

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





class 				lowerCamelCase  (nn.Module     ):

			def __init__(   self			:  Dict      ,			__UpperCAmelCase			:  Optional[int]      ,			__UpperCAmelCase			:  Dict="gpt2-medium"      ,			__UpperCAmelCase			:  Tuple=False      ,			__UpperCAmelCase			:  Tuple="cpu"    )				->      List[Any]:
						super().__init__()
						SCREAMING_SNAKE_CASE__									= GPTaTokenizer.from_pretrained(__UpperCAmelCase    )
						SCREAMING_SNAKE_CASE__									= GPTaLMHeadModel.from_pretrained(__UpperCAmelCase    )
						SCREAMING_SNAKE_CASE__									= self.encoder.transformer.config.hidden_size
						SCREAMING_SNAKE_CASE__									= ClassificationHead(class_size=__UpperCAmelCase      ,			embed_size=self.embed_size    )
						SCREAMING_SNAKE_CASE__									= cached_mode
						SCREAMING_SNAKE_CASE__									= device

			def    SCREAMING_SNAKE_CASE			(   self			:  Dict    )				->      Dict:
						return self.classifier_head

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

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





			def    SCREAMING_SNAKE_CASE			(   self			:  Tuple      ,			__UpperCAmelCase			:  Union[str, Any]    )				->      Dict:
						if self.cached_mode:
									SCREAMING_SNAKE_CASE__									= x.to(self.device    )
						else:
									SCREAMING_SNAKE_CASE__									= self.avg_representation(x.to(self.device    )    )

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

						return probs





class 				lowerCamelCase  (data.Dataset     ):

			def __init__(   self			:  Tuple      ,			__UpperCAmelCase			:  Optional[Any]      ,			__UpperCAmelCase			:  int    )				->      Optional[Any]:
						SCREAMING_SNAKE_CASE__									= X
						SCREAMING_SNAKE_CASE__									= y

			def __len__(   self			:  Optional[int]    )				->      Tuple:
						return len(self.X    )





			def __getitem__(   self			:  Optional[int]      ,			__UpperCAmelCase			:  Union[str, Any]    )				->      Optional[int]:
						SCREAMING_SNAKE_CASE__									= {}
						SCREAMING_SNAKE_CASE__									= self.X[index]
						SCREAMING_SNAKE_CASE__									= self.y[index]
						return data

def 		A    (       snake_case__      ):




			'''simple docstring'''


			def pad_sequences(snake_case__      ):
						SCREAMING_SNAKE_CASE__									= [len(snake_case__      ) for seq in sequences]

						SCREAMING_SNAKE_CASE__									= torch.zeros(len(snake_case__      )						,			max(snake_case__      )      ).long()  # padding value = 0

						for i, seq in enumerate(snake_case__      ):
									SCREAMING_SNAKE_CASE__									= lengths[i]
									SCREAMING_SNAKE_CASE__									= seq[:end]

						return padded_sequences, lengths

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

			SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= pad_sequences(item_info["""X"""]      )
			SCREAMING_SNAKE_CASE__									= torch.tensor(item_info["""y"""]						,			dtype=torch.long      )

			return x_batch, y_batch

def 		A    (       snake_case__      ):




			'''simple docstring'''


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

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

			return x_batch, y_batch

def 		A    (       snake_case__						,			snake_case__						,			snake_case__						,			snake_case__=0						,			snake_case__=10						,			snake_case__="cpu"      ):




			'''simple docstring'''


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

						optimizer.zero_grad()

						SCREAMING_SNAKE_CASE__									= discriminator(snake_case__      )
						SCREAMING_SNAKE_CASE__									= nn.functional.nll_loss(snake_case__						,			snake_case__      )
						loss.backward(retain_graph=snake_case__      )
						optimizer.step()

						samples_so_far += len(snake_case__      )

						if batch_idx % log_interval == 0:
									print(
									    """Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}""".format(
									        epoch + 1						,			snake_case__						,			len(data_loader.dataset      )						,			1_00 * samples_so_far / len(data_loader.dataset      )						,			loss.item()						,			)      )

def 		A    (       snake_case__						,			snake_case__						,			snake_case__="cpu"      ):




