
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


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


def 					__lowerCAmelCase    (    a__="gpt2"						) ->						List[Any]:
				__a    							=					GPTaLMHeadModel.from_pretrained(a__		,					output_hidden_states=a__						)
				torch.save(model.state_dict()		,					model_name + '''local.pt'''						)
				return model


def 					__lowerCAmelCase    (    a__		,					a__		,					a__						) ->						str:
				__a    							=					copy.deepcopy(a__						)
				model.to(a__						)

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


def 					__lowerCAmelCase    (    a__		,					a__		,					a__		,					a__						) ->						Any:
				__a    							=					np.array(a__						).mean()
				__a    							=					np.array(a__						).std()
				__a    							=					(real_perps - avg) / std
				__a    							=					list(zip(a__		,					a__		,					a__						)						)
				joblib.dump(a__		,					a__						)


def 					__lowerCAmelCase    (    a__		,					a__		,					a__		,					a__		,					a__		,					a__		,					a__		,					a__="dev.jbl"		,					a__=recopy_gpta		,					) ->						Dict:
				__a    							=					[]
				__a    							=					[]
				__a    							=					[]

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

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

				for step in tqdm(range(a__						)						):
								__a    							=					torch.zeros((1, context_len)		,					dtype=torch.long		,					device=a__						)
								__a    							=					random.choice(a__						)
								__a    							=					random.randint(0		,					len(story[0]						) - context_len - 1						)
								__a    							=					story[0][start : start + context_len]
								lm_optimizer.zero_grad()
								__a    							=					model(a__		,					labels=a__						)
								__a    							=					outputs[0]
								__a    							=					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
								__a    							=					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
								__a							,  __a							,  __a    							=					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__		,					a__="data/tokenized_stories_train_wikitext103.jbl"		,					a__=100		,					a__=1026		,					a__=True						) ->						Optional[Any]:
				__a    							=					joblib.load(a__						)
				print('''data loaded'''						)
				__a    							=					[]
				if trim:
								for i, example in enumerate(a__						):
												if len(example[0]						) > min_len:
																__a    							=					random.randint(0		,					len(example[0]						) - context_len - 1						)
																objective_set.append(example[0, start : start + context_len]						)
												if len(a__						) >= number:
																break
								__a    							=					[]
								for j in range(i + 1		,					len(a__						)						):
												if len(data[j][0]						) > min_len:
																train_data.append(data[j]						)
				else:
								__a    							=					data[0:number]
								__a    							=					data[number:]

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


def 					__lowerCAmelCase    (    a__		,					a__		,					a__		,					a__		,					a__=50		,					a__="secondary_learner.pt"						) ->						int:
				__a    							=					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
				__a    							=					train_dataset[:512]
				__a    							=					train_dataset[512:]
				__a    							=					DataLoader(a__		,					shuffle=a__		,					batch_size=a__						)
				__a    							=					DataLoader(a__		,					shuffle=a__		,					batch_size=a__						)

				# secondary learner model set up
				__a    							=					nn.MSELoss()
				__a    							=					nn.MSELoss(reduction='''sum'''						)
				secondary_learner.to(a__						)
				__a    							=					torch.optim.Adam(secondary_learner.parameters()		,					lr=0.00_001						)
				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
				__a    							=					float('''inf'''						)
				# Iterate through batches until we've used max_steps batches
				for epoch in range(int(a__						)						):
								__a    							=					0.0
								secondary_learner.train()
								for step, batch in enumerate(a__						):
												__a    							=					batch[0].to(a__						)
												__a    							=					batch[1].to(a__						)
												__a    							=					secondary_learner(a__						)
												q_optimizer.zero_grad()
												__a    							=					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__						)):
																__a    							=					tr_q_loss / (step + 1)

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

												secondary_learner.train()
				return secondary_learner







class     __A(				nn.Module    ):




			def __init__(  self	,  _snake_case       )					->   Union[str, Any]:


							'''simple docstring'''




							super(UpperCamelCase__	,  self       ).__init__()
							__a    							=					model.transformer.wte
							__a    							=					copy.deepcopy(model.transformer.wte.weight       )
							__a    							=					nn.Convad(self.embeddings.weight.size(1       )	,  256	,  3	,  padding=1       )
							__a    							=					nn.Sequential(nn.Linear(256	,  32       )	,  nn.Dropout(p=0.1       )	,  nn.Linear(32	,  32       )	,  nn.Linear(32	,  1       )       )




			def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Dict:


							'''simple docstring'''




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




			@classmethod
			def        SCREAMING_SNAKE_CASE_	(  cls	,  _snake_case	,  _snake_case       )					->   List[Any]:


							'''simple docstring'''




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

import os

from apache_beam.io.filesystems import FileSystems
from apache_beam.pipeline import Pipeline

from .logging import get_logger


A  :			Optional[int]     				=				2 << 2_0  # 2mb
A  :			Optional[int]     				=				get_logger(__name__)







class     __A(				a    ):




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Union[str, Any]:


								'''simple docstring'''




								__a    							=					self._options.get_all_options().get('''runner'''       )
								return runner in [None, "DirectRunner", "PortableRunner"]



def 					__lowerCAmelCase    (    a__		,					a__		,					a__=False						) ->						Union[str, Any]:
				__a    							=					FileSystems
				if fs.exists(a__						):
								if force_upload:
												logger.info(F"""Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True."""						)
								else:
												logger.info(F"""Remote path already exist: {remote_file_path}. Skipping it as force_upload=False."""						)
												return
				with fs.create(a__						) as remote_file:
								with open(a__		,					'''rb'''						) as local_file:
												__a    							=					local_file.read(a__						)
												while chunk:
																remote_file.write(a__						)
																__a    							=					local_file.read(a__						)






def 					__lowerCAmelCase    (    a__		,					a__		,					a__=False						) ->						Dict:
				__a    							=					FileSystems
				if os.path.exists(a__						):
								if force_download:
												logger.info(F"""Local path already exist: {remote_file_path}. Overwriting it as force_upload=True."""						)
								else:
												logger.info(F"""Local path already exist: {remote_file_path}. Skipping it as force_upload=False."""						)
												return
				with fs.open(a__						) as remote_file:
								with open(a__		,					'''wb'''						) as local_file:
												__a    							=					remote_file.read(a__						)
												while chunk:
																local_file.write(a__						)
																__a    							=					remote_file.read(a__						)