





import timeit



class 				_UpperCamelCase       :

       def __init__(		self:			Any       , _SCREAMING_SNAKE_CASE:			List[Any]		)							->			Optional[Any]:




              """simple docstring"""


              if isinstance(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		):  # Initializes a matrix identical to the one provided.
                     UpperCamelCase_ 							=	arg
                     UpperCamelCase_ 							=	len(_SCREAMING_SNAKE_CASE		)
              else:  # Initializes a square matrix of the given size and set values to zero.
                     UpperCamelCase_ 							=	arg
                     UpperCamelCase_ 							=	[[0 for _ in range(self.n		)] for _ in range(self.n		)]



       def __mul__(		self:			Union[str, Any]       , _SCREAMING_SNAKE_CASE:			List[str]		)							->			Dict:




              """simple docstring"""


              UpperCamelCase_ 							=	Matrix(self.n		)
              for i in range(self.n		):
                     for j in range(self.n		):
                            for k in range(self.n		):
                                   matrix.t[i][j] += self.t[i][k] * b.t[k][j]
              return matrix




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_	)  ->		Optional[Any]:
       UpperCamelCase_ 							=	Matrix([[1, 0], [0, 1]]	)
       while b > 0:
              if b & 1:
                     matrix *= a
              a *= a
              b >>= 1
       return matrix




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)  ->		Union[str, Any]:
       # Trivial Cases
       if n == 1:
              return fa
       elif n == 2:
              return fa
       UpperCamelCase_ 							=	Matrix([[1, 1], [1, 0]]	)
       UpperCamelCase_ 							=	modular_exponentiation(UpperCamelCase_       ,		n - 2	)
       return fa * matrix.t[0][0] + fa * matrix.t[0][1]




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)  ->		List[Any]:
       # Trivial Cases
       if n == 1:
              return fa
       elif n == 2:
              return fa

       UpperCamelCase_ 							=	fa
       UpperCamelCase_ 							=	fa
       n -= 2

       while n > 0:
              UpperCamelCase_							,			UpperCamelCase_ 							=	fn_a + fn_a, fn_a
              n -= 1

       return fn_a




def 			lowerCAmelCase_			(				)  ->		Optional[int]:
       UpperCamelCase_ 							=	"\nfrom random import randint\nfrom __main__ import fibonacci_with_matrix_exponentiation\n"
       UpperCamelCase_ 							=	"fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)"
       UpperCamelCase_ 							=	timeit.timeit(setup=UpperCamelCase_       ,		stmt=UpperCamelCase_       ,		number=100	)
       print("With matrix exponentiation the average execution time is "       ,		exec_time / 100	)
       return exec_time




def 			lowerCAmelCase_			(				)  ->		Tuple:
       UpperCamelCase_ 							=	"\nfrom random import randint\nfrom __main__ import simple_fibonacci\n"
       UpperCamelCase_ 							=	"simple_fibonacci(randint(1,70000), 1, 1)"
       UpperCamelCase_ 							=	timeit.timeit(setup=UpperCamelCase_       ,		stmt=UpperCamelCase_       ,		number=100	)
       print(
           "Without matrix exponentiation the average execution time is "       ,		exec_time / 100	)
       return exec_time




def 			lowerCAmelCase_			(				)  ->		List[Any]:
       matrix_exponentiation_time()
       simple_fibonacci_time()


if __name__ == "__main__":
     main()







# 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)
UpperCAmelCase_   							=      1E-10
UpperCAmelCase_   							=      'This is incredible! I love it, this is the best chicken I have ever had.'
UpperCAmelCase_   							=      100





class        lowerCamelCase__(   nn.Module):
      def __init__(							self:					Optional[Any]       ,			UpperCamelCase_:					int       ,			UpperCamelCase_:					Any="gpt2-medium"       ,			UpperCamelCase_:					Any=False       ,			UpperCamelCase_:					List[Any]="cpu"  ):
             super().__init__()
             __lowerCamelCase  =		GPTaTokenizer.from_pretrained(UpperCamelCase_  )
             __lowerCamelCase  =		GPTaLMHeadModel.from_pretrained(UpperCamelCase_  )
             __lowerCamelCase  =		self.encoder.transformer.config.hidden_size
             __lowerCamelCase  =		ClassificationHead(class_size=UpperCamelCase_       ,			embed_size=self.embed_size  )
             __lowerCamelCase  =		cached_mode
             __lowerCamelCase  =		device
      def      lowerCAmelCase__		(							self:					List[Any]  ):
             return self.classifier_head
      def      lowerCAmelCase__		(							self:					int  ):
             for param in self.encoder.parameters():
                    __lowerCamelCase  =		False
             self.classifier_head.train()
      def      lowerCAmelCase__		(							self:					Optional[Any]       ,			UpperCamelCase_:					Optional[int]  ):
             __lowerCamelCase  =		x.ne(0  ).unsqueeze(2  ).repeat(1       ,			1       ,			self.embed_size  ).float().to(self.device  ).detach()
             __lowerCamelCase  =		self.encoder.transformer(UpperCamelCase_  )["""last_hidden_state"""]
             __lowerCamelCase  =		hidden * mask
             __lowerCamelCase  =		torch.sum(UpperCamelCase_       ,			dim=1  ) / (torch.sum(UpperCamelCase_       ,			dim=1  ).detach() + EPSILON)
             return avg_hidden




