


'''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   :List[str]								=    logging.getLogger(__name__)





def lowerCamelCase    (    lowerCAmelCase       :			Tuple	):





	"""simple docstring"""







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





def lowerCamelCase    (    lowerCAmelCase       :			List[Any]	,	lowerCAmelCase       :			List[Any]	,	lowerCAmelCase       :			List[str]	):





	"""simple docstring"""







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





def lowerCamelCase    (    lowerCAmelCase       :			List[Any]="gpt2"	):





	"""simple docstring"""







	__magic_name__ :					Optional[Any]  			=  GPTaLMHeadModel.from_pretrained(lowerCAmelCase	,	output_hidden_states=lowerCAmelCase	)
	torch.save(model.state_dict()	,	model_name + 'local.pt'	)
	return model





def lowerCamelCase    (    lowerCAmelCase       :			List[str]	,	lowerCAmelCase       :			Optional[Any]	,	lowerCAmelCase       :			str	):





	"""simple docstring"""







	__magic_name__ :					List[str]  			=  copy.deepcopy(lowerCAmelCase	)
	model.to(lowerCAmelCase	)

	__magic_name__ :					List[Any]  			=  ['bias', 'LayerNorm.weight']
	__magic_name__ :					List[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},
	]
	__magic_name__ :					Tuple  			=  AdamW(lowerCAmelCase	,	lr=5e-5	,	eps=1e-8	)
	__magic_name__ :					int  			=  get_linear_schedule_with_warmup(lowerCAmelCase	,	0	,	lowerCAmelCase	)
	torch.cuda.empty_cache()
	return model, lm_optimizer, lm_scheduler





def lowerCamelCase    (    lowerCAmelCase       :			Union[str, Any]	,	lowerCAmelCase       :			str	,	lowerCAmelCase       :			Dict	,	lowerCAmelCase       :			List[Any]	):





	"""simple docstring"""







	__magic_name__ :					int  			=  np.array(lowerCAmelCase	).mean()
	__magic_name__ :					Tuple  			=  np.array(lowerCAmelCase	).std()
	__magic_name__ :					str  			=  (real_perps - avg) / std
	__magic_name__ :					List[str]  			=  list(zip(lowerCAmelCase	,	lowerCAmelCase	,	lowerCAmelCase	)	)
	joblib.dump(lowerCAmelCase	,	lowerCAmelCase	)





def lowerCamelCase    (    lowerCAmelCase       :			Optional[Any]	,	lowerCAmelCase       :			int	,	lowerCAmelCase       :			Tuple	,	lowerCAmelCase       :			Any	,	lowerCAmelCase       :			Optional[int]	,	lowerCAmelCase       :			Union[str, Any]	,	lowerCAmelCase       :			Union[str, Any]	,	lowerCAmelCase       :			Union[str, Any]="dev.jbl"	,	lowerCAmelCase       :			Dict=recopy_gpta	,	):





	"""simple docstring"""







	__magic_name__ :					str  			=  []
	__magic_name__ :					Tuple  			=  []
	__magic_name__ :					List[str]  			=  []

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

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

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

		# Periodically save the stored (X, IG(X)) pairs
		if step % 1000 == 0 and step > 1:
			intermittent_save(lowerCAmelCase	,	lowerCAmelCase	,	lowerCAmelCase	,	lowerCAmelCase	)

		# Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
		__magic_name__ :					Union[str, Any]  			=  recopy_model(lowerCAmelCase	,	lowerCAmelCase	,	lowerCAmelCase	)

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

	intermittent_save(lowerCAmelCase	,	lowerCAmelCase	,	lowerCAmelCase	,	lowerCAmelCase	)





def lowerCamelCase    (    lowerCAmelCase       :			int	,	lowerCAmelCase       :			Any="data/tokenized_stories_train_wikitext103.jbl"	,	lowerCAmelCase       :			str=100	,	lowerCAmelCase       :			Any=1026	,	lowerCAmelCase       :			List[str]=True	):





	"""simple docstring"""







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

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





def lowerCamelCase    (    lowerCAmelCase       :			List[str]	,	lowerCAmelCase       :			Union[str, Any]	,	lowerCAmelCase       :			Tuple	,	lowerCAmelCase       :			Union[str, Any]	,	lowerCAmelCase       :			Optional[Any]=50	,	lowerCAmelCase       :			Dict="secondary_learner.pt"	):





	"""simple docstring"""







	__magic_name__ :					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
	__magic_name__ :					int  			=  train_dataset[:512]
	__magic_name__ :					Optional[int]  			=  train_dataset[512:]
	__magic_name__ :					Dict  			=  DataLoader(lowerCAmelCase	,	shuffle=lowerCAmelCase	,	batch_size=lowerCAmelCase	)
	__magic_name__ :					Optional[int]  			=  DataLoader(lowerCAmelCase	,	shuffle=lowerCAmelCase	,	batch_size=lowerCAmelCase	)

