# 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)
_SCREAMING_SNAKE_CASE:						Union[str, Any]    	=       1e-10
_SCREAMING_SNAKE_CASE:						str    	=       '''This is incredible! I love it, this is the best chicken I have ever had.'''
_SCREAMING_SNAKE_CASE:						Optional[Any]    	=       100

class 	_SCREAMING_SNAKE_CASE							(     nn.Module							):






		"""simple docstring"""







		def __init__(			self    :				List[str]  ,					__SCREAMING_SNAKE_CASE    :				List[Any]  ,					__SCREAMING_SNAKE_CASE    :				Dict="gpt2-medium"  ,					__SCREAMING_SNAKE_CASE    :				int=False  ,					__SCREAMING_SNAKE_CASE    :				Union[str, Any]="cpu"   ):






					"""simple docstring"""
					super().__init__()
					_snake_case              =      GPTaTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE   )
					_snake_case              =      GPTaLMHeadModel.from_pretrained(__SCREAMING_SNAKE_CASE   )
					_snake_case              =      self.encoder.transformer.config.hidden_size
					_snake_case              =      ClassificationHead(class_size=__SCREAMING_SNAKE_CASE  ,					embed_size=self.embed_size   )
					_snake_case              =      cached_mode
					_snake_case              =      device



		def 				_a				(			self    :				Dict   ):






					"""simple docstring"""
					return self.classifier_head



		def 				_a				(			self    :				Dict   ):






					"""simple docstring"""
					for param in self.encoder.parameters():
								_snake_case              =      False
					self.classifier_head.train()



		def 				_a				(			self    :				Optional[Any]  ,					__SCREAMING_SNAKE_CASE    :				Dict   ):






					"""simple docstring"""
					_snake_case              =      x.ne(0   ).unsqueeze(2   ).repeat(1  ,					1  ,					self.embed_size   ).float().to(self.device   ).detach()
					_snake_case              =      self.encoder.transformer(__SCREAMING_SNAKE_CASE   )["last_hidden_state"]
					_snake_case              =      hidden * mask
					_snake_case              =      torch.sum(__SCREAMING_SNAKE_CASE  ,					dim=1   ) / (torch.sum(__SCREAMING_SNAKE_CASE  ,					dim=1   ).detach() + EPSILON)
					return avg_hidden







		def 				_a				(			self    :				Optional[Any]  ,					__SCREAMING_SNAKE_CASE    :				Tuple   ):






					"""simple docstring"""
					if self.cached_mode:
								_snake_case              =      x.to(self.device   )
					else:
								_snake_case              =      self.avg_representation(x.to(self.device   )   )

					_snake_case              =      self.classifier_head(__SCREAMING_SNAKE_CASE   )
					_snake_case              =      nn.functional.log_softmax(__SCREAMING_SNAKE_CASE  ,					dim=-1   )

					return probs

class 	_SCREAMING_SNAKE_CASE							(     data.Dataset							):






		"""simple docstring"""







		def __init__(			self    :				str  ,					__SCREAMING_SNAKE_CASE    :				Optional[Any]  ,					__SCREAMING_SNAKE_CASE    :				Tuple   ):






					"""simple docstring"""
					_snake_case              =      X
					_snake_case              =      y



		def __len__(			self    :				List[str]   ):






					"""simple docstring"""
					return len(self.X   )







		def __getitem__(			self    :				str  ,					__SCREAMING_SNAKE_CASE    :				Optional[int]   ):






					"""simple docstring"""
					_snake_case              =      {}
					_snake_case              =      self.X[index]
					_snake_case              =      self.y[index]
					return data






def    UpperCAmelCase__(						lowerCamelCase_  ):



			"""simple docstring"""





			def pad_sequences(lowerCamelCase_  ):
						_snake_case              =      [len(lowerCamelCase_  ) for seq in sequences]

