





# 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			):






       '''simple docstring'''


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




       def 			UpperCamelCase_	(					self							)	->   List[Any]:
            return self.classifier_head




       def 			UpperCamelCase_	(					self							)	->   Tuple:
            for param in self.encoder.parameters():
                 _SCREAMING_SNAKE_CASE				:		int  			=							False
            self.classifier_head.train()




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







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

            _SCREAMING_SNAKE_CASE				:		str  			=							self.classifier_head(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							nn.functional.log_softmax(__lowerCamelCase			,  dim=-1							)

            return probs





class 	lowerCAmelCase__(       data.Dataset			):






       '''simple docstring'''


       def __init__(					self			,  __lowerCamelCase			,  __lowerCamelCase							)	->   List[str]:
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							X
            _SCREAMING_SNAKE_CASE				:		Dict  			=							y




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







       def __getitem__(					self			,  __lowerCamelCase							)	->   str:
            _SCREAMING_SNAKE_CASE				:		str  			=							{}
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							self.X[index]
            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							self.y[index]
            return data






def 			lowerCamelCase__	(__lowerCamelCase		):
     def pad_sequences(__lowerCamelCase		):
          _SCREAMING_SNAKE_CASE				:		Any  			=							[len(__lowerCamelCase		) for seq in sequences]

          _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							torch.zeros(len(__lowerCamelCase		),				max(__lowerCamelCase		)		).long()  # padding value = 0

          for i, seq in enumerate(__lowerCamelCase		):
               _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							lengths[i]
               _SCREAMING_SNAKE_CASE				:		Tuple  			=							seq[:end]

          return padded_sequences, lengths

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

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

     return x_batch, y_batch






def 			lowerCamelCase__	(__lowerCamelCase		):
     _SCREAMING_SNAKE_CASE				:		List[Any]  			=							{}
     for key in data[0].keys():
          _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							[d[key] for d in data]

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

     return x_batch, y_batch






def 			lowerCamelCase__	(__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase=0,				__lowerCamelCase=10,				__lowerCamelCase="cpu"		):
     _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				:		Optional[int]  			=							input_t.to(__lowerCamelCase		), target_t.to(__lowerCamelCase		)

          optimizer.zero_grad()

          _SCREAMING_SNAKE_CASE				:		List[str]  			=							discriminator(__lowerCamelCase		)
          _SCREAMING_SNAKE_CASE				:		List[str]  			=							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 			lowerCamelCase__	(__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase="cpu"		):
     discriminator.eval()
     _SCREAMING_SNAKE_CASE				:		Dict  			=							0
     _SCREAMING_SNAKE_CASE				:		Any  			=							0
     with torch.no_grad():
          for input_t, target_t in data_loader:
               _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							input_t.to(__lowerCamelCase		), target_t.to(__lowerCamelCase		)
               _SCREAMING_SNAKE_CASE				:		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
               _SCREAMING_SNAKE_CASE				:		Optional[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 			lowerCamelCase__	(__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase=False,				__lowerCamelCase="cpu"		):
     _SCREAMING_SNAKE_CASE				:		List[str]  			=							model.tokenizer.encode(__lowerCamelCase		)
     _SCREAMING_SNAKE_CASE				:		str  			=							torch.tensor([input_t],				dtype=torch.long,				device=__lowerCamelCase		)
     if cached:
          _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							model.avg_representation(__lowerCamelCase		)

     _SCREAMING_SNAKE_CASE				:		List[str]  			=							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 			lowerCamelCase__	(__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase=False,				__lowerCamelCase="cpu"		):
     _SCREAMING_SNAKE_CASE				:		Tuple  			=							torch.utils.data.DataLoader(dataset=__lowerCamelCase,				batch_size=__lowerCamelCase,				collate_fn=__lowerCamelCase		)

