
"""simple docstring"""




# 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)
A__   :						Union[str, Any] 							=     1e-10
A__   :						Dict 							=     """This is incredible! I love it, this is the best chicken I have ever had."""
A__   :						Optional[int] 							=     100







class 	_lowercase    (							nn.Module					):


     '''simple docstring'''





     def __init__(   self			,      __UpperCamelCase			,      __UpperCamelCase="gpt2-medium"			,      __UpperCamelCase=False			,      __UpperCamelCase="cpu"	)->       Any:
          super().__init__()
          UpperCAmelCase__				:			Union[str, Any]							=    GPTaTokenizer.from_pretrained(__UpperCamelCase	)
          UpperCAmelCase__				:			List[str]							=    GPTaLMHeadModel.from_pretrained(__UpperCamelCase	)
          UpperCAmelCase__				:			int							=    self.encoder.transformer.config.hidden_size
          UpperCAmelCase__				:			List[Any]							=    ClassificationHead(class_size=__UpperCamelCase			,      embed_size=self.embed_size	)
          UpperCAmelCase__				:			Union[str, Any]							=    cached_mode
          UpperCAmelCase__				:			Optional[int]							=    device



     def 			lowerCAmelCase__							(   self	)->       Union[str, Any]:
          return self.classifier_head



     def 			lowerCAmelCase__							(   self	)->       Union[str, Any]:
          for param in self.encoder.parameters():
               UpperCAmelCase__				:			List[Any]							=    False
          self.classifier_head.train()



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



     def 			lowerCAmelCase__							(   self			,      __UpperCamelCase	)->       Optional[int]:
          if self.cached_mode:
               UpperCAmelCase__				:			int							=    x.to(self.device	)
          else:
               UpperCAmelCase__				:			Tuple							=    self.avg_representation(x.to(self.device	)	)

          UpperCAmelCase__				:			int							=    self.classifier_head(__UpperCamelCase	)
          UpperCAmelCase__				:			str							=    nn.functional.log_softmax(__UpperCamelCase			,      dim=-1	)

          return probs







class 	_lowercase    (							data.Dataset					):


     '''simple docstring'''





     def __init__(   self			,      __UpperCamelCase			,      __UpperCamelCase	)->       Dict:
          UpperCAmelCase__				:			List[str]							=    X
          UpperCAmelCase__				:			int							=    y



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



     def __getitem__(   self			,      __UpperCamelCase	)->       str:
          UpperCAmelCase__				:			List[str]							=    {}
          UpperCAmelCase__				:			str							=    self.X[index]
          UpperCAmelCase__				:			Tuple							=    self.y[index]
          return data





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






     '''simple docstring'''
     def pad_sequences(lowerCAmelCase						:							str		):
          UpperCAmelCase__				:			Union[str, Any]							=    [len(lowerCAmelCase		) for seq in sequences]

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

          for i, seq in enumerate(lowerCAmelCase		):
               UpperCAmelCase__				:			str							=    lengths[i]
               UpperCAmelCase__				:			Optional[int]							=    seq[:end]

          return padded_sequences, lengths

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

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

     return x_batch, y_batch



def 				a__			(					lowerCAmelCase						:							int		):






     '''simple docstring'''
     UpperCAmelCase__				:			str							=    {}
     for key in data[0].keys():
          UpperCAmelCase__				:			Optional[int]							=    [d[key] for d in data]

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

     return x_batch, y_batch



def 				a__			(					lowerCAmelCase						:							int							,     lowerCAmelCase						:							Dict							,     lowerCAmelCase						:							int							,     lowerCAmelCase						:							Any=0							,     lowerCAmelCase						:							Dict=10							,     lowerCAmelCase						:							str="cpu"		):






     '''simple docstring'''
     UpperCAmelCase__				:			Dict							=    0
     discriminator.train_custom()
     for batch_idx, (input_t, target_t) in enumerate(lowerCAmelCase		):
          UpperCAmelCase__				,						UpperCAmelCase__				:			Union[str, Any]							=    input_t.to(lowerCAmelCase		), target_t.to(lowerCAmelCase		)

          optimizer.zero_grad()

