code: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  from __future__ import annotations
  
  import unittest
  from collections.abc import Iterator
  
  
  
  
  
  class   lowerCamelCase_							:
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					def __init__(					self,						__UpperCamelCase,						__UpperCamelCase				):
  							_snake_case			 = label
  							_snake_case			 = parent
  							_snake_case			 = None
  							_snake_case			 = None
  
  
  
  
  
  class   lowerCamelCase_							:
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					def __init__(					self				):
  							_snake_case			 = None
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = None
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							return self.root is None
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase				):
  							_snake_case			 = self._put(self.root,						__UpperCamelCase				)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase = None				):
  							if node is None:
  									_snake_case			 = Node(__UpperCamelCase,						__UpperCamelCase				)
  							else:
  									if label < node.label:
  											_snake_case			 = self._put(node.left,						__UpperCamelCase,						__UpperCamelCase				)
  									elif label > node.label:
  											_snake_case			 = self._put(node.right,						__UpperCamelCase,						__UpperCamelCase				)
  									else:
  											_snake_case			 = f'''Node with label {label} already exists'''
  											raise Exception(__UpperCamelCase				)
  
  							return node
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase				):
  							return self._search(self.root,						__UpperCamelCase				)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase,						__UpperCamelCase				):
  							if node is None:
  									_snake_case			 = f'''Node with label {label} does not exist'''
  									raise Exception(__UpperCamelCase				)
  							else:
  									if label < node.label:
  											_snake_case			 = self._search(node.left,						__UpperCamelCase				)
  									elif label > node.label:
  											_snake_case			 = self._search(node.right,						__UpperCamelCase				)
  
  							return node
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase				):
  							_snake_case			 = self.search(__UpperCamelCase				)
  							if node.right and node.left:
  									_snake_case			 = self._get_lowest_node(node.right				)
  									_snake_case			 = node.left
  									_snake_case			 = node.right
  									_snake_case			 = lowest_node
  									if node.right:
  											_snake_case			 = lowest_node
  									self._reassign_nodes(__UpperCamelCase,						__UpperCamelCase				)
  							elif not node.right and node.left:
  									self._reassign_nodes(__UpperCamelCase,						node.left				)
  							elif node.right and not node.left:
  									self._reassign_nodes(__UpperCamelCase,						node.right				)
  							else:
  									self._reassign_nodes(__UpperCamelCase,						__UpperCamelCase				)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase,						__UpperCamelCase				):
  							if new_children:
  									_snake_case			 = node.parent
  
  							if node.parent:
  									if node.parent.right == node:
  											_snake_case			 = new_children
  									else:
  											_snake_case			 = new_children
  							else:
  									_snake_case			 = new_children
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase				):
  							if node.left:
  									_snake_case			 = self._get_lowest_node(node.left				)
  							else:
  									_snake_case			 = node
  									self._reassign_nodes(__UpperCamelCase,						node.right				)
  
  							return lowest_node
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase				):
  							try:
  									self.search(__UpperCamelCase				)
  									return True
  							except Exception:
  									return False
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							if self.root is None:
  									raise Exception("Binary search tree is empty"				)
  
  							_snake_case			 = self.root
  							while node.right is not None:
  									_snake_case			 = node.right
  
  							return node.label
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							if self.root is None:
  									raise Exception("Binary search tree is empty"				)
  
  							_snake_case			 = self.root
  							while node.left is not None:
  									_snake_case			 = node.left
  
  							return node.label
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							return self._inorder_traversal(self.root				)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase				):
  							if node is not None:
  									yield from self._inorder_traversal(node.left				)
  									yield node
  									yield from self._inorder_traversal(node.right				)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							return self._preorder_traversal(self.root				)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase				):
  							if node is not None:
  									yield node
  									yield from self._preorder_traversal(node.left				)
  									yield from self._preorder_traversal(node.right				)
  
  
  
  
  
  class   lowerCamelCase_							( unittest.TestCase							):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					@staticmethod
  					def        __SCREAMING_SNAKE_CASE			(					):
  							_snake_case			 = BinarySearchTree()
  							t.put(8				)
  							t.put(3				)
  							t.put(6				)
  							t.put(1				)
  							t.put(10				)
  							t.put(14				)
  							t.put(13				)
  							t.put(4				)
  							t.put(7				)
  							t.put(5				)
  
  							return t
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = BinarySearchTree()
  							assert t.is_empty()
  
  							t.put(8				)
  							assert t.root is not None
  							assert t.root.parent is None
  							assert t.root.label == 8
  
  							t.put(10				)
  							assert t.root.right is not None
  							assert t.root.right.parent == t.root
  							assert t.root.right.label == 10
  
  							t.put(3				)
  							assert t.root.left is not None
  							assert t.root.left.parent == t.root
  							assert t.root.left.label == 3
  
  							t.put(6				)
  							assert t.root.left.right is not None
  							assert t.root.left.right.parent == t.root.left
  							assert t.root.left.right.label == 6
  
  							t.put(1				)
  							assert t.root.left.left is not None
  							assert t.root.left.left.parent == t.root.left
  							assert t.root.left.left.label == 1
  
  							with self.assertRaises(__UpperCamelCase				):  # noqa: B017
  									t.put(1				)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = self._get_binary_search_tree()
  
