'''simple docstring'''




import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


_a   :	List[Any] 							=   logging.getLogger(__name__)






@dataclass
class 		__A       :
       snake_case       :str				    =					field(
           metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}   )
       snake_case       :Optional[str]				    =					field(
           default=__magic_name__ ,					metadata={"help": "Pretrained config name or path if not the same as model_name"}   )
       snake_case       :Optional[str]				    =					field(
           default="NER" ,					metadata={"help": "Task type to fine tune in training (e.g. NER, POS, etc)"}   )
       snake_case       :Optional[str]				    =					field(
           default=__magic_name__ ,					metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}   )
       snake_case       :bool				    =					field(default=__magic_name__ ,					metadata={"help": "Set this flag to use fast tokenization."}   )
       # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
       # or just modify its tokenizer_config.json.
       snake_case       :Optional[str]				    =					field(
           default=__magic_name__ ,					metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} ,					)






@dataclass
class 		__A       :
       snake_case       :str				    =					field(
           metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."}   )
       snake_case       :Optional[str]				    =					field(
           default=__magic_name__ ,					metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."} ,					)
       snake_case       :int				    =					field(
           default=128 ,					metadata={
               "help": (
                   "The maximum total input sequence length after tokenization. Sequences longer "
                   "than this will be truncated, sequences shorter will be padded."
               )
           } ,					)
       snake_case       :bool				    =					field(
           default=__magic_name__ ,					metadata={"help": "Overwrite the cached training and evaluation sets"}   )





def _lowercase	(      )					->       str:





       """simple docstring"""


       __UpperCAmelCase  :				int       				=		HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)						)
       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.
              __UpperCAmelCase  :				str       				=		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
       else:
              __UpperCAmelCase  :				List[Any]       				=		parser.parse_args_into_dataclasses()

       if (
           os.path.exists(training_args.output_dir						)
           and os.listdir(training_args.output_dir						)
           and training_args.do_train
           and not training_args.overwrite_output_dir
       ):
              raise ValueError(
                  f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
                  " --overwrite_output_dir to overcome."						)

       __UpperCAmelCase  :				List[str]       				=		import_module("tasks"						)
       try:
              __UpperCAmelCase  :				Optional[int]       				=		getattr(lowerCamelCase__							,    model_args.task_type						)
              __UpperCAmelCase  :				TokenClassificationTask       				=		token_classification_task_clazz()
       except AttributeError:
              raise ValueError(
                  f"""Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
                  f"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}"""						)

       # Setup logging
       logging.basicConfig(
           format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"							,    datefmt="%m/%d/%Y %H:%M:%S"							,    level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN							,    )
       logger.warning(
           "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"							,    training_args.local_rank							,    training_args.device							,    training_args.n_gpu							,    bool(training_args.local_rank != -1						)							,    training_args.fpaa							,    )
       # Set the verbosity to info of the Transformers logger (on main process only):
       if is_main_process(training_args.local_rank						):
              transformers.utils.logging.set_verbosity_info()
              transformers.utils.logging.enable_default_handler()
              transformers.utils.logging.enable_explicit_format()
       logger.info("Training/evaluation parameters %s"							,    lowerCamelCase__						)

       # Set seed
       set_seed(training_args.seed						)

       # Prepare CONLL-2003 task
       __UpperCAmelCase  :				Any       				=		token_classification_task.get_labels(data_args.labels						)
       __UpperCAmelCase  :				Dict[int, str]       				=		dict(enumerate(lowerCamelCase__						)						)
       __UpperCAmelCase  :				Any       				=		len(lowerCamelCase__						)

       # Load pretrained model and tokenizer
       #
       # Distributed training:
       # The .from_pretrained methods guarantee that only one local process can concurrently
       # download model & vocab.

       __UpperCAmelCase  :				List[str]       				=		AutoConfig.from_pretrained(
           model_args.config_name if model_args.config_name else model_args.model_name_or_path							,    num_labels=lowerCamelCase__							,    idalabel=lowerCamelCase__							,    labelaid={label: i for i, label in enumerate(lowerCamelCase__						)}							,    cache_dir=model_args.cache_dir							,    )
       __UpperCAmelCase  :				List[Any]       				=		AutoTokenizer.from_pretrained(
           model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path							,    cache_dir=model_args.cache_dir							,    use_fast=model_args.use_fast							,    )
       __UpperCAmelCase  :				Union[str, Any]       				=		AutoModelForTokenClassification.from_pretrained(
           model_args.model_name_or_path							,    from_tf=bool(".ckpt" in model_args.model_name_or_path						)							,    config=lowerCamelCase__							,    cache_dir=model_args.cache_dir							,    )

