






# 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     :     Tuple       =  1e-10
__snake_case     :     Optional[Any]       =  '''This is incredible! I love it, this is the best chicken I have ever had.'''
__snake_case     :     Optional[Any]       =  1_00


class      __SCREAMING_SNAKE_CASE       ( nn.Module):






						def __init__(						self  ,    _UpperCamelCase  ,    _UpperCamelCase="gpt2-medium"  ,    _UpperCamelCase=False  ,    _UpperCamelCase="cpu" ):






												"""simple docstring"""
												super().__init__()
												lowerCAmelCase__         =    GPTaTokenizer.from_pretrained(_lowercase )
												lowerCAmelCase__         =    GPTaLMHeadModel.from_pretrained(_lowercase )
												lowerCAmelCase__         =    self.encoder.transformer.config.hidden_size
												lowerCAmelCase__         =    ClassificationHead(class_size=_lowercase  ,    embed_size=self.embed_size )
												lowerCAmelCase__         =    cached_mode
												lowerCAmelCase__         =    device






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												return self.classifier_head






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""






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






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase ):






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







						def    UpperCamelCase__   (						self  ,    _UpperCamelCase ):






												"""simple docstring"""






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

												lowerCAmelCase__         =    self.classifier_head(_lowercase )
												lowerCAmelCase__         =    nn.functional.log_softmax(_lowercase  ,    dim=-1 )

												return probs




class      __SCREAMING_SNAKE_CASE       ( data.Dataset):






						def __init__(						self  ,    _UpperCamelCase  ,    _UpperCamelCase ):






												"""simple docstring"""
												lowerCAmelCase__         =    X
												lowerCAmelCase__         =    y






						def __len__(						self ):






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







						def __getitem__(						self  ,    _UpperCamelCase ):






												"""simple docstring"""
												lowerCAmelCase__         =    {}
												lowerCAmelCase__         =    self.X[index]
												lowerCAmelCase__         =    self.y[index]
												return data







def 						_UpperCamelCase (     UpperCamelCase_							:       Optional[int]		)	-> Dict:

						"""simple docstring"""



						def pad_sequences(UpperCamelCase_							:       List[Any]		):
												lowerCAmelCase__         =    [len(__UpperCamelCase		) for seq in sequences]

												lowerCAmelCase__         =    torch.zeros(len(__UpperCamelCase		)     ,							max(__UpperCamelCase		)		).long()  # padding value = 0

												for i, seq in enumerate(__UpperCamelCase		):
																		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 						_UpperCamelCase (     UpperCamelCase_							:       Optional[Any]		)	-> List[str]:

						"""simple docstring"""



						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 						_UpperCamelCase (     UpperCamelCase_							:       List[str]     ,							UpperCamelCase_							:       List[Any]     ,							UpperCamelCase_							:       Optional[Any]     ,							UpperCamelCase_							:       List[str]=0     ,							UpperCamelCase_							:       Union[str, Any]=10     ,							UpperCamelCase_							:       List[Any]="cpu"		)	-> str:

						"""simple docstring"""



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

												optimizer.zero_grad()

												lowerCAmelCase__         =    discriminator(__UpperCamelCase		)
												lowerCAmelCase__         =    nn.functional.nll_loss(__UpperCamelCase     ,							__UpperCamelCase		)
												loss.backward(retain_graph=__UpperCamelCase		)
												optimizer.step()

												samples_so_far += len(__UpperCamelCase		)

												if batch_idx % log_interval == 0:
																		print(
																		    'Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
																		        epoch + 1     ,							__UpperCamelCase     ,							len(data_loader.dataset		)     ,							100 * samples_so_far / len(data_loader.dataset		)     ,							loss.item()     ,							)		)







def 						_UpperCamelCase (     UpperCamelCase_							:       Dict     ,							UpperCamelCase_							:       str     ,							UpperCamelCase_							:       Union[str, Any]="cpu"		)	-> Optional[Any]:

						"""simple docstring"""



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

						test_loss /= len(data_loader.dataset		)

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







def 						_UpperCamelCase (     UpperCamelCase_							:       Dict     ,							UpperCamelCase_							:       str     ,							UpperCamelCase_							:       int     ,							UpperCamelCase_							:       Dict=False     ,							UpperCamelCase_							:       Union[str, Any]="cpu"		)	-> Any:

						"""simple docstring"""



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

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







def 						_UpperCamelCase (     UpperCamelCase_							:       Union[str, Any]     ,							UpperCamelCase_							:       str     ,							UpperCamelCase_							:       Dict     ,							UpperCamelCase_							:       Optional[Any]=False     ,							UpperCamelCase_							:       Any="cpu"		)	-> Dict:

						"""simple docstring"""



						lowerCAmelCase__         =    torch.utils.data.DataLoader(dataset=__UpperCamelCase     ,							batch_size=__UpperCamelCase     ,							collate_fn=__UpperCamelCase		)

						lowerCAmelCase__         =    []
						lowerCAmelCase__         =    []
						for batch_idx, (x, y) in enumerate(tqdm(__UpperCamelCase     ,							ascii=__UpperCamelCase		)		):
												with torch.no_grad():
																		lowerCAmelCase__         =    x.to(__UpperCamelCase		)
																		lowerCAmelCase__         =    discriminator.avg_representation(__UpperCamelCase		).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(__UpperCamelCase     ,							__UpperCamelCase		)     ,							batch_size=__UpperCamelCase     ,							shuffle=__UpperCamelCase     ,							collate_fn=__UpperCamelCase		)

						return data_loader







def 						_UpperCamelCase (     UpperCamelCase_							:       str     ,							UpperCamelCase_							:       Any=None     ,							UpperCamelCase_							:       str="gpt2-medium"     ,							UpperCamelCase_							:       List[Any]=10     ,							UpperCamelCase_							:       int=64     ,							UpperCamelCase_							:       Optional[Any]=10     ,							UpperCamelCase_							:       str=False     ,							UpperCamelCase_							:       Tuple=False     ,							UpperCamelCase_							:       Optional[int]=False     ,							)	-> int:

						"""simple docstring"""



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

						print('Preprocessing {} dataset...'.format(__UpperCamelCase		)		)
						lowerCAmelCase__         =    time.time()

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

												lowerCAmelCase__         =    Discriminator(
												    class_size=len(__UpperCamelCase		)     ,							pretrained_model=__UpperCamelCase     ,							cached_mode=__UpperCamelCase     ,							device=__UpperCamelCase		).to(__UpperCamelCase		)

												lowerCAmelCase__         =    torchtext_data.Field()
												lowerCAmelCase__         =    torchtext_data.Field(sequential=__UpperCamelCase		)
												lowerCAmelCase__    ,							lowerCAmelCase__    ,							lowerCAmelCase__         =    datasets.SST.splits(
												    __UpperCamelCase     ,							__UpperCamelCase     ,							fine_grained=__UpperCamelCase     ,							train_subtrees=__UpperCamelCase     ,							)

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

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

												lowerCAmelCase__         =    {
												    'class_size': len(__UpperCamelCase		),
												    '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(__UpperCamelCase		)}

												lowerCAmelCase__         =    Discriminator(
												    class_size=len(__UpperCamelCase		)     ,							pretrained_model=__UpperCamelCase     ,							cached_mode=__UpperCamelCase     ,							device=__UpperCamelCase		).to(__UpperCamelCase		)

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

																														if len(__UpperCamelCase		) < max_length_seq:
																																				lowerCAmelCase__         =    torch.tensor([5_0256] + seq     ,							device=__UpperCamelCase     ,							dtype=torch.long		)
																														else:
																																				print('Line {} is longer than maximum length {}'.format(__UpperCamelCase     ,							__UpperCamelCase		)		)
																																				continue
																														x.append(__UpperCamelCase		)
																														y.append(d['label']		)
																								except Exception:
																														print('Error evaluating / tokenizing line {}, skipping it'.format(__UpperCamelCase		)		)
																														pass

