




'''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)
lowercase__								=						1E-10
lowercase__								=						'''This is incredible! I love it, this is the best chicken I have ever had.'''
lowercase__								=						1_0_0






class     lowercase  ( nn.Module):






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






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






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






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






      def  A__(self			,	a__       )							-> Dict:
         if self.cached_mode:
            SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			x.to(self.device       )
         else:
            SCREAMING_SNAKE_CASE__      :					str							    =			self.avg_representation(x.to(self.device       )       )

         SCREAMING_SNAKE_CASE__      :					List[str]							    =			self.classifier_head(a__       )
         SCREAMING_SNAKE_CASE__      :					Dict							    =			nn.functional.log_softmax(a__			,	dim=-1       )

         return probs






class     lowercase  ( data.Dataset):






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






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






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


def 					UpperCamelCase						(	snake_case ):




   '''simple docstring'''

   def pad_sequences(snake_case ):
      SCREAMING_SNAKE_CASE__      :					str							    =			[len(snake_case ) for seq in sequences]

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

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

      return padded_sequences, lengths

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

   SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			pad_sequences(item_info['''X'''] )
   SCREAMING_SNAKE_CASE__      :					List[str]							    =			torch.tensor(item_info['''y''']						,  dtype=torch.long )

   return x_batch, y_batch


def 					UpperCamelCase						(	snake_case ):




   '''simple docstring'''

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

   SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			torch.cat(item_info['''X''']						,  0 )
   SCREAMING_SNAKE_CASE__      :					List[str]							    =			torch.tensor(item_info['''y''']						,  dtype=torch.long )

   return x_batch, y_batch


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




   '''simple docstring'''

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

      optimizer.zero_grad()

      SCREAMING_SNAKE_CASE__      :					int							    =			discriminator(snake_case )
      SCREAMING_SNAKE_CASE__      :					List[str]							    =			nn.functional.nll_loss(snake_case						,  snake_case )
      loss.backward(retain_graph=snake_case )
      optimizer.step()

      samples_so_far += len(snake_case )

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


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




   '''simple docstring'''

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

   test_loss /= len(data_loader.dataset )

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


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




   '''simple docstring'''

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

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


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




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					str							    =			torch.utils.data.DataLoader(dataset=snake_case						,  batch_size=snake_case						,  collate_fn=snake_case )

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

   SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			torch.utils.data.DataLoader(
       dataset=Dataset(snake_case						,  snake_case )						,  batch_size=snake_case						,  shuffle=snake_case						,  collate_fn=snake_case )

   return data_loader


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




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					Dict							    =			'''cuda''' if torch.cuda.is_available() and not no_cuda else '''cpu'''

   print('''Preprocessing {} dataset...'''.format(snake_case ) )
   SCREAMING_SNAKE_CASE__      :					Dict							    =			time.time()

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

      SCREAMING_SNAKE_CASE__      :					Tuple							    =			Discriminator(
          class_size=len(snake_case )						,  pretrained_model=snake_case						,  cached_mode=snake_case						,  device=snake_case ).to(snake_case )

      SCREAMING_SNAKE_CASE__      :					Tuple							    =			torchtext_data.Field()
      SCREAMING_SNAKE_CASE__      :					List[str]							    =			torchtext_data.Field(sequential=snake_case )
      SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					Tuple							    =			datasets.SST.splits(
          snake_case						,  snake_case						,  fine_grained=snake_case						,  train_subtrees=snake_case						,  )

      SCREAMING_SNAKE_CASE__      :					int							    =			[]
      SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			[]
      for i in trange(len(snake_case )						,  ascii=snake_case ):
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			TreebankWordDetokenizer().detokenize(vars(train_data[i] )['''text'''] )
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			discriminator.tokenizer.encode(snake_case )
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			torch.tensor([5_02_56] + seq						,  device=snake_case						,  dtype=torch.long )
         x.append(snake_case )
         y.append(classaidx[vars(train_data[i] )['''label''']] )
      SCREAMING_SNAKE_CASE__      :					List[str]							    =			Dataset(snake_case						,  snake_case )

