






"""simple docstring"""


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_								=	100


class    snake_case						(				nn.Module		):


   '''simple docstring'''

   def __init__(      self					:	int,			_lowerCamelCase					:	List[str],			_lowerCamelCase					:	List[str]="gpt2-medium",			_lowerCamelCase					:	Tuple=False,			_lowerCamelCase					:	List[Any]="cpu"    ):

     '''simple docstring'''
     super().__init__()
     __A		     =					GPTaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_    )
     __A		     =					GPTaLMHeadModel.from_pretrained(SCREAMING_SNAKE_CASE_    )
     __A		     =					self.encoder.transformer.config.hidden_size
     __A		     =					ClassificationHead(class_size=SCREAMING_SNAKE_CASE_,			embed_size=self.embed_size    )
     __A		     =					cached_mode
     __A		     =					device


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict    ):

     '''simple docstring'''
     return self.classifier_head


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[Any]    ):

     '''simple docstring'''
     for param in self.encoder.parameters():
       __A		     =					False
     self.classifier_head.train()


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple,			_lowerCamelCase					:	List[Any]    ):

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






   def 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple,			_lowerCamelCase					:	Union[str, Any]    ):

     '''simple docstring'''
     if self.cached_mode:
       __A		     =					x.to(self.device    )
     else:
       __A		     =					self.avg_representation(x.to(self.device    )    )

     __A		     =					self.classifier_head(SCREAMING_SNAKE_CASE_    )
     __A		     =					nn.functional.log_softmax(SCREAMING_SNAKE_CASE_,			dim=-1    )

     return probs





class    snake_case						(				data.Dataset		):


   '''simple docstring'''

   def __init__(      self					:	Any,			_lowerCamelCase					:	int,			_lowerCamelCase					:	Any    ):

     '''simple docstring'''
     __A		     =					X
     __A		     =					y


   def __len__(      self					:	Union[str, Any]    ):

     '''simple docstring'''
     return len(self.X    )






   def __getitem__(      self					:	List[Any],			_lowerCamelCase					:	List[str]    ):

     '''simple docstring'''
     __A		     =					{}
     __A		     =					self.X[index]
     __A		     =					self.y[index]
     return data





def    lowerCAmelCase							(       __UpperCamelCase					):




  """simple docstring"""







  def pad_sequences(__UpperCamelCase					):
    __A		     =					[len(A_					) for seq in sequences]

    __A		     =					torch.zeros(len(A_					)						,			max(A_					)					).long()  # padding value = 0

    for i, seq in enumerate(A_					):
      __A		     =					lengths[i]
      __A		     =					seq[:end]

    return padded_sequences, lengths

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

  __A		     =					pad_sequences(item_info['''X''']					)
  __A		     =					torch.tensor(item_info['''y''']						,			dtype=torch.long					)

  return x_batch, y_batch



def    lowerCAmelCase							(       __UpperCamelCase					):




  """simple docstring"""







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

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

  return x_batch, y_batch



def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase=0						,			__UpperCamelCase=1_0						,			__UpperCamelCase="cpu"					):




  """simple docstring"""







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

    optimizer.zero_grad()

    __A		     =					discriminator(A_					)
    __A		     =					nn.functional.nll_loss(A_						,			A_					)
    loss.backward(retain_graph=A_					)
    optimizer.step()

    samples_so_far += len(A_					)

    if batch_idx % log_interval == 0:
      print(
          '''Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'''.format(
              epoch + 1						,			A_						,			len(data_loader.dataset					)						,			1_0_0 * samples_so_far / len(data_loader.dataset					)						,			loss.item()						,			)					)



def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase="cpu"					):




  """simple docstring"""







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

  test_loss /= len(data_loader.dataset					)

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



def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase=False						,			__UpperCamelCase="cpu"					):




  """simple docstring"""







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

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



def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase=False						,			__UpperCamelCase="cpu"					):




  """simple docstring"""







  __A		     =					torch.utils.data.DataLoader(dataset=A_						,			batch_size=A_						,			collate_fn=A_					)

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

  __A		     =					torch.utils.data.DataLoader(
      dataset=Dataset(A_						,			A_					)						,			batch_size=A_						,			shuffle=A_						,			collate_fn=A_					)

  return data_loader



def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase=None						,			__UpperCamelCase="gpt2-medium"						,			__UpperCamelCase=1_0						,			__UpperCamelCase=6_4						,			__UpperCamelCase=1_0						,			__UpperCamelCase=False						,			__UpperCamelCase=False						,			__UpperCamelCase=False						,			):




  """simple docstring"""







  __A		     =					'''cuda''' if torch.cuda.is_available() and not no_cuda else '''cpu'''

  print('''Preprocessing {} dataset...'''.format(A_					)					)
  __A		     =					time.time()

