"""simple docstring"""




import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import MODEL_MAPPING, PoolFormerConfig, PoolFormerForImageClassification, PoolFormerModel
				from transformers.models.poolformer.modeling_poolformer import POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import PoolFormerImageProcessor
class      UpperCamelCase__(      __A			):





						def 					snake_case__						(							self  )       ->					int:
													A__						  =				self.config_class(**self.inputs_dict  )
													self.parent.assertTrue(hasattr(__UpperCAmelCase		,'hidden_sizes'  )  )
													self.parent.assertTrue(hasattr(__UpperCAmelCase		,'num_encoder_blocks'  )  )






class      UpperCamelCase__:





						def __init__(							self		,__UpperCAmelCase		,__UpperCAmelCase=13		,__UpperCAmelCase=64		,__UpperCAmelCase=3		,__UpperCAmelCase=4		,__UpperCAmelCase=[2, 2, 2, 2]		,__UpperCAmelCase=[8, 4, 2, 1]		,__UpperCAmelCase=[16, 32, 64, 1_28]		,__UpperCAmelCase=[1, 4, 8, 16]		,__UpperCAmelCase=False		,__UpperCAmelCase=True		,__UpperCAmelCase="gelu"		,__UpperCAmelCase=0.1		,__UpperCAmelCase=0.0_2		,__UpperCAmelCase=3		,__UpperCAmelCase=None		,)       ->					Tuple:
													A__						  =				parent
													A__						  =				batch_size
													A__						  =				image_size
													A__						  =				num_channels
													A__						  =				num_encoder_blocks
													A__						  =				sr_ratios
													A__						  =				depths
													A__						  =				hidden_sizes
													A__						  =				downsampling_rates
													A__						  =				is_training
													A__						  =				use_labels
													A__						  =				hidden_act
													A__						  =				hidden_dropout_prob
													A__						  =				initializer_range
													A__						  =				num_labels
													A__						  =				scope





						def 					snake_case__						(							self  )       ->					Tuple:
													A__						  =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

													A__						  =				None
													if self.use_labels:
																				A__						  =				ids_tensor([self.batch_size, self.image_size, self.image_size]		,self.num_labels  )

													A__						  =				PoolFormerConfig(
													    image_size=self.image_size		,num_channels=self.num_channels		,num_encoder_blocks=self.num_encoder_blocks		,depths=self.depths		,hidden_sizes=self.hidden_sizes		,hidden_act=self.hidden_act		,hidden_dropout_prob=self.hidden_dropout_prob		,initializer_range=self.initializer_range		,)

													return config, pixel_values, labels





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  )       ->					Union[str, Any]:
													A__						  =				PoolFormerModel(config=__UpperCAmelCase  )
													model.to(__UpperCAmelCase  )
													model.eval()
													A__						  =				model(__UpperCAmelCase  )
													A__						  =				A__						  =				self.image_size // 3_2.0
													self.parent.assertEqual(
													    result.last_hidden_state.shape		,(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)  )





						def 					snake_case__						(							self  )       ->					Any:
													A__						  =				self.prepare_config_and_inputs()
													A__  ,	A__  ,	A__						  =				config_and_inputs
													A__						  =				{'pixel_values': pixel_values}
													return config, inputs_dict






@require_torch
class      UpperCamelCase__(      __A		,    __A		,    unittest.TestCase			):
						lowerCAmelCase__	:    Optional[int]    				=		(PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
						lowerCAmelCase__	:    Optional[Any]    				=		(
						    {'feature-extraction': PoolFormerModel, 'image-classification': PoolFormerForImageClassification}
						    if is_torch_available()
						    else {}
						)

						lowerCAmelCase__	:    Any    				=		False
						lowerCAmelCase__	:    str    				=		False
						lowerCAmelCase__	:    Any    				=		False
						lowerCAmelCase__	:    str    				=		False
						lowerCAmelCase__	:    Optional[int]    				=		False





						def 					snake_case__						(							self  )       ->					Any:
													A__						  =				PoolFormerModelTester(self  )
													A__						  =				PoolFormerConfigTester(self		,config_class=__UpperCAmelCase  )





						def 					snake_case__						(							self  )       ->					int:
													self.config_tester.run_common_tests()





						def 					snake_case__						(							self  )       ->					Tuple:
													A__						  =				self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*__UpperCAmelCase  )





						@unittest.skip('PoolFormer does not use inputs_embeds'  )
						def 					snake_case__						(							self  )       ->					int:
													pass





