






"""simple docstring"""




import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


lowerCamelCase__			=   logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

lowerCamelCase__			=   {
    "base": AutoModel,
    "sequence-classification": AutoModelForSequenceClassification,
    "question-answering": AutoModelForQuestionAnswering,
    "pretraining": AutoModelForPreTraining,
    "token-classification": AutoModelForTokenClassification,
    "language-modeling": AutoModelWithLMHead,
    "summarization": AutoModelForSeqaSeqLM,
    "translation": AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
lowerCamelCase__			=   {
    "linear": get_linear_schedule_with_warmup,
    "cosine": get_cosine_schedule_with_warmup,
    "cosine_w_restarts": get_cosine_with_hard_restarts_schedule_with_warmup,
    "polynomial": get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
lowerCamelCase__			=   sorted(arg_to_scheduler.keys())
lowerCamelCase__			=   "{" + ", ".join(arg_to_scheduler_choices) + "}"







class 					_UpperCamelCase		( pl.LightningModule):






    def __init__(self						,      lowerCamelCase__						,      lowerCamelCase__=None						,      lowerCamelCase__="base"						,      lowerCamelCase__=None						,      lowerCamelCase__=None						,      lowerCamelCase__=None						,      **lowerCamelCase__						,      ):


      """simple docstring"""
      super().__init__()
      # TODO: move to self.save_hyperparameters()
      # self.save_hyperparameters()
      # can also expand arguments into trainer signature for easier reading

      self.save_hyperparameters(lowerCamelCase__   )
      A__											=							0
      A__											=							Path(self.hparams.output_dir   )
      A__											=							self.hparams.cache_dir if self.hparams.cache_dir else None
      if config is None:
        A__											=							AutoConfig.from_pretrained(
            self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path						,      **({"""num_labels""": num_labels} if num_labels is not None else {})						,      cache_dir=lowerCamelCase__						,      **lowerCamelCase__						,      )
      else:
        A__											=							config

      A__											=							("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
      for p in extra_model_params:
        if getattr(self.hparams						,      lowerCamelCase__						,      lowerCamelCase__   ):
          assert hasattr(self.config						,      lowerCamelCase__   ), F"""model config doesn't have a `{p}` attribute"""
          setattr(self.config						,      lowerCamelCase__						,      getattr(self.hparams						,      lowerCamelCase__   )   )

      if tokenizer is None:
        A__											=							AutoTokenizer.from_pretrained(
            self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path						,      cache_dir=lowerCamelCase__						,      )
      else:
        A__											=							tokenizer
      A__											=							MODEL_MODES[mode]
      if model is None:
        A__											=							self.model_type.from_pretrained(
            self.hparams.model_name_or_path						,      from_tf=bool(""".ckpt""" in self.hparams.model_name_or_path   )						,      config=self.config						,      cache_dir=lowerCamelCase__						,      )
      else:
        A__											=							model






    def  A    (self						,      *lowerCamelCase__						,      **lowerCamelCase__   ):


      """simple docstring"""
      A__											=							self.model_type.from_pretrained(*lowerCamelCase__						,      **lowerCamelCase__   )






    def  A    (self   ):


      """simple docstring"""
      A__											=							arg_to_scheduler[self.hparams.lr_scheduler]
      A__											=							get_schedule_func(
          self.opt						,      num_warmup_steps=self.hparams.warmup_steps						,      num_training_steps=self.total_steps()   )
      A__											=							{"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
      return scheduler






    def  A    (self   ):


      """simple docstring"""
      A__											=							self.model
      A__											=							["""bias""", """LayerNorm.weight"""]
      A__											=							[
          {
              """params""": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay   )],
              """weight_decay""": self.hparams.weight_decay,
          },
          {
              """params""": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay   )],
              """weight_decay""": 0.0,
          },
      ]
      if self.hparams.adafactor:
        A__											=							Adafactor(
            lowerCamelCase__						,      lr=self.hparams.learning_rate						,      scale_parameter=lowerCamelCase__						,      relative_step=lowerCamelCase__   )

      else:
        A__											=							AdamW(
            lowerCamelCase__						,      lr=self.hparams.learning_rate						,      eps=self.hparams.adam_epsilon   )
      A__											=							optimizer

      A__											=							self.get_lr_scheduler()

      return [optimizer], [scheduler]






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__   ):


      """simple docstring"""
      return self.validation_step(lowerCamelCase__						,      lowerCamelCase__   )






    def  A    (self						,      lowerCamelCase__   ):


      """simple docstring"""
      return self.validation_end(lowerCamelCase__   )






    def  A    (self   ):


