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      		=					logging.getLogger(__name__)


def SCREAMING_SNAKE_CASE					(  lowercase_:						Optional[Any]					):
       random.seed(lowercase_					)
       np.random.seed(lowercase_					)
       torch.manual_seed(lowercase_					)
       torch.cuda.manual_seed_all(lowercase_					)


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


def SCREAMING_SNAKE_CASE					(  lowercase_:						Any="gpt2"					):
       _UpperCamelCase  :							str	    =	GPTaLMHeadModel.from_pretrained(lowercase_		,    output_hidden_states=lowercase_					)
       torch.save(model.state_dict()		,    model_name + 'local.pt'					)
       return model


def SCREAMING_SNAKE_CASE					(  lowercase_:						Dict		,    lowercase_:						Any		,    lowercase_:						Any					):
       _UpperCamelCase  :							Any	    =	copy.deepcopy(lowercase_					)
       model.to(lowercase_					)

       _UpperCamelCase  :							List[str]	    =	['bias', 'LayerNorm.weight']
       _UpperCamelCase  :							List[Any]	    =	[
           {
               '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  :							Optional[int]	    =	AdamW(lowercase_		,    lr=5e-5		,    eps=1e-8					)
       _UpperCamelCase  :							Union[str, Any]	    =	get_linear_schedule_with_warmup(lowercase_		,    0		,    lowercase_					)
       torch.cuda.empty_cache()
       return model, lm_optimizer, lm_scheduler


def SCREAMING_SNAKE_CASE					(  lowercase_:						List[Any]		,    lowercase_:						List[Any]		,    lowercase_:						Tuple		,    lowercase_:						List[Any]					):
       _UpperCamelCase  :							Union[str, Any]	    =	np.array(lowercase_					).mean()
       _UpperCamelCase  :							List[Any]	    =	np.array(lowercase_					).std()
       _UpperCamelCase  :							int	    =	(real_perps - avg) / std
       _UpperCamelCase  :							Any	    =	list(zip(lowercase_		,    lowercase_		,    lowercase_					)					)
       joblib.dump(lowercase_		,    lowercase_					)


def SCREAMING_SNAKE_CASE					(  lowercase_:						Union[str, Any]		,    lowercase_:						Tuple		,    lowercase_:						Optional[int]		,    lowercase_:						str		,    lowercase_:						Dict		,    lowercase_:						Optional[Any]		,    lowercase_:						str		,    lowercase_:						str="dev.jbl"		,    lowercase_:						Optional[Any]=recopy_gpta		,    ):
       _UpperCamelCase  :							Optional[Any]	    =	[]
       _UpperCamelCase  :							List[Any]	    =	[]
       _UpperCamelCase  :							int	    =	[]

       # Initialize the transformer model
       _UpperCamelCase  :							str	    =	copy.deepcopy(lowercase_					)
       orig_model.to(device='cpu'					)
       torch.cuda.empty_cache()

       # Compute perplexity of initial transformer model for comparison
       model.train()
       _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase  :							Any	    =	recopy_model(lowercase_		,    lowercase_		,    lowercase_					)

       for step in tqdm(range(lowercase_					)					):
              _UpperCamelCase  :							Tuple	    =	torch.zeros((1, context_len)		,    dtype=torch.long		,    device=lowercase_					)
              _UpperCamelCase  :							Any	    =	random.choice(lowercase_					)
              _UpperCamelCase  :							List[Any]	    =	random.randint(0		,    len(story[0]					) - context_len - 1					)
              _UpperCamelCase  :							Optional[int]	    =	story[0][start : start + context_len]
              lm_optimizer.zero_grad()
              _UpperCamelCase  :							Any	    =	model(lowercase_		,    labels=lowercase_					)
              _UpperCamelCase  :							Union[str, Any]	    =	outputs[0]
              _UpperCamelCase  :							int	    =	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
              _UpperCamelCase  :							Optional[int]	    =	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
              _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase  :							Dict	    =	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 SCREAMING_SNAKE_CASE					(  lowercase_:						Union[str, Any]		,    lowercase_:						Tuple="data/tokenized_stories_train_wikitext103.jbl"		,    lowercase_:						Tuple=100		,    lowercase_:						Tuple=1026		,    lowercase_:						Optional[int]=True					):
       _UpperCamelCase  :							Union[str, Any]	    =	joblib.load(lowercase_					)
       print('data loaded'					)
       _UpperCamelCase  :							int	    =	[]
       if trim:
              for i, example in enumerate(lowercase_					):
                     if len(example[0]					) > min_len:
                            _UpperCamelCase  :							List[Any]	    =	random.randint(0		,    len(example[0]					) - context_len - 1					)
                            objective_set.append(example[0, start : start + context_len]					)
                     if len(lowercase_					) >= number:
                            break
              _UpperCamelCase  :							Optional[int]	    =	[]
              for j in range(i + 1		,    len(lowercase_					)					):
                     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(lowercase_		,    'objective_set.jbl'					)
       print('objective set saved'					)
       return train_data, objective_set


def SCREAMING_SNAKE_CASE					(  lowercase_:						Optional[Any]		,    lowercase_:						List[Any]		,    lowercase_:						Union[str, Any]		,    lowercase_:						Dict		,    lowercase_:						Optional[Any]=50		,    lowercase_:						Dict="secondary_learner.pt"					):
       _UpperCamelCase  :							Any	    =	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  :							Tuple	    =	train_dataset[:512]
       _UpperCamelCase  :							Tuple	    =	train_dataset[512:]
       _UpperCamelCase  :							Dict	    =	DataLoader(lowercase_		,    shuffle=lowercase_		,    batch_size=lowercase_					)
       _UpperCamelCase  :							List[Any]	    =	DataLoader(lowercase_		,    shuffle=lowercase_		,    batch_size=lowercase_					)