						_snake_case              =      torch.zeros(len(lowerCamelCase_  )							,	max(lowerCamelCase_  )  ).long()  # padding value = 0

						for i, seq in enumerate(lowerCamelCase_  ):
									_snake_case              =      lengths[i]
									_snake_case              =      seq[:end]

						return padded_sequences, lengths

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

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

			return x_batch, y_batch






def    UpperCAmelCase__(						lowerCamelCase_  ):



			"""simple docstring"""





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

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

			return x_batch, y_batch






def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_=0							,	lowerCamelCase_=10							,	lowerCamelCase_="cpu"  ):



			"""simple docstring"""





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

						optimizer.zero_grad()

						_snake_case              =      discriminator(lowerCamelCase_  )
						_snake_case              =      nn.functional.nll_loss(lowerCamelCase_							,	lowerCamelCase_  )
						loss.backward(retain_graph=lowerCamelCase_  )
						optimizer.step()

						samples_so_far += len(lowerCamelCase_  )

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






def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_="cpu"  ):



			"""simple docstring"""





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

			test_loss /= len(data_loader.dataset  )

			print(
			    "Performance on test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)".format(
			        lowerCamelCase_							,	lowerCamelCase_							,	len(data_loader.dataset  )							,	1_0_0.0 * correct / len(data_loader.dataset  )  )  )






def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_=False							,	lowerCamelCase_="cpu"  ):



			"""simple docstring"""





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

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






def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_=False							,	lowerCamelCase_="cpu"  ):



			"""simple docstring"""





			_snake_case              =      torch.utils.data.DataLoader(dataset=lowerCamelCase_							,	batch_size=lowerCamelCase_							,	collate_fn=lowerCamelCase_  )

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

			_snake_case              =      torch.utils.data.DataLoader(
			    dataset=Dataset(lowerCamelCase_							,	lowerCamelCase_  )							,	batch_size=lowerCamelCase_							,	shuffle=lowerCamelCase_							,	collate_fn=lowerCamelCase_  )

			return data_loader






def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_=None							,	lowerCamelCase_="gpt2-medium"							,	lowerCamelCase_=10							,	lowerCamelCase_=64							,	lowerCamelCase_=10							,	lowerCamelCase_=False							,	lowerCamelCase_=False							,	lowerCamelCase_=False							,	):



			"""simple docstring"""





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

			print("Preprocessing {} dataset...".format(lowerCamelCase_  )  )
			_snake_case              =      time.time()

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

						_snake_case              =      Discriminator(
						    class_size=len(lowerCamelCase_  )							,	pretrained_model=lowerCamelCase_							,	cached_mode=lowerCamelCase_							,	device=lowerCamelCase_  ).to(lowerCamelCase_  )

						_snake_case              =      torchtext_data.Field()
						_snake_case              =      torchtext_data.Field(sequential=lowerCamelCase_  )
						_snake_case       , _snake_case       , _snake_case              =      datasets.SST.splits(
						    lowerCamelCase_							,	lowerCamelCase_							,	fine_grained=lowerCamelCase_							,	train_subtrees=lowerCamelCase_							,	)

						_snake_case              =      []
						_snake_case              =      []
						for i in trange(len(lowerCamelCase_  )							,	ascii=lowerCamelCase_  ):
									_snake_case              =      TreebankWordDetokenizer().detokenize(vars(train_data[i]  )["text"]  )
									_snake_case              =      discriminator.tokenizer.encode(lowerCamelCase_  )
									_snake_case              =      torch.tensor([50256] + seq							,	device=lowerCamelCase_							,	dtype=torch.long  )
									x.append(lowerCamelCase_  )
									y.append(classaidx[vars(train_data[i]  )["label"]]  )
						_snake_case              =      Dataset(lowerCamelCase_							,	lowerCamelCase_  )

