






"""simple docstring"""



import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the checkpointing capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

_snake_case       :      List[Any]       =					16
_snake_case       :      str       =					32




def 						lowerCamelCase_				(  __A  :		Accelerator			,	__A  :		int = 16			):
					lowerCamelCase__				=AutoTokenizer.from_pretrained('bert-base-cased'			)
					lowerCamelCase__				=load_dataset('glue'			,	'mrpc'			)

					def tokenize_function(__A  :		Tuple			):
										# max_length=None => use the model max length (it's actually the default)
										lowerCamelCase__				=tokenizer(examples['sentence1']			,	examples['sentence2']			,	truncation=UpperCamelCase__			,	max_length=UpperCamelCase__			)
										return outputs

					# Apply the method we just defined to all the examples in all the splits of the dataset
					# starting with the main process first:
					with accelerator.main_process_first():
										lowerCamelCase__				=datasets.map(
										    UpperCamelCase__			,	batched=UpperCamelCase__			,	remove_columns=['idx', 'sentence1', 'sentence2']			,	)

					# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
					# transformers library
					lowerCamelCase__				=tokenized_datasets.rename_column('label'			,	'labels'			)

					def collate_fn(__A  :		List[str]			):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										lowerCamelCase__				=128 if accelerator.distributed_type == DistributedType.TPU else None
										# When using mixed precision we want round multiples of 8/16
										if accelerator.mixed_precision == "fp8":
															lowerCamelCase__				=16
										elif accelerator.mixed_precision != "no":
															lowerCamelCase__				=8
										else:
															lowerCamelCase__				=None

										return tokenizer.pad(
										    UpperCamelCase__			,	padding='longest'			,	max_length=UpperCamelCase__			,	pad_to_multiple_of=UpperCamelCase__			,	return_tensors='pt'			,	)

					# Instantiate dataloaders.
					lowerCamelCase__				=DataLoader(
					    tokenized_datasets['train']			,	shuffle=UpperCamelCase__			,	collate_fn=UpperCamelCase__			,	batch_size=UpperCamelCase__			)
					lowerCamelCase__				=DataLoader(
					    tokenized_datasets['validation']			,	shuffle=UpperCamelCase__			,	collate_fn=UpperCamelCase__			,	batch_size=UpperCamelCase__			)

					return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
			from accelerate.test_utils.training import mocked_dataloaders

			_snake_case       :      Optional[int]       =					mocked_dataloaders  # noqa: F811




def 						lowerCamelCase_				(  __A  :		Dict			,	__A  :		Dict			):
					# For testing only
					if os.environ.get('TESTING_MOCKED_DATALOADERS'			,	UpperCamelCase__			) == "1":
										lowerCamelCase__				=2
					# Initialize accelerator
					lowerCamelCase__				=Accelerator(cpu=args.cpu			,	mixed_precision=args.mixed_precision			)
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					lowerCamelCase__				=config["lr"]
					lowerCamelCase__				=int(config['num_epochs']			)
					lowerCamelCase__				=int(config['seed']			)
					lowerCamelCase__				=int(config['batch_size']			)

					# New Code #
					# Parse out whether we are saving every epoch or after a certain number of batches
					if hasattr(args.checkpointing_steps			,	'isdigit'			):
										if args.checkpointing_steps == "epoch":
															lowerCamelCase__				=args.checkpointing_steps
										elif args.checkpointing_steps.isdigit():
															lowerCamelCase__				=int(args.checkpointing_steps			)
										else:
															raise ValueError(
															    f"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."			)
					else:
										lowerCamelCase__				=None

					set_seed(UpperCamelCase__			)

					lowerCamelCase__				=get_dataloaders(UpperCamelCase__			,	UpperCamelCase__			)
					lowerCamelCase__				=evaluate.load('glue'			,	'mrpc'			)

					# If the batch size is too big we use gradient accumulation
					lowerCamelCase__				=1
					if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
										lowerCamelCase__				=batch_size // MAX_GPU_BATCH_SIZE
										lowerCamelCase__				=MAX_GPU_BATCH_SIZE

					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					lowerCamelCase__				=AutoModelForSequenceClassification.from_pretrained('bert-base-cased'			,	return_dict=UpperCamelCase__			)

					# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
					# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
					# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
					lowerCamelCase__				=model.to(accelerator.device			)

					# Instantiate optimizer
					lowerCamelCase__				=AdamW(params=model.parameters()			,	lr=UpperCamelCase__			)

					# Instantiate scheduler
					lowerCamelCase__				=get_linear_schedule_with_warmup(
					    optimizer=UpperCamelCase__			,	num_warmup_steps=100			,	num_training_steps=(len(UpperCamelCase__			) * num_epochs) // gradient_accumulation_steps			,	)

