


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


SCREAMING_SNAKE_CASE		:Dict											=       logging.getLogger(__name__)



def      _lowerCAmelCase  (							lowerCAmelCase_							:List[str]							)->str:
			'''simple docstring'''






			random.seed(lowerCAmelCase_							)
			np.random.seed(lowerCAmelCase_							)
			torch.manual_seed(lowerCAmelCase_							)
			torch.cuda.manual_seed_all(lowerCAmelCase_							)



def      _lowerCAmelCase  (							lowerCAmelCase_							:Optional[int]						,  lowerCAmelCase_							:Union[str, Any]						,  lowerCAmelCase_							:Optional[Any]							)->Dict:
			'''simple docstring'''






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



def      _lowerCAmelCase  (							lowerCAmelCase_							:Union[str, Any]="gpt2"							)->Union[str, Any]:
			'''simple docstring'''






			snake_case_         =   GPTaLMHeadModel.from_pretrained(lowerCAmelCase_						,  output_hidden_states=lowerCAmelCase_							)
			torch.save(model.state_dict()						,  model_name + "local.pt"							)
			return model



def      _lowerCAmelCase  (							lowerCAmelCase_							:List[str]						,  lowerCAmelCase_							:List[str]						,  lowerCAmelCase_							:List[Any]							)->int:
			'''simple docstring'''






			snake_case_         =   copy.deepcopy(lowerCAmelCase_							)
			model.to(lowerCAmelCase_							)

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



def      _lowerCAmelCase  (							lowerCAmelCase_							:Tuple						,  lowerCAmelCase_							:List[str]						,  lowerCAmelCase_							:Optional[int]						,  lowerCAmelCase_							:str							)->Tuple:
			'''simple docstring'''






			snake_case_         =   np.array(lowerCAmelCase_							).mean()
			snake_case_         =   np.array(lowerCAmelCase_							).std()
			snake_case_         =   (real_perps - avg) / std
			snake_case_         =   list(zip(lowerCAmelCase_						,  lowerCAmelCase_						,  lowerCAmelCase_							)							)
			joblib.dump(lowerCAmelCase_						,  lowerCAmelCase_							)



def      _lowerCAmelCase  (							lowerCAmelCase_							:Optional[int]						,  lowerCAmelCase_							:List[str]						,  lowerCAmelCase_							:Optional[Any]						,  lowerCAmelCase_							:str						,  lowerCAmelCase_							:Optional[int]						,  lowerCAmelCase_							:Any						,  lowerCAmelCase_							:Any						,  lowerCAmelCase_							:str="dev.jbl"						,  lowerCAmelCase_							:int=recopy_gpta						,  )->Tuple:
			'''simple docstring'''






			snake_case_         =   []
			snake_case_         =   []
			snake_case_         =   []

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

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

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

						# Periodically save the stored (X, IG(X)) pairs
						if step % 1_000 == 0 and step > 1:
									intermittent_save(lowerCAmelCase_						,  lowerCAmelCase_						,  lowerCAmelCase_						,  lowerCAmelCase_							)

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

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

			intermittent_save(lowerCAmelCase_						,  lowerCAmelCase_						,  lowerCAmelCase_						,  lowerCAmelCase_							)



def      _lowerCAmelCase  (							lowerCAmelCase_							:List[Any]						,  lowerCAmelCase_							:Union[str, Any]="data/tokenized_stories_train_wikitext103.jbl"						,  lowerCAmelCase_							:Tuple=100						,  lowerCAmelCase_							:int=1_026						,  lowerCAmelCase_							:int=True							)->List[Any]:
			'''simple docstring'''






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

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



def      _lowerCAmelCase  (							lowerCAmelCase_							:Union[str, Any]						,  lowerCAmelCase_							:Tuple						,  lowerCAmelCase_							:List[Any]						,  lowerCAmelCase_							:Optional[Any]						,  lowerCAmelCase_							:Dict=50						,  lowerCAmelCase_							:Dict="secondary_learner.pt"							)->Union[str, Any]:
			'''simple docstring'''






			snake_case_         =   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
			snake_case_         =   train_dataset[:512]
			snake_case_         =   train_dataset[512:]
			snake_case_         =   DataLoader(lowerCAmelCase_						,  shuffle=lowerCAmelCase_						,  batch_size=lowerCAmelCase_							)
			snake_case_         =   DataLoader(lowerCAmelCase_						,  shuffle=lowerCAmelCase_						,  batch_size=lowerCAmelCase_							)

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

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

									secondary_learner.train()
			return secondary_learner



class       __lowerCAmelCase       (	nn.Module   ):






		"""simple docstring"""





		def __init__(       self    :    Optional[int]      ,			_lowerCAmelCase    :    Dict					)				->	List[Any]:




					"""simple docstring"""
					super(_lowerCAmelCase      ,			self					).__init__()
					snake_case_         =   model.transformer.wte
					snake_case_         =   copy.deepcopy(model.transformer.wte.weight					)
					snake_case_         =   nn.Convad(self.embeddings.weight.size(1					)      ,			2_5_6      ,			3      ,			padding=1					)
					snake_case_         =   nn.Sequential(nn.Linear(2_5_6      ,			3_2					)      ,			nn.Dropout(p=0.1					)      ,			nn.Linear(3_2      ,			3_2					)      ,			nn.Linear(3_2      ,			1					)					)



		def    lowerCAmelCase__					(       self    :    Union[str, Any]      ,			_lowerCAmelCase    :    Optional[int]					)				->	List[Any]:




					"""simple docstring"""
					snake_case_         =   torch.max(self.conv(self.embeddings(_lowerCAmelCase					).squeeze(1					).transpose(1      ,			2					)					)      ,			2					)[0]
					snake_case_         =   self.fc(_lowerCAmelCase					)
					return qs.squeeze(1					)



		@classmethod
		def    lowerCAmelCase__					(       cls    :    List[Any]      ,			_lowerCAmelCase    :    List[Any]      ,			_lowerCAmelCase    :    Dict					)				->	Dict:




					"""simple docstring"""
					snake_case_         =   cls(_lowerCAmelCase					)  # this calls __init__
					snake_case_         =   torch.load(_lowerCAmelCase					)
					secondary_learner.load_state_dict(_lowerCAmelCase					)
					snake_case_         =   model.transformer.wte
					snake_case_         =   copy.deepcopy(model.transformer.wte.weight					)
					return secondary_learner








from typing import Any

import numpy as np



def      _lowerCAmelCase  (							lowerCAmelCase_							:np.ndarray							)->bool:
   '''simple docstring'''






   assert np.shape(lowerCAmelCase_							)[0] == np.shape(lowerCAmelCase_							)[1]

   # If matrix not symmetric, exit right away.
   if np.allclose(lowerCAmelCase_						,  matrix.T							) is False:
      return False

   # Get eigenvalues and eignevectors for a symmetric matrix.
   snake_case_      ,      snake_case_         =   np.linalg.eigh(lowerCAmelCase_							)

   # Check sign of all eigenvalues.
   # np.all returns a value of type np.bool_
   return bool(np.all(eigen_values > 0							)							)



def      _lowerCAmelCase  (							lowerCAmelCase_							:int							)->Any:
   '''simple docstring'''






   snake_case_         =   np.random.randn(lowerCAmelCase_						,  lowerCAmelCase_							)
   snake_case_         =   np.dot(lowerCAmelCase_						,  random_matrix.T							)
   assert _is_matrix_spd(lowerCAmelCase_							)
   return spd_matrix



def      _lowerCAmelCase  (							lowerCAmelCase_							:np.ndarray						,  lowerCAmelCase_							:np.ndarray						,  lowerCAmelCase_							:int = 1_000						,  lowerCAmelCase_							:float = 1e-8						,  )->Any:
   '''simple docstring'''






   assert np.shape(lowerCAmelCase_							)[0] == np.shape(lowerCAmelCase_							)[1]
   assert np.shape(lowerCAmelCase_							)[0] == np.shape(lowerCAmelCase_							)[0]
   assert _is_matrix_spd(lowerCAmelCase_							)

   # Initialize solution guess, residual, search direction.
   snake_case_         =   np.zeros((np.shape(lowerCAmelCase_							)[0], 1)							)
   snake_case_         =   np.copy(lowerCAmelCase_							)
   snake_case_         =   np.copy(lowerCAmelCase_							)

   # Set initial errors in solution guess and residual.
   snake_case_         =   1e9
   snake_case_         =   1e9
   snake_case_         =   1e9

   # Set iteration counter to threshold number of iterations.
   snake_case_         =   0

   while error > tol:
      # Save this value so we only calculate the matrix-vector product once.
      snake_case_         =   np.dot(lowerCAmelCase_						,  lowerCAmelCase_							)

      # The main algorithm.

      # Update search direction magnitude.
      snake_case_         =   np.dot(ra.T						,  lowerCAmelCase_							) / np.dot(pa.T						,  lowerCAmelCase_							)
      # Update solution guess.
      snake_case_         =   xa + alpha * pa
      # Calculate new residual.
      snake_case_         =   ra - alpha * w
      # Calculate new Krylov subspace scale.
      snake_case_         =   np.dot(r.T						,  lowerCAmelCase_							) / np.dot(ra.T						,  lowerCAmelCase_							)
      # Calculate new A conjuage search direction.
      snake_case_         =   r + beta * pa

      # Calculate errors.
      snake_case_         =   np.linalg.norm(r - ra							)
      snake_case_         =   np.linalg.norm(x - xa							)
      snake_case_         =   np.maximum(lowerCAmelCase_						,  lowerCAmelCase_							)

      # Update variables.
      snake_case_         =   np.copy(lowerCAmelCase_							)
      snake_case_         =   np.copy(lowerCAmelCase_							)
      snake_case_         =   np.copy(lowerCAmelCase_							)

      # Update number of iterations.
      iterations += 1
      if iterations > max_iterations:
         break

   return x



def      _lowerCAmelCase  (							)->None:
   '''simple docstring'''






   snake_case_         =   3
   snake_case_         =   _create_spd_matrix(lowerCAmelCase_							)
   snake_case_         =   np.random.randn(lowerCAmelCase_						,  1							)
   snake_case_         =   np.dot(lowerCAmelCase_						,  lowerCAmelCase_							)

   # Numpy solution.
   snake_case_         =   np.linalg.solve(lowerCAmelCase_						,  lowerCAmelCase_							)

   # Our implementation.
   snake_case_         =   conjugate_gradient(lowerCAmelCase_						,  lowerCAmelCase_							)

   # Ensure both solutions are close to x_true (and therefore one another).
   assert np.linalg.norm(x_numpy - x_true							) <= 1e-6
   assert np.linalg.norm(x_conjugate_gradient - x_true							) <= 1e-6


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   test_conjugate_gradient()




