



import argparse
import csv
import json
import math
import time

import numpy as np
import torch
import torch.optim as optim
import torch.utils.data as data
from nltk.tokenize.treebank import TreebankWordDetokenizer
from pplm_classification_head import ClassificationHead
from torch import nn
from torchtext import data as torchtext_data
from torchtext import datasets
from tqdm import tqdm, trange

from transformers import GPTaLMHeadModel, GPTaTokenizer


torch.manual_seed(0)
np.random.seed(0)
UpperCAmelCase__			:   Dict      			=    1e-10
UpperCAmelCase__			:   str      			=    'This is incredible! I love it, this is the best chicken I have ever had.'
UpperCAmelCase__			:   Union[str, Any]      			=    100





class 							lowerCAmelCase_		(		nn.Module     ):







		def __init__(    self  :   Any       ,		UpperCAmelCase_  :   List[str]       ,		UpperCAmelCase_  :   List[Any]="gpt2-medium"       ,		UpperCAmelCase_  :   Union[str, Any]=False       ,		UpperCAmelCase_  :   Optional[Any]="cpu"					)       ->		List[Any]:


						'''simple docstring'''







						super().__init__()
						_UpperCAmelCase						:  Optional[Any]     					=	GPTaTokenizer.from_pretrained(UpperCAmelCase_					)
						_UpperCAmelCase						:  Union[str, Any]     					=	GPTaLMHeadModel.from_pretrained(UpperCAmelCase_					)
						_UpperCAmelCase						:  List[Any]     					=	self.encoder.transformer.config.hidden_size
						_UpperCAmelCase						:  Dict     					=	ClassificationHead(class_size=UpperCAmelCase_       ,		embed_size=self.embed_size					)
						_UpperCAmelCase						:  Dict     					=	cached_mode
						_UpperCAmelCase						:  Any     					=	device







		def 					a_  (    self  :   Dict					)       ->		Tuple:


						'''simple docstring'''







						return self.classifier_head







		def 					a_  (    self  :   Tuple					)       ->		List[str]:


						'''simple docstring'''







						for param in self.encoder.parameters():
										_UpperCAmelCase						:  List[str]     					=	False
						self.classifier_head.train()







		def 					a_  (    self  :   str       ,		UpperCAmelCase_  :   Dict					)       ->		Union[str, Any]:


						'''simple docstring'''







						_UpperCAmelCase						:  Dict     					=	x.ne(0					).unsqueeze(2					).repeat(1       ,		1       ,		self.embed_size					).float().to(self.device					).detach()
						_UpperCAmelCase						:  str     					=	self.encoder.transformer(UpperCAmelCase_					)['''last_hidden_state''']
						_UpperCAmelCase						:  Optional[int]     					=	hidden * mask
						_UpperCAmelCase						:  List[Any]     					=	torch.sum(UpperCAmelCase_       ,		dim=1					) / (torch.sum(UpperCAmelCase_       ,		dim=1					).detach() + EPSILON)
						return avg_hidden







		def 					a_  (    self  :   Optional[Any]       ,		UpperCAmelCase_  :   str					)       ->		Dict:


						'''simple docstring'''







						if self.cached_mode:
										_UpperCAmelCase						:  str     					=	x.to(self.device					)
						else:
										_UpperCAmelCase						:  Optional[Any]     					=	self.avg_representation(x.to(self.device					)					)

						_UpperCAmelCase						:  Union[str, Any]     					=	self.classifier_head(UpperCAmelCase_					)
						_UpperCAmelCase						:  List[str]     					=	nn.functional.log_softmax(UpperCAmelCase_       ,		dim=-1					)

						return probs





class 							lowerCAmelCase_		(		data.Dataset     ):







		def __init__(    self  :   Any       ,		UpperCAmelCase_  :   List[str]       ,		UpperCAmelCase_  :   Dict					)       ->		Union[str, Any]:


						'''simple docstring'''







						_UpperCAmelCase						:  Tuple     					=	X
						_UpperCAmelCase						:  Optional[int]     					=	y







		def __len__(    self  :   Dict					)       ->		Union[str, Any]:


						'''simple docstring'''







						return len(self.X					)







		def __getitem__(    self  :   Optional[Any]       ,		UpperCAmelCase_  :   List[str]					)       ->		Any:


						'''simple docstring'''







						_UpperCAmelCase						:  Optional[int]     					=	{}
						_UpperCAmelCase						:  Any     					=	self.X[index]
						_UpperCAmelCase						:  int     					=	self.y[index]
						return data


def 	_A	(   _UpperCamelCase			):






				def pad_sequences(_UpperCamelCase			):
								_UpperCAmelCase						:  Any     					=	[len(a__			) for seq in sequences]

