import logging
import os
import sys
from dataclasses import dataclass, field
from typing import List, Optional

import torch
from datasets import Dataset
from torch import nn
from tqdm.auto import tqdm

from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
    utils,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


UpperCamelCase    				=		'\nDistills an NLI-based zero-shot classifier to a smaller, more efficient model with a fixed set of candidate class\nnames. Useful for speeding up zero-shot classification in cases where labeled training data is not available, but\nwhen only a single fixed set of classes is needed. Takes a teacher NLI model, student classifier model, unlabeled\ndataset, and set of K possible class names. Yields a single classifier with K outputs corresponding to the provided\nclass names.\n'

UpperCamelCase    				=		logging.getLogger(__name__)



@dataclass
class     snake_case_			:
   __A      :						Optional[str]   =							field(
       default="roberta-large-mnli"    ,metadata={"help": "The NLI/zero-shot teacher model to be distilled."}			)
   __A      :						Optional[str]   =							field(
       default="This example is {}."    ,metadata={
           "help": (
               "Template used to turn class names into mock hypotheses for teacher NLI model. Must include {{}}"
               "where class name is inserted."
           )
       }    ,)
   __A      :						Optional[int]   =							field(
       default=32    ,metadata={"help": "Batch size for generating teacher predictions."}			)
   __A      :						Optional[bool]   =							field(
       default=__A    ,metadata={
           "help": (
               "Allow multiple classes to be true rather than forcing them to sum to 1 (sometimes called"
               "multi-class multi-label classification)."
           )
       }    ,)
   __A      :						Optional[float]   =							field(
       default=1.0    ,metadata={"help": "Temperature applied to teacher softmax for distillation."}			)



@dataclass
class     snake_case_			:
   __A      :						Optional[str]   =							field(
       default="distilbert-base-uncased"    ,metadata={"help": "The NLI/zero-shot teacher model to be distilled."}			)



@dataclass
class     snake_case_			:
   __A      :						str   =							field(metadata={"help": "Text file with one unlabeled instance per line."}			)
   __A      :						str   =							field(metadata={"help": "Text file with one class name per line."}			)
   __A      :						bool   =							field(
       default=__A    ,metadata={"help": "Whether to use one of the fast tokenizer (backed by the Rust tokenizers library) or not."}    ,)



@dataclass
class     snake_case_			(						__A			):
   __A      :						Optional[str]   =							field(
       default=__A    ,metadata={"help": "The output directory where the model predictions and checkpoints will be written."}    ,)
   __A      :						int   =							field(
       default=32    ,metadata={"help": "Batch size per GPU/TPU core/CPU for training."}			)
   __A      :						int   =							field(
       default=128    ,metadata={"help": "Batch size per GPU/TPU core/CPU for evaluation."}			)
   __A      :						float   =							field(default=1.0    ,metadata={"help": "Total number of training epochs to perform."}			)
   __A      :						bool   =							field(default=__A    ,metadata={"help": "Whether to run training of student model."}			)
   __A      :						bool   =							field(
       default=__A    ,metadata={
           "help": (
               "Whether to evaluate the agreement of the final student predictions and the teacher predictions"
               "after training."
           )
       }    ,)
   __A      :						Optional[int]   =							field(
       default=0    ,metadata={
           "help": (
               "Limit the total amount of checkpoints."
               "Deletes the older checkpoints in the output_dir. Default is 0 (no checkpoints)."
           )
       }    ,)



class     snake_case_			(						__A			):






   def  __UpperCamelCase	( self							:	List[Any]      ,		lowercase_							:	Any      ,		lowercase_							:	Any      ,		lowercase_							:	int=False    )   ->		List[str]:
       lowercase__					:  Optional[Any]          =							inputs['labels']
       lowercase__					:  Optional[int]          =							model(inputs["input_ids"]      ,		attention_mask=inputs["attention_mask"]    )
       lowercase__					:  List[Any]          =							outputs[0]

       lowercase__					:  int          =							-torch.sum(target_p * logits.log_softmax(dim=-1    )      ,		axis=-1    ).mean()

       if return_outputs:
           return loss, outputs

       return loss



def      lowercase_							(     _lowerCamelCase				:			List[Any]):
    lowercase__					:  Optional[Any]          =							[]
    with open(_A						,      "r") as f:
        for line in f:
            lowercase__					:  Optional[int]          =							line.strip()
            if len(_A) > 0:
                lines.append(_A)
    return lines

