

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


lowerCAmelCase__						=	logging.getLogger(__name__)


def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Any )     -> Optional[Any]:




				'''simple docstring'''





				random.seed(SCREAMING_SNAKE_CASE_ )
				np.random.seed(SCREAMING_SNAKE_CASE_ )
				torch.manual_seed(SCREAMING_SNAKE_CASE_ )
				torch.cuda.manual_seed_all(SCREAMING_SNAKE_CASE_ )


def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Tuple		,    SCREAMING_SNAKE_CASE_:							List[str]		,    SCREAMING_SNAKE_CASE_:							Union[str, Any] )     -> Optional[Any]:




				'''simple docstring'''





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


def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							List[str]="gpt2" )     -> Dict:




				'''simple docstring'''





				A__            =     GPTaLMHeadModel.from_pretrained(SCREAMING_SNAKE_CASE_		,    output_hidden_states=SCREAMING_SNAKE_CASE_ )
				torch.save(model.state_dict()		,    model_name + "local.pt" )
				return model


def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Optional[Any]		,    SCREAMING_SNAKE_CASE_:							Any		,    SCREAMING_SNAKE_CASE_:							int )     -> List[Any]:




				'''simple docstring'''





				A__            =     copy.deepcopy(SCREAMING_SNAKE_CASE_ )
				model.to(SCREAMING_SNAKE_CASE_ )

				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(SCREAMING_SNAKE_CASE_		,    lr=5e-5		,    eps=1e-8 )
				A__            =     get_linear_schedule_with_warmup(SCREAMING_SNAKE_CASE_		,    0		,    SCREAMING_SNAKE_CASE_ )
				torch.cuda.empty_cache()
				return model, lm_optimizer, lm_scheduler


def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Tuple		,    SCREAMING_SNAKE_CASE_:							Union[str, Any]		,    SCREAMING_SNAKE_CASE_:							List[str]		,    SCREAMING_SNAKE_CASE_:							Tuple )     -> int:




				'''simple docstring'''





				A__            =     np.array(SCREAMING_SNAKE_CASE_ ).mean()
				A__            =     np.array(SCREAMING_SNAKE_CASE_ ).std()
				A__            =     (real_perps - avg) / std
				A__            =     list(zip(SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_ ) )
				joblib.dump(SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_ )


def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							List[Any]		,    SCREAMING_SNAKE_CASE_:							List[Any]		,    SCREAMING_SNAKE_CASE_:							str		,    SCREAMING_SNAKE_CASE_:							Tuple		,    SCREAMING_SNAKE_CASE_:							int		,    SCREAMING_SNAKE_CASE_:							List[str]		,    SCREAMING_SNAKE_CASE_:							List[Any]		,    SCREAMING_SNAKE_CASE_:							int="dev.jbl"		,    SCREAMING_SNAKE_CASE_:							List[str]=recopy_gpta		,    )     -> Dict:




				'''simple docstring'''





				A__            =     []
				A__            =     []
				A__            =     []

				# Initialize the transformer model
				A__            =     copy.deepcopy(SCREAMING_SNAKE_CASE_ )
				orig_model.to(device="cpu" )
				torch.cuda.empty_cache()

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

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

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

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

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

				intermittent_save(SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_ )


def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Tuple		,    SCREAMING_SNAKE_CASE_:							int="data/tokenized_stories_train_wikitext103.jbl"		,    SCREAMING_SNAKE_CASE_:							List[Any]=1_0_0		,    SCREAMING_SNAKE_CASE_:							int=1_0_2_6		,    SCREAMING_SNAKE_CASE_:							Tuple=True )     -> Optional[Any]:




				'''simple docstring'''





				A__            =     joblib.load(SCREAMING_SNAKE_CASE_ )
				print("data loaded" )
				A__            =     []
				if trim:
								for i, example in enumerate(SCREAMING_SNAKE_CASE_ ):
												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(SCREAMING_SNAKE_CASE_ ) >= number:
																break
								A__            =     []
								for j in range(i + 1		,    len(SCREAMING_SNAKE_CASE_ ) ):
												if len(data[j][0] ) > min_len:
																train_data.append(data[j] )
				else:
								A__            =     data[0:number]
								A__            =     data[number:]

				joblib.dump(SCREAMING_SNAKE_CASE_		,    "objective_set.jbl" )
				print("objective set saved" )
				return train_data, objective_set


def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Optional[int]		,    SCREAMING_SNAKE_CASE_:							List[str]		,    SCREAMING_SNAKE_CASE_:							List[Any]		,    SCREAMING_SNAKE_CASE_:							List[Any]		,    SCREAMING_SNAKE_CASE_:							str=5_0		,    SCREAMING_SNAKE_CASE_:							int="secondary_learner.pt" )     -> Optional[Any]:




				'''simple docstring'''





				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[:5_1_2]
				A__            =     train_dataset[5_1_2:]
				A__            =     DataLoader(SCREAMING_SNAKE_CASE_		,    shuffle=SCREAMING_SNAKE_CASE_		,    batch_size=SCREAMING_SNAKE_CASE_ )
				A__            =     DataLoader(SCREAMING_SNAKE_CASE_		,    shuffle=SCREAMING_SNAKE_CASE_		,    batch_size=SCREAMING_SNAKE_CASE_ )

				# secondary learner model set up
				A__            =     nn.MSELoss()
				A__            =     nn.MSELoss(reduction="sum" )
				secondary_learner.to(SCREAMING_SNAKE_CASE_ )
				A__            =     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
				A__            =     float("inf" )
				# Iterate through batches until we've used max_steps batches
				for epoch in range(int(SCREAMING_SNAKE_CASE_ ) ):
								A__            =     0.0
								secondary_learner.train()
								for step, batch in enumerate(SCREAMING_SNAKE_CASE_ ):
												A__            =     batch[0].to(SCREAMING_SNAKE_CASE_ )
												A__            =     batch[1].to(SCREAMING_SNAKE_CASE_ )
												A__            =     secondary_learner(SCREAMING_SNAKE_CASE_ )
												q_optimizer.zero_grad()
												A__            =     loss(SCREAMING_SNAKE_CASE_		,    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(SCREAMING_SNAKE_CASE_ )):
																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(SCREAMING_SNAKE_CASE_ ):
																				A__            =     batcha[0].to(SCREAMING_SNAKE_CASE_ )
																				A__            =     batcha[1].to(SCREAMING_SNAKE_CASE_ )
																				A__            =     secondary_learner(SCREAMING_SNAKE_CASE_ )
																				q_lossa += test_loss(SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_ ).item()
																				sum_qa += torch.sum(SCREAMING_SNAKE_CASE_ ).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(SCREAMING_SNAKE_CASE_ )
																print(
																    "Epoch: "		,    SCREAMING_SNAKE_CASE_		,    "step: "		,    SCREAMING_SNAKE_CASE_		,    "Avg. q:"		,    sum_qa / len(SCREAMING_SNAKE_CASE_ )		,    "Train Loss: "		,    SCREAMING_SNAKE_CASE_		,    "Test Loss: "		,    SCREAMING_SNAKE_CASE_		,    )
																if q_lossa < best_test_loss:
																				joblib.dump((predicted, actual)		,    "pred_vs_actual.jbl" )
																				torch.save(secondary_learner.state_dict()		,    SCREAMING_SNAKE_CASE_ )
																				A__            =     q_lossa

												secondary_learner.train()
				return secondary_learner





class    a__  (   nn.Module     ):
			"""simple docstring"""







			def __init__(      self , lowercase    )					->  List[Any]:
							'''simple docstring'''






							super(lowercase , 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        UpperCamelCase						(      self , lowercase    )					->  List[str]:
							'''simple docstring'''






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


			@classmethod
			def        UpperCamelCase						(      cls , lowercase , lowercase    )					->  Dict:
							'''simple docstring'''






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




from typing import Optional

from ..utils.logging import get_logger
from .audio_classificiation import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageModeling
from .question_answering import QuestionAnsweringExtractive
from .summarization import Summarization
from .text_classification import TextClassification


lowerCAmelCase__						=	[
    """AutomaticSpeechRecognition""",
    """AudioClassification""",
    """ImageClassification""",
    """LanguageModeling""",
    """QuestionAnsweringExtractive""",
    """Summarization""",
    """TaskTemplate""",
    """TextClassification""",
]

lowerCAmelCase__						=	get_logger(__name__)


lowerCAmelCase__						=	{
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}



def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							dict )     -> Optional[TaskTemplate]:




				'''simple docstring'''





				A__            =     task_template_dict.get("task" )
				if task_name is None:
								logger.warning(F'Couldn\'t find template for task \'{task_name}\'. Available templates: {list(SCREAMING_SNAKE_CASE_ )}' )
								return None
				A__            =     NAMEaTEMPLATE.get(SCREAMING_SNAKE_CASE_ )
				return template.from_dict(SCREAMING_SNAKE_CASE_ )