          UpperCAmelCase__				:			int							=    discriminator(lowerCAmelCase		)
          UpperCAmelCase__				:			Optional[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 				a__			(					lowerCAmelCase						:							List[Any]							,     lowerCAmelCase						:							int							,     lowerCAmelCase						:							Dict="cpu"		):






     '''simple docstring'''
     discriminator.eval()
     UpperCAmelCase__				:			List[str]							=    0
     UpperCAmelCase__				:			str							=    0
     with torch.no_grad():
          for input_t, target_t in data_loader:
               UpperCAmelCase__				,						UpperCAmelCase__				:			int							=    input_t.to(lowerCAmelCase		), target_t.to(lowerCAmelCase		)
               UpperCAmelCase__				:			Union[str, Any]							=    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
               UpperCAmelCase__				:			Union[str, 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		)							,     100.0 * correct / len(data_loader.dataset		)		)		)



def 				a__			(					lowerCAmelCase						:							int							,     lowerCAmelCase						:							List[str]							,     lowerCAmelCase						:							Optional[int]							,     lowerCAmelCase						:							str=False							,     lowerCAmelCase						:							int="cpu"		):






     '''simple docstring'''
     UpperCAmelCase__				:			Dict							=    model.tokenizer.encode(lowerCAmelCase		)
     UpperCAmelCase__				:			Tuple							=    torch.tensor([input_t]							,     dtype=torch.long							,     device=lowerCAmelCase		)
     if cached:
          UpperCAmelCase__				:			Optional[Any]							=    model.avg_representation(lowerCAmelCase		)

     UpperCAmelCase__				:			Any							=    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 				a__			(					lowerCAmelCase						:							Any							,     lowerCAmelCase						:							int							,     lowerCAmelCase						:							Any							,     lowerCAmelCase						:							Optional[int]=False							,     lowerCAmelCase						:							List[str]="cpu"		):






     '''simple docstring'''
     UpperCAmelCase__				:			Optional[int]							=    torch.utils.data.DataLoader(dataset=lowerCAmelCase							,     batch_size=lowerCAmelCase							,     collate_fn=lowerCAmelCase		)

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

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

     return data_loader



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






     '''simple docstring'''
     UpperCAmelCase__				:			List[str]							=    "cuda" if torch.cuda.is_available() and not no_cuda else "cpu"

     print("Preprocessing {} dataset...".format(lowerCAmelCase		)		)
     UpperCAmelCase__				:			int							=    time.time()

     if dataset == "SST":
          UpperCAmelCase__				:			List[str]							=    ["positive", "negative", "very positive", "very negative", "neutral"]
          UpperCAmelCase__				:			int							=    {c: i for i, c in enumerate(lowerCAmelCase		)}

          UpperCAmelCase__				:			int							=    Discriminator(
              class_size=len(lowerCAmelCase		)							,     pretrained_model=lowerCAmelCase							,     cached_mode=lowerCAmelCase							,     device=lowerCAmelCase		).to(lowerCAmelCase		)

          UpperCAmelCase__				:			Dict							=    torchtext_data.Field()
          UpperCAmelCase__				:			List[Any]							=    torchtext_data.Field(sequential=lowerCAmelCase		)
          UpperCAmelCase__				,						UpperCAmelCase__				,						UpperCAmelCase__				:			Dict							=    datasets.SST.splits(
              lowerCAmelCase							,     lowerCAmelCase							,     fine_grained=lowerCAmelCase							,     train_subtrees=lowerCAmelCase							,     )

          UpperCAmelCase__				:			Dict							=    []
          UpperCAmelCase__				:			Optional[int]							=    []
          for i in trange(len(lowerCAmelCase		)							,     ascii=lowerCAmelCase		):
               UpperCAmelCase__				:			int							=    TreebankWordDetokenizer().detokenize(vars(train_data[i]		)["text"]		)
               UpperCAmelCase__				:			str							=    discriminator.tokenizer.encode(lowerCAmelCase		)
               UpperCAmelCase__				:			Dict							=    torch.tensor([5_0256] + seq							,     device=lowerCAmelCase							,     dtype=torch.long		)
               x.append(lowerCAmelCase		)
               y.append(classaidx[vars(train_data[i]		)["label"]]		)
          UpperCAmelCase__				:			Tuple							=    Dataset(lowerCAmelCase							,     lowerCAmelCase		)