						@unittest.skip('PoolFormer does not have get_input_embeddings method and get_output_embeddings methods'  )
						def 					snake_case__						(							self  )       ->					List[str]:
													pass





						def 					snake_case__						(							self  )       ->					List[Any]:
													def check_hidden_states_output(__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  ):
																				A__						  =				model_class(__UpperCAmelCase  )
																				model.to(__UpperCAmelCase  )
																				model.eval()

																				with torch.no_grad():
																											A__						  =				model(**self._prepare_for_class(__UpperCAmelCase		,__UpperCAmelCase  )  )

																				A__						  =				outputs.hidden_states

																				A__						  =				self.model_tester.num_encoder_blocks
																				self.assertEqual(len(__UpperCAmelCase  )		,__UpperCAmelCase  )

																				# verify the first hidden states (first block)
																				self.assertListEqual(
																				    list(hidden_states[0].shape[-3:]  )		,[
																				        self.model_tester.hidden_sizes[0],
																				        self.model_tester.image_size // 4,
																				        self.model_tester.image_size // 4,
																				    ]		,)

													A__  ,	A__						  =				self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				A__						  =				True
																				check_hidden_states_output(__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  )

																				# check that output_hidden_states also work using config
																				del inputs_dict["output_hidden_states"]
																				A__						  =				True

																				check_hidden_states_output(__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  )





						def 					snake_case__						(							self  )       ->					Union[str, Any]:
													if not self.model_tester.is_training:
																				return

													A__  ,	A__						  =				self.model_tester.prepare_config_and_inputs_for_common()
													A__						  =				True

													for model_class in self.all_model_classes:
																				if model_class in get_values(__UpperCAmelCase  ):
																											continue
																				A__						  =				model_class(__UpperCAmelCase  )
																				model.to(__UpperCAmelCase  )
																				model.train()
																				A__						  =				self._prepare_for_class(__UpperCAmelCase		,__UpperCAmelCase		,return_labels=__UpperCAmelCase  )
																				A__						  =				model(**__UpperCAmelCase  ).loss
																				loss.backward()





						def 					snake_case__						(							self  )       ->					Union[str, Any]:
													A__  ,	A__						  =				self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				A__						  =				model_class(__UpperCAmelCase  )
																				A__						  =				inspect.signature(model.forward  )
																				# signature.parameters is an OrderedDict => so arg_names order is deterministic
																				A__						  =				[*signature.parameters.keys()]

																				A__						  =				['pixel_values']
																				self.assertListEqual(arg_names[:1]		,__UpperCAmelCase  )





						@slow
						def 					snake_case__						(							self  )       ->					Union[str, Any]:
													for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				A__						  =				PoolFormerModel.from_pretrained(__UpperCAmelCase  )
																				self.assertIsNotNone(__UpperCAmelCase  )






def 	UpperCAmelCase       (						):






							"""simple docstring"""





							A__						  =				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
							return image


@require_torch
class      UpperCamelCase__(      unittest.TestCase			):





						@slow
						def 					snake_case__						(							self  )       ->					Union[str, Any]:
													A__						  =				PoolFormerImageProcessor()
													A__						  =				PoolFormerForImageClassification.from_pretrained('sail/poolformer_s12'  ).to(__UpperCAmelCase  )

													A__						  =				image_processor(images=prepare_img()		,return_tensors='pt'  ).to(__UpperCAmelCase  )

													# forward pass
													with torch.no_grad():
																				A__						  =				model(**__UpperCAmelCase  )

													# verify the logits
													A__						  =				torch.Size((1, 10_00)  )
													self.assertEqual(outputs.logits.shape		,__UpperCAmelCase  )

													A__						  =				torch.tensor([-0.6_1_1_3, 0.1_6_8_5, -0.0_4_9_2]  ).to(__UpperCAmelCase  )
													self.assertTrue(torch.allclose(outputs.logits[0, :3]		,__UpperCAmelCase		,atol=1e-4  )  )