  							_snake_case			 = t.search(6				)
  							assert node.label == 6
  
  							_snake_case			 = t.search(13				)
  							assert node.label == 13
  
  							with self.assertRaises(__UpperCamelCase				):  # noqa: B017
  									t.search(2				)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = self._get_binary_search_tree()
  
  							t.remove(13				)
  							assert t.root is not None
  							assert t.root.right is not None
  							assert t.root.right.right is not None
  							assert t.root.right.right.right is None
  							assert t.root.right.right.left is None
  
  							t.remove(7				)
  							assert t.root.left is not None
  							assert t.root.left.right is not None
  							assert t.root.left.right.left is not None
  							assert t.root.left.right.right is None
  							assert t.root.left.right.left.label == 4
  
  							t.remove(6				)
  							assert t.root.left.left is not None
  							assert t.root.left.right.right is not None
  							assert t.root.left.left.label == 1
  							assert t.root.left.right.label == 4
  							assert t.root.left.right.right.label == 5
  							assert t.root.left.right.left is None
  							assert t.root.left.left.parent == t.root.left
  							assert t.root.left.right.parent == t.root.left
  
  							t.remove(3				)
  							assert t.root is not None
  							assert t.root.left.label == 4
  							assert t.root.left.right.label == 5
  							assert t.root.left.left.label == 1
  							assert t.root.left.parent == t.root
  							assert t.root.left.left.parent == t.root.left
  							assert t.root.left.right.parent == t.root.left
  
  							t.remove(4				)
  							assert t.root.left is not None
  							assert t.root.left.left is not None
  							assert t.root.left.label == 5
  							assert t.root.left.right is None
  							assert t.root.left.left.label == 1
  							assert t.root.left.parent == t.root
  							assert t.root.left.left.parent == t.root.left
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = self._get_binary_search_tree()
  
  							t.remove(3				)
  							assert t.root is not None
  							assert t.root.left is not None
  							assert t.root.left.left is not None
  							assert t.root.left.right is not None
  							assert t.root.left.right.left is not None
  							assert t.root.left.right.right is not None
  							assert t.root.left.label == 4
  							assert t.root.left.right.label == 6
  							assert t.root.left.left.label == 1
  							assert t.root.left.right.right.label == 7
  							assert t.root.left.right.left.label == 5
  							assert t.root.left.parent == t.root
  							assert t.root.left.right.parent == t.root.left
  							assert t.root.left.left.parent == t.root.left
  							assert t.root.left.right.left.parent == t.root.left.right
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = self._get_binary_search_tree()
  							t.empty()
  							assert t.root is None
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = self._get_binary_search_tree()
  							assert not t.is_empty()
  
  							t.empty()
  							assert t.is_empty()
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = self._get_binary_search_tree()
  
  							assert t.exists(6				)
  							assert not t.exists(-1				)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = self._get_binary_search_tree()
  
  							assert t.get_max_label() == 14
  
  							t.empty()
  							with self.assertRaises(__UpperCamelCase				):  # noqa: B017
  									t.get_max_label()
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = self._get_binary_search_tree()
  
  							assert t.get_min_label() == 1
  
  							t.empty()
  							with self.assertRaises(__UpperCamelCase				):  # noqa: B017
  									t.get_min_label()
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = self._get_binary_search_tree()
  
  							_snake_case			 = [i.label for i in t.inorder_traversal()]
  							assert inorder_traversal_nodes == [1, 3, 4, 5, 6, 7, 8, 10, 13, 14]
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = self._get_binary_search_tree()
  
  							_snake_case			 = [i.label for i in t.preorder_traversal()]
  							assert preorder_traversal_nodes == [8, 3, 1, 6, 4, 5, 7, 10, 14, 13]
  
  
  
