCAmelCase     :		Optional[Any]		     = model(_UpperCamelCase			,    labels=_UpperCamelCase						)
		eval_loss += outputs[0].sum().item()
		nb_eval_examples += batch.size(0						)
	__lowerCAmelCase     :		Dict		     = eval_loss / nb_eval_examples
	__lowerCAmelCase     :		int		     = torch.exp(_UpperCamelCase						)
	model.train()
	return perplexity
def        __lowerCAmelCase							(_UpperCamelCase="gpt2"						):
	__lowerCAmelCase     :		Optional[Any]		     = GPTaLMHeadModel.from_pretrained(_UpperCamelCase			,    output_hidden_states=_UpperCamelCase						)
	torch.save(model.state_dict()			,    model_name + 'local.pt'						)
	return model
def        __lowerCAmelCase							(_UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase						):
	__lowerCAmelCase     :		Optional[int]		     = copy.deepcopy(_UpperCamelCase						)
	model.to(_UpperCamelCase						)

	__lowerCAmelCase     :		int		     = ['bias', 'LayerNorm.weight']
	__lowerCAmelCase     :		List[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},
	]
	__lowerCAmelCase     :		Optional[Any]		     = AdamW(_UpperCamelCase			,    lr=5e-5			,    eps=1e-8						)
	__lowerCAmelCase     :		Tuple		     = get_linear_schedule_with_warmup(_UpperCamelCase			,    0			,    _UpperCamelCase						)
	torch.cuda.empty_cache()
	return model, lm_optimizer, lm_scheduler
def        __lowerCAmelCase							(_UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase						):
	__lowerCAmelCase     :		Tuple		     = np.array(_UpperCamelCase						).mean()
	__lowerCAmelCase     :		str		     = np.array(_UpperCamelCase						).std()
	__lowerCAmelCase     :		Tuple		     = (real_perps - avg) / std
	__lowerCAmelCase     :		str		     = list(zip(_UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase						)						)
	joblib.dump(_UpperCamelCase			,    _UpperCamelCase						)
def        __lowerCAmelCase							(_UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase="dev.jbl"			,    _UpperCamelCase=recopy_gpta			,    ):
	__lowerCAmelCase     :		int		     = []
	__lowerCAmelCase     :		Any		     = []
	__lowerCAmelCase     :		Dict		     = []

	# Initialize the transformer model
	__lowerCAmelCase     :		List[Any]		     = copy.deepcopy(_UpperCamelCase						)
	orig_model.to(device='cpu'						)
	torch.cuda.empty_cache()

	# Compute perplexity of initial transformer model for comparison
	model.train()
	__lowerCAmelCase     ,  __lowerCAmelCase     ,  __lowerCAmelCase     :		int		     = recopy_model(_UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase						)

	for step in tqdm(range(_UpperCamelCase						)						):
		__lowerCAmelCase     :		List[Any]		     = torch.zeros((1, context_len)			,    dtype=torch.long			,    device=_UpperCamelCase						)
		__lowerCAmelCase     :		List[str]		     = random.choice(_UpperCamelCase						)
		__lowerCAmelCase     :		List[str]		     = random.randint(0			,    len(story[0]						) - context_len - 1						)
		__lowerCAmelCase     :		Dict		     = story[0][start : start + context_len]
		lm_optimizer.zero_grad()
		__lowerCAmelCase     :		Union[str, Any]		     = model(_UpperCamelCase			,    labels=_UpperCamelCase						)
		__lowerCAmelCase     :		Union[str, Any]		     = outputs[0]
		__lowerCAmelCase     :		Tuple		     = 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
		__lowerCAmelCase     :		str		     = 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
		__lowerCAmelCase     ,  __lowerCAmelCase     ,  __lowerCAmelCase     :		Dict		     = 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        __lowerCAmelCase							(_UpperCamelCase			,    _UpperCamelCase="data/tokenized_stories_train_wikitext103.jbl"			,    _UpperCamelCase=100			,    _UpperCamelCase=1026			,    _UpperCamelCase=True						):
	__lowerCAmelCase     :		List[Any]		     = joblib.load(_UpperCamelCase						)
	print('data loaded'						)
	__lowerCAmelCase     :		Optional[Any]		     = []
	if trim:
		for i, example in enumerate(_UpperCamelCase						):
			if len(example[0]						) > min_len:
				__lowerCAmelCase     :		int		     = random.randint(0			,    len(example[0]						) - context_len - 1						)
				objective_set.append(example[0, start : start + context_len]						)
			if len(_UpperCamelCase						) >= number:
				break
		__lowerCAmelCase     :		List[str]		     = []
		for j in range(i + 1			,    len(_UpperCamelCase						)						):
			if len(data[j][0]						) > min_len:
				train_data.append(data[j]						)
	else:
		__lowerCAmelCase     :		List[Any]		     = data[0:number]
		__lowerCAmelCase     :		Union[str, Any]		     = data[number:]

