





# Copyright (c) 2019 Uber Technologies, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

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)
__lowercase							:List[str]							=	1e-10
__lowercase							:str							=	"This is incredible! I love it, this is the best chicken I have ever had."
__lowercase							:Any							=	100



class  _a  (					nn.Module				):






							"""simple docstring"""

							def __init__(   self					:  Dict				,						a					:  int				,						a					:  Optional[Any]="gpt2-medium"				,						a					:  Dict=False				,						a					:  Any="cpu"     )						->Optional[int]:
													super().__init__()
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       GPTaTokenizer.from_pretrained(a     )
													SCREAMING_SNAKE_CASE__  :					Tuple =       GPTaLMHeadModel.from_pretrained(a     )
													SCREAMING_SNAKE_CASE__  :					Any =       self.encoder.transformer.config.hidden_size
													SCREAMING_SNAKE_CASE__  :					Dict =       ClassificationHead(class_size=a				,						embed_size=self.embed_size     )
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       cached_mode
													SCREAMING_SNAKE_CASE__  :					Any =       device

							def   A_	(   self					:  Dict     )						->List[str]:
													return self.classifier_head

							def   A_	(   self					:  Tuple     )						->Optional[int]:
													for param in self.encoder.parameters():
																			SCREAMING_SNAKE_CASE__  :					Optional[Any] =       False
													self.classifier_head.train()

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






							def   A_	(   self					:  Optional[int]				,						a					:  List[Any]     )						->Union[str, Any]:
													if self.cached_mode:
																			SCREAMING_SNAKE_CASE__  :					int =       x.to(self.device     )
													else:
																			SCREAMING_SNAKE_CASE__  :					Optional[Any] =       self.avg_representation(x.to(self.device     )     )

													SCREAMING_SNAKE_CASE__  :					Tuple =       self.classifier_head(a     )
													SCREAMING_SNAKE_CASE__  :					int =       nn.functional.log_softmax(a				,						dim=-1     )

													return probs







class  _a  (					data.Dataset				):






							"""simple docstring"""

							def __init__(   self					:  List[Any]				,						a					:  int				,						a					:  List[str]     )						->Any:
													SCREAMING_SNAKE_CASE__  :					Any =       X
													SCREAMING_SNAKE_CASE__  :					str =       y

							def __len__(   self					:  List[Any]     )						->List[Any]:
													return len(self.X     )






							def __getitem__(   self					:  Union[str, Any]				,						a					:  List[Any]     )						->Dict:
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       {}
													SCREAMING_SNAKE_CASE__  :					Dict =       self.X[index]
													SCREAMING_SNAKE_CASE__  :					List[Any] =       self.y[index]
													return data







def    UpperCAmelCase				(  _lowerCamelCase   :	List[Any]  ):





						'''simple docstring'''


						def pad_sequences(_lowerCamelCase   :	List[str]  ):
												SCREAMING_SNAKE_CASE__  :					Optional[Any] =       [len(_lowerCamelCase  ) for seq in sequences]

												SCREAMING_SNAKE_CASE__  :					List[str] =       torch.zeros(len(_lowerCamelCase  )      , max(_lowerCamelCase  )  ).long()  # padding value = 0

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

												return padded_sequences, lengths

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

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

						return x_batch, y_batch







def    UpperCAmelCase				(  _lowerCamelCase   :	List[str]  ):





						'''simple docstring'''


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

						SCREAMING_SNAKE_CASE__  :					Any =       torch.cat(item_info["X"]      , 0  )
						SCREAMING_SNAKE_CASE__  :					int =       torch.tensor(item_info["y"]      , dtype=torch.long  )

						return x_batch, y_batch







def    UpperCAmelCase				(  _lowerCamelCase   :	List[Any]      , _lowerCamelCase   :	Optional[Any]      , _lowerCamelCase   :	Dict      , _lowerCamelCase   :	Union[str, Any]=0      , _lowerCamelCase   :	int=10      , _lowerCamelCase   :	List[Any]="cpu"  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       0
						discriminator.train_custom()
						for batch_idx, (input_t, target_t) in enumerate(_lowerCamelCase  ):
												SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					List[Any] =       input_t.to(_lowerCamelCase  ), target_t.to(_lowerCamelCase  )

												optimizer.zero_grad()