					# Prepare everything
					# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
					# prepare method.
					lowerCamelCase__				=accelerator.prepare(
					    UpperCamelCase__			,	UpperCamelCase__			,	UpperCamelCase__			,	UpperCamelCase__			,	UpperCamelCase__			)

					# New Code #
					# We need to keep track of how many total steps we have iterated over
					lowerCamelCase__				=0
					# We also need to keep track of the stating epoch so files are named properly
					lowerCamelCase__				=0

					# We need to load the checkpoint back in before training here with `load_state`
					# The total number of epochs is adjusted based on where the state is being loaded from,
					# as we assume continuation of the same training script
					if args.resume_from_checkpoint:
										if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
															accelerator.print(f"Resumed from checkpoint: {args.resume_from_checkpoint}"			)
															accelerator.load_state(args.resume_from_checkpoint			)
															lowerCamelCase__				=os.path.basename(args.resume_from_checkpoint			)
										else:
															# Get the most recent checkpoint
															lowerCamelCase__				=[f.name for f in os.scandir(os.getcwd()			) if f.is_dir()]
															dirs.sort(key=os.path.getctime			)
															lowerCamelCase__				=dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
										# Extract `epoch_{i}` or `step_{i}`
										lowerCamelCase__				=os.path.splitext(UpperCamelCase__			)[0]

										if "epoch" in training_difference:
															lowerCamelCase__				=int(training_difference.replace('epoch_'			,	''			)			) + 1
															lowerCamelCase__				=None
										else:
															lowerCamelCase__				=int(training_difference.replace('step_'			,	''			)			)
															lowerCamelCase__				=resume_step // len(UpperCamelCase__			)
															resume_step -= starting_epoch * len(UpperCamelCase__			)

    # Now we train the model
					for epoch in range(UpperCamelCase__			,	UpperCamelCase__			):
										model.train()
										# New Code #
										if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
															# We need to skip steps until we reach the resumed step
															lowerCamelCase__				=accelerator.skip_first_batches(UpperCamelCase__			,	UpperCamelCase__			)
															overall_step += resume_step
										else:
															# After the first iteration though, we need to go back to the original dataloader
															lowerCamelCase__				=train_dataloader
										for step, batch in enumerate(UpperCamelCase__			):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															batch.to(accelerator.device			)
															lowerCamelCase__				=model(**UpperCamelCase__			)
															lowerCamelCase__				=outputs.loss
															lowerCamelCase__				=loss / gradient_accumulation_steps
															accelerator.backward(UpperCamelCase__			)
															if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()
															# New Code #
															overall_step += 1

															# New Code #
															# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
															# These are saved to folders named `step_{overall_step}`
															# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
															# If mixed precision was used, will also save a "scalar.bin" file
															if isinstance(UpperCamelCase__			,	UpperCamelCase__			):
																				lowerCamelCase__				=f"step_{overall_step}"
																				if overall_step % checkpointing_steps == 0:
																									if args.output_dir is not None:
																														lowerCamelCase__				=os.path.join(args.output_dir			,	UpperCamelCase__			)
																									accelerator.save_state(UpperCamelCase__			)

										model.eval()
										for step, batch in enumerate(UpperCamelCase__			):
															# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
															batch.to(accelerator.device			)
															with torch.no_grad():
																				lowerCamelCase__				=model(**UpperCamelCase__			)
															lowerCamelCase__				=outputs.logits.argmax(dim=-1			)
															lowerCamelCase__				=accelerator.gather_for_metrics((predictions, batch['labels'])			)
															metric.add_batch(
															    predictions=UpperCamelCase__			,	references=UpperCamelCase__			,	)

										lowerCamelCase__				=metric.compute()
										# Use accelerator.print to print only on the main process.
										accelerator.print(f"epoch {epoch}:"			,	UpperCamelCase__			)