			'''simple docstring'''


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

			test_loss /= len(data_loader.dataset      )

			print(
			    """Performance on test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)""".format(
			        snake_case__						,			snake_case__						,			len(data_loader.dataset      )						,			1_00.0 * correct / len(data_loader.dataset      )      )      )

def 		A    (       snake_case__						,			snake_case__						,			snake_case__						,			snake_case__=False						,			snake_case__="cpu"      ):




			'''simple docstring'''


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

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

def 		A    (       snake_case__						,			snake_case__						,			snake_case__						,			snake_case__=False						,			snake_case__="cpu"      ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__									= torch.utils.data.DataLoader(dataset=snake_case__						,			batch_size=snake_case__						,			collate_fn=snake_case__      )

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

			SCREAMING_SNAKE_CASE__									= torch.utils.data.DataLoader(
			    dataset=Dataset(snake_case__						,			snake_case__      )						,			batch_size=snake_case__						,			shuffle=snake_case__						,			collate_fn=snake_case__      )

			return data_loader

def 		A    (       snake_case__						,			snake_case__=None						,			snake_case__="gpt2-medium"						,			snake_case__=10						,			snake_case__=64						,			snake_case__=10						,			snake_case__=False						,			snake_case__=False						,			snake_case__=False						,			):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__									= """cuda""" if torch.cuda.is_available() and not no_cuda else """cpu"""

			print("""Preprocessing {} dataset...""".format(snake_case__      )      )
			SCREAMING_SNAKE_CASE__									= time.time()

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

						SCREAMING_SNAKE_CASE__									= Discriminator(
						    class_size=len(snake_case__      )						,			pretrained_model=snake_case__						,			cached_mode=snake_case__						,			device=snake_case__      ).to(snake_case__      )

						SCREAMING_SNAKE_CASE__									= torchtext_data.Field()
						SCREAMING_SNAKE_CASE__									= torchtext_data.Field(sequential=snake_case__      )
						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= datasets.SST.splits(
						    snake_case__						,			snake_case__						,			fine_grained=snake_case__						,			train_subtrees=snake_case__						,			)

						SCREAMING_SNAKE_CASE__									= []
						SCREAMING_SNAKE_CASE__									= []
						for i in trange(len(snake_case__      )						,			ascii=snake_case__      ):
									SCREAMING_SNAKE_CASE__									= TreebankWordDetokenizer().detokenize(vars(train_data[i]      )["""text"""]      )
									SCREAMING_SNAKE_CASE__									= discriminator.tokenizer.encode(snake_case__      )
									SCREAMING_SNAKE_CASE__									= torch.tensor([5_02_56] + seq						,			device=snake_case__						,			dtype=torch.long      )
									x.append(snake_case__      )
									y.append(classaidx[vars(train_data[i]      )["""label"""]]      )
						SCREAMING_SNAKE_CASE__									= Dataset(snake_case__						,			snake_case__      )

						SCREAMING_SNAKE_CASE__									= []
						SCREAMING_SNAKE_CASE__									= []
						for i in trange(len(snake_case__      )						,			ascii=snake_case__      ):
									SCREAMING_SNAKE_CASE__									= TreebankWordDetokenizer().detokenize(vars(test_data[i]      )["""text"""]      )
									SCREAMING_SNAKE_CASE__									= discriminator.tokenizer.encode(snake_case__      )
									SCREAMING_SNAKE_CASE__									= torch.tensor([5_02_56] + seq						,			device=snake_case__						,			dtype=torch.long      )
									test_x.append(snake_case__      )
									test_y.append(classaidx[vars(test_data[i]      )["""label"""]]      )
						SCREAMING_SNAKE_CASE__									= Dataset(snake_case__						,			snake_case__      )

