




'''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 						lowerCAmelCase_       (	__a )       ->			Optional[Any]:





							"""simple docstring"""





							random.seed(__a )
							np.random.seed(__a )
							torch.manual_seed(__a )
							torch.cuda.manual_seed_all(__a )
def 						lowerCAmelCase_       (	__a	,	__a	,	__a )       ->			Tuple:





							"""simple docstring"""





							model.eval()
							SCREAMING_SNAKE_CASE			: str	     =next(model.parameters() ).device
							SCREAMING_SNAKE_CASE			: str	     =1
							SCREAMING_SNAKE_CASE			: str	     =torch.zeros((eval_batch_size, context_len)	,	dtype=torch.long	,	device=__a )
							SCREAMING_SNAKE_CASE			: Tuple	     =DataLoader(__a	,	shuffle=__a	,	batch_size=__a )
							SCREAMING_SNAKE_CASE			: List[Any]	     =torch.zeros(1	,	device=__a )
							SCREAMING_SNAKE_CASE			: List[str]	     =0
							for batch in eval_dataloader:
														batch.to(__a )
														# pad
														context.zero_()
														for i in range(__a ):
																					SCREAMING_SNAKE_CASE			: List[Any]	     =batch[i]
														SCREAMING_SNAKE_CASE			: Tuple	     =model(__a	,	labels=__a )
														eval_loss += outputs[0].sum().item()
														nb_eval_examples += batch.size(0 )
							SCREAMING_SNAKE_CASE			: int	     =eval_loss / nb_eval_examples
							SCREAMING_SNAKE_CASE			: Tuple	     =torch.exp(__a )
							model.train()
							return perplexity
def 						lowerCAmelCase_       (	__a="gpt2" )       ->			Any:





							"""simple docstring"""





							SCREAMING_SNAKE_CASE			: Any	     =GPTaLMHeadModel.from_pretrained(__a	,	output_hidden_states=__a )
							torch.save(model.state_dict()	,	model_name + '''local.pt''' )
							return model
def 						lowerCAmelCase_       (	__a	,	__a	,	__a )       ->			int:





							"""simple docstring"""





							SCREAMING_SNAKE_CASE			: Any	     =copy.deepcopy(__a )
							model.to(__a )

							SCREAMING_SNAKE_CASE			: str	     =['''bias''', '''LayerNorm.weight''']
							SCREAMING_SNAKE_CASE			: 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},
							]
							SCREAMING_SNAKE_CASE			: int	     =AdamW(__a	,	lr=5E-5	,	eps=1E-8 )
							SCREAMING_SNAKE_CASE			: Tuple	     =get_linear_schedule_with_warmup(__a	,	0	,	__a )
							torch.cuda.empty_cache()
							return model, lm_optimizer, lm_scheduler
def 						lowerCAmelCase_       (	__a	,	__a	,	__a	,	__a )       ->			Optional[Any]:





							"""simple docstring"""





							SCREAMING_SNAKE_CASE			: Any	     =np.array(__a ).mean()
							SCREAMING_SNAKE_CASE			: Optional[int]	     =np.array(__a ).std()
							SCREAMING_SNAKE_CASE			: List[Any]	     =(real_perps - avg) / std
							SCREAMING_SNAKE_CASE			: List[Any]	     =list(zip(__a	,	__a	,	__a ) )
							joblib.dump(__a	,	__a )
def 						lowerCAmelCase_       (	__a	,	__a	,	__a	,	__a	,	__a	,	__a	,	__a	,	__a="dev.jbl"	,	__a=recopy_gpta	,	)       ->			Optional[Any]:





							"""simple docstring"""





							SCREAMING_SNAKE_CASE			: Dict	     =[]
							SCREAMING_SNAKE_CASE			: str	     =[]
							SCREAMING_SNAKE_CASE			: Optional[int]	     =[]

							# Initialize the transformer model
							SCREAMING_SNAKE_CASE			: int	     =copy.deepcopy(__a )
							orig_model.to(device='''cpu''' )
							torch.cuda.empty_cache()

							# Compute perplexity of initial transformer model for comparison
							model.train()
							SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: List[Any]	     =recopy_model(__a	,	__a	,	__a )

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

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

														# Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
														SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: Dict	     =recopy_model(__a	,	__a	,	__a )

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

							intermittent_save(__a	,	__a	,	__a	,	__a )
def 						lowerCAmelCase_       (	__a	,	__a="data/tokenized_stories_train_wikitext103.jbl"	,	__a=100	,	__a=1026	,	__a=True )       ->			List[str]:





							"""simple docstring"""





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

							joblib.dump(__a	,	'''objective_set.jbl''' )
							print('''objective set saved''' )
							return train_data, objective_set
def 						lowerCAmelCase_       (	__a	,	__a	,	__a	,	__a	,	__a=50	,	__a="secondary_learner.pt" )       ->			Union[str, Any]:





							"""simple docstring"""





							SCREAMING_SNAKE_CASE			: Optional[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
							SCREAMING_SNAKE_CASE			: Any	     =train_dataset[:512]
							SCREAMING_SNAKE_CASE			: List[Any]	     =train_dataset[512:]
							SCREAMING_SNAKE_CASE			: List[Any]	     =DataLoader(__a	,	shuffle=__a	,	batch_size=__a )
							SCREAMING_SNAKE_CASE			: int	     =DataLoader(__a	,	shuffle=__a	,	batch_size=__a )

