




'''simple docstring'''







import json
import os
import re
import unicodedata
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...utils import logging


_lowerCamelCase				:		List[Any]				      =    logging.get_logger(__name__)

_lowerCamelCase				:		str				      =    {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

_lowerCamelCase				:		Any				      =    {
    'vocab_file': {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/vocab.json'},
    'merges_file': {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/merges.txt'},
}

_lowerCamelCase				:		Union[str, Any]				      =    {
    'openai-gpt': 512,
}






def 						_lowercase						(      lowercase_    ):



		'''simple docstring'''







		_snake_case      			=			text.strip()
		if not text:
				return []
		_snake_case      			=			text.split()
		return tokens





class 	lowerCamelCase_				(			__A				):




					'''simple docstring'''






					def __init__(       self,    __UpperCamelCase=True,    __UpperCamelCase=None,    __UpperCamelCase=True,    __UpperCamelCase=None,    __UpperCamelCase=True,    ):
							if never_split is None:
									_snake_case      			=			[]
							_snake_case      			=			do_lower_case
							_snake_case      			=			set(__UpperCAmelCase					)
							_snake_case      			=			tokenize_chinese_chars
							_snake_case      			=			strip_accents
							_snake_case      			=			do_split_on_punc






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase,    __UpperCamelCase=None					):
							_snake_case      			=			self.never_split.union(set(__UpperCAmelCase					)					) if never_split else self.never_split
							_snake_case      			=			self._clean_text(__UpperCAmelCase					)

							# This was added on November 1st, 2018 for the multilingual and Chinese
							# models. This is also applied to the English models now, but it doesn't
							# matter since the English models were not trained on any Chinese data
							# and generally don't have any Chinese data in them (there are Chinese
							# characters in the vocabulary because Wikipedia does have some Chinese
							# words in the English Wikipedia.).
							if self.tokenize_chinese_chars:
									_snake_case      			=			self._tokenize_chinese_chars(__UpperCAmelCase					)
							# prevents treating the same character with different unicode codepoints as different characters
							_snake_case      			=			unicodedata.normalize("NFC",    __UpperCAmelCase					)
							_snake_case      			=			whitespace_tokenize(__UpperCAmelCase					)
							_snake_case      			=			[]
							for token in orig_tokens:
									if token not in never_split:
											if self.do_lower_case:
													_snake_case      			=			token.lower()
													if self.strip_accents is not False:
															_snake_case      			=			self._run_strip_accents(__UpperCAmelCase					)
											elif self.strip_accents:
													_snake_case      			=			self._run_strip_accents(__UpperCAmelCase					)
									split_tokens.extend(self._run_split_on_punc(__UpperCAmelCase,    __UpperCAmelCase					)					)

							_snake_case      			=			whitespace_tokenize(" ".join(__UpperCAmelCase					)					)
							return output_tokens






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase					):
							_snake_case      			=			unicodedata.normalize("NFD",    __UpperCAmelCase					)
							_snake_case      			=			[]
							for char in text:
									_snake_case      			=			unicodedata.category(__UpperCAmelCase					)
									if cat == "Mn":
											continue
									output.append(__UpperCAmelCase					)
							return "".join(__UpperCAmelCase					)






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase,    __UpperCamelCase=None					):
							if not self.do_split_on_punc or (never_split is not None and text in never_split):
									return [text]
							_snake_case      			=			list(__UpperCAmelCase					)
							_snake_case      			=			0
							_snake_case      			=			True
							_snake_case      			=			[]
							while i < len(__UpperCAmelCase					):
									_snake_case      			=			chars[i]
									if _is_punctuation(__UpperCAmelCase					):
											output.append([char]					)
											_snake_case      			=			True
									else:
											if start_new_word:
													output.append([]					)
											_snake_case      			=			False
											output[-1].append(__UpperCAmelCase					)
									i += 1

							return ["".join(__UpperCAmelCase					) for x in output]






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase					):
							_snake_case      			=			[]
							for char in text:
									_snake_case      			=			ord(__UpperCAmelCase					)
									if self._is_chinese_char(__UpperCAmelCase					):
											output.append(" "					)
											output.append(__UpperCAmelCase					)
											output.append(" "					)
									else:
											output.append(__UpperCAmelCase					)
							return "".join(__UpperCAmelCase					)






