

import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

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


lowerCAmelCase__       = logging.get_logger(__name__)

lowerCAmelCase__       = "▁"

lowerCAmelCase__       = {"vocab_file": "prophetnet.tokenizer"}

lowerCAmelCase__       = {
    "vocab_file": {
        "microsoft/xprophetnet-large-wiki100-cased": (
            "https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer"
        ),
    }
}

lowerCAmelCase__       = {
    "microsoft/xprophetnet-large-wiki100-cased": {"do_lower_case": False},
}

lowerCAmelCase__       = {
    "microsoft/xprophetnet-large-wiki100-cased": 512,
}
def      _lowerCAmelCase(	__A			):
  UpperCAmelCase					      =  collections.OrderedDict()
  with open(__A   ,       "r"   ,       encoding="utf-8"			) as reader:
    UpperCAmelCase					      =  reader.readlines()
  for index, token in enumerate(__A			):
    UpperCAmelCase					      =  token.rstrip("\n"			)
    UpperCAmelCase					      =  index
  return vocab






class 						__magic_name__    (       _snake_case    ):
 UpperCAmelCase							    =						VOCAB_FILES_NAMES
 UpperCAmelCase							    =						PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase							    =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase							    =						["""input_ids""", """attention_mask"""]


 def __init__(						self		: List[str]			,						lowerCAmelCase__		: Any			,						lowerCAmelCase__		: Optional[Any]="[SEP]"			,						lowerCAmelCase__		: Any="[SEP]"			,						lowerCAmelCase__		: Tuple="[SEP]"			,						lowerCAmelCase__		: List[Any]="[UNK]"			,						lowerCAmelCase__		: Dict="[PAD]"			,						lowerCAmelCase__		: Tuple="[CLS]"			,						lowerCAmelCase__		: str="[MASK]"			,						lowerCAmelCase__		: Optional[Dict[str, Any]] = None			,						**lowerCAmelCase__		: int			,						)    ->      None:
   UpperCAmelCase					      =  {} if sp_model_kwargs is None else sp_model_kwargs

   super().__init__(
       bos_token=lowerCAmelCase__			,						eos_token=lowerCAmelCase__			,						sep_token=lowerCAmelCase__			,						unk_token=lowerCAmelCase__			,						pad_token=lowerCAmelCase__			,						cls_token=lowerCAmelCase__			,						mask_token=lowerCAmelCase__			,						sp_model_kwargs=self.sp_model_kwargs			,						**lowerCAmelCase__			,						)

   try:
     import sentencepiece as spm
   except ImportError:
     logger.warning(
         "You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"
         " pip install sentencepiece"    )
     raise

   UpperCAmelCase					      =  spm.SentencePieceProcessor(**self.sp_model_kwargs    )
   self.sp_model.Load(str(lowerCAmelCase__    )    )
   UpperCAmelCase					      =  vocab