

# 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_         =      1E-10
a_         =      'This is incredible! I love it, this is the best chicken I have ever had.'
a_         =      100






class _UpperCamelCase							(							nn.Module				):
			'''simple docstring'''







			def __init__(     self							:	List[str] , a							:	Dict , a							:	List[str]="gpt2-medium" , a							:	Tuple=False , a							:	Dict="cpu"		)							->    int:

				"""simple docstring"""

				super().__init__()
				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    GPTaTokenizer.from_pretrained(a		)
				SCREAMING_SNAKE_CASE				: Any			       =    GPTaLMHeadModel.from_pretrained(a		)
				SCREAMING_SNAKE_CASE				: List[str]			       =    self.encoder.transformer.config.hidden_size
				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    ClassificationHead(class_size=a , embed_size=self.embed_size		)
				SCREAMING_SNAKE_CASE				: List[str]			       =    cached_mode
				SCREAMING_SNAKE_CASE				: int			       =    device
			def 							__UpperCamelCase	(     self							:	Union[str, Any]		)							->    Optional[Any]:

				"""simple docstring"""

				return self.classifier_head
			def 							__UpperCamelCase	(     self							:	str		)							->    Any:

				"""simple docstring"""

				for param in self.encoder.parameters():
					SCREAMING_SNAKE_CASE				: int			       =    False
				self.classifier_head.train()
			def 							__UpperCamelCase	(     self							:	List[str] , a							:	Union[str, Any]		)							->    Dict:

				"""simple docstring"""

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






			def 							__UpperCamelCase	(     self							:	str , a							:	Optional[int]		)							->    Any:

				"""simple docstring"""

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

				SCREAMING_SNAKE_CASE				: List[str]			       =    self.classifier_head(a		)
				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    nn.functional.log_softmax(a , dim=-1		)

				return probs






class _UpperCamelCase							(							data.Dataset				):
			'''simple docstring'''







			def __init__(     self							:	Optional[int] , a							:	List[str] , a							:	Optional[int]		)							->    Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: List[str]			       =    X
				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    y
			def __len__(     self							:	str		)							->    Dict:

				"""simple docstring"""

				return len(self.X		)






			def __getitem__(     self							:	List[str] , a							:	str		)							->    Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    {}
				SCREAMING_SNAKE_CASE				: Dict			       =    self.X[index]
				SCREAMING_SNAKE_CASE				: List[Any]			       =    self.y[index]
				return data

def 	lowerCamelCase__	(   _a):
	def pad_sequences(_a):
		SCREAMING_SNAKE_CASE				: Optional[int]			       =    [len(_a) for seq in sequences]

		SCREAMING_SNAKE_CASE				: str			       =    torch.zeros(len(_a)							,     max(_a)).long()  # padding value = 0

		for i, seq in enumerate(_a):
			SCREAMING_SNAKE_CASE				: int			       =    lengths[i]
			SCREAMING_SNAKE_CASE				: List[Any]			       =    seq[:end]

		return padded_sequences, lengths

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

	SCREAMING_SNAKE_CASE				: Any			       =    pad_sequences(item_info["X"])
	SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    torch.tensor(item_info["y"]							,     dtype=torch.long)

	return x_batch, y_batch

def 	lowerCamelCase__	(   _a):
	SCREAMING_SNAKE_CASE				: int			       =    {}
	for key in data[0].keys():
		SCREAMING_SNAKE_CASE				: str			       =    [d[key] for d in data]

	SCREAMING_SNAKE_CASE				: Optional[int]			       =    torch.cat(item_info["X"]							,     0)
	SCREAMING_SNAKE_CASE				: Dict			       =    torch.tensor(item_info["y"]							,     dtype=torch.long)

	return x_batch, y_batch

def 	lowerCamelCase__	(   _a							,     _a							,     _a							,     _a=0							,     _a=10							,     _a="cpu"):
	SCREAMING_SNAKE_CASE				: Tuple			       =    0
	discriminator.train_custom()
	for batch_idx, (input_t, target_t) in enumerate(_a):
		SCREAMING_SNAKE_CASE				: Optional[int]			       =    input_t.to(_a), target_t.to(_a)

		optimizer.zero_grad()