						_snake_case              =      []
						_snake_case              =      []
						for i in trange(len(lowerCamelCase_  )							,	ascii=lowerCamelCase_  ):
									_snake_case              =      TreebankWordDetokenizer().detokenize(vars(test_data[i]  )["text"]  )
									_snake_case              =      discriminator.tokenizer.encode(lowerCamelCase_  )
									_snake_case              =      torch.tensor([50256] + seq							,	device=lowerCamelCase_							,	dtype=torch.long  )
									test_x.append(lowerCamelCase_  )
									test_y.append(classaidx[vars(test_data[i]  )["label"]]  )
						_snake_case              =      Dataset(lowerCamelCase_							,	lowerCamelCase_  )

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

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

						_snake_case              =      Discriminator(
						    class_size=len(lowerCamelCase_  )							,	pretrained_model=lowerCamelCase_							,	cached_mode=lowerCamelCase_							,	device=lowerCamelCase_  ).to(lowerCamelCase_  )

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

															if len(lowerCamelCase_  ) < max_length_seq:
																		_snake_case              =      torch.tensor([50256] + seq							,	device=lowerCamelCase_							,	dtype=torch.long  )
															else:
																		print("Line {} is longer than maximum length {}".format(lowerCamelCase_							,	lowerCamelCase_  )  )
																		continue
															x.append(lowerCamelCase_  )
															y.append(d["label"]  )
												except Exception:
															print("Error evaluating / tokenizing line {}, skipping it".format(lowerCamelCase_  )  )
															pass

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

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

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

						_snake_case              =      Discriminator(
						    class_size=len(lowerCamelCase_  )							,	pretrained_model=lowerCamelCase_							,	cached_mode=lowerCamelCase_							,	device=lowerCamelCase_  ).to(lowerCamelCase_  )

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

															if len(lowerCamelCase_  ) < max_length_seq:
																		_snake_case              =      torch.tensor([50256] + seq							,	device=lowerCamelCase_							,	dtype=torch.long  )
															else:
																		print("Line {} is longer than maximum length {}".format(lowerCamelCase_							,	lowerCamelCase_  )  )
																		continue
															x.append(lowerCamelCase_  )
															y.append(int(np.sum(d["label"]  ) > 0  )  )
												except Exception:
															print("Error evaluating / tokenizing line {}, skipping it".format(lowerCamelCase_  )  )
															pass

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

						_snake_case              =      {
						    "class_size": len(lowerCamelCase_  ),
						    "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."  )

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

						_snake_case              =      sorted(lowerCamelCase_  )
						_snake_case              =      {c: i for i, c in enumerate(lowerCamelCase_  )}

						_snake_case              =      Discriminator(
						    class_size=len(lowerCamelCase_  )							,	pretrained_model=lowerCamelCase_							,	cached_mode=lowerCamelCase_							,	device=lowerCamelCase_  ).to(lowerCamelCase_  )

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

															try:
																		_snake_case              =      discriminator.tokenizer.encode(lowerCamelCase_  )
																		if len(lowerCamelCase_  ) < max_length_seq:
																					_snake_case              =      torch.tensor([50256] + seq							,	device=lowerCamelCase_							,	dtype=torch.long  )

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

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

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

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

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

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

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

						_snake_case              =      time.time()

						_snake_case              =      get_cached_data_loader(lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_							,	shuffle=lowerCamelCase_							,	device=lowerCamelCase_  )

						_snake_case              =      get_cached_data_loader(lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_							,	device=lowerCamelCase_  )

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

			else:
						_snake_case              =      torch.utils.data.DataLoader(
						    dataset=lowerCamelCase_							,	batch_size=lowerCamelCase_							,	shuffle=lowerCamelCase_							,	collate_fn=lowerCamelCase_  )
						_snake_case              =      torch.utils.data.DataLoader(dataset=lowerCamelCase_							,	batch_size=lowerCamelCase_							,	collate_fn=lowerCamelCase_  )