												SCREAMING_SNAKE_CASE__  :					Any =       discriminator(_lowerCamelCase  )
												SCREAMING_SNAKE_CASE__  :					Any =       nn.functional.nll_loss(_lowerCamelCase      , _lowerCamelCase  )
												loss.backward(retain_graph=_lowerCamelCase  )
												optimizer.step()

												samples_so_far += len(_lowerCamelCase  )

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







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





						'''simple docstring'''


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

						test_loss /= len(data_loader.dataset  )

						print(
						    "Performance on test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)".format(
						        _lowerCamelCase      , _lowerCamelCase      , len(data_loader.dataset  )      , 1_0_0.0 * correct / len(data_loader.dataset  )  )  )







def    UpperCAmelCase				(  _lowerCamelCase   :	Optional[int]      , _lowerCamelCase   :	str      , _lowerCamelCase   :	Optional[int]      , _lowerCamelCase   :	str=False      , _lowerCamelCase   :	Tuple="cpu"  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       model.tokenizer.encode(_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					List[str] =       torch.tensor([input_t]      , dtype=torch.long      , device=_lowerCamelCase  )
						if cached:
												SCREAMING_SNAKE_CASE__  :					Optional[Any] =       model.avg_representation(_lowerCamelCase  )

						SCREAMING_SNAKE_CASE__  :					Optional[int] =       model(_lowerCamelCase  ).data.cpu().numpy().flatten().tolist()
						print("Input sentence:"      , _lowerCamelCase  )
						print(
						    "Predictions:"      , ", ".join("{}: {:.4f}".format(_lowerCamelCase      , math.exp(_lowerCamelCase  )  ) for c, log_prob in zip(_lowerCamelCase      , _lowerCamelCase  )  )      , )







def    UpperCAmelCase				(  _lowerCamelCase   :	int      , _lowerCamelCase   :	int      , _lowerCamelCase   :	Optional[Any]      , _lowerCamelCase   :	List[Any]=False      , _lowerCamelCase   :	Any="cpu"  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					List[str] =       torch.utils.data.DataLoader(dataset=_lowerCamelCase      , batch_size=_lowerCamelCase      , collate_fn=_lowerCamelCase  )

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

						SCREAMING_SNAKE_CASE__  :					List[str] =       torch.utils.data.DataLoader(
						    dataset=Dataset(_lowerCamelCase      , _lowerCamelCase  )      , batch_size=_lowerCamelCase      , shuffle=_lowerCamelCase      , collate_fn=_lowerCamelCase  )

						return data_loader







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





						'''simple docstring'''


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

						print("Preprocessing {} dataset...".format(_lowerCamelCase  )  )
						SCREAMING_SNAKE_CASE__  :					Dict =       time.time()

						if dataset == "SST":
												SCREAMING_SNAKE_CASE__  :					Optional[int] =       ["positive", "negative", "very positive", "very negative", "neutral"]
												SCREAMING_SNAKE_CASE__  :					Tuple =       {c: i for i, c in enumerate(_lowerCamelCase  )}

												SCREAMING_SNAKE_CASE__  :					Dict =       Discriminator(
												    class_size=len(_lowerCamelCase  )      , pretrained_model=_lowerCamelCase      , cached_mode=_lowerCamelCase      , device=_lowerCamelCase  ).to(_lowerCamelCase  )

												SCREAMING_SNAKE_CASE__  :					Any =       torchtext_data.Field()
												SCREAMING_SNAKE_CASE__  :					str =       torchtext_data.Field(sequential=_lowerCamelCase  )
												SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					Any =       datasets.SST.splits(
												    _lowerCamelCase      , _lowerCamelCase      , fine_grained=_lowerCamelCase      , train_subtrees=_lowerCamelCase      , )

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

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

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

						elif dataset == "clickbait":
												SCREAMING_SNAKE_CASE__  :					Any =       ["non_clickbait", "clickbait"]
												SCREAMING_SNAKE_CASE__  :					Optional[Any] =       {c: i for i, c in enumerate(_lowerCamelCase  )}

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

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

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

												SCREAMING_SNAKE_CASE__  :					int =       Dataset(_lowerCamelCase      , _lowerCamelCase  )
												SCREAMING_SNAKE_CASE__  :					int =       int(0.9 * len(_lowerCamelCase  )  )
												SCREAMING_SNAKE_CASE__  :					int =       len(_lowerCamelCase  ) - train_size
												SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					int =       torch.utils.data.random_split(_lowerCamelCase      , [train_size, test_size]  )

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

						elif dataset == "toxic":
												SCREAMING_SNAKE_CASE__  :					str =       ["non_toxic", "toxic"]
												SCREAMING_SNAKE_CASE__  :					Tuple =       {c: i for i, c in enumerate(_lowerCamelCase  )}

