






"""simple docstring"""



from typing import TYPE_CHECKING

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


_snake_case       :      Dict       =					{
    "configuration_esm": ["ESM_PRETRAINED_CONFIG_ARCHIVE_MAP", "EsmConfig"],
    "tokenization_esm": ["EsmTokenizer"],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case       :      str       =					[
						    "ESM_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "EsmForMaskedLM",
						    "EsmForSequenceClassification",
						    "EsmForTokenClassification",
						    "EsmModel",
						    "EsmPreTrainedModel",
						]
						_snake_case       :      List[Any]       =					["EsmForProteinFolding", "EsmFoldPreTrainedModel"]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case       :      List[str]       =					[
						    "TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TFEsmForMaskedLM",
						    "TFEsmForSequenceClassification",
						    "TFEsmForTokenClassification",
						    "TFEsmModel",
						    "TFEsmPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_esm import ESM_PRETRAINED_CONFIG_ARCHIVE_MAP, EsmConfig
						from .tokenization_esm import EsmTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_esm import (
												    ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    EsmForMaskedLM,
												    EsmForSequenceClassification,
												    EsmForTokenClassification,
												    EsmModel,
												    EsmPreTrainedModel,
												)
												from .modeling_esmfold import EsmFoldPreTrainedModel, EsmForProteinFolding

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_esm import (
												    TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFEsmForMaskedLM,
												    TFEsmForSequenceClassification,
												    TFEsmForTokenClassification,
												    TFEsmModel,
												    TFEsmPreTrainedModel,
												)


else:
						import sys

						_snake_case       :      str       =					_LazyModule(__name__, globals()["__file__"], _import_structure)







"""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


_snake_case       :      Union[str, Any]       =					logging.getLogger(__name__)




def 						lowerCamelCase_				(  __A  :		Any			):
					random.seed(__A			)
					np.random.seed(__A			)
					torch.manual_seed(__A			)
					torch.cuda.manual_seed_all(__A			)




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




def 						lowerCamelCase_				(  __A  :		List[str]="gpt2"			):
					lowerCamelCase__				=GPTaLMHeadModel.from_pretrained(__A			,	output_hidden_states=__A			)
					torch.save(model.state_dict()			,	model_name + 'local.pt'			)
					return model




def 						lowerCamelCase_				(  __A  :		Optional[Any]			,	__A  :		Tuple			,	__A  :		List[str]			):
					lowerCamelCase__				=copy.deepcopy(__A			)
					model.to(__A			)

					lowerCamelCase__				=['bias', 'LayerNorm.weight']
					lowerCamelCase__				=[
					    {
					        '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},
					]
					lowerCamelCase__				=AdamW(__A			,	lr=5E-5			,	eps=1E-8			)
					lowerCamelCase__				=get_linear_schedule_with_warmup(__A			,	0			,	__A			)
					torch.cuda.empty_cache()
					return model, lm_optimizer, lm_scheduler




def 						lowerCamelCase_				(  __A  :		Union[str, Any]			,	__A  :		Optional[int]			,	__A  :		str			,	__A  :		Tuple			):
					lowerCamelCase__				=np.array(__A			).mean()
					lowerCamelCase__				=np.array(__A			).std()
					lowerCamelCase__				=(real_perps - avg) / std
					lowerCamelCase__				=list(zip(__A			,	__A			,	__A			)			)
					joblib.dump(__A			,	__A			)




def 						lowerCamelCase_				(  __A  :		Optional[int]			,	__A  :		Union[str, Any]			,	__A  :		Any			,	__A  :		Optional[Any]			,	__A  :		Any			,	__A  :		Dict			,	__A  :		Any			,	__A  :		List[str]="dev.jbl"			,	__A  :		List[str]=recopy_gpta			,	):
					lowerCamelCase__				=[]
					lowerCamelCase__				=[]
					lowerCamelCase__				=[]

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

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

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

										# Periodically save the stored (X, IG(X)) pairs
										if step % 1000 == 0 and step > 1:
															intermittent_save(__A			,	__A			,	__A			,	__A			)

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

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

					intermittent_save(__A			,	__A			,	__A			,	__A			)




def 						lowerCamelCase_				(  __A  :		Optional[Any]			,	__A  :		Any="data/tokenized_stories_train_wikitext103.jbl"			,	__A  :		Any=100			,	__A  :		str=1026			,	__A  :		int=True			):
					lowerCamelCase__				=joblib.load(__A			)
					print('data loaded'			)
					lowerCamelCase__				=[]
					if trim:
										for i, example in enumerate(__A			):
															if len(example[0]			) > min_len:
																				lowerCamelCase__				=random.randint(0			,	len(example[0]			) - context_len - 1			)
																				objective_set.append(example[0, start : start + context_len]			)
															if len(__A			) >= number:
																				break
										lowerCamelCase__				=[]
										for j in range(i + 1			,	len(__A			)			):
															if len(data[j][0]			) > min_len:
																				train_data.append(data[j]			)
					else:
										lowerCamelCase__				=data[0:number]
										lowerCamelCase__				=data[number:]

					joblib.dump(__A			,	'objective_set.jbl'			)
					print('objective set saved'			)
					return train_data, objective_set




def 						lowerCamelCase_				(  __A  :		Optional[int]			,	__A  :		List[str]			,	__A  :		Optional[Any]			,	__A  :		str			,	__A  :		Optional[int]=50			,	__A  :		Optional[int]="secondary_learner.pt"			):
					lowerCamelCase__				=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
					lowerCamelCase__				=train_dataset[:512]
					lowerCamelCase__				=train_dataset[512:]
					lowerCamelCase__				=DataLoader(__A			,	shuffle=__A			,	batch_size=__A			)
					lowerCamelCase__				=DataLoader(__A			,	shuffle=__A			,	batch_size=__A			)

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

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

															secondary_learner.train()
					return secondary_learner




class        __lowercase(       nn.Module  ):






						def __init__(					self    ,						_SCREAMING_SNAKE_CASE)			->  Optional[int]:




											"""simple docstring"""





											super(_SCREAMING_SNAKE_CASE    ,						self).__init__()
											lowerCamelCase__				=model.transformer.wte
											lowerCamelCase__				=copy.deepcopy(model.transformer.wte.weight)
											lowerCamelCase__				=nn.Convad(self.embeddings.weight.size(1)    ,						2_56    ,						3    ,						padding=1)
											lowerCamelCase__				=nn.Sequential(nn.Linear(2_56    ,						32)    ,						nn.Dropout(p=0.1)    ,						nn.Linear(32    ,						32)    ,						nn.Linear(32    ,						1))






						def 		lowerCAmelCase   (					self    ,						_SCREAMING_SNAKE_CASE)			->  Optional[int]:




											"""simple docstring"""





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






						@classmethod
						def 		lowerCAmelCase   (					cls    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE)			->  List[Any]:




											"""simple docstring"""





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