       # secondary learner model set up
       _UpperCamelCase  :							Optional[int]	    =	nn.MSELoss()
       _UpperCamelCase  :							List[Any]	    =	nn.MSELoss(reduction='sum'					)
       secondary_learner.to(lowercase_					)
       _UpperCamelCase  :							Union[str, Any]	    =	torch.optim.Adam(secondary_learner.parameters()		,    lr=0.0_0001					)
       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  :							List[str]	    =	float('inf'					)
       # Iterate through batches until we've used max_steps batches
       for epoch in range(int(lowercase_					)					):
              _UpperCamelCase  :							Optional[Any]	    =	0.0
              secondary_learner.train()
              for step, batch in enumerate(lowercase_					):
                     _UpperCamelCase  :							List[str]	    =	batch[0].to(lowercase_					)
                     _UpperCamelCase  :							int	    =	batch[1].to(lowercase_					)
                     _UpperCamelCase  :							Optional[Any]	    =	secondary_learner(lowercase_					)
                     q_optimizer.zero_grad()
                     _UpperCamelCase  :							int	    =	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_					)):
                            _UpperCamelCase  :							Any	    =	tr_q_loss / (step + 1)

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

                     secondary_learner.train()
       return secondary_learner





class  __lowerCAmelCase	(							nn.Module    ):



 '''simple docstring'''

 def __init__(      self							:		Dict							,      lowerCamelCase							:		Optional[int]	)			->				Optional[Any]:
        super(lowerCamelCase_							,      self	).__init__()
        _UpperCamelCase  :							Dict	    =	model.transformer.wte
        _UpperCamelCase  :							Optional[Any]	    =	copy.deepcopy(model.transformer.wte.weight	)
        _UpperCamelCase  :							List[str]	    =	nn.Convad(self.embeddings.weight.size(1	)							,      2_5_6							,      3							,      padding=1	)
        _UpperCamelCase  :							List[Any]	    =	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        __snake_case       (      self							:		Optional[int]							,      lowerCamelCase							:		Union[str, Any]	)			->				Dict:
        _UpperCamelCase  :							Union[str, Any]	    =	torch.max(self.conv(self.embeddings(lowerCamelCase_	).squeeze(1	).transpose(1							,      2	)	)							,      2	)[0]
        _UpperCamelCase  :							List[Any]	    =	self.fc(lowerCamelCase_	)
        return qs.squeeze(1	)

 @classmethod
 def        __snake_case       (      cls							:		Optional[int]							,      lowerCamelCase							:		Optional[int]							,      lowerCamelCase							:		Dict	)			->				str:
        _UpperCamelCase  :							Tuple	    =	cls(lowerCamelCase_	)  # this calls __init__
        _UpperCamelCase  :							Union[str, Any]	    =	torch.load(lowerCamelCase_	)
        secondary_learner.load_state_dict(lowerCamelCase_	)
        _UpperCamelCase  :							Tuple	    =	model.transformer.wte
        _UpperCamelCase  :							Any	    =	copy.deepcopy(model.transformer.wte.weight	)
        return secondary_learner








import platform
from argparse import ArgumentParser

import huggingface_hub
import pandas
import pyarrow

from datasets import __version__ as version
from datasets.commands import BaseDatasetsCLICommand






def        a( A			:       Dict			)						->			Optional[int]:
					"""simple docstring"""





					return EnvironmentCommand()







class       _lowercase				(  lowerCAmelCase      ):





	"""simple docstring"""




	@staticmethod
	def 	UpperCamelCase_			(lowerCamelCase_						):
						"""simple docstring"""

						a   	=  parser.add_parser("env"						,			help="Print relevant system environment info."						)
						download_parser.set_defaults(func=lowerCamelCase_						)




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  {
						    "`datasets` version": version,
						    "Platform": platform.platform(),
						    "Python version": platform.python_version(),
						    "Huggingface_hub version": huggingface_hub.__version__,
						    "PyArrow version": pyarrow.__version__,
						    "Pandas version": pandas.__version__,
						}

						print("\nCopy-and-paste the text below in your GitHub issue.\n"						)
						print(self.format_dict(lowerCamelCase_						)						)

						return info




	@staticmethod
	def 	UpperCamelCase_			(lowerCamelCase_						):
						"""simple docstring"""

						return "\n".join([F'''- {prop}: {val}''' for prop, val in d.items()]						) + "\n"
