
'''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)
__lowercase							:				str					=       1E-10
__lowercase							:				List[Any]					=       '''This is incredible! I love it, this is the best chicken I have ever had.'''
__lowercase							:				Optional[int]					=       100

class 		__lowercase			(					nn.Module      ):

			def __init__(self     ,				A     ,				A="gpt2-medium"     ,				A=False     ,				A="cpu" ):
								super().__init__()
								lowerCamelCase_   : Optional[Any]       =						GPTaTokenizer.from_pretrained(A )
								lowerCamelCase_   : Union[str, Any]       =						GPTaLMHeadModel.from_pretrained(A )
								lowerCamelCase_   : Tuple       =						self.encoder.transformer.config.hidden_size
								lowerCamelCase_   : Union[str, Any]       =						ClassificationHead(class_size=A     ,				embed_size=self.embed_size )
								lowerCamelCase_   : Optional[int]       =						cached_mode
								lowerCamelCase_   : Any       =						device

			def     UpperCAmelCase__	(self ):
								return self.classifier_head

			def     UpperCAmelCase__	(self ):
								for param in self.encoder.parameters():
													lowerCamelCase_   : List[str]       =						False
								self.classifier_head.train()

			def     UpperCAmelCase__	(self     ,				A ):
								lowerCamelCase_   : Any       =						x.ne(0 ).unsqueeze(2 ).repeat(1     ,				1     ,				self.embed_size ).float().to(self.device ).detach()
								lowerCamelCase_   : int       =						self.encoder.transformer(A )['''last_hidden_state''']
								lowerCamelCase_   : List[Any]       =						hidden * mask
								lowerCamelCase_   : Optional[int]       =						torch.sum(A     ,				dim=1 ) / (torch.sum(A     ,				dim=1 ).detach() + EPSILON)
								return avg_hidden






			def     UpperCAmelCase__	(self     ,				A ):
								if self.cached_mode:
													lowerCamelCase_   : Union[str, Any]       =						x.to(self.device )
								else:
													lowerCamelCase_   : Optional[int]       =						self.avg_representation(x.to(self.device ) )

								lowerCamelCase_   : Tuple       =						self.classifier_head(A )
								lowerCamelCase_   : Optional[Any]       =						nn.functional.log_softmax(A     ,				dim=-1 )

								return probs

class 		__lowercase			(					data.Dataset      ):

			def __init__(self     ,				A     ,				A ):
								lowerCamelCase_   : Optional[Any]       =						X
								lowerCamelCase_   : Optional[Any]       =						y

			def __len__(self ):
								return len(self.X )






			def __getitem__(self     ,				A ):
								lowerCamelCase_   : Optional[Any]       =						{}
								lowerCamelCase_   : Optional[int]       =						self.X[index]
								lowerCamelCase_   : Dict       =						self.y[index]
								return data




def lowercase_		(				_lowercase			)       ->							Union[str, Any]:
					'''simple docstring'''

					def pad_sequences(_lowercase			):
										lowerCamelCase_   : List[str]       =						[len(_lowercase			) for seq in sequences]

										lowerCamelCase_   : List[str]       =						torch.zeros(len(_lowercase			)     ,    max(_lowercase			)			).long()  # padding value = 0

										for i, seq in enumerate(_lowercase			):
															lowerCamelCase_   : List[str]       =						lengths[i]
															lowerCamelCase_   : Optional[Any]       =						seq[:end]

										return padded_sequences, lengths

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

					lowerCamelCase_, lowerCamelCase_   : Optional[int]       =						pad_sequences(item_info['''X''']			)
					lowerCamelCase_   : str       =						torch.tensor(item_info['''y''']     ,    dtype=torch.long			)

					return x_batch, y_batch




def lowercase_		(				_lowercase			)       ->							Optional[int]:
					'''simple docstring'''