	joblib.dump(_UpperCamelCase			,    'objective_set.jbl'						)
	print('objective set saved'						)
	return train_data, objective_set
def        __lowerCAmelCase							(_UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase=50			,    _UpperCamelCase="secondary_learner.pt"						):
	__lowerCAmelCase     :		str		     = 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
	__lowerCAmelCase     :		List[Any]		     = train_dataset[:512]
	__lowerCAmelCase     :		Optional[Any]		     = train_dataset[512:]
	__lowerCAmelCase     :		Dict		     = DataLoader(_UpperCamelCase			,    shuffle=_UpperCamelCase			,    batch_size=_UpperCamelCase						)
	__lowerCAmelCase     :		str		     = DataLoader(_UpperCamelCase			,    shuffle=_UpperCamelCase			,    batch_size=_UpperCamelCase						)

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

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

			secondary_learner.train()
	return secondary_learner




class 					A__			(      nn.Module):

		def __init__( self			,						_SCREAMING_SNAKE_CASE					):
			super(_SCREAMING_SNAKE_CASE			,						self					).__init__()
			__lowerCAmelCase     :		str		     = model.transformer.wte
			__lowerCAmelCase     :		Optional[Any]		     = copy.deepcopy(model.transformer.wte.weight					)
			__lowerCAmelCase     :		List[str]		     = nn.Convad(self.embeddings.weight.size(1					)			,						2_56			,						3			,						padding=1					)
			__lowerCAmelCase     :		Tuple		     = nn.Sequential(nn.Linear(2_56			,						32					)			,						nn.Dropout(p=0.1					)			,						nn.Linear(32			,						32					)			,						nn.Linear(32			,						1					)					)

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







		@classmethod
		def 				__lowerCamelCase				( cls			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					):
			__lowerCAmelCase     :		Optional[int]		     = cls(_SCREAMING_SNAKE_CASE					)  # this calls __init__
			__lowerCAmelCase     :		Dict		     = torch.load(_SCREAMING_SNAKE_CASE					)
			secondary_learner.load_state_dict(_SCREAMING_SNAKE_CASE					)
			__lowerCAmelCase     :		Dict		     = model.transformer.wte
			__lowerCAmelCase     :		Any		     = copy.deepcopy(model.transformer.wte.weight					)
			return secondary_learner

"""simple docstring"""







import os
import sys
from pathlib import Path

import pytest

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node

from .utils import execute_subprocess_async, get_torch_dist_unique_port, require_torch
def        __lowerCAmelCase							():
	__lowerCAmelCase     :		Optional[int]		     = Dataset.from_dict({'i': range(17						)}						)
	__lowerCAmelCase     :		List[Any]		     = len(_UpperCamelCase						)
	__lowerCAmelCase     :		Tuple		     = 3
	__lowerCAmelCase     :		List[Any]		     = [
	    split_dataset_by_node(_UpperCamelCase			,    rank=_UpperCamelCase			,    world_size=_UpperCamelCase						) for rank in range(_UpperCamelCase						)
	]
	assert sum(len(_UpperCamelCase						) for ds in datasets_per_rank						) == full_size
	assert len({tuple(x.values()						) for ds in datasets_per_rank for x in ds}						) == full_size
def        __lowerCAmelCase							():
	def gen():
		return ({"i": i} for i in range(17						))

	__lowerCAmelCase     :		List[Any]		     = 3
	__lowerCAmelCase     :		Optional[Any]		     = IterableDataset.from_generator(_UpperCamelCase						)
	__lowerCAmelCase     :		List[Any]		     = len(list(_UpperCamelCase						)						)
	__lowerCAmelCase     :		str		     = [
	    split_dataset_by_node(_UpperCamelCase			,    rank=_UpperCamelCase			,    world_size=_UpperCamelCase						) for rank in range(_UpperCamelCase						)
	]
	assert sum(len(list(_UpperCamelCase						)						) for ds in datasets_per_rank						) == full_size
	assert len({tuple(x.values()						) for ds in datasets_per_rank for x in ds}						) == full_size
@pytest.mark.parametrize('shards_per_node'			,    [1, 2, 3]						)
def        __lowerCAmelCase							(_UpperCamelCase						):
	def gen(_UpperCamelCase						):
		for shard in shards:
			yield from ({"i": i, "shard": shard} for i in range(17						))