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

    __A		     =					Discriminator(
        class_size=len(A_					)						,			pretrained_model=A_						,			cached_mode=A_						,			device=A_					).to(A_					)

    __A		     =					torchtext_data.Field()
    __A		     =					torchtext_data.Field(sequential=A_					)
    __A		     =					datasets.SST.splits(
        A_						,			A_						,			fine_grained=A_						,			train_subtrees=A_						,			)

    __A		     =					[]
    __A		     =					[]
    for i in trange(len(A_					)						,			ascii=A_					):
      __A		     =					TreebankWordDetokenizer().detokenize(vars(train_data[i]					)['''text''']					)
      __A		     =					discriminator.tokenizer.encode(A_					)
      __A		     =					torch.tensor([5_0_2_5_6] + seq						,			device=A_						,			dtype=torch.long					)
      x.append(A_					)
      y.append(classaidx[vars(train_data[i]					)['''label''']]					)
    __A		     =					Dataset(A_						,			A_					)

    __A		     =					[]
    __A		     =					[]
    for i in trange(len(A_					)						,			ascii=A_					):
      __A		     =					TreebankWordDetokenizer().detokenize(vars(test_data[i]					)['''text''']					)
      __A		     =					discriminator.tokenizer.encode(A_					)
      __A		     =					torch.tensor([5_0_2_5_6] + seq						,			device=A_						,			dtype=torch.long					)
      test_x.append(A_					)
      test_y.append(classaidx[vars(test_data[i]					)['''label''']]					)
    __A		     =					Dataset(A_						,			A_					)

    __A		     =					{
        '''class_size''': len(A_					),
        '''embed_size''': discriminator.embed_size,
        '''pretrained_model''': pretrained_model,
        '''class_vocab''': classaidx,
        '''default_class''': 2,
    }

  elif dataset == "clickbait":
    __A		     =					['''non_clickbait''', '''clickbait''']
    __A		     =					{c: i for i, c in enumerate(A_					)}

    __A		     =					Discriminator(
        class_size=len(A_					)						,			pretrained_model=A_						,			cached_mode=A_						,			device=A_					).to(A_					)

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

          if len(A_					) < max_length_seq:
            __A		     =					torch.tensor([5_0_2_5_6] + seq						,			device=A_						,			dtype=torch.long					)
          else:
            print('''Line {} is longer than maximum length {}'''.format(A_						,			A_					)					)
            continue
          x.append(A_					)
          y.append(d['''label''']					)
        except Exception:
          print('''Error evaluating / tokenizing line {}, skipping it'''.format(A_					)					)
          pass

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

    __A		     =					{
        '''class_size''': len(A_					),
        '''embed_size''': discriminator.embed_size,
        '''pretrained_model''': pretrained_model,
        '''class_vocab''': classaidx,
        '''default_class''': 1,
    }

  elif dataset == "toxic":
    __A		     =					['''non_toxic''', '''toxic''']
    __A		     =					{c: i for i, c in enumerate(A_					)}

    __A		     =					Discriminator(
        class_size=len(A_					)						,			pretrained_model=A_						,			cached_mode=A_						,			device=A_					).to(A_					)

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

          if len(A_					) < max_length_seq:
            __A		     =					torch.tensor([5_0_2_5_6] + seq						,			device=A_						,			dtype=torch.long					)
          else:
            print('''Line {} is longer than maximum length {}'''.format(A_						,			A_					)					)
            continue
          x.append(A_					)
          y.append(int(np.sum(d['''label''']					) > 0					)					)
        except Exception:
          print('''Error evaluating / tokenizing line {}, skipping it'''.format(A_					)					)
          pass

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

    __A		     =					{
        '''class_size''': len(A_					),
        '''embed_size''': discriminator.embed_size,
        '''pretrained_model''': pretrained_model,
        '''class_vocab''': classaidx,
        '''default_class''': 0,
    }

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

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

    __A		     =					set()
    with open(A_					) as f:
      __A		     =					csv.reader(A_						,			delimiter='''\t'''					)
      for row in tqdm(A_						,			ascii=A_					):
        if row:
          classes.add(row[0]					)

    __A		     =					sorted(A_					)
    __A		     =					{c: i for i, c in enumerate(A_					)}

    __A		     =					Discriminator(
        class_size=len(A_					)						,			pretrained_model=A_						,			cached_mode=A_						,			device=A_					).to(A_					)

    __A		     =					[]
    __A		     =					[]
    with open(A_					) as f:
      __A		     =					csv.reader(A_						,			delimiter='''\t'''					)
      for i, row in enumerate(tqdm(A_						,			ascii=A_					)					):
        if row:
          __A		     =					row[0]
          __A		     =					row[1]

          try:
            __A		     =					discriminator.tokenizer.encode(A_					)
            if len(A_					) < max_length_seq:
              __A		     =					torch.tensor([5_0_2_5_6] + seq						,			device=A_						,			dtype=torch.long					)

