


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


snake_case       :					str        =  logging.getLogger(__name__)
def        __lowerCamelCase		(				UpperCAmelCase_		:	Dict		):




      """simple docstring"""





      random.seed(lowercase_		)
      np.random.seed(lowercase_		)
      torch.manual_seed(lowercase_		)
      torch.cuda.manual_seed_all(lowercase_		)
def        __lowerCamelCase		(				UpperCAmelCase_		:	Any				,						UpperCAmelCase_		:	Optional[Any]				,						UpperCAmelCase_		:	List[Any]		):




      """simple docstring"""





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




      """simple docstring"""





      a		:Tuple											= GPTaLMHeadModel.from_pretrained(lowercase_				,						output_hidden_states=lowercase_		)
      torch.save(model.state_dict()				,						model_name + '''local.pt'''		)
      return model
def        __lowerCamelCase		(				UpperCAmelCase_		:	Optional[Any]				,						UpperCAmelCase_		:	List[Any]				,						UpperCAmelCase_		:	str		):




      """simple docstring"""





      a		:List[str]											= copy.deepcopy(lowercase_		)
      model.to(lowercase_		)

      a		:Dict											= ['''bias''', '''LayerNorm.weight''']
      a		: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},
      ]
      a		:Dict											= AdamW(lowercase_				,						lr=5E-5				,						eps=1E-8		)
      a		:List[Any]											= get_linear_schedule_with_warmup(lowercase_				,						0				,						lowercase_		)
      torch.cuda.empty_cache()
      return model, lm_optimizer, lm_scheduler
def        __lowerCamelCase		(				UpperCAmelCase_		:	Dict				,						UpperCAmelCase_		:	List[str]				,						UpperCAmelCase_		:	List[Any]				,						UpperCAmelCase_		:	Any		):




      """simple docstring"""





      a		:Any											= np.array(lowercase_		).mean()
      a		:int											= np.array(lowercase_		).std()
      a		:Optional[int]											= (real_perps - avg) / std
      a		:Any											= list(zip(lowercase_				,						lowercase_				,						lowercase_		)		)
      joblib.dump(lowercase_				,						lowercase_		)
def        __lowerCamelCase		(				UpperCAmelCase_		:	Optional[int]				,						UpperCAmelCase_		:	List[Any]				,						UpperCAmelCase_		:	Dict				,						UpperCAmelCase_		:	Optional[Any]				,						UpperCAmelCase_		:	List[Any]				,						UpperCAmelCase_		:	List[str]				,						UpperCAmelCase_		:	Optional[int]				,						UpperCAmelCase_		:	Tuple="dev.jbl"				,						UpperCAmelCase_		:	Optional[int]=recopy_gpta				,						):




      """simple docstring"""





      a		:Dict											= []
      a		:Union[str, Any]											= []
      a		:str											= []

      # Initialize the transformer model
      a		:Union[str, Any]											= copy.deepcopy(lowercase_		)
      orig_model.to(device='''cpu'''		)
      torch.cuda.empty_cache()

      # Compute perplexity of initial transformer model for comparison
      model.train()
      a							,       a							,       a		:Tuple											= recopy_model(lowercase_				,						lowercase_				,						lowercase_		)

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

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

            # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
            a							,       a							,       a		:Tuple											= recopy_model(lowercase_				,						lowercase_				,						lowercase_		)

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

      intermittent_save(lowercase_				,						lowercase_				,						lowercase_				,						lowercase_		)
def        __lowerCamelCase		(				UpperCAmelCase_		:	Union[str, Any]				,						UpperCAmelCase_		:	Union[str, Any]="data/tokenized_stories_train_wikitext103.jbl"				,						UpperCAmelCase_		:	int=100				,						UpperCAmelCase_		:	Any=1026				,						UpperCAmelCase_		:	Any=True		):




      """simple docstring"""





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

      joblib.dump(lowercase_				,						'''objective_set.jbl'''		)
      print('''objective set saved'''		)
      return train_data, objective_set
def        __lowerCamelCase		(				UpperCAmelCase_		:	Union[str, Any]				,						UpperCAmelCase_		:	str				,						UpperCAmelCase_		:	Tuple				,						UpperCAmelCase_		:	Dict				,						UpperCAmelCase_		:	Tuple=50				,						UpperCAmelCase_		:	int="secondary_learner.pt"		):




      """simple docstring"""





      a		:List[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
      a		:List[str]											= train_dataset[:512]
      a		:Optional[int]											= train_dataset[512:]
      a		:Any											= DataLoader(lowercase_				,						shuffle=lowercase_				,						batch_size=lowercase_		)
      a		:Any											= DataLoader(lowercase_				,						shuffle=lowercase_				,						batch_size=lowercase_		)

