


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)
SCREAMING_SNAKE_CASE : List[Any]          =    1e-1_0
SCREAMING_SNAKE_CASE : Dict          =    "This is incredible! I love it, this is the best chicken I have ever had."
SCREAMING_SNAKE_CASE : Optional[int]          =    1_00





class 					snake_case__  ( nn.Module       ):





	def __init__(  self				,    UpperCamelCase_				,    UpperCamelCase_="gpt2-medium"				,    UpperCamelCase_=False				,    UpperCamelCase_="cpu"							)		->			int:
								"""simple docstring"""






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





	def     A				(  self							)		->			Union[str, Any]:
								"""simple docstring"""






								return self.classifier_head





	def     A				(  self							)		->			Tuple:
								"""simple docstring"""






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





	def     A				(  self				,    UpperCamelCase_							)		->			Union[str, Any]:
								"""simple docstring"""






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





	def     A				(  self				,    UpperCamelCase_							)		->			Union[str, Any]:
								"""simple docstring"""






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

								a_       :   Optional[Any]			=	self.classifier_head(UpperCamelCase__							)
								a_       :   Dict			=	nn.functional.log_softmax(UpperCamelCase__				,    dim=-1							)

								return probs





class 					snake_case__  ( data.Dataset       ):





	def __init__(  self				,    UpperCamelCase_				,    UpperCamelCase_							)		->			Union[str, Any]:
								"""simple docstring"""






								a_       :   Tuple			=	X
								a_       :   List[Any]			=	y





	def __len__(  self							)		->			List[str]:
								"""simple docstring"""






								return len(self.X							)





	def __getitem__(  self				,    UpperCamelCase_							)		->			Dict:
								"""simple docstring"""






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





def 						_lowerCamelCase	(							SCREAMING_SNAKE_CASE_				: Tuple      ):






							"""simple docstring"""



							def pad_sequences(SCREAMING_SNAKE_CASE_				: Optional[Any]      ):
														a_       :   Union[str, Any]			=	[len(SCREAMING_SNAKE_CASE_      ) for seq in sequences]

														a_       :   int			=	torch.zeros(len(SCREAMING_SNAKE_CASE_      )	,	max(SCREAMING_SNAKE_CASE_      )      ).long()  # padding value = 0

														for i, seq in enumerate(SCREAMING_SNAKE_CASE_      ):
																					a_       :   List[str]			=	lengths[i]
																					a_       :   List[Any]			=	seq[:end]

														return padded_sequences, lengths

							a_       :   Optional[Any]			=	{}
							for key in data[0].keys():
														a_       :   List[str]			=	[d[key] for d in data]

							a_		,				a_       :   Optional[Any]			=	pad_sequences(item_info["""X"""]      )
							a_       :   List[Any]			=	torch.tensor(item_info["""y"""]	,	dtype=torch.long      )

							return x_batch, y_batch





def 						_lowerCamelCase	(							SCREAMING_SNAKE_CASE_				: Optional[Any]      ):






							"""simple docstring"""



							a_       :   Any			=	{}
							for key in data[0].keys():
														a_       :   str			=	[d[key] for d in data]

							a_       :   Optional[int]			=	torch.cat(item_info["""X"""]	,	0      )
							a_       :   Dict			=	torch.tensor(item_info["""y"""]	,	dtype=torch.long      )

							return x_batch, y_batch





def 						_lowerCamelCase	(							SCREAMING_SNAKE_CASE_				: Tuple	,	SCREAMING_SNAKE_CASE_				: Tuple	,	SCREAMING_SNAKE_CASE_				: Optional[Any]	,	SCREAMING_SNAKE_CASE_				: int=0	,	SCREAMING_SNAKE_CASE_				: Any=10	,	SCREAMING_SNAKE_CASE_				: str="cpu"      ):






