



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)
A__     				=	1e-1_0
A__     				=	"""This is incredible! I love it, this is the best chicken I have ever had."""
A__     				=	1_00





class  __lowerCAmelCase			(					nn.Module	):




    def __init__(							self						,    _snake_case						,    _snake_case="gpt2-medium"						,    _snake_case=False						,    _snake_case="cpu"		):



         """simple docstring"""





         super().__init__()
         _lowerCAmelCase      =   GPTaTokenizer.from_pretrained(_snake_case		)
         _lowerCAmelCase      =   GPTaLMHeadModel.from_pretrained(_snake_case		)
         _lowerCAmelCase      =   self.encoder.transformer.config.hidden_size
         _lowerCAmelCase      =   ClassificationHead(class_size=_snake_case						,    embed_size=self.embed_size		)
         _lowerCAmelCase      =   cached_mode
         _lowerCAmelCase      =   device




    def 							snake_case       (							self		):



         """simple docstring"""





         return self.classifier_head




    def 							snake_case       (							self		):



         """simple docstring"""





         for param in self.encoder.parameters():
              _lowerCAmelCase      =   False
         self.classifier_head.train()




    def 							snake_case       (							self						,    _snake_case		):



         """simple docstring"""





         _lowerCAmelCase      =   x.ne(0		).unsqueeze(2		).repeat(1						,    1						,    self.embed_size		).float().to(self.device		).detach()
         _lowerCAmelCase      =   self.encoder.transformer(_snake_case		)["last_hidden_state"]
         _lowerCAmelCase      =   hidden * mask
         _lowerCAmelCase      =   torch.sum(_snake_case						,    dim=1		) / (torch.sum(_snake_case						,    dim=1		).detach() + EPSILON)
         return avg_hidden




    def 							snake_case       (							self						,    _snake_case		):



         """simple docstring"""





         if self.cached_mode:
              _lowerCAmelCase      =   x.to(self.device		)
         else:
              _lowerCAmelCase      =   self.avg_representation(x.to(self.device		)		)

         _lowerCAmelCase      =   self.classifier_head(_snake_case		)
         _lowerCAmelCase      =   nn.functional.log_softmax(_snake_case						,    dim=-1		)

         return probs





class  __lowerCAmelCase			(					data.Dataset	):




    def __init__(							self						,    _snake_case						,    _snake_case		):



         """simple docstring"""





         _lowerCAmelCase      =   X
         _lowerCAmelCase      =   y




    def __len__(							self		):



         """simple docstring"""





         return len(self.X		)




    def __getitem__(							self						,    _snake_case		):



         """simple docstring"""





         _lowerCAmelCase      =   {}
         _lowerCAmelCase      =   self.X[index]
         _lowerCAmelCase      =   self.y[index]
         return data


def 		_UpperCAmelCase    (				snake_case      ):





     """simple docstring"""

     def pad_sequences(snake_case      ):
          _lowerCAmelCase      =   [len(_UpperCAmelCase      ) for seq in sequences]

          _lowerCAmelCase      =   torch.zeros(len(_UpperCAmelCase      )     ,						max(_UpperCAmelCase      )      ).long()  # padding value = 0

          for i, seq in enumerate(_UpperCAmelCase      ):
               _lowerCAmelCase      =   lengths[i]
               _lowerCAmelCase      =   seq[:end]

          return padded_sequences, lengths

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

     _lowerCAmelCase      =   pad_sequences(item_info["""X"""]      )
     _lowerCAmelCase      =   torch.tensor(item_info["""y"""]     ,						dtype=torch.long      )

     return x_batch, y_batch


def 		_UpperCAmelCase    (				snake_case      ):





     """simple docstring"""

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

     _lowerCAmelCase      =   torch.cat(item_info["""X"""]     ,						0      )
     _lowerCAmelCase      =   torch.tensor(item_info["""y"""]     ,						dtype=torch.long      )

     return x_batch, y_batch


def 		_UpperCAmelCase    (				snake_case     ,						snake_case     ,						snake_case     ,						snake_case=0     ,						snake_case=10     ,						snake_case="cpu"      ):





     """simple docstring"""

     _lowerCAmelCase      =   0
     discriminator.train_custom()
     for batch_idx, (input_t, target_t) in enumerate(_UpperCAmelCase      ):
          _lowerCAmelCase      =   input_t.to(_UpperCAmelCase      ), target_t.to(_UpperCAmelCase      )

          optimizer.zero_grad()

          _lowerCAmelCase      =   discriminator(_UpperCAmelCase      )
          _lowerCAmelCase      =   nn.functional.nll_loss(_UpperCAmelCase     ,						_UpperCAmelCase      )
          loss.backward(retain_graph=_UpperCAmelCase      )
          optimizer.step()

          samples_so_far += len(_UpperCAmelCase      )

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


def 		_UpperCAmelCase    (				snake_case     ,						snake_case     ,						snake_case="cpu"      ):