								_UpperCAmelCase						:  str     					=	torch.zeros(len(a__			)      ,					max(a__			)			).long()  # padding value = 0

								for i, seq in enumerate(a__			):
												_UpperCAmelCase						:  str     					=	lengths[i]
												_UpperCAmelCase						:  Dict     					=	seq[:end]

								return padded_sequences, lengths

				_UpperCAmelCase						:  List[Any]     					=	{}
				for key in data[0].keys():
								_UpperCAmelCase						:  int     					=	[d[key] for d in data]

				_UpperCAmelCase					, _UpperCAmelCase						:  Optional[int]     					=	pad_sequences(item_info['''X''']			)
				_UpperCAmelCase						:  List[str]     					=	torch.tensor(item_info['''y''']      ,					dtype=torch.long			)

				return x_batch, y_batch


def 	_A	(   _UpperCamelCase			):
				_UpperCAmelCase						:  str     					=	{}
				for key in data[0].keys():
								_UpperCAmelCase						:  Union[str, Any]     					=	[d[key] for d in data]

				_UpperCAmelCase						:  Dict     					=	torch.cat(item_info['''X''']      ,					0			)
				_UpperCAmelCase						:  Dict     					=	torch.tensor(item_info['''y''']      ,					dtype=torch.long			)

				return x_batch, y_batch


def 	_A	(   _UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase=0      ,					_UpperCamelCase=10      ,					_UpperCamelCase="cpu"			):
				_UpperCAmelCase						:  Tuple     					=	0
				discriminator.train_custom()
				for batch_idx, (input_t, target_t) in enumerate(a__			):
								_UpperCAmelCase					, _UpperCAmelCase						:  List[Any]     					=	input_t.to(a__			), target_t.to(a__			)

								optimizer.zero_grad()

								_UpperCAmelCase						:  List[str]     					=	discriminator(a__			)
								_UpperCAmelCase						:  Tuple     					=	nn.functional.nll_loss(a__      ,					a__			)
								loss.backward(retain_graph=a__			)
								optimizer.step()

								samples_so_far += len(a__			)

								if batch_idx % log_interval == 0:
												print(
												    '''Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'''.format(
												        epoch + 1      ,					a__      ,					len(data_loader.dataset			)      ,					100 * samples_so_far / len(data_loader.dataset			)      ,					loss.item()      ,					)			)


def 	_A	(   _UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase="cpu"			):
				discriminator.eval()
				_UpperCAmelCase						:  str     					=	0
				_UpperCAmelCase						:  Optional[int]     					=	0
				with torch.no_grad():
								for input_t, target_t in data_loader:
												_UpperCAmelCase					, _UpperCAmelCase						:  Optional[int]     					=	input_t.to(a__			), target_t.to(a__			)
												_UpperCAmelCase						:  Any     					=	discriminator(a__			)
												# sum up batch loss
												test_loss += nn.functional.nll_loss(a__      ,					a__      ,					reduction='''sum'''			).item()
												# get the index of the max log-probability
												_UpperCAmelCase						:  List[str]     					=	output_t.argmax(dim=1      ,					keepdim=a__			)
												correct += pred_t.eq(target_t.view_as(a__			)			).sum().item()

				test_loss /= len(data_loader.dataset			)

				print(
				    '''Performance on test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)'''.format(
				        a__      ,					a__      ,					len(data_loader.dataset			)      ,					100.0 * correct / len(data_loader.dataset			)			)			)


def 	_A	(   _UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase=False      ,					_UpperCamelCase="cpu"			):
				_UpperCAmelCase						:  Optional[int]     					=	model.tokenizer.encode(a__			)
				_UpperCAmelCase						:  int     					=	torch.tensor([input_t]      ,					dtype=torch.long      ,					device=a__			)
				if cached:
								_UpperCAmelCase						:  Optional[Any]     					=	model.avg_representation(a__			)

				_UpperCAmelCase						:  List[Any]     					=	model(a__			).data.cpu().numpy().flatten().tolist()
				print('''Input sentence:'''      ,					a__			)
				print(
				    '''Predictions:'''      ,					''', '''.join('''{}: {:.4f}'''.format(a__      ,					math.exp(a__			)			) for c, log_prob in zip(a__      ,					a__			)			)      ,					)


def 	_A	(   _UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase=False      ,					_UpperCamelCase="cpu"			):
				_UpperCAmelCase						:  Tuple     					=	torch.utils.data.DataLoader(dataset=a__      ,					batch_size=a__      ,					collate_fn=a__			)