							"""simple docstring"""



							a_       :   Tuple			=	0
							discriminator.train_custom()
							for batch_idx, (input_t, target_t) in enumerate(SCREAMING_SNAKE_CASE_      ):
														a_		,				a_       :   Union[str, Any]			=	input_t.to(SCREAMING_SNAKE_CASE_      ), target_t.to(SCREAMING_SNAKE_CASE_      )

														optimizer.zero_grad()

														a_       :   Optional[Any]			=	discriminator(SCREAMING_SNAKE_CASE_      )
														a_       :   Tuple			=	nn.functional.nll_loss(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_      )
														loss.backward(retain_graph=SCREAMING_SNAKE_CASE_      )
														optimizer.step()

														samples_so_far += len(SCREAMING_SNAKE_CASE_      )

														if batch_idx % log_interval == 0:
																					print(
																					    """Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}""".format(
																					        epoch + 1	,	SCREAMING_SNAKE_CASE_	,	len(data_loader.dataset      )	,	1_00 * samples_so_far / len(data_loader.dataset      )	,	loss.item()	,	)      )





def 						_lowerCamelCase	(							SCREAMING_SNAKE_CASE_				: Any	,	SCREAMING_SNAKE_CASE_				: List[Any]	,	SCREAMING_SNAKE_CASE_				: List[Any]="cpu"      ):






							"""simple docstring"""



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

							test_loss /= len(data_loader.dataset      )

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





def 						_lowerCamelCase	(							SCREAMING_SNAKE_CASE_				: Optional[Any]	,	SCREAMING_SNAKE_CASE_				: int	,	SCREAMING_SNAKE_CASE_				: Dict	,	SCREAMING_SNAKE_CASE_				: Optional[int]=False	,	SCREAMING_SNAKE_CASE_				: int="cpu"      ):






							"""simple docstring"""



							a_       :   List[str]			=	model.tokenizer.encode(SCREAMING_SNAKE_CASE_      )
							a_       :   List[str]			=	torch.tensor([input_t]	,	dtype=torch.long	,	device=SCREAMING_SNAKE_CASE_      )
							if cached:
														a_       :   List[str]			=	model.avg_representation(SCREAMING_SNAKE_CASE_      )

							a_       :   Any			=	model(SCREAMING_SNAKE_CASE_      ).data.cpu().numpy().flatten().tolist()
							print("""Input sentence:"""	,	SCREAMING_SNAKE_CASE_      )
							print(
							    """Predictions:"""	,	""", """.join("""{}: {:.4f}""".format(SCREAMING_SNAKE_CASE_	,	math.exp(SCREAMING_SNAKE_CASE_      )      ) for c, log_prob in zip(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_      )      )	,	)





def 						_lowerCamelCase	(							SCREAMING_SNAKE_CASE_				: Tuple	,	SCREAMING_SNAKE_CASE_				: List[Any]	,	SCREAMING_SNAKE_CASE_				: Dict	,	SCREAMING_SNAKE_CASE_				: Dict=False	,	SCREAMING_SNAKE_CASE_				: int="cpu"      ):






							"""simple docstring"""



							a_       :   Tuple			=	torch.utils.data.DataLoader(dataset=SCREAMING_SNAKE_CASE_	,	batch_size=SCREAMING_SNAKE_CASE_	,	collate_fn=SCREAMING_SNAKE_CASE_      )

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

							a_       :   Any			=	torch.utils.data.DataLoader(
							    dataset=Dataset(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_      )	,	batch_size=SCREAMING_SNAKE_CASE_	,	shuffle=SCREAMING_SNAKE_CASE_	,	collate_fn=SCREAMING_SNAKE_CASE_      )

							return data_loader





def 						_lowerCamelCase	(							SCREAMING_SNAKE_CASE_				: str	,	SCREAMING_SNAKE_CASE_				: List[Any]=None	,	SCREAMING_SNAKE_CASE_				: Any="gpt2-medium"	,	SCREAMING_SNAKE_CASE_				: int=10	,	SCREAMING_SNAKE_CASE_				: int=64	,	SCREAMING_SNAKE_CASE_				: Optional[int]=10	,	SCREAMING_SNAKE_CASE_				: Optional[Any]=False	,	SCREAMING_SNAKE_CASE_				: Optional[int]=False	,	SCREAMING_SNAKE_CASE_				: int=False	,	):