  
  
  
  def 	_lowercase	(			):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		_snake_case			 = BinarySearchTree()
  		t.put(8						)
  		t.put(3						)
  		t.put(6						)
  		t.put(1						)
  		t.put(10						)
  		t.put(14						)
  		t.put(13						)
  		t.put(4						)
  		t.put(7						)
  		t.put(5						)
  
  		print(
  		    "\n            8\n           / \\\n          3   10\n         / \\    \\\n        1   6    14\n           / \\   /\n          4   7 13\n           \\\n            5\n        "						)
  
  		print("Label 6 exists:"		,							t.exists(6						)						)
  		print("Label 13 exists:"		,							t.exists(13						)						)
  		print("Label -1 exists:"		,							t.exists(-1						)						)
  		print("Label 12 exists:"		,							t.exists(12						)						)
  
  		# Prints all the elements of the list in inorder traversal
  		_snake_case			 = [i.label for i in t.inorder_traversal()]
  		print("Inorder traversal:"		,							lowercase_						)
  
  		# Prints all the elements of the list in preorder traversal
  		_snake_case			 = [i.label for i in t.preorder_traversal()]
  		print("Preorder traversal:"		,							lowercase_						)
  
  		print("Max. label:"		,							t.get_max_label()						)
  		print("Min. label:"		,							t.get_min_label()						)
  
  		# Delete elements
  		print("\nDeleting elements 13, 10, 8, 3, 6, 14"						)
  		print(
  		    "\n          4\n         / \\\n        1   7\n             \\\n              5\n        "						)
  		t.remove(13						)
  		t.remove(10						)
  		t.remove(8						)
  		t.remove(3						)
  		t.remove(6						)
  		t.remove(14						)
  
  		# Prints all the elements of the list in inorder traversal after delete
  		_snake_case			 = [i.label for i in t.inorder_traversal()]
  		print("Inorder traversal after delete:"		,							lowercase_						)
  
  		# Prints all the elements of the list in preorder traversal after delete
  		_snake_case			 = [i.label for i in t.preorder_traversal()]
  		print("Preorder traversal after delete:"		,							lowercase_						)
  
  		print("Max. label:"		,							t.get_max_label()						)
  		print("Min. label:"		,							t.get_min_label()						)
  
  
  if __name__ == "__main__":
  						binary_search_tree_example()
  
  
  
  
  
  
style_context: 
  
  
  
  
  '''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
  
  
  _lowerCamelCase							:		str										=						logging.getLogger(__name__)
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		random.seed(lowercase_						)
  		np.random.seed(lowercase_						)
  		torch.manual_seed(lowercase_						)
  		torch.cuda.manual_seed_all(lowercase_						)
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_		,							lowercase_		,							lowercase_						):
  
  
  
  		'''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=lowercase_						)
  		_snake_case			 = DataLoader(lowercase_		,							shuffle=lowercase_		,							batch_size=lowercase_						)
  		_snake_case			 = torch.zeros(1		,							device=lowercase_						)
  		_snake_case			 = 0
  		for batch in eval_dataloader:
  				batch.to(lowercase_						)
  				# pad
  				context.zero_()
  				for i in range(lowercase_						):
  						_snake_case			 = batch[i]
  				_snake_case			 = model(lowercase_		,							labels=lowercase_						)
  				eval_loss += outputs[0].sum().item()
  				nb_eval_examples += batch.size(0						)
  		_snake_case			 = eval_loss / nb_eval_examples
  		_snake_case			 = torch.exp(lowercase_						)
  		model.train()
  		return perplexity
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_="gpt2"						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		_snake_case			 = GPTaLMHeadModel.from_pretrained(lowercase_		,							output_hidden_states=lowercase_						)
  		torch.save(model.state_dict()		,							model_name + "local.pt"						)
  		return model
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_		,							lowercase_		,							lowercase_						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		_snake_case			 = copy.deepcopy(lowercase_						)
  		model.to(lowercase_						)
  
  		_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(lowercase_		,							lr=5e-5		,							eps=1e-8						)
  		_snake_case			 = get_linear_schedule_with_warmup(lowercase_		,							0		,							lowercase_						)
  		torch.cuda.empty_cache()
  		return model, lm_optimizer, lm_scheduler
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_		,							lowercase_		,							lowercase_		,							lowercase_						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		_snake_case			 = np.array(lowercase_						).mean()
  		_snake_case			 = np.array(lowercase_						).std()
  		_snake_case			 = (real_perps - avg) / std
  		_snake_case			 = list(zip(lowercase_		,							lowercase_		,							lowercase_						)						)
  		joblib.dump(lowercase_		,							lowercase_						)
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_		,							lowercase_		,							lowercase_		,							lowercase_		,							lowercase_		,							lowercase_		,							lowercase_		,							lowercase_="dev.jbl"		,							lowercase_=recopy_gpta		,							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		_snake_case			 = []
  		_snake_case			 = []
  		_snake_case			 = []
  
