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						:    Dict     = logging.getLogger(__name__)
def 					snake_case						(__lowercase						)       ->       List[str]:

						'''simple docstring'''



						random.seed(__lowercase						)
						np.random.seed(__lowercase						)
						torch.manual_seed(__lowercase						)
						torch.cuda.manual_seed_all(__lowercase						)
def 					snake_case						(__lowercase					,			__lowercase					,			__lowercase						)       ->       Optional[int]:

						'''simple docstring'''



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

						'''simple docstring'''



						_snake_case						:     Union[str, Any]				=    GPTaLMHeadModel.from_pretrained(__lowercase					,			output_hidden_states=__lowercase						)
						torch.save(model.state_dict()					,			model_name + "local.pt"						)
						return model
def 					snake_case						(__lowercase					,			__lowercase					,			__lowercase						)       ->       List[str]:

						'''simple docstring'''



						_snake_case						:     List[Any]				=    copy.deepcopy(__lowercase						)
						model.to(__lowercase						)

						_snake_case						:     Any				=    ["bias", "LayerNorm.weight"]
						_snake_case						:     List[str]				=    [
						    {
						        "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},
						]
						_snake_case						:     Union[str, Any]				=    AdamW(__lowercase					,			lr=5e-5					,			eps=1e-8						)
						_snake_case						:     Tuple				=    get_linear_schedule_with_warmup(__lowercase					,			0					,			__lowercase						)
						torch.cuda.empty_cache()
						return model, lm_optimizer, lm_scheduler
def 					snake_case						(__lowercase					,			__lowercase					,			__lowercase					,			__lowercase						)       ->       Tuple:

						'''simple docstring'''



						_snake_case						:     Dict				=    np.array(__lowercase						).mean()
						_snake_case						:     Optional[Any]				=    np.array(__lowercase						).std()
						_snake_case						:     Any				=    (real_perps - avg) / std
						_snake_case						:     Tuple				=    list(zip(__lowercase					,			__lowercase					,			__lowercase						)						)
						joblib.dump(__lowercase					,			__lowercase						)
def 					snake_case						(__lowercase					,			__lowercase					,			__lowercase					,			__lowercase					,			__lowercase					,			__lowercase					,			__lowercase					,			__lowercase="dev.jbl"					,			__lowercase=recopy_gpta					,			)       ->       int:

						'''simple docstring'''



						_snake_case						:     Optional[int]				=    []
						_snake_case						:     Any				=    []
						_snake_case						:     Dict				=    []

						# Initialize the transformer model
						_snake_case						:     str				=    copy.deepcopy(__lowercase						)
						orig_model.to(device="cpu"						)
						torch.cuda.empty_cache()

						# Compute perplexity of initial transformer model for comparison
						model.train()
						_snake_case				,_snake_case				,_snake_case						:     Union[str, Any]				=    recopy_model(__lowercase					,			__lowercase					,			__lowercase						)

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

												# Periodically save the stored (X, IG(X)) pairs
												if step % 1_000 == 0 and step > 1:
																		intermittent_save(__lowercase					,			__lowercase					,			__lowercase					,			__lowercase						)

												# Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
												_snake_case				,_snake_case				,_snake_case						:     Tuple				=    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 					snake_case						(__lowercase					,			__lowercase="data/tokenized_stories_train_wikitext103.jbl"					,			__lowercase=100					,			__lowercase=1_026					,			__lowercase=True						)       ->       str:

						'''simple docstring'''



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

						joblib.dump(__lowercase					,			"objective_set.jbl"						)
						print("objective set saved"						)
						return train_data, objective_set
def 					snake_case						(__lowercase					,			__lowercase					,			__lowercase					,			__lowercase					,			__lowercase=50					,			__lowercase="secondary_learner.pt"						)       ->       List[str]:

						'''simple docstring'''



						_snake_case						:     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
						_snake_case						:     Any				=    train_dataset[:512]
						_snake_case						:     Optional[int]				=    train_dataset[512:]
						_snake_case						:     Tuple				=    DataLoader(__lowercase					,			shuffle=__lowercase					,			batch_size=__lowercase						)
						_snake_case						:     Any				=    DataLoader(__lowercase					,			shuffle=__lowercase					,			batch_size=__lowercase						)

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

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

																		secondary_learner.train()
						return secondary_learner



class    lowercase_    (     nn.Module		):



							def __init__(						self      ,				lowercase_				):
													super(lowercase_      ,				self				).__init__()
													_snake_case						:     Optional[Any]				=    model.transformer.wte
													_snake_case						:     Union[str, Any]				=    copy.deepcopy(model.transformer.wte.weight				)
													_snake_case						:     Tuple				=    nn.Convad(self.embeddings.weight.size(1				)      ,				256      ,				3      ,				padding=1				)
													_snake_case						:     Any				=    nn.Sequential(nn.Linear(256      ,				32				)      ,				nn.Dropout(p=0.1				)      ,				nn.Linear(32      ,				32				)      ,				nn.Linear(32      ,				1				)				)



							def        UpperCamelCase       (						self      ,				lowercase_				):
													_snake_case						:     List[Any]				=    torch.max(self.conv(self.embeddings(lowercase_				).squeeze(1				).transpose(1      ,				2				)				)      ,				2				)[0]
													_snake_case						:     Union[str, Any]				=    self.fc(lowercase_				)
													return qs.squeeze(1				)



							@classmethod
							def        UpperCamelCase       (						cls      ,				lowercase_      ,				lowercase_				):
													_snake_case						:     int				=    cls(lowercase_				)  # this calls __init__
													_snake_case						:     List[Any]				=    torch.load(lowercase_				)
													secondary_learner.load_state_dict(lowercase_				)
													_snake_case						:     Dict				=    model.transformer.wte
													_snake_case						:     Optional[int]				=    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


UpperCamelCase__              =						[
    'AutomaticSpeechRecognition',
    'AudioClassification',
    'ImageClassification',
    'LanguageModeling',
    'QuestionAnsweringExtractive',
    'Summarization',
    'TaskTemplate',
    'TextClassification',
]

UpperCamelCase__              =						get_logger(__name__)


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






def 					snake_case					(							A			)							->							Optional[TaskTemplate]:




	'''simple docstring'''


	lowerCamelCase_							:		Any							   =							task_template_dict.get('task'			)
	if task_name is None:
		logger.warning(f"Couldn't find template for task '{task_name}'. Available templates: {list(A			)}"			)
		return None
	lowerCamelCase_							:		Any							   =							NAMEaTEMPLATE.get(A			)
	return template.from_dict(A			)