						SCREAMING_SNAKE_CASE__									= {
						    """class_size""": len(snake_case__      ),
						    """embed_size""": discriminator.embed_size,
						    """pretrained_model""": pretrained_model,
						    """class_vocab""": classaidx,
						    """default_class""": 2,
						}

			elif dataset == "clickbait":
						SCREAMING_SNAKE_CASE__									= ["""non_clickbait""", """clickbait"""]
						SCREAMING_SNAKE_CASE__									= {c: i for i, c in enumerate(snake_case__      )}

						SCREAMING_SNAKE_CASE__									= Discriminator(
						    class_size=len(snake_case__      )						,			pretrained_model=snake_case__						,			cached_mode=snake_case__						,			device=snake_case__      ).to(snake_case__      )

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

															if len(snake_case__      ) < max_length_seq:
																		SCREAMING_SNAKE_CASE__									= torch.tensor([5_02_56] + seq						,			device=snake_case__						,			dtype=torch.long      )
															else:
																		print("""Line {} is longer than maximum length {}""".format(snake_case__						,			snake_case__      )      )
																		continue
															x.append(snake_case__      )
															y.append(d["""label"""]      )
												except Exception:
															print("""Error evaluating / tokenizing line {}, skipping it""".format(snake_case__      )      )
															pass

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

						SCREAMING_SNAKE_CASE__									= {
						    """class_size""": len(snake_case__      ),
						    """embed_size""": discriminator.embed_size,
						    """pretrained_model""": pretrained_model,
						    """class_vocab""": classaidx,
						    """default_class""": 1,
						}

			elif dataset == "toxic":
						SCREAMING_SNAKE_CASE__									= ["""non_toxic""", """toxic"""]
						SCREAMING_SNAKE_CASE__									= {c: i for i, c in enumerate(snake_case__      )}

						SCREAMING_SNAKE_CASE__									= Discriminator(
						    class_size=len(snake_case__      )						,			pretrained_model=snake_case__						,			cached_mode=snake_case__						,			device=snake_case__      ).to(snake_case__      )

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

															if len(snake_case__      ) < max_length_seq:
																		SCREAMING_SNAKE_CASE__									= torch.tensor([5_02_56] + seq						,			device=snake_case__						,			dtype=torch.long      )
															else:
																		print("""Line {} is longer than maximum length {}""".format(snake_case__						,			snake_case__      )      )
																		continue
															x.append(snake_case__      )
															y.append(int(np.sum(d["""label"""]      ) > 0      )      )
												except Exception:
															print("""Error evaluating / tokenizing line {}, skipping it""".format(snake_case__      )      )
															pass

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

						SCREAMING_SNAKE_CASE__									= {
						    """class_size""": len(snake_case__      ),
						    """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."""      )

						SCREAMING_SNAKE_CASE__									= set()
						with open(snake_case__      ) as f:
									SCREAMING_SNAKE_CASE__									= csv.reader(snake_case__						,			delimiter="""\t"""      )
									for row in tqdm(snake_case__						,			ascii=snake_case__      ):
												if row:
															classes.add(row[0]      )

						SCREAMING_SNAKE_CASE__									= sorted(snake_case__      )
						SCREAMING_SNAKE_CASE__									= {c: i for i, c in enumerate(snake_case__      )}

						SCREAMING_SNAKE_CASE__									= Discriminator(
						    class_size=len(snake_case__      )						,			pretrained_model=snake_case__						,			cached_mode=snake_case__						,			device=snake_case__      ).to(snake_case__      )

						SCREAMING_SNAKE_CASE__									= []
						SCREAMING_SNAKE_CASE__									= []
						with open(snake_case__      ) as f:
									SCREAMING_SNAKE_CASE__									= csv.reader(snake_case__						,			delimiter="""\t"""      )
									for i, row in enumerate(tqdm(snake_case__						,			ascii=snake_case__      )      ):
												if row:
															SCREAMING_SNAKE_CASE__									= row[0]
															SCREAMING_SNAKE_CASE__									= row[1]

															try:
																		SCREAMING_SNAKE_CASE__									= discriminator.tokenizer.encode(snake_case__      )
																		if len(snake_case__      ) < max_length_seq:
																					SCREAMING_SNAKE_CASE__									= torch.tensor([5_02_56] + seq						,			device=snake_case__						,			dtype=torch.long      )

