

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 						UpperCamelCase_(			snake_case__:  List[str]   )       -> Optional[Any]:
 random.seed(snake_case__   )
 np.random.seed(snake_case__   )
 torch.manual_seed(snake_case__   )
 torch.cuda.manual_seed_all(snake_case__   )
def 						UpperCamelCase_(			snake_case__:  Optional[Any]  ,    snake_case__:  Optional[int]  ,    snake_case__:  str   )       -> Dict:
 model.eval()
 UpperCAmelCase__							=				next(model.parameters()   ).device
 UpperCAmelCase__							=				1
 UpperCAmelCase__							=				torch.zeros((eval_batch_size, context_len)  ,    dtype=torch.long  ,    device=snake_case__   )
 UpperCAmelCase__							=				DataLoader(snake_case__  ,    shuffle=snake_case__  ,    batch_size=snake_case__   )
 UpperCAmelCase__							=				torch.zeros(1  ,    device=snake_case__   )
 UpperCAmelCase__							=				0
 for batch in eval_dataloader:
  batch.to(snake_case__   )
  # pad
  context.zero_()
  for i in range(snake_case__   ):
   UpperCAmelCase__							=				batch[i]
  UpperCAmelCase__							=				model(snake_case__  ,    labels=snake_case__   )
  eval_loss += outputs[0].sum().item()
  nb_eval_examples += batch.size(0   )
 UpperCAmelCase__							=				eval_loss / nb_eval_examples
 UpperCAmelCase__							=				torch.exp(snake_case__   )
 model.train()
 return perplexity
def 						UpperCamelCase_(			snake_case__:  Dict="gpt2"   )       -> Optional[Any]:
 UpperCAmelCase__							=				GPTaLMHeadModel.from_pretrained(snake_case__  ,    output_hidden_states=snake_case__   )
 torch.save(model.state_dict()  ,    model_name + 'local.pt'   )
 return model
def 						UpperCamelCase_(			snake_case__:  Dict  ,    snake_case__:  List[Any]  ,    snake_case__:  Dict   )       -> Union[str, Any]:
 UpperCAmelCase__							=				copy.deepcopy(snake_case__   )
 model.to(snake_case__   )

 UpperCAmelCase__							=				['bias', 'LayerNorm.weight']
 UpperCAmelCase__							=				[
     {
         '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},
 ]
 UpperCAmelCase__							=				AdamW(snake_case__  ,    lr=5e-5  ,    eps=1e-8   )
 UpperCAmelCase__							=				get_linear_schedule_with_warmup(snake_case__  ,    0  ,    snake_case__   )
 torch.cuda.empty_cache()
 return model, lm_optimizer, lm_scheduler
def 						UpperCamelCase_(			snake_case__:  str  ,    snake_case__:  Optional[int]  ,    snake_case__:  int  ,    snake_case__:  Tuple   )       -> Union[str, Any]:
 UpperCAmelCase__							=				np.array(snake_case__   ).mean()
 UpperCAmelCase__							=				np.array(snake_case__   ).std()
 UpperCAmelCase__							=				(real_perps - avg) / std
 UpperCAmelCase__							=				list(zip(snake_case__  ,    snake_case__  ,    snake_case__   )   )
 joblib.dump(snake_case__  ,    snake_case__   )
def 						UpperCamelCase_(			snake_case__:  Any  ,    snake_case__:  Optional[Any]  ,    snake_case__:  Optional[int]  ,    snake_case__:  List[str]  ,    snake_case__:  Optional[Any]  ,    snake_case__:  List[str]  ,    snake_case__:  List[Any]  ,    snake_case__:  Dict="dev.jbl"  ,    snake_case__:  Optional[int]=recopy_gpta  ,    )       -> Dict:
 UpperCAmelCase__							=				[]
 UpperCAmelCase__							=				[]
 UpperCAmelCase__							=				[]

 # Initialize the transformer model
 UpperCAmelCase__							=				copy.deepcopy(snake_case__   )
 orig_model.to(device='cpu'   )
 torch.cuda.empty_cache()

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

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

  # Periodically save the stored (X, IG(X)) pairs
  if step % 10_00 == 0 and step > 1:
   intermittent_save(snake_case__  ,    snake_case__  ,    snake_case__  ,    snake_case__   )

  # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
  UpperCAmelCase__				,    UpperCAmelCase__				,    UpperCAmelCase__							=				recopy_model(snake_case__  ,    snake_case__  ,    snake_case__   )

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

 intermittent_save(snake_case__  ,    snake_case__  ,    snake_case__  ,    snake_case__   )
def 						UpperCamelCase_(			snake_case__:  Dict  ,    snake_case__:  str="data/tokenized_stories_train_wikitext103.jbl"  ,    snake_case__:  Union[str, Any]=1_00  ,    snake_case__:  Tuple=10_26  ,    snake_case__:  Tuple=True   )       -> int:
 UpperCAmelCase__							=				joblib.load(snake_case__   )
 print('data loaded'   )
 UpperCAmelCase__							=				[]
 if trim:
  for i, example in enumerate(snake_case__   ):
   if len(example[0]   ) > min_len:
    UpperCAmelCase__							=				random.randint(0  ,    len(example[0]   ) - context_len - 1   )
    objective_set.append(example[0, start : start + context_len]   )
   if len(snake_case__   ) >= number:
    break
  UpperCAmelCase__							=				[]
  for j in range(i + 1  ,    len(snake_case__   )   ):
   if len(data[j][0]   ) > min_len:
    train_data.append(data[j]   )
 else:
  UpperCAmelCase__							=				data[0:number]
  UpperCAmelCase__							=				data[number:]

 joblib.dump(snake_case__  ,    'objective_set.jbl'   )
 print('objective set saved'   )
 return train_data, objective_set
def 						UpperCamelCase_(			snake_case__:  Dict  ,    snake_case__:  List[Any]  ,    snake_case__:  Union[str, Any]  ,    snake_case__:  int  ,    snake_case__:  Union[str, Any]=50  ,    snake_case__:  str="secondary_learner.pt"   )       -> int:
 UpperCAmelCase__							=				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
 UpperCAmelCase__							=				train_dataset[:5_12]
 UpperCAmelCase__							=				train_dataset[5_12:]
 UpperCAmelCase__							=				DataLoader(snake_case__  ,    shuffle=snake_case__  ,    batch_size=snake_case__   )
 UpperCAmelCase__							=				DataLoader(snake_case__  ,    shuffle=snake_case__  ,    batch_size=snake_case__   )

 # secondary learner model set up
 UpperCAmelCase__							=				nn.MSELoss()
 UpperCAmelCase__							=				nn.MSELoss(reduction='sum'   )
 secondary_learner.to(snake_case__   )
 UpperCAmelCase__							=				torch.optim.Adam(secondary_learner.parameters()  ,    lr=0.0_0_0_0_1   )
 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
 UpperCAmelCase__							=				float('inf'   )
 # Iterate through batches until we've used max_steps batches
 for epoch in range(int(snake_case__   )   ):
  UpperCAmelCase__							=				0.0
  secondary_learner.train()
  for step, batch in enumerate(snake_case__   ):
   UpperCAmelCase__							=				batch[0].to(snake_case__   )
   UpperCAmelCase__							=				batch[1].to(snake_case__   )
   UpperCAmelCase__							=				secondary_learner(snake_case__   )
   q_optimizer.zero_grad()
   UpperCAmelCase__							=				loss(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(snake_case__   )):
    UpperCAmelCase__							=				tr_q_loss / (step + 1)

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

   secondary_learner.train()
 return secondary_learner







class     lowercase						(       nn.Module					):



 '''simple docstring'''


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






  """simple docstring"""





  super(__a       ,       self ).__init__()
  UpperCAmelCase__							=				model.transformer.wte
  UpperCAmelCase__							=				copy.deepcopy(model.transformer.wte.weight )
  UpperCAmelCase__							=				nn.Convad(self.embeddings.weight.size(1 )       ,       256       ,       3       ,       padding=1 )
  UpperCAmelCase__							=				nn.Sequential(nn.Linear(256       ,       32 )       ,       nn.Dropout(p=0.1 )       ,       nn.Linear(32       ,       32 )       ,       nn.Linear(32       ,       1 ) )




 def   UpperCamelCase__							(self       ,       __a ) ->					Tuple:






  """simple docstring"""





  UpperCAmelCase__							=				torch.max(self.conv(self.embeddings(__a ).squeeze(1 ).transpose(1       ,       2 ) )       ,       2 )[0]
  UpperCAmelCase__							=				self.fc(__a )
  return qs.squeeze(1 )




 @classmethod
 def   UpperCamelCase__							(cls       ,       __a       ,       __a ) ->					Dict:






  """simple docstring"""





  UpperCAmelCase__							=				cls(__a )  # this calls __init__
  UpperCAmelCase__							=				torch.load(__a )
  secondary_learner.load_state_dict(__a )
  UpperCAmelCase__							=				model.transformer.wte
  UpperCAmelCase__							=				copy.deepcopy(model.transformer.wte.weight )
  return secondary_learner





import torch

from transformers import AutoTokenizer

class 				_UpperCamelCase (    _A     ):






			'''simple docstring'''

			def __init__(				self       :				List[str]  ,		snake_case_       :				Dict					):
							UpperCamelCase_:							int		=  AutoTokenizer.from_pretrained(snake_case_					)

			def 					lowerCAmelCase__		(				self       :				Union[str, Any]  ,		snake_case_       :				Optional[Any]					):

							if isinstance(snake_case_  ,		snake_case_					) and all(isinstance(_x  ,		snake_case_					) for _x in x					):
											UpperCamelCase_:							Tuple		=  None
											for l in x:
															UpperCamelCase_:							Optional[Any]		=  self.tokenizer(
															    snake_case_  ,		padding="""max_length"""  ,		max_length=384  ,		truncation=snake_case_  ,		return_tensors="""pt"""  ,		)
															UpperCamelCase_:							str		=  torch.tensor([len(snake_case_					)]					)
															if d is not None:
																			for k in d.keys():
																							UpperCamelCase_:							List[Any]		=  torch.cat((d[k], t[k])  ,		0					)
															else:
																			UpperCamelCase_:							Optional[int]		=  t

											UpperCamelCase_:							Union[str, Any]		=  torch.tensor(self.tokenizer.convert_tokens_to_ids("""[E]"""					)					)
											UpperCamelCase_:							List[str]		=  torch.tensor(self.tokenizer.convert_tokens_to_ids("""[/E]"""					)					)

							elif isinstance(snake_case_  ,		snake_case_					) and all(isinstance(_x  ,		snake_case_					) for _x in x					):
											UpperCamelCase_:							List[str]		=  self.tokenizer(
											    snake_case_  ,		padding="""max_length"""  ,		max_length=384  ,		truncation=snake_case_  ,		return_tensors="""pt"""  ,		)

							else:
											raise Exception(
											    """Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of"""
											    """ strings` for supports are supported."""					)

							return d




			def 					lowerCAmelCase__		(				self       :				int  ,		snake_case_       :				List[str]  ,		snake_case_       :				Optional[int]  ,		snake_case_       :				Optional[Any]  ,		snake_case_       :				Optional[int]  ,		snake_case_       :				List[Any]=0.70					):
							UpperCamelCase_:							int		=  []
							for idx in range(len(W_query["""input_ids"""]					)					):
											UpperCamelCase_:							List[str]		=  range(p_start.shape[1]					)

											UpperCamelCase_:							str		=  []
											for start_id in start_indexes:
															for end_id in end_indexes:
																			if start_id < end_id:
																							output.append(
																							    (
																							        start_id,
																							        end_id,
																							        p_start[idx][start_id].item(),
																							        p_end[idx][end_id].item(),
																							    )					)

											output.sort(key=lambda snake_case_					: (tup[2] * tup[3])  ,		reverse=snake_case_					)
											UpperCamelCase_:							Optional[int]		=  []
											for k in range(len(snake_case_					)					):
															if output[k][2] * output[k][3] >= thresh:
																			UpperCamelCase_,      UpperCamelCase_:							Tuple		=  output[k][0], output[k][1]
																			UpperCamelCase_:							Any		=  self.tokenizer.decode(W_query["""input_ids"""][idx][c_start_pos:c_end_pos]					)
																			temp.append((decoded, output[k][2] * output[k][3])					)

											final_outputs.append(snake_case_					)

							return final_outputs