      SCREAMING_SNAKE_CASE__      :					List[Any]							    =			[]
      SCREAMING_SNAKE_CASE__      :					Tuple							    =			[]
      for i in trange(len(snake_case )						,  ascii=snake_case ):
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			TreebankWordDetokenizer().detokenize(vars(test_data[i] )['''text'''] )
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			discriminator.tokenizer.encode(snake_case )
         SCREAMING_SNAKE_CASE__      :					Dict							    =			torch.tensor([5_02_56] + seq						,  device=snake_case						,  dtype=torch.long )
         test_x.append(snake_case )
         test_y.append(classaidx[vars(test_data[i] )['''label''']] )
      SCREAMING_SNAKE_CASE__      :					Dict							    =			Dataset(snake_case						,  snake_case )

      SCREAMING_SNAKE_CASE__      :					Any							    =			{
          '''class_size''': len(snake_case ),
          '''embed_size''': discriminator.embed_size,
          '''pretrained_model''': pretrained_model,
          '''class_vocab''': classaidx,
          '''default_class''': 2,
      }

   elif dataset == "clickbait":
      SCREAMING_SNAKE_CASE__      :					Any							    =			['''non_clickbait''', '''clickbait''']
      SCREAMING_SNAKE_CASE__      :					List[str]							    =			{c: i for i, c in enumerate(snake_case )}

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

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

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

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

      SCREAMING_SNAKE_CASE__      :					int							    =			{
          '''class_size''': len(snake_case ),
          '''embed_size''': discriminator.embed_size,
          '''pretrained_model''': pretrained_model,
          '''class_vocab''': classaidx,
          '''default_class''': 1,
      }

   elif dataset == "toxic":
      SCREAMING_SNAKE_CASE__      :					Dict							    =			['''non_toxic''', '''toxic''']
      SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			{c: i for i, c in enumerate(snake_case )}

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

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

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

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

      SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			{
          '''class_size''': len(snake_case ),
          '''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__      :					str							    =			set()
      with open(snake_case ) as f:
         SCREAMING_SNAKE_CASE__      :					Any							    =			csv.reader(snake_case						,  delimiter='''\t''' )
         for row in tqdm(snake_case						,  ascii=snake_case ):
            if row:
               classes.add(row[0] )

      SCREAMING_SNAKE_CASE__      :					List[Any]							    =			sorted(snake_case )
      SCREAMING_SNAKE_CASE__      :					Tuple							    =			{c: i for i, c in enumerate(snake_case )}

      SCREAMING_SNAKE_CASE__      :					Any							    =			Discriminator(
          class_size=len(snake_case )						,  pretrained_model=snake_case						,  cached_mode=snake_case						,  device=snake_case ).to(snake_case )

      SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			[]
      SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			[]
      with open(snake_case ) as f:
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			csv.reader(snake_case						,  delimiter='''\t''' )
         for i, row in enumerate(tqdm(snake_case						,  ascii=snake_case ) ):
            if row:
               SCREAMING_SNAKE_CASE__      :					Dict							    =			row[0]
               SCREAMING_SNAKE_CASE__      :					List[str]							    =			row[1]

               try:
                  SCREAMING_SNAKE_CASE__      :					str							    =			discriminator.tokenizer.encode(snake_case )
                  if len(snake_case ) < max_length_seq:
                     SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			torch.tensor([5_02_56] + seq						,  device=snake_case						,  dtype=torch.long )

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

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

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

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

      SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			{
          '''class_size''': len(snake_case ),
          '''embed_size''': discriminator.embed_size,
          '''pretrained_model''': pretrained_model,
          '''class_vocab''': classaidx,
          '''default_class''': 0,
      }

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

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

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

      SCREAMING_SNAKE_CASE__      :					int							    =			get_cached_data_loader(snake_case						,  snake_case						,  snake_case						,  shuffle=snake_case						,  device=snake_case )

      SCREAMING_SNAKE_CASE__      :					int							    =			get_cached_data_loader(snake_case						,  snake_case						,  snake_case						,  device=snake_case )