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

					lowerCamelCase_   : Optional[Any]       =						torch.cat(item_info['''X''']     ,    0			)
					lowerCamelCase_   : Optional[Any]       =						torch.tensor(item_info['''y''']     ,    dtype=torch.long			)

					return x_batch, y_batch




def lowercase_		(				_lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase=0     ,    _lowercase=10     ,    _lowercase="cpu"			)       ->							Optional[int]:
					'''simple docstring'''

					lowerCamelCase_   : Optional[Any]       =						0
					discriminator.train_custom()
					for batch_idx, (input_t, target_t) in enumerate(_lowercase			):
										lowerCamelCase_, lowerCamelCase_   : List[str]       =						input_t.to(_lowercase			), target_t.to(_lowercase			)

										optimizer.zero_grad()

										lowerCamelCase_   : Tuple       =						discriminator(_lowercase			)
										lowerCamelCase_   : Union[str, Any]       =						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 lowercase_		(				_lowercase     ,    _lowercase     ,    _lowercase="cpu"			)       ->							Optional[Any]:
					'''simple docstring'''

					discriminator.eval()
					lowerCamelCase_   : List[Any]       =						0
					lowerCamelCase_   : List[str]       =						0
					with torch.no_grad():
										for input_t, target_t in data_loader:
															lowerCamelCase_, lowerCamelCase_   : int       =						input_t.to(_lowercase			), target_t.to(_lowercase			)
															lowerCamelCase_   : Any       =						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_   : Tuple       =						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			)     ,    1_00.0 * correct / len(data_loader.dataset			)			)			)




def lowercase_		(				_lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase=False     ,    _lowercase="cpu"			)       ->							Optional[Any]:
					'''simple docstring'''

					lowerCamelCase_   : Dict       =						model.tokenizer.encode(_lowercase			)
					lowerCamelCase_   : List[str]       =						torch.tensor([input_t]     ,    dtype=torch.long     ,    device=_lowercase			)
					if cached:
										lowerCamelCase_   : List[str]       =						model.avg_representation(_lowercase			)

					lowerCamelCase_   : Tuple       =						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 lowercase_		(				_lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase=False     ,    _lowercase="cpu"			)       ->							int:
					'''simple docstring'''

					lowerCamelCase_   : Tuple       =						torch.utils.data.DataLoader(dataset=_lowercase     ,    batch_size=_lowercase     ,    collate_fn=_lowercase			)

					lowerCamelCase_   : Optional[int]       =						[]
					lowerCamelCase_   : Optional[Any]       =						[]
					for batch_idx, (x, y) in enumerate(tqdm(_lowercase     ,    ascii=_lowercase			)			):
										with torch.no_grad():
															lowerCamelCase_   : Any       =						x.to(_lowercase			)
															lowerCamelCase_   : Any       =						discriminator.avg_representation(_lowercase			).cpu().detach()
															lowerCamelCase_   : Union[str, Any]       =						torch.unbind(avg_rep.unsqueeze(1			)			)
															xs += avg_rep_list
															ys += y.cpu().numpy().tolist()

					lowerCamelCase_   : int       =						torch.utils.data.DataLoader(
					    dataset=Dataset(_lowercase     ,    _lowercase			)     ,    batch_size=_lowercase     ,    shuffle=_lowercase     ,    collate_fn=_lowercase			)

					return data_loader




def lowercase_		(				_lowercase     ,    _lowercase=None     ,    _lowercase="gpt2-medium"     ,    _lowercase=10     ,    _lowercase=64     ,    _lowercase=10     ,    _lowercase=False     ,    _lowercase=False     ,    _lowercase=False     ,    )       ->							Dict:
					'''simple docstring'''

					lowerCamelCase_   : Any       =						'''cuda''' if torch.cuda.is_available() and not no_cuda else '''cpu'''

					print('''Preprocessing {} dataset...'''.format(_lowercase			)			)
					lowerCamelCase_   : Dict       =						time.time()

					if dataset == "SST":
										lowerCamelCase_   : Optional[int]       =						['''positive''', '''negative''', '''very positive''', '''very negative''', '''neutral''']
										lowerCamelCase_   : Any       =						{c: i for i, c in enumerate(_lowercase			)}

										lowerCamelCase_   : Union[str, Any]       =						Discriminator(
										    class_size=len(_lowercase			)     ,    pretrained_model=_lowercase     ,    cached_mode=_lowercase     ,    device=_lowercase			).to(_lowercase			)

										lowerCamelCase_   : str       =						torchtext_data.Field()
										lowerCamelCase_   : Tuple       =						torchtext_data.Field(sequential=_lowercase			)
										lowerCamelCase_, lowerCamelCase_, lowerCamelCase_   : Tuple       =						datasets.SST.splits(
										    _lowercase     ,    _lowercase     ,    fine_grained=_lowercase     ,    train_subtrees=_lowercase     ,    )