				_UpperCAmelCase						:  Optional[int]     					=	[]
				_UpperCAmelCase						:  Any     					=	[]
				for batch_idx, (x, y) in enumerate(tqdm(a__      ,					ascii=a__			)			):
								with torch.no_grad():
												_UpperCAmelCase						:  str     					=	x.to(a__			)
												_UpperCAmelCase						:  Any     					=	discriminator.avg_representation(a__			).cpu().detach()
												_UpperCAmelCase						:  Optional[Any]     					=	torch.unbind(avg_rep.unsqueeze(1			)			)
												xs += avg_rep_list
												ys += y.cpu().numpy().tolist()

				_UpperCAmelCase						:  int     					=	torch.utils.data.DataLoader(
				    dataset=Dataset(a__      ,					a__			)      ,					batch_size=a__      ,					shuffle=a__      ,					collate_fn=a__			)

				return data_loader


def 	_A	(   _UpperCamelCase      ,					_UpperCamelCase=None      ,					_UpperCamelCase="gpt2-medium"      ,					_UpperCamelCase=10      ,					_UpperCamelCase=64      ,					_UpperCamelCase=10      ,					_UpperCamelCase=False      ,					_UpperCamelCase=False      ,					_UpperCamelCase=False      ,					):
				_UpperCAmelCase						:  Optional[Any]     					=	'''cuda''' if torch.cuda.is_available() and not no_cuda else '''cpu'''

				print('''Preprocessing {} dataset...'''.format(a__			)			)
				_UpperCAmelCase						:  Dict     					=	time.time()

				if dataset == "SST":
								_UpperCAmelCase						:  Union[str, Any]     					=	['''positive''', '''negative''', '''very positive''', '''very negative''', '''neutral''']
								_UpperCAmelCase						:  List[str]     					=	{c: i for i, c in enumerate(a__			)}

								_UpperCAmelCase						:  Tuple     					=	Discriminator(
								    class_size=len(a__			)      ,					pretrained_model=a__      ,					cached_mode=a__      ,					device=a__			).to(a__			)

								_UpperCAmelCase						:  Tuple     					=	torchtext_data.Field()
								_UpperCAmelCase						:  str     					=	torchtext_data.Field(sequential=a__			)
								_UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase						:  List[Any]     					=	datasets.SST.splits(
								    a__      ,					a__      ,					fine_grained=a__      ,					train_subtrees=a__      ,					)

								_UpperCAmelCase						:  List[str]     					=	[]
								_UpperCAmelCase						:  Dict     					=	[]
								for i in trange(len(a__			)      ,					ascii=a__			):
												_UpperCAmelCase						:  Optional[int]     					=	TreebankWordDetokenizer().detokenize(vars(train_data[i]			)['''text''']			)
												_UpperCAmelCase						:  List[str]     					=	discriminator.tokenizer.encode(a__			)
												_UpperCAmelCase						:  Dict     					=	torch.tensor([50_256] + seq      ,					device=a__      ,					dtype=torch.long			)
												x.append(a__			)
												y.append(classaidx[vars(train_data[i]			)['''label''']]			)
								_UpperCAmelCase						:  int     					=	Dataset(a__      ,					a__			)

								_UpperCAmelCase						:  int     					=	[]
								_UpperCAmelCase						:  Union[str, Any]     					=	[]
								for i in trange(len(a__			)      ,					ascii=a__			):
												_UpperCAmelCase						:  Optional[int]     					=	TreebankWordDetokenizer().detokenize(vars(test_data[i]			)['''text''']			)
												_UpperCAmelCase						:  int     					=	discriminator.tokenizer.encode(a__			)
												_UpperCAmelCase						:  str     					=	torch.tensor([50_256] + seq      ,					device=a__      ,					dtype=torch.long			)
												test_x.append(a__			)
												test_y.append(classaidx[vars(test_data[i]			)['''label''']]			)
								_UpperCAmelCase						:  Dict     					=	Dataset(a__      ,					a__			)

								_UpperCAmelCase						:  Optional[int]     					=	{
								    '''class_size''': len(a__			),
								    '''embed_size''': discriminator.embed_size,
								    '''pretrained_model''': pretrained_model,
								    '''class_vocab''': classaidx,
								    '''default_class''': 2,
								}

				elif dataset == "clickbait":
								_UpperCAmelCase						:  List[str]     					=	['''non_clickbait''', '''clickbait''']
								_UpperCAmelCase						:  Dict     					=	{c: i for i, c in enumerate(a__			)}

