


"""simple docstring"""



import argparse
import json
import os
import pickle
import shutil

import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset

from transformers import (
    BertConfig,
    BertForMaskedLM,
    BertTokenizer,
    DistilBertConfig,
    DistilBertForMaskedLM,
    DistilBertTokenizer,
    GPTaConfig,
    GPTaLMHeadModel,
    GPTaTokenizer,
    RobertaConfig,
    RobertaForMaskedLM,
    RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed


__snake_case       =       {
    """distilbert""": (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    """roberta""": (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    """bert""": (BertConfig, BertForMaskedLM, BertTokenizer),
    """gpt2""": (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}







def     __lowerCAmelCase		(		lowercase    :    Union[str, Any]					)      ->		Union[str, Any]:


    """simple docstring"""





    assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
    assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
    if args.mlm:
        assert os.path.isfile(args.token_counts					)
        assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
    else:
        assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])

    assert args.teacher_type == args.student_type or (
        args.student_type == "distilbert" and args.teacher_type == "bert"
    )
    assert os.path.isfile(args.student_config					)
    if args.student_pretrained_weights is not None:
        assert os.path.isfile(args.student_pretrained_weights					)

    if args.freeze_token_type_embds:
        assert args.student_type in ["roberta"]

    assert args.alpha_ce >= 0.0
    assert args.alpha_mlm >= 0.0
    assert args.alpha_clm >= 0.0
    assert args.alpha_mse >= 0.0
    assert args.alpha_cos >= 0.0
    assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0







def     __lowerCAmelCase		(		lowercase    :    int		,				lowercase    :    Tuple					)      ->		Tuple:


    """simple docstring"""





    if args.student_type == "roberta":
        snake_case       : Optional[Any]    =       False
    elif args.student_type == "gpt2":
        snake_case       : Optional[int]    =       False







def     __lowerCAmelCase		(		lowercase    :    int		,				lowercase    :    Optional[Any]					)      ->		Optional[Any]:


    """simple docstring"""





    if args.student_type == "roberta":
        snake_case       : Tuple    =       False







def     __lowerCAmelCase		(		)      ->		Dict:


    """simple docstring"""





    snake_case       : List[str]    =       argparse.ArgumentParser(description="Training"					)
    parser.add_argument("--force"		,				action="store_true"		,				help="Overwrite dump_path if it already exists."					)

    parser.add_argument(
        "--dump_path"		,				type=lowercase		,				required=lowercase		,				help="The output directory (log, checkpoints, parameters, etc.)"					)
    parser.add_argument(
        "--data_file"		,				type=lowercase		,				required=lowercase		,				help="The binarized file (tokenized + tokens_to_ids) and grouped by sequence."		,				)

    parser.add_argument(
        "--student_type"		,				type=lowercase		,				choices=["distilbert", "roberta", "gpt2"]		,				required=lowercase		,				help="The student type (DistilBERT, RoBERTa)."		,				)
    parser.add_argument("--student_config"		,				type=lowercase		,				required=lowercase		,				help="Path to the student configuration."					)
    parser.add_argument(
        "--student_pretrained_weights"		,				default=lowercase		,				type=lowercase		,				help="Load student initialization checkpoint."					)

    parser.add_argument(
        "--teacher_type"		,				choices=["bert", "roberta", "gpt2"]		,				required=lowercase		,				help="Teacher type (BERT, RoBERTa)."					)
    parser.add_argument("--teacher_name"		,				type=lowercase		,				required=lowercase		,				help="The teacher model."					)

    parser.add_argument("--temperature"		,				default=2.0		,				type=lowercase		,				help="Temperature for the softmax temperature."					)
    parser.add_argument(
        "--alpha_ce"		,				default=0.5		,				type=lowercase		,				help="Linear weight for the distillation loss. Must be >=0."					)
    parser.add_argument(
        "--alpha_mlm"		,				default=0.0		,				type=lowercase		,				help="Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag."		,				)
    parser.add_argument("--alpha_clm"		,				default=0.5		,				type=lowercase		,				help="Linear weight for the CLM loss. Must be >=0."					)
    parser.add_argument("--alpha_mse"		,				default=0.0		,				type=lowercase		,				help="Linear weight of the MSE loss. Must be >=0."					)
    parser.add_argument(
        "--alpha_cos"		,				default=0.0		,				type=lowercase		,				help="Linear weight of the cosine embedding loss. Must be >=0."					)