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

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

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

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

												SCREAMING_SNAKE_CASE__  :					int =       {
												    "class_size": len(_lowerCamelCase  ),
												    "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."  )

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

												SCREAMING_SNAKE_CASE__  :					Dict =       sorted(_lowerCamelCase  )
												SCREAMING_SNAKE_CASE__  :					Any =       {c: i for i, c in enumerate(_lowerCamelCase  )}

												SCREAMING_SNAKE_CASE__  :					int =       Discriminator(
												    class_size=len(_lowerCamelCase  )      , pretrained_model=_lowerCamelCase      , cached_mode=_lowerCamelCase      , device=_lowerCamelCase  ).to(_lowerCamelCase  )

												SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       []
												SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       []
												with open(_lowerCamelCase  ) as f:
																		SCREAMING_SNAKE_CASE__  :					int =       csv.reader(_lowerCamelCase      , delimiter="\t"  )
																		for i, row in enumerate(tqdm(_lowerCamelCase      , ascii=_lowerCamelCase  )  ):
																								if row:
																														SCREAMING_SNAKE_CASE__  :					Tuple =       row[0]
																														SCREAMING_SNAKE_CASE__  :					Dict =       row[1]

																														try:
																																				SCREAMING_SNAKE_CASE__  :					Dict =       discriminator.tokenizer.encode(_lowerCamelCase  )
																																				if len(_lowerCamelCase  ) < max_length_seq:
																																										SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       torch.tensor([50_256] + seq      , device=_lowerCamelCase      , dtype=torch.long  )

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

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

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

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

												SCREAMING_SNAKE_CASE__  :					Tuple =       {
												    "class_size": len(_lowerCamelCase  ),
												    "embed_size": discriminator.embed_size,
												    "pretrained_model": pretrained_model,
												    "class_vocab": classaidx,
												    "default_class": 0,
												}

						SCREAMING_SNAKE_CASE__  :					Dict =       time.time()
						print("Preprocessed {} data points".format(len(_lowerCamelCase  ) + len(_lowerCamelCase  )  )  )
						print("Data preprocessing took: {:.3f}s".format(end - start  )  )

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

												SCREAMING_SNAKE_CASE__  :					str =       time.time()

												SCREAMING_SNAKE_CASE__  :					List[Any] =       get_cached_data_loader(_lowerCamelCase      , _lowerCamelCase      , _lowerCamelCase      , shuffle=_lowerCamelCase      , device=_lowerCamelCase  )

												SCREAMING_SNAKE_CASE__  :					Any =       get_cached_data_loader(_lowerCamelCase      , _lowerCamelCase      , _lowerCamelCase      , device=_lowerCamelCase  )

												SCREAMING_SNAKE_CASE__  :					Optional[int] =       time.time()
												print("Building representation cache took: {:.3f}s".format(end - start  )  )

						else:
												SCREAMING_SNAKE_CASE__  :					Tuple =       torch.utils.data.DataLoader(
												    dataset=_lowerCamelCase      , batch_size=_lowerCamelCase      , shuffle=_lowerCamelCase      , collate_fn=_lowerCamelCase  )
												SCREAMING_SNAKE_CASE__  :					int =       torch.utils.data.DataLoader(dataset=_lowerCamelCase      , batch_size=_lowerCamelCase      , collate_fn=_lowerCamelCase  )

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

						SCREAMING_SNAKE_CASE__  :					List[str] =       optim.Adam(discriminator.parameters()      , lr=0.0_0_0_1  )

						for epoch in range(_lowerCamelCase  ):
												SCREAMING_SNAKE_CASE__  :					int =       time.time()
												print("\nEpoch"      , epoch + 1  )

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

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

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

												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(_lowerCamelCase      , epoch + 1  )      , )


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

				train_discriminator(**(vars(args)))






import argparse
import json
import re
import string
import sys

import numpy as np


__lowercase							:Optional[Any]							=	0.5







def    UpperCAmelCase				(  _lowerCamelCase   :	int      , _lowerCamelCase   :	Tuple  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					List[Any] =       [".", ",", ";", ":"]

						for token in remove_tokens:
												SCREAMING_SNAKE_CASE__  :					List[Any] =       ground_truth.replace(_lowerCamelCase      , ""  )
												SCREAMING_SNAKE_CASE__  :					List[Any] =       prediction.replace(_lowerCamelCase      , ""  )

						SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					List[Any] =       ground_truth.lower(), prediction.lower()
						SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					Dict =       ground_truth.replace("/"      , " "  ), prediction.replace("/"      , " "  )
						SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					str =       set(ground_truth.split(" "  )  ), set(prediction.split(" "  )  )

						SCREAMING_SNAKE_CASE__  :					Optional[int] =       ground_truth.intersection(_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       ground_truth.union(_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					Dict =       len(_lowerCamelCase  ) / len(_lowerCamelCase  )
						return jaccard







def    UpperCAmelCase				(  _lowerCamelCase   :	List[Any]  ):





						'''simple docstring'''


						def remove_articles(_lowerCamelCase   :	Any  ):
												return re.sub(r"\b(a|an|the)\b"      , " "      , _lowerCamelCase  )

						def white_space_fix(_lowerCamelCase   :	Any  ):
												return " ".join(text.split()  )

						def remove_punc(_lowerCamelCase   :	List[Any]  ):
												SCREAMING_SNAKE_CASE__  :					Tuple =       set(string.punctuation  )
												return "".join(ch for ch in text if ch not in exclude  )

						def lower(_lowerCamelCase   :	str  ):
												return text.lower()

						return white_space_fix(remove_articles(remove_punc(lower(_lowerCamelCase  )  )  )  )







def    UpperCAmelCase				(  _lowerCamelCase   :	Optional[Any]      , _lowerCamelCase   :	Optional[Any]      , _lowerCamelCase   :	Tuple  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					Optional[int] =       0, 0, 0

						SCREAMING_SNAKE_CASE__  :					Optional[int] =       "Parties" in qa_id

						# first check if ground truth is empty
						if len(_lowerCamelCase  ) == 0:
												if len(_lowerCamelCase  ) > 0:
																		fp += len(_lowerCamelCase  )  # false positive for each one
						else:
												for ground_truth in ground_truths:
																		assert len(_lowerCamelCase  ) > 0
																		# check if there is a match
																		SCREAMING_SNAKE_CASE__  :					Tuple =       False
																		for pred in predictions:
																								if substr_ok:
																														SCREAMING_SNAKE_CASE__  :					Optional[Any] =       get_jaccard(_lowerCamelCase      , _lowerCamelCase  ) >= IOU_THRESH or ground_truth in pred
																								else:
																														SCREAMING_SNAKE_CASE__  :					Tuple =       get_jaccard(_lowerCamelCase      , _lowerCamelCase  ) >= IOU_THRESH
																								if is_match:
																														SCREAMING_SNAKE_CASE__  :					str =       True

																		if match_found:
																								tp += 1
																		else:
																								fn += 1

        # now also get any fps by looping through preds
												for pred in predictions:
																		# Check if there's a match. if so, don't count (don't want to double count based on the above)
																		# but if there's no match, then this is a false positive.
																		# (Note: we get the true positives in the above loop instead of this loop so that we don't double count
																		# multiple predictions that are matched with the same answer.)
																		SCREAMING_SNAKE_CASE__  :					Tuple =       False
																		for ground_truth in ground_truths:
																								assert len(_lowerCamelCase  ) > 0
																								if substr_ok:
																														SCREAMING_SNAKE_CASE__  :					Tuple =       get_jaccard(_lowerCamelCase      , _lowerCamelCase  ) >= IOU_THRESH or ground_truth in pred
																								else:
																														SCREAMING_SNAKE_CASE__  :					Dict =       get_jaccard(_lowerCamelCase      , _lowerCamelCase  ) >= IOU_THRESH
																								if is_match:
																														SCREAMING_SNAKE_CASE__  :					List[Any] =       True

																		if not match_found:
																								fp += 1

						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       tp / (tp + fp) if tp + fp > 0 else np.nan
						SCREAMING_SNAKE_CASE__  :					Dict =       tp / (tp + fn) if tp + fn > 0 else np.nan

						return precision, recall







def    UpperCAmelCase				(  _lowerCamelCase   :	Optional[int]  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					int =       precisions[::-1]
						for i in range(1      , len(_lowerCamelCase  )  ):
												SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       max(precision_best[i - 1]      , precision_best[i]  )
						SCREAMING_SNAKE_CASE__  :					Optional[Any] =       precision_best[::-1]
						return precisions







def    UpperCAmelCase				(  _lowerCamelCase   :	Dict      , _lowerCamelCase   :	str  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					Tuple =       process_precisions(_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					List[Any] =       np.trapz(_lowerCamelCase      , _lowerCamelCase  )
						if np.isnan(_lowerCamelCase  ):
												return 0
						return aupr