								_UpperCAmelCase						:  Any     					=	Discriminator(
								    class_size=len(a__			)      ,					pretrained_model=a__      ,					cached_mode=a__      ,					device=a__			).to(a__			)

								with open('''datasets/clickbait/clickbait_train_prefix.txt'''			) as f:
												_UpperCAmelCase						:  Union[str, Any]     					=	[]
												for i, line in enumerate(a__			):
																try:
																				data.append(eval(a__			)			)
																except Exception:
																				print('''Error evaluating line {}: {}'''.format(a__      ,					a__			)			)
																				continue
								_UpperCAmelCase						:  List[Any]     					=	[]
								_UpperCAmelCase						:  List[str]     					=	[]
								with open('''datasets/clickbait/clickbait_train_prefix.txt'''			) as f:
												for i, line in enumerate(tqdm(a__      ,					ascii=a__			)			):
																try:
																				_UpperCAmelCase						:  Dict     					=	eval(a__			)
																				_UpperCAmelCase						:  str     					=	discriminator.tokenizer.encode(d['''text''']			)

																				if len(a__			) < max_length_seq:
																								_UpperCAmelCase						:  Tuple     					=	torch.tensor([50_256] + seq      ,					device=a__      ,					dtype=torch.long			)
																				else:
																								print('''Line {} is longer than maximum length {}'''.format(a__      ,					a__			)			)
																								continue
																				x.append(a__			)
																				y.append(d['''label''']			)
																except Exception:
																				print('''Error evaluating / tokenizing line {}, skipping it'''.format(a__			)			)
																				pass

								_UpperCAmelCase						:  Any     					=	Dataset(a__      ,					a__			)
								_UpperCAmelCase						:  Dict     					=	int(0.9 * len(a__			)			)
								_UpperCAmelCase						:  int     					=	len(a__			) - train_size
								_UpperCAmelCase					, _UpperCAmelCase						:  Optional[int]     					=	torch.utils.data.random_split(a__      ,					[train_size, test_size]			)

								_UpperCAmelCase						:  Union[str, Any]     					=	{
								    '''class_size''': len(a__			),
								    '''embed_size''': discriminator.embed_size,
								    '''pretrained_model''': pretrained_model,
								    '''class_vocab''': classaidx,
								    '''default_class''': 1,
								}

				elif dataset == "toxic":
								_UpperCAmelCase						:  Union[str, Any]     					=	['''non_toxic''', '''toxic''']
								_UpperCAmelCase						:  Optional[int]     					=	{c: i for i, c in enumerate(a__			)}

								_UpperCAmelCase						:  List[Any]     					=	Discriminator(
								    class_size=len(a__			)      ,					pretrained_model=a__      ,					cached_mode=a__      ,					device=a__			).to(a__			)

								_UpperCAmelCase						:  List[str]     					=	[]
								_UpperCAmelCase						:  Any     					=	[]
								with open('''datasets/toxic/toxic_train.txt'''			) as f:
												for i, line in enumerate(tqdm(a__      ,					ascii=a__			)			):
																try:
																				_UpperCAmelCase						:  Optional[int]     					=	eval(a__			)
																				_UpperCAmelCase						:  List[Any]     					=	discriminator.tokenizer.encode(d['''text''']			)

																				if len(a__			) < max_length_seq:
																								_UpperCAmelCase						:  str     					=	torch.tensor([50_256] + seq      ,					device=a__      ,					dtype=torch.long			)
																				else:
																								print('''Line {} is longer than maximum length {}'''.format(a__      ,					a__			)			)
																								continue
																				x.append(a__			)
																				y.append(int(np.sum(d['''label''']			) > 0			)			)
																except Exception:
																				print('''Error evaluating / tokenizing line {}, skipping it'''.format(a__			)			)
																				pass

								_UpperCAmelCase						:  Any     					=	Dataset(a__      ,					a__			)
								_UpperCAmelCase						:  Union[str, Any]     					=	int(0.9 * len(a__			)			)
								_UpperCAmelCase						:  List[str]     					=	len(a__			) - train_size
								_UpperCAmelCase					, _UpperCAmelCase						:  List[Any]     					=	torch.utils.data.random_split(a__      ,					[train_size, test_size]			)

								_UpperCAmelCase						:  Optional[Any]     					=	{
								    '''class_size''': len(a__			),
								    '''embed_size''': discriminator.embed_size,
								    '''pretrained_model''': pretrained_model,
								    '''class_vocab''': classaidx,
								    '''default_class''': 0,
								}

				else:  # if dataset == "generic":
								# This assumes the input dataset is a TSV with the following structure:
								# class \t text

								if dataset_fp is None:
												raise ValueError('''When generic dataset is selected, dataset_fp needs to be specified aswell.'''			)