     """simple docstring"""

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

     test_loss /= len(data_loader.dataset      )

     print(
         """Performance on test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)""".format(
             _UpperCAmelCase     ,						_UpperCAmelCase     ,						len(data_loader.dataset      )     ,						1_00.0 * correct / len(data_loader.dataset      )      )      )


def 		_UpperCAmelCase    (				snake_case     ,						snake_case     ,						snake_case     ,						snake_case=False     ,						snake_case="cpu"      ):





     """simple docstring"""

     _lowerCAmelCase      =   model.tokenizer.encode(_UpperCAmelCase      )
     _lowerCAmelCase      =   torch.tensor([input_t]     ,						dtype=torch.long     ,						device=_UpperCAmelCase      )
     if cached:
          _lowerCAmelCase      =   model.avg_representation(_UpperCAmelCase      )

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


def 		_UpperCAmelCase    (				snake_case     ,						snake_case     ,						snake_case     ,						snake_case=False     ,						snake_case="cpu"      ):





     """simple docstring"""

     _lowerCAmelCase      =   torch.utils.data.DataLoader(dataset=_UpperCAmelCase     ,						batch_size=_UpperCAmelCase     ,						collate_fn=_UpperCAmelCase      )

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

     _lowerCAmelCase      =   torch.utils.data.DataLoader(
         dataset=Dataset(_UpperCAmelCase     ,						_UpperCAmelCase      )     ,						batch_size=_UpperCAmelCase     ,						shuffle=_UpperCAmelCase     ,						collate_fn=_UpperCAmelCase      )

     return data_loader


def 		_UpperCAmelCase    (				snake_case     ,						snake_case=None     ,						snake_case="gpt2-medium"     ,						snake_case=10     ,						snake_case=64     ,						snake_case=10     ,						snake_case=False     ,						snake_case=False     ,						snake_case=False     ,						):





     """simple docstring"""

     _lowerCAmelCase      =   "cuda" if torch.cuda.is_available() and not no_cuda else "cpu"

     print("""Preprocessing {} dataset...""".format(_UpperCAmelCase      )      )
     _lowerCAmelCase      =   time.time()

     if dataset == "SST":
          _lowerCAmelCase      =   ["positive", "negative", "very positive", "very negative", "neutral"]
          _lowerCAmelCase      =   {c: i for i, c in enumerate(_UpperCAmelCase      )}

          _lowerCAmelCase      =   Discriminator(
              class_size=len(_UpperCAmelCase      )     ,						pretrained_model=_UpperCAmelCase     ,						cached_mode=_UpperCAmelCase     ,						device=_UpperCAmelCase      ).to(_UpperCAmelCase      )

          _lowerCAmelCase      =   torchtext_data.Field()
          _lowerCAmelCase      =   torchtext_data.Field(sequential=_UpperCAmelCase      )
          _lowerCAmelCase      =   datasets.SST.splits(
              _UpperCAmelCase     ,						_UpperCAmelCase     ,						fine_grained=_UpperCAmelCase     ,						train_subtrees=_UpperCAmelCase     ,						)

          _lowerCAmelCase      =   []
          _lowerCAmelCase      =   []
          for i in trange(len(_UpperCAmelCase      )     ,						ascii=_UpperCAmelCase      ):
               _lowerCAmelCase      =   TreebankWordDetokenizer().detokenize(vars(train_data[i]      )["""text"""]      )
               _lowerCAmelCase      =   discriminator.tokenizer.encode(_UpperCAmelCase      )
               _lowerCAmelCase      =   torch.tensor([5_02_56] + seq     ,						device=_UpperCAmelCase     ,						dtype=torch.long      )
               x.append(_UpperCAmelCase      )
               y.append(classaidx[vars(train_data[i]      )["""label"""]]      )
          _lowerCAmelCase      =   Dataset(_UpperCAmelCase     ,						_UpperCAmelCase      )

          _lowerCAmelCase      =   []
          _lowerCAmelCase      =   []
          for i in trange(len(_UpperCAmelCase      )     ,						ascii=_UpperCAmelCase      ):
               _lowerCAmelCase      =   TreebankWordDetokenizer().detokenize(vars(test_data[i]      )["""text"""]      )
               _lowerCAmelCase      =   discriminator.tokenizer.encode(_UpperCAmelCase      )
               _lowerCAmelCase      =   torch.tensor([5_02_56] + seq     ,						device=_UpperCAmelCase     ,						dtype=torch.long      )
               test_x.append(_UpperCAmelCase      )
               test_y.append(classaidx[vars(test_data[i]      )["""label"""]]      )
          _lowerCAmelCase      =   Dataset(_UpperCAmelCase     ,						_UpperCAmelCase      )