      """simple docstring"""
      A__											=							max(1						,      self.hparams.gpus   )  # TODO: consider num_tpu_cores
      A__											=							self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
      return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs






    def  A    (self						,      lowerCamelCase__   ):


      """simple docstring"""
      if mode == "test":
        A__											=							len(self.test_dataloader().dataset   )
      else:
        A__											=							self.get_dataloader("""train"""						,      self.hparams.train_batch_size						,      shuffle=lowerCamelCase__   )
        A__											=							len(self.train_dataloader().dataset   )






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__ = False   ):


      """simple docstring"""
      raise NotImplementedError("""You must implement this for your task"""   )






    def  A    (self   ):


      """simple docstring"""
      return self.train_loader






    def  A    (self   ):


      """simple docstring"""
      return self.get_dataloader("""dev"""						,      self.hparams.eval_batch_size						,      shuffle=lowerCamelCase__   )






    def  A    (self   ):


      """simple docstring"""
      return self.get_dataloader("""test"""						,      self.hparams.eval_batch_size						,      shuffle=lowerCamelCase__   )






    def  A    (self						,      lowerCamelCase__   ):


      """simple docstring"""
      return os.path.join(
          self.hparams.data_dir						,      """cached_{}_{}_{}""".format(
              lowerCamelCase__						,      list(filter(lowerCamelCase__						,      self.hparams.model_name_or_path.split("""/"""   )   )   ).pop()						,      str(self.hparams.max_seq_length   )						,      )						,      )






    @pl.utilities.rank_zero_only
    def  A    (self						,      lowerCamelCase__   ):


      """simple docstring"""
      A__											=							self.output_dir.joinpath("""best_tfmr"""   )
      A__											=							self.step_count
      self.model.save_pretrained(lowerCamelCase__   )
      self.tokenizer.save_pretrained(lowerCamelCase__   )






    @staticmethod
    def  A    (lowerCamelCase__						,      lowerCamelCase__   ):


      """simple docstring"""
      parser.add_argument(
          """--model_name_or_path"""						,      default=lowerCamelCase__						,      type=lowerCamelCase__						,      required=lowerCamelCase__						,      help="""Path to pretrained model or model identifier from huggingface.co/models"""						,      )
      parser.add_argument(
          """--config_name"""						,      default=""""""						,      type=lowerCamelCase__						,      help="""Pretrained config name or path if not the same as model_name"""   )
      parser.add_argument(
          """--tokenizer_name"""						,      default=lowerCamelCase__						,      type=lowerCamelCase__						,      help="""Pretrained tokenizer name or path if not the same as model_name"""						,      )
      parser.add_argument(
          """--cache_dir"""						,      default=""""""						,      type=lowerCamelCase__						,      help="""Where do you want to store the pre-trained models downloaded from huggingface.co"""						,      )
      parser.add_argument(
          """--encoder_layerdrop"""						,      type=lowerCamelCase__						,      help="""Encoder layer dropout probability (Optional). Goes into model.config"""						,      )
      parser.add_argument(
          """--decoder_layerdrop"""						,      type=lowerCamelCase__						,      help="""Decoder layer dropout probability (Optional). Goes into model.config"""						,      )
      parser.add_argument(
          """--dropout"""						,      type=lowerCamelCase__						,      help="""Dropout probability (Optional). Goes into model.config"""						,      )
      parser.add_argument(
          """--attention_dropout"""						,      type=lowerCamelCase__						,      help="""Attention dropout probability (Optional). Goes into model.config"""						,      )
      parser.add_argument("""--learning_rate"""						,      default=5E-5						,      type=lowerCamelCase__						,      help="""The initial learning rate for Adam."""   )
      parser.add_argument(
          """--lr_scheduler"""						,      default="""linear"""						,      choices=lowerCamelCase__						,      metavar=lowerCamelCase__						,      type=lowerCamelCase__						,      help="""Learning rate scheduler"""						,      )
      parser.add_argument("""--weight_decay"""						,      default=0.0						,      type=lowerCamelCase__						,      help="""Weight decay if we apply some."""   )
      parser.add_argument("""--adam_epsilon"""						,      default=1E-8						,      type=lowerCamelCase__						,      help="""Epsilon for Adam optimizer."""   )
      parser.add_argument("""--warmup_steps"""						,      default=0						,      type=lowerCamelCase__						,      help="""Linear warmup over warmup_steps."""   )
      parser.add_argument("""--num_workers"""						,      default=4						,      type=lowerCamelCase__						,      help="""kwarg passed to DataLoader"""   )
      parser.add_argument("""--num_train_epochs"""						,      dest="""max_epochs"""						,      default=3						,      type=lowerCamelCase__   )
      parser.add_argument("""--train_batch_size"""						,      default=3_2						,      type=lowerCamelCase__   )
      parser.add_argument("""--eval_batch_size"""						,      default=3_2						,      type=lowerCamelCase__   )
      parser.add_argument("""--adafactor"""						,      action="""store_true"""   )







class 					_UpperCamelCase		( pl.Callback):