								_UpperCAmelCase						:  List[Any]     					=	set()
								with open(a__			) as f:
												_UpperCAmelCase						:  int     					=	csv.reader(a__      ,					delimiter='''\t'''			)
												for row in tqdm(a__      ,					ascii=a__			):
																if row:
																				classes.add(row[0]			)

								_UpperCAmelCase						:  str     					=	sorted(a__			)
								_UpperCAmelCase						:  Dict     					=	{c: i for i, c in enumerate(a__			)}

								_UpperCAmelCase						:  Optional[Any]     					=	Discriminator(
								    class_size=len(a__			)      ,					pretrained_model=a__      ,					cached_mode=a__      ,					device=a__			).to(a__			)

								_UpperCAmelCase						:  List[Any]     					=	[]
								_UpperCAmelCase						:  Dict     					=	[]
								with open(a__			) as f:
												_UpperCAmelCase						:  Any     					=	csv.reader(a__      ,					delimiter='''\t'''			)
												for i, row in enumerate(tqdm(a__      ,					ascii=a__			)			):
																if row:
																				_UpperCAmelCase						:  Dict     					=	row[0]
																				_UpperCAmelCase						:  Tuple     					=	row[1]

																				try:
																								_UpperCAmelCase						:  str     					=	discriminator.tokenizer.encode(a__			)
																								if len(a__			) < max_length_seq:
																												_UpperCAmelCase						:  Optional[int]     					=	torch.tensor([50_256] + seq      ,					device=a__      ,					dtype=torch.long			)

																								else:
																												print('''Line {} is longer than maximum length {}'''.format(a__      ,					a__			)			)
																												continue

																								x.append(a__			)
																								y.append(classaidx[label]			)

																				except Exception:
																								print('''Error tokenizing line {}, skipping it'''.format(a__			)			)
																								pass

								_UpperCAmelCase						:  Optional[int]     					=	Dataset(a__      ,					a__			)
								_UpperCAmelCase						:  Any     					=	int(0.9 * len(a__			)			)
								_UpperCAmelCase						:  Optional[Any]     					=	len(a__			) - train_size
								_UpperCAmelCase					, _UpperCAmelCase						:  Optional[Any]     					=	torch.utils.data.random_split(a__      ,					[train_size, test_size]			)

								_UpperCAmelCase						:  List[str]     					=	{
								    '''class_size''': len(a__			),
								    '''embed_size''': discriminator.embed_size,
								    '''pretrained_model''': pretrained_model,
								    '''class_vocab''': classaidx,
								    '''default_class''': 0,
								}

				_UpperCAmelCase						:  Optional[Any]     					=	time.time()
				print('''Preprocessed {} data points'''.format(len(a__			) + len(a__			)			)			)
				print('''Data preprocessing took: {:.3f}s'''.format(end - start			)			)

				if cached:
								print('''Building representation cache...'''			)

								_UpperCAmelCase						:  Optional[int]     					=	time.time()

								_UpperCAmelCase						:  List[str]     					=	get_cached_data_loader(a__      ,					a__      ,					a__      ,					shuffle=a__      ,					device=a__			)

								_UpperCAmelCase						:  Any     					=	get_cached_data_loader(a__      ,					a__      ,					a__      ,					device=a__			)

								_UpperCAmelCase						:  List[Any]     					=	time.time()
								print('''Building representation cache took: {:.3f}s'''.format(end - start			)			)

				else:
								_UpperCAmelCase						:  Dict     					=	torch.utils.data.DataLoader(
								    dataset=a__      ,					batch_size=a__      ,					shuffle=a__      ,					collate_fn=a__			)
								_UpperCAmelCase						:  Union[str, Any]     					=	torch.utils.data.DataLoader(dataset=a__      ,					batch_size=a__      ,					collate_fn=a__			)

				if save_model:
								with open('''{}_classifier_head_meta.json'''.format(a__			)      ,					'''w'''			) as meta_file:
												json.dump(a__      ,					a__			)

				_UpperCAmelCase						:  str     					=	optim.Adam(discriminator.parameters()      ,					lr=0.0_001			)

				for epoch in range(a__			):
								_UpperCAmelCase						:  Optional[int]     					=	time.time()
								print('''\nEpoch'''      ,					epoch + 1			)

								train_epoch(
								    discriminator=a__      ,					data_loader=a__      ,					optimizer=a__      ,					epoch=a__      ,					log_interval=a__      ,					device=a__      ,					)
								evaluate_performance(data_loader=a__      ,					discriminator=a__      ,					device=a__			)