"""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)
__lowerCamelCase									=	1E-10
__lowerCamelCase									=	"This is incredible! I love it, this is the best chicken I have ever had."
__lowerCamelCase									=	1_00
class      UpperCamelCase__(      nn.Module			):





						def __init__(							self		,__UpperCAmelCase		,__UpperCAmelCase="gpt2-medium"		,__UpperCAmelCase=False		,__UpperCAmelCase="cpu"  )       ->					Any:
													super().__init__()
													A__						  =				GPTaTokenizer.from_pretrained(__UpperCAmelCase  )
													A__						  =				GPTaLMHeadModel.from_pretrained(__UpperCAmelCase  )
													A__						  =				self.encoder.transformer.config.hidden_size
													A__						  =				ClassificationHead(class_size=__UpperCAmelCase		,embed_size=self.embed_size  )
													A__						  =				cached_mode
													A__						  =				device





						def 					snake_case__						(							self  )       ->					List[str]:
													return self.classifier_head





						def 					snake_case__						(							self  )       ->					Optional[int]:
													for param in self.encoder.parameters():
																				A__						  =				False
													self.classifier_head.train()





						def 					snake_case__						(							self		,__UpperCAmelCase  )       ->					List[Any]:
													A__						  =				x.ne(0  ).unsqueeze(2  ).repeat(1		,1		,self.embed_size  ).float().to(self.device  ).detach()
													A__						  =				self.encoder.transformer(__UpperCAmelCase  )['last_hidden_state']
													A__						  =				hidden * mask
													A__						  =				torch.sum(__UpperCAmelCase		,dim=1  ) / (torch.sum(__UpperCAmelCase		,dim=1  ).detach() + EPSILON)
													return avg_hidden





						def 					snake_case__						(							self		,__UpperCAmelCase  )       ->					Dict:
													if self.cached_mode:
																				A__						  =				x.to(self.device  )
													else:
																				A__						  =				self.avg_representation(x.to(self.device  )  )

													A__						  =				self.classifier_head(__UpperCAmelCase  )
													A__						  =				nn.functional.log_softmax(__UpperCAmelCase		,dim=-1  )

													return probs






class      UpperCamelCase__(      data.Dataset			):





						def __init__(							self		,__UpperCAmelCase		,__UpperCAmelCase  )       ->					Union[str, Any]:
													A__						  =				X
													A__						  =				y





						def __len__(							self  )       ->					List[str]:
													return len(self.X  )





						def __getitem__(							self		,__UpperCAmelCase  )       ->					Any:
													A__						  =				{}
													A__						  =				self.X[index]
													A__						  =				self.y[index]
													return data






def 	UpperCAmelCase       (						UpperCamelCase__					):






							"""simple docstring"""





							def pad_sequences(UpperCamelCase__					):
														A__						  =				[len(UpperCamelCase__					) for seq in sequences]

														A__						  =				torch.zeros(len(UpperCamelCase__					)							,     max(UpperCamelCase__					)					).long()  # padding value = 0

														for i, seq in enumerate(UpperCamelCase__					):
																					A__						  =				lengths[i]
																					A__						  =				seq[:end]

														return padded_sequences, lengths

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

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

							return x_batch, y_batch






def 	UpperCAmelCase       (						UpperCamelCase__					):






							"""simple docstring"""





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

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

							return x_batch, y_batch






def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__=0							,     UpperCamelCase__=10							,     UpperCamelCase__="cpu"					):






							"""simple docstring"""





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

														optimizer.zero_grad()

														A__						  =				discriminator(UpperCamelCase__					)
														A__						  =				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__							,     UpperCamelCase__							,     UpperCamelCase__="cpu"					):






							"""simple docstring"""





							discriminator.eval()
							A__						  =				0
							A__						  =				0
							with torch.no_grad():
														for input_t, target_t in data_loader:
																					A__  ,	A__						  =				input_t.to(UpperCamelCase__					), target_t.to(UpperCamelCase__					)
																					A__						  =				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
																					A__						  =				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					)							,     1_0_0.0 * correct / len(data_loader.dataset					)					)					)






def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__=False							,     UpperCamelCase__="cpu"					):






							"""simple docstring"""





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

							A__						  =				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__							,     UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__=False							,     UpperCamelCase__="cpu"					):






							"""simple docstring"""





							A__						  =				torch.utils.data.DataLoader(dataset=UpperCamelCase__							,     batch_size=UpperCamelCase__							,     collate_fn=UpperCamelCase__					)

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

							A__						  =				torch.utils.data.DataLoader(
							    dataset=Dataset(UpperCamelCase__							,     UpperCamelCase__					)							,     batch_size=UpperCamelCase__							,     shuffle=UpperCamelCase__							,     collate_fn=UpperCamelCase__					)

							return data_loader






def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__=None							,     UpperCamelCase__="gpt2-medium"							,     UpperCamelCase__=10							,     UpperCamelCase__=64							,     UpperCamelCase__=10							,     UpperCamelCase__=False							,     UpperCamelCase__=False							,     UpperCamelCase__=False							,     ):






