code: 
  
  
  """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)
  __SCREAMING_SNAKE_CASE					=						1E-1_0
  __SCREAMING_SNAKE_CASE					=						"""This is incredible! I love it, this is the best chicken I have ever had."""
  __SCREAMING_SNAKE_CASE					=						1_00
  
  
  
  
  
  class __snake_case		(					nn.Module      ):
  
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			def __init__(		self							:Union[str, Any]				,  UpperCamelCase__							:Dict				,  UpperCamelCase__							:Union[str, Any]="gpt2-medium"				,  UpperCamelCase__							:Dict=False				,  UpperCamelCase__							:Any="cpu"       ):
  										super().__init__()
  										_a          =				GPTaTokenizer.from_pretrained(UpperCamelCase__       )
  										_a          =				GPTaLMHeadModel.from_pretrained(UpperCamelCase__       )
  										_a          =				self.encoder.transformer.config.hidden_size
  										_a          =				ClassificationHead(class_size=UpperCamelCase__				,  embed_size=self.embed_size       )
  										_a          =				cached_mode
  										_a          =				device
  
  
  
  
  			def SCREAMING_SNAKE_CASE_      (		self							:str       ):
  										return self.classifier_head
  
  
  
  
  			def SCREAMING_SNAKE_CASE_      (		self							:Tuple       ):
  										for param in self.encoder.parameters():
  																	_a          =				False
  										self.classifier_head.train()
  
  
  
  
  			def SCREAMING_SNAKE_CASE_      (		self							:Tuple				,  UpperCamelCase__							:List[str]       ):
  										_a          =				x.ne(0       ).unsqueeze(2       ).repeat(1				,  1				,  self.embed_size       ).float().to(self.device       ).detach()
  										_a          =				self.encoder.transformer(UpperCamelCase__       )["last_hidden_state"]
  										_a          =				hidden * mask
  										_a          =				torch.sum(UpperCamelCase__				,  dim=1       ) / (torch.sum(UpperCamelCase__				,  dim=1       ).detach() + EPSILON)
  										return avg_hidden
  
  
  
  
  			def SCREAMING_SNAKE_CASE_      (		self							:Union[str, Any]				,  UpperCamelCase__							:str       ):
  										if self.cached_mode:
  																	_a          =				x.to(self.device       )
  										else:
  																	_a          =				self.avg_representation(x.to(self.device       )       )
  
  										_a          =				self.classifier_head(UpperCamelCase__       )
  										_a          =				nn.functional.log_softmax(UpperCamelCase__				,  dim=-1       )
  
  										return probs
  
  
  
  
  
  
  class __snake_case		(					data.Dataset      ):
  
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			def __init__(		self							:str				,  UpperCamelCase__							:Union[str, Any]				,  UpperCamelCase__							:Union[str, Any]       ):
  										_a          =				X
  										_a          =				y
  
  
  
  
  			def __len__(		self							:Tuple       ):
  										return len(self.X       )
  
  
  
  
  			def __getitem__(		self							:List[str]				,  UpperCamelCase__							:List[str]       ):
  										_a          =				{}
  										_a          =				self.X[index]
  										_a          =				self.y[index]
  										return data
  
  
  
  
  
  
  def 						__a      (  a					):
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							def pad_sequences(a					):
  														_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   , _a          =				pad_sequences(item_info["X"]					)
  							_a          =				torch.tensor(item_info["y"],		dtype=torch.long					)
  
  							return x_batch, y_batch
  
  
  
  def 						__a      (  a					):
  
  
  
  
  
  							"""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 						__a      (  a,		a,		a,		a=0,		a=1_0,		a="cpu"					):
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							_a          =				0
  							discriminator.train_custom()
  							for batch_idx, (input_t, target_t) in enumerate(a					):
  														_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 						__a      (  a,		a,		a="cpu"					):
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							discriminator.eval()
  							_a          =				0
  							_a          =				0
  							with torch.no_grad():
  														for input_t, target_t in data_loader:
  																					_a   , _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 						__a      (  a,		a,		a,		a=False,		a="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 						__a      (  a,		a,		a,		a=False,		a="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 						__a      (  a,		a=None,		a="gpt2-medium",		a=1_0,		a=6_4,		a=1_0,		a=False,		a=False,		a=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   , _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   , _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   , _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   , _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__":
  			__SCREAMING_SNAKE_CASE					=						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""")
  			__SCREAMING_SNAKE_CASE					=						parser.parse_args()
  
  			train_discriminator(**(vars(args)))
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  # Copyright 2022 The HuggingFace Team. All rights reserved.
  #
  # 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 os
  from typing import List
  