    parser.add_argument(
        "--mlm"		,				action="store_true"		,				help="The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM."					)
    parser.add_argument(
        "--mlm_mask_prop"		,				default=0.15		,				type=lowercase		,				help="Proportion of tokens for which we need to make a prediction."		,				)
    parser.add_argument("--word_mask"		,				default=0.8		,				type=lowercase		,				help="Proportion of tokens to mask out."					)
    parser.add_argument("--word_keep"		,				default=0.1		,				type=lowercase		,				help="Proportion of tokens to keep."					)
    parser.add_argument("--word_rand"		,				default=0.1		,				type=lowercase		,				help="Proportion of tokens to randomly replace."					)
    parser.add_argument(
        "--mlm_smoothing"		,				default=0.7		,				type=lowercase		,				help="Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec)."		,				)
    parser.add_argument("--token_counts"		,				type=lowercase		,				help="The token counts in the data_file for MLM."					)

    parser.add_argument(
        "--restrict_ce_to_mask"		,				action="store_true"		,				help="If true, compute the distillation loss only the [MLM] prediction distribution."		,				)
    parser.add_argument(
        "--freeze_pos_embs"		,				action="store_true"		,				help="Freeze positional embeddings during distillation. For student_type in ['roberta', 'gpt2'] only."		,				)
    parser.add_argument(
        "--freeze_token_type_embds"		,				action="store_true"		,				help="Freeze token type embeddings during distillation if existent. For student_type in ['roberta'] only."		,				)

    parser.add_argument("--n_epoch"		,				type=lowercase		,				default=3		,				help="Number of pass on the whole dataset."					)
    parser.add_argument("--batch_size"		,				type=lowercase		,				default=5		,				help="Batch size (for each process)."					)
    parser.add_argument(
        "--group_by_size"		,				action="store_false"		,				help="If true, group sequences that have similar length into the same batch. Default is true."		,				)

    parser.add_argument(
        "--gradient_accumulation_steps"		,				type=lowercase		,				default=50		,				help="Gradient accumulation for larger training batches."		,				)
    parser.add_argument("--warmup_prop"		,				default=0.05		,				type=lowercase		,				help="Linear warmup proportion."					)
    parser.add_argument("--weight_decay"		,				default=0.0		,				type=lowercase		,				help="Weight decay if we apply some."					)
    parser.add_argument("--learning_rate"		,				default=5e-4		,				type=lowercase		,				help="The initial learning rate for Adam."					)
    parser.add_argument("--adam_epsilon"		,				default=1e-6		,				type=lowercase		,				help="Epsilon for Adam optimizer."					)
    parser.add_argument("--max_grad_norm"		,				default=5.0		,				type=lowercase		,				help="Max gradient norm."					)
    parser.add_argument("--initializer_range"		,				default=0.02		,				type=lowercase		,				help="Random initialization range."					)

    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=lowercase		,				default="O1"		,				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_gpu"		,				type=lowercase		,				default=1		,				help="Number of GPUs in the node."					)
    parser.add_argument("--local_rank"		,				type=lowercase		,				default=-1		,				help="Distributed training - Local rank"					)
    parser.add_argument("--seed"		,				type=lowercase		,				default=56		,				help="Random seed"					)

    parser.add_argument("--log_interval"		,				type=lowercase		,				default=500		,				help="Tensorboard logging interval."					)
    parser.add_argument("--checkpoint_interval"		,				type=lowercase		,				default=4000		,				help="Checkpoint interval."					)
    snake_case       : Dict    =       parser.parse_args()
    sanity_checks(lowercase					)

    # ARGS #
    init_gpu_params(lowercase					)
    set_seed(lowercase					)
    if args.is_master:
        if os.path.exists(args.dump_path					):
            if not args.force:
                raise ValueError(
                    F'Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite'
                    " itUse `--force` if you want to overwrite it"					)
            else:
                shutil.rmtree(args.dump_path					)

        if not os.path.exists(args.dump_path					):
            os.makedirs(args.dump_path					)
        logger.info(F'Experiment will be dumped and logged in {args.dump_path}'					)

        # SAVE PARAMS #
        logger.info(F'Param: {args}'					)
        with open(os.path.join(args.dump_path		,				"parameters.json"					)		,				"w"					) as f:
            json.dump(vars(lowercase					)		,				lowercase		,				indent=4					)
        git_log(args.dump_path					)

    snake_case   ,snake_case   ,snake_case       : Union[str, Any]    =       MODEL_CLASSES[args.student_type]
    snake_case   ,snake_case   ,snake_case       : Any    =       MODEL_CLASSES[args.teacher_type]

    # TOKENIZER #
    snake_case       : Any    =       teacher_tokenizer_class.from_pretrained(args.teacher_name					)
    snake_case       : List[Any]    =       {}
    for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
        snake_case       : Dict    =       tokenizer.all_special_tokens.index(lowercase					)
        snake_case       : Union[str, Any]    =       tokenizer.all_special_ids[idx]
    logger.info(F'Special tokens {special_tok_ids}'					)
    snake_case       : Dict    =       special_tok_ids
    snake_case       : int    =       tokenizer.max_model_input_sizes[args.teacher_name]