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

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

     return data_loader






def 			lowerCamelCase__	(__lowerCamelCase,				__lowerCamelCase=None,				__lowerCamelCase="gpt2-medium",				__lowerCamelCase=10,				__lowerCamelCase=64,				__lowerCamelCase=10,				__lowerCamelCase=False,				__lowerCamelCase=False,				__lowerCamelCase=False,				):
     _SCREAMING_SNAKE_CASE				:		Any  			=							"cuda" if torch.cuda.is_available() and not no_cuda else "cpu"

     print("Preprocessing {} dataset...".format(__lowerCamelCase		)		)
     _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							time.time()

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

          _SCREAMING_SNAKE_CASE				:		str  			=							Discriminator(
              class_size=len(__lowerCamelCase		),				pretrained_model=__lowerCamelCase,				cached_mode=__lowerCamelCase,				device=__lowerCamelCase		).to(__lowerCamelCase		)

          _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							torchtext_data.Field()
          _SCREAMING_SNAKE_CASE				:		str  			=							torchtext_data.Field(sequential=__lowerCamelCase		)
          _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							datasets.SST.splits(
              __lowerCamelCase,				__lowerCamelCase,				fine_grained=__lowerCamelCase,				train_subtrees=__lowerCamelCase,				)

          _SCREAMING_SNAKE_CASE				:		List[Any]  			=							[]
          _SCREAMING_SNAKE_CASE				:		Tuple  			=							[]
          for i in trange(len(__lowerCamelCase		),				ascii=__lowerCamelCase		):
               _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							TreebankWordDetokenizer().detokenize(vars(train_data[i]		)["text"]		)
               _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							discriminator.tokenizer.encode(__lowerCamelCase		)
               _SCREAMING_SNAKE_CASE				:		str  			=							torch.tensor([50256] + 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				:		Any  			=							[]
          _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							[]
          for i in trange(len(__lowerCamelCase		),				ascii=__lowerCamelCase		):
               _SCREAMING_SNAKE_CASE				:		Tuple  			=							TreebankWordDetokenizer().detokenize(vars(test_data[i]		)["text"]		)
               _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							discriminator.tokenizer.encode(__lowerCamelCase		)
               _SCREAMING_SNAKE_CASE				:		int  			=							torch.tensor([50256] + seq,				device=__lowerCamelCase,				dtype=torch.long		)
               test_x.append(__lowerCamelCase		)
               test_y.append(classaidx[vars(test_data[i]		)["label"]]		)
          _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							Dataset(__lowerCamelCase,				__lowerCamelCase		)

          _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							{
              "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				:		List[str]  			=							["non_clickbait", "clickbait"]
          _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							{c: i for i, c in enumerate(__lowerCamelCase		)}

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

                         if len(__lowerCamelCase		) < max_length_seq:
                              _SCREAMING_SNAKE_CASE				:		List[Any]  			=							torch.tensor([50256] + 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				:		List[str]  			=							Dataset(__lowerCamelCase,				__lowerCamelCase		)
          _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							int(0.9 * len(__lowerCamelCase		)		)
          _SCREAMING_SNAKE_CASE				:		List[str]  			=							len(__lowerCamelCase		) - train_size
          _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE				:		int  			=							torch.utils.data.random_split(__lowerCamelCase,				[train_size, test_size]		)

          _SCREAMING_SNAKE_CASE				:		Dict  			=							{
              "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				:		List[str]  			=							["non_toxic", "toxic"]
          _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							{c: i for i, c in enumerate(__lowerCamelCase		)}

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

          _SCREAMING_SNAKE_CASE				:		List[Any]  			=							[]
          _SCREAMING_SNAKE_CASE				:		Any  			=							[]
          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				:		List[Any]  			=							discriminator.tokenizer.encode(d["text"]		)

                         if len(__lowerCamelCase		) < max_length_seq:
                              _SCREAMING_SNAKE_CASE				:		str  			=							torch.tensor([50256] + 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				:		List[Any]  			=							Dataset(__lowerCamelCase,				__lowerCamelCase		)
          _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							int(0.9 * len(__lowerCamelCase		)		)
          _SCREAMING_SNAKE_CASE				:		str  			=							len(__lowerCamelCase		) - train_size
          _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE				:		List[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				:		List[str]  			=							set()
          with open(__lowerCamelCase		) as f:
               _SCREAMING_SNAKE_CASE				:		Tuple  			=							csv.reader(__lowerCamelCase,				delimiter="\t"		)
               for row in tqdm(__lowerCamelCase,				ascii=__lowerCamelCase		):
                    if row:
                         classes.add(row[0]		)

