



'''simple docstring'''

import os
from shutil import copyfile
from typing import Optional, Tuple

from ...file_utils import is_sentencepiece_available
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if is_sentencepiece_available():
				from .tokenization_deberta_va import DebertaVaTokenizer
else:
				UpperCAmelCase			=    None

UpperCAmelCase			=    logging.get_logger(__name__)

UpperCAmelCase			=    {'vocab_file': 'spm.model', 'tokenizer_file': 'tokenizer.json'}

UpperCAmelCase			=    {
    'vocab_file': {
        'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model',
        'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model',
        'microsoft/deberta-v2-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model'
        ),
        'microsoft/deberta-v2-xxlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model'
        ),
    }
}

UpperCAmelCase			=    {
    'microsoft/deberta-v2-xlarge': 512,
    'microsoft/deberta-v2-xxlarge': 512,
    'microsoft/deberta-v2-xlarge-mnli': 512,
    'microsoft/deberta-v2-xxlarge-mnli': 512,
}

UpperCAmelCase			=    {
    'microsoft/deberta-v2-xlarge': {'do_lower_case': False},
    'microsoft/deberta-v2-xxlarge': {'do_lower_case': False},
    'microsoft/deberta-v2-xlarge-mnli': {'do_lower_case': False},
    'microsoft/deberta-v2-xxlarge-mnli': {'do_lower_case': False},
}

class __snake_case(      _lowerCAmelCase					):


						'''simple docstring'''



						UpperCAmelCase							:    int           =		VOCAB_FILES_NAMES
						UpperCAmelCase							:    str           =		PRETRAINED_VOCAB_FILES_MAP
						UpperCAmelCase							:    List[Any]           =		PRETRAINED_INIT_CONFIGURATION
						UpperCAmelCase							:    List[Any]           =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCAmelCase							:    int           =		DebertaVaTokenizer


						def __init__(					self	,       A_=None	,       A_=None	,       A_=False	,       A_=False	,       A_="[CLS]"	,       A_="[SEP]"	,       A_="[UNK]"	,       A_="[SEP]"	,       A_="[PAD]"	,       A_="[CLS]"	,       A_="[MASK]"	,       **A_	,       )	->	None:
							super().__init__(
							    A_	,       tokenizer_file=A_	,       do_lower_case=A_	,       bos_token=A_	,       eos_token=A_	,       unk_token=A_	,       sep_token=A_	,       pad_token=A_	,       cls_token=A_	,       mask_token=A_	,       split_by_punct=A_	,       **A_	,       )

							lowerCAmelCase    =	do_lower_case
							lowerCAmelCase    =	split_by_punct
							lowerCAmelCase    =	vocab_file
							lowerCAmelCase    =	False if not self.vocab_file else True


						def 				__snake_case    (					self	,       A_	,       A_=None      )	->	Optional[int]:

							if token_ids_a is None:
								return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							lowerCAmelCase    =	[self.cls_token_id]
							lowerCAmelCase    =	[self.sep_token_id]
							return cls + token_ids_a + sep + token_ids_a + sep


						def 				__snake_case    (					self	,       A_	,       A_=None	,       A_=False      )	->	int:

							if already_has_special_tokens:
								return super().get_special_tokens_mask(
								    token_ids_a=A_	,       token_ids_a=A_	,       already_has_special_tokens=A_      )

							if token_ids_a is not None:
								return [1] + ([0] * len(A_      )) + [1] + ([0] * len(A_      )) + [1]
							return [1] + ([0] * len(A_      )) + [1]


						def 				__snake_case    (					self	,       A_	,       A_=None      )	->	Tuple:
							lowerCAmelCase    =	[self.sep_token_id]
							lowerCAmelCase    =	[self.cls_token_id]
							if token_ids_a is None:
								return len(cls + token_ids_a + sep      ) * [0]
							return len(cls + token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1]




						def 				__snake_case    (					self	,       A_	,       A_ = None      )	->	Tuple[str]:
							if not self.can_save_slow_tokenizer:
								raise ValueError(
								    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
								    """tokenizer."""      )

							if not os.path.isdir(A_      ):
								logger.error(f'Vocabulary path ({save_directory}) should be a directory'      )
								return
							lowerCAmelCase    =	os.path.join(
							    A_	,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]      )

							if os.path.abspath(self.vocab_file      ) != os.path.abspath(A_      ):
								copyfile(self.vocab_file	,       A_      )

							return (out_vocab_file,)




'''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


UpperCAmelCase			=    logging.getLogger(__name__)






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						Tuple							)			->						Dict:







	"""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 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						List[Any]							,		_SCREAMING_SNAKE_CASE     :						int							,		_SCREAMING_SNAKE_CASE     :						Optional[int]							)			->						int:







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






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						int="gpt2"							)			->						List[Any]:







	"""simple docstring"""
	lowerCAmelCase    =	GPTaLMHeadModel.from_pretrained(_SCREAMING_SNAKE_CASE							,		output_hidden_states=_SCREAMING_SNAKE_CASE							)
	torch.save(model.state_dict()							,		model_name + """local.pt"""							)
	return model






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						Optional[Any]							,		_SCREAMING_SNAKE_CASE     :						Union[str, Any]							,		_SCREAMING_SNAKE_CASE     :						Tuple							)			->						Union[str, Any]:







	"""simple docstring"""
	lowerCAmelCase    =	copy.deepcopy(_SCREAMING_SNAKE_CASE							)
	model.to(_SCREAMING_SNAKE_CASE							)

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






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						Union[str, Any]							,		_SCREAMING_SNAKE_CASE     :						Optional[int]							,		_SCREAMING_SNAKE_CASE     :						Optional[Any]							,		_SCREAMING_SNAKE_CASE     :						Any							)			->						List[str]:







	"""simple docstring"""
	lowerCAmelCase    =	np.array(_SCREAMING_SNAKE_CASE							).mean()
	lowerCAmelCase    =	np.array(_SCREAMING_SNAKE_CASE							).std()
	lowerCAmelCase    =	(real_perps - avg) / std
	lowerCAmelCase    =	list(zip(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)							)
	joblib.dump(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)






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







	"""simple docstring"""
	lowerCAmelCase    =	[]
	lowerCAmelCase    =	[]
	lowerCAmelCase    =	[]

	# Initialize the transformer model
	lowerCAmelCase    =	copy.deepcopy(_SCREAMING_SNAKE_CASE							)
	orig_model.to(device="""cpu"""							)
	torch.cuda.empty_cache()

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

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

		# Periodically save the stored (X, IG(X)) pairs
		if step % 1_000 == 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
		lowerCAmelCase,   lowerCAmelCase,   lowerCAmelCase    =	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 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						Union[str, Any]							,		_SCREAMING_SNAKE_CASE     :						int="data/tokenized_stories_train_wikitext103.jbl"							,		_SCREAMING_SNAKE_CASE     :						Any=100							,		_SCREAMING_SNAKE_CASE     :						Optional[int]=1_026							,		_SCREAMING_SNAKE_CASE     :						List[str]=True							)			->						Optional[Any]:







	"""simple docstring"""
	lowerCAmelCase    =	joblib.load(_SCREAMING_SNAKE_CASE							)
	print("""data loaded"""							)
	lowerCAmelCase    =	[]
	if trim:
		for i, example in enumerate(_SCREAMING_SNAKE_CASE							):
			if len(example[0]							) > min_len:
				lowerCAmelCase    =	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
		lowerCAmelCase    =	[]
		for j in range(i + 1							,		len(_SCREAMING_SNAKE_CASE							)							):
			if len(data[j][0]							) > min_len:
				train_data.append(data[j]							)
	else:
		lowerCAmelCase    =	data[0:number]
		lowerCAmelCase    =	data[number:]

	joblib.dump(_SCREAMING_SNAKE_CASE							,		"""objective_set.jbl"""							)
	print("""objective set saved"""							)
	return train_data, objective_set






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						Any							,		_SCREAMING_SNAKE_CASE     :						Optional[Any]							,		_SCREAMING_SNAKE_CASE     :						Optional[Any]							,		_SCREAMING_SNAKE_CASE     :						List[str]							,		_SCREAMING_SNAKE_CASE     :						str=50							,		_SCREAMING_SNAKE_CASE     :						Tuple="secondary_learner.pt"							)			->						List[Any]:







	"""simple docstring"""
	lowerCAmelCase    =	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    =	train_dataset[:512]
	lowerCAmelCase    =	train_dataset[512:]
	lowerCAmelCase    =	DataLoader(_SCREAMING_SNAKE_CASE							,		shuffle=_SCREAMING_SNAKE_CASE							,		batch_size=_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase    =	DataLoader(_SCREAMING_SNAKE_CASE							,		shuffle=_SCREAMING_SNAKE_CASE							,		batch_size=_SCREAMING_SNAKE_CASE							)

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

				secondary_learner.eval()
				lowerCAmelCase    =	0.0
				lowerCAmelCase    =	0.0
				lowerCAmelCase    =	[]
				lowerCAmelCase    =	[]
				# Compute performance of the secondary learner after this batch
				for stepa, batcha in enumerate(_SCREAMING_SNAKE_CASE							):
					lowerCAmelCase    =	batcha[0].to(_SCREAMING_SNAKE_CASE							)
					lowerCAmelCase    =	batcha[1].to(_SCREAMING_SNAKE_CASE							)
					lowerCAmelCase    =	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							)
					lowerCAmelCase    =	q_lossa

			secondary_learner.train()
	return secondary_learner





class __snake_case(      nn.Module					):


						'''simple docstring'''



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


						def 				__snake_case    (					self	,       A_      )	->	Optional[int]:
							lowerCAmelCase    =	torch.max(self.conv(self.embeddings(A_      ).squeeze(1      ).transpose(1	,       2      )      )	,       2      )[0]
							lowerCAmelCase    =	self.fc(A_      )
							return qs.squeeze(1      )




						@classmethod
						def 				__snake_case    (					cls	,       A_	,       A_      )	->	str:
							lowerCAmelCase    =	cls(A_      )  # this calls __init__
							lowerCAmelCase    =	torch.load(A_      )
							secondary_learner.load_state_dict(A_      )
							lowerCAmelCase    =	model.transformer.wte
							lowerCAmelCase    =	copy.deepcopy(model.transformer.wte.weight      )
							return secondary_learner