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

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

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

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

						SCREAMING_SNAKE_CASE__									= {
						    """class_size""": len(snake_case__      ),
						    """embed_size""": discriminator.embed_size,
						    """pretrained_model""": pretrained_model,
						    """class_vocab""": classaidx,
						    """default_class""": 0,
						}

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

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

						SCREAMING_SNAKE_CASE__									= time.time()

						SCREAMING_SNAKE_CASE__									= get_cached_data_loader(snake_case__						,			snake_case__						,			snake_case__						,			shuffle=snake_case__						,			device=snake_case__      )

						SCREAMING_SNAKE_CASE__									= get_cached_data_loader(snake_case__						,			snake_case__						,			snake_case__						,			device=snake_case__      )

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

			else:
						SCREAMING_SNAKE_CASE__									= torch.utils.data.DataLoader(
						    dataset=snake_case__						,			batch_size=snake_case__						,			shuffle=snake_case__						,			collate_fn=snake_case__      )
						SCREAMING_SNAKE_CASE__									= torch.utils.data.DataLoader(dataset=snake_case__						,			batch_size=snake_case__						,			collate_fn=snake_case__      )

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

			SCREAMING_SNAKE_CASE__									= optim.Adam(discriminator.parameters()						,			lr=0.00_01      )

			for epoch in range(snake_case__      ):
						SCREAMING_SNAKE_CASE__									= time.time()
						print("""\nEpoch"""						,			epoch + 1      )

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

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

						print("""\nExample prediction"""      )
						predict(snake_case__						,			snake_case__						,			snake_case__						,			cached=snake_case__						,			device=snake_case__      )

						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(snake_case__						,			epoch + 1      )						,			)


if __name__ == "__main__":
						A_							:     Dict     =	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")
						A_							:     int     =	parser.parse_args()

						train_discriminator(**(vars(args)))





"""simple docstring"""




import os
import pickle
import tempfile
import unittest
from typing import Callable, Optional

import numpy as np

from transformers import (
    BatchEncoding,
    BertTokenizer,
    BertTokenizerFast,
    PreTrainedTokenizer,
    PreTrainedTokenizerFast,
    TensorType,
    TokenSpan,
    is_tokenizers_available,
)
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import CaptureStderr, require_flax, require_tf, require_tokenizers, require_torch, slow


if is_tokenizers_available():
						from tokenizers import Tokenizer
						from tokenizers.models import WordPiece





class 				lowerCamelCase  (unittest.TestCase     ):

			def    SCREAMING_SNAKE_CASE			(   self			:  Union[str, Any]      ,			__UpperCAmelCase			:  str    )				->      int:
						SCREAMING_SNAKE_CASE__									= list(tokenizer_class.max_model_input_sizes.keys()    )
						for model_name in sa_models[:1]:
									SCREAMING_SNAKE_CASE__									= tokenizer_class.from_pretrained(__UpperCAmelCase    )
									self.assertIsNotNone(__UpperCAmelCase    )
									self.assertIsInstance(__UpperCAmelCase      ,			__UpperCAmelCase    )
									self.assertIsInstance(__UpperCAmelCase      ,			__UpperCAmelCase    )

									for special_tok in tokenizer.all_special_tokens:
												self.assertIsInstance(__UpperCAmelCase      ,			__UpperCAmelCase    )
												SCREAMING_SNAKE_CASE__									= tokenizer.convert_tokens_to_ids(__UpperCAmelCase    )
												self.assertIsInstance(__UpperCAmelCase      ,			__UpperCAmelCase    )

			def    SCREAMING_SNAKE_CASE			(   self			:  Dict      ,			__UpperCAmelCase			:  BatchEncoding      ,			__UpperCAmelCase			:  Optional[Callable] = None    )				->      Tuple:
						SCREAMING_SNAKE_CASE__									= pickle.dumps(__UpperCAmelCase    )
						self.assertIsNotNone(__UpperCAmelCase    )