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__   ):


      """simple docstring"""
      A__											=							trainer.lr_schedulers[0]["""scheduler"""]
      A__											=							{F"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()   )}
      pl_module.logger.log_metrics(lowerCamelCase__   )






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__   ):


      """simple docstring"""
      rank_zero_info("""***** Validation results *****"""   )
      A__											=							trainer.callback_metrics
      # Log results
      for key in sorted(lowerCamelCase__   ):
        if key not in ["log", "progress_bar"]:
          rank_zero_info("""{} = {}\n""".format(lowerCamelCase__						,      str(metrics[key]   )   )   )






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__   ):


      """simple docstring"""
      rank_zero_info("""***** Test results *****"""   )
      A__											=							trainer.callback_metrics
      # Log and save results to file
      A__											=							os.path.join(pl_module.hparams.output_dir						,      """test_results.txt"""   )
      with open(lowerCamelCase__						,      """w"""   ) as writer:
        for key in sorted(lowerCamelCase__   ):
          if key not in ["log", "progress_bar"]:
            rank_zero_info("""{} = {}\n""".format(lowerCamelCase__						,      str(metrics[key]   )   )   )
            writer.write("""{} = {}\n""".format(lowerCamelCase__						,      str(metrics[key]   )   )   )






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	List[str]		,				UpperCamelCase     :	int    ):
  #  To allow all pl args uncomment the following line
  #  parser = pl.Trainer.add_argparse_args(parser)
  parser.add_argument(
      """--output_dir"""		,				default=UpperCamelCase		,				type=UpperCamelCase		,				required=UpperCamelCase		,				help="""The output directory where the model predictions and checkpoints will be written."""		,				)
  parser.add_argument(
      """--fp16"""		,				action="""store_true"""		,				help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"""		,				)

  parser.add_argument(
      """--fp16_opt_level"""		,				type=UpperCamelCase		,				default="""O2"""		,				help=(
          """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."""
          """See details at https://nvidia.github.io/apex/amp.html"""
      )		,				)
  parser.add_argument("""--n_tpu_cores"""		,				dest="""tpu_cores"""		,				type=UpperCamelCase    )
  parser.add_argument("""--max_grad_norm"""		,				dest="""gradient_clip_val"""		,				default=1.0		,				type=UpperCamelCase		,				help="""Max gradient norm"""    )
  parser.add_argument("""--do_train"""		,				action="""store_true"""		,				help="""Whether to run training."""    )
  parser.add_argument("""--do_predict"""		,				action="""store_true"""		,				help="""Whether to run predictions on the test set."""    )
  parser.add_argument(
      """--gradient_accumulation_steps"""		,				dest="""accumulate_grad_batches"""		,				type=UpperCamelCase		,				default=1		,				help="""Number of updates steps to accumulate before performing a backward/update pass."""		,				)
  parser.add_argument("""--seed"""		,				type=UpperCamelCase		,				default=42		,				help="""random seed for initialization"""    )
  parser.add_argument(
      """--data_dir"""		,				default=UpperCamelCase		,				type=UpperCamelCase		,				required=UpperCamelCase		,				help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task."""		,				)






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	BaseTransformer		,				UpperCamelCase     :	argparse.Namespace		,				UpperCamelCase     :	Optional[Any]=None		,				UpperCamelCase     :	List[Any]=True		,				UpperCamelCase     :	Union[str, Any]=[]		,				UpperCamelCase     :	Dict=None		,				UpperCamelCase     :	Tuple=None		,				**UpperCamelCase     :	List[Any]		,				):
  pl.seed_everything(args.seed    )

  # init model
  A__											=							Path(model.hparams.output_dir    )
  odir.mkdir(exist_ok=UpperCamelCase    )

  # add custom checkpoints
  if checkpoint_callback is None:
    A__											=							pl.callbacks.ModelCheckpoint(
        filepath=args.output_dir		,				prefix="""checkpoint"""		,				monitor="""val_loss"""		,				mode="""min"""		,				save_top_k=1    )
  if early_stopping_callback:
    extra_callbacks.append(UpperCamelCase    )
  if logging_callback is None:
    A__											=							LoggingCallback()