def      lowercase_							(     _lowerCamelCase				:			Tuple						,      _lowerCamelCase				:			Tuple						,      _lowerCamelCase				:			List[str]):
    lowercase__					:  int          =							[]
    lowercase__					:  Tuple          =							[]
    for example in examples:
        for name in class_names:
            premises.append(_A)
            hypotheses.append(hypothesis_template.format(_A))
    return premises, hypotheses

def      lowercase_							(     _lowerCamelCase				:			Optional[Any]):





    for label, ind in config.labelaid.items():
        if label.lower().startswith("entail"):
            return ind
    logger.warning("Could not identify entailment dimension from teacher config label2id. Setting to -1.")
    return -1

def      lowercase_							(     _lowerCamelCase				:			str						,      _lowerCamelCase				:			List[str]						,      _lowerCamelCase				:			List[str]						,      _lowerCamelCase				:			str						,      _lowerCamelCase				:			int						,      _lowerCamelCase				:			float						,      _lowerCamelCase				:			bool						,      _lowerCamelCase				:			bool						,      _lowerCamelCase				:			bool						,      _lowerCamelCase				:			bool						,      ):
    lowercase__					:  Any          =							AutoModelForSequenceClassification.from_pretrained(_A)
    lowercase__					:  int          =							model.config
    if not no_cuda and torch.cuda.is_available():
        lowercase__					:  Dict          =							nn.DataParallel(model.cuda())
        batch_size *= len(model.device_ids)
    lowercase__					:  Tuple          =							AutoTokenizer.from_pretrained(_A						,      use_fast=_A)

    lowercase__					:  str          =							get_premise_hypothesis_pairs(_A						,      _A						,      _A)
    lowercase__					:  Optional[Any]          =							[]

    for i in tqdm(range(0						,      len(_A)						,      _A)):
        lowercase__					:  Optional[Any]          =							premises[i : i + batch_size]
        lowercase__					:  List[Any]          =							hypotheses[i : i + batch_size]

        lowercase__					:  Optional[Any]          =							tokenizer(
            _A						,      _A						,      padding=_A						,      truncation="only_first"						,      return_tensors="pt"						,      )

        with torch.cuda.amp.autocast(enabled=_A):
            with torch.no_grad():
                lowercase__					:  int          =							model(**_A)
        logits.append(outputs.logits.detach().cpu().float())

    lowercase__					:  Optional[Any]          =							get_entailment_id(_A)
    lowercase__					:  Any          =							-1 if entail_id == 0 else 0
    lowercase__					:  int          =							torch.cat(_A						,      dim=0)  # N*K x 3
    lowercase__					:  Optional[int]          =							logits.reshape(len(_A)						,      len(_A)						,      -1)[..., [contr_id, entail_id]]  # N x K x 2

    if multi_label:
        # softmax over (contr, entail) logits for each class independently
        lowercase__					:  Optional[Any]          =							(nli_logits / temperature).softmax(-1)
    else:
        # softmax over entail logits across classes s.t. class probabilities sum to 1.
        lowercase__					:  Optional[Any]          =							(nli_logits / temperature).softmax(1)

    return nli_prob[..., 1]  # N x K

def      lowercase_							(     ):
    lowercase__					:  Optional[int]          =							HfArgumentParser(
        (DataTrainingArguments, TeacherModelArguments, StudentModelArguments, DistillTrainingArguments)						,      description=_A						,      )

    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        lowercase__					:  Optional[Any]          =							parser.parse_json_file(
            json_file=os.path.abspath(sys.argv[1]))
    else:
        lowercase__					:  Union[str, Any]          =							parser.parse_args_into_dataclasses()

    # Detecting last checkpoint.
    lowercase__					:  Tuple          =							None
    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
        lowercase__					:  Optional[Any]          =							get_last_checkpoint(training_args.output_dir)
        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
            raise ValueError(
                f'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
                "Use --overwrite_output_dir to overcome.")
        elif last_checkpoint is not None:
            logger.info(
                f'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch.")