          UpperCAmelCase__				:			Optional[Any]							=    []
          UpperCAmelCase__				:			str							=    []
          for i in trange(len(lowerCAmelCase		)							,     ascii=lowerCAmelCase		):
               UpperCAmelCase__				:			Optional[Any]							=    TreebankWordDetokenizer().detokenize(vars(test_data[i]		)["text"]		)
               UpperCAmelCase__				:			Optional[Any]							=    discriminator.tokenizer.encode(lowerCAmelCase		)
               UpperCAmelCase__				:			Optional[int]							=    torch.tensor([5_0256] + seq							,     device=lowerCAmelCase							,     dtype=torch.long		)
               test_x.append(lowerCAmelCase		)
               test_y.append(classaidx[vars(test_data[i]		)["label"]]		)
          UpperCAmelCase__				:			Union[str, Any]							=    Dataset(lowerCAmelCase							,     lowerCAmelCase		)

          UpperCAmelCase__				:			Optional[Any]							=    {
              "class_size": len(lowerCAmelCase		),
              "embed_size": discriminator.embed_size,
              "pretrained_model": pretrained_model,
              "class_vocab": classaidx,
              "default_class": 2,
          }

     elif dataset == "clickbait":
          UpperCAmelCase__				:			Optional[int]							=    ["non_clickbait", "clickbait"]
          UpperCAmelCase__				:			List[Any]							=    {c: i for i, c in enumerate(lowerCAmelCase		)}

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

                         if len(lowerCAmelCase		) < max_length_seq:
                              UpperCAmelCase__				:			Dict							=    torch.tensor([5_0256] + 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

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

          UpperCAmelCase__				:			int							=    {
              "class_size": len(lowerCAmelCase		),
              "embed_size": discriminator.embed_size,
              "pretrained_model": pretrained_model,
              "class_vocab": classaidx,
              "default_class": 1,
          }

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

          UpperCAmelCase__				:			Tuple							=    Discriminator(
              class_size=len(lowerCAmelCase		)							,     pretrained_model=lowerCAmelCase							,     cached_mode=lowerCAmelCase							,     device=lowerCAmelCase		).to(lowerCAmelCase		)

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

                         if len(lowerCAmelCase		) < max_length_seq:
                              UpperCAmelCase__				:			int							=    torch.tensor([5_0256] + 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

          UpperCAmelCase__				:			List[str]							=    Dataset(lowerCAmelCase							,     lowerCAmelCase		)
          UpperCAmelCase__				:			List[str]							=    int(0.9 * len(lowerCAmelCase		)		)
          UpperCAmelCase__				:			List[str]							=    len(lowerCAmelCase		) - train_size
          UpperCAmelCase__				,						UpperCAmelCase__				:			int							=    torch.utils.data.random_split(lowerCAmelCase							,     [train_size, test_size]		)

          UpperCAmelCase__				:			str							=    {
              "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."		)

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

          UpperCAmelCase__				:			Union[str, Any]							=    sorted(lowerCAmelCase		)
          UpperCAmelCase__				:			Any							=    {c: i for i, c in enumerate(lowerCAmelCase		)}

          UpperCAmelCase__				:			int							=    Discriminator(
              class_size=len(lowerCAmelCase		)							,     pretrained_model=lowerCAmelCase							,     cached_mode=lowerCAmelCase							,     device=lowerCAmelCase		).to(lowerCAmelCase		)

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

                         try:
                              UpperCAmelCase__				:			int							=    discriminator.tokenizer.encode(lowerCAmelCase		)
                              if len(lowerCAmelCase		) < max_length_seq:
                                   UpperCAmelCase__				:			str							=    torch.tensor([5_0256] + 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

