


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 :				int						    =       logging.getLogger(__name__)
def 							UpperCamelCase_  (				__snake_case			:						Union[str, Any]     )->      List[Any]:




						'''simple docstring'''
						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[int]	,			__snake_case			:						List[str]	,			__snake_case			:						Union[str, Any]     )->      Any:




						'''simple docstring'''
						model.eval()
						__magic_name__ 		=			next(model.parameters()     ).device
						__magic_name__ 		=			1
						__magic_name__ 		=			torch.zeros((eval_batch_size, context_len)	,			dtype=torch.long	,			device=__snake_case     )
						__magic_name__ 		=			DataLoader(__snake_case	,			shuffle=__snake_case	,			batch_size=__snake_case     )
						__magic_name__ 		=			torch.zeros(1	,			device=__snake_case     )
						__magic_name__ 		=			0
						for batch in eval_dataloader:
												batch.to(__snake_case     )
												# pad
												context.zero_()
												for i in range(__snake_case     ):
																		__magic_name__ 		=			batch[i]
												__magic_name__ 		=			model(__snake_case	,			labels=__snake_case     )
												eval_loss += outputs[0].sum().item()
												nb_eval_examples += batch.size(0     )
						__magic_name__ 		=			eval_loss / nb_eval_examples
						__magic_name__ 		=			torch.exp(__snake_case     )
						model.train()
						return perplexity
def 							UpperCamelCase_  (				__snake_case			:						Dict="gpt2"     )->      Tuple:




						'''simple docstring'''
						__magic_name__ 		=			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			:						Optional[int]	,			__snake_case			:						Optional[int]	,			__snake_case			:						Any     )->      int:




						'''simple docstring'''
						__magic_name__ 		=			copy.deepcopy(__snake_case     )
						model.to(__snake_case     )

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




						'''simple docstring'''
						__magic_name__ 		=			np.array(__snake_case     ).mean()
						__magic_name__ 		=			np.array(__snake_case     ).std()
						__magic_name__ 		=			(real_perps - avg) / std
						__magic_name__ 		=			list(zip(__snake_case	,			__snake_case	,			__snake_case     )     )
						joblib.dump(__snake_case	,			__snake_case     )
def 							UpperCamelCase_  (				__snake_case			:						Optional[int]	,			__snake_case			:						Any	,			__snake_case			:						int	,			__snake_case			:						Optional[Any]	,			__snake_case			:						Optional[int]	,			__snake_case			:						Union[str, Any]	,			__snake_case			:						int	,			__snake_case			:						Union[str, Any]="dev.jbl"	,			__snake_case			:						Union[str, Any]=recopy_gpta	,			)->      str:




						'''simple docstring'''
						__magic_name__ 		=			[]
						__magic_name__ 		=			[]
						__magic_name__ 		=			[]

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

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

						for step in tqdm(range(__snake_case     )     ):
												__magic_name__ 		=			torch.zeros((1, context_len)	,			dtype=torch.long	,			device=__snake_case     )
												__magic_name__ 		=			random.choice(__snake_case     )
												__magic_name__ 		=			random.randint(0	,			len(story[0]     ) - context_len - 1     )
												__magic_name__ 		=			story[0][start : start + context_len]
												lm_optimizer.zero_grad()
												__magic_name__ 		=			model(__snake_case	,			labels=__snake_case     )
												__magic_name__ 		=			outputs[0]
												__magic_name__ 		=			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
												__magic_name__ 		=			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
												__magic_name__		,			__magic_name__		,			__magic_name__ 		=			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			:						Optional[Any]	,			__snake_case			:						Any="data/tokenized_stories_train_wikitext103.jbl"	,			__snake_case			:						Optional[int]=1_00	,			__snake_case			:						Optional[Any]=10_26	,			__snake_case			:						Tuple=True     )->      Union[str, Any]:




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

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




						'''simple docstring'''
						__magic_name__ 		=			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
						__magic_name__ 		=			train_dataset[:5_12]
						__magic_name__ 		=			train_dataset[5_12:]
						__magic_name__ 		=			DataLoader(__snake_case	,			shuffle=__snake_case	,			batch_size=__snake_case     )
						__magic_name__ 		=			DataLoader(__snake_case	,			shuffle=__snake_case	,			batch_size=__snake_case     )

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

																								secondary_learner.eval()
																								__magic_name__ 		=			0.0
																								__magic_name__ 		=			0.0
																								__magic_name__ 		=			[]
																								__magic_name__ 		=			[]
																								# Compute performance of the secondary learner after this batch
																								for stepa, batcha in enumerate(__snake_case     ):
																														__magic_name__ 		=			batcha[0].to(__snake_case     )
																														__magic_name__ 		=			batcha[1].to(__snake_case     )
																														__magic_name__ 		=			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     )
																														__magic_name__ 		=			q_lossa

																		secondary_learner.train()
						return secondary_learner




class     __snake_case							(      nn.Module):
					def __init__(	self					:	str  ,			_lowerCAmelCase					:	Optional[int]     )       ->				List[Any]:



											'''simple docstring'''





											super(__A  ,			self     ).__init__()
											__magic_name__ 		=			model.transformer.wte
											__magic_name__ 		=			copy.deepcopy(model.transformer.wte.weight     )
											__magic_name__ 		=			nn.Convad(self.embeddings.weight.size(1     )  ,			2_5_6  ,			3  ,			padding=1     )
											__magic_name__ 		=			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   a (	self					:	Tuple  ,			_lowerCAmelCase					:	List[Any]     )       ->				List[Any]:



											'''simple docstring'''





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







					@classmethod
					def   a (	cls					:	Union[str, Any]  ,			_lowerCAmelCase					:	List[str]  ,			_lowerCAmelCase					:	Dict     )       ->				int:



											'''simple docstring'''





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



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class     __snake_case							(      _UpperCAmelCase):
					SCREAMING_SNAKE_CASE_		:					Union[str, Any] 				= ["image_processor", "tokenizer"]
					SCREAMING_SNAKE_CASE_		:					Tuple 				= "AutoImageProcessor"
					SCREAMING_SNAKE_CASE_		:					Dict 				= "AutoTokenizer"
					def __init__(	self					:	List[str]  ,			_lowerCAmelCase					:	str=None  ,			_lowerCAmelCase					:	Dict=None  ,			**_lowerCAmelCase					:	List[str]     )       ->				Optional[int]:



											'''simple docstring'''





											__magic_name__ 		=			None
											if "feature_extractor" in kwargs:
																	warnings.warn(
																	    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
																	    ' instead.'  ,			_lowerCAmelCase  ,			)
																	__magic_name__ 		=			kwargs.pop('feature_extractor'     )

											__magic_name__ 		=			image_processor if image_processor is not None else feature_extractor
											if image_processor is None:
																	raise ValueError('You have to specify an image_processor.'     )
											if tokenizer is None:
																	raise ValueError('You have to specify a tokenizer.'     )

											super().__init__(_lowerCAmelCase  ,			_lowerCAmelCase     )
											__magic_name__ 		=			self.image_processor
					def __call__(	self					:	str  ,			_lowerCAmelCase					:	Any=None  ,			_lowerCAmelCase					:	int=None  ,			_lowerCAmelCase					:	List[str]=None  ,			**_lowerCAmelCase					:	int     )       ->				int:



											'''simple docstring'''





											if text is None and images is None:
																	raise ValueError('You have to specify either text or images. Both cannot be none.'     )

											if text is not None:
																	__magic_name__ 		=			self.tokenizer(_lowerCAmelCase  ,			return_tensors=_lowerCAmelCase  ,			**_lowerCAmelCase     )

											if images is not None:
																	__magic_name__ 		=			self.image_processor(_lowerCAmelCase  ,			return_tensors=_lowerCAmelCase  ,			**_lowerCAmelCase     )

											if text is not None and images is not None:
																	__magic_name__ 		=			image_features.pixel_values
																	return encoding
											elif text is not None:
																	return encoding
											else:
																	return BatchEncoding(data=dict(**_lowerCAmelCase     )  ,			tensor_type=_lowerCAmelCase     )
					def   a (	self					:	Optional[int]  ,			*_lowerCAmelCase					:	Optional[int]  ,			**_lowerCAmelCase					:	Optional[Any]     )       ->				Optional[int]:



											'''simple docstring'''





											return self.tokenizer.batch_decode(*_lowerCAmelCase  ,			**_lowerCAmelCase     )
					def   a (	self					:	Union[str, Any]  ,			*_lowerCAmelCase					:	str  ,			**_lowerCAmelCase					:	Tuple     )       ->				int:



											'''simple docstring'''





											return self.tokenizer.decode(*_lowerCAmelCase  ,			**_lowerCAmelCase     )
					@property
					def   a (	self					:	List[Any]     )       ->				Tuple:



											'''simple docstring'''





											__magic_name__ 		=			self.tokenizer.model_input_names
											__magic_name__ 		=			self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )
					@property
					def   a (	self					:	Optional[Any]     )       ->				Dict:



											'''simple docstring'''





											warnings.warn(
											    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'  ,			_lowerCAmelCase  ,			)
											return self.image_processor_class







					@property
					def   a (	self					:	Dict     )       ->				Tuple:



											'''simple docstring'''





											warnings.warn(
											    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'  ,			_lowerCAmelCase  ,			)
											return self.image_processor