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase					):
							# This defines a "chinese character" as anything in the CJK Unicode block:
							#   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
							#
							# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
							# despite its name. The modern Korean Hangul alphabet is a different block,
							# as is Japanese Hiragana and Katakana. Those alphabets are used to write
							# space-separated words, so they are not treated specially and handled
							# like the all of the other languages.
							if (
							    (cp >= 0X4e_00 and cp <= 0X9f_ff)
							    or (cp >= 0X34_00 and cp <= 0X4d_bf)  #
							    or (cp >= 0X2_00_00 and cp <= 0X2_a6_df)  #
							    or (cp >= 0X2_a7_00 and cp <= 0X2_b7_3f)  #
							    or (cp >= 0X2_b7_40 and cp <= 0X2_b8_1f)  #
							    or (cp >= 0X2_b8_20 and cp <= 0X2_ce_af)  #
							    or (cp >= 0Xf9_00 and cp <= 0Xfa_ff)
							    or (cp >= 0X2_f8_00 and cp <= 0X2_fa_1f)  #
							):  #
									return True

							return False






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase					):
							_snake_case      			=			[]
							for char in text:
									_snake_case      			=			ord(__UpperCAmelCase					)
									if cp == 0 or cp == 0Xff_fd or _is_control(__UpperCAmelCase					):
											continue
									if _is_whitespace(__UpperCAmelCase					):
											output.append(" "					)
									else:
											output.append(__UpperCAmelCase					)
							return "".join(__UpperCAmelCase					)






def 						_lowercase						(      lowercase_    ):



		'''simple docstring'''







		_snake_case      			=			set()
		_snake_case      			=			word[0]
		for char in word[1:]:
				pairs.add((prev_char, char)    )
				_snake_case      			=			char
		return pairs






def 						_lowercase						(      lowercase_    ):



		'''simple docstring'''







		_snake_case      			=			text.replace("—"		,				"-"    )
		_snake_case      			=			text.replace("–"		,				"-"    )
		_snake_case      			=			text.replace("―"		,				"-"    )
		_snake_case      			=			text.replace("…"		,				"..."    )
		_snake_case      			=			text.replace("´"		,				"\'"    )
		_snake_case      			=			re.sub(r"(-+|~+|!+|\"+|;+|\?+|\++|,+|\)+|\(+|\\+|\/+|\*+|\[+|\]+|}+|{+|\|+|_+)"		,				r" \1 "		,				UpperCamelCase__    )
		_snake_case      			=			re.sub(r"\s*\n\s*"		,				" \n "		,				UpperCamelCase__    )
		_snake_case      			=			re.sub(r"[^\S\n]+"		,				" "		,				UpperCamelCase__    )
		return text.strip()





class 	lowerCamelCase_				(			__A				):




					'''simple docstring'''






					lowercase__       =					VOCAB_FILES_NAMES
					lowercase__       =					PRETRAINED_VOCAB_FILES_MAP
					lowercase__       =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowercase__       =					['input_ids', 'attention_mask']






					def __init__(       self,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase="<unk>",    **__UpperCamelCase					):
							super().__init__(unk_token=__UpperCAmelCase,    **__UpperCAmelCase					)

							try:
									import ftfy
									from spacy.lang.en import English

									_snake_case      			=			English()
									_snake_case      			=			_nlp.tokenizer
									_snake_case      			=			ftfy.fix_text
							except ImportError:
									logger.warning("ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy."					)
									_snake_case      			=			BasicTokenizer(do_lower_case=__UpperCAmelCase					)
									_snake_case      			=			None

							with open(__UpperCAmelCase,    encoding="utf-8"					) as vocab_handle:
									_snake_case      			=			json.load(__UpperCAmelCase					)
							_snake_case      			=			{v: k for k, v in self.encoder.items()}
							with open(__UpperCAmelCase,    encoding="utf-8"					) as merges_handle:
									_snake_case      			=			merges_handle.read().split("\n"					)[1:-1]
							_snake_case      			=			[tuple(merge.split()					) for merge in merges]
							_snake_case      			=			dict(zip(__UpperCAmelCase,    range(len(__UpperCAmelCase					)					)					)					)
							_snake_case      			=			{}