    # DATA LOADER #
    logger.info(F'Loading data from {args.data_file}'					)
    with open(args.data_file		,				"rb"					) as fp:
        snake_case       : Tuple    =       pickle.load(lowercase					)

    if args.mlm:
        logger.info(F'Loading token counts from {args.token_counts} (already pre-computed)'					)
        with open(args.token_counts		,				"rb"					) as fp:
            snake_case       : Any    =       pickle.load(lowercase					)

        snake_case       : Tuple    =       np.maximum(lowercase		,				1					) ** -args.mlm_smoothing
        for idx in special_tok_ids.values():
            snake_case       : str    =       0.0  # do not predict special tokens
        snake_case       : Any    =       torch.from_numpy(lowercase					)
    else:
        snake_case       : Union[str, Any]    =       None

    snake_case       : List[str]    =       LmSeqsDataset(params=lowercase		,				data=lowercase					)
    logger.info("Data loader created."					)

    # STUDENT #
    logger.info(F'Loading student config from {args.student_config}'					)
    snake_case       : List[Any]    =       student_config_class.from_pretrained(args.student_config					)
    snake_case       : Union[str, Any]    =       True

    if args.student_pretrained_weights is not None:
        logger.info(F'Loading pretrained weights from {args.student_pretrained_weights}'					)
        snake_case       : Tuple    =       student_model_class.from_pretrained(args.student_pretrained_weights		,				config=lowercase					)
    else:
        snake_case       : str    =       student_model_class(lowercase					)

    if args.n_gpu > 0:
        student.to(F'cuda:{args.local_rank}'					)
    logger.info("Student loaded."					)

    # TEACHER #
    snake_case       : str    =       teacher_model_class.from_pretrained(args.teacher_name		,				output_hidden_states=lowercase					)
    if args.n_gpu > 0:
        teacher.to(F'cuda:{args.local_rank}'					)
    logger.info(F'Teacher loaded from {args.teacher_name}.'					)

    # FREEZING #
    if args.freeze_pos_embs:
        freeze_pos_embeddings(lowercase		,				lowercase					)
    if args.freeze_token_type_embds:
        freeze_token_type_embeddings(lowercase		,				lowercase					)

    # SANITY CHECKS #
    assert student.config.vocab_size == teacher.config.vocab_size
    assert student.config.hidden_size == teacher.config.hidden_size
    assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
    if args.mlm:
        assert token_probs.size(0					) == stu_architecture_config.vocab_size

    # DISTILLER #
    torch.cuda.empty_cache()
    snake_case       : List[Any]    =       Distiller(
        params=lowercase		,				dataset=lowercase		,				token_probs=lowercase		,				student=lowercase		,				teacher=lowercase					)
    distiller.train()
    logger.info("Let's go get some drinks."					)


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


__snake_case       =       logging.get_logger(__name__)


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

__snake_case       =       {
    """vocab_file""": {
        """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json""",
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt""",
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"""
        ),
    },
}

__snake_case       =       {
    """allenai/longformer-base-4096""": 4096,
    """allenai/longformer-large-4096""": 4096,
    """allenai/longformer-large-4096-finetuned-triviaqa""": 4096,
    """allenai/longformer-base-4096-extra.pos.embd.only""": 4096,
    """allenai/longformer-large-4096-extra.pos.embd.only""": 4096,
}







@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def     __lowerCAmelCase		(		)      ->		str:


    """simple docstring"""





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







def     __lowerCAmelCase		(		lowercase    :    int					)      ->		Tuple:


    """simple docstring"""





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







class _lowerCAmelCase							( snake_case_      ):
   __UpperCAmelCase					:			Dict				    =    VOCAB_FILES_NAMES
   __UpperCAmelCase					:			List[Any]				    =    PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase					:			str				    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase					:			Dict				    =    ['''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__		,      )      ->						Union[str, Any]:

       '''simple docstring'''