	__lowerCAmelCase     :		List[Any]		     = 3
	__lowerCAmelCase     :		Optional[int]		     = shards_per_node * world_size
	__lowerCAmelCase     :		Dict		     = {'shards': [F"shard_{shard_idx}.txt" for shard_idx in range(_UpperCamelCase						)]}
	__lowerCAmelCase     :		Dict		     = IterableDataset.from_generator(_UpperCamelCase			,    gen_kwargs=_UpperCamelCase						)
	__lowerCAmelCase     :		str		     = len(list(_UpperCamelCase						)						)
	assert full_ds.n_shards == world_size * shards_per_node
	__lowerCAmelCase     :		Union[str, Any]		     = [
	    split_dataset_by_node(_UpperCamelCase			,    rank=_UpperCamelCase			,    world_size=_UpperCamelCase						) for rank in range(_UpperCamelCase						)
	]
	assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
	assert sum(len(list(_UpperCamelCase						)						) for ds in datasets_per_rank						) == full_size
	assert len({tuple(x.values()						) for ds in datasets_per_rank for x in ds}						) == full_size
def        __lowerCAmelCase							():
	def gen():
		return ({"i": i} for i in range(17						))

	__lowerCAmelCase     :		str		     = 2
	__lowerCAmelCase     :		Any		     = IterableDataset.from_generator(_UpperCamelCase						)
	__lowerCAmelCase     :		Optional[int]		     = len(list(_UpperCamelCase						)						)

	__lowerCAmelCase     :		Dict		     = split_dataset_by_node(_UpperCamelCase			,    rank=0			,    world_size=_UpperCamelCase						).shuffle(seed=42						)
	assert len(list(_UpperCamelCase						)						) == 1 + full_size // world_size
	with pytest.raises(_UpperCamelCase						):
		split_dataset_by_node(_UpperCamelCase			,    rank=0			,    world_size=_UpperCamelCase						).shuffle()

	__lowerCAmelCase     :		List[str]		     = split_dataset_by_node(full_ds.shuffle(seed=42						)			,    rank=0			,    world_size=_UpperCamelCase						)
	assert len(list(_UpperCamelCase						)						) == 1 + full_size // world_size
	with pytest.raises(_UpperCamelCase						):
		split_dataset_by_node(full_ds.shuffle()			,    rank=0			,    world_size=_UpperCamelCase						)
@pytest.mark.parametrize('streaming'			,    [False, True]						)
@require_torch
@pytest.mark.skipif(os.name == 'nt'			,    reason='execute_subprocess_async doesn\'t support windows'						)
@pytest.mark.integration
def        __lowerCAmelCase							(_UpperCamelCase						):
	__lowerCAmelCase     :		Tuple		     = 2
	__lowerCAmelCase     :		Optional[Any]		     = get_torch_dist_unique_port()
	__lowerCAmelCase     :		List[Any]		     = Path(_UpperCamelCase						).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
	__lowerCAmelCase     :		int		     = F"\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    ".split()
	__lowerCAmelCase     :		Tuple		     = F"\n        --streaming={streaming}\n    ".split()
	__lowerCAmelCase     :		Optional[Any]		     = [sys.executable] + distributed_args + args
	execute_subprocess_async(_UpperCamelCase			,    env=os.environ.copy()						)
@pytest.mark.parametrize(
    'nproc_per_node, num_workers'			,    [
        (2, 2),  # each node has 2 shards and each worker has 1 shards
        (3, 2),  # each node uses all the shards but skips examples, and each worker has 2 shards
    ]			,    )
@require_torch
@pytest.mark.skipif(os.name == 'nt'			,    reason='execute_subprocess_async doesn\'t support windows'						)
@pytest.mark.integration
def        __lowerCAmelCase							(_UpperCamelCase			,    _UpperCamelCase						):
	__lowerCAmelCase     :		Optional[int]		     = True
	__lowerCAmelCase     :		Optional[Any]		     = get_torch_dist_unique_port()
	__lowerCAmelCase     :		Tuple		     = Path(_UpperCamelCase						).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
	__lowerCAmelCase     :		Optional[Any]		     = F"\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    ".split()
	__lowerCAmelCase     :		List[Any]		     = F"\n        --streaming={streaming}\n        --num_workers={num_workers}\n    ".split()
	__lowerCAmelCase     :		List[Any]		     = [sys.executable] + distributed_args + args
	execute_subprocess_async(_UpperCamelCase			,    env=os.environ.copy()						)