



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


__snake_case       :	Any            =logging.getLogger(__name__)

__snake_case       :	List[str]            ='pytorch_model.bin'



@dataclasses.dataclass
class  lowerCamelCase__    :




  '''simple docstring'''



  snake_case_			=dataclasses.field(
      metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models."""})
  snake_case_			=dataclasses.field(
      default=lowerCamelCase__   ,       metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co."""}   ,       )



@dataclasses.dataclass
class  lowerCamelCase__    :




  '''simple docstring'''



  snake_case_			=dataclasses.field(metadata={"""help""": """A csv or a json file containing the training data."""})
  snake_case_			=dataclasses.field(metadata={"""help""": """A csv or a json file containing the data to predict on."""})
  snake_case_			=dataclasses.field(
      default=lowerCamelCase__   ,       metadata={"""help""": """A csv or a json file containing the validation data."""})
  snake_case_			=dataclasses.field(
      default=lowerCamelCase__   ,       metadata={"""help""": """The name of the task to train on."""}   ,       )
  snake_case_			=dataclasses.field(
      default=lowerCamelCase__   ,       metadata={"""help""": """The list of labels for the task."""})



@dataclasses.dataclass
class  lowerCamelCase__    :




  '''simple docstring'''



  snake_case_			=dataclasses.field(
      metadata={"""help""": """The output directory where the model predictions and checkpoints will be written."""})
  snake_case_			=dataclasses.field(
      default="""accuracy"""   ,       metadata={"""help""": """The evaluation metric used for the task."""})
  snake_case_			=dataclasses.field(
      default="""no"""   ,       metadata={
          """help""": """The evaluation strategy to adopt during training. Possible values are: [\"no\", \"step\", \"epoch]"""
      }   ,       )
  snake_case_			=dataclasses.field(
      default=10   ,       metadata={"""help""": """Number of evaluation calls with no improvement after which training will be stopped."""}   ,       )
  snake_case_			=dataclasses.field(
      default=0.0   ,       metadata={
          """help""": """How much the specified evaluation metric must improve to satisfy early stopping conditions."""
      }   ,       )
  snake_case_			=dataclasses.field(
      default=lowerCamelCase__   ,       metadata={"""help""": """Whether to filter the pseudo-labeled data based on the confidence score."""}   ,       )
  snake_case_			=dataclasses.field(
      default=lowerCamelCase__   ,       metadata={"""help""": """Whether to filter the pseudo-labeled data based on the validation performance."""}   ,       )
  snake_case_			=dataclasses.field(
      default=lowerCamelCase__   ,       metadata={"""help""": """Whether to fine-tune on labeled data after pseudo training."""}   ,       )
  snake_case_			=dataclasses.field(
      default=0.0   ,       metadata={"""help""": """Confidence threshold for pseudo-labeled data filtering."""}   ,       )
  snake_case_			=dataclasses.field(
      default=100   ,       metadata={"""help""": """Number of evaluation calls with no improvement after which training will be stopped."""}   ,       )
  snake_case_			=dataclasses.field(
      default=lowerCamelCase__   ,       metadata={"""help""": """Random seed for initialization."""}   ,       )






def 			lowerCAmelCase__	(					lowerCamelCase_	:	int ,lowerCamelCase_	:	List[Any] ,lowerCamelCase_	:	Any ,lowerCamelCase_	:	Dict ,lowerCamelCase_	:	int ,lowerCamelCase_	:	Union[str, Any]):





    '''simple docstring'''

    lowerCAmelCase__  :       Optional[int]				=  datasets.concatenate_datasets([infer_input, infer_output] ,axis=1)

    if args.do_filter_by_confidence:
        lowerCAmelCase__  :       Union[str, Any]				=  dataset.filter(lambda lowerCamelCase_: example["probability"] > args.confidence_threshold)