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

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

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

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

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

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

						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(lowerCamelCase_							,	epoch + 1  )							,	)


if __name__ == "__main__":
			_SCREAMING_SNAKE_CASE:						List[Any]    	=       argparse.ArgumentParser(description='''Train a discriminator on top of GPT-2 representations''')
			parser.add_argument(
			    '''--dataset''',
			    type=str,
			    default='''SST''',
			    choices=('''SST''', '''clickbait''', '''toxic''', '''generic'''),
			    help=(
			        '''dataset to train the discriminator on.'''
			        '''In case of generic, the dataset is expected'''
			        '''to be a TSBV file with structure: class \\t text'''
			    ),
			)
			parser.add_argument(
			    '''--dataset_fp''',
			    type=str,
			    default='''''',
			    help='''File path of the dataset to use. Needed only in case of generic datadset''',
			)
			parser.add_argument(
			    '''--pretrained_model''', type=str, default='''gpt2-medium''', help='''Pretrained model to use as encoder'''
			)
			parser.add_argument('''--epochs''', type=int, default=10, metavar='''N''', help='''Number of training epochs''')
			parser.add_argument(
			    '''--batch_size''', type=int, default=64, metavar='''N''', help='''input batch size for training (default: 64)'''
			)
			parser.add_argument(
			    '''--log_interval''',
			    type=int,
			    default=10,
			    metavar='''N''',
			    help='''how many batches to wait before logging training status''',
			)
			parser.add_argument('''--save_model''', action='''store_true''', help='''whether to save the model''')
			parser.add_argument('''--cached''', action='''store_true''', help='''whether to cache the input representations''')
			parser.add_argument('''--no_cuda''', action='''store_true''', help='''use to turn off cuda''')
			_SCREAMING_SNAKE_CASE:						Tuple    	=       parser.parse_args()

			train_discriminator(**(vars(args)))


import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


_SCREAMING_SNAKE_CASE:						List[str]    	=       logging.get_logger(__name__)

@dataclass
class 	_SCREAMING_SNAKE_CASE							:






		"""simple docstring"""







		A_						 =      42
		A_						 =      42
		A_						 =      None
		A_						 =      None



		def 				_a				(			self    :				Union[str, Any]   ):






					"""simple docstring"""
					return json.dumps(dataclasses.asdict(self   )  ,					indent=2   ) + "\n"

@dataclass(frozen=_lowercase							)
class 	_SCREAMING_SNAKE_CASE							:






		"""simple docstring"""







		A_						 =      42
		A_						 =      None
		A_						 =      None
		A_						 =      None



		def 				_a				(			self    :				List[Any]   ):






					"""simple docstring"""
					return json.dumps(dataclasses.asdict(self   )   ) + "\n"

class 	_SCREAMING_SNAKE_CASE							:






		"""simple docstring"""







		def 				_a				(			self    :				Tuple  ,					__SCREAMING_SNAKE_CASE    :				Tuple   ):






					"""simple docstring"""
					raise NotImplementedError()



		def 				_a				(			self    :				Any  ,					__SCREAMING_SNAKE_CASE    :				Optional[Any]   ):






					"""simple docstring"""
					raise NotImplementedError()



		def 				_a				(			self    :				str  ,					__SCREAMING_SNAKE_CASE    :				Optional[int]   ):






					"""simple docstring"""
					raise NotImplementedError()



		def 				_a				(			self    :				str  ,					__SCREAMING_SNAKE_CASE    :				Tuple   ):






					"""simple docstring"""
					raise NotImplementedError()



		def 				_a				(			self    :				Dict   ):






					"""simple docstring"""
					raise NotImplementedError()



		def 				_a				(			self    :				Optional[Any]  ,					__SCREAMING_SNAKE_CASE    :				str   ):






					"""simple docstring"""
					if len(self.get_labels()   ) > 1:
								_snake_case              =      self.get_labels()[int(example.label   )]
					return example







		@classmethod
		def 				_a				(			cls    :				List[Any]  ,					__SCREAMING_SNAKE_CASE    :				List[str]  ,					__SCREAMING_SNAKE_CASE    :				Tuple=None   ):






					"""simple docstring"""
					with open(__SCREAMING_SNAKE_CASE  ,					"r"  ,					encoding="utf-8-sig"   ) as f:
								return list(csv.reader(__SCREAMING_SNAKE_CASE  ,					delimiter="\t"  ,					quotechar=__SCREAMING_SNAKE_CASE   )   )

class 	_SCREAMING_SNAKE_CASE							(     _lowercase							):