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

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

                  secondary_learner.train()
      return secondary_learner







class 	_snake_case						(   nn.Module							):




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




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase					):
       a		:List[str]											= torch.max(self.conv(self.embeddings(__UpperCamelCase					).squeeze(1					).transpose(1      ,						2					)					)      ,						2					)[0]
       a		:Dict											= self.fc(__UpperCamelCase					)
       return qs.squeeze(1					)




 @classmethod
 def  SCREAMING_SNAKE_CASE__       (	cls      ,						_lowerCamelCase      ,						_lowerCamelCase					):
       a		:Tuple											= cls(__UpperCamelCase					)  # this calls __init__
       a		:List[str]											= torch.load(__UpperCamelCase					)
       secondary_learner.load_state_dict(__UpperCamelCase					)
       a		:Any											= model.transformer.wte
       a		:Union[str, Any]											= copy.deepcopy(model.transformer.wte.weight					)
       return secondary_learner









from typing import Optional, Union

from ...file_utils import TensorType
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy







class 	_snake_case						(   _snake_case							):
 SCREAMING_SNAKE_CASE__   = 'MarkupLMFeatureExtractor'
 SCREAMING_SNAKE_CASE__   = ('MarkupLMTokenizer', 'MarkupLMTokenizerFast')
 SCREAMING_SNAKE_CASE__   = True




 def __call__(	self      ,						_lowerCamelCase=None      ,						_lowerCamelCase=None      ,						_lowerCamelCase=None      ,						_lowerCamelCase=None      ,						_lowerCamelCase=None      ,						_lowerCamelCase = True      ,						_lowerCamelCase = False      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = 0      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = False      ,						_lowerCamelCase = False      ,						_lowerCamelCase = False      ,						_lowerCamelCase = False      ,						_lowerCamelCase = True      ,						_lowerCamelCase = None      ,						**_lowerCamelCase      ,						):
       # first, create nodes and xpaths
       if self.parse_html:
             if html_strings is None:
                   raise ValueError('''Make sure to pass HTML strings in case `parse_html` is set to `True`'''					)

             if nodes is not None or xpaths is not None or node_labels is not None:
                   raise ValueError(
                       '''Please don\'t pass nodes, xpaths nor node labels in case `parse_html` is set to `True`'''					)

             a		:int											= self.feature_extractor(_lowerCamelCase					)
             a		:Tuple											= features['''nodes''']
             a		:Dict											= features['''xpaths''']
       else:
             if html_strings is not None:
                   raise ValueError('''You have passed HTML strings but `parse_html` is set to `False`.'''					)
             if nodes is None or xpaths is None:
                   raise ValueError('''Make sure to pass nodes and xpaths in case `parse_html` is set to `False`'''					)

        # # second, apply the tokenizer
       if questions is not None and self.parse_html:
             if isinstance(_lowerCamelCase      ,						_lowerCamelCase					):
                   a		:Tuple											= [questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

       a		:Optional[int]											= self.tokenizer(
           text=questions if questions is not None else nodes      ,						text_pair=nodes if questions is not None else None      ,						xpaths=_lowerCamelCase      ,						node_labels=_lowerCamelCase      ,						add_special_tokens=_lowerCamelCase      ,						padding=_lowerCamelCase      ,						truncation=_lowerCamelCase      ,						max_length=_lowerCamelCase      ,						stride=_lowerCamelCase      ,						pad_to_multiple_of=_lowerCamelCase      ,						return_token_type_ids=_lowerCamelCase      ,						return_attention_mask=_lowerCamelCase      ,						return_overflowing_tokens=_lowerCamelCase      ,						return_special_tokens_mask=_lowerCamelCase      ,						return_offsets_mapping=_lowerCamelCase      ,						return_length=_lowerCamelCase      ,						verbose=_lowerCamelCase      ,						return_tensors=_lowerCamelCase      ,						**_lowerCamelCase      ,						)

       return encoded_inputs




 def  SCREAMING_SNAKE_CASE__       (	self      ,						*_lowerCamelCase      ,						**_lowerCamelCase					):
       return self.tokenizer.batch_decode(*_lowerCamelCase      ,						**_lowerCamelCase					)




 def  SCREAMING_SNAKE_CASE__       (	self      ,						*_lowerCamelCase      ,						**_lowerCamelCase					):
       return self.tokenizer.decode(*_lowerCamelCase      ,						**_lowerCamelCase					)




 @property
 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Any											= self.tokenizer.model_input_names
       return tokenizer_input_names





