






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			:			Any			   =   1e-10
__snake_case			:			List[Any]			   =   """This is incredible! I love it, this is the best chicken I have ever had."""
__snake_case			:			List[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(a__      )
												lowerCAmelCase__			  =  GPTaLMHeadModel.from_pretrained(a__      )
												lowerCAmelCase__			  =  self.encoder.transformer.config.hidden_size
												lowerCAmelCase__			  =  ClassificationHead(class_size=a__   ,  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(a__      )['last_hidden_state']
												lowerCAmelCase__			  =  hidden * mask
												lowerCAmelCase__			  =  torch.sum(a__   ,  dim=1      ) / (torch.sum(a__   ,  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(a__      )
												lowerCAmelCase__			  =  nn.functional.log_softmax(a__   ,  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_				:    str						)     ->      Optional[Any]:

						"""simple docstring"""



						def pad_sequences(UpperCamelCase_				:    Optional[int]						):
												lowerCAmelCase__			  =  [len(__lowercase						) for seq in sequences]

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

												for i, seq in enumerate(__lowercase						):
																		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_				:    Union[str, Any]						)     ->      Optional[int]:

						"""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_				:    str			,				UpperCamelCase_				:    int			,				UpperCamelCase_				:    str			,				UpperCamelCase_				:    str=0			,				UpperCamelCase_				:    str=10			,				UpperCamelCase_				:    Tuple="cpu"						)     ->      Dict:

						"""simple docstring"""



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

												optimizer.zero_grad()

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

												samples_so_far += len(__lowercase						)

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







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

						"""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(__lowercase						), target_t.to(__lowercase						)
																		lowerCAmelCase__			  =  discriminator(__lowercase						)
																		# sum up batch loss
																		test_loss += nn.functional.nll_loss(__lowercase			,				__lowercase			,				reduction='sum'						).item()
																		# get the index of the max log-probability
																		lowerCAmelCase__			  =  output_t.argmax(dim=1			,				keepdim=__lowercase						)
																		correct += pred_t.eq(target_t.view_as(__lowercase						)						).sum().item()

						test_loss /= len(data_loader.dataset						)

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







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

						"""simple docstring"""



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

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







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

						"""simple docstring"""



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

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

						return data_loader







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

						"""simple docstring"""



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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

												lowerCAmelCase__			  =  time.time()

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

												lowerCAmelCase__			  =  get_cached_data_loader(__lowercase			,				__lowercase			,				__lowercase			,				device=__lowercase						)

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

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

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

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

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

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

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

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

												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(__lowercase			,				epoch + 1						)			,				)


if __name__ == "__main__":
					__snake_case			:			Union[str, Any]			   =   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			:			int			   =   parser.parse_args()

					train_discriminator(**(vars(args)))









"""simple docstring"""


import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
		import torch

		from transformers import CamembertModel
@require_torch
@require_sentencepiece
@require_tokenizers
class snake_case					(     unittest.TestCase):







		@slow
		def        a_       (    self					:				int						)				->  Tuple:





						'''simple docstring'''







						_A         =	CamembertModel.from_pretrained("camembert-base"						)
						model.to(a__						)

						_A         =	torch.tensor(
						    [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]]			,      device=a__			,      dtype=torch.long			,      )  # J'aime le camembert !
						with torch.no_grad():
										_A         =	model(a__						)["last_hidden_state"]
						_A         =	torch.Size((1, 10, 7_68)						)
						self.assertEqual(output.shape			,      a__						)
						# compare the actual values for a slice.
						_A         =	torch.tensor(
						    [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]			,      device=a__			,      dtype=torch.float			,      )
						# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
						# camembert.eval()
						# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

						self.assertTrue(torch.allclose(output[:, :3, :3]			,      a__			,      atol=1E-4						)						)