          _SCREAMING_SNAKE_CASE				:		Tuple  			=							sorted(__lowerCamelCase		)
          _SCREAMING_SNAKE_CASE				:		Union[str, 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		)

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

                         try:
                              _SCREAMING_SNAKE_CASE				:		int  			=							discriminator.tokenizer.encode(__lowerCamelCase		)
                              if len(__lowerCamelCase		) < max_length_seq:
                                   _SCREAMING_SNAKE_CASE				:		int  			=							torch.tensor([50256] + 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				:		Tuple  			=							Dataset(__lowerCamelCase,				__lowerCamelCase		)
          _SCREAMING_SNAKE_CASE				:		Any  			=							int(0.9 * len(__lowerCamelCase		)		)
          _SCREAMING_SNAKE_CASE				:		List[Any]  			=							len(__lowerCamelCase		) - train_size
          _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE				:		int  			=							torch.utils.data.random_split(__lowerCamelCase,				[train_size, test_size]		)

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

     _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							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				:		Dict  			=							time.time()

          _SCREAMING_SNAKE_CASE				:		Tuple  			=							get_cached_data_loader(__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase,				shuffle=__lowerCamelCase,				device=__lowerCamelCase		)

          _SCREAMING_SNAKE_CASE				:		Dict  			=							get_cached_data_loader(__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase,				device=__lowerCamelCase		)

          _SCREAMING_SNAKE_CASE				:		Tuple  			=							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				:		Union[str, 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		)

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

     for epoch in range(__lowerCamelCase		):
          _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							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				:		List[str]  			=							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__":
       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)))






import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import CLIPImageProcessor





class 	lowerCAmelCase__(       unittest.TestCase			):






       '''simple docstring'''


       def __init__(					self			,  __lowerCamelCase			,  __lowerCamelCase=7			,  __lowerCamelCase=3			,  __lowerCamelCase=1_8			,  __lowerCamelCase=3_0			,  __lowerCamelCase=4_0_0			,  __lowerCamelCase=True			,  __lowerCamelCase=None			,  __lowerCamelCase=True			,  __lowerCamelCase=None			,  __lowerCamelCase=True			,  __lowerCamelCase=[0.4814_5466, 0.457_8275, 0.4082_1073]			,  __lowerCamelCase=[0.2686_2954, 0.2613_0258, 0.2757_7711]			,  __lowerCamelCase=True			,  )	->   List[Any]:
            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							size if size is not None else {"shortest_edge": 2_0}
            _SCREAMING_SNAKE_CASE				:		int  			=							crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
            _SCREAMING_SNAKE_CASE				:		str  			=							parent
            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							batch_size
            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							num_channels
            _SCREAMING_SNAKE_CASE				:		Any  			=							image_size
            _SCREAMING_SNAKE_CASE				:		Any  			=							min_resolution
            _SCREAMING_SNAKE_CASE				:		int  			=							max_resolution
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							do_resize
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							size
            _SCREAMING_SNAKE_CASE				:		int  			=							do_center_crop
            _SCREAMING_SNAKE_CASE				:		Dict  			=							crop_size
            _SCREAMING_SNAKE_CASE				:		int  			=							do_normalize
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							image_mean
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							image_std
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							do_convert_rgb




       def 			UpperCamelCase_	(					self							)	->   List[str]:
            return {
                "do_resize": self.do_resize,
                "size": self.size,
                "do_center_crop": self.do_center_crop,
                "crop_size": self.crop_size,
                "do_normalize": self.do_normalize,
                "image_mean": self.image_mean,
                "image_std": self.image_std,
                "do_convert_rgb": self.do_convert_rgb,
            }







       def 			UpperCamelCase_	(					self			,  __lowerCamelCase=False			,  __lowerCamelCase=False			,  __lowerCamelCase=False							)	->   Tuple:
            assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

