






'''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


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

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

			# Initialize the transformer model
			UpperCAmelCase_     :   int       =   copy.deepcopy(_lowerCamelCase						)
			orig_model.to(device='''cpu'''						)
			torch.cuda.empty_cache()

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

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

						# Periodically save the stored (X, IG(X)) pairs
						if step % 1_0_0_0 == 0 and step > 1:
									intermittent_save(_lowerCamelCase	,   _lowerCamelCase	,   _lowerCamelCase	,   _lowerCamelCase						)

						# Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
						UpperCAmelCase_     :   List[str]       =   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 __UpperCAmelCase (			A			:	Tuple	,   A			:	Tuple="data/tokenized_stories_train_wikitext103.jbl"	,   A			:	int=1_0_0	,   A			:	Any=1_0_2_6	,   A			:	List[str]=True						)  -> Union[str, Any]:
			UpperCAmelCase_     :   int       =   joblib.load(_lowerCamelCase						)
			print('''data loaded'''						)
			UpperCAmelCase_     :   List[Any]       =   []
			if trim:
						for i, example in enumerate(_lowerCamelCase						):
									if len(example[0]						) > min_len:
												UpperCAmelCase_     :   Dict       =   random.randint(0	,   len(example[0]						) - context_len - 1						)
												objective_set.append(example[0, start : start + context_len]						)
									if len(_lowerCamelCase						) >= number:
												break
						UpperCAmelCase_     :   Any       =   []
						for j in range(i + 1	,   len(_lowerCamelCase						)						):
									if len(data[j][0]						) > min_len:
												train_data.append(data[j]						)
			else:
						UpperCAmelCase_     :   List[str]       =   data[0:number]
						UpperCAmelCase_     :   Optional[int]       =   data[number:]

			joblib.dump(_lowerCamelCase	,   '''objective_set.jbl'''						)
			print('''objective set saved'''						)
			return train_data, objective_set
def __UpperCAmelCase (			A			:	Optional[Any]	,   A			:	List[str]	,   A			:	Optional[int]	,   A			:	str	,   A			:	Any=5_0	,   A			:	str="secondary_learner.pt"						)  -> int:
			UpperCAmelCase_     :   Optional[int]       =   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_     :   List[Any]       =   train_dataset[:5_1_2]
			UpperCAmelCase_     :   List[str]       =   train_dataset[5_1_2:]
			UpperCAmelCase_     :   List[Any]       =   DataLoader(_lowerCamelCase	,   shuffle=_lowerCamelCase	,   batch_size=_lowerCamelCase						)
			UpperCAmelCase_     :   List[Any]       =   DataLoader(_lowerCamelCase	,   shuffle=_lowerCamelCase	,   batch_size=_lowerCamelCase						)

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

												secondary_learner.eval()
												UpperCAmelCase_     :   Any       =   0.0
												UpperCAmelCase_     :   str       =   0.0
												UpperCAmelCase_     :   List[str]       =   []
												UpperCAmelCase_     :   Optional[Any]       =   []
												# Compute performance of the secondary learner after this batch
												for stepa, batcha in enumerate(_lowerCamelCase						):
															UpperCAmelCase_     :   Tuple       =   batcha[0].to(_lowerCamelCase						)
															UpperCAmelCase_     :   Union[str, Any]       =   batcha[1].to(_lowerCamelCase						)
															UpperCAmelCase_     :   Union[str, Any]       =   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						)
															UpperCAmelCase_     :   Union[str, Any]       =   q_lossa

									secondary_learner.train()
			return secondary_learner







class 				_UpperCamelCase     (			nn.Module):




					def __init__(       self       :						Optional[Any]							,				_A       :						int     )  ->    List[Any]:
								super(snake_case_							,				self     ).__init__()
								UpperCAmelCase_     :   Union[str, Any]       =   model.transformer.wte
								UpperCAmelCase_     :   Union[str, Any]       =   copy.deepcopy(model.transformer.wte.weight     )
								UpperCAmelCase_     :   Optional[int]       =   nn.Convad(self.embeddings.weight.size(1     )							,				2_56							,				3							,				padding=1     )
								UpperCAmelCase_     :   Dict       =   nn.Sequential(nn.Linear(2_56							,				32     )							,				nn.Dropout(p=0.1     )							,				nn.Linear(32							,				32     )							,				nn.Linear(32							,				1     )     )




					def 						A     (       self       :						Dict							,				_A       :						Union[str, Any]     )  ->    Union[str, Any]:
								UpperCAmelCase_     :   Tuple       =   torch.max(self.conv(self.embeddings(snake_case_     ).squeeze(1     ).transpose(1							,				2     )     )							,				2     )[0]
								UpperCAmelCase_     :   List[str]       =   self.fc(snake_case_     )
								return qs.squeeze(1     )





					@classmethod
					def 						A     (       cls       :						Optional[Any]							,				_A       :						Union[str, Any]							,				_A       :						str     )  ->    Optional[int]:
								UpperCAmelCase_     :   List[str]       =   cls(snake_case_     )  # this calls __init__
								UpperCAmelCase_     :   Union[str, Any]       =   torch.load(snake_case_     )
								secondary_learner.load_state_dict(snake_case_     )
								UpperCAmelCase_     :   int       =   model.transformer.wte
								UpperCAmelCase_     :   Tuple       =   copy.deepcopy(model.transformer.wte.weight     )
								return secondary_learner










'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_UpperCamelCase		:		Optional[Any]								=			{'configuration_vit_hybrid': ['VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTHybridConfig']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_UpperCamelCase		:		Optional[int]								=			[
						    'VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'ViTHybridForImageClassification',
						    'ViTHybridModel',
						    'ViTHybridPreTrainedModel',
						]

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_UpperCamelCase		:		str								=			['ViTHybridImageProcessor']


if TYPE_CHECKING:
						from .configuration_vit_hybrid import VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTHybridConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_vit_hybrid import (
												    VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ViTHybridForImageClassification,
												    ViTHybridModel,
												    ViTHybridPreTrainedModel,
												)

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .image_processing_vit_hybrid import ViTHybridImageProcessor


else:
						import sys

						_UpperCamelCase		:		Tuple								=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