		"""simple docstring"""







		def __init__(			self    :				int  ,					__SCREAMING_SNAKE_CASE    :				List[str]=None  ,					__SCREAMING_SNAKE_CASE    :				Any=None  ,					__SCREAMING_SNAKE_CASE    :				List[str]="classification"  ,					__SCREAMING_SNAKE_CASE    :				Any=False   ):






					"""simple docstring"""
					_snake_case              =      [] if labels is None else labels
					_snake_case              =      [] if examples is None else examples
					_snake_case              =      mode
					_snake_case              =      verbose



		def __len__(			self    :				List[str]   ):






					"""simple docstring"""
					return len(self.examples   )



		def __getitem__(			self    :				List[str]  ,					__SCREAMING_SNAKE_CASE    :				Tuple   ):






					"""simple docstring"""
					if isinstance(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   ):
								return SingleSentenceClassificationProcessor(labels=self.labels  ,					examples=self.examples[idx]   )
					return self.examples[idx]



		@classmethod
		def 				_a				(			cls    :				int  ,					__SCREAMING_SNAKE_CASE    :				Tuple  ,					__SCREAMING_SNAKE_CASE    :				Optional[int]=""  ,					__SCREAMING_SNAKE_CASE    :				Tuple=0  ,					__SCREAMING_SNAKE_CASE    :				Dict=1  ,					__SCREAMING_SNAKE_CASE    :				List[Any]=None  ,					__SCREAMING_SNAKE_CASE    :				Optional[Any]=False  ,					**__SCREAMING_SNAKE_CASE    :				str   ):






					"""simple docstring"""
					_snake_case              =      cls(**__SCREAMING_SNAKE_CASE   )
					processor.add_examples_from_csv(
					    __SCREAMING_SNAKE_CASE  ,					split_name=__SCREAMING_SNAKE_CASE  ,					column_label=__SCREAMING_SNAKE_CASE  ,					column_text=__SCREAMING_SNAKE_CASE  ,					column_id=__SCREAMING_SNAKE_CASE  ,					skip_first_row=__SCREAMING_SNAKE_CASE  ,					overwrite_labels=__SCREAMING_SNAKE_CASE  ,					overwrite_examples=__SCREAMING_SNAKE_CASE  ,					)
					return processor



		@classmethod
		def 				_a				(			cls    :				Any  ,					__SCREAMING_SNAKE_CASE    :				Optional[Any]  ,					__SCREAMING_SNAKE_CASE    :				List[str]=None  ,					**__SCREAMING_SNAKE_CASE    :				List[str]   ):






					"""simple docstring"""
					_snake_case              =      cls(**__SCREAMING_SNAKE_CASE   )
					processor.add_examples(__SCREAMING_SNAKE_CASE  ,					labels=__SCREAMING_SNAKE_CASE   )
					return processor



		def 				_a				(			self    :				Tuple  ,					__SCREAMING_SNAKE_CASE    :				List[Any]  ,					__SCREAMING_SNAKE_CASE    :				Dict=""  ,					__SCREAMING_SNAKE_CASE    :				str=0  ,					__SCREAMING_SNAKE_CASE    :				List[str]=1  ,					__SCREAMING_SNAKE_CASE    :				Optional[int]=None  ,					__SCREAMING_SNAKE_CASE    :				List[str]=False  ,					__SCREAMING_SNAKE_CASE    :				Any=False  ,					__SCREAMING_SNAKE_CASE    :				str=False  ,					):