		SCREAMING_SNAKE_CASE				: List[str]			       =    discriminator(_a)
		SCREAMING_SNAKE_CASE				: Dict			       =    nn.functional.nll_loss(_a							,     _a)
		loss.backward(retain_graph=_a)
		optimizer.step()

		samples_so_far += len(_a)

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

def 	lowerCamelCase__	(   _a							,     _a							,     _a="cpu"):
	discriminator.eval()
	SCREAMING_SNAKE_CASE				: int			       =    0
	SCREAMING_SNAKE_CASE				: Optional[int]			       =    0
	with torch.no_grad():
		for input_t, target_t in data_loader:
			SCREAMING_SNAKE_CASE				: Any			       =    input_t.to(_a), target_t.to(_a)
			SCREAMING_SNAKE_CASE				: str			       =    discriminator(_a)
			# sum up batch loss
			test_loss += nn.functional.nll_loss(_a							,     _a							,     reduction="sum").item()
			# get the index of the max log-probability
			SCREAMING_SNAKE_CASE				: List[Any]			       =    output_t.argmax(dim=1							,     keepdim=_a)
			correct += pred_t.eq(target_t.view_as(_a)).sum().item()

	test_loss /= len(data_loader.dataset)

	print(
	    "Performance on test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)".format(
	        _a							,     _a							,     len(data_loader.dataset)							,     100.0 * correct / len(data_loader.dataset)))

def 	lowerCamelCase__	(   _a							,     _a							,     _a							,     _a=False							,     _a="cpu"):
	SCREAMING_SNAKE_CASE				: str			       =    model.tokenizer.encode(_a)
	SCREAMING_SNAKE_CASE				: List[Any]			       =    torch.tensor([input_t]							,     dtype=torch.long							,     device=_a)
	if cached:
		SCREAMING_SNAKE_CASE				: int			       =    model.avg_representation(_a)

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

def 	lowerCamelCase__	(   _a							,     _a							,     _a							,     _a=False							,     _a="cpu"):
	SCREAMING_SNAKE_CASE				: Tuple			       =    torch.utils.data.DataLoader(dataset=_a							,     batch_size=_a							,     collate_fn=_a)

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

	SCREAMING_SNAKE_CASE				: str			       =    torch.utils.data.DataLoader(
	    dataset=Dataset(_a							,     _a)							,     batch_size=_a							,     shuffle=_a							,     collate_fn=_a)

	return data_loader

def 	lowerCamelCase__	(   _a							,     _a=None							,     _a="gpt2-medium"							,     _a=10							,     _a=64							,     _a=10							,     _a=False							,     _a=False							,     _a=False							,     ):
	SCREAMING_SNAKE_CASE				: Any			       =    "cuda" if torch.cuda.is_available() and not no_cuda else "cpu"

	print("Preprocessing {} dataset...".format(_a))
	SCREAMING_SNAKE_CASE				: Optional[int]			       =    time.time()

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

		SCREAMING_SNAKE_CASE				: Optional[int]			       =    Discriminator(
		    class_size=len(_a)							,     pretrained_model=_a							,     cached_mode=_a							,     device=_a).to(_a)

		SCREAMING_SNAKE_CASE				: str			       =    torchtext_data.Field()
		SCREAMING_SNAKE_CASE				: List[Any]			       =    torchtext_data.Field(sequential=_a)
		SCREAMING_SNAKE_CASE				: Dict			       =    datasets.SST.splits(
		    _a							,     _a							,     fine_grained=_a							,     train_subtrees=_a							,     )

		SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    []
		SCREAMING_SNAKE_CASE				: int			       =    []
		for i in trange(len(_a)							,     ascii=_a):
			SCREAMING_SNAKE_CASE				: int			       =    TreebankWordDetokenizer().detokenize(vars(train_data[i])["text"])
			SCREAMING_SNAKE_CASE				: List[Any]			       =    discriminator.tokenizer.encode(_a)
			SCREAMING_SNAKE_CASE				: str			       =    torch.tensor([50256] + seq							,     device=_a							,     dtype=torch.long)
			x.append(_a)
			y.append(classaidx[vars(train_data[i])["label"]])
		SCREAMING_SNAKE_CASE				: Tuple			       =    Dataset(_a							,     _a)

		SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    []
		SCREAMING_SNAKE_CASE				: Dict			       =    []
		for i in trange(len(_a)							,     ascii=_a):
			SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    TreebankWordDetokenizer().detokenize(vars(test_data[i])["text"])
			SCREAMING_SNAKE_CASE				: Optional[Any]			       =    discriminator.tokenizer.encode(_a)
			SCREAMING_SNAKE_CASE				: Optional[Any]			       =    torch.tensor([50256] + seq							,     device=_a							,     dtype=torch.long)
			test_x.append(_a)
			test_y.append(classaidx[vars(test_data[i])["label"]])
		SCREAMING_SNAKE_CASE				: Optional[int]			       =    Dataset(_a							,     _a)

		SCREAMING_SNAKE_CASE				: Optional[Any]			       =    {
		    "class_size": len(_a),
		    "embed_size": discriminator.embed_size,
		    "pretrained_model": pretrained_model,
		    "class_vocab": classaidx,
		    "default_class": 2,
		}

	elif dataset == "clickbait":
		SCREAMING_SNAKE_CASE				: Optional[Any]			       =    ["non_clickbait", "clickbait"]
		SCREAMING_SNAKE_CASE				: Tuple			       =    {c: i for i, c in enumerate(_a)}

		SCREAMING_SNAKE_CASE				: List[Any]			       =    Discriminator(
		    class_size=len(_a)							,     pretrained_model=_a							,     cached_mode=_a							,     device=_a).to(_a)

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

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

		SCREAMING_SNAKE_CASE				: Optional[Any]			       =    Dataset(_a							,     _a)
		SCREAMING_SNAKE_CASE				: Optional[int]			       =    int(0.9 * len(_a))
		SCREAMING_SNAKE_CASE				: Optional[Any]			       =    len(_a) - train_size
		SCREAMING_SNAKE_CASE				: Optional[Any]			       =    torch.utils.data.random_split(_a							,     [train_size, test_size])

		SCREAMING_SNAKE_CASE				: Any			       =    {
		    "class_size": len(_a),
		    "embed_size": discriminator.embed_size,
		    "pretrained_model": pretrained_model,
		    "class_vocab": classaidx,
		    "default_class": 1,
		}

	elif dataset == "toxic":
		SCREAMING_SNAKE_CASE				: Optional[Any]			       =    ["non_toxic", "toxic"]
		SCREAMING_SNAKE_CASE				: int			       =    {c: i for i, c in enumerate(_a)}

		SCREAMING_SNAKE_CASE				: int			       =    Discriminator(
		    class_size=len(_a)							,     pretrained_model=_a							,     cached_mode=_a							,     device=_a).to(_a)

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

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

		SCREAMING_SNAKE_CASE				: Any			       =    Dataset(_a							,     _a)
		SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    int(0.9 * len(_a))
		SCREAMING_SNAKE_CASE				: Dict			       =    len(_a) - train_size
		SCREAMING_SNAKE_CASE				: Any			       =    torch.utils.data.random_split(_a							,     [train_size, test_size])

		SCREAMING_SNAKE_CASE				: List[Any]			       =    {
		    "class_size": len(_a),
		    "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				: str			       =    set()
		with open(_a) as f:
			SCREAMING_SNAKE_CASE				: Any			       =    csv.reader(_a							,     delimiter="\t")
			for row in tqdm(_a							,     ascii=_a):
				if row:
					classes.add(row[0])

		SCREAMING_SNAKE_CASE				: Optional[Any]			       =    sorted(_a)
		SCREAMING_SNAKE_CASE				: List[Any]			       =    {c: i for i, c in enumerate(_a)}

		SCREAMING_SNAKE_CASE				: Tuple			       =    Discriminator(
		    class_size=len(_a)							,     pretrained_model=_a							,     cached_mode=_a							,     device=_a).to(_a)

		SCREAMING_SNAKE_CASE				: Optional[int]			       =    []
		SCREAMING_SNAKE_CASE				: List[str]			       =    []
		with open(_a) as f:
			SCREAMING_SNAKE_CASE				: Any			       =    csv.reader(_a							,     delimiter="\t")
			for i, row in enumerate(tqdm(_a							,     ascii=_a)):
				if row:
					SCREAMING_SNAKE_CASE				: Dict			       =    row[0]
					SCREAMING_SNAKE_CASE				: List[Any]			       =    row[1]

					try:
						SCREAMING_SNAKE_CASE				: int			       =    discriminator.tokenizer.encode(_a)
						if len(_a) < max_length_seq:
							SCREAMING_SNAKE_CASE				: List[str]			       =    torch.tensor([50256] + seq							,     device=_a							,     dtype=torch.long)