						SCREAMING_SNAKE_CASE__									= pickle.loads(__UpperCAmelCase    )

						# Ensure is_fast is correctly restored
						self.assertEqual(be_restored.is_fast      ,			be_original.is_fast    )

						# Ensure encodings are potentially correctly restored
						if be_original.is_fast:
									self.assertIsNotNone(be_restored.encodings    )
						else:
									self.assertIsNone(be_restored.encodings    )

						# Ensure the keys are the same
						for original_v, restored_v in zip(be_original.values()      ,			be_restored.values()    ):
									if equal_op:
												self.assertTrue(equal_op(__UpperCAmelCase      ,			__UpperCAmelCase    )    )
									else:
												self.assertEqual(__UpperCAmelCase      ,			__UpperCAmelCase    )

			@slow
			def    SCREAMING_SNAKE_CASE			(   self			:  str    )				->      Optional[Any]:
						self.check_tokenizer_from_pretrained(__UpperCAmelCase    )

			def    SCREAMING_SNAKE_CASE			(   self			:  Tuple    )				->      Optional[int]:
						self.assertEqual(TensorType("""tf"""    )      ,			TensorType.TENSORFLOW    )
						self.assertEqual(TensorType("""pt"""    )      ,			TensorType.PYTORCH    )
						self.assertEqual(TensorType("""np"""    )      ,			TensorType.NUMPY    )

			@require_tokenizers
			def    SCREAMING_SNAKE_CASE			(   self			:  Dict    )				->      List[str]:
						import numpy as np

						SCREAMING_SNAKE_CASE__									= BertTokenizer.from_pretrained("""bert-base-cased"""    )
						SCREAMING_SNAKE_CASE__									= BertTokenizerFast.from_pretrained("""bert-base-cased"""    )

						# Python no tensor
						with self.subTest("""BatchEncoding (Python, return_tensors=None)"""    ):
									self.assert_dump_and_restore(tokenizer_p("""Small example to encode"""    )    )

						with self.subTest("""BatchEncoding (Python, return_tensors=NUMPY)"""    ):
									self.assert_dump_and_restore(
									    tokenizer_p("""Small example to encode"""      ,			return_tensors=TensorType.NUMPY    )      ,			np.array_equal    )

						with self.subTest("""BatchEncoding (Rust, return_tensors=None)"""    ):
									self.assert_dump_and_restore(tokenizer_r("""Small example to encode"""    )    )

						with self.subTest("""BatchEncoding (Rust, return_tensors=NUMPY)"""    ):
									self.assert_dump_and_restore(
									    tokenizer_r("""Small example to encode"""      ,			return_tensors=TensorType.NUMPY    )      ,			np.array_equal    )

			@require_tf
			@require_tokenizers
			def    SCREAMING_SNAKE_CASE			(   self			:  Dict    )				->      int:
						import tensorflow as tf

						def tf_array_equals(__UpperCAmelCase			:  Optional[int]      ,			__UpperCAmelCase			:  str    ):
									return tf.reduce_all(tf.equal(__UpperCAmelCase      ,			__UpperCAmelCase    )    )

						SCREAMING_SNAKE_CASE__									= BertTokenizer.from_pretrained("""bert-base-cased"""    )
						SCREAMING_SNAKE_CASE__									= BertTokenizerFast.from_pretrained("""bert-base-cased"""    )

						with self.subTest("""BatchEncoding (Python, return_tensors=TENSORFLOW)"""    ):
									self.assert_dump_and_restore(
									    tokenizer_p("""Small example to encode"""      ,			return_tensors=TensorType.TENSORFLOW    )      ,			__UpperCAmelCase    )

						with self.subTest("""BatchEncoding (Rust, return_tensors=TENSORFLOW)"""    ):
									self.assert_dump_and_restore(
									    tokenizer_r("""Small example to encode"""      ,			return_tensors=TensorType.TENSORFLOW    )      ,			__UpperCAmelCase    )