										# New Code #
										# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
										# These are saved to folders named `epoch_{epoch}`
										# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
										# If mixed precision was used, will also save a "scalar.bin" file
										if checkpointing_steps == "epoch":
															lowerCamelCase__				=f"epoch_{epoch}"
															if args.output_dir is not None:
																				lowerCamelCase__				=os.path.join(args.output_dir			,	UpperCamelCase__			)
															accelerator.save_state(UpperCamelCase__			)




def 						lowerCamelCase_				(  ):
					lowerCamelCase__				=argparse.ArgumentParser(description='Simple example of training script.'			)
					parser.add_argument(
					    '--mixed_precision'			,	type=UpperCamelCase__			,	default=UpperCamelCase__			,	choices=['no', 'fp16', 'bf16', 'fp8']			,	help='Whether to use mixed precision. Choose'
					    'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
					    'and an Nvidia Ampere GPU.'			,	)
					parser.add_argument('--cpu'			,	action='store_true'			,	help='If passed, will train on the CPU.'			)
					parser.add_argument(
					    '--checkpointing_steps'			,	type=UpperCamelCase__			,	default=UpperCamelCase__			,	help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'			,	)
					parser.add_argument(
					    '--output_dir'			,	type=UpperCamelCase__			,	default='.'			,	help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'			,	)
					parser.add_argument(
					    '--resume_from_checkpoint'			,	type=UpperCamelCase__			,	default=UpperCamelCase__			,	help='If the training should continue from a checkpoint folder.'			,	)
					lowerCamelCase__				=parser.parse_args()
					lowerCamelCase__				={"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
					training_function(UpperCamelCase__			,	UpperCamelCase__			)


if __name__ == "__main__":
			main()







"""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)
_snake_case       :      Union[str, Any]       =					1E-1_0
_snake_case       :      Union[str, Any]       =					"This is incredible! I love it, this is the best chicken I have ever had."
_snake_case       :      List[str]       =					100




class        __lowercase(       nn.Module  ):






						def __init__(					self    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE="gpt2-medium"    ,						_SCREAMING_SNAKE_CASE=False    ,						_SCREAMING_SNAKE_CASE="cpu")			->  Tuple:




											"""simple docstring"""





											super().__init__()
											lowerCamelCase__				=GPTaTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE)
											lowerCamelCase__				=GPTaLMHeadModel.from_pretrained(_SCREAMING_SNAKE_CASE)
											lowerCamelCase__				=self.encoder.transformer.config.hidden_size
											lowerCamelCase__				=ClassificationHead(class_size=_SCREAMING_SNAKE_CASE    ,						embed_size=self.embed_size)
											lowerCamelCase__				=cached_mode
											lowerCamelCase__				=device






						def 		lowerCAmelCase   (					self)			->  int:




											"""simple docstring"""





											return self.classifier_head






						def 		lowerCAmelCase   (					self)			->  Tuple:




											"""simple docstring"""





											for param in self.encoder.parameters():
																lowerCamelCase__				=False
											self.classifier_head.train()






						def 		lowerCAmelCase   (					self    ,						_SCREAMING_SNAKE_CASE)			->  Optional[Any]:




											"""simple docstring"""





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






						def 		lowerCAmelCase   (					self    ,						_SCREAMING_SNAKE_CASE)			->  Any:




											"""simple docstring"""





											if self.cached_mode:
																lowerCamelCase__				=x.to(self.device)
											else:
																lowerCamelCase__				=self.avg_representation(x.to(self.device))

											lowerCamelCase__				=self.classifier_head(_SCREAMING_SNAKE_CASE)
											lowerCamelCase__				=nn.functional.log_softmax(_SCREAMING_SNAKE_CASE    ,						dim=-1)

											return probs




class        __lowercase(       data.Dataset  ):






						def __init__(					self    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE)			->  Dict:




											"""simple docstring"""





											lowerCamelCase__				=X
											lowerCamelCase__				=y






						def __len__(					self)			->  Optional[int]:




											"""simple docstring"""





											return len(self.X)






						def __getitem__(					self    ,						_SCREAMING_SNAKE_CASE)			->  Optional[Any]:




											"""simple docstring"""





											lowerCamelCase__				={}
											lowerCamelCase__				=self.X[index]
											lowerCamelCase__				=self.y[index]
											return data




def 						lowerCamelCase_				(  __A  :		List[Any]			):
					def pad_sequences(__A  :		Any			):
										lowerCamelCase__				=[len(__A			) for seq in sequences]

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

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

										return padded_sequences, lengths

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

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

					return x_batch, y_batch




def 						lowerCamelCase_				(  __A  :		Optional[int]			):
					lowerCamelCase__				={}
					for key in data[0].keys():
										lowerCamelCase__				=[d[key] for d in data]