  A__											=							{}

  # TODO: remove with PyTorch 1.6 since pl uses native amp
  if args.fpaa:
    A__											=							16
    A__											=							args.fpaa_opt_level

  if args.gpus > 1:
    A__											=							"""ddp"""

  A__											=							args.accumulate_grad_batches
  A__											=							extra_train_kwargs.get("""accelerator"""		,				UpperCamelCase    )
  A__											=							extra_train_kwargs.get("""profiler"""		,				UpperCamelCase    )

  A__											=							pl.Trainer.from_argparse_args(
      UpperCamelCase		,				weights_summary=UpperCamelCase		,				callbacks=[logging_callback] + extra_callbacks		,				logger=UpperCamelCase		,				checkpoint_callback=UpperCamelCase		,				**UpperCamelCase		,				)

  if args.do_train:
    trainer.fit(UpperCamelCase    )

  return trainer












"""simple docstring"""




import json
import os
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

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


if TYPE_CHECKING:
      from transformers.pipelines.conversational import Conversation

lowerCamelCase__			=   logging.get_logger(__name__)

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

lowerCamelCase__			=   {
    "vocab_file": {
        "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json",
        "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json",
        "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json",
        "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json",
        "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json",
        "microsoft/deberta-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt",
        "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt",
        "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt",
        "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt",
        "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt",
        "microsoft/deberta-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"
        ),
    },
}

lowerCamelCase__			=   {
    "microsoft/deberta-base": 512,
    "microsoft/deberta-large": 512,
    "microsoft/deberta-xlarge": 512,
    "microsoft/deberta-base-mnli": 512,
    "microsoft/deberta-large-mnli": 512,
    "microsoft/deberta-xlarge-mnli": 512,
}

lowerCamelCase__			=   {
    "microsoft/deberta-base": {"do_lower_case": False},
    "microsoft/deberta-large": {"do_lower_case": False},
}






def 						_SCREAMING_SNAKE_CASE							( ):
  A__											=							(
      list(range(ord("""!"""    )		,				ord("""~"""    ) + 1    )    ) + list(range(ord("""¡"""    )		,				ord("""¬"""    ) + 1    )    ) + list(range(ord("""®"""    )		,				ord("""ÿ"""    ) + 1    )    )
  )
  A__											=							bs[:]
  A__											=							0
  for b in range(2**8    ):
    if b not in bs:
      bs.append(UpperCamelCase    )
      cs.append(2**8 + n    )
      n += 1
  A__											=							[chr(UpperCamelCase    ) for n in cs]
  return dict(zip(UpperCamelCase		,				UpperCamelCase    )    )






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	str    ):
  A__											=							set()
  A__											=							word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)    )
    A__											=							char
  return pairs







class 					_UpperCamelCase		( __snake_case):
    __lowerCamelCase         =  VOCAB_FILES_NAMES
    __lowerCamelCase         =  PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase         =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase         =  ["input_ids", "attention_mask", "token_type_ids"]






    def __init__(self						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__="replace"						,      lowerCamelCase__="[CLS]"						,      lowerCamelCase__="[SEP]"						,      lowerCamelCase__="[SEP]"						,      lowerCamelCase__="[CLS]"						,      lowerCamelCase__="[UNK]"						,      lowerCamelCase__="[PAD]"						,      lowerCamelCase__="[MASK]"						,      lowerCamelCase__=False						,      lowerCamelCase__=False						,      **lowerCamelCase__						,      ):


      """simple docstring"""
      A__											=							AddedToken(lowerCamelCase__						,      lstrip=lowerCamelCase__						,      rstrip=lowerCamelCase__   ) if isinstance(lowerCamelCase__						,      lowerCamelCase__   ) else bos_token
      A__											=							AddedToken(lowerCamelCase__						,      lstrip=lowerCamelCase__						,      rstrip=lowerCamelCase__   ) if isinstance(lowerCamelCase__						,      lowerCamelCase__   ) else eos_token
      A__											=							AddedToken(lowerCamelCase__						,      lstrip=lowerCamelCase__						,      rstrip=lowerCamelCase__   ) if isinstance(lowerCamelCase__						,      lowerCamelCase__   ) else sep_token
      A__											=							AddedToken(lowerCamelCase__						,      lstrip=lowerCamelCase__						,      rstrip=lowerCamelCase__   ) if isinstance(lowerCamelCase__						,      lowerCamelCase__   ) else cls_token
      A__											=							AddedToken(lowerCamelCase__						,      lstrip=lowerCamelCase__						,      rstrip=lowerCamelCase__   ) if isinstance(lowerCamelCase__						,      lowerCamelCase__   ) else unk_token
      A__											=							AddedToken(lowerCamelCase__						,      lstrip=lowerCamelCase__						,      rstrip=lowerCamelCase__   ) if isinstance(lowerCamelCase__						,      lowerCamelCase__   ) else pad_token