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

          UpperCAmelCase__				:			List[Any]							=    {
              "class_size": len(lowerCAmelCase		),
              "embed_size": discriminator.embed_size,
              "pretrained_model": pretrained_model,
              "class_vocab": classaidx,
              "default_class": 0,
          }

     UpperCAmelCase__				:			List[str]							=    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..."		)

          UpperCAmelCase__				:			List[Any]							=    time.time()

          UpperCAmelCase__				:			int							=    get_cached_data_loader(lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     shuffle=lowerCAmelCase							,     device=lowerCAmelCase		)

          UpperCAmelCase__				:			Optional[Any]							=    get_cached_data_loader(lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     device=lowerCAmelCase		)

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

     else:
          UpperCAmelCase__				:			int							=    torch.utils.data.DataLoader(
              dataset=lowerCAmelCase							,     batch_size=lowerCAmelCase							,     shuffle=lowerCAmelCase							,     collate_fn=lowerCAmelCase		)
          UpperCAmelCase__				:			List[Any]							=    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		)

     UpperCAmelCase__				:			int							=    optim.Adam(discriminator.parameters()							,     lr=0.0001		)

     for epoch in range(lowerCAmelCase		):
          UpperCAmelCase__				:			Dict							=    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		)

          UpperCAmelCase__				:			int							=    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__":
    A__   :						str 							=     argparse.ArgumentParser(description="""Train a discriminator on top of GPT-2 representations""")
    parser.add_argument(
        """--dataset""",
        type=str,
        default="""SST""",
        choices=("""SST""", """clickbait""", """toxic""", """generic"""),
        help=(
            """dataset to train the discriminator on."""
            """In case of generic, the dataset is expected"""
            """to be a TSBV file with structure: class \\t text"""
        ),
    )
    parser.add_argument(
        """--dataset_fp""",
        type=str,
        default="""""",
        help="""File path of the dataset to use. Needed only in case of generic datadset""",
    )
    parser.add_argument(
        """--pretrained_model""", type=str, default="""gpt2-medium""", help="""Pretrained model to use as encoder"""
    )
    parser.add_argument("""--epochs""", type=int, default=10, metavar="""N""", help="""Number of training epochs""")
    parser.add_argument(
        """--batch_size""", type=int, default=64, metavar="""N""", help="""input batch size for training (default: 64)"""
    )
    parser.add_argument(
        """--log_interval""",
        type=int,
        default=10,
        metavar="""N""",
        help="""how many batches to wait before logging training status""",
    )
    parser.add_argument("""--save_model""", action="""store_true""", help="""whether to save the model""")
    parser.add_argument("""--cached""", action="""store_true""", help="""whether to cache the input representations""")
    parser.add_argument("""--no_cuda""", action="""store_true""", help="""use to turn off cuda""")
    A__   :						Optional[Any] 							=     parser.parse_args()

    train_discriminator(**(vars(args)))





"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A__   :						Optional[Any] 							=     {"""configuration_vilt""": ["""VILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViltConfig"""]}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A__   :						List[Any] 							=     ["""ViltFeatureExtractor"""]
    A__   :						Any 							=     ["""ViltImageProcessor"""]
    A__   :						int 							=     ["""ViltProcessor"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A__   :						str 							=     [
        """VILT_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """ViltForImageAndTextRetrieval""",
        """ViltForImagesAndTextClassification""",
        """ViltForTokenClassification""",
        """ViltForMaskedLM""",
        """ViltForQuestionAnswering""",
        """ViltLayer""",
        """ViltModel""",
        """ViltPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_vilt import VILT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViltConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_vilt import ViltFeatureExtractor
        from .image_processing_vilt import ViltImageProcessor
        from .processing_vilt import ViltProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_vilt import (
            VILT_PRETRAINED_MODEL_ARCHIVE_LIST,
            ViltForImageAndTextRetrieval,
            ViltForImagesAndTextClassification,
            ViltForMaskedLM,
            ViltForQuestionAnswering,
            ViltForTokenClassification,
            ViltLayer,
            ViltModel,
            ViltPreTrainedModel,
        )


else:
    import sys

    A__   :						Any 							=     _LazyModule(__name__, globals()["""__file__"""], _import_structure)