								_UpperCAmelCase						:  Union[str, Any]     					=	time.time()
								print('''Epoch took: {:.3f}s'''.format(end - start			)			)

								print('''\nExample prediction'''			)
								predict(a__      ,					a__      ,					a__      ,					cached=a__      ,					device=a__			)

								if save_model:
												# torch.save(discriminator.state_dict(),
												#           "{}_discriminator_{}.pt".format(
												#               args.dataset, epoch + 1
												#               ))
												torch.save(
												    discriminator.get_classifier().state_dict()      ,					'''{}_classifier_head_epoch_{}.pt'''.format(a__      ,					epoch + 1			)      ,					)


if __name__ == "__main__":
					UpperCAmelCase__			:   List[str]      			=    argparse.ArgumentParser(description='Train a discriminator on top of GPT-2 representations')
					parser.add_argument(
					    '--dataset',
					    type=str,
					    default='SST',
					    choices=('SST', 'clickbait', 'toxic', 'generic'),
					    help=(
					        'dataset to train the discriminator on.'
					        'In case of generic, the dataset is expected'
					        'to be a TSBV file with structure: class \\t text'
					    ),
					)
					parser.add_argument(
					    '--dataset_fp',
					    type=str,
					    default='',
					    help='File path of the dataset to use. Needed only in case of generic datadset',
					)
					parser.add_argument(
					    '--pretrained_model', type=str, default='gpt2-medium', help='Pretrained model to use as encoder'
					)
					parser.add_argument('--epochs', type=int, default=10, metavar='N', help='Number of training epochs')
					parser.add_argument(
					    '--batch_size', type=int, default=64, metavar='N', help='input batch size for training (default: 64)'
					)
					parser.add_argument(
					    '--log_interval',
					    type=int,
					    default=10,
					    metavar='N',
					    help='how many batches to wait before logging training status',
					)
					parser.add_argument('--save_model', action='store_true', help='whether to save the model')
					parser.add_argument('--cached', action='store_true', help='whether to cache the input representations')
					parser.add_argument('--no_cuda', action='store_true', help='use to turn off cuda')
					UpperCAmelCase__			:   Union[str, Any]      			=    parser.parse_args()

					train_discriminator(**(vars(args)))


'''simple docstring'''







import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
lowerCAmelCase_      =   logging.get_logger(__name__)

lowerCAmelCase_      =   {
    "post_extract_proj": "feature_projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "attention.self.query_proj": "encoder.encoder.layer.*.attention.self.query_proj",
    "attention.self.key_proj": "encoder.encoder.layer.*.attention.self.key_proj",
    "attention.self.value_proj": "encoder.encoder.layer.*.attention.self.value_proj",
    "attention.output.dense": "encoder.encoder.layer.*.attention.output.dense",
    "attention.output.LayerNorm": "encoder.encoder.layer.*.attention.output.LayerNorm",
    "intermediate.dense": "encoder.encoder.layer.*.intermediate.dense",
    "output.dense": "encoder.encoder.layer.*.output.dense",
    "output.LayerNorm": "encoder.encoder.layer.*.output.LayerNorm",
    "encoder.encoder.rel_embeddings": "encoder.encoder.rel_embeddings",
    "encoder.encoder.LayerNorm": "encoder.encoder.LayerNorm",
    "encoder.upsample.0": "encoder.upsample.projection",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}







def   lowerCAmelCase(  a__    :  List[Any]      , a__    :  List[str]      , a__    :  Optional[int]      , a__    :  Optional[Any]      , a__    :  List[Any]      ):







       '''simple docstring'''






       for attribute in key.split("."      ):
              lowerCamelCase__				   =      getattr(a__      , a__      )

       if weight_type is not None:
              lowerCamelCase__				   =      getattr(a__      , a__      ).shape
       else:
              lowerCamelCase__				   =      hf_pointer.shape

       assert hf_shape == value.shape, (
           f"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
           f""" {value.shape} for {full_name}"""
       )

       if weight_type == "weight":
              lowerCamelCase__				   =      value
       elif weight_type == "weight_g":
              lowerCamelCase__				   =      value
       elif weight_type == "weight_v":
              lowerCamelCase__				   =      value
       elif weight_type == "bias":
              lowerCamelCase__				   =      value
       else:
              lowerCamelCase__				   =      value

       logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""      )







def   lowerCAmelCase(  a__    :  Any      , a__    :  List[str]      , a__    :  str      ):