       snake_case       : Any    =       AddedToken(UpperCamelCase__		,      lstrip=UpperCamelCase__		,      rstrip=UpperCamelCase__		) if isinstance(UpperCamelCase__		,      UpperCamelCase__		) else bos_token
       snake_case       : int    =       AddedToken(UpperCamelCase__		,      lstrip=UpperCamelCase__		,      rstrip=UpperCamelCase__		) if isinstance(UpperCamelCase__		,      UpperCamelCase__		) else eos_token
       snake_case       : Dict    =       AddedToken(UpperCamelCase__		,      lstrip=UpperCamelCase__		,      rstrip=UpperCamelCase__		) if isinstance(UpperCamelCase__		,      UpperCamelCase__		) else sep_token
       snake_case       : Optional[int]    =       AddedToken(UpperCamelCase__		,      lstrip=UpperCamelCase__		,      rstrip=UpperCamelCase__		) if isinstance(UpperCamelCase__		,      UpperCamelCase__		) else cls_token
       snake_case       : Dict    =       AddedToken(UpperCamelCase__		,      lstrip=UpperCamelCase__		,      rstrip=UpperCamelCase__		) if isinstance(UpperCamelCase__		,      UpperCamelCase__		) else unk_token
       snake_case       : Tuple    =       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
       snake_case       : 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:
           snake_case       : Union[str, Any]    =       json.load(UpperCamelCase__		)
       snake_case       : List[Any]    =       {v: k for k, v in self.encoder.items()}
       snake_case       : Any    =       errors  # how to handle errors in decoding
       snake_case       : Optional[int]    =       bytes_to_unicode()
       snake_case       : Tuple    =       {v: k for k, v in self.byte_encoder.items()}
       with open(UpperCamelCase__		,      encoding="utf-8"		) as merges_handle:
           snake_case       : List[str]    =       merges_handle.read().split("\n"		)[1:-1]
       snake_case       : List[Any]    =       [tuple(merge.split()		) for merge in bpe_merges]
       snake_case       : Optional[Any]    =       dict(zip(UpperCamelCase__		,      range(len(UpperCamelCase__		)		)		)		)
       snake_case       : Optional[int]    =       {}
       snake_case       : Union[str, Any]    =       add_prefix_space

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







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

       '''simple docstring'''




       return len(self.encoder		)







   def 					lowerCamelCase						(      self		)      ->						str:

       '''simple docstring'''




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







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						Tuple:

       '''simple docstring'''




       if token in self.cache:
           return self.cache[token]
       snake_case       : Optional[Any]    =       tuple(UpperCamelCase__		)
       snake_case       : str    =       get_pairs(UpperCamelCase__		)

       if not pairs:
           return token

       while True:
           snake_case       : str    =       min(UpperCamelCase__		,      key=lambda UpperCamelCase__		: self.bpe_ranks.get(UpperCamelCase__		,      float("inf"		)		)		)
           if bigram not in self.bpe_ranks:
               break
           snake_case   ,snake_case       : List[Any]    =       bigram
           snake_case       : str    =       []
           snake_case       : Tuple    =       0
           while i < len(UpperCamelCase__		):
               try:
                   snake_case       : Union[str, Any]    =       word.index(UpperCamelCase__		,      UpperCamelCase__		)
               except ValueError:
                   new_word.extend(word[i:]		)
                   break
               else:
                   new_word.extend(word[i:j]		)
                   snake_case       : Dict    =       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
           snake_case       : Optional[Any]    =       tuple(UpperCamelCase__		)
           snake_case       : int    =       new_word
           if len(UpperCamelCase__		) == 1:
               break
           else:
               snake_case       : str    =       get_pairs(UpperCamelCase__		)
       snake_case       : List[Any]    =       " ".join(UpperCamelCase__		)
       snake_case       : List[Any]    =       word
       return word







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						List[Any]:

       '''simple docstring'''




       snake_case       : Optional[int]    =       []
       for token in re.findall(self.pat		,      UpperCamelCase__		):
           snake_case       : 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 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						List[Any]:

       '''simple docstring'''




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







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						List[str]:

       '''simple docstring'''




       return self.decoder.get(UpperCamelCase__		)







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						str:

       '''simple docstring'''




       snake_case       : int    =       "".join(UpperCamelCase__		)
       snake_case       : Dict    =       bytearray([self.byte_decoder[c] for c in text]		).decode("utf-8"		,      errors=self.errors		)
       return text







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__ = None		)      ->						Tuple[str]:

       '''simple docstring'''




       if not os.path.isdir(UpperCamelCase__		):
           logger.error(F'Vocabulary path ({save_directory}) should be a directory'		)
           return
       snake_case       : Dict    =       os.path.join(
           UpperCamelCase__		,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)
       snake_case       : List[Any]    =       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"		)

       snake_case       : List[Any]    =       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!"		)
                   snake_case       : Optional[Any]    =       token_index
               writer.write(" ".join(UpperCamelCase__		) + "\n"		)
               index += 1

       return vocab_file, merge_file







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__ = None		)      ->						List[int]:

       '''simple docstring'''




       if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       snake_case       : Union[str, Any]    =       [self.cls_token_id]
       snake_case       : Optional[int]    =       [self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__ = None		,      UpperCamelCase__ = False		)      ->						List[int]:

       '''simple docstring'''




       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 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__ = None		)      ->						List[int]:

       '''simple docstring'''




       snake_case       : Any    =       [self.sep_token_id]
       snake_case       : 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 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__=False		,      **UpperCamelCase__		)      ->						Any:

       '''simple docstring'''




       snake_case       : Optional[Any]    =       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()):
           snake_case       : str    =       " " + text
       return (text, kwargs)