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

												lowerCAmelCase__         =    {
												    'class_size': len(__UpperCamelCase		),
												    '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(__UpperCamelCase		)}

												lowerCAmelCase__         =    Discriminator(
												    class_size=len(__UpperCamelCase		)     ,							pretrained_model=__UpperCamelCase     ,							cached_mode=__UpperCamelCase     ,							device=__UpperCamelCase		).to(__UpperCamelCase		)

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

																														if len(__UpperCamelCase		) < max_length_seq:
																																				lowerCAmelCase__         =    torch.tensor([5_0256] + seq     ,							device=__UpperCamelCase     ,							dtype=torch.long		)
																														else:
																																				print('Line {} is longer than maximum length {}'.format(__UpperCamelCase     ,							__UpperCamelCase		)		)
																																				continue
																														x.append(__UpperCamelCase		)
																														y.append(int(np.sum(d['label']		) > 0		)		)
																								except Exception:
																														print('Error evaluating / tokenizing line {}, skipping it'.format(__UpperCamelCase		)		)
																														pass

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

												lowerCAmelCase__         =    {
												    'class_size': len(__UpperCamelCase		),
												    '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(__UpperCamelCase		) as f:
																		lowerCAmelCase__         =    csv.reader(__UpperCamelCase     ,							delimiter='\t'		)
																		for row in tqdm(__UpperCamelCase     ,							ascii=__UpperCamelCase		):
																								if row:
																														classes.add(row[0]		)

												lowerCAmelCase__         =    sorted(__UpperCamelCase		)
												lowerCAmelCase__         =    {c: i for i, c in enumerate(__UpperCamelCase		)}

												lowerCAmelCase__         =    Discriminator(
												    class_size=len(__UpperCamelCase		)     ,							pretrained_model=__UpperCamelCase     ,							cached_mode=__UpperCamelCase     ,							device=__UpperCamelCase		).to(__UpperCamelCase		)

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

																														try:
																																				lowerCAmelCase__         =    discriminator.tokenizer.encode(__UpperCamelCase		)
																																				if len(__UpperCamelCase		) < max_length_seq:
																																										lowerCAmelCase__         =    torch.tensor([5_0256] + seq     ,							device=__UpperCamelCase     ,							dtype=torch.long		)

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

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

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

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

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

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

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

												lowerCAmelCase__         =    time.time()

												lowerCAmelCase__         =    get_cached_data_loader(__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							shuffle=__UpperCamelCase     ,							device=__UpperCamelCase		)

												lowerCAmelCase__         =    get_cached_data_loader(__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							device=__UpperCamelCase		)

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

						else:
												lowerCAmelCase__         =    torch.utils.data.DataLoader(
												    dataset=__UpperCamelCase     ,							batch_size=__UpperCamelCase     ,							shuffle=__UpperCamelCase     ,							collate_fn=__UpperCamelCase		)
												lowerCAmelCase__         =    torch.utils.data.DataLoader(dataset=__UpperCamelCase     ,							batch_size=__UpperCamelCase     ,							collate_fn=__UpperCamelCase		)

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

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

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

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

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

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

												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(__UpperCamelCase     ,							epoch + 1		)     ,							)


if __name__ == "__main__":
					__snake_case     :     int       =  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     :     str       =  parser.parse_args()

					train_discriminator(**(vars(args)))






# This test is meant to be run in on an instance with TPUs like this:
#
#   python examples/pytorch/xla_spawn.py --num_cores=8 tests/test_trainer_tpu.py
#
# Replace 8 with the number of TPU cores you have.
#

import sys
from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.utils import logging