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

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

																					secondary_learner.train()
							return secondary_learner


class 							_lowerCAmelCase		(     nn.Module	):




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




				def     __a		( self		,		snake_case_							)    ->      Optional[int]:
											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =torch.max(self.conv(self.embeddings(UpperCamelCase__							).squeeze(1							).transpose(1		,		2							)							)		,		2							)[0]
											SCREAMING_SNAKE_CASE			: Tuple	     =self.fc(UpperCamelCase__							)
											return qs.squeeze(1							)







				@classmethod
				def     __a		( cls		,		snake_case_		,		snake_case_							)    ->      Any:
											SCREAMING_SNAKE_CASE			: Tuple	     =cls(UpperCamelCase__							)  # this calls __init__
											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =torch.load(UpperCamelCase__							)
											secondary_learner.load_state_dict(UpperCamelCase__							)
											SCREAMING_SNAKE_CASE			: Optional[int]	     =model.transformer.wte
											SCREAMING_SNAKE_CASE			: str	     =copy.deepcopy(model.transformer.wte.weight							)
											return secondary_learner












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

from ...tokenization_utils_fast import AddedToken, PreTrainedTokenizerFast
from ...utils import logging


_A							    =	logging.get_logger(__name__)

_A							    =	{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

_A							    =	{
    """vocab_file""": {
        """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json""",
    },
}


class 							_lowerCAmelCase		(     UpperCamelCase__	):




				def __init__( self		,		snake_case_=None		,		snake_case_=None		,		snake_case_=False		,		snake_case_=True		,		snake_case_=False		,		snake_case_="<s>"		,		snake_case_="</s>"		,		snake_case_="<unk>"		,		snake_case_="<sep>"		,		snake_case_="<pad>"		,		snake_case_="<cls>"		,		snake_case_="<mask>"		,		snake_case_=["<eop>", "<eod>"]		,		**snake_case_		,		)    ->      str:
											SCREAMING_SNAKE_CASE			: Optional[int]	     =AddedToken(snake_case_		,		lstrip=snake_case_		,		rstrip=snake_case_							) if isinstance(snake_case_		,		snake_case_							) else mask_token

											super().__init__(
											    vocab_file=snake_case_		,		tokenizer_file=snake_case_		,		do_lower_case=snake_case_		,		remove_space=snake_case_		,		keep_accents=snake_case_		,		bos_token=snake_case_		,		eos_token=snake_case_		,		unk_token=snake_case_		,		sep_token=snake_case_		,		pad_token=snake_case_		,		cls_token=snake_case_		,		mask_token=snake_case_		,		additional_special_tokens=snake_case_		,		**snake_case_		,		)

											SCREAMING_SNAKE_CASE			: Dict	     =3
											SCREAMING_SNAKE_CASE			: Dict	     =do_lower_case
											SCREAMING_SNAKE_CASE			: int	     =remove_space
											SCREAMING_SNAKE_CASE			: Optional[Any]	     =keep_accents
											SCREAMING_SNAKE_CASE			: Tuple	     =vocab_file
											SCREAMING_SNAKE_CASE			: Optional[Any]	     =False if not self.vocab_file else True

											try:
																		import jieba
											except ModuleNotFoundError as error:
																		raise error.__class__(
																		    '''You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '''
																		    '''See https://pypi.org/project/jieba/ for installation.'''							)
											SCREAMING_SNAKE_CASE			: str	     =jieba
											SCREAMING_SNAKE_CASE			: Dict	     =str.maketrans(''' \n'''		,		'''\u2582\u2583'''							)




				def     __a		( self		,		snake_case_		,		snake_case_ = None							)    ->      List[int]:
											SCREAMING_SNAKE_CASE			: Dict	     =[self.sep_token_id]
											SCREAMING_SNAKE_CASE			: Any	     =[self.cls_token_id]
											if token_ids_a is None:
																		return token_ids_a + sep + cls
											return token_ids_a + sep + token_ids_a + sep + cls




				def     __a		( self		,		snake_case_		,		snake_case_ = None							)    ->      List[int]:
											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =[self.sep_token_id]
											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =[2]

											if token_ids_a is None:
																		return len(token_ids_a + sep							) * [0] + cls_segment_id
											return len(token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1] + cls_segment_id




				def     __a		( self		,		snake_case_		,		snake_case_ = 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(snake_case_							):
																		logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
																		return
											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =os.path.join(
											    snake_case_		,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)

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

											return (out_vocab_file,)




				def     __a		( self		,		snake_case_		,		*snake_case_		,		**snake_case_							)    ->      Optional[Any]:
											SCREAMING_SNAKE_CASE			: int	     =[
											    ''' '''.join([x.translate(self.translator							) for x in self.jieba.cut(snake_case_		,		cut_all=snake_case_							)]							)
											    for text in batch_text_or_text_pairs
											]
											return super()._batch_encode_plus(snake_case_		,		*snake_case_		,		**snake_case_							)







				def     __a		( self		,		*snake_case_		,		**snake_case_							)    ->      str:
											SCREAMING_SNAKE_CASE			: List[Any]	     =super()._decode(*snake_case_		,		**snake_case_							)
											SCREAMING_SNAKE_CASE			: List[Any]	     =text.replace(''' '''		,		''''''							).replace('''\u2582'''		,		''' '''							).replace('''\u2583'''		,		'''\n'''							)
											return text






