


"""simple docstring"""



import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional

import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm

import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy


UpperCAmelCase      		=			logging.getLogger(__name__)

UpperCAmelCase      		=			'''pytorch_model.bin'''
@dataclasses.dataclass
class 					__magic_name__		:
       __A			:      str        =   dataclasses.field(
           metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models."}     )
       __A			:      Optional[str]        =   dataclasses.field(
           default=__UpperCAmelCase			,						metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co."}			,						)







@dataclasses.dataclass
class 					__magic_name__		:
       __A			:      str        =   dataclasses.field(metadata={"help": "A csv or a json file containing the training data."}     )
       __A			:      str        =   dataclasses.field(metadata={"help": "A csv or a json file containing the data to predict on."}     )
       __A			:      Optional[str]        =   dataclasses.field(
           default=__UpperCAmelCase			,						metadata={"help": "A csv or a json file containing the validation data."}     )
       __A			:      Optional[str]        =   dataclasses.field(
           default=__UpperCAmelCase			,						metadata={"help": "The name of the task to train on."}			,						)
       __A			:      Optional[List[str]]        =   dataclasses.field(
           default=__UpperCAmelCase			,						metadata={"help": "The list of labels for the task."}     )







@dataclasses.dataclass
class 					__magic_name__		:
       __A			:      str        =   dataclasses.field(
           metadata={"help": "The output directory where the model predictions and checkpoints will be written."}     )
       __A			:      Optional[str]        =   dataclasses.field(
           default="accuracy"			,						metadata={"help": "The evaluation metric used for the task."}     )
       __A			:      Optional[str]        =   dataclasses.field(
           default="no"			,						metadata={
               "help": "The evaluation strategy to adopt during training. Possible values are: [\"no\", \"step\", \"epoch]"
           }			,						)
       __A			:      Optional[int]        =   dataclasses.field(
           default=10			,						metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."}			,						)
       __A			:      Optional[float]        =   dataclasses.field(
           default=0.0			,						metadata={
               "help": "How much the specified evaluation metric must improve to satisfy early stopping conditions."
           }			,						)
       __A			:      Optional[bool]        =   dataclasses.field(
           default=__UpperCAmelCase			,						metadata={"help": "Whether to filter the pseudo-labeled data based on the confidence score."}			,						)
       __A			:      Optional[bool]        =   dataclasses.field(
           default=__UpperCAmelCase			,						metadata={"help": "Whether to filter the pseudo-labeled data based on the validation performance."}			,						)
       __A			:      Optional[bool]        =   dataclasses.field(
           default=__UpperCAmelCase			,						metadata={"help": "Whether to fine-tune on labeled data after pseudo training."}			,						)
       __A			:      Optional[float]        =   dataclasses.field(
           default=0.0			,						metadata={"help": "Confidence threshold for pseudo-labeled data filtering."}			,						)
       __A			:      Optional[int]        =   dataclasses.field(
           default=1_00			,						metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."}			,						)
       __A			:      Optional[int]        =   dataclasses.field(
           default=__UpperCAmelCase			,						metadata={"help": "Random seed for initialization."}			,						)







def       lowerCamelCase					(a_						:List[str]				,				a_						:List[Any]				,				a_						:List[Any]				,				a_						:Tuple				,				a_						:Optional[Any]				,				a_						:Any)     ->							List[str]:
       lowercase		:str       							=				datasets.concatenate_datasets([infer_input, infer_output]				,				axis=1)

       if args.do_filter_by_confidence:
              lowercase		:int       							=				dataset.filter(lambda a_: example["probability"] > args.confidence_threshold)

       if args.do_filter_by_val_performance:
              assert eval_result >= 0.0 and eval_result <= 1.0
              lowercase		:str       							=				int(eval_result * len(a_))
              print(a_)
              lowercase		:str       							=				dataset.sort('''probability'''				,				reverse=a_)
              lowercase		:List[Any]       							=				dataset.select(range(a_))

       lowercase		:int       							=				dataset.remove_columns(['''label''', '''probability'''])
       lowercase		:List[str]       							=				dataset.rename_column('''prediction'''				,				'''label''')
       lowercase		:List[str]       							=				dataset.map(lambda a_: {"label": idalabel[example["label"]]})
       lowercase		:List[Any]       							=				dataset.shuffle(seed=args.seed)