def    UpperCAmelCase				(  _lowerCamelCase   :	Optional[int]      , _lowerCamelCase   :	Any      , _lowerCamelCase   :	List[str]  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					Any =       process_precisions(_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					List[str] =       0
						for prec, recall in zip(_lowerCamelCase      , _lowerCamelCase  ):
												if recall >= recall_thresh:
																		SCREAMING_SNAKE_CASE__  :					Optional[int] =       prec
																		break
						return prec_at_recall







def    UpperCAmelCase				(  _lowerCamelCase   :	Optional[int]      , _lowerCamelCase   :	Optional[Any]  ):





						'''simple docstring'''


						return normalize_answer(_lowerCamelCase  ) == normalize_answer(_lowerCamelCase  )







def    UpperCAmelCase				(  _lowerCamelCase   :	Optional[int]      , _lowerCamelCase   :	str      , _lowerCamelCase   :	List[Any]  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					Optional[Any] =       0
						for pred in predictions:
												for ground_truth in ground_truths:
																		SCREAMING_SNAKE_CASE__  :					Optional[Any] =       metric_fn(_lowerCamelCase      , _lowerCamelCase  )
																		if score == 1:  # break the loop when one prediction matches the ground truth
																								break
												if score == 1:
																		break
						return score







def    UpperCAmelCase				(  _lowerCamelCase   :	Optional[int]      , _lowerCamelCase   :	int  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					Any =       0
						SCREAMING_SNAKE_CASE__  :					List[str] =       []
						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       []
						for article in dataset:
												for paragraph in article["paragraphs"]:
																		for qa in paragraph["qas"]:
																								total += 1
																								if qa["id"] not in predictions:
																														SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       "Unanswered question " + qa["id"] + " will receive score 0."
																														print(_lowerCamelCase      , file=sys.stderr  )
																														continue
																								SCREAMING_SNAKE_CASE__  :					Any =       [x["text"] for x in qa["answers"]]
																								SCREAMING_SNAKE_CASE__  :					int =       predictions[qa["id"]]
																								SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					Tuple =       compute_precision_recall(_lowerCamelCase      , _lowerCamelCase      , qa["id"]  )

																								precisions.append(_lowerCamelCase  )
																								recalls.append(_lowerCamelCase  )

																								if precision == 0 and recall == 0:
																														fa += 0
																								else:
																														fa += 2 * (precision * recall) / (precision + recall)

																								exact_match += metric_max_over_ground_truths(_lowerCamelCase      , _lowerCamelCase      , _lowerCamelCase  )

						SCREAMING_SNAKE_CASE__  :					Optional[int] =       [x for _, x in sorted(zip(_lowerCamelCase      , _lowerCamelCase  )  )]
						recalls.sort()

						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       1_0_0.0 * fa / total
						SCREAMING_SNAKE_CASE__  :					Dict =       1_0_0.0 * exact_match / total
						SCREAMING_SNAKE_CASE__  :					str =       get_aupr(_lowerCamelCase      , _lowerCamelCase  )

						SCREAMING_SNAKE_CASE__  :					Optional[int] =       get_prec_at_recall(_lowerCamelCase      , _lowerCamelCase      , recall_thresh=0.9  )
						SCREAMING_SNAKE_CASE__  :					Optional[Any] =       get_prec_at_recall(_lowerCamelCase      , _lowerCamelCase      , recall_thresh=0.8  )

						return {
						    "exact_match": exact_match,
						    "f1": fa,
						    "aupr": aupr,
						    "prec_at_80_recall": prec_at_aa_recall,
						    "prec_at_90_recall": prec_at_aa_recall,
						}


if __name__ == "__main__":
				__lowercase							:Tuple							=	argparse.ArgumentParser(description="Evaluation for CUAD")
				parser.add_argument("dataset_file", help="Dataset file")
				parser.add_argument("prediction_file", help="Prediction File")
				__lowercase							:Tuple							=	parser.parse_args()
				with open(args.dataset_file) as dataset_file:
								__lowercase							:Union[str, Any]							=	json.load(dataset_file)
								__lowercase							:Union[str, Any]							=	dataset_json["data"]
				with open(args.prediction_file) as prediction_file:
								__lowercase							:Optional[Any]							=	json.load(prediction_file)
				print(json.dumps(evaluate(dataset, predictions)))