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"						,      datefmt="%m/%d/%Y %H:%M:%S"						,      handlers=[logging.StreamHandler(sys.stdout)]						,      )
    logger.setLevel(logging.INFO if is_main_process(training_args.local_rank) else logging.WARN)

    # Log on each process the small summary:
    logger.warning(
        f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
        + f'''distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fpaa}''')
    # Set the verbosity to info of the Transformers logger (on main process only):
    if is_main_process(training_args.local_rank):
        utils.logging.set_verbosity_info()
        utils.logging.enable_default_handler()
        utils.logging.enable_explicit_format()

    if training_args.local_rank != -1:
        raise ValueError("Distributed training is not currently supported.")
    if training_args.tpu_num_cores is not None:
        raise ValueError("TPU acceleration is not currently supported.")

    logger.info(f'''Training/evaluation parameters {training_args}''')

    # Set seed before initializing model.
    set_seed(training_args.seed)

    # 1. read in data
    lowercase__					:  Optional[Any]          =							read_lines(data_args.data_file)
    lowercase__					:  str          =							read_lines(data_args.class_names_file)

    # 2. get teacher predictions and load into dataset
    logger.info("Generating predictions from zero-shot teacher model")
    lowercase__					:  Optional[Any]          =							get_teacher_predictions(
        teacher_args.teacher_name_or_path						,      _A						,      _A						,      teacher_args.hypothesis_template						,      teacher_args.teacher_batch_size						,      teacher_args.temperature						,      teacher_args.multi_label						,      data_args.use_fast_tokenizer						,      training_args.no_cuda						,      training_args.fpaa						,      )
    lowercase__					:  Any          =							Dataset.from_dict(
        {
            "text": examples,
            "labels": teacher_soft_preds,
        })

    # 3. create student
    logger.info("Initializing student model")
    lowercase__					:  int          =							AutoModelForSequenceClassification.from_pretrained(
        student_args.student_name_or_path						,      num_labels=len(_A))
    lowercase__					:  str          =							AutoTokenizer.from_pretrained(student_args.student_name_or_path						,      use_fast=data_args.use_fast_tokenizer)
    lowercase__					:  Tuple          =							dict(enumerate(_A))
    lowercase__					:  Optional[int]          =							{label: i for i, label in enumerate(_A)}

    # 4. train student on teacher predictions
    lowercase__					:  Optional[int]          =							dataset.map(_A						,      input_columns="text")
    dataset.set_format("torch")

    def compute_metrics(_lowerCamelCase				:			Tuple						,      _lowerCamelCase				:			int=False):
        lowercase__					:  List[Any]          =							p.predictions.argmax(-1)
        lowercase__					:  Any          =							p.label_ids.argmax(-1)  # "label_ids" are actually distributions
        return {"agreement": (preds == proxy_labels).mean().item()}

    lowercase__					:  List[str]          =							DistillationTrainer(
        model=_A						,      tokenizer=_A						,      args=_A						,      train_dataset=_A						,      compute_metrics=_A						,      )

    if training_args.do_train:
        logger.info("Training student model on teacher predictions")
        trainer.train()

    if training_args.do_eval:
        lowercase__					:  Any          =							trainer.evaluate(eval_dataset=_A)['eval_agreement']
        logger.info(f'''Agreement of student and teacher predictions: {agreement * 100:0.2f}%''')

    trainer.save_model()


if __name__ == "__main__":
      main()







'''simple docstring'''




import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_lowerCAmelCase	:		Tuple		  =						logging.get_logger(__name__)

_lowerCAmelCase	:		Dict		  =						{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

_lowerCAmelCase	:		Optional[int]		  =						{
    'vocab_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/vocab.json',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/vocab.json',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/vocab.json',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/merges.txt',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/merges.txt',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/merges.txt',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'
        ),
    },
}

_lowerCAmelCase	:		Tuple		  =						{
    'roberta-base': 512,
    'roberta-large': 512,
    'roberta-large-mnli': 512,
    'distilroberta-base': 512,
    'roberta-base-openai-detector': 512,
    'roberta-large-openai-detector': 512,
}

@lru_cache()
def       __UpperCamelCase     (     )     ->   Any:




	"""simple docstring"""





	lowerCAmelCase       :     Any   				=			(
	    list(range(ord('!'					)       , ord('~'					) + 1					)					) + list(range(ord('¡'					)       , ord('¬'					) + 1					)					) + list(range(ord('®'					)       , ord('ÿ'					) + 1					)					)
	)
	lowerCAmelCase       :     Optional[int]   				=			bs[:]
	lowerCAmelCase       :     Optional[int]   				=			0
	for b in range(2**8					):
		if b not in bs:
			bs.append(_A					)
			cs.append(2**8 + n					)
			n += 1
	lowerCAmelCase       :     int   				=			[chr(_A					) for n in cs]
	return dict(zip(_A       , _A					)					)

def       __UpperCamelCase     (     _A     :					Union[str, Any]					)     ->   Any:




	"""simple docstring"""





	lowerCAmelCase       :     Tuple   				=			set()
	lowerCAmelCase       :     List[Any]   				=			word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)					)
		lowerCAmelCase       :     Dict   				=			char
	return pairs






class 			lowerCAmelCase					(					a				):
	_lowerCamelCase :       int         =			VOCAB_FILES_NAMES
	_lowerCamelCase :       Any         =			PRETRAINED_VOCAB_FILES_MAP
	_lowerCamelCase :       int         =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_lowerCamelCase :       Optional[Any]         =			["""input_ids""", """attention_mask"""]



	def __init__(       self			,       snake_case__			,       snake_case__			,       snake_case__="replace"			,       snake_case__="<s>"			,       snake_case__="</s>"			,       snake_case__="</s>"			,       snake_case__="<s>"			,       snake_case__="<unk>"			,       snake_case__="<pad>"			,       snake_case__="<mask>"			,       snake_case__=False			,       **snake_case__			,       ):
		lowerCAmelCase       :     Optional[Any]   				=			AddedToken(snake_case__			,       lstrip=snake_case__			,       rstrip=snake_case__     ) if isinstance(snake_case__			,       snake_case__     ) else bos_token
		lowerCAmelCase       :     List[Any]   				=			AddedToken(snake_case__			,       lstrip=snake_case__			,       rstrip=snake_case__     ) if isinstance(snake_case__			,       snake_case__     ) else eos_token
		lowerCAmelCase       :     Any   				=			AddedToken(snake_case__			,       lstrip=snake_case__			,       rstrip=snake_case__     ) if isinstance(snake_case__			,       snake_case__     ) else sep_token
		lowerCAmelCase       :     str   				=			AddedToken(snake_case__			,       lstrip=snake_case__			,       rstrip=snake_case__     ) if isinstance(snake_case__			,       snake_case__     ) else cls_token
		lowerCAmelCase       :     Tuple   				=			AddedToken(snake_case__			,       lstrip=snake_case__			,       rstrip=snake_case__     ) if isinstance(snake_case__			,       snake_case__     ) else unk_token
		lowerCAmelCase       :     List[Any]   				=			AddedToken(snake_case__			,       lstrip=snake_case__			,       rstrip=snake_case__     ) if isinstance(snake_case__			,       snake_case__     ) else pad_token

		# Mask token behave like a normal word, i.e. include the space before it
		lowerCAmelCase       :     List[str]   				=			AddedToken(snake_case__			,       lstrip=snake_case__			,       rstrip=snake_case__     ) if isinstance(snake_case__			,       snake_case__     ) else mask_token

		super().__init__(
		    errors=snake_case__			,       bos_token=snake_case__			,       eos_token=snake_case__			,       unk_token=snake_case__			,       sep_token=snake_case__			,       cls_token=snake_case__			,       pad_token=snake_case__			,       mask_token=snake_case__			,       add_prefix_space=snake_case__			,       **snake_case__			,       )

		with open(snake_case__			,       encoding='utf-8'     ) as vocab_handle:
			lowerCAmelCase       :     Tuple   				=			json.load(snake_case__     )
		lowerCAmelCase       :     Union[str, Any]   				=			{v: k for k, v in self.encoder.items()}
		lowerCAmelCase       :     List[str]   				=			errors  # how to handle errors in decoding
		lowerCAmelCase       :     Tuple   				=			bytes_to_unicode()
		lowerCAmelCase       :     Dict   				=			{v: k for k, v in self.byte_encoder.items()}
		with open(snake_case__			,       encoding='utf-8'     ) as merges_handle:
			lowerCAmelCase       :     List[str]   				=			merges_handle.read().split('\n'     )[1:-1]
		lowerCAmelCase       :     Tuple   				=			[tuple(merge.split()     ) for merge in bpe_merges]
		lowerCAmelCase       :     Optional[Any]   				=			dict(zip(snake_case__			,       range(len(snake_case__     )     )     )     )
		lowerCAmelCase       :     List[str]   				=			{}
		lowerCAmelCase       :     Dict   				=			add_prefix_space

		# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
		lowerCAmelCase       :     Dict   				=			re.compile(R'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'     )



