




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  :		Optional[int]		     =   logging.getLogger(__name__)
def  snake_case_					(		SCREAMING_SNAKE_CASE_    )      ->			Optional[Any]:
							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_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )      ->			int:
							model.eval()
							lowercase__  :	Any        =							next(model.parameters()    ).device
							lowercase__  :	int        =							1
							lowercase__  :	Optional[int]        =							torch.zeros((eval_batch_size, context_len)    ,dtype=torch.long    ,device=SCREAMING_SNAKE_CASE_    )
							lowercase__  :	Optional[Any]        =							DataLoader(SCREAMING_SNAKE_CASE_    ,shuffle=SCREAMING_SNAKE_CASE_    ,batch_size=SCREAMING_SNAKE_CASE_    )
							lowercase__  :	Tuple        =							torch.zeros(1    ,device=SCREAMING_SNAKE_CASE_    )
							lowercase__  :	Tuple        =							0
							for batch in eval_dataloader:
														batch.to(SCREAMING_SNAKE_CASE_    )
														# pad
														context.zero_()
														for i in range(SCREAMING_SNAKE_CASE_    ):
																					lowercase__  :	Any        =							batch[i]
														lowercase__  :	Union[str, Any]        =							model(SCREAMING_SNAKE_CASE_    ,labels=SCREAMING_SNAKE_CASE_    )
														eval_loss += outputs[0].sum().item()
														nb_eval_examples += batch.size(0    )
							lowercase__  :	Dict        =							eval_loss / nb_eval_examples
							lowercase__  :	Union[str, Any]        =							torch.exp(SCREAMING_SNAKE_CASE_    )
							model.train()
							return perplexity
def  snake_case_					(		SCREAMING_SNAKE_CASE_="gpt2"    )      ->			Optional[int]:
							lowercase__  :	List[Any]        =							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_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )      ->			Dict:
							lowercase__  :	List[Any]        =							copy.deepcopy(SCREAMING_SNAKE_CASE_    )
							model.to(SCREAMING_SNAKE_CASE_    )

							lowercase__  :	int        =							["bias", "LayerNorm.weight"]
							lowercase__  :	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},
							]
							lowercase__  :	Any        =							AdamW(SCREAMING_SNAKE_CASE_    ,lr=5E-5    ,eps=1E-8    )
							lowercase__  :	List[str]        =							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_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )      ->			Any:
							lowercase__  :	List[str]        =							np.array(SCREAMING_SNAKE_CASE_    ).mean()
							lowercase__  :	Tuple        =							np.array(SCREAMING_SNAKE_CASE_    ).std()
							lowercase__  :	Tuple        =							(real_perps - avg) / std
							lowercase__  :	int        =							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_    ,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    ,)      ->			Tuple:
							lowercase__  :	Union[str, Any]        =							[]
							lowercase__  :	Optional[int]        =							[]
							lowercase__  :	List[Any]        =							[]

							# Initialize the transformer model
							lowercase__  :	List[Any]        =							copy.deepcopy(SCREAMING_SNAKE_CASE_    )
							orig_model.to(device="cpu"    )
							torch.cuda.empty_cache()

							# Compute perplexity of initial transformer model for comparison
							model.train()
							lowercase__  :	Optional[int]        =							recopy_model(SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )

							for step in tqdm(range(SCREAMING_SNAKE_CASE_    )    ):
														lowercase__  :	Optional[int]        =							torch.zeros((1, context_len)    ,dtype=torch.long    ,device=SCREAMING_SNAKE_CASE_    )
														lowercase__  :	int        =							random.choice(SCREAMING_SNAKE_CASE_    )
														lowercase__  :	str        =							random.randint(0    ,len(story[0]    ) - context_len - 1    )
														lowercase__  :	int        =							story[0][start : start + context_len]
														lm_optimizer.zero_grad()
														lowercase__  :	Tuple        =							model(SCREAMING_SNAKE_CASE_    ,labels=SCREAMING_SNAKE_CASE_    )
														lowercase__  :	str        =							outputs[0]
														lowercase__  :	str        =							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
														lowercase__  :	List[str]        =							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
														lowercase__  :	Optional[int]        =							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_    ,SCREAMING_SNAKE_CASE_="data/tokenized_stories_train_wikitext103.jbl"    ,SCREAMING_SNAKE_CASE_=1_00    ,SCREAMING_SNAKE_CASE_=10_26    ,SCREAMING_SNAKE_CASE_=True    )      ->			str:
							lowercase__  :	List[Any]        =							joblib.load(SCREAMING_SNAKE_CASE_    )
							print("data loaded"    )
							lowercase__  :	str        =							[]
							if trim:
														for i, example in enumerate(SCREAMING_SNAKE_CASE_    ):
																					if len(example[0]    ) > min_len:
																												lowercase__  :	Dict        =							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
														lowercase__  :	List[Any]        =							[]
														for j in range(i + 1    ,len(SCREAMING_SNAKE_CASE_    )    ):
																					if len(data[j][0]    ) > min_len:
																												train_data.append(data[j]    )
							else:
														lowercase__  :	Dict        =							data[0:number]
														lowercase__  :	str        =							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_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_=50    ,SCREAMING_SNAKE_CASE_="secondary_learner.pt"    )      ->			Union[str, Any]:
							lowercase__  :	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
							lowercase__  :	Any        =							train_dataset[:5_12]
							lowercase__  :	int        =							train_dataset[5_12:]
							lowercase__  :	Optional[Any]        =							DataLoader(SCREAMING_SNAKE_CASE_    ,shuffle=SCREAMING_SNAKE_CASE_    ,batch_size=SCREAMING_SNAKE_CASE_    )
							lowercase__  :	Any        =							DataLoader(SCREAMING_SNAKE_CASE_    ,shuffle=SCREAMING_SNAKE_CASE_    ,batch_size=SCREAMING_SNAKE_CASE_    )