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

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

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

		SCREAMING_SNAKE_CASE				: str			       =    Dataset(_a							,     _a)
		SCREAMING_SNAKE_CASE				: Optional[int]			       =    int(0.9 * len(_a))
		SCREAMING_SNAKE_CASE				: Optional[int]			       =    len(_a) - train_size
		SCREAMING_SNAKE_CASE				: Optional[Any]			       =    torch.utils.data.random_split(_a							,     [train_size, test_size])

		SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    {
		    "class_size": len(_a),
		    "embed_size": discriminator.embed_size,
		    "pretrained_model": pretrained_model,
		    "class_vocab": classaidx,
		    "default_class": 0,
		}

	SCREAMING_SNAKE_CASE				: Optional[Any]			       =    time.time()
	print("Preprocessed {} data points".format(len(_a) + len(_a)))
	print("Data preprocessing took: {:.3f}s".format(end - start))

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

		SCREAMING_SNAKE_CASE				: List[Any]			       =    time.time()

		SCREAMING_SNAKE_CASE				: int			       =    get_cached_data_loader(_a							,     _a							,     _a							,     shuffle=_a							,     device=_a)

		SCREAMING_SNAKE_CASE				: Tuple			       =    get_cached_data_loader(_a							,     _a							,     _a							,     device=_a)

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

	else:
		SCREAMING_SNAKE_CASE				: Tuple			       =    torch.utils.data.DataLoader(
		    dataset=_a							,     batch_size=_a							,     shuffle=_a							,     collate_fn=_a)
		SCREAMING_SNAKE_CASE				: Any			       =    torch.utils.data.DataLoader(dataset=_a							,     batch_size=_a							,     collate_fn=_a)

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

	SCREAMING_SNAKE_CASE				: Dict			       =    optim.Adam(discriminator.parameters()							,     lr=0.0001)

	for epoch in range(_a):
		SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    time.time()
		print("\nEpoch"							,     epoch + 1)

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

		SCREAMING_SNAKE_CASE				: List[str]			       =    time.time()
		print("Epoch took: {:.3f}s".format(end - start))

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

		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(_a							,     epoch + 1)							,     )


if __name__ == "__main__":
	a_         =      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_         =      parser.parse_args()

	train_discriminator(**(vars(args)))


from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(     self							:	Optional[int] , a							:	Any		)							->    None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: List[str]			       =    vocab
				SCREAMING_SNAKE_CASE				: str			       =    normalizers.BertNormalizer(
				    clean_text=a , handle_chinese_chars=a , strip_accents=a , lowercase=a , )
				try:
					import rjieba
				except ImportError:
					raise ImportError(
					    "You need to install rjieba to use RoFormerTokenizer. "
					    "See https://pypi.org/project/rjieba/ for installation."		)
				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    rjieba
			def 							__UpperCamelCase	(     self							:	Optional[int] , a							:	int , a							:	NormalizedString		)							->    List[NormalizedString]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Dict			       =    []

				# this code slice normalized_string is too slow (6s) but test_alignement_methods can pass
				for token, start, end in self.jieba.tokenize(str(a		) , hmm=a		):
					if token in self.vocab:
						splits.append(normalized_string[start:end]		)
					else:
						SCREAMING_SNAKE_CASE				: Any			       =    self.normalizers.normalize_str(a		).split()
						for token in token_list:
							if token:
								SCREAMING_SNAKE_CASE				: Optional[int]			       =    start + len(a		)
								splits.append(normalized_string[start:end]		)
								SCREAMING_SNAKE_CASE				: List[Any]			       =    end

        # this code test_alignement_methods can't pass but fast (300ms)
        # for token in self.jieba.cut(str(normalized_string), False):
        #     if token in self.vocab:
        #         splits.append(NormalizedString(token))
        #     else:
        #         token_list = self.normalizers.normalize_str(token).split()
        #         for token in token_list:
        #             if token:
        #                 splits.append(NormalizedString(token))

				return splits






			def 							__UpperCamelCase	(     self							:	Optional[int] , a							:	PreTokenizedString		)							->    Tuple:

				"""simple docstring"""

				pretok.split(self.jieba_split		)