       lowercase		:str       							=				os.path.join(a_				,				F"""train_pseudo.{args.data_file_extension}""")
       if args.data_file_extension == "csv":
              dataset.to_csv(a_				,				index=a_)
       else:
              dataset.to_json(a_)






def       lowerCamelCase					(a_						:Any				,				a_						:int				,				a_						:Tuple				,				a_						:Tuple				,				**a_						:Optional[int])     ->							Any:
       lowercase		:Tuple       							=				Accelerator()
       # Make one log on every process with the configuration for debugging.
       logging.basicConfig(
           format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''				,				datefmt='''%m/%d/%Y %H:%M:%S'''				,				level=logging.INFO				,				)
       logger.info(accelerator.state)

       # Setup logging, we only want one process per machine to log things on the
       # screen. accelerator.is_local_main_process is only True for one process per
       # machine.
       logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)

       if accelerator.is_local_main_process:
              datasets.utils.logging.set_verbosity_warning()
              transformers.utils.logging.set_verbosity_info()
       else:
              datasets.utils.logging.set_verbosity_error()
              transformers.utils.logging.set_verbosity_error()

       lowercase		:Optional[int]       							=				STModelArguments(model_name_or_path=a_)
       lowercase		:Union[str, Any]       							=				STDataArguments(train_file=a_				,				infer_file=a_)
       lowercase		:Dict       							=				STTrainingArguments(output_dir=a_)
       lowercase		:Dict       							=				argparse.Namespace()

       for arg_class in (model_args, data_args, training_args):
              for key, value in vars(a_).items():
                     setattr(a_				,				a_				,				a_)

       for key, value in kwargs.items():
              if hasattr(a_				,				a_):
                     setattr(a_				,				a_				,				a_)

    # Sanity checks
       lowercase		:Dict       							=				{}
       lowercase		:Any       							=				None

       # You need to provide the training data and the data to predict on
       assert args.train_file is not None
       assert args.infer_file is not None
       lowercase		:Union[str, Any]       							=				args.train_file
       lowercase		:int       							=				args.infer_file

       if args.evaluation_strategy != IntervalStrategy.NO.value:
              assert args.eval_file is not None
              lowercase		:Union[str, Any]       							=				args.eval_file

       for key in data_files:
              lowercase		:Dict       							=				data_files[key].split('''.''')[-1]
              assert extension in ["csv", "json"], F"""`{key}_file` should be a csv or a json file."""
              if args.data_file_extension is None:
                     lowercase		:Union[str, Any]       							=				extension
              else:
                     assert extension == args.data_file_extension, F"""`{key}_file` should be a {args.data_file_extension} file`."""

       assert (
           args.eval_metric in datasets.list_metrics()
       ), F"""{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}."""

       # If passed along, set the training seed now.
       if args.seed is not None:
              set_seed(args.seed)

       logger.info('''Creating the initial data directory for self-training...''')
       lowercase		:Any       							=				F"""{args.output_dir}/self-train_iter-{{}}""".format
       lowercase		:Any       							=				data_dir_format(0)

       if accelerator.is_main_process:
              if args.output_dir is not None:
                     os.makedirs(args.output_dir				,				exist_ok=a_)
                     os.makedirs(a_				,				exist_ok=a_)
       accelerator.wait_for_everyone()

       lowercase		:Optional[int]       							=				None
       lowercase		:List[Any]       							=				None
       lowercase		:Optional[Any]       							=				0
       lowercase		:List[str]       							=				False
       # Show the progress bar
       lowercase		:Optional[int]       							=				tqdm(range(args.max_selftrain_iterations)				,				disable=not accelerator.is_local_main_process)

       # Self-train
       for iteration in range(0				,				int(args.max_selftrain_iterations)):
              lowercase		:List[Any]       							=				data_dir_format(a_)
              assert os.path.exists(a_)

              # Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
              # iteration > 0
              lowercase		:Dict       							=				os.path.join(a_				,				'''stage-1''')
              lowercase		:Dict       							=				{
                  '''accelerator''': accelerator,
                  '''model_name_or_path''': args.model_name_or_path,
                  '''cache_dir''': args.cache_dir,
                  '''do_train''': True,
                  '''train_file''': data_files['''train'''] if iteration == 0 else data_files['''train_pseudo'''],
                  '''do_eval''': True if args.eval_file is not None else False,
                  '''eval_file''': data_files['''eval'''],
                  '''do_predict''': True,
                  '''infer_file''': data_files['''infer'''],
                  '''task_name''': args.task_name,
                  '''label_list''': args.label_list,
                  '''output_dir''': current_output_dir,
                  '''eval_metric''': args.eval_metric,
                  '''evaluation_strategy''': args.evaluation_strategy,
                  '''early_stopping_patience''': args.early_stopping_patience,
                  '''early_stopping_threshold''': args.early_stopping_threshold,
                  '''seed''': args.seed,
              }
              # Add additional training arguments
              for key, value in kwargs.items():
                     if key not in arguments_dict and not hasattr(a_				,				a_):
                            arguments_dict.update({key: value})

              lowercase		:Tuple       							=				os.path.join(a_				,				'''best-checkpoint'''				,				a_)
              if os.path.exists(a_):
                     logger.info(
                         '''Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1.'''				,				a_				,				a_				,				)
              else:
                     logger.info('''***** Running self-training: iteration: %d, stage: 1 *****'''				,				a_)
                     finetune(**a_)
                     accelerator.wait_for_everyone()
                     assert os.path.exists(a_)
                     logger.info('''Self-training job completed: iteration: %d, stage: 1.'''				,				a_)

              if iteration > 0 and args.finetune_on_labeled_data:
                     # Stage 2 (optional): fine-tuning on the original labeled data
                     lowercase		:Tuple       							=				os.path.join(a_				,				'''best-checkpoint''')
                     lowercase		:str       							=				os.path.join(a_				,				'''stage-2''')
                     # Update arguments_dict
                     lowercase		:List[Any]       							=				model_path
                     lowercase		:Optional[int]       							=				data_files['''train''']
                     lowercase		:str       							=				current_output_dir

                     lowercase		:List[str]       							=				os.path.join(a_				,				'''best-checkpoint'''				,				a_)
                     if os.path.exists(a_):
                            logger.info(
                                '''Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2.'''				,				a_				,				a_				,				)
                     else:
                            logger.info('''***** Running self-training: iteration: %d, stage: 2 *****'''				,				a_)
                            finetune(**a_)
                            accelerator.wait_for_everyone()
                            assert os.path.exists(a_)
                            logger.info('''Self-training job completed: iteration: %d, stage: 2.'''				,				a_)

              lowercase		:Union[str, Any]       							=				iteration
              lowercase		:Optional[Any]       							=				data_dir_format(iteration + 1)

              lowercase		:Optional[Any]       							=				AutoConfig.from_pretrained(os.path.join(a_				,				'''best-checkpoint'''))
              lowercase		:List[Any]       							=				config.idalabel
              lowercase		:Optional[Any]       							=				os.path.join(a_				,				'''eval_results_best-checkpoint.json''')
              lowercase		:Optional[int]       							=				os.path.join(a_				,				'''test_results_best-checkpoint.json''')
              assert os.path.exists(a_)

              with open(a_				,				'''r''') as f:
                     lowercase		:Dict       							=				float(json.load(a_)[args.eval_metric])
              lowercase		:Optional[int]       							=				os.path.join(a_				,				'''infer_output_best-checkpoint.csv''')
              assert os.path.exists(a_)
              # Loading the dataset from local csv or json files.
              lowercase		:Tuple       							=				load_dataset(args.data_file_extension				,				data_files={'''data''': data_files['''infer''']})['''data''']
              lowercase		:Optional[Any]       							=				load_dataset('''csv'''				,				data_files={'''data''': infer_output_file})['''data''']

              if accelerator.is_main_process:
                     os.makedirs(a_				,				exist_ok=a_)
                     shutil.copy(a_				,				os.path.join(a_				,				F"""eval_results_iter-{iteration}.json"""))
                     if os.path.exists(a_):
                            shutil.copy(a_				,				os.path.join(a_				,				F"""test_results_iter-{iteration}.json"""))
                     create_pseudo_labeled_data(a_				,				a_				,				a_				,				a_				,				a_				,				a_)
              accelerator.wait_for_everyone()