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

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

										lowerCamelCase_   : List[str]       =						{
										    '''class_size''': len(_lowercase			),
										    '''embed_size''': discriminator.embed_size,
										    '''pretrained_model''': pretrained_model,
										    '''class_vocab''': classaidx,
										    '''default_class''': 2,
										}

					elif dataset == "clickbait":
										lowerCamelCase_   : Optional[Any]       =						['''non_clickbait''', '''clickbait''']
										lowerCamelCase_   : int       =						{c: i for i, c in enumerate(_lowercase			)}

										lowerCamelCase_   : Dict       =						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_   : Optional[int]       =						[]
															for i, line in enumerate(_lowercase			):
																				try:
																									data.append(eval(_lowercase			)			)
																				except Exception:
																									print('''Error evaluating line {}: {}'''.format(_lowercase     ,    _lowercase			)			)
																									continue
										lowerCamelCase_   : Tuple       =						[]
										lowerCamelCase_   : Dict       =						[]
										with open('''datasets/clickbait/clickbait_train_prefix.txt'''			) as f:
															for i, line in enumerate(tqdm(_lowercase     ,    ascii=_lowercase			)			):
																				try:
																									lowerCamelCase_   : Optional[int]       =						eval(_lowercase			)
																									lowerCamelCase_   : Union[str, Any]       =						discriminator.tokenizer.encode(d['''text''']			)

																									if len(_lowercase			) < max_length_seq:
																														lowerCamelCase_   : Any       =						torch.tensor([50_256] + 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_   : List[Any]       =						Dataset(_lowercase     ,    _lowercase			)
										lowerCamelCase_   : Dict       =						int(0.9 * len(_lowercase			)			)
										lowerCamelCase_   : List[Any]       =						len(_lowercase			) - train_size
										lowerCamelCase_, lowerCamelCase_   : int       =						torch.utils.data.random_split(_lowercase     ,    [train_size, test_size]			)

										lowerCamelCase_   : Union[str, Any]       =						{
										    '''class_size''': len(_lowercase			),
										    '''embed_size''': discriminator.embed_size,
										    '''pretrained_model''': pretrained_model,
										    '''class_vocab''': classaidx,
										    '''default_class''': 1,
										}

					elif dataset == "toxic":
										lowerCamelCase_   : str       =						['''non_toxic''', '''toxic''']
										lowerCamelCase_   : int       =						{c: i for i, c in enumerate(_lowercase			)}

										lowerCamelCase_   : Dict       =						Discriminator(
										    class_size=len(_lowercase			)     ,    pretrained_model=_lowercase     ,    cached_mode=_lowercase     ,    device=_lowercase			).to(_lowercase			)

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

																									if len(_lowercase			) < max_length_seq:
																														lowerCamelCase_   : Union[str, Any]       =						torch.tensor([50_256] + 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_   : Optional[Any]       =						Dataset(_lowercase     ,    _lowercase			)
										lowerCamelCase_   : Tuple       =						int(0.9 * len(_lowercase			)			)
										lowerCamelCase_   : Optional[Any]       =						len(_lowercase			) - train_size
										lowerCamelCase_, lowerCamelCase_   : int       =						torch.utils.data.random_split(_lowercase     ,    [train_size, test_size]			)

										lowerCamelCase_   : Union[str, Any]       =						{
										    '''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_   : Optional[int]       =						set()
										with open(_lowercase			) as f:
															lowerCamelCase_   : int       =						csv.reader(_lowercase     ,    delimiter='''\t'''			)
															for row in tqdm(_lowercase     ,    ascii=_lowercase			):
																				if row:
																									classes.add(row[0]			)

										lowerCamelCase_   : Optional[int]       =						sorted(_lowercase			)
										lowerCamelCase_   : Tuple       =						{c: i for i, c in enumerate(_lowercase			)}

										lowerCamelCase_   : Tuple       =						Discriminator(
										    class_size=len(_lowercase			)     ,    pretrained_model=_lowercase     ,    cached_mode=_lowercase     ,    device=_lowercase			).to(_lowercase			)

										lowerCamelCase_   : str       =						[]
										lowerCamelCase_   : Union[str, Any]       =						[]
										with open(_lowercase			) as f:
															lowerCamelCase_   : List[Any]       =						csv.reader(_lowercase     ,    delimiter='''\t'''			)
															for i, row in enumerate(tqdm(_lowercase     ,    ascii=_lowercase			)			):
																				if row:
																									lowerCamelCase_   : Optional[int]       =						row[0]
																									lowerCamelCase_   : Dict       =						row[1]