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

    lowerCAmelCase__  :       Tuple				=  dataset.remove_columns(['''label''', '''probability'''])
    lowerCAmelCase__  :       str				=  dataset.rename_column('''prediction''' ,'''label''')
    lowerCAmelCase__  :       List[str]				=  dataset.map(lambda lowerCamelCase_: {"label": idalabel[example["label"]]})
    lowerCAmelCase__  :       Dict				=  dataset.shuffle(seed=args.seed)

    lowerCAmelCase__  :       Union[str, Any]				=  os.path.join(lowerCamelCase_ ,f"""train_pseudo.{args.data_file_extension}""")
    if args.data_file_extension == "csv":
        dataset.to_csv(lowerCamelCase_ ,index=lowerCamelCase_)
    else:
        dataset.to_json(lowerCamelCase_)






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Dict ,lowerCamelCase_	:	Any ,lowerCamelCase_	:	Any ,lowerCamelCase_	:	int ,**lowerCamelCase_	:	Any):





    '''simple docstring'''

    lowerCAmelCase__  :       Any				=  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()

    lowerCAmelCase__  :       Tuple				=  STModelArguments(model_name_or_path=lowerCamelCase_)
    lowerCAmelCase__  :       Tuple				=  STDataArguments(train_file=lowerCamelCase_ ,infer_file=lowerCamelCase_)
    lowerCAmelCase__  :       Any				=  STTrainingArguments(output_dir=lowerCamelCase_)
    lowerCAmelCase__  :       Optional[int]				=  argparse.Namespace()

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

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

    # Sanity checks
    lowerCAmelCase__  :       str				=  {}
    lowerCAmelCase__  :       Optional[int]				=  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
    lowerCAmelCase__  :       List[Any]				=  args.train_file
    lowerCAmelCase__  :       int				=  args.infer_file

    if args.evaluation_strategy != IntervalStrategy.NO.value:
        assert args.eval_file is not None
        lowerCAmelCase__  :       str				=  args.eval_file

    for key in data_files:
        lowerCAmelCase__  :       List[Any]				=  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:
            lowerCAmelCase__  :       Optional[int]				=  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...''')
    lowerCAmelCase__  :       str				=  f"""{args.output_dir}/self-train_iter-{{}}""".format
    lowerCAmelCase__  :       Union[str, Any]				=  data_dir_format(0)

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

    lowerCAmelCase__  :       List[Any]				=  None
    lowerCAmelCase__  :       Union[str, Any]				=  None
    lowerCAmelCase__  :       Tuple				=  0
    lowerCAmelCase__  :       int				=  False
    # Show the progress bar
    lowerCAmelCase__  :       Dict				=  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)):
        lowerCAmelCase__  :       int				=  data_dir_format(lowerCamelCase_)
        assert os.path.exists(lowerCamelCase_)

        # Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
        # iteration > 0
        lowerCAmelCase__  :       Optional[int]				=  os.path.join(lowerCamelCase_ ,'''stage-1''')
        lowerCAmelCase__  :       List[Any]				=  {
            '''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(lowerCamelCase_ ,lowerCamelCase_):
                arguments_dict.update({key: value})

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

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

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

        lowerCAmelCase__  :       List[str]				=  iteration
        lowerCAmelCase__  :       Dict				=  data_dir_format(iteration + 1)

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

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

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

        lowerCAmelCase__  :       Union[str, Any]				=  os.path.join(lowerCamelCase_ ,f"""train_pseudo.{args.data_file_extension}""")

        if args.evaluation_strategy != IntervalStrategy.NO.value:
            lowerCAmelCase__  :       Optional[Any]				=  eval_result

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

                if early_stopping_patience_counter >= args.early_stopping_patience:
                    lowerCAmelCase__  :       Union[str, Any]				=  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''' ,lowerCamelCase_)
        logger.info('''Best evaluation result: %s = %f''' ,args.eval_metric ,lowerCamelCase_)
        accelerator.wait_for_everyone()
        if accelerator.is_main_process:
            shutil.copy(
                os.path.join(lowerCamelCase_ ,f"""eval_results_iter-{iteration}.json""") ,os.path.join(lowerCamelCase_ ,'''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 ,lowerCamelCase_)
        accelerator.wait_for_everyone()
        if accelerator.is_main_process:
            shutil.copy(
                os.path.join(lowerCamelCase_ ,f"""eval_results_iter-{args.max_selftrain_iterations - 1}.json""") ,os.path.join(lowerCamelCase_ ,'''eval_results_best-iteration.json''') ,)






import json
import os
from typing import Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__snake_case       :	int            =logging.get_logger(__name__)