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

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

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

   SCREAMING_SNAKE_CASE__      :					List[str]							    =			optim.Adam(discriminator.parameters()						,  lr=0.0001 )

   for epoch in range(snake_case ):
      SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			time.time()
      print('''\nEpoch'''						,  epoch + 1 )

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

      SCREAMING_SNAKE_CASE__      :					Tuple							    =			time.time()
      print('''Epoch took: {:.3f}s'''.format(end - start ) )

      print('''\nExample prediction''' )
      predict(snake_case						,  snake_case						,  snake_case						,  cached=snake_case						,  device=snake_case )

      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(snake_case						,  epoch + 1 )						,  )


if __name__ == "__main__":
       lowercase__								=						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=1_0, metavar='''N''', help='''Number of training epochs''')
       parser.add_argument(
           '''--batch_size''', type=int, default=6_4, metavar='''N''', help='''input batch size for training (default: 64)'''
       )
       parser.add_argument(
           '''--log_interval''',
           type=int,
           default=1_0,
           metavar='''N''',
           help='''how many batches to wait before logging training status''',
       )
       parser.add_argument('''--save_model''', action='''store_true''', help='''whether to save the model''')
       parser.add_argument('''--cached''', action='''store_true''', help='''whether to cache the input representations''')
       parser.add_argument('''--no_cuda''', action='''store_true''', help='''use to turn off cuda''')
       lowercase__								=						parser.parse_args()

       train_discriminator(**(vars(args)))







'''simple docstring'''





import unittest

from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    PreTrainedTokenizer,
    is_vision_available,
)
from transformers.pipelines import ImageClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
       from PIL import Image
else:






       class     lowercase  :






             @staticmethod
             def  A__(*a__			,	**a__       )							-> Union[str, Any]:
                pass






@is_pipeline_test
@require_torch_or_tf
@require_vision
class     lowercase  ( unittest.TestCase):
      __lowercase					=	MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
      __lowercase					=	TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING






      def  A__(self			,	a__			,	a__			,	a__       )							-> Dict:
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			ImageClassificationPipeline(model=a__			,	image_processor=a__			,	top_k=2       )
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			[
             Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       ),
             '''http://images.cocodataset.org/val2017/000000039769.jpg''',
         ]
         return image_classifier, examples






      def  A__(self			,	a__			,	a__       )							-> List[Any]:
         SCREAMING_SNAKE_CASE__      :					int							    =			image_classifier('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )

         self.assertEqual(
             a__			,	[
                 {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
                 {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
             ]			,	)

         import datasets

         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''			,	'''image'''			,	split='''test'''       )

         # Accepts URL + PIL.Image + lists
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			image_classifier(
             [
                 Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       ),
                 '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                 # RGBA
                 dataset[0]['''file'''],
                 # LA
                 dataset[1]['''file'''],
                 # L
                 dataset[2]['''file'''],
             ]       )
         self.assertEqual(
             a__			,	[
                 [
                     {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
                     {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
                 ],
                 [
                     {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
                     {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
                 ],
                 [
                     {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
                     {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
                 ],
                 [
                     {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
                     {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
                 ],
                 [
                     {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
                     {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
                 ],
             ]			,	)






      @require_torch
      def  A__(self       )							-> List[str]:
         SCREAMING_SNAKE_CASE__      :					List[str]							    =			'''hf-internal-testing/tiny-random-vit'''
         SCREAMING_SNAKE_CASE__      :					Any							    =			pipeline('''image-classification'''			,	model=a__       )

         SCREAMING_SNAKE_CASE__      :					Any							    =			image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''       )
         self.assertEqual(
             nested_simplify(a__			,	decimals=4       )			,	[{'''label''': '''LABEL_1''', '''score''': 0.5_74}, {'''label''': '''LABEL_0''', '''score''': 0.4_26}]			,	)

         SCREAMING_SNAKE_CASE__      :					Any							    =			image_classifier(
             [
                 '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                 '''http://images.cocodataset.org/val2017/000000039769.jpg''',
             ]			,	top_k=2			,	)
         self.assertEqual(
             nested_simplify(a__			,	decimals=4       )			,	[
                 [{'''label''': '''LABEL_1''', '''score''': 0.5_74}, {'''label''': '''LABEL_0''', '''score''': 0.4_26}],
                 [{'''label''': '''LABEL_1''', '''score''': 0.5_74}, {'''label''': '''LABEL_0''', '''score''': 0.4_26}],
             ]			,	)