          _lowerCAmelCase      =   {
              "class_size": len(_UpperCAmelCase      ),
              "embed_size": discriminator.embed_size,
              "pretrained_model": pretrained_model,
              "class_vocab": classaidx,
              "default_class": 2,
          }

     elif dataset == "clickbait":
          _lowerCAmelCase      =   ["non_clickbait", "clickbait"]
          _lowerCAmelCase      =   {c: i for i, c in enumerate(_UpperCAmelCase      )}

          _lowerCAmelCase      =   Discriminator(
              class_size=len(_UpperCAmelCase      )     ,						pretrained_model=_UpperCAmelCase     ,						cached_mode=_UpperCAmelCase     ,						device=_UpperCAmelCase      ).to(_UpperCAmelCase      )

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

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

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

          _lowerCAmelCase      =   {
              "class_size": len(_UpperCAmelCase      ),
              "embed_size": discriminator.embed_size,
              "pretrained_model": pretrained_model,
              "class_vocab": classaidx,
              "default_class": 1,
          }

     elif dataset == "toxic":
          _lowerCAmelCase      =   ["non_toxic", "toxic"]
          _lowerCAmelCase      =   {c: i for i, c in enumerate(_UpperCAmelCase      )}

          _lowerCAmelCase      =   Discriminator(
              class_size=len(_UpperCAmelCase      )     ,						pretrained_model=_UpperCAmelCase     ,						cached_mode=_UpperCAmelCase     ,						device=_UpperCAmelCase      ).to(_UpperCAmelCase      )

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

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

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

          _lowerCAmelCase      =   {
              "class_size": len(_UpperCAmelCase      ),
              "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."""      )

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

          _lowerCAmelCase      =   sorted(_UpperCAmelCase      )
          _lowerCAmelCase      =   {c: i for i, c in enumerate(_UpperCAmelCase      )}

          _lowerCAmelCase      =   Discriminator(
              class_size=len(_UpperCAmelCase      )     ,						pretrained_model=_UpperCAmelCase     ,						cached_mode=_UpperCAmelCase     ,						device=_UpperCAmelCase      ).to(_UpperCAmelCase      )

          _lowerCAmelCase      =   []
          _lowerCAmelCase      =   []
          with open(_UpperCAmelCase      ) as f:
               _lowerCAmelCase      =   csv.reader(_UpperCAmelCase     ,						delimiter="""\t"""      )
               for i, row in enumerate(tqdm(_UpperCAmelCase     ,						ascii=_UpperCAmelCase      )      ):
                    if row:
                         _lowerCAmelCase      =   row[0]
                         _lowerCAmelCase      =   row[1]

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

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

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

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

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

          _lowerCAmelCase      =   {
              "class_size": len(_UpperCAmelCase      ),
              "embed_size": discriminator.embed_size,
              "pretrained_model": pretrained_model,
              "class_vocab": classaidx,
              "default_class": 0,
          }

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

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

          _lowerCAmelCase      =   time.time()

          _lowerCAmelCase      =   get_cached_data_loader(_UpperCAmelCase     ,						_UpperCAmelCase     ,						_UpperCAmelCase     ,						shuffle=_UpperCAmelCase     ,						device=_UpperCAmelCase      )

          _lowerCAmelCase      =   get_cached_data_loader(_UpperCAmelCase     ,						_UpperCAmelCase     ,						_UpperCAmelCase     ,						device=_UpperCAmelCase      )

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

     else:
          _lowerCAmelCase      =   torch.utils.data.DataLoader(
              dataset=_UpperCAmelCase     ,						batch_size=_UpperCAmelCase     ,						shuffle=_UpperCAmelCase     ,						collate_fn=_UpperCAmelCase      )
          _lowerCAmelCase      =   torch.utils.data.DataLoader(dataset=_UpperCAmelCase     ,						batch_size=_UpperCAmelCase     ,						collate_fn=_UpperCAmelCase      )

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

     _lowerCAmelCase      =   optim.Adam(discriminator.parameters()     ,						lr=0.0_001      )

     for epoch in range(_UpperCAmelCase      ):
          _lowerCAmelCase      =   time.time()
          print("""\nEpoch"""     ,						epoch + 1      )

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

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

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

          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(_UpperCAmelCase     ,						epoch + 1      )     ,						)


if __name__ == "__main__":
 A__     				=	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""")
 A__     				=	parser.parse_args()

 train_discriminator(**(vars(args)))








from math import exp


def 		_UpperCAmelCase    (				snake_case      ):





					"""simple docstring"""

					return 8 * x - 2 * exp(-x      )


def 		_UpperCAmelCase    (				snake_case     ,						snake_case     ,						snake_case      ):





					"""simple docstring"""

					_lowerCAmelCase      =   lower_bound
					_lowerCAmelCase      =   upper_bound
					for _ in range(0     ,						snake_case      ):
										_lowerCAmelCase     ,     _lowerCAmelCase      =   xa, xa - (f(snake_case      ) * (xa - xa)) / (f(snake_case      ) - f(snake_case      ))
					return xa


if __name__ == "__main__":
	print(f"Example: {secant_method(1, 3, 2)}")