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

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

																					secondary_learner.train()
							return secondary_learner







class 				UpperCAmelCase(					nn.Module					):



	"""simple docstring"""






	def __init__(					self	,				lowerCamelCase    ) ->      List[str]:





								"""simple docstring"""


								super(lowerCamelCase	,				self    ).__init__()
								lowercase__  :	Any        =							model.transformer.wte
								lowercase__  :	str        =							copy.deepcopy(model.transformer.wte.weight    )
								lowercase__  :	Dict        =							nn.Convad(self.embeddings.weight.size(1    )	,				256	,				3	,				padding=1    )
								lowercase__  :	Optional[int]        =							nn.Sequential(nn.Linear(256	,				32    )	,				nn.Dropout(p=0.1    )	,				nn.Linear(32	,				32    )	,				nn.Linear(32	,				1    )    )

	def    __a     (					self	,				lowerCamelCase    ) ->      str:





								"""simple docstring"""


								lowercase__  :	Optional[Any]        =							torch.max(self.conv(self.embeddings(lowerCamelCase    ).squeeze(1    ).transpose(1	,				2    )    )	,				2    )[0]
								lowercase__  :	Dict        =							self.fc(lowerCamelCase    )
								return qs.squeeze(1    )







	@classmethod
	def    __a     (					cls	,				lowerCamelCase	,				lowerCamelCase    ) ->      List[Any]:





								"""simple docstring"""


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





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:
						__a  :		Optional[Any]		     =   None

__a  :		List[str]		     =   logging.get_logger(__name__)

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

__a  :		int		     =   {
    '''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'''
        ),
    }
}

__a  :		Any		     =   {
    '''microsoft/deberta-v2-xlarge''': 5_1_2,
    '''microsoft/deberta-v2-xxlarge''': 5_1_2,
    '''microsoft/deberta-v2-xlarge-mnli''': 5_1_2,
    '''microsoft/deberta-v2-xxlarge-mnli''': 5_1_2,
}

__a  :		Union[str, Any]		     =   {
    '''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 				UpperCAmelCase(					snake_case_					):



	"""simple docstring"""






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

	def __init__(					self	,				lowerCamelCase=None	,				lowerCamelCase=None	,				lowerCamelCase=False	,				lowerCamelCase=False	,				lowerCamelCase="[CLS]"	,				lowerCamelCase="[SEP]"	,				lowerCamelCase="[UNK]"	,				lowerCamelCase="[SEP]"	,				lowerCamelCase="[PAD]"	,				lowerCamelCase="[CLS]"	,				lowerCamelCase="[MASK]"	,				**lowerCamelCase	,				) ->      None:





								"""simple docstring"""


								super().__init__(
								    lowerCamelCase	,				tokenizer_file=lowerCamelCase	,				do_lower_case=lowerCamelCase	,				bos_token=lowerCamelCase	,				eos_token=lowerCamelCase	,				unk_token=lowerCamelCase	,				sep_token=lowerCamelCase	,				pad_token=lowerCamelCase	,				cls_token=lowerCamelCase	,				mask_token=lowerCamelCase	,				split_by_punct=lowerCamelCase	,				**lowerCamelCase	,				)

								lowercase__  :	Optional[int]        =							do_lower_case
								lowercase__  :	Optional[Any]        =							split_by_punct
								lowercase__  :	Tuple        =							vocab_file
								lowercase__  :	Optional[int]        =							False if not self.vocab_file else True

	def    __a     (					self	,				lowerCamelCase	,				lowerCamelCase=None    ) ->      List[Any]:





								"""simple docstring"""


								if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								lowercase__  :	Union[str, Any]        =							[self.cls_token_id]
								lowercase__  :	Union[str, Any]        =							[self.sep_token_id]
								return cls + token_ids_a + sep + token_ids_a + sep

	def    __a     (					self	,				lowerCamelCase	,				lowerCamelCase=None	,				lowerCamelCase=False    ) ->      Union[str, Any]:





								"""simple docstring"""


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

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

	def    __a     (					self	,				lowerCamelCase	,				lowerCamelCase=None    ) ->      Optional[int]:





								"""simple docstring"""


								lowercase__  :	Any        =							[self.sep_token_id]
								lowercase__  :	List[str]        =							[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    __a     (					self	,				lowerCamelCase	,				lowerCamelCase = None    ) ->      Tuple[str]:





								"""simple docstring"""


								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(lowerCamelCase    ):
															logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""    )
															return
								lowercase__  :	int        =							os.path.join(
								    lowerCamelCase	,				(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]    )

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

								return (out_vocab_file,)