  
  
  def 						__a      (  a,		a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         if name != "training_function" and name != "main":
                raise ValueError(F'Incorrect function name passed: {name}, choose either \'main\' or \'training_function\''					)
         _a   , _a          =				[], False
         for line in lines:
                if not found_start and F'def {name}' in line:
                       _a          =				True
                       good_lines.append(a					)
                       continue
                if found_start:
                       if name == "training_function" and "def main" in line:
                              return good_lines
                       if name == "main" and "if __name__" in line:
                              return good_lines
                       good_lines.append(a					)
  
  
  
  def 						__a      (  a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         return [line for line in lines if not line.lstrip().startswith("#"					) and line != "\n"]
  
  
  
  def 						__a      (  a,		a,		a,		a = None					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         with open(a,		"r"					) as f:
                _a          =				f.readlines()
         with open(os.path.abspath(os.path.join("examples",		"nlp_example.py"					)					),		"r"					) as f:
                _a          =				f.readlines()
         with open(a,		"r"					) as f:
                _a          =				f.readlines()
         if secondary_filename is not None:
                with open(a,		"r"					) as f:
                       _a          =				f.readlines()
  
      # This is our base, we remove all the code from here in our `full_filename` and `feature_filename` to find the new content
         if parser_only:
                _a          =				clean_lines(get_function_contents_by_name(a,		"main"					)					)
                _a          =				clean_lines(get_function_contents_by_name(a,		"main"					)					)
                _a          =				clean_lines(get_function_contents_by_name(a,		"main"					)					)
                if secondary_filename is not None:
                       _a          =				clean_lines(get_function_contents_by_name(a,		"main"					)					)
         else:
                _a          =				clean_lines(get_function_contents_by_name(a,		"training_function"					)					)
                _a          =				clean_lines(get_function_contents_by_name(a,		"training_function"					)					)
                _a          =				clean_lines(get_function_contents_by_name(a,		"training_function"					)					)
                if secondary_filename is not None:
                       _a          =				clean_lines(
                           get_function_contents_by_name(a,		"training_function"					)					)
  
         _a          =				"train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size)\n"
  
         # Specific code in our script that differs from the full version, aka what is new
         _a          =				[]
         _a          =				[]  # We keep track of the idxs just in case it's a repeated statement
         _a          =				iter(a					)
         for i in range(len(a					) - 1					):
                if i not in passed_idxs:
                       _a          =				next(a					)
                       if (line not in full_file_func) and (line.lstrip() != _dl_line):
                              if "TESTING_MOCKED_DATALOADERS" not in line:
                                     new_feature_code.append(a					)
                                     passed_idxs.append(a					)
                              else:
                                     # Skip over the `config['num_epochs'] = 2` statement
                                     _a          =				next(a					)
  
      # Extract out just the new parts from the full_file_training_func
         _a          =				[]
         _a          =				[]  # We keep track of the idxs just in case it's a repeated statement
         for i, line in enumerate(a					):
                if i not in passed_idxs:
                       if (line not in full_file_func) and (line.lstrip() != _dl_line):
                              if "TESTING_MOCKED_DATALOADERS" not in line:
                                     new_full_example_parts.append(a					)
                                     passed_idxs.append(a					)
  
      # Finally, get the overall diff
         _a          =				[line for line in new_feature_code if line not in new_full_example_parts]
         if secondary_filename is not None:
                _a          =				[line for line in full_file_contents if line not in secondary_file_func]
                _a          =				[line for line in diff_from_example if line not in diff_from_two]
  
         return diff_from_example
  