              lowercase		:Optional[int]       							=				os.path.join(a_				,				F"""train_pseudo.{args.data_file_extension}""")

              if args.evaluation_strategy != IntervalStrategy.NO.value:
                     lowercase		:List[Any]       							=				eval_result

                     if best_iteration is None:
                            lowercase		:List[str]       							=				new_iteration
                            lowercase		:Union[str, Any]       							=				new_eval_result
                     else:
                            if new_eval_result - best_eval_result > args.early_stopping_threshold:
                                   lowercase		:Optional[int]       							=				new_iteration
                                   lowercase		:Tuple       							=				new_eval_result
                                   lowercase		:Union[str, Any]       							=				0
                            else:
                                   if new_eval_result == best_eval_result:
                                          lowercase		:Optional[int]       							=				new_iteration
                                          lowercase		:Any       							=				new_eval_result
                                   early_stopping_patience_counter += 1

                            if early_stopping_patience_counter >= args.early_stopping_patience:
                                   lowercase		:Optional[int]       							=				True

              progress_bar.update(1)

              if should_training_stop:
                     break

       if best_iteration is not None:
              # Save the best iteration
              logger.info('''Best iteration: %d'''				,				a_)
              logger.info('''Best evaluation result: %s = %f'''				,				args.eval_metric				,				a_)
              accelerator.wait_for_everyone()
              if accelerator.is_main_process:
                     shutil.copy(
                         os.path.join(a_				,				F"""eval_results_iter-{iteration}.json""")				,				os.path.join(a_				,				'''eval_results_best-iteration.json''')				,				)
       else:
              # Assume that the last iteration is the best
              logger.info('''Best iteration: %d'''				,				args.max_selftrain_iterations - 1)
              logger.info('''Best evaluation result: %s = %f'''				,				args.eval_metric				,				a_)
              accelerator.wait_for_everyone()
              if accelerator.is_main_process:
                     shutil.copy(
                         os.path.join(a_				,				F"""eval_results_iter-{args.max_selftrain_iterations - 1}.json""")				,				os.path.join(a_				,				'''eval_results_best-iteration.json''')				,				)





"""simple docstring"""



import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


UpperCAmelCase      		=			logging.get_logger(__name__)


UpperCAmelCase      		=			{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

# See all BART models at https://huggingface.co/models?filter=bart
UpperCAmelCase      		=			{
    '''vocab_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/vocab.json''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/vocab.json''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/merges.txt''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/merges.txt''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json''',
    },
}

UpperCAmelCase      		=			{
    '''facebook/bart-base''': 1_024,
    '''facebook/bart-large''': 1_024,
    '''facebook/bart-large-mnli''': 1_024,
    '''facebook/bart-large-cnn''': 1_024,
    '''facebook/bart-large-xsum''': 1_024,
    '''yjernite/bart_eli5''': 1_024,
}
class 					__magic_name__		(			__UpperCAmelCase     ):
       __A			:      str        =   VOCAB_FILES_NAMES
       __A			:      int        =   PRETRAINED_VOCAB_FILES_MAP
       __A			:      List[Any]        =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __A			:      Optional[int]        =   ["input_ids", "attention_mask"]
       __A			:      List[Any]        =   BartTokenizer






       def __init__(   self  :    List[Any]  ,     snake_case__  :    Dict=None  ,     snake_case__  :    Dict=None  ,     snake_case__  :    Dict=None  ,     snake_case__  :    Optional[int]="replace"  ,     snake_case__  :    int="<s>"  ,     snake_case__  :    Optional[Any]="</s>"  ,     snake_case__  :    str="</s>"  ,     snake_case__  :    Union[str, Any]="<s>"  ,     snake_case__  :    Optional[Any]="<unk>"  ,     snake_case__  :    Tuple="<pad>"  ,     snake_case__  :    str="<mask>"  ,     snake_case__  :    List[Any]=False  ,     snake_case__  :    Tuple=True  ,     **snake_case__  :    Dict  ,     ):

              '''simple docstring'''