							"""simple docstring"""





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

							print('Preprocessing {} dataset...'.format(UpperCamelCase__					)					)
							A__						  =				time.time()

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

														A__						  =				Discriminator(
														    class_size=len(UpperCamelCase__					)							,     pretrained_model=UpperCamelCase__							,     cached_mode=UpperCamelCase__							,     device=UpperCamelCase__					).to(UpperCamelCase__					)

														A__						  =				torchtext_data.Field()
														A__						  =				torchtext_data.Field(sequential=UpperCamelCase__					)
														A__  ,	A__  ,	A__						  =				datasets.SST.splits(
														    UpperCamelCase__							,     UpperCamelCase__							,     fine_grained=UpperCamelCase__							,     train_subtrees=UpperCamelCase__							,     )

														A__						  =				[]
														A__						  =				[]
														for i in trange(len(UpperCamelCase__					)							,     ascii=UpperCamelCase__					):
																					A__						  =				TreebankWordDetokenizer().detokenize(vars(train_data[i]					)['text']					)
																					A__						  =				discriminator.tokenizer.encode(UpperCamelCase__					)
																					A__						  =				torch.tensor([50_256] + seq							,     device=UpperCamelCase__							,     dtype=torch.long					)
																					x.append(UpperCamelCase__					)
																					y.append(classaidx[vars(train_data[i]					)['label']]					)
														A__						  =				Dataset(UpperCamelCase__							,     UpperCamelCase__					)

														A__						  =				[]
														A__						  =				[]
														for i in trange(len(UpperCamelCase__					)							,     ascii=UpperCamelCase__					):
																					A__						  =				TreebankWordDetokenizer().detokenize(vars(test_data[i]					)['text']					)
																					A__						  =				discriminator.tokenizer.encode(UpperCamelCase__					)
																					A__						  =				torch.tensor([50_256] + seq							,     device=UpperCamelCase__							,     dtype=torch.long					)
																					test_x.append(UpperCamelCase__					)
																					test_y.append(classaidx[vars(test_data[i]					)['label']]					)
														A__						  =				Dataset(UpperCamelCase__							,     UpperCamelCase__					)

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

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

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

																																			if len(UpperCamelCase__					) < max_length_seq:
																																										A__						  =				torch.tensor([50_256] + 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

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

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

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

														A__						  =				Discriminator(
														    class_size=len(UpperCamelCase__					)							,     pretrained_model=UpperCamelCase__							,     cached_mode=UpperCamelCase__							,     device=UpperCamelCase__					).to(UpperCamelCase__					)

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

																																			if len(UpperCamelCase__					) < max_length_seq:
																																										A__						  =				torch.tensor([50_256] + 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

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

														A__						  =				{
														    '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.'					)

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

														A__						  =				sorted(UpperCamelCase__					)
														A__						  =				{c: i for i, c in enumerate(UpperCamelCase__					)}

														A__						  =				Discriminator(
														    class_size=len(UpperCamelCase__					)							,     pretrained_model=UpperCamelCase__							,     cached_mode=UpperCamelCase__							,     device=UpperCamelCase__					).to(UpperCamelCase__					)

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

																																			try:
																																										A__						  =				discriminator.tokenizer.encode(UpperCamelCase__					)
																																										if len(UpperCamelCase__					) < max_length_seq:
																																																	A__						  =				torch.tensor([50_256] + 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

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

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

							A__						  =				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...'					)

														A__						  =				time.time()

														A__						  =				get_cached_data_loader(UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__							,     shuffle=UpperCamelCase__							,     device=UpperCamelCase__					)

														A__						  =				get_cached_data_loader(UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__							,     device=UpperCamelCase__					)

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

							else:
														A__						  =				torch.utils.data.DataLoader(
														    dataset=UpperCamelCase__							,     batch_size=UpperCamelCase__							,     shuffle=UpperCamelCase__							,     collate_fn=UpperCamelCase__					)
														A__						  =				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__					)

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

							for epoch in range(UpperCamelCase__					):
														A__						  =				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__					)

														A__						  =				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__":
				__lowerCamelCase									=	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")
				__lowerCamelCase									=	parser.parse_args()

				train_discriminator(**(vars(args)))