																									try:
																														lowerCamelCase_   : Union[str, Any]       =						discriminator.tokenizer.encode(_lowercase			)
																														if len(_lowercase			) < max_length_seq:
																																			lowerCamelCase_   : List[Any]       =						torch.tensor([50_256] + 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_   : Any       =						Dataset(_lowercase     ,    _lowercase			)
										lowerCamelCase_   : List[str]       =						int(0.9 * len(_lowercase			)			)
										lowerCamelCase_   : Optional[int]       =						len(_lowercase			) - train_size
										lowerCamelCase_, lowerCamelCase_   : str       =						torch.utils.data.random_split(_lowercase     ,    [train_size, test_size]			)

										lowerCamelCase_   : Optional[int]       =						{
										    '''class_size''': len(_lowercase			),
										    '''embed_size''': discriminator.embed_size,
										    '''pretrained_model''': pretrained_model,
										    '''class_vocab''': classaidx,
										    '''default_class''': 0,
										}

					lowerCamelCase_   : Any       =						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_   : Optional[int]       =						time.time()

										lowerCamelCase_   : Tuple       =						get_cached_data_loader(_lowercase     ,    _lowercase     ,    _lowercase     ,    shuffle=_lowercase     ,    device=_lowercase			)

										lowerCamelCase_   : Optional[Any]       =						get_cached_data_loader(_lowercase     ,    _lowercase     ,    _lowercase     ,    device=_lowercase			)

										lowerCamelCase_   : Optional[int]       =						time.time()
										print('''Building representation cache took: {:.3f}s'''.format(end - start			)			)

					else:
										lowerCamelCase_   : Optional[Any]       =						torch.utils.data.DataLoader(
										    dataset=_lowercase     ,    batch_size=_lowercase     ,    shuffle=_lowercase     ,    collate_fn=_lowercase			)
										lowerCamelCase_   : Dict       =						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_   : Dict       =						optim.Adam(discriminator.parameters()     ,    lr=0.00_01			)

					for epoch in range(_lowercase			):
										lowerCamelCase_   : List[str]       =						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_   : int       =						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__":
					__lowercase							:				Tuple					=       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''')
					__lowercase							:				int					=       parser.parse_args()

					train_discriminator(**(vars(args)))




'''simple docstring'''


import argparse
import logging
import math
import time

import torch

from transformers import TransfoXLCorpus, TransfoXLLMHeadModel


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
__lowercase							:				Optional[Any]					=       logging.getLogger(__name__)




def lowercase_		(				)       ->							Union[str, Any]:
					'''simple docstring'''

					lowerCamelCase_   : Optional[int]       =						argparse.ArgumentParser(description='''PyTorch Transformer Language Model'''			)
					parser.add_argument('''--model_name'''     ,    type=_lowercase     ,    default='''transfo-xl-wt103'''     ,    help='''pretrained model name'''			)
					parser.add_argument(
					    '''--split'''     ,    type=_lowercase     ,    default='''test'''     ,    choices=['''all''', '''valid''', '''test''']     ,    help='''which split to evaluate'''			)
					parser.add_argument('''--batch_size'''     ,    type=_lowercase     ,    default=10     ,    help='''batch size'''			)
					parser.add_argument('''--tgt_len'''     ,    type=_lowercase     ,    default=128     ,    help='''number of tokens to predict'''			)
					parser.add_argument('''--ext_len'''     ,    type=_lowercase     ,    default=0     ,    help='''length of the extended context'''			)
					parser.add_argument('''--mem_len'''     ,    type=_lowercase     ,    default=1_600     ,    help='''length of the retained previous heads'''			)
					parser.add_argument('''--clamp_len'''     ,    type=_lowercase     ,    default=1_000     ,    help='''max positional embedding index'''			)
					parser.add_argument('''--no_cuda'''     ,    action='''store_true'''     ,    help='''Do not use CUDA even though CUA is available'''			)
					parser.add_argument('''--work_dir'''     ,    type=_lowercase     ,    required=_lowercase     ,    help='''path to the work_dir'''			)
					parser.add_argument('''--no_log'''     ,    action='''store_true'''     ,    help='''do not log the eval result'''			)
					parser.add_argument('''--same_length'''     ,    action='''store_true'''     ,    help='''set same length attention with masking'''			)
					parser.add_argument('''--server_ip'''     ,    type=_lowercase     ,    default=''''''     ,    help='''Can be used for distant debugging.'''			)
					parser.add_argument('''--server_port'''     ,    type=_lowercase     ,    default=''''''     ,    help='''Can be used for distant debugging.'''			)
					lowerCamelCase_   : Dict       =						parser.parse_args()
					assert args.ext_len >= 0, "extended context length must be non-negative"