      # Mask token behave like a normal word, i.e. include the space before it
      A__											=							AddedToken(lowerCamelCase__						,      lstrip=lowerCamelCase__						,      rstrip=lowerCamelCase__   ) if isinstance(lowerCamelCase__						,      lowerCamelCase__   ) else mask_token

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

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

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






    @property
    # Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.vocab_size
    def  A    (self   ):


      """simple docstring"""
      return len(self.encoder   )






    def  A    (self   ):


      """simple docstring"""
      return dict(self.encoder						,      **self.added_tokens_encoder   )






    def  A    (self						,      lowerCamelCase__   ):


      """simple docstring"""
      if token in self.cache:
        return self.cache[token]
      A__											=							tuple(lowerCamelCase__   )
      A__											=							get_pairs(lowerCamelCase__   )

      if not pairs:
        return token

      while True:
        A__											=							min(lowerCamelCase__						,      key=lambda lowerCamelCase__   : self.bpe_ranks.get(lowerCamelCase__						,      float("""inf"""   )   )   )
        if bigram not in self.bpe_ranks:
          break
        A__						,A__											=							bigram
        A__											=							[]
        A__											=							0
        while i < len(lowerCamelCase__   ):
          try:
            A__											=							word.index(lowerCamelCase__						,      lowerCamelCase__   )
          except ValueError:
            new_word.extend(word[i:]   )
            break
          else:
            new_word.extend(word[i:j]   )
            A__											=							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
        A__											=							tuple(lowerCamelCase__   )
        A__											=							new_word
        if len(lowerCamelCase__   ) == 1:
          break
        else:
          A__											=							get_pairs(lowerCamelCase__   )
      A__											=							""" """.join(lowerCamelCase__   )
      A__											=							word
      return word






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__ = None   ):


      """simple docstring"""
      if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      A__											=							[self.cls_token_id]
      A__											=							[self.sep_token_id]
      return cls + token_ids_a + sep + token_ids_a + sep






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__ = None						,      lowerCamelCase__ = False   ):


      """simple docstring"""
      if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=lowerCamelCase__						,      token_ids_a=lowerCamelCase__						,      already_has_special_tokens=lowerCamelCase__   )

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






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__ = None   ):


      """simple docstring"""
      A__											=							[self.sep_token_id]
      A__											=							[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   ) * [0] + len(token_ids_a + sep   ) * [1]






    def  A    (self						,      lowerCamelCase__   ):


      """simple docstring"""
      A__											=							[]
      for token in re.findall(self.pat						,      lowerCamelCase__   ):
        A__											=							"""""".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(lowerCamelCase__   ).split(""" """   )   )
      return bpe_tokens






    def  A    (self						,      lowerCamelCase__   ):


      """simple docstring"""
      return self.encoder.get(lowerCamelCase__						,      self.encoder.get(self.unk_token   )   )






    def  A    (self						,      lowerCamelCase__   ):


      """simple docstring"""
      return self.decoder.get(lowerCamelCase__   )






    def  A    (self						,      lowerCamelCase__   ):


      """simple docstring"""
      A__											=							"""""".join(lowerCamelCase__   )
      A__											=							bytearray([self.byte_decoder[c] for c in text]   ).decode("""utf-8"""						,      errors=self.errors   )
      return text






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__ = None   ):


      """simple docstring"""
      if not os.path.isdir(lowerCamelCase__   ):
        logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""   )
        return
      A__											=							os.path.join(
          lowerCamelCase__						,      (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]   )
      A__											=							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"""   )

      A__											=							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!"""   )
            A__											=							token_index
          writer.write(""" """.join(lowerCamelCase__   ) + """\n"""   )
          index += 1

      return vocab_file, merge_file






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__=False						,      **lowerCamelCase__   ):


      """simple docstring"""
      A__											=							kwargs.pop("""add_prefix_space"""						,      self.add_prefix_space   )
      if (is_split_into_words or add_prefix_space) and (len(lowerCamelCase__   ) > 0 and not text[0].isspace()):
        A__											=							""" """ + text
      return (text, kwargs)






    def  A    (self						,      lowerCamelCase__   ):


      """simple docstring"""
      A__											=							[]
      for is_user, text in conversation.iter_texts():
        input_ids.extend(self.encode(lowerCamelCase__						,      add_special_tokens=lowerCamelCase__   ) + [self.eos_token_id]   )
      if len(lowerCamelCase__   ) > self.model_max_length:
        A__											=							input_ids[-self.model_max_length :]
      return input_ids