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

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

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

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

    __A		     =					{
        '''class_size''': len(A_					),
        '''embed_size''': discriminator.embed_size,
        '''pretrained_model''': pretrained_model,
        '''class_vocab''': classaidx,
        '''default_class''': 0,
    }

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

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

    __A		     =					time.time()

    __A		     =					get_cached_data_loader(A_						,			A_						,			A_						,			shuffle=A_						,			device=A_					)

    __A		     =					get_cached_data_loader(A_						,			A_						,			A_						,			device=A_					)

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

  else:
    __A		     =					torch.utils.data.DataLoader(
        dataset=A_						,			batch_size=A_						,			shuffle=A_						,			collate_fn=A_					)
    __A		     =					torch.utils.data.DataLoader(dataset=A_						,			batch_size=A_						,			collate_fn=A_					)

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

  __A		     =					optim.Adam(discriminator.parameters()						,			lr=0.0001					)

  for epoch in range(A_					):
    __A		     =					time.time()
    print('''\nEpoch'''						,			epoch + 1					)

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

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

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

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


if __name__ == "__main__":
       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=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')
       lowercase_								=	parser.parse_args()

       train_discriminator(**(vars(args)))






'''simple docstring'''





import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


UpperCAmelCase            = logging.get_logger(__name__)

UpperCAmelCase            = {
    "google/vit-hybrid-base-bit-384": "https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json",
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}

class     a (					__magic_name__     ):
				_snake_case		     =			'''vit-hybrid'''




				def __init__(				self						:					Any,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]=None,     SCREAMING_SNAKE_CASE_						:					Tuple=7_68,     SCREAMING_SNAKE_CASE_						:					List[str]=12,     SCREAMING_SNAKE_CASE_						:					Optional[Any]=12,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]=30_72,     SCREAMING_SNAKE_CASE_						:					List[Any]="gelu",     SCREAMING_SNAKE_CASE_						:					Any=0.0,     SCREAMING_SNAKE_CASE_						:					Dict=0.0,     SCREAMING_SNAKE_CASE_						:					List[Any]=0.02,     SCREAMING_SNAKE_CASE_						:					List[Any]=1e-12,     SCREAMING_SNAKE_CASE_						:					int=2_24,     SCREAMING_SNAKE_CASE_						:					int=1,     SCREAMING_SNAKE_CASE_						:					Optional[Any]=3,     SCREAMING_SNAKE_CASE_						:					Any=[1, 10_24, 24, 24],     SCREAMING_SNAKE_CASE_						:					List[str]=True,     **SCREAMING_SNAKE_CASE_						:					List[str],     ):
										super().__init__(**SCREAMING_SNAKE_CASE_   )

										if backbone_config is None:
																logger.info('''`backbone_config` is `None`. Initializing the config with a `BiT` backbone.'''   )
																snake_case							:  int			=     {
																    '''global_padding''': '''same''',
																    '''layer_type''': '''bottleneck''',
																    '''depths''': [3, 4, 9],
																    '''out_features''': ['''stage3'''],
																    '''embedding_dynamic_padding''': True,
																}

										if isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   ):
																if "model_type" in backbone_config:
																						snake_case							:  Any			=     CONFIG_MAPPING[backbone_config['''model_type''']]
																else:
																						logger.info(
																						    '''`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class.'''   )
																						snake_case							:  Any			=     BitConfig
																snake_case							:  Optional[Any]			=     backbone_config_class(**SCREAMING_SNAKE_CASE_   )

										snake_case							:  str			=     backbone_featmap_shape
										snake_case							:  Union[str, Any]			=     backbone_config
										snake_case							:  List[str]			=     hidden_size
										snake_case							:  List[Any]			=     num_hidden_layers
										snake_case							:  Dict			=     num_attention_heads
										snake_case							:  List[str]			=     intermediate_size
										snake_case							:  Optional[int]			=     hidden_act
										snake_case							:  List[str]			=     hidden_dropout_prob
										snake_case							:  Tuple			=     attention_probs_dropout_prob
										snake_case							:  Optional[int]			=     initializer_range
										snake_case							:  Optional[int]			=     layer_norm_eps
										snake_case							:  List[Any]			=     image_size
										snake_case							:  Any			=     patch_size
										snake_case							:  str			=     num_channels
										snake_case							:  List[str]			=     qkv_bias




				def 						__snake_case				(				self						:					Any   ):
										snake_case							:  Tuple			=     copy.deepcopy(self.__dict__   )
										snake_case							:  List[Any]			=     self.backbone_config.to_dict()
										snake_case							:  Optional[Any]			=     self.__class__.model_type
										return output