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

              lowercase		:Dict       							=				json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()				)
              if pre_tok_state.get('''add_prefix_space'''  ,     snake_case__				) != add_prefix_space:
                     lowercase		:Union[str, Any]       							=				getattr(snake_case__  ,     pre_tok_state.pop('''type'''				)				)
                     lowercase		:Tuple       							=				add_prefix_space
                     lowercase		:int       							=				pre_tok_class(**snake_case__				)

              lowercase		:str       							=				add_prefix_space

              # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
              lowercase		:Dict       							=				'''post_processor'''
              lowercase		:Optional[int]       							=				getattr(self.backend_tokenizer  ,     snake_case__  ,     snake_case__				)
              if tokenizer_component_instance:
                     lowercase		:Tuple       							=				json.loads(tokenizer_component_instance.__getstate__()				)

                     # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
                     if "sep" in state:
                            lowercase		:Optional[Any]       							=				tuple(state['''sep''']				)
                     if "cls" in state:
                            lowercase		:Tuple       							=				tuple(state['''cls''']				)

                     lowercase		:int       							=				False

                     if state.get('''add_prefix_space'''  ,     snake_case__				) != add_prefix_space:
                            lowercase		:Dict       							=				add_prefix_space
                            lowercase		:Optional[int]       							=				True

                     if state.get('''trim_offsets'''  ,     snake_case__				) != trim_offsets:
                            lowercase		:Any       							=				trim_offsets
                            lowercase		:Dict       							=				True

                     if changes_to_apply:
                            lowercase		:Dict       							=				getattr(snake_case__  ,     state.pop('''type'''				)				)
                            lowercase		:List[str]       							=				component_class(**snake_case__				)
                            setattr(self.backend_tokenizer  ,     snake_case__  ,     snake_case__				)






       @property
       def  __snake_case						(   self  :    Dict				):

              '''simple docstring'''






              if self._mask_token is None:
                     if self.verbose:
                            logger.error('''Using mask_token, but it is not set yet.'''				)
                     return None
              return str(self._mask_token				)






       @mask_token.setter
       def  __snake_case						(   self  :    List[Any]  ,     snake_case__  :    Optional[int]				):

              '''simple docstring'''






              lowercase		:Dict       							=				AddedToken(snake_case__  ,     lstrip=snake_case__  ,     rstrip=snake_case__				) if isinstance(snake_case__  ,     snake_case__				) else value
              lowercase		:Dict       							=				value






       def  __snake_case						(   self  :    Optional[int]  ,     *snake_case__  :    str  ,     **snake_case__  :    str				):

              '''simple docstring'''






              lowercase		:Any       							=				kwargs.get('''is_split_into_words'''  ,     snake_case__				)

              if is_split_into_words and not self.add_prefix_space:
                     raise ValueError(
                         f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
                         '''to use it with pretokenized inputs.'''				)

              return super()._batch_encode_plus(*snake_case__  ,     **snake_case__				)






       def  __snake_case						(   self  :    int  ,     *snake_case__  :    int  ,     **snake_case__  :    int				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				kwargs.get('''is_split_into_words'''  ,     snake_case__				)

              if is_split_into_words and not self.add_prefix_space:
                     raise ValueError(
                         f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
                         '''to use it with pretokenized inputs.'''				)

              return super()._encode_plus(*snake_case__  ,     **snake_case__				)






       def  __snake_case						(   self  :    Dict  ,     snake_case__  :    str  ,     snake_case__  :    Optional[str] = None				):

              '''simple docstring'''






              lowercase		:List[Any]       							=				self._tokenizer.model.save(snake_case__  ,     name=snake_case__				)
              return tuple(snake_case__				)






       def  __snake_case						(   self  :    int  ,     snake_case__  :    Any  ,     snake_case__  :    int=None				):

              '''simple docstring'''






              lowercase		:Any       							=				[self.bos_token_id] + token_ids_a + [self.eos_token_id]
              if token_ids_a is None:
                     return output

              return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







       def  __snake_case						(   self  :    List[Any]  ,     snake_case__  :    List[int]  ,     snake_case__  :    Optional[List[int]] = None				):

              '''simple docstring'''






              lowercase		:int       							=				[self.sep_token_id]
              lowercase		: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]