	# secondary learner model set up
	__magic_name__ :					List[Any]  			=  nn.MSELoss()
	__magic_name__ :					Any  			=  nn.MSELoss(reduction='sum'	)
	secondary_learner.to(lowerCAmelCase	)
	__magic_name__ :					Union[str, Any]  			=  torch.optim.Adam(secondary_learner.parameters()	,	lr=0.0_0001	)
	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
	__magic_name__ :					Any  			=  float('inf'	)
	# Iterate through batches until we've used max_steps batches
	for epoch in range(int(lowerCAmelCase	)	):
		__magic_name__ :					int  			=  0.0
		secondary_learner.train()
		for step, batch in enumerate(lowerCAmelCase	):
			__magic_name__ :					List[str]  			=  batch[0].to(lowerCAmelCase	)
			__magic_name__ :					Optional[Any]  			=  batch[1].to(lowerCAmelCase	)
			__magic_name__ :					Dict  			=  secondary_learner(lowerCAmelCase	)
			q_optimizer.zero_grad()
			__magic_name__ :					List[Any]  			=  loss(lowerCAmelCase	,	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(lowerCAmelCase	)):
				__magic_name__ :					Optional[int]  			=  tr_q_loss / (step + 1)

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

			secondary_learner.train()
	return secondary_learner





class _lowerCamelCase (		nn.Module							):



		'''simple docstring'''







		def __init__(      self	:		List[Any]						,  _A	:		Dict    )						->				str:
			super(_A						,  self    ).__init__()
			__magic_name__ :					Union[str, Any]  			=  model.transformer.wte
			__magic_name__ :					List[Any]  			=  copy.deepcopy(model.transformer.wte.weight    )
			__magic_name__ :					int  			=  nn.Convad(self.embeddings.weight.size(1    )						,  256						,  3						,  padding=1    )
			__magic_name__ :					List[Any]  			=  nn.Sequential(nn.Linear(256						,  32    )						,  nn.Dropout(p=0.1    )						,  nn.Linear(32						,  32    )						,  nn.Linear(32						,  1    )    )







		def      __lowerCAmelCase	(      self	:		Optional[int]						,  _A	:		Union[str, Any]    )						->				Optional[int]:
			__magic_name__ :					Optional[int]  			=  torch.max(self.conv(self.embeddings(_A    ).squeeze(1    ).transpose(1						,  2    )    )						,  2    )[0]
			__magic_name__ :					Any  			=  self.fc(_A    )
			return qs.squeeze(1    )







		@classmethod
		def      __lowerCAmelCase	(      cls	:		List[Any]						,  _A	:		List[Any]						,  _A	:		str    )						->				int:
			__magic_name__ :					List[str]  			=  cls(_A    )  # this calls __init__
			__magic_name__ :					List[Any]  			=  torch.load(_A    )
			secondary_learner.load_state_dict(_A    )
			__magic_name__ :					List[Any]  			=  model.transformer.wte
			__magic_name__ :					Any  			=  copy.deepcopy(model.transformer.wte.weight    )
			return secondary_learner



'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase   :List[Any]								=    logging.get_logger(__name__)

lowerCAmelCase   :Dict								=    {
    '''uw-madison/yoso-4096''': '''https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json''',
    # See all YOSO models at https://huggingface.co/models?filter=yoso
}
class _lowerCamelCase (		lowercase__							):



		'''simple docstring'''







		A_			:    Optional[Any]   					=       """yoso"""







		def __init__(      self	:		Any						,  _A	:		List[Any]=50265						,  _A	:		Any=768						,  _A	:		List[str]=12						,  _A	:		Dict=12						,  _A	:		Union[str, Any]=3072						,  _A	:		List[Any]="gelu"						,  _A	:		Dict=0.1						,  _A	:		Union[str, Any]=0.1						,  _A	:		Union[str, Any]=4096						,  _A	:		Dict=1						,  _A	:		Tuple=0.02						,  _A	:		Any=1E-12						,  _A	:		Optional[int]="absolute"						,  _A	:		Tuple=True						,  _A	:		Any=9						,  _A	:		Tuple=64						,  _A	:		str=None						,  _A	:		Dict=True						,  _A	:		str=True						,  _A	:		int=1						,  _A	:		Optional[int]=0						,  _A	:		Tuple=2						,  **_A	:		Optional[Any]						,  )						->				Any:
			super().__init__(pad_token_id=_A						,  bos_token_id=_A						,  eos_token_id=_A						,  **_A    )

			__magic_name__ :					List[Any]  			=  vocab_size
			__magic_name__ :					int  			=  max_position_embeddings
			__magic_name__ :					int  			=  hidden_size
			__magic_name__ :					Any  			=  num_hidden_layers
			__magic_name__ :					List[str]  			=  num_attention_heads
			__magic_name__ :					str  			=  intermediate_size
			__magic_name__ :					Any  			=  hidden_act
			__magic_name__ :					Union[str, Any]  			=  hidden_dropout_prob
			__magic_name__ :					str  			=  attention_probs_dropout_prob
			__magic_name__ :					int  			=  initializer_range
			__magic_name__ :					List[Any]  			=  type_vocab_size
			__magic_name__ :					Tuple  			=  layer_norm_eps
			__magic_name__ :					Tuple  			=  position_embedding_type
			__magic_name__ :					Optional[Any]  			=  use_expectation
			__magic_name__ :					Union[str, Any]  			=  hash_code_len
			__magic_name__ :					int  			=  num_hash
			__magic_name__ :					List[Any]  			=  conv_window
			__magic_name__ :					Optional[Any]  			=  use_fast_hash
			__magic_name__ :					int  			=  lsh_backward