  		# Initialize the transformer model
  		_snake_case			 = copy.deepcopy(lowercase_						)
  		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(lowercase_		,							lowercase_		,							lowercase_						)
  
  		for step in tqdm(range(lowercase_						)						):
  				_snake_case			 = torch.zeros((1, context_len)		,							dtype=torch.long		,							device=lowercase_						)
  				_snake_case			 = random.choice(lowercase_						)
  				_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(lowercase_		,							labels=lowercase_						)
  				_snake_case			 = outputs[0]
  				_snake_case			 = 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
  				_snake_case			 = 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
  				_snake_case ,					_snake_case ,					_snake_case			 = 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 	_lowercase	(			lowercase_		,							lowercase_="data/tokenized_stories_train_wikitext103.jbl"		,							lowercase_=100		,							lowercase_=1026		,							lowercase_=True						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		_snake_case			 = joblib.load(lowercase_						)
  		print("data loaded"						)
  		_snake_case			 = []
  		if trim:
  				for i, example in enumerate(lowercase_						):
  						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(lowercase_						) >= number:
  								break
  				_snake_case			 = []
  				for j in range(i + 1		,							len(lowercase_						)						):
  						if len(data[j][0]						) > min_len:
  								train_data.append(data[j]						)
  		else:
  				_snake_case			 = data[0:number]
  				_snake_case			 = data[number:]
  
  		joblib.dump(lowercase_		,							"objective_set.jbl"						)
  		print("objective set saved"						)
  		return train_data, objective_set
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_		,							lowercase_		,							lowercase_		,							lowercase_		,							lowercase_=50		,							lowercase_="secondary_learner.pt"						):
  
  
  
  		'''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(lowercase_		,							shuffle=lowercase_		,							batch_size=lowercase_						)
  		_snake_case			 = DataLoader(lowercase_		,							shuffle=lowercase_		,							batch_size=lowercase_						)
  
  		# secondary learner model set up
  		_snake_case			 = nn.MSELoss()
  		_snake_case			 = nn.MSELoss(reduction="sum"						)
  		secondary_learner.to(lowercase_						)
  		_snake_case			 = 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
  		_snake_case			 = float("inf"						)
  		# Iterate through batches until we've used max_steps batches
  		for epoch in range(int(lowercase_						)						):
  				_snake_case			 = 0.0
  				secondary_learner.train()
  				for step, batch in enumerate(lowercase_						):
  						_snake_case			 = batch[0].to(lowercase_						)
  						_snake_case			 = batch[1].to(lowercase_						)
  						_snake_case			 = secondary_learner(lowercase_						)
  						q_optimizer.zero_grad()
  						_snake_case			 = 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_						)):
  								_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(lowercase_						):
  										_snake_case			 = batcha[0].to(lowercase_						)
  										_snake_case			 = batcha[1].to(lowercase_						)
  										_snake_case			 = 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_						)
  										_snake_case			 = q_lossa
  
  						secondary_learner.train()
  		return secondary_learner
  
  
  
  
  
  class   lowerCamelCase_							( nn.Module							):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					def __init__(					self,						__UpperCamelCase				):
  							super(__UpperCamelCase,						self				).__init__()
  							_snake_case			 = model.transformer.wte
  							_snake_case			 = copy.deepcopy(model.transformer.wte.weight				)
  							_snake_case			 = nn.Convad(self.embeddings.weight.size(1				),						256,						3,						padding=1				)
  							_snake_case			 = nn.Sequential(nn.Linear(256,						32				),						nn.Dropout(p=0.1				),						nn.Linear(32,						32				),						nn.Linear(32,						1				)				)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase				):
  							_snake_case			 = torch.max(self.conv(self.embeddings(__UpperCamelCase				).squeeze(1				).transpose(1,						2				)				),						2				)[0]
  							_snake_case			 = self.fc(__UpperCamelCase				)
  							return qs.squeeze(1				)
  
  
  
  
  
  
  					@classmethod
  					def        __SCREAMING_SNAKE_CASE			(					cls,						__UpperCamelCase,						__UpperCamelCase				):
  							_snake_case			 = cls(__UpperCamelCase				)  # this calls __init__
  							_snake_case			 = torch.load(__UpperCamelCase				)
  							secondary_learner.load_state_dict(__UpperCamelCase				)
  							_snake_case			 = model.transformer.wte
  							_snake_case			 = copy.deepcopy(model.transformer.wte.weight				)
  							return secondary_learner
  
  
  
  
  
  