					if args.server_ip and args.server_port:
										# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
										import ptvsd

										print('''Waiting for debugger attach'''			)
										ptvsd.enable_attach(address=(args.server_ip, args.server_port)     ,    redirect_output=_lowercase			)
										ptvsd.wait_for_attach()

					lowerCamelCase_   : List[str]       =						torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''			)
					logger.info('''device: {}'''.format(_lowercase			)			)

					# Load a pre-processed dataset
					# You can also build the corpus yourself using TransfoXLCorpus methods
					# The pre-processing involve computing word frequencies to prepare the Adaptive input and SoftMax
					# and tokenizing the dataset
					# The pre-processed corpus is a convertion (using the conversion script )
					lowerCamelCase_   : str       =						TransfoXLCorpus.from_pretrained(args.model_name			)

					lowerCamelCase_   : Optional[int]       =						corpus.get_iterator('''valid'''     ,    args.batch_size     ,    args.tgt_len     ,    device=_lowercase     ,    ext_len=args.ext_len			)
					lowerCamelCase_   : str       =						corpus.get_iterator('''test'''     ,    args.batch_size     ,    args.tgt_len     ,    device=_lowercase     ,    ext_len=args.ext_len			)

					# Load a pre-trained model
					lowerCamelCase_   : str       =						TransfoXLLMHeadModel.from_pretrained(args.model_name			)
					model.to(_lowercase			)

					logger.info(
					    '''Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}'''.format(
					        args.batch_size     ,    args.tgt_len     ,    args.ext_len     ,    args.mem_len     ,    args.clamp_len			)			)

					model.reset_memory_length(args.mem_len			)
					if args.clamp_len > 0:
										lowerCamelCase_   : Union[str, Any]       =						args.clamp_len
					if args.same_length:
										lowerCamelCase_   : int       =						True

					###############################################################################
					# Evaluation code
					###############################################################################
					def evaluate(_lowercase			):
										# Turn on evaluation mode which disables dropout.
										model.eval()
										lowerCamelCase_, lowerCamelCase_   : Union[str, Any]       =						0, 0.0
										lowerCamelCase_   : Optional[int]       =						time.time()
										with torch.no_grad():
															lowerCamelCase_   : Dict       =						None
															for idx, (data, target, seq_len) in enumerate(_lowercase			):
																				lowerCamelCase_   : str       =						model(_lowercase     ,    lm_labels=_lowercase     ,    mems=_lowercase			)
																				lowerCamelCase_, lowerCamelCase_, lowerCamelCase_   : int       =						ret
																				lowerCamelCase_   : List[str]       =						loss.mean()
																				total_loss += seq_len * loss.item()
																				total_len += seq_len
															lowerCamelCase_   : List[str]       =						time.time() - start_time
										logger.info('''Time : {:.2f}s, {:.2f}ms/segment'''.format(_lowercase     ,    1_000 * total_time / (idx + 1)			)			)
										return total_loss / total_len

					# Run on test data.
					if args.split == "all":
										lowerCamelCase_   : Tuple       =						evaluate(_lowercase			)
										lowerCamelCase_   : List[Any]       =						evaluate(_lowercase			)
					elif args.split == "valid":
										lowerCamelCase_   : Union[str, Any]       =						evaluate(_lowercase			)
										lowerCamelCase_   : str       =						None
					elif args.split == "test":
										lowerCamelCase_   : Any       =						evaluate(_lowercase			)
										lowerCamelCase_   : int       =						None

					def format_log(_lowercase     ,    _lowercase			):
										lowerCamelCase_   : Optional[Any]       =						'''| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '''.format(_lowercase     ,    _lowercase     ,    math.exp(_lowercase			)			)
										return log_str

					lowerCamelCase_   : List[Any]       =						''''''
					if valid_loss is not None:
										log_str += format_log(_lowercase     ,    '''valid'''			)
					if test_loss is not None:
										log_str += format_log(_lowercase     ,    '''test'''			)

					logger.info('''=''' * 100			)
					logger.info(_lowercase			)
					logger.info('''=''' * 100			)


if __name__ == "__main__":
					main()