       '''simple docstring'''






       lowerCamelCase__				   =      []
       lowerCamelCase__				   =      fairseq_model.state_dict()

       lowerCamelCase__				   =      hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

       for name, value in fairseq_dict.items():
              lowerCamelCase__				   =      False
              if "conv_layers" in name:
                     load_conv_layer(
                         a__      , a__      , a__      , a__      , hf_model.config.feat_extract_norm == "group"      , )
                     lowerCamelCase__				   =      True
              else:
                     for key, mapped_key in MAPPING.items():
                            lowerCamelCase__				   =      "sew_d." + mapped_key if (is_finetuned and mapped_key != "lm_head") else mapped_key

                            if key in name or key.split("w2v_model."      )[-1] == name.split("."      )[0]:
                                   lowerCamelCase__				   =      True
                                   if "*" in mapped_key:
                                          lowerCamelCase__				   =      name.split(a__      )[0].split("."      )[-2]
                                          if not layer_index.isnumeric():
                                                 continue
                                          lowerCamelCase__				   =      mapped_key.replace("*"      , a__      )
                                   if "weight_g" in name:
                                          lowerCamelCase__				   =      "weight_g"
                                   elif "weight_v" in name:
                                          lowerCamelCase__				   =      "weight_v"
                                   elif "weight" in name:
                                          lowerCamelCase__				   =      "weight"
                                   elif "bias" in name:
                                          lowerCamelCase__				   =      "bias"
                                   else:
                                          lowerCamelCase__				   =      None
                                   set_recursively(a__      , a__      , a__      , a__      , a__      )
                            continue
              if not is_used:
                     unused_weights.append(a__      )

       logger.warning(f"""Unused weights: {unused_weights}"""      )







def   lowerCAmelCase(  a__    :  Optional[Any]      , a__    :  Dict      , a__    :  List[Any]      , a__    :  Union[str, Any]      , a__    :  List[str]      ):







       '''simple docstring'''






       lowerCamelCase__				   =      full_name.split("conv_layers."      )[-1]
       lowerCamelCase__				   =      name.split("."      )
       lowerCamelCase__				   =      int(items[0]      )
       lowerCamelCase__				   =      int(items[1]      )

       if type_id == 0:
              if "bias" in name:
                     assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                         f"""{full_name} has size {value.shape}, but"""
                         f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
                     )
                     lowerCamelCase__				   =      value
                     logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""      )
              elif "weight" in name:
                     assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                         f"""{full_name} has size {value.shape}, but"""
                         f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
                     )
                     lowerCamelCase__				   =      value
                     logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""      )
       elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
              if "bias" in name:
                     assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                         f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
                         " found."
                     )
                     lowerCamelCase__				   =      value
                     logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""      )
              elif "weight" in name:
                     assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                         f"""{full_name} has size {value.shape}, but"""
                         f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
                     )
                     lowerCamelCase__				   =      value
                     logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""      )
       else:
              unused_weights.append(a__      )







def   lowerCAmelCase(  a__    :  Optional[int]      , a__    :  Tuple      ):







       '''simple docstring'''






       lowerCamelCase__				   =      SEWDConfig()
       if is_finetuned:
              lowerCamelCase__				   =      model.wav_encoder.wav_model.cfg
       else:
              lowerCamelCase__				   =      model.cfg

       lowerCamelCase__				   =      fs_config.conv_bias
       lowerCamelCase__				   =      eval(fs_config.conv_feature_layers      )
       lowerCamelCase__				   =      [x[0] for x in conv_layers]
       lowerCamelCase__				   =      [x[1] for x in conv_layers]
       lowerCamelCase__				   =      [x[2] for x in conv_layers]
       lowerCamelCase__				   =      "gelu"
       lowerCamelCase__				   =      "layer" if fs_config.extractor_mode == "layer_norm" else "group"
       lowerCamelCase__				   =      0.0
       lowerCamelCase__				   =      fs_config.activation_fn.name
       lowerCamelCase__				   =      fs_config.encoder_embed_dim
       lowerCamelCase__				   =      0.0_2
       lowerCamelCase__				   =      fs_config.encoder_ffn_embed_dim
       lowerCamelCase__				   =      1E-5
       lowerCamelCase__				   =      fs_config.encoder_layerdrop
       lowerCamelCase__				   =      fs_config.encoder_attention_heads
       lowerCamelCase__				   =      fs_config.conv_pos_groups
       lowerCamelCase__				   =      fs_config.conv_pos
       lowerCamelCase__				   =      len(a__      )
       lowerCamelCase__				   =      fs_config.encoder_layers
       lowerCamelCase__				   =      fs_config.squeeze_factor
       # DeBERTa-specific parameters:
       lowerCamelCase__				   =      fs_config.max_position_embeddings
       lowerCamelCase__				   =      fs_config.position_buckets
       lowerCamelCase__				   =      fs_config.share_att_key
       lowerCamelCase__				   =      fs_config.relative_attention
       lowerCamelCase__				   =      fs_config.position_biased_input
       lowerCamelCase__				   =      tuple(fs_config.pos_att_type.split("|"      )      )
       lowerCamelCase__				   =      fs_config.norm_rel_ebd