__lowerCamelCase     :					List[str]				  =						logging.get_logger(__name__)


if is_torch_available():
   import torch
   from torch import nn
   from torch.utils.data import Dataset

   from transformers import Trainer







   class __snake_case							(		lowerCamelCase_						):




      def __init__(       self     :							Tuple			,      _lowercase     :							int = 1_01 ):



        """simple docstring"""






        SCREAMING_SNAKE_CASE__     				= length




      def __len__(       self     :							str ):



        """simple docstring"""






        return self.length




      def __getitem__(       self     :							Optional[Any]			,      _lowercase     :							Union[str, Any] ):



        """simple docstring"""






        return i







   class __snake_case							:




      def __call__(       self     :							Dict			,      _lowercase     :							Dict ):



        """simple docstring"""






        return {"input_ids": torch.tensor(_lowercase ), "labels": torch.tensor(_lowercase )}







   class __snake_case							(		nn.Module						):




      def __init__(       self     :							Tuple ):



        """simple docstring"""






        super().__init__()
        # Add some (unused) params otherwise DDP will complain.
        SCREAMING_SNAKE_CASE__     				= nn.Linear(1_20			,      80 )




      def       __a		(       self     :							Any			,      _lowercase     :							Union[str, Any]			,      _lowercase     :							Dict=None ):



        """simple docstring"""






        if labels is not None:
          return torch.tensor(0.0			,      device=input_ids.device ), input_ids
        else:
          return input_ids



def       __SCREAMING_SNAKE_CASE    (   )				->   Optional[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= HfArgumentParser((TrainingArguments,)      )
  sys.argv += ["--output_dir", "./examples"]
  SCREAMING_SNAKE_CASE__     				= parser.parse_args_into_dataclasses()[0]

  logger.warning(
      f"""Process rank: {training_args.local_rank}, device: {training_args.device}, """
      f"""tpu_num_cores: {training_args.tpu_num_cores}"""					,	)

  # Essentially, what we want to verify in the distributed case is
  # that we get all samples back, in the right order.
  # (this is crucial for prediction for instance)
  for dataset_length in [10_01, 2_56, 15]:
    SCREAMING_SNAKE_CASE__     				= DummyDataset(__UpperCamelCase      )

    def compute_metrics(__UpperCamelCase       :			EvalPrediction      ) -> Dict:
      SCREAMING_SNAKE_CASE__     				= list(range(len(__UpperCamelCase      )      )      )
      SCREAMING_SNAKE_CASE__     				= p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
      return {"success": success}

    SCREAMING_SNAKE_CASE__     				= Trainer(
        model=DummyModel()					,	args=__UpperCamelCase					,	data_collator=DummyDataCollator()					,	eval_dataset=__UpperCamelCase					,	compute_metrics=__UpperCamelCase					,	)
    SCREAMING_SNAKE_CASE__     				= trainer.evaluate()
    logger.info(__UpperCamelCase      )
    if metrics["eval_success"] is not True:
      logger.error(__UpperCamelCase      )
      exit(1      )

    SCREAMING_SNAKE_CASE__     				= trainer.predict(__UpperCamelCase      )
    logger.info(p.metrics      )
    if p.metrics["test_success"] is not True:
      logger.error(p.metrics      )
      exit(1      )

    SCREAMING_SNAKE_CASE__     				= 2

    SCREAMING_SNAKE_CASE__     				= trainer.evaluate()
    logger.info(__UpperCamelCase      )
    if metrics["eval_success"] is not True:
      logger.error(__UpperCamelCase      )
      exit(1      )

    SCREAMING_SNAKE_CASE__     				= trainer.predict(__UpperCamelCase      )
    logger.info(p.metrics      )
    if p.metrics["test_success"] is not True:
      logger.error(p.metrics      )
      exit(1      )

    SCREAMING_SNAKE_CASE__     				= None

  logger.info("""🔥 All distributed tests successful"""      )



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			Union[str, Any]      )				->   Dict:







  """simple docstring"""





  main()


if __name__ == "__main__":
   main()