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

					return x_batch, y_batch




def 						lowerCamelCase_				(  __A  :		List[Any]			,	__A  :		int			,	__A  :		str			,	__A  :		Any=0			,	__A  :		List[Any]=10			,	__A  :		Optional[Any]="cpu"			):
					lowerCamelCase__				=0
					discriminator.train_custom()
					for batch_idx, (input_t, target_t) in enumerate(__A			):
										lowerCamelCase__				,     lowerCamelCase__				=input_t.to(__A			), target_t.to(__A			)

										optimizer.zero_grad()

										lowerCamelCase__				=discriminator(__A			)
										lowerCamelCase__				=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			)			,	100 * samples_so_far / len(data_loader.dataset			)			,	loss.item()			,	)			)




def 						lowerCamelCase_				(  __A  :		Dict			,	__A  :		int			,	__A  :		Any="cpu"			):
					discriminator.eval()
					lowerCamelCase__				=0
					lowerCamelCase__				=0
					with torch.no_grad():
										for input_t, target_t in data_loader:
															lowerCamelCase__				,     lowerCamelCase__				=input_t.to(__A			), target_t.to(__A			)
															lowerCamelCase__				=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
															lowerCamelCase__				=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			)			,	1_0_0.0 * correct / len(data_loader.dataset			)			)			)




def 						lowerCamelCase_				(  __A  :		Tuple			,	__A  :		Any			,	__A  :		int			,	__A  :		Optional[int]=False			,	__A  :		Any="cpu"			):
					lowerCamelCase__				=model.tokenizer.encode(__A			)
					lowerCamelCase__				=torch.tensor([input_t]			,	dtype=torch.long			,	device=__A			)
					if cached:
										lowerCamelCase__				=model.avg_representation(__A			)

					lowerCamelCase__				=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_				(  __A  :		List[str]			,	__A  :		str			,	__A  :		Dict			,	__A  :		List[Any]=False			,	__A  :		Any="cpu"			):
					lowerCamelCase__				=torch.utils.data.DataLoader(dataset=__A			,	batch_size=__A			,	collate_fn=__A			)

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

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

					return data_loader




def 						lowerCamelCase_				(  __A  :		Union[str, Any]			,	__A  :		Any=None			,	__A  :		Union[str, Any]="gpt2-medium"			,	__A  :		Optional[int]=10			,	__A  :		Optional[Any]=64			,	__A  :		int=10			,	__A  :		Tuple=False			,	__A  :		Optional[int]=False			,	__A  :		List[Any]=False			,	):
					lowerCamelCase__				='cuda' if torch.cuda.is_available() and not no_cuda else 'cpu'

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

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

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

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

										lowerCamelCase__				=[]
										lowerCamelCase__				=[]
										for i in trange(len(__A			)			,	ascii=__A			):
															lowerCamelCase__				=TreebankWordDetokenizer().detokenize(vars(train_data[i]			)['text']			)
															lowerCamelCase__				=discriminator.tokenizer.encode(__A			)
															lowerCamelCase__				=torch.tensor([5_0256] + seq			,	device=__A			,	dtype=torch.long			)
															x.append(__A			)
															y.append(classaidx[vars(train_data[i]			)['label']]			)
										lowerCamelCase__				=Dataset(__A			,	__A			)

										lowerCamelCase__				=[]
										lowerCamelCase__				=[]
										for i in trange(len(__A			)			,	ascii=__A			):
															lowerCamelCase__				=TreebankWordDetokenizer().detokenize(vars(test_data[i]			)['text']			)
															lowerCamelCase__				=discriminator.tokenizer.encode(__A			)
															lowerCamelCase__				=torch.tensor([5_0256] + seq			,	device=__A			,	dtype=torch.long			)
															test_x.append(__A			)
															test_y.append(classaidx[vars(test_data[i]			)['label']]			)
										lowerCamelCase__				=Dataset(__A			,	__A			)

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

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

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

																									if len(__A			) < max_length_seq:
																														lowerCamelCase__				=torch.tensor([5_0256] + 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

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

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

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

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

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

																									if len(__A			) < max_length_seq:
																														lowerCamelCase__				=torch.tensor([5_0256] + 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

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

										lowerCamelCase__				={
										    '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.'			)

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

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

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

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

																									try:
																														lowerCamelCase__				=discriminator.tokenizer.encode(__A			)
																														if len(__A			) < max_length_seq:
																																			lowerCamelCase__				=torch.tensor([5_0256] + 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

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

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

					lowerCamelCase__				=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...'			)

										lowerCamelCase__				=time.time()

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

										lowerCamelCase__				=get_cached_data_loader(__A			,	__A			,	__A			,	device=__A			)

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

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

					lowerCamelCase__				=optim.Adam(discriminator.parameters()			,	lr=0.0_0_0_1			)

					for epoch in range(__A			):
										lowerCamelCase__				=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			)

										lowerCamelCase__				=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__":
						_snake_case       :      Dict       =					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")
						_snake_case       :      Optional[Any]       =					parser.parse_args()

						train_discriminator(**(vars(args)))