					@property
					def 							__SCREAMING_SNAKE_CASE      (       self					):
							return True






					@property
					def 							__SCREAMING_SNAKE_CASE      (       self					):
							return len(self.encoder					)






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							return dict(self.encoder,    **self.added_tokens_encoder					)






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase					):
							_snake_case      			=			tuple(token[:-1]					) + (token[-1] + "</w>",)
							if token in self.cache:
									return self.cache[token]
							_snake_case      			=			get_pairs(__UpperCAmelCase					)

							if not pairs:
									return token + "</w>"

							while True:
									_snake_case      			=			min(__UpperCAmelCase,    key=lambda __UpperCamelCase					: self.bpe_ranks.get(__UpperCAmelCase,    float("inf"					)					)					)
									if bigram not in self.bpe_ranks:
											break
									_snake_case			,       _snake_case      			=			bigram
									_snake_case      			=			[]
									_snake_case      			=			0
									while i < len(__UpperCAmelCase					):
											try:
													_snake_case      			=			word.index(__UpperCAmelCase,    __UpperCAmelCase					)
											except ValueError:
													new_word.extend(word[i:]					)
													break
											else:
													new_word.extend(word[i:j]					)
													_snake_case      			=			j

											if word[i] == first and i < len(__UpperCAmelCase					) - 1 and word[i + 1] == second:
													new_word.append(first + second					)
													i += 2
											else:
													new_word.append(word[i]					)
													i += 1
									_snake_case      			=			tuple(__UpperCAmelCase					)
									_snake_case      			=			new_word
									if len(__UpperCAmelCase					) == 1:
											break
									else:
											_snake_case      			=			get_pairs(__UpperCAmelCase					)
							_snake_case      			=			" ".join(__UpperCAmelCase					)
							if word == "\n  </w>":
									_snake_case      			=			"\n</w>"
							_snake_case      			=			word
							return word






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase					):
							_snake_case      			=			[]
							if self.fix_text is None:
									# Using BERT's BasicTokenizer
									_snake_case      			=			self.nlp.tokenize(__UpperCAmelCase					)
									for token in text:
											split_tokens.extend(list(self.bpe(__UpperCAmelCase					).split(" "					)					)					)
							else:
									# Using SpaCy & ftfy (original tokenization process of OpenAI GPT)
									_snake_case      			=			self.nlp(text_standardize(self.fix_text(__UpperCAmelCase					)					)					)
									for token in text:
											split_tokens.extend(list(self.bpe(token.text.lower()					).split(" "					)					)					)
							return split_tokens






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase					):
							return self.encoder.get(__UpperCAmelCase,    self.encoder.get(self.unk_token					)					)






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase					):
							return self.decoder.get(__UpperCAmelCase,    self.unk_token					)






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase					):
							_snake_case      			=			"".join(__UpperCAmelCase					).replace("</w>",    " "					).strip()
							return out_string






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase,    __UpperCamelCase = None					):
							if not os.path.isdir(__UpperCAmelCase					):
									logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''					)
									return
							_snake_case      			=			os.path.join(
							    __UpperCAmelCase,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]					)
							_snake_case      			=			os.path.join(
							    __UpperCAmelCase,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]					)

							with open(__UpperCAmelCase,    "w",    encoding="utf-8"					) as f:
									f.write(json.dumps(self.encoder,    indent=2,    sort_keys=__UpperCAmelCase,    ensure_ascii=__UpperCAmelCase					) + "\n"					)

							_snake_case      			=			0
							with open(__UpperCAmelCase,    "w",    encoding="utf-8"					) as writer:
									writer.write("#version: 0.2\n"					)
									for bpe_tokens, token_index in sorted(self.bpe_ranks.items(),    key=lambda __UpperCamelCase					: kv[1]					):
											if index != token_index:
													logger.warning(
													    f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
													    " Please check that the tokenizer is not corrupted!"					)
													_snake_case      			=			token_index
											writer.write(" ".join(__UpperCAmelCase					) + "\n"					)
											index += 1

							return vocab_file, merge_file






"""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)))