					"""simple docstring"""
					_snake_case              =      self._read_tsv(__SCREAMING_SNAKE_CASE   )
					if skip_first_row:
								_snake_case              =      lines[1:]
					_snake_case              =      []
					_snake_case              =      []
					_snake_case              =      []
					for i, line in enumerate(__SCREAMING_SNAKE_CASE   ):
								texts.append(line[column_text]   )
								labels.append(line[column_label]   )
								if column_id is not None:
											ids.append(line[column_id]   )
								else:
											_snake_case              =      F'{split_name}-{i}' if split_name else str(__SCREAMING_SNAKE_CASE   )
											ids.append(__SCREAMING_SNAKE_CASE   )

					return self.add_examples(
					    __SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					overwrite_labels=__SCREAMING_SNAKE_CASE  ,					overwrite_examples=__SCREAMING_SNAKE_CASE   )



		def 				_a				(			self    :				Optional[Any]  ,					__SCREAMING_SNAKE_CASE    :				Dict  ,					__SCREAMING_SNAKE_CASE    :				int=None  ,					__SCREAMING_SNAKE_CASE    :				Union[str, Any]=None  ,					__SCREAMING_SNAKE_CASE    :				Tuple=False  ,					__SCREAMING_SNAKE_CASE    :				str=False   ):






					"""simple docstring"""
					if labels is not None and len(__SCREAMING_SNAKE_CASE   ) != len(__SCREAMING_SNAKE_CASE   ):
								raise ValueError(
								    F'Text and labels have mismatched lengths {len(__SCREAMING_SNAKE_CASE   )} and {len(__SCREAMING_SNAKE_CASE   )}'   )
					if ids is not None and len(__SCREAMING_SNAKE_CASE   ) != len(__SCREAMING_SNAKE_CASE   ):
								raise ValueError(F'Text and ids have mismatched lengths {len(__SCREAMING_SNAKE_CASE   )} and {len(__SCREAMING_SNAKE_CASE   )}'   )
					if ids is None:
								_snake_case              =      [None] * len(__SCREAMING_SNAKE_CASE   )
					if labels is None:
								_snake_case              =      [None] * len(__SCREAMING_SNAKE_CASE   )
					_snake_case              =      []
					_snake_case              =      set()
					for text_or_text_and_label, label, guid in zip(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   ):
								if isinstance(__SCREAMING_SNAKE_CASE  ,					(tuple, list)   ) and label is None:
											_snake_case       , _snake_case              =      text_or_text_and_label
								else:
											_snake_case              =      text_or_text_and_label
								added_labels.add(__SCREAMING_SNAKE_CASE   )
								examples.append(InputExample(guid=__SCREAMING_SNAKE_CASE  ,					text_a=__SCREAMING_SNAKE_CASE  ,					text_b=__SCREAMING_SNAKE_CASE  ,					label=__SCREAMING_SNAKE_CASE   )   )

					# Update examples
					if overwrite_examples:
								_snake_case              =      examples
					else:
								self.examples.extend(__SCREAMING_SNAKE_CASE   )

					# Update labels
					if overwrite_labels:
								_snake_case              =      list(__SCREAMING_SNAKE_CASE   )
					else:
								_snake_case              =      list(set(self.labels   ).union(__SCREAMING_SNAKE_CASE   )   )

					return self.examples







		def 				_a				(			self    :				int  ,					__SCREAMING_SNAKE_CASE    :				str  ,					__SCREAMING_SNAKE_CASE    :				Optional[Any]=None  ,					__SCREAMING_SNAKE_CASE    :				Any=False  ,					__SCREAMING_SNAKE_CASE    :				Any=0  ,					__SCREAMING_SNAKE_CASE    :				Tuple=True  ,					__SCREAMING_SNAKE_CASE    :				List[str]=None  ,					):






					"""simple docstring"""
					if max_length is None:
								_snake_case              =      tokenizer.max_len

					_snake_case              =      {label: i for i, label in enumerate(self.labels   )}

					_snake_case              =      []
					for ex_index, example in enumerate(self.examples   ):
								if ex_index % 10_000 == 0:
											logger.info(F'Tokenizing example {ex_index}'   )

								_snake_case              =      tokenizer.encode(
								    example.text_a  ,					add_special_tokens=__SCREAMING_SNAKE_CASE  ,					max_length=min(__SCREAMING_SNAKE_CASE  ,					tokenizer.max_len   )  ,					)
								all_input_ids.append(__SCREAMING_SNAKE_CASE   )