       # Get datasets
       __UpperCAmelCase  :				Tuple       				=		(
           TokenClassificationDataset(
               token_classification_task=lowerCamelCase__							,    data_dir=data_args.data_dir							,    tokenizer=lowerCamelCase__							,    labels=lowerCamelCase__							,    model_type=config.model_type							,    max_seq_length=data_args.max_seq_length							,    overwrite_cache=data_args.overwrite_cache							,    mode=Split.train							,    )
           if training_args.do_train
           else None
       )
       __UpperCAmelCase  :				int       				=		(
           TokenClassificationDataset(
               token_classification_task=lowerCamelCase__							,    data_dir=data_args.data_dir							,    tokenizer=lowerCamelCase__							,    labels=lowerCamelCase__							,    model_type=config.model_type							,    max_seq_length=data_args.max_seq_length							,    overwrite_cache=data_args.overwrite_cache							,    mode=Split.dev							,    )
           if training_args.do_eval
           else None
       )

       def align_predictions(lowerCamelCase__							,    lowerCamelCase__						) -> Tuple[List[int], List[int]]:
              __UpperCAmelCase  :				Tuple       				=		np.argmax(lowerCamelCase__							,    axis=2						)

              __UpperCAmelCase  :				str       				=		preds.shape

              __UpperCAmelCase  :				List[Any]       				=		[[] for _ in range(lowerCamelCase__						)]
              __UpperCAmelCase  :				Union[str, Any]       				=		[[] for _ in range(lowerCamelCase__						)]

              for i in range(lowerCamelCase__						):
                     for j in range(lowerCamelCase__						):
                            if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
                                   out_label_list[i].append(label_map[label_ids[i][j]]						)
                                   preds_list[i].append(label_map[preds[i][j]]						)

              return preds_list, out_label_list

       def compute_metrics(lowerCamelCase__						) -> Dict:
              __UpperCAmelCase  :				Any       				=		align_predictions(p.predictions							,    p.label_ids						)
              return {
                  "accuracy_score": accuracy_score(lowerCamelCase__							,    lowerCamelCase__						),
                  "precision": precision_score(lowerCamelCase__							,    lowerCamelCase__						),
                  "recall": recall_score(lowerCamelCase__							,    lowerCamelCase__						),
                  "f1": fa_score(lowerCamelCase__							,    lowerCamelCase__						),
              }

       # Data collator
       __UpperCAmelCase  :				Dict       				=		DataCollatorWithPadding(lowerCamelCase__							,    pad_to_multiple_of=8						) if training_args.fpaa else None

       # Initialize our Trainer
       __UpperCAmelCase  :				List[str]       				=		Trainer(
           model=lowerCamelCase__							,    args=lowerCamelCase__							,    train_dataset=lowerCamelCase__							,    eval_dataset=lowerCamelCase__							,    compute_metrics=lowerCamelCase__							,    data_collator=lowerCamelCase__							,    )

       # Training
       if training_args.do_train:
              trainer.train(
                  model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path						) else None						)
              trainer.save_model()
              # For convenience, we also re-save the tokenizer to the same directory,
              # so that you can share your model easily on huggingface.co/models =)
              if trainer.is_world_process_zero():
                     tokenizer.save_pretrained(training_args.output_dir						)

    # Evaluation
       __UpperCAmelCase  :				List[str]       				=		{}
       if training_args.do_eval:
              logger.info("*** Evaluate ***"						)

              __UpperCAmelCase  :				Tuple       				=		trainer.evaluate()

              __UpperCAmelCase  :				List[str]       				=		os.path.join(training_args.output_dir							,    "eval_results.txt"						)
              if trainer.is_world_process_zero():
                     with open(lowerCamelCase__							,    "w"						) as writer:
                            logger.info("***** Eval results *****"						)
                            for key, value in result.items():
                                   logger.info("  %s = %s"							,    lowerCamelCase__							,    lowerCamelCase__						)
                                   writer.write("%s = %s\n" % (key, value)						)

                     results.update(lowerCamelCase__						)

    # Predict
       if training_args.do_predict:
              __UpperCAmelCase  :				List[Any]       				=		TokenClassificationDataset(
                  token_classification_task=lowerCamelCase__							,    data_dir=data_args.data_dir							,    tokenizer=lowerCamelCase__							,    labels=lowerCamelCase__							,    model_type=config.model_type							,    max_seq_length=data_args.max_seq_length							,    overwrite_cache=data_args.overwrite_cache							,    mode=Split.test							,    )

              __UpperCAmelCase  :				List[Any]       				=		trainer.predict(lowerCamelCase__						)
              __UpperCAmelCase  :				int       				=		align_predictions(lowerCamelCase__							,    lowerCamelCase__						)

              __UpperCAmelCase  :				str       				=		os.path.join(training_args.output_dir							,    "test_results.txt"						)
              if trainer.is_world_process_zero():
                     with open(lowerCamelCase__							,    "w"						) as writer:
                            for key, value in metrics.items():
                                   logger.info("  %s = %s"							,    lowerCamelCase__							,    lowerCamelCase__						)
                                   writer.write("%s = %s\n" % (key, value)						)

        # Save predictions
              __UpperCAmelCase  :				Any       				=		os.path.join(training_args.output_dir							,    "test_predictions.txt"						)
              if trainer.is_world_process_zero():
                     with open(lowerCamelCase__							,    "w"						) as writer:
                            with open(os.path.join(data_args.data_dir							,    "test.txt"						)							,    "r"						) as f:
                                   token_classification_task.write_predictions_to_file(lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__						)

       return results

def _lowercase	(      lowerCamelCase__						)					->       Optional[int]:





       """simple docstring"""


       main()


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


_a   :	Optional[Any] 							=   logging.get_logger(__name__)


_a   :	int 							=   {"vocab_file": "vocab.json", "merges_file": "merges.txt"}

# See all BART models at https://huggingface.co/models?filter=bart
_a   :	Tuple 							=   {
    "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",
    },
}

_a   :	List[Any] 							=   {
    "facebook/bart-base": 1024,
    "facebook/bart-large": 1024,
    "facebook/bart-large-mnli": 1024,
    "facebook/bart-large-cnn": 1024,
    "facebook/bart-large-xsum": 1024,
    "yjernite/bart_eli5": 1024,
}

@lru_cache()
def _lowercase	(      )					->       List[Any]:





       """simple docstring"""


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

def _lowercase	(      lowerCamelCase__						)					->       str:





       """simple docstring"""


       __UpperCAmelCase  :				Dict       				=		set()
       __UpperCAmelCase  :				Union[str, Any]       				=		word[0]
       for char in word[1:]:
              pairs.add((prev_char, char)						)
              __UpperCAmelCase  :				Optional[Any]       				=		char
       return pairs






class 		__A       (__magic_name__   ):
       snake_case       :Optional[int]				    =					VOCAB_FILES_NAMES
       snake_case       :List[Any]				    =					PRETRAINED_VOCAB_FILES_MAP
       snake_case       :Any				    =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       snake_case       :Optional[int]				    =					["input_ids", "attention_mask"]






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

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

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

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

              # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
              __UpperCAmelCase  :				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 			_snake_case						(      self      ):
              return len(self.encoder      )






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






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              if token in self.cache:
                     return self.cache[token]
              __UpperCAmelCase  :				List[str]       				=		tuple(UpperCamelCase_      )
              __UpperCAmelCase  :				str       				=		get_pairs(UpperCamelCase_      )

              if not pairs:
                     return token

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

                            if word[i] == first and i < len(UpperCamelCase_      ) - 1 and word[i + 1] == second:
                                   new_word.append(first + second      )
                                   i += 2
                            else:
                                   new_word.append(word[i]      )
                                   i += 1
                     __UpperCAmelCase  :				Dict       				=		tuple(UpperCamelCase_      )
                     __UpperCAmelCase  :				str       				=		new_word
                     if len(UpperCamelCase_      ) == 1:
                            break
                     else:
                            __UpperCAmelCase  :				int       				=		get_pairs(UpperCamelCase_      )
              __UpperCAmelCase  :				Optional[int]       				=		" ".join(UpperCamelCase_      )
              __UpperCAmelCase  :				Dict       				=		word
              return word






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				Optional[Any]       				=		[]
              for token in re.findall(self.pat				,     UpperCamelCase_      ):
                     __UpperCAmelCase  :				Any       				=		"".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(UpperCamelCase_      ).split(" "      )      )
              return bpe_tokens






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              return self.encoder.get(UpperCamelCase_				,     self.encoder.get(self.unk_token      )      )






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              return self.decoder.get(UpperCamelCase_      )






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				List[str]       				=		"".join(UpperCamelCase_      )
              __UpperCAmelCase  :				Union[str, Any]       				=		bytearray([self.byte_decoder[c] for c in text]      ).decode("utf-8"				,     errors=self.errors      )
              return text






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_ = None      ):
              if not os.path.isdir(UpperCamelCase_      ):
                     logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""      )
                     return
              __UpperCAmelCase  :				Any       				=		os.path.join(
                  UpperCamelCase_				,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]      )
              __UpperCAmelCase  :				Optional[int]       				=		os.path.join(
                  UpperCamelCase_				,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]      )

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

              __UpperCAmelCase  :				str       				=		0
              with open(UpperCamelCase_				,     "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 UpperCamelCase_      : 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!"      )
                                   __UpperCAmelCase  :				str       				=		token_index
                            writer.write(" ".join(UpperCamelCase_      ) + "\n"      )
                            index += 1

              return vocab_file, merge_file






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_ = None      ):
              if token_ids_a is None:
                     return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
              __UpperCAmelCase  :				List[Any]       				=		[self.cls_token_id]
              __UpperCAmelCase  :				Tuple       				=		[self.sep_token_id]
              return cls + token_ids_a + sep + sep + token_ids_a + sep






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

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






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_ = None      ):
              __UpperCAmelCase  :				int       				=		[self.sep_token_id]
              __UpperCAmelCase  :				List[str]       				=		[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 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_=False				,     **UpperCamelCase_      ):
              __UpperCAmelCase  :				List[str]       				=		kwargs.pop("add_prefix_space"				,     self.add_prefix_space      )
              if (is_split_into_words or add_prefix_space) and (len(UpperCamelCase_      ) > 0 and not text[0].isspace()):
                     __UpperCAmelCase  :				Tuple       				=		" " + text
              return (text, kwargs)