            if equal_resolution:
                 _SCREAMING_SNAKE_CASE				:		Tuple  			=							[]
                 for i in range(self.batch_size							):
                      image_inputs.append(
                          np.random.randint(
                              2_5_5			,  size=(self.num_channels, self.max_resolution, self.max_resolution)			,  dtype=np.uinta							)							)
            else:
                 _SCREAMING_SNAKE_CASE				:		Dict  			=							[]
                 for i in range(self.batch_size							):
                      _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE				:		Tuple  			=							np.random.choice(np.arange(self.min_resolution			,  self.max_resolution							)			,  2							)
                      image_inputs.append(np.random.randint(2_5_5			,  size=(self.num_channels, width, height)			,  dtype=np.uinta							)							)

            if not numpify and not torchify:
                 # PIL expects the channel dimension as last dimension
                 _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							[Image.fromarray(np.moveaxis(__lowerCamelCase			,  0			,  -1							)							) for x in image_inputs]

            if torchify:
                 _SCREAMING_SNAKE_CASE				:		List[Any]  			=							[torch.from_numpy(__lowerCamelCase							) for x in image_inputs]

            return image_inputs





@require_torch
@require_vision
class 	lowerCAmelCase__(       __lowercase     ,   unittest.TestCase			):






       '''simple docstring'''


       __snake_case       	=			CLIPImageProcessor if is_vision_available() else None




       def 			UpperCamelCase_	(					self							)	->   Dict:
            _SCREAMING_SNAKE_CASE				:		str  			=							CLIPImageProcessingTester(self							)




       @property
       def 			UpperCamelCase_	(					self							)	->   int:
            return self.image_processor_tester.prepare_image_processor_dict()




       def 			UpperCamelCase_	(					self							)	->   int:
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							self.image_processing_class(**self.image_processor_dict							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "do_resize"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "size"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "do_center_crop"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "center_crop"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "do_normalize"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "image_mean"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "image_std"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "do_convert_rgb"							)							)




       def 			UpperCamelCase_	(					self							)	->   Optional[Any]:
            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							self.image_processing_class.from_dict(self.image_processor_dict							)
            self.assertEqual(image_processor.size			,  {"shortest_edge": 2_0}							)
            self.assertEqual(image_processor.crop_size			,  {"height": 1_8, "width": 1_8}							)

            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							self.image_processing_class.from_dict(self.image_processor_dict			,  size=4_2			,  crop_size=8_4							)
            self.assertEqual(image_processor.size			,  {"shortest_edge": 4_2}							)
            self.assertEqual(image_processor.crop_size			,  {"height": 8_4, "width": 8_4}							)




       def 			UpperCamelCase_	(					self							)	->   Tuple:
            pass




       def 			UpperCamelCase_	(					self							)	->   str:
            # Initialize image_processing
            _SCREAMING_SNAKE_CASE				:		str  			=							self.image_processing_class(**self.image_processor_dict							)
            # create random PIL images
            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							self.image_processor_tester.prepare_inputs(equal_resolution=__lowerCamelCase							)
            for image in image_inputs:
                 self.assertIsInstance(__lowerCamelCase			,  Image.Image							)

            # Test not batched input
            _SCREAMING_SNAKE_CASE				:		int  			=							image_processing(image_inputs[0]			,  return_tensors="pt"							).pixel_values
            self.assertEqual(
                encoded_images.shape			,  (
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size["height"],
                    self.image_processor_tester.crop_size["width"],
                )			,  )

            # Test batched
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							image_processing(__lowerCamelCase			,  return_tensors="pt"							).pixel_values
            self.assertEqual(
                encoded_images.shape			,  (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size["height"],
                    self.image_processor_tester.crop_size["width"],
                )			,  )




       def 			UpperCamelCase_	(					self							)	->   Union[str, Any]:
            # Initialize image_processing
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							self.image_processing_class(**self.image_processor_dict							)
            # create random numpy tensors
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							self.image_processor_tester.prepare_inputs(equal_resolution=__lowerCamelCase			,  numpify=__lowerCamelCase							)
            for image in image_inputs:
                 self.assertIsInstance(__lowerCamelCase			,  np.ndarray							)

