
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__				:  List[Any]       					=				logging.getLogger(__name__)



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



def       _lowercase					(      __SCREAMING_SNAKE_CASE="gpt2" )	->     Optional[int]:
			UpperCamelCase__    :		Optional[int]     =				GPTaLMHeadModel.from_pretrained(__SCREAMING_SNAKE_CASE				,    output_hidden_states=__SCREAMING_SNAKE_CASE )
			torch.save(model.state_dict()				,    model_name + 'local.pt' )
			return model



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )	->     Optional[int]:
			UpperCamelCase__    :		List[str]     =				copy.deepcopy(__SCREAMING_SNAKE_CASE )
			model.to(__SCREAMING_SNAKE_CASE )

			UpperCamelCase__    :		Tuple     =				['bias', 'LayerNorm.weight']
			UpperCamelCase__    :		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},
			]
			UpperCamelCase__    :		Dict     =				AdamW(__SCREAMING_SNAKE_CASE				,    lr=5E-5				,    eps=1E-8 )
			UpperCamelCase__    :		List[str]     =				get_linear_schedule_with_warmup(__SCREAMING_SNAKE_CASE				,    0				,    __SCREAMING_SNAKE_CASE )
			torch.cuda.empty_cache()
			return model, lm_optimizer, lm_scheduler



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )	->     Optional[int]:
			UpperCamelCase__    :		str     =				np.array(__SCREAMING_SNAKE_CASE ).mean()
			UpperCamelCase__    :		int     =				np.array(__SCREAMING_SNAKE_CASE ).std()
			UpperCamelCase__    :		Any     =				(real_perps - avg) / std
			UpperCamelCase__    :		Optional[int]     =				list(zip(__SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE ) )
			joblib.dump(__SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE="dev.jbl"				,    __SCREAMING_SNAKE_CASE=recopy_gpta				,    )	->     Union[str, Any]:
			UpperCamelCase__    :		Any     =				[]
			UpperCamelCase__    :		Optional[Any]     =				[]
			UpperCamelCase__    :		Optional[Any]     =				[]

			# Initialize the transformer model
			UpperCamelCase__    :		List[str]     =				copy.deepcopy(__SCREAMING_SNAKE_CASE )
			orig_model.to(device='cpu' )
			torch.cuda.empty_cache()

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

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

						# Periodically save the stored (X, IG(X)) pairs
						if step % 1000 == 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
						UpperCamelCase__    :		List[Any]     =				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       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE="data/tokenized_stories_train_wikitext103.jbl"				,    __SCREAMING_SNAKE_CASE=100				,    __SCREAMING_SNAKE_CASE=1026				,    __SCREAMING_SNAKE_CASE=True )	->     Union[str, Any]:
			UpperCamelCase__    :		Union[str, Any]     =				joblib.load(__SCREAMING_SNAKE_CASE )
			print('data loaded' )
			UpperCamelCase__    :		Dict     =				[]
			if trim:
						for i, example in enumerate(__SCREAMING_SNAKE_CASE ):
									if len(example[0] ) > min_len:
												UpperCamelCase__    :		Any     =				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
						UpperCamelCase__    :		List[Any]     =				[]
						for j in range(i + 1				,    len(__SCREAMING_SNAKE_CASE ) ):
									if len(data[j][0] ) > min_len:
												train_data.append(data[j] )
			else:
						UpperCamelCase__    :		Optional[Any]     =				data[0:number]
						UpperCamelCase__    :		Any     =				data[number:]

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



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE=50				,    __SCREAMING_SNAKE_CASE="secondary_learner.pt" )	->     List[Any]:
			UpperCamelCase__    :		Union[str, 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__    :		int     =				train_dataset[:512]
			UpperCamelCase__    :		Optional[Any]     =				train_dataset[512:]
			UpperCamelCase__    :		Dict     =				DataLoader(__SCREAMING_SNAKE_CASE				,    shuffle=__SCREAMING_SNAKE_CASE				,    batch_size=__SCREAMING_SNAKE_CASE )
			UpperCamelCase__    :		Optional[Any]     =				DataLoader(__SCREAMING_SNAKE_CASE				,    shuffle=__SCREAMING_SNAKE_CASE				,    batch_size=__SCREAMING_SNAKE_CASE )

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

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

									secondary_learner.train()
			return secondary_learner



class 			UpperCamelCase_			(						nn.Module   ):




							'''simple docstring'''

							def __init__(				self     ,						UpperCamelCase) ->	Optional[int]:
										super(UpperCamelCase     ,						self).__init__()
										UpperCamelCase__    :		Optional[int]     =				model.transformer.wte
										UpperCamelCase__    :		Dict     =				copy.deepcopy(model.transformer.wte.weight)
										UpperCamelCase__    :		Optional[Any]     =				nn.Convad(self.embeddings.weight.size(1)     ,						2_56     ,						3     ,						padding=1)
										UpperCamelCase__    :		Tuple     =				nn.Sequential(nn.Linear(2_56     ,						32)     ,						nn.Dropout(p=0.1)     ,						nn.Linear(32     ,						32)     ,						nn.Linear(32     ,						1))
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase) ->	Optional[Any]:
										UpperCamelCase__    :		List[Any]     =				torch.max(self.conv(self.embeddings(UpperCamelCase).squeeze(1).transpose(1     ,						2))     ,						2)[0]
										UpperCamelCase__    :		Union[str, Any]     =				self.fc(UpperCamelCase)
										return qs.squeeze(1)






							@classmethod
							def        lowerCAmelCase__ (				cls     ,						UpperCamelCase     ,						UpperCamelCase) ->	List[str]:
										UpperCamelCase__    :		int     =				cls(UpperCamelCase)  # this calls __init__
										UpperCamelCase__    :		Optional[int]     =				torch.load(UpperCamelCase)
										secondary_learner.load_state_dict(UpperCamelCase)
										UpperCamelCase__    :		List[Any]     =				model.transformer.wte
										UpperCamelCase__    :		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__				:  Tuple       					=				[
    '''AutomaticSpeechRecognition''',
    '''AudioClassification''',
    '''ImageClassification''',
    '''LanguageModeling''',
    '''QuestionAnsweringExtractive''',
    '''Summarization''',
    '''TaskTemplate''',
    '''TextClassification''',
]

UpperCAmelCase__				:  Dict       					=				get_logger(__name__)


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



def       _lowercase					(      __SCREAMING_SNAKE_CASE )	->     Optional[TaskTemplate]:
			UpperCamelCase__    :		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(__SCREAMING_SNAKE_CASE )}""" )
						return None
			UpperCamelCase__    :		Any     =				NAMEaTEMPLATE.get(__SCREAMING_SNAKE_CASE )
			return template.from_dict(__SCREAMING_SNAKE_CASE )