							"""simple docstring"""



							a_       :   List[str]			=	"""cuda""" if torch.cuda.is_available() and not no_cuda else """cpu"""

							print("""Preprocessing {} dataset...""".format(SCREAMING_SNAKE_CASE_      )      )
							a_       :   Optional[Any]			=	time.time()

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

														a_       :   Dict			=	Discriminator(
														    class_size=len(SCREAMING_SNAKE_CASE_      )	,	pretrained_model=SCREAMING_SNAKE_CASE_	,	cached_mode=SCREAMING_SNAKE_CASE_	,	device=SCREAMING_SNAKE_CASE_      ).to(SCREAMING_SNAKE_CASE_      )

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

														a_       :   Dict			=	[]
														a_       :   List[str]			=	[]
														for i in trange(len(SCREAMING_SNAKE_CASE_      )	,	ascii=SCREAMING_SNAKE_CASE_      ):
																					a_       :   Optional[Any]			=	TreebankWordDetokenizer().detokenize(vars(train_data[i]      )["""text"""]      )
																					a_       :   str			=	discriminator.tokenizer.encode(SCREAMING_SNAKE_CASE_      )
																					a_       :   Tuple			=	torch.tensor([5_02_56] + seq	,	device=SCREAMING_SNAKE_CASE_	,	dtype=torch.long      )
																					x.append(SCREAMING_SNAKE_CASE_      )
																					y.append(classaidx[vars(train_data[i]      )["""label"""]]      )
														a_       :   Optional[Any]			=	Dataset(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_      )

														a_       :   int			=	[]
														a_       :   Union[str, Any]			=	[]
														for i in trange(len(SCREAMING_SNAKE_CASE_      )	,	ascii=SCREAMING_SNAKE_CASE_      ):
																					a_       :   Any			=	TreebankWordDetokenizer().detokenize(vars(test_data[i]      )["""text"""]      )
																					a_       :   Union[str, Any]			=	discriminator.tokenizer.encode(SCREAMING_SNAKE_CASE_      )
																					a_       :   List[str]			=	torch.tensor([5_02_56] + seq	,	device=SCREAMING_SNAKE_CASE_	,	dtype=torch.long      )
																					test_x.append(SCREAMING_SNAKE_CASE_      )
																					test_y.append(classaidx[vars(test_data[i]      )["""label"""]]      )
														a_       :   Tuple			=	Dataset(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_      )

														a_       :   Tuple			=	{
														    """class_size""": len(SCREAMING_SNAKE_CASE_      ),
														    """embed_size""": discriminator.embed_size,
														    """pretrained_model""": pretrained_model,
														    """class_vocab""": classaidx,
														    """default_class""": 2,
														}

							elif dataset == "clickbait":
														a_       :   List[Any]			=	["""non_clickbait""", """clickbait"""]
														a_       :   Any			=	{c: i for i, c in enumerate(SCREAMING_SNAKE_CASE_      )}

														a_       :   List[str]			=	Discriminator(
														    class_size=len(SCREAMING_SNAKE_CASE_      )	,	pretrained_model=SCREAMING_SNAKE_CASE_	,	cached_mode=SCREAMING_SNAKE_CASE_	,	device=SCREAMING_SNAKE_CASE_      ).to(SCREAMING_SNAKE_CASE_      )

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

																																			if len(SCREAMING_SNAKE_CASE_      ) < max_length_seq:
																																										a_       :   Any			=	torch.tensor([5_02_56] + seq	,	device=SCREAMING_SNAKE_CASE_	,	dtype=torch.long      )
																																			else:
																																										print("""Line {} is longer than maximum length {}""".format(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_      )      )
																																										continue
																																			x.append(SCREAMING_SNAKE_CASE_      )
																																			y.append(d["""label"""]      )
																												except Exception:
																																			print("""Error evaluating / tokenizing line {}, skipping it""".format(SCREAMING_SNAKE_CASE_      )      )
																																			pass