			@require_torch
			@require_tokenizers
			def    SCREAMING_SNAKE_CASE			(   self			:  Optional[int]    )				->      Optional[int]:
						import torch

						SCREAMING_SNAKE_CASE__									= BertTokenizer.from_pretrained("""bert-base-cased"""    )
						SCREAMING_SNAKE_CASE__									= BertTokenizerFast.from_pretrained("""bert-base-cased"""    )

						with self.subTest("""BatchEncoding (Python, return_tensors=PYTORCH)"""    ):
									self.assert_dump_and_restore(
									    tokenizer_p("""Small example to encode"""      ,			return_tensors=TensorType.PYTORCH    )      ,			torch.equal    )

						with self.subTest("""BatchEncoding (Rust, return_tensors=PYTORCH)"""    ):
									self.assert_dump_and_restore(
									    tokenizer_r("""Small example to encode"""      ,			return_tensors=TensorType.PYTORCH    )      ,			torch.equal    )

			@require_tokenizers
			def    SCREAMING_SNAKE_CASE			(   self			:  Any    )				->      int:
						SCREAMING_SNAKE_CASE__									= BertTokenizer.from_pretrained("""bert-base-cased"""    )
						SCREAMING_SNAKE_CASE__									= BertTokenizerFast.from_pretrained("""bert-base-cased"""    )

						with self.subTest("""Python Tokenizer"""    ):
									self.assertFalse(tokenizer_p("""Small example to_encode"""    ).is_fast    )

						with self.subTest("""Rust Tokenizer"""    ):
									self.assertTrue(tokenizer_r("""Small example to_encode"""    ).is_fast    )

			@require_tokenizers
			def    SCREAMING_SNAKE_CASE			(   self			:  Optional[int]    )				->      Optional[Any]:
						SCREAMING_SNAKE_CASE__									= BertTokenizerFast.from_pretrained("""bert-base-cased"""    )
						SCREAMING_SNAKE_CASE__									= tokenizer_r(["""Test""", """\xad""", """test"""]      ,			is_split_into_words=__UpperCAmelCase    )

						self.assertEqual(encoded.word_to_tokens(0    )      ,			TokenSpan(start=1      ,			end=2    )    )
						self.assertEqual(encoded.word_to_tokens(1    )      ,			__UpperCAmelCase    )
						self.assertEqual(encoded.word_to_tokens(2    )      ,			TokenSpan(start=2      ,			end=3    )    )

			def    SCREAMING_SNAKE_CASE			(   self			:  Optional[Any]    )				->      Optional[int]:
						SCREAMING_SNAKE_CASE__									= BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]}    )
						SCREAMING_SNAKE_CASE__									= batch.convert_to_tensors(tensor_type="""np"""    )
						self.assertEqual(tensor_batch["""inputs"""].shape      ,			(2, 3)    )
						self.assertEqual(tensor_batch["""labels"""].shape      ,			(2,)    )
						# test converting the converted
						with CaptureStderr() as cs:
									SCREAMING_SNAKE_CASE__									= batch.convert_to_tensors(tensor_type="""np"""    )
						self.assertFalse(len(cs.err    )      ,			msg=F"""should have no warning, but got {cs.err}"""    )

						SCREAMING_SNAKE_CASE__									= BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0}    )
						SCREAMING_SNAKE_CASE__									= batch.convert_to_tensors(tensor_type="""np"""      ,			prepend_batch_axis=__UpperCAmelCase    )
						self.assertEqual(tensor_batch["""inputs"""].shape      ,			(1, 3)    )
						self.assertEqual(tensor_batch["""labels"""].shape      ,			(1,)    )