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



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



	def 					lowercase  (       self			,       snake_case__     ):
		if token in self.cache:
			return self.cache[token]
		lowerCAmelCase       :     List[str]   				=			tuple(snake_case__     )
		lowerCAmelCase       :     Union[str, Any]   				=			get_pairs(snake_case__     )

		if not pairs:
			return token

		while True:
			lowerCAmelCase       :     str   				=			min(snake_case__			,       key=lambda snake_case__     : self.bpe_ranks.get(snake_case__			,       float('inf'     )     )     )
			if bigram not in self.bpe_ranks:
				break
			lowerCAmelCase				, lowerCAmelCase       :     List[Any]   				=			bigram
			lowerCAmelCase       :     Optional[int]   				=			[]
			lowerCAmelCase       :     Union[str, Any]   				=			0
			while i < len(snake_case__     ):
				try:
					lowerCAmelCase       :     Union[str, Any]   				=			word.index(snake_case__			,       snake_case__     )
				except ValueError:
					new_word.extend(word[i:]     )
					break
				else:
					new_word.extend(word[i:j]     )
					lowerCAmelCase       :     List[str]   				=			j

				if word[i] == first and i < len(snake_case__     ) - 1 and word[i + 1] == second:
					new_word.append(first + second     )
					i += 2
				else:
					new_word.append(word[i]     )
					i += 1
			lowerCAmelCase       :     int   				=			tuple(snake_case__     )
			lowerCAmelCase       :     List[Any]   				=			new_word
			if len(snake_case__     ) == 1:
				break
			else:
				lowerCAmelCase       :     Union[str, Any]   				=			get_pairs(snake_case__     )
		lowerCAmelCase       :     Optional[Any]   				=			' '.join(snake_case__     )
		lowerCAmelCase       :     Any   				=			word
		return word



	def 					lowercase  (       self			,       snake_case__     ):
		lowerCAmelCase       :     List[Any]   				=			[]
		for token in re.findall(self.pat			,       snake_case__     ):
			lowerCAmelCase       :     Dict   				=			''.join(
			    self.byte_encoder[b] for b in token.encode('utf-8'     )     )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
			bpe_tokens.extend(bpe_token for bpe_token in self.bpe(snake_case__     ).split(' '     )     )
		return bpe_tokens



	def 					lowercase  (       self			,       snake_case__     ):
		return self.encoder.get(snake_case__			,       self.encoder.get(self.unk_token     )     )



	def 					lowercase  (       self			,       snake_case__     ):
		return self.decoder.get(snake_case__     )



	def 					lowercase  (       self			,       snake_case__     ):
		lowerCAmelCase       :     Union[str, Any]   				=			''.join(snake_case__     )
		lowerCAmelCase       :     Any   				=			bytearray([self.byte_decoder[c] for c in text]     ).decode('utf-8'			,       errors=self.errors     )
		return text



	def 					lowercase  (       self			,       snake_case__			,       snake_case__ = None     ):
		if not os.path.isdir(snake_case__     ):
			logger.error(f"Vocabulary path ({save_directory}) should be a directory"     )
			return
		lowerCAmelCase       :     Union[str, Any]   				=			os.path.join(
		    snake_case__			,       (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']     )
		lowerCAmelCase       :     int   				=			os.path.join(
		    snake_case__			,       (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']     )

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

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

		return vocab_file, merge_file



	def 					lowercase  (       self			,       snake_case__			,       snake_case__ = None     ):
		if token_ids_a is None:
			return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
		lowerCAmelCase       :     Optional[Any]   				=			[self.cls_token_id]
		lowerCAmelCase       :     List[str]   				=			[self.sep_token_id]
		return cls + token_ids_a + sep + sep + token_ids_a + sep



	def 					lowercase  (       self			,       snake_case__			,       snake_case__ = None			,       snake_case__ = False     ):
		if already_has_special_tokens:
			return super().get_special_tokens_mask(
			    token_ids_a=snake_case__			,       token_ids_a=snake_case__			,       already_has_special_tokens=snake_case__     )

		if token_ids_a is None:
			return [1] + ([0] * len(snake_case__     )) + [1]
		return [1] + ([0] * len(snake_case__     )) + [1, 1] + ([0] * len(snake_case__     )) + [1]



	def 					lowercase  (       self			,       snake_case__			,       snake_case__ = None     ):
		lowerCAmelCase       :     Union[str, Any]   				=			[self.sep_token_id]
		lowerCAmelCase       :     Tuple   				=			[self.cls_token_id]

		if token_ids_a is None:
			return len(cls + token_ids_a + sep     ) * [0]
		return len(cls + token_ids_a + sep + sep + token_ids_a + sep     ) * [0]



	def 					lowercase  (       self			,       snake_case__			,       snake_case__=False			,       **snake_case__     ):
		lowerCAmelCase       :     List[str]   				=			kwargs.pop('add_prefix_space'			,       self.add_prefix_space     )
		if (is_split_into_words or add_prefix_space) and (len(snake_case__     ) > 0 and not text[0].isspace()):
			lowerCAmelCase       :     Optional[Any]   				=			' ' + text
		return (text, kwargs)