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

														a_       :   Any			=	{
														    """class_size""": len(SCREAMING_SNAKE_CASE_      ),
														    """embed_size""": discriminator.embed_size,
														    """pretrained_model""": pretrained_model,
														    """class_vocab""": classaidx,
														    """default_class""": 1,
														}

							elif dataset == "toxic":
														a_       :   Optional[int]			=	["""non_toxic""", """toxic"""]
														a_       :   Optional[Any]			=	{c: i for i, c in enumerate(SCREAMING_SNAKE_CASE_      )}

														a_       :   Optional[int]			=	Discriminator(
														    class_size=len(SCREAMING_SNAKE_CASE_      )	,	pretrained_model=SCREAMING_SNAKE_CASE_	,	cached_mode=SCREAMING_SNAKE_CASE_	,	device=SCREAMING_SNAKE_CASE_      ).to(SCREAMING_SNAKE_CASE_      )

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

																																			if len(SCREAMING_SNAKE_CASE_      ) < max_length_seq:
																																										a_       :   Tuple			=	torch.tensor([5_02_56] + seq	,	device=SCREAMING_SNAKE_CASE_	,	dtype=torch.long      )
																																			else:
																																										print("""Line {} is longer than maximum length {}""".format(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_      )      )
																																										continue
																																			x.append(SCREAMING_SNAKE_CASE_      )
																																			y.append(int(np.sum(d["""label"""]      ) > 0      )      )
																												except Exception:
																																			print("""Error evaluating / tokenizing line {}, skipping it""".format(SCREAMING_SNAKE_CASE_      )      )
																																			pass

														a_       :   Optional[int]			=	Dataset(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_      )
														a_       :   Optional[Any]			=	int(0.9 * len(SCREAMING_SNAKE_CASE_      )      )
														a_       :   List[Any]			=	len(SCREAMING_SNAKE_CASE_      ) - train_size
														a_		,				a_       :   Tuple			=	torch.utils.data.random_split(SCREAMING_SNAKE_CASE_	,	[train_size, test_size]      )

														a_       :   Union[str, Any]			=	{
														    """class_size""": len(SCREAMING_SNAKE_CASE_      ),
														    """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."""      )

														a_       :   List[str]			=	set()
														with open(SCREAMING_SNAKE_CASE_      ) as f:
																					a_       :   List[Any]			=	csv.reader(SCREAMING_SNAKE_CASE_	,	delimiter="""\t"""      )
																					for row in tqdm(SCREAMING_SNAKE_CASE_	,	ascii=SCREAMING_SNAKE_CASE_      ):
																												if row:
																																			classes.add(row[0]      )

														a_       :   Tuple			=	sorted(SCREAMING_SNAKE_CASE_      )
														a_       :   Any			=	{c: i for i, c in enumerate(SCREAMING_SNAKE_CASE_      )}

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

														a_       :   List[Any]			=	[]
														a_       :   Optional[Any]			=	[]
														with open(SCREAMING_SNAKE_CASE_      ) as f:
																					a_       :   List[Any]			=	csv.reader(SCREAMING_SNAKE_CASE_	,	delimiter="""\t"""      )
																					for i, row in enumerate(tqdm(SCREAMING_SNAKE_CASE_	,	ascii=SCREAMING_SNAKE_CASE_      )      ):
																												if row:
																																			a_       :   Optional[int]			=	row[0]
																																			a_       :   List[str]			=	row[1]

																																			try:
																																										a_       :   Any			=	discriminator.tokenizer.encode(SCREAMING_SNAKE_CASE_      )
																																										if len(SCREAMING_SNAKE_CASE_      ) < max_length_seq:
																																																	a_       :   Dict			=	torch.tensor([5_02_56] + seq	,	device=SCREAMING_SNAKE_CASE_	,	dtype=torch.long      )