			@require_torch
			def    SCREAMING_SNAKE_CASE			(   self			:  Dict    )				->      Optional[Any]:
						SCREAMING_SNAKE_CASE__									= BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]}    )
						SCREAMING_SNAKE_CASE__									= batch.convert_to_tensors(tensor_type="""pt"""    )
						self.assertEqual(tensor_batch["""inputs"""].shape      ,			(2, 3)    )
						self.assertEqual(tensor_batch["""labels"""].shape      ,			(2,)    )
						# test converting the converted
						with CaptureStderr() as cs:
									SCREAMING_SNAKE_CASE__									= batch.convert_to_tensors(tensor_type="""pt"""    )
						self.assertFalse(len(cs.err    )      ,			msg=F"""should have no warning, but got {cs.err}"""    )

						SCREAMING_SNAKE_CASE__									= BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0}    )
						SCREAMING_SNAKE_CASE__									= batch.convert_to_tensors(tensor_type="""pt"""      ,			prepend_batch_axis=__UpperCAmelCase    )
						self.assertEqual(tensor_batch["""inputs"""].shape      ,			(1, 3)    )
						self.assertEqual(tensor_batch["""labels"""].shape      ,			(1,)    )

			@require_tf
			def    SCREAMING_SNAKE_CASE			(   self			:  Any    )				->      int:
						SCREAMING_SNAKE_CASE__									= BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]}    )
						SCREAMING_SNAKE_CASE__									= batch.convert_to_tensors(tensor_type="""tf"""    )
						self.assertEqual(tensor_batch["""inputs"""].shape      ,			(2, 3)    )
						self.assertEqual(tensor_batch["""labels"""].shape      ,			(2,)    )
						# test converting the converted
						with CaptureStderr() as cs:
									SCREAMING_SNAKE_CASE__									= batch.convert_to_tensors(tensor_type="""tf"""    )
						self.assertFalse(len(cs.err    )      ,			msg=F"""should have no warning, but got {cs.err}"""    )

						SCREAMING_SNAKE_CASE__									= BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0}    )
						SCREAMING_SNAKE_CASE__									= batch.convert_to_tensors(tensor_type="""tf"""      ,			prepend_batch_axis=__UpperCAmelCase    )
						self.assertEqual(tensor_batch["""inputs"""].shape      ,			(1, 3)    )
						self.assertEqual(tensor_batch["""labels"""].shape      ,			(1,)    )

			@require_flax
			def    SCREAMING_SNAKE_CASE			(   self			:  int    )				->      Tuple:
						SCREAMING_SNAKE_CASE__									= BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]}    )
						SCREAMING_SNAKE_CASE__									= batch.convert_to_tensors(tensor_type="""jax"""    )
						self.assertEqual(tensor_batch["""inputs"""].shape      ,			(2, 3)    )
						self.assertEqual(tensor_batch["""labels"""].shape      ,			(2,)    )
						# test converting the converted
						with CaptureStderr() as cs:
									SCREAMING_SNAKE_CASE__									= batch.convert_to_tensors(tensor_type="""jax"""    )
						self.assertFalse(len(cs.err    )      ,			msg=F"""should have no warning, but got {cs.err}"""    )

						SCREAMING_SNAKE_CASE__									= BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0}    )
						SCREAMING_SNAKE_CASE__									= batch.convert_to_tensors(tensor_type="""jax"""      ,			prepend_batch_axis=__UpperCAmelCase    )
						self.assertEqual(tensor_batch["""inputs"""].shape      ,			(1, 3)    )
						self.assertEqual(tensor_batch["""labels"""].shape      ,			(1,)    )

			def    SCREAMING_SNAKE_CASE			(   self			:  List[Any]    )				->      List[Any]:
						SCREAMING_SNAKE_CASE__									= [{"""input_ids""": np.array([0, 1, 2]    )}, {"""input_ids""": np.array([0, 1, 2, 3]    )}]
						SCREAMING_SNAKE_CASE__									= BertTokenizer.from_pretrained("""bert-base-cased"""    )