            # Test not batched input
            _SCREAMING_SNAKE_CASE				:		Dict  			=							image_processing(image_inputs[0]			,  return_tensors="pt"							).pixel_values
            self.assertEqual(
                encoded_images.shape			,  (
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size["height"],
                    self.image_processor_tester.crop_size["width"],
                )			,  )

            # Test batched
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							image_processing(__lowerCamelCase			,  return_tensors="pt"							).pixel_values
            self.assertEqual(
                encoded_images.shape			,  (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size["height"],
                    self.image_processor_tester.crop_size["width"],
                )			,  )







       def 			UpperCamelCase_	(					self							)	->   Any:
            # Initialize image_processing
            _SCREAMING_SNAKE_CASE				:		str  			=							self.image_processing_class(**self.image_processor_dict							)
            # create random PyTorch tensors
            _SCREAMING_SNAKE_CASE				:		int  			=							self.image_processor_tester.prepare_inputs(equal_resolution=__lowerCamelCase			,  torchify=__lowerCamelCase							)
            for image in image_inputs:
                 self.assertIsInstance(__lowerCamelCase			,  torch.Tensor							)

            # Test not batched input
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							image_processing(image_inputs[0]			,  return_tensors="pt"							).pixel_values
            self.assertEqual(
                encoded_images.shape			,  (
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size["height"],
                    self.image_processor_tester.crop_size["width"],
                )			,  )

            # Test batched
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							image_processing(__lowerCamelCase			,  return_tensors="pt"							).pixel_values
            self.assertEqual(
                encoded_images.shape			,  (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size["height"],
                    self.image_processor_tester.crop_size["width"],
                )			,  )





@require_torch
@require_vision
class 	lowerCAmelCase__(       __lowercase     ,   unittest.TestCase			):






       '''simple docstring'''


       __snake_case       	=			CLIPImageProcessor if is_vision_available() else None




       def 			UpperCamelCase_	(					self							)	->   int:
            _SCREAMING_SNAKE_CASE				:		Any  			=							CLIPImageProcessingTester(self			,  num_channels=4							)
            _SCREAMING_SNAKE_CASE				:		Any  			=							3




       @property
       def 			UpperCamelCase_	(					self							)	->   Dict:
            return self.image_processor_tester.prepare_image_processor_dict()




       def 			UpperCamelCase_	(					self							)	->   Union[str, Any]:
            _SCREAMING_SNAKE_CASE				:		str  			=							self.image_processing_class(**self.image_processor_dict							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "do_resize"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "size"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "do_center_crop"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "center_crop"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "do_normalize"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "image_mean"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "image_std"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "do_convert_rgb"							)							)




       def 			UpperCamelCase_	(					self							)	->   List[str]:
            pass







       def 			UpperCamelCase_	(					self							)	->   Union[str, Any]:
            # Initialize image_processing
            _SCREAMING_SNAKE_CASE				:		Dict  			=							self.image_processing_class(**self.image_processor_dict							)
            # create random PIL images
            _SCREAMING_SNAKE_CASE				:		Any  			=							self.image_processor_tester.prepare_inputs(equal_resolution=__lowerCamelCase							)
            for image in image_inputs:
                 self.assertIsInstance(__lowerCamelCase			,  Image.Image							)

            # Test not batched input
            _SCREAMING_SNAKE_CASE				:		Dict  			=							image_processing(image_inputs[0]			,  return_tensors="pt"							).pixel_values
            self.assertEqual(
                encoded_images.shape			,  (
                    1,
                    self.expected_encoded_image_num_channels,
                    self.image_processor_tester.crop_size["height"],
                    self.image_processor_tester.crop_size["width"],
                )			,  )

            # Test batched
            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							image_processing(__lowerCamelCase			,  return_tensors="pt"							).pixel_values
            self.assertEqual(
                encoded_images.shape			,  (
                    self.image_processor_tester.batch_size,
                    self.expected_encoded_image_num_channels,
                    self.image_processor_tester.crop_size["height"],
                    self.image_processor_tester.crop_size["width"],
                )			,  )