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

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

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

														a_       :   Any			=	Dataset(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_      )
														a_       :   Dict			=	int(0.9 * len(SCREAMING_SNAKE_CASE_      )      )
														a_       :   int			=	len(SCREAMING_SNAKE_CASE_      ) - train_size
														a_		,				a_       :   Union[str, Any]			=	torch.utils.data.random_split(SCREAMING_SNAKE_CASE_	,	[train_size, test_size]      )

														a_       :   List[str]			=	{
														    """class_size""": len(SCREAMING_SNAKE_CASE_      ),
														    """embed_size""": discriminator.embed_size,
														    """pretrained_model""": pretrained_model,
														    """class_vocab""": classaidx,
														    """default_class""": 0,
														}

							a_       :   List[Any]			=	time.time()
							print("""Preprocessed {} data points""".format(len(SCREAMING_SNAKE_CASE_      ) + len(SCREAMING_SNAKE_CASE_      )      )      )
							print("""Data preprocessing took: {:.3f}s""".format(end - start      )      )

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

														a_       :   str			=	time.time()

														a_       :   str			=	get_cached_data_loader(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	shuffle=SCREAMING_SNAKE_CASE_	,	device=SCREAMING_SNAKE_CASE_      )

														a_       :   Dict			=	get_cached_data_loader(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	device=SCREAMING_SNAKE_CASE_      )

														a_       :   Tuple			=	time.time()
														print("""Building representation cache took: {:.3f}s""".format(end - start      )      )

							else:
														a_       :   Tuple			=	torch.utils.data.DataLoader(
														    dataset=SCREAMING_SNAKE_CASE_	,	batch_size=SCREAMING_SNAKE_CASE_	,	shuffle=SCREAMING_SNAKE_CASE_	,	collate_fn=SCREAMING_SNAKE_CASE_      )
														a_       :   Optional[Any]			=	torch.utils.data.DataLoader(dataset=SCREAMING_SNAKE_CASE_	,	batch_size=SCREAMING_SNAKE_CASE_	,	collate_fn=SCREAMING_SNAKE_CASE_      )

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

							a_       :   Optional[Any]			=	optim.Adam(discriminator.parameters()	,	lr=0.0_001      )

							for epoch in range(SCREAMING_SNAKE_CASE_      ):
														a_       :   Union[str, Any]			=	time.time()
														print("""\nEpoch"""	,	epoch + 1      )

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

														a_       :   Any			=	time.time()
														print("""Epoch took: {:.3f}s""".format(end - start      )      )

														print("""\nExample prediction"""      )
														predict(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	cached=SCREAMING_SNAKE_CASE_	,	device=SCREAMING_SNAKE_CASE_      )

														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(SCREAMING_SNAKE_CASE_	,	epoch + 1      )	,	)


if __name__ == "__main__":
	SCREAMING_SNAKE_CASE : Dict          =    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")
	SCREAMING_SNAKE_CASE : Dict          =    parser.parse_args()

	train_discriminator(**(vars(args)))






import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


SCREAMING_SNAKE_CASE : List[str]          =    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE : List[Any]          =    {
    "shi-labs/oneformer_ade20k_swin_tiny": (
        "https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json"
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}





class 					snake_case__  ( __A       ):
	UpperCAmelCase   :							Tuple         = """oneformer"""
	UpperCAmelCase   :							Union[str, Any]         = {"""hidden_size""": """hidden_dim"""}





	def __init__(  self				,    UpperCamelCase_ = None				,    UpperCamelCase_ = 255				,    UpperCamelCase_ = 150				,    UpperCamelCase_ = 0.1				,    UpperCamelCase_ = 2.0				,    UpperCamelCase_ = 5.0				,    UpperCamelCase_ = 5.0				,    UpperCamelCase_ = 0.5				,    UpperCamelCase_ = 0.07				,    UpperCamelCase_ = 12544				,    UpperCamelCase_ = 3.0				,    UpperCamelCase_ = 0.75				,    UpperCamelCase_ = 0.02				,    UpperCamelCase_ = 1.0				,    UpperCamelCase_ = 1e-05				,    UpperCamelCase_ = False				,    UpperCamelCase_ = True				,    UpperCamelCase_ = True				,    UpperCamelCase_ = [4, 8, 16, 32]				,    UpperCamelCase_ = 77				,    UpperCamelCase_ = 256				,    UpperCamelCase_ = 77				,    UpperCamelCase_ = 6				,    UpperCamelCase_ = 49408				,    UpperCamelCase_ = 2				,    UpperCamelCase_ = 16				,    UpperCamelCase_ = 256				,    UpperCamelCase_ = 256				,    UpperCamelCase_ = 256				,    UpperCamelCase_ = 1024				,    UpperCamelCase_ = "GN"				,    UpperCamelCase_ = 6				,    UpperCamelCase_ = 10				,    UpperCamelCase_ = True				,    UpperCamelCase_ = 8				,    UpperCamelCase_ = 0.1				,    UpperCamelCase_ = 2048				,    UpperCamelCase_ = False				,    UpperCamelCase_ = False				,    UpperCamelCase_ = 2				,    UpperCamelCase_ = 4				,    **UpperCamelCase_				,    )		->			List[Any]:
								"""simple docstring"""