       # take care of any params that are overridden by the Wav2VecCtc model
       if is_finetuned:
              lowerCamelCase__				   =      model.cfg
              lowerCamelCase__				   =      fs_config.final_dropout
              lowerCamelCase__				   =      fs_config.layerdrop
       lowerCamelCase__				   =      fs_config.activation_dropout
       lowerCamelCase__				   =      fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
       lowerCamelCase__				   =      fs_config.attention_dropout
       lowerCamelCase__				   =      fs_config.dropout_input
       lowerCamelCase__				   =      fs_config.dropout
       lowerCamelCase__				   =      fs_config.mask_channel_length
       lowerCamelCase__				   =      fs_config.mask_channel_prob
       lowerCamelCase__				   =      fs_config.mask_length
       lowerCamelCase__				   =      fs_config.mask_prob

       lowerCamelCase__				   =      "Wav2Vec2FeatureExtractor"
       lowerCamelCase__				   =      "Wav2Vec2CTCTokenizer"

       return config







@torch.no_grad()
def   lowerCAmelCase(  a__    :  List[Any]      , a__    :  int      , a__    :  Any=None      , a__    :  Any=None      , a__    :  Any=True      ):







       '''simple docstring'''






       if is_finetuned:
              lowerCamelCase__   ,		lowerCamelCase__   ,		lowerCamelCase__				   =      fairseq.checkpoint_utils.load_model_ensemble_and_task(
                  [checkpoint_path]      , arg_overrides={"data": "/".join(dict_path.split("/"      )[:-1]      )}      )
       else:
              lowerCamelCase__   ,		lowerCamelCase__   ,		lowerCamelCase__				   =      fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]      )

       if config_path is not None:
              lowerCamelCase__				   =      SEWDConfig.from_pretrained(a__      )
       else:
              lowerCamelCase__				   =      convert_config(model[0]      , a__      )
       lowerCamelCase__				   =      model[0].eval()

       lowerCamelCase__				   =      True if config.feat_extract_norm == "layer" else False
       lowerCamelCase__				   =      WavaVecaFeatureExtractor(
           feature_size=1      , sampling_rate=1_6000      , padding_value=0      , do_normalize=a__      , return_attention_mask=a__      , )

       if is_finetuned:
              if dict_path:
                     lowerCamelCase__				   =      Dictionary.load(a__      )

                     # important change bos & pad token id since CTC symbol is <pad> and
                     # not <s> as in fairseq
                     lowerCamelCase__				   =      target_dict.pad_index
                     lowerCamelCase__				   =      target_dict.bos_index
                     lowerCamelCase__				   =      target_dict.pad_index
                     lowerCamelCase__				   =      target_dict.bos_index
                     lowerCamelCase__				   =      target_dict.eos_index
                     lowerCamelCase__				   =      len(target_dict.symbols      )
                     lowerCamelCase__				   =      os.path.join(a__      , "vocab.json"      )
                     if not os.path.isdir(a__      ):
                            logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(a__      )      )
                            return
                     os.makedirs(a__      , exist_ok=a__      )
                     with open(a__      , "w"      , encoding="utf-8"      ) as vocab_handle:
                            json.dump(target_dict.indices      , a__      )
                     lowerCamelCase__				   =      WavaVecaCTCTokenizer(
                         a__      , unk_token=target_dict.unk_word      , pad_token=target_dict.pad_word      , bos_token=target_dict.bos_word      , eos_token=target_dict.eos_word      , word_delimiter_token="|"      , do_lower_case=a__      , )
                     lowerCamelCase__				   =      WavaVecaProcessor(feature_extractor=a__      , tokenizer=a__      )
                     processor.save_pretrained(a__      )

              lowerCamelCase__				   =      SEWDForCTC(a__      )
       else:
              lowerCamelCase__				   =      SEWDModel(a__      )
              feature_extractor.save_pretrained(a__      )

       recursively_load_weights(a__      , a__      , a__      )

       hf_model.save_pretrained(a__      )


if __name__ == "__main__":
    lowerCAmelCase_      =   argparse.ArgumentParser()
    parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
    parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
    parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
    parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
    parser.add_argument(
        "--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
    )
    lowerCAmelCase_      =   parser.parse_args()
    convert_sew_checkpoint(
        args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
    )