      def      lowerCAmelCase__		(							self:					Dict       ,			UpperCamelCase_:					Optional[int]  ):
             if self.cached_mode:
                    __lowerCamelCase  =		x.to(self.device  )
             else:
                    __lowerCamelCase  =		self.avg_representation(x.to(self.device  )  )

             __lowerCamelCase  =		self.classifier_head(UpperCamelCase_  )
             __lowerCamelCase  =		nn.functional.log_softmax(UpperCamelCase_       ,			dim=-1  )

             return probs





class        lowerCamelCase__(   data.Dataset):
      def __init__(							self:					List[Any]       ,			UpperCamelCase_:					Optional[Any]       ,			UpperCamelCase_:					Optional[int]  ):
             __lowerCamelCase  =		X
             __lowerCamelCase  =		y
      def __len__(							self:					Dict  ):
             return len(self.X  )




      def __getitem__(							self:					Dict       ,			UpperCamelCase_:					Dict  ):
             __lowerCamelCase  =		{}
             __lowerCamelCase  =		self.X[index]
             __lowerCamelCase  =		self.y[index]
             return data




def    lowerCamelCase__			(		A__			:   List[Any]      ):





       '''simple docstring'''



       def pad_sequences(A__			:   List[str]      ):
              __lowerCamelCase  =		[len(A__      ) for seq in sequences]

              __lowerCamelCase  =		torch.zeros(len(A__      )    ,     max(A__      )      ).long()  # padding value = 0

              for i, seq in enumerate(A__      ):
                     __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,  __lowerCamelCase  =		pad_sequences(item_info["""X"""]      )
       __lowerCamelCase  =		torch.tensor(item_info["""y"""]    ,     dtype=torch.long      )

       return x_batch, y_batch




def    lowerCamelCase__			(		A__			:   Optional[int]      ):





       '''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    lowerCamelCase__			(		A__			:   List[Any]    ,     A__			:   Optional[Any]    ,     A__			:   List[str]    ,     A__			:   int=0    ,     A__			:   Optional[Any]=10    ,     A__			:   List[str]="cpu"      ):





       '''simple docstring'''



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

              optimizer.zero_grad()

              __lowerCamelCase  =		discriminator(A__      )
              __lowerCamelCase  =		nn.functional.nll_loss(A__    ,     A__      )
              loss.backward(retain_graph=A__      )
              optimizer.step()

              samples_so_far += len(A__      )

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




def    lowerCamelCase__			(		A__			:   Tuple    ,     A__			:   str    ,     A__			:   int="cpu"      ):





       '''simple docstring'''



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

       test_loss /= len(data_loader.dataset      )

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




def    lowerCamelCase__			(		A__			:   Tuple    ,     A__			:   str    ,     A__			:   str    ,     A__			:   List[str]=False    ,     A__			:   Optional[Any]="cpu"      ):





       '''simple docstring'''



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

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




def    lowerCamelCase__			(		A__			:   Tuple    ,     A__			:   Any    ,     A__			:   Tuple    ,     A__			:   List[str]=False    ,     A__			:   Union[str, Any]="cpu"      ):





       '''simple docstring'''



       __lowerCamelCase  =		torch.utils.data.DataLoader(dataset=A__    ,     batch_size=A__    ,     collate_fn=A__      )

       __lowerCamelCase  =		[]
       __lowerCamelCase  =		[]
       for batch_idx, (x, y) in enumerate(tqdm(A__    ,     ascii=A__      )      ):
              with torch.no_grad():
                     __lowerCamelCase  =		x.to(A__      )
                     __lowerCamelCase  =		discriminator.avg_representation(A__      ).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(A__    ,     A__      )    ,     batch_size=A__    ,     shuffle=A__    ,     collate_fn=A__      )

       return data_loader




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





       '''simple docstring'''



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

       print("""Preprocessing {} dataset...""".format(A__      )      )
       __lowerCamelCase  =		time.time()

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

              __lowerCamelCase  =		Discriminator(
                  class_size=len(A__      )    ,     pretrained_model=A__    ,     cached_mode=A__    ,     device=A__      ).to(A__      )