__snake_case       :	List[Any]            ={
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

__snake_case       :	Any            ={
    'vocab_file': {'ctrl': 'https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json'},
    'merges_file': {'ctrl': 'https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt'},
}

__snake_case       :	Optional[int]            ={
    'ctrl': 2_5_6,
}

__snake_case       :	Optional[int]            ={
    'Pregnancy': 1_6_8_6_2_9,
    'Christianity': 7_6_7_5,
    'Explain': 1_0_6_4_2_3,
    'Fitness': 6_3_4_4_0,
    'Saving': 6_3_1_6_3,
    'Ask': 2_7_1_7_1,
    'Ass': 9_5_9_8_5,
    'Joke': 1_6_3_5_0_9,
    'Questions': 4_5_6_2_2,
    'Thoughts': 4_9_6_0_5,
    'Retail': 5_2_3_4_2,
    'Feminism': 1_6_4_3_3_8,
    'Writing': 1_1_9_9_2,
    'Atheism': 1_9_2_2_6_3,
    'Netflix': 4_8_6_1_6,
    'Computing': 3_9_6_3_9,
    'Opinion': 4_3_2_1_3,
    'Alone': 4_4_9_6_7,
    'Funny': 5_8_9_1_7,
    'Gaming': 4_0_3_5_8,
    'Human': 4_0_8_8,
    'India': 1_3_3_1,
    'Joker': 7_7_1_3_8,
    'Diet': 3_6_2_0_6,
    'Legal': 1_1_8_5_9,
    'Norman': 4_9_3_9,
    'Tip': 7_2_6_8_9,
    'Weight': 5_2_3_4_3,
    'Movies': 4_6_2_7_3,
    'Running': 2_3_4_2_5,
    'Science': 2_0_9_0,
    'Horror': 3_7_7_9_3,
    'Confession': 6_0_5_7_2,
    'Finance': 1_2_2_5_0,
    'Politics': 1_6_3_6_0,
    'Scary': 1_9_1_9_8_5,
    'Support': 1_2_6_5_4,
    'Technologies': 3_2_5_1_6,
    'Teenage': 6_6_1_6_0,
    'Event': 3_2_7_6_9,
    'Learned': 6_7_4_6_0,
    'Notion': 1_8_2_7_7_0,
    'Wikipedia': 3_7_5_8_3,
    'Books': 6_6_6_5,
    'Extract': 7_6_0_5_0,
    'Confessions': 1_0_2_7_0_1,
    'Conspiracy': 7_5_9_3_2,
    'Links': 6_3_6_7_4,
    'Narcissus': 1_5_0_4_2_5,
    'Relationship': 5_4_7_6_6,
    'Relationships': 1_3_4_7_9_6,
    'Reviews': 4_1_6_7_1,
    'News': 4_2_5_6,
    'Translation': 2_6_8_2_0,
    'multilingual': 1_2_8_4_0_6,
}






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Optional[Any]):





				'''simple docstring'''

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

				lowerCAmelCase__  :       Dict				=  set(lowerCamelCase_)
				return pairs



class  lowerCamelCase__    (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_			=VOCAB_FILES_NAMES
		snake_case_			=PRETRAINED_VOCAB_FILES_MAP
		snake_case_			=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		snake_case_			=CONTROL_CODES



		def __init__(self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase="<unk>"	,**__lowerCamelCase				)  ->    Union[str, Any]:

						"""simple docstring"""






						super().__init__(unk_token=__lowerCamelCase	,**__lowerCamelCase				)

						with open(__lowerCamelCase	,encoding='''utf-8'''				) as vocab_handle:
										lowerCAmelCase__  :       List[str]				=  json.load(__lowerCamelCase				)
						lowerCAmelCase__  :       int				=  {v: k for k, v in self.encoder.items()}
						with open(__lowerCamelCase	,encoding='''utf-8'''				) as merges_handle:
										lowerCAmelCase__  :       str				=  merges_handle.read().split('''\n'''				)[1:-1]
						lowerCAmelCase__  :       Optional[Any]				=  [tuple(merge.split()				) for merge in merges]
						lowerCAmelCase__  :       int				=  dict(zip(__lowerCamelCase	,range(len(__lowerCamelCase				)				)				)				)
						lowerCAmelCase__  :       Union[str, Any]				=  {}



		@property
		def 	lowerCAmelCase__   (self				)  ->    Optional[Any]:

						"""simple docstring"""






						return len(self.encoder				)



		def 	lowerCAmelCase__   (self				)  ->    int:

						"""simple docstring"""






						return dict(self.encoder	,**self.added_tokens_encoder				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    int:

						"""simple docstring"""






						if token in self.cache:
										return self.cache[token]
						lowerCAmelCase__  :       Union[str, Any]				=  tuple(__lowerCamelCase				)
						lowerCAmelCase__  :       Tuple				=  tuple(list(word[:-1]				) + [word[-1] + '''</w>''']				)
						lowerCAmelCase__  :       Union[str, Any]				=  get_pairs(__lowerCamelCase				)

						if not pairs:
										return token

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

														if word[i] == first and i < len(__lowerCamelCase				) - 1 and word[i + 1] == second:
																		new_word.append(first + second				)
																		i += 2
														else:
																		new_word.append(word[i]				)
																		i += 1
										lowerCAmelCase__  :       Optional[Any]				=  tuple(__lowerCamelCase				)
										lowerCAmelCase__  :       Any				=  new_word
										if len(__lowerCamelCase				) == 1:
														break
										else:
														lowerCAmelCase__  :       Optional[Any]				=  get_pairs(__lowerCamelCase				)
						lowerCAmelCase__  :       List[Any]				=  '''@@ '''.join(__lowerCamelCase				)
						lowerCAmelCase__  :       Optional[int]				=  word[:-4]
						lowerCAmelCase__  :       List[Any]				=  word
						return word



		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    List[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       List[Any]				=  []

						lowerCAmelCase__  :       int				=  re.findall(R'''\S+\n?'''	,__lowerCamelCase				)

						for token in words:
										split_tokens.extend(list(self.bpe(__lowerCamelCase				).split(''' '''				)				)				)
						return split_tokens



		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    List[str]:

						"""simple docstring"""






						return self.encoder.get(__lowerCamelCase	,self.encoder.get(self.unk_token				)				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    Union[str, Any]:

						"""simple docstring"""






						return self.decoder.get(__lowerCamelCase	,self.unk_token				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    Optional[int]:

						"""simple docstring"""






						lowerCAmelCase__  :       Dict				=  ''' '''.join(__lowerCamelCase				).replace('''@@ '''	,''''''				).strip()
						return out_string



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase = None				)  ->    Tuple[str]:

						"""simple docstring"""






						if not os.path.isdir(__lowerCamelCase				):
										logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
										return
						lowerCAmelCase__  :       Optional[int]				=  os.path.join(
						    __lowerCamelCase	,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)
						lowerCAmelCase__  :       List[Any]				=  os.path.join(
						    __lowerCamelCase	,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']				)

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

						lowerCAmelCase__  :       Optional[Any]				=  0
						with open(__lowerCamelCase	,'''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 __lowerCamelCase				: 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__  :       Optional[int]				=  token_index
														writer.write(''' '''.join(__lowerCamelCase				) + '''\n'''				)
														index += 1

						return vocab_file, merge_file

		# def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
		#     filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
		#     tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
		#     tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
		#     return ''.join(tokens_generated_so_far)