      @require_tf
      def  A__(self       )							-> Optional[int]:
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			'''hf-internal-testing/tiny-random-vit'''
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			pipeline('''image-classification'''			,	model=a__			,	framework='''tf'''       )

         SCREAMING_SNAKE_CASE__      :					List[str]							    =			image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''       )
         self.assertEqual(
             nested_simplify(a__			,	decimals=4       )			,	[{'''label''': '''LABEL_1''', '''score''': 0.5_74}, {'''label''': '''LABEL_0''', '''score''': 0.4_26}]			,	)

         SCREAMING_SNAKE_CASE__      :					Dict							    =			image_classifier(
             [
                 '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                 '''http://images.cocodataset.org/val2017/000000039769.jpg''',
             ]			,	top_k=2			,	)
         self.assertEqual(
             nested_simplify(a__			,	decimals=4       )			,	[
                 [{'''label''': '''LABEL_1''', '''score''': 0.5_74}, {'''label''': '''LABEL_0''', '''score''': 0.4_26}],
                 [{'''label''': '''LABEL_1''', '''score''': 0.5_74}, {'''label''': '''LABEL_0''', '''score''': 0.4_26}],
             ]			,	)






      def  A__(self       )							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			PreTrainedTokenizer()

         # Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			pipeline(
             '''image-classification'''			,	model='''hf-internal-testing/tiny-random-vit'''			,	tokenizer=a__       )

         self.assertIs(image_classifier.tokenizer			,	a__       )






      @slow
      @require_torch
      def  A__(self       )							-> int:
         # Perceiver is not tested by `run_pipeline_test` properly.
         # That is because the type of feature_extractor and model preprocessor need to be kept
         # in sync, which is not the case in the current design
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			pipeline('''image-classification'''			,	model='''deepmind/vision-perceiver-conv'''       )
         SCREAMING_SNAKE_CASE__      :					str							    =			image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''       )
         self.assertEqual(
             nested_simplify(a__			,	decimals=4       )			,	[
                 {'''score''': 0.43_85, '''label''': '''tabby, tabby cat'''},
                 {'''score''': 0.3_21, '''label''': '''tiger cat'''},
                 {'''score''': 0.05_02, '''label''': '''Egyptian cat'''},
                 {'''score''': 0.01_37, '''label''': '''crib, cot'''},
                 {'''score''': 0.0_07, '''label''': '''radiator'''},
             ]			,	)

         SCREAMING_SNAKE_CASE__      :					str							    =			pipeline('''image-classification'''			,	model='''deepmind/vision-perceiver-fourier'''       )
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''       )
         self.assertEqual(
             nested_simplify(a__			,	decimals=4       )			,	[
                 {'''score''': 0.56_58, '''label''': '''tabby, tabby cat'''},
                 {'''score''': 0.13_09, '''label''': '''tiger cat'''},
                 {'''score''': 0.07_22, '''label''': '''Egyptian cat'''},
                 {'''score''': 0.07_07, '''label''': '''remote control, remote'''},
                 {'''score''': 0.00_82, '''label''': '''computer keyboard, keypad'''},
             ]			,	)

         SCREAMING_SNAKE_CASE__      :					Dict							    =			pipeline('''image-classification'''			,	model='''deepmind/vision-perceiver-learned'''       )
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''       )
         self.assertEqual(
             nested_simplify(a__			,	decimals=4       )			,	[
                 {'''score''': 0.30_22, '''label''': '''tabby, tabby cat'''},
                 {'''score''': 0.23_62, '''label''': '''Egyptian cat'''},
                 {'''score''': 0.18_56, '''label''': '''tiger cat'''},
                 {'''score''': 0.03_24, '''label''': '''remote control, remote'''},
                 {'''score''': 0.00_96, '''label''': '''quilt, comforter, comfort, puff'''},
             ]			,	)