              __lowerCamelCase  =		torchtext_data.Field()
              __lowerCamelCase  =		torchtext_data.Field(sequential=A__      )
              __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase  =		datasets.SST.splits(
                  A__    ,     A__    ,     fine_grained=A__    ,     train_subtrees=A__    ,     )

              __lowerCamelCase  =		[]
              __lowerCamelCase  =		[]
              for i in trange(len(A__      )    ,     ascii=A__      ):
                     __lowerCamelCase  =		TreebankWordDetokenizer().detokenize(vars(train_data[i]      )["""text"""]      )
                     __lowerCamelCase  =		discriminator.tokenizer.encode(A__      )
                     __lowerCamelCase  =		torch.tensor([50256] + seq    ,     device=A__    ,     dtype=torch.long      )
                     x.append(A__      )
                     y.append(classaidx[vars(train_data[i]      )["""label"""]]      )
              __lowerCamelCase  =		Dataset(A__    ,     A__      )

              __lowerCamelCase  =		[]
              __lowerCamelCase  =		[]
              for i in trange(len(A__      )    ,     ascii=A__      ):
                     __lowerCamelCase  =		TreebankWordDetokenizer().detokenize(vars(test_data[i]      )["""text"""]      )
                     __lowerCamelCase  =		discriminator.tokenizer.encode(A__      )
                     __lowerCamelCase  =		torch.tensor([50256] + seq    ,     device=A__    ,     dtype=torch.long      )
                     test_x.append(A__      )
                     test_y.append(classaidx[vars(test_data[i]      )["""label"""]]      )
              __lowerCamelCase  =		Dataset(A__    ,     A__      )

              __lowerCamelCase  =		{
                  """class_size""": len(A__      ),
                  """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(A__      )}

              __lowerCamelCase  =		Discriminator(
                  class_size=len(A__      )    ,     pretrained_model=A__    ,     cached_mode=A__    ,     device=A__      ).to(A__      )

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

                                   if len(A__      ) < max_length_seq:
                                          __lowerCamelCase  =		torch.tensor([50256] + seq    ,     device=A__    ,     dtype=torch.long      )
                                   else:
                                          print("""Line {} is longer than maximum length {}""".format(A__    ,     A__      )      )
                                          continue
                                   x.append(A__      )
                                   y.append(d["""label"""]      )
                            except Exception:
                                   print("""Error evaluating / tokenizing line {}, skipping it""".format(A__      )      )
                                   pass

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

              __lowerCamelCase  =		{
                  """class_size""": len(A__      ),
                  """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(A__      )}

              __lowerCamelCase  =		Discriminator(
                  class_size=len(A__      )    ,     pretrained_model=A__    ,     cached_mode=A__    ,     device=A__      ).to(A__      )

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

                                   if len(A__      ) < max_length_seq:
                                          __lowerCamelCase  =		torch.tensor([50256] + seq    ,     device=A__    ,     dtype=torch.long      )
                                   else:
                                          print("""Line {} is longer than maximum length {}""".format(A__    ,     A__      )      )
                                          continue
                                   x.append(A__      )
                                   y.append(int(np.sum(d["""label"""]      ) > 0      )      )
                            except Exception:
                                   print("""Error evaluating / tokenizing line {}, skipping it""".format(A__      )      )
                                   pass

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

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

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

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

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

              __lowerCamelCase  =		sorted(A__      )
              __lowerCamelCase  =		{c: i for i, c in enumerate(A__      )}

              __lowerCamelCase  =		Discriminator(
                  class_size=len(A__      )    ,     pretrained_model=A__    ,     cached_mode=A__    ,     device=A__      ).to(A__      )

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

                                   try:
                                          __lowerCamelCase  =		discriminator.tokenizer.encode(A__      )
                                          if len(A__      ) < max_length_seq:
                                                 __lowerCamelCase  =		torch.tensor([50256] + seq    ,     device=A__    ,     dtype=torch.long      )

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

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

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

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

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

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

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

              __lowerCamelCase  =		time.time()

              __lowerCamelCase  =		get_cached_data_loader(A__    ,     A__    ,     A__    ,     shuffle=A__    ,     device=A__      )

              __lowerCamelCase  =		get_cached_data_loader(A__    ,     A__    ,     A__    ,     device=A__      )

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

       else:
              __lowerCamelCase  =		torch.utils.data.DataLoader(
                  dataset=A__    ,     batch_size=A__    ,     shuffle=A__    ,     collate_fn=A__      )
              __lowerCamelCase  =		torch.utils.data.DataLoader(dataset=A__    ,     batch_size=A__    ,     collate_fn=A__      )

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

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

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

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

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

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

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


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

       train_discriminator(**(vars(args)))