					_snake_case              =      max(len(__SCREAMING_SNAKE_CASE   ) for input_ids in all_input_ids   )

					_snake_case              =      []
					for ex_index, (input_ids, example) in enumerate(zip(__SCREAMING_SNAKE_CASE  ,					self.examples   )   ):
								if ex_index % 10_000 == 0:
											logger.info(F'Writing example {ex_index}/{len(self.examples   )}'   )
								# The mask has 1 for real tokens and 0 for padding tokens. Only real
								# tokens are attended to.
								_snake_case              =      [1 if mask_padding_with_zero else 0] * len(__SCREAMING_SNAKE_CASE   )

								# Zero-pad up to the sequence length.
								_snake_case              =      batch_length - len(__SCREAMING_SNAKE_CASE   )
								if pad_on_left:
											_snake_case              =      ([pad_token] * padding_length) + input_ids
											_snake_case              =      ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
								else:
											_snake_case              =      input_ids + ([pad_token] * padding_length)
											_snake_case              =      attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

								if len(__SCREAMING_SNAKE_CASE   ) != batch_length:
											raise ValueError(F'Error with input length {len(__SCREAMING_SNAKE_CASE   )} vs {batch_length}'   )
								if len(__SCREAMING_SNAKE_CASE   ) != batch_length:
											raise ValueError(F'Error with input length {len(__SCREAMING_SNAKE_CASE   )} vs {batch_length}'   )

								if self.mode == "classification":
											_snake_case              =      label_map[example.label]
								elif self.mode == "regression":
											_snake_case              =      float(example.label   )
								else:
											raise ValueError(self.mode   )

								if ex_index < 5 and self.verbose:
											logger.info("*** Example ***"   )
											logger.info(F'guid: {example.guid}'   )
											logger.info(F'input_ids: {" ".join([str(__SCREAMING_SNAKE_CASE   ) for x in input_ids]   )}'   )
											logger.info(F'attention_mask: {" ".join([str(__SCREAMING_SNAKE_CASE   ) for x in attention_mask]   )}'   )
											logger.info(F'label: {example.label} (id = {label})'   )

								features.append(InputFeatures(input_ids=__SCREAMING_SNAKE_CASE  ,					attention_mask=__SCREAMING_SNAKE_CASE  ,					label=__SCREAMING_SNAKE_CASE   )   )

					if return_tensors is None:
								return features
					elif return_tensors == "tf":
								if not is_tf_available():
											raise RuntimeError("return_tensors set to 'tf' but TensorFlow 2.0 can't be imported"   )
								import tensorflow as tf

								def gen():
											for ex in features:
														yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

								_snake_case              =      tf.data.Dataset.from_generator(
								    __SCREAMING_SNAKE_CASE  ,					({"input_ids": tf.intaa, "attention_mask": tf.intaa}, tf.intaa)  ,					({"input_ids": tf.TensorShape([None]   ), "attention_mask": tf.TensorShape([None]   )}, tf.TensorShape([]   ))  ,					)
								return dataset
					elif return_tensors == "pt":
								if not is_torch_available():
											raise RuntimeError("return_tensors set to 'pt' but PyTorch can't be imported"   )
								import torch
								from torch.utils.data import TensorDataset

								_snake_case              =      torch.tensor([f.input_ids for f in features]  ,					dtype=torch.long   )
								_snake_case              =      torch.tensor([f.attention_mask for f in features]  ,					dtype=torch.long   )
								if self.mode == "classification":
											_snake_case              =      torch.tensor([f.label for f in features]  ,					dtype=torch.long   )
								elif self.mode == "regression":
											_snake_case              =      torch.tensor([f.label for f in features]  ,					dtype=torch.float   )

								_snake_case              =      TensorDataset(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )
								return dataset
					else:
								raise ValueError("return_tensors should be one of 'tf' or 'pt'"   )