						SCREAMING_SNAKE_CASE__									= tokenizer.pad(__UpperCAmelCase      ,			padding=__UpperCAmelCase    )
						self.assertTrue(isinstance(batch["""input_ids"""]      ,			np.ndarray    )    )
						self.assertEqual(batch["""input_ids"""].tolist()      ,			[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )
						SCREAMING_SNAKE_CASE__									= tokenizer.pad(__UpperCAmelCase      ,			padding=__UpperCAmelCase      ,			return_tensors="""np"""    )
						self.assertTrue(isinstance(batch["""input_ids"""]      ,			np.ndarray    )    )
						self.assertEqual(batch["""input_ids"""].tolist()      ,			[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )

			@require_torch
			def    SCREAMING_SNAKE_CASE			(   self			:  Dict    )				->      Optional[int]:
						import torch

						SCREAMING_SNAKE_CASE__									= [{"""input_ids""": torch.tensor([0, 1, 2]    )}, {"""input_ids""": torch.tensor([0, 1, 2, 3]    )}]
						SCREAMING_SNAKE_CASE__									= BertTokenizer.from_pretrained("""bert-base-cased"""    )

						SCREAMING_SNAKE_CASE__									= tokenizer.pad(__UpperCAmelCase      ,			padding=__UpperCAmelCase    )
						self.assertTrue(isinstance(batch["""input_ids"""]      ,			torch.Tensor    )    )
						self.assertEqual(batch["""input_ids"""].tolist()      ,			[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )
						SCREAMING_SNAKE_CASE__									= tokenizer.pad(__UpperCAmelCase      ,			padding=__UpperCAmelCase      ,			return_tensors="""pt"""    )
						self.assertTrue(isinstance(batch["""input_ids"""]      ,			torch.Tensor    )    )
						self.assertEqual(batch["""input_ids"""].tolist()      ,			[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )

			@require_tf
			def    SCREAMING_SNAKE_CASE			(   self			:  List[Any]    )				->      Dict:
						import tensorflow as tf

						SCREAMING_SNAKE_CASE__									= [{"""input_ids""": tf.constant([0, 1, 2]    )}, {"""input_ids""": tf.constant([0, 1, 2, 3]    )}]
						SCREAMING_SNAKE_CASE__									= BertTokenizer.from_pretrained("""bert-base-cased"""    )

						SCREAMING_SNAKE_CASE__									= tokenizer.pad(__UpperCAmelCase      ,			padding=__UpperCAmelCase    )
						self.assertTrue(isinstance(batch["""input_ids"""]      ,			tf.Tensor    )    )
						self.assertEqual(batch["""input_ids"""].numpy().tolist()      ,			[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )
						SCREAMING_SNAKE_CASE__									= tokenizer.pad(__UpperCAmelCase      ,			padding=__UpperCAmelCase      ,			return_tensors="""tf"""    )
						self.assertTrue(isinstance(batch["""input_ids"""]      ,			tf.Tensor    )    )
						self.assertEqual(batch["""input_ids"""].numpy().tolist()      ,			[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )

			@require_tokenizers
			def    SCREAMING_SNAKE_CASE			(   self			:  Optional[Any]    )				->      Optional[int]:
						SCREAMING_SNAKE_CASE__									= Tokenizer(WordPiece(unk_token="""[UNK]"""    )    )
						PreTrainedTokenizerFast(tokenizer_object=__UpperCAmelCase    )





			@require_tokenizers
			def    SCREAMING_SNAKE_CASE			(   self			:  str    )				->      int:
						SCREAMING_SNAKE_CASE__									= Tokenizer(WordPiece(unk_token="""[UNK]"""    )    )
						with tempfile.TemporaryDirectory() as tmpdirname:
									bert_tokenizer.save(os.path.join(__UpperCAmelCase      ,			"""tokenizer.json"""    )    )
									PreTrainedTokenizerFast(tokenizer_file=os.path.join(__UpperCAmelCase      ,			"""tokenizer.json"""    )    )


