
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


__SCREAMING_SNAKE_CASE     :				Any             =       logging.getLogger(__name__)
def       UpperCAmelCase__							(    __magic_name__  :       Optional[int]					):


					'''simple docstring'''

					random.seed(__magic_name__					)
					np.random.seed(__magic_name__					)
					torch.manual_seed(__magic_name__					)
					torch.cuda.manual_seed_all(__magic_name__					)
def       UpperCAmelCase__							(    __magic_name__  :       Optional[int]							,					__magic_name__  :       Tuple							,					__magic_name__  :       List[str]					):


					'''simple docstring'''

					model.eval()
					lowerCAmelCase						:   Union[str, Any]							=       next(model.parameters()					).device
					lowerCAmelCase						:   List[Any]							=       1
					lowerCAmelCase						:   Union[str, Any]							=       torch.zeros((eval_batch_size, context_len)							,					dtype=torch.long							,					device=__magic_name__					)
					lowerCAmelCase						:   List[Any]							=       DataLoader(__magic_name__							,					shuffle=__magic_name__							,					batch_size=__magic_name__					)
					lowerCAmelCase						:   Tuple							=       torch.zeros(1							,					device=__magic_name__					)
					lowerCAmelCase						:   Tuple							=       0
					for batch in eval_dataloader:
										batch.to(__magic_name__					)
										# pad
										context.zero_()
										for i in range(__magic_name__					):
															lowerCAmelCase						:   List[Any]							=       batch[i]
										lowerCAmelCase						:   Optional[Any]							=       model(__magic_name__							,					labels=__magic_name__					)
										eval_loss += outputs[0].sum().item()
										nb_eval_examples += batch.size(0					)
					lowerCAmelCase						:   Dict							=       eval_loss / nb_eval_examples
					lowerCAmelCase						:   Union[str, Any]							=       torch.exp(__magic_name__					)
					model.train()
					return perplexity
def       UpperCAmelCase__							(    __magic_name__  :       int="gpt2"					):


					'''simple docstring'''

					lowerCAmelCase						:   Any							=       GPTaLMHeadModel.from_pretrained(__magic_name__							,					output_hidden_states=__magic_name__					)
					torch.save(model.state_dict()							,					model_name + '''local.pt'''					)
					return model
def       UpperCAmelCase__							(    __magic_name__  :       str							,					__magic_name__  :       Optional[Any]							,					__magic_name__  :       Dict					):


					'''simple docstring'''

					lowerCAmelCase						:   List[str]							=       copy.deepcopy(__magic_name__					)
					model.to(__magic_name__					)

					lowerCAmelCase						:   List[str]							=       ['''bias''', '''LayerNorm.weight''']
					lowerCAmelCase						:   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},
					]
					lowerCAmelCase						:   Optional[Any]							=       AdamW(__magic_name__							,					lr=5e-5							,					eps=1e-8					)
					lowerCAmelCase						:   Tuple							=       get_linear_schedule_with_warmup(__magic_name__							,					0							,					__magic_name__					)
					torch.cuda.empty_cache()
					return model, lm_optimizer, lm_scheduler
def       UpperCAmelCase__							(    __magic_name__  :       List[Any]							,					__magic_name__  :       Optional[Any]							,					__magic_name__  :       List[Any]							,					__magic_name__  :       Optional[Any]					):


					'''simple docstring'''

					lowerCAmelCase						:   Tuple							=       np.array(__magic_name__					).mean()
					lowerCAmelCase						:   str							=       np.array(__magic_name__					).std()
					lowerCAmelCase						:   str							=       (real_perps - avg) / std
					lowerCAmelCase						:   List[Any]							=       list(zip(__magic_name__							,					__magic_name__							,					__magic_name__					)					)
					joblib.dump(__magic_name__							,					__magic_name__					)
def       UpperCAmelCase__							(    __magic_name__  :       List[str]							,					__magic_name__  :       Optional[Any]							,					__magic_name__  :       Optional[int]							,					__magic_name__  :       List[str]							,					__magic_name__  :       Optional[int]							,					__magic_name__  :       Tuple							,					__magic_name__  :       Any							,					__magic_name__  :       Tuple="dev.jbl"							,					__magic_name__  :       Optional[int]=recopy_gpta							,					):


					'''simple docstring'''

					lowerCAmelCase						:   Any							=       []
					lowerCAmelCase						:   List[str]							=       []
					lowerCAmelCase						:   str							=       []

					# Initialize the transformer model
					lowerCAmelCase						:   List[Any]							=       copy.deepcopy(__magic_name__					)
					orig_model.to(device='''cpu'''					)
					torch.cuda.empty_cache()

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

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

										# Periodically save the stored (X, IG(X)) pairs
										if step % 10_00 == 0 and step > 1:
															intermittent_save(__magic_name__							,					__magic_name__							,					__magic_name__							,					__magic_name__					)

										# Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
										lowerCAmelCase		,							lowerCAmelCase		,							lowerCAmelCase						:   Optional[Any]							=       recopy_model(__magic_name__							,					__magic_name__							,					__magic_name__					)

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

					intermittent_save(__magic_name__							,					__magic_name__							,					__magic_name__							,					__magic_name__					)
def       UpperCAmelCase__							(    __magic_name__  :       int							,					__magic_name__  :       Dict="data/tokenized_stories_train_wikitext103.jbl"							,					__magic_name__  :       Optional[int]=1_00							,					__magic_name__  :       str=10_26							,					__magic_name__  :       str=True					):


					'''simple docstring'''

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

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


					'''simple docstring'''

					lowerCAmelCase						:   str							=       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						:   int							=       train_dataset[:5_12]
					lowerCAmelCase						:   List[str]							=       train_dataset[5_12:]
					lowerCAmelCase						:   Optional[Any]							=       DataLoader(__magic_name__							,					shuffle=__magic_name__							,					batch_size=__magic_name__					)
					lowerCAmelCase						:   int							=       DataLoader(__magic_name__							,					shuffle=__magic_name__							,					batch_size=__magic_name__					)

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

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

															secondary_learner.train()
					return secondary_learner


class 						__magic_name__				(  nn.Module      ):





		def __init__(							self      :		Tuple  ,					lowerCamelCase__      :		Any			):
							super(lowerCamelCase__  ,					self			).__init__()
							lowerCAmelCase						:   Optional[int]							=       model.transformer.wte
							lowerCAmelCase						:   Any							=       copy.deepcopy(model.transformer.wte.weight			)
							lowerCAmelCase						:   int							=       nn.Convad(self.embeddings.weight.size(1			)  ,					2_5_6  ,					3  ,					padding=1			)
							lowerCAmelCase						:   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  _A							(							self      :		Any  ,					lowerCamelCase__      :		Union[str, Any]			):
							lowerCAmelCase						:   Any							=       torch.max(self.conv(self.embeddings(lowerCamelCase__			).squeeze(1			).transpose(1  ,					2			)			)  ,					2			)[0]
							lowerCAmelCase						:   Optional[Any]							=       self.fc(lowerCamelCase__			)
							return qs.squeeze(1			)





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


from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


__SCREAMING_SNAKE_CASE     :				Optional[Any]             =       logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE     :				int             =       {
    'gpt2': 'https://huggingface.co/gpt2/resolve/main/config.json',
    'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/config.json',
    'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/config.json',
    'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/config.json',
    'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/config.json',
}


class 						__magic_name__				(  snake_case      ):
		_lowerCAmelCase						=		"gpt2"
		_lowerCAmelCase						=		["past_key_values"]
		_lowerCAmelCase						=		{
		    "hidden_size": "n_embd",
		    "max_position_embeddings": "n_positions",
		    "num_attention_heads": "n_head",
		    "num_hidden_layers": "n_layer",
		}





		def __init__(							self      :		Union[str, Any]  ,					lowerCamelCase__      :		Tuple=5_0_2_5_7  ,					lowerCamelCase__      :		str=1_0_2_4  ,					lowerCamelCase__      :		Union[str, Any]=7_6_8  ,					lowerCamelCase__      :		Tuple=1_2  ,					lowerCamelCase__      :		Tuple=1_2  ,					lowerCamelCase__      :		List[str]=None  ,					lowerCamelCase__      :		Tuple="gelu_new"  ,					lowerCamelCase__      :		List[str]=0.1  ,					lowerCamelCase__      :		str=0.1  ,					lowerCamelCase__      :		List[str]=0.1  ,					lowerCamelCase__      :		Dict=1E-5  ,					lowerCamelCase__      :		Optional[Any]=0.0_2  ,					lowerCamelCase__      :		Tuple="cls_index"  ,					lowerCamelCase__      :		str=True  ,					lowerCamelCase__      :		Optional[int]=None  ,					lowerCamelCase__      :		int=True  ,					lowerCamelCase__      :		Tuple=0.1  ,					lowerCamelCase__      :		List[str]=True  ,					lowerCamelCase__      :		Dict=True  ,					lowerCamelCase__      :		Tuple=5_0_2_5_6  ,					lowerCamelCase__      :		Optional[int]=5_0_2_5_6  ,					lowerCamelCase__      :		Dict=False  ,					lowerCamelCase__      :		Tuple=False  ,					**lowerCamelCase__      :		List[str]  ,					):
							lowerCAmelCase						:   List[str]							=       vocab_size
							lowerCAmelCase						:   Dict							=       n_positions
							lowerCAmelCase						:   int							=       n_embd
							lowerCAmelCase						:   Union[str, Any]							=       n_layer
							lowerCAmelCase						:   List[str]							=       n_head
							lowerCAmelCase						:   str							=       n_inner
							lowerCAmelCase						:   Tuple							=       activation_function
							lowerCAmelCase						:   List[Any]							=       resid_pdrop
							lowerCAmelCase						:   Any							=       embd_pdrop
							lowerCAmelCase						:   int							=       attn_pdrop
							lowerCAmelCase						:   Dict							=       layer_norm_epsilon
							lowerCAmelCase						:   List[str]							=       initializer_range
							lowerCAmelCase						:   List[Any]							=       summary_type
							lowerCAmelCase						:   int							=       summary_use_proj
							lowerCAmelCase						:   List[str]							=       summary_activation
							lowerCAmelCase						:   List[str]							=       summary_first_dropout
							lowerCAmelCase						:   Optional[int]							=       summary_proj_to_labels
							lowerCAmelCase						:   Optional[int]							=       scale_attn_weights
							lowerCAmelCase						:   List[str]							=       use_cache
							lowerCAmelCase						:   List[Any]							=       scale_attn_by_inverse_layer_idx
							lowerCAmelCase						:   int							=       reorder_and_upcast_attn