								if backbone_config is None:
															logger.info("""`backbone_config` is unset. Initializing the config with the default `Swin` backbone."""							)
															a_       :   Tuple			=	CONFIG_MAPPING["""swin"""](
															    image_size=224				,    in_channels=3				,    patch_size=4				,    embed_dim=96				,    depths=[2, 2, 6, 2]				,    num_heads=[3, 6, 12, 24]				,    window_size=7				,    drop_path_rate=0.3				,    use_absolute_embeddings=UpperCamelCase_				,    out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]				,    )
								elif isinstance(UpperCamelCase_				,    UpperCamelCase_							):
															a_       :   str			=	backbone_config.get("""model_type"""							)
															a_       :   Optional[Any]			=	CONFIG_MAPPING[backbone_model_type]
															a_       :   List[Any]			=	config_class.from_dict(UpperCamelCase_							)

								a_       :   str			=	backbone_config

								a_       :   List[str]			=	ignore_value
								a_       :   Optional[int]			=	num_queries
								a_       :   Optional[Any]			=	no_object_weight
								a_       :   List[Any]			=	class_weight
								a_       :   Union[str, Any]			=	mask_weight
								a_       :   Union[str, Any]			=	dice_weight
								a_       :   Optional[Any]			=	contrastive_weight
								a_       :   Any			=	contrastive_temperature
								a_       :   Optional[int]			=	train_num_points
								a_       :   Tuple			=	oversample_ratio
								a_       :   str			=	importance_sample_ratio
								a_       :   List[Any]			=	init_std
								a_       :   int			=	init_xavier_std
								a_       :   List[Any]			=	layer_norm_eps
								a_       :   Optional[int]			=	is_training
								a_       :   List[Any]			=	use_auxiliary_loss
								a_       :   str			=	output_auxiliary_logits
								a_       :   Dict			=	strides
								a_       :   List[Any]			=	task_seq_len
								a_       :   int			=	text_encoder_width
								a_       :   List[Any]			=	text_encoder_context_length
								a_       :   List[str]			=	text_encoder_num_layers
								a_       :   Union[str, Any]			=	text_encoder_vocab_size
								a_       :   Optional[Any]			=	text_encoder_proj_layers
								a_       :   Optional[int]			=	text_encoder_n_ctx
								a_       :   Optional[Any]			=	conv_dim
								a_       :   str			=	mask_dim
								a_       :   Optional[Any]			=	hidden_dim
								a_       :   Any			=	encoder_feedforward_dim
								a_       :   Dict			=	norm
								a_       :   List[Any]			=	encoder_layers
								a_       :   int			=	decoder_layers
								a_       :   Any			=	use_task_norm
								a_       :   Union[str, Any]			=	num_attention_heads
								a_       :   List[str]			=	dropout
								a_       :   List[Any]			=	dim_feedforward
								a_       :   Union[str, Any]			=	pre_norm
								a_       :   Dict			=	enforce_input_proj
								a_       :   Tuple			=	query_dec_layers
								a_       :   Any			=	common_stride
								a_       :   List[str]			=	decoder_layers

								super().__init__(**UpperCamelCase_							)





	def     A				(  self							)		->			Dict[str, any]:
								"""simple docstring"""






								a_       :   Dict			=	copy.deepcopy(self.__dict__							)
								a_       :   Any			=	self.backbone_config.to_dict()
								a_       :   Union[str, Any]			=	self.__class__.model_type
								return output