							lowerCAmelCase						:   List[Any]							=       bos_token_id
							lowerCAmelCase						:   Optional[int]							=       eos_token_id

							super().__init__(bos_token_id=lowerCamelCase__  ,					eos_token_id=lowerCamelCase__  ,					**lowerCamelCase__			)



class 						__magic_name__				(  snake_case      ):





		def __init__(							self      :		str  ,					lowerCamelCase__      :		PretrainedConfig  ,					lowerCamelCase__      :		str = "default"  ,					lowerCamelCase__      :		List[PatchingSpec] = None  ,					lowerCamelCase__      :		bool = False  ,					):
							super().__init__(lowerCamelCase__  ,					task=lowerCamelCase__  ,					patching_specs=lowerCamelCase__  ,					use_past=lowerCamelCase__			)
							if not getattr(self._config  ,					'''pad_token_id'''  ,					lowerCamelCase__			):
												# TODO: how to do that better?
												lowerCAmelCase						:   str							=       0





		@property
		def  _A							(							self      :		Optional[Any]			):
							lowerCAmelCase						:   List[Any]							=       OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}			)
							if self.use_past:
												self.fill_with_past_key_values_(lowerCamelCase__  ,					direction='''inputs'''			)
												lowerCAmelCase						:   str							=       {0: '''batch''', 1: '''past_sequence + sequence'''}
							else:
												lowerCAmelCase						:   int							=       {0: '''batch''', 1: '''sequence'''}

							return common_inputs





		@property
		def  _A							(							self      :		Optional[int]			):
							return self._config.n_layer





		@property
		def  _A							(							self      :		List[str]			):
							return self._config.n_head





		def  _A							(							self      :		List[str]  ,					lowerCamelCase__      :		PreTrainedTokenizer  ,					lowerCamelCase__      :		int = -1  ,					lowerCamelCase__      :		int = -1  ,					lowerCamelCase__      :		bool = False  ,					lowerCamelCase__      :		Optional[TensorType] = None  ,					):
							lowerCAmelCase						:   Union[str, Any]							=       super(lowerCamelCase__  ,					self			).generate_dummy_inputs(
							    lowerCamelCase__  ,					batch_size=lowerCamelCase__  ,					seq_length=lowerCamelCase__  ,					is_pair=lowerCamelCase__  ,					framework=lowerCamelCase__			)

							# We need to order the input in the way they appears in the forward()
							lowerCAmelCase						:   Tuple							=       OrderedDict({'''input_ids''': common_inputs['''input_ids''']}			)

							# Need to add the past_keys
							if self.use_past:
												if not is_torch_available():
																	raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''			)
												else:
																	import torch

																	lowerCAmelCase		,							lowerCAmelCase						:   Any							=       common_inputs['''input_ids'''].shape
																	# Not using the same length for past_key_values
																	lowerCAmelCase						:   Optional[int]							=       seqlen + 2
																	lowerCAmelCase						:   Any							=       (
																	    batch,
																	    self.num_attention_heads,
																	    past_key_values_length,
																	    self._config.hidden_size // self.num_attention_heads,
																	)
																	lowerCAmelCase						:   Any							=       [
																	    (torch.zeros(lowerCamelCase__			), torch.zeros(lowerCamelCase__			)) for _ in range(self.num_layers			)
																	]

							lowerCAmelCase						:   List[str]							=       common_inputs['''attention_mask''']
							if self.use_past:
												lowerCAmelCase						:   Optional[Any]							=       ordered_inputs['''attention_mask'''].dtype
												lowerCAmelCase						:   Optional[Any]							=       torch.cat(
												    [ordered_inputs['''attention_mask'''], torch.ones(lowerCamelCase__  ,					lowerCamelCase__  ,					dtype=lowerCamelCase__			)]  ,					dim=1			)

							return ordered_inputs





		@property
		def  _A							(							self      :		Union[str, Any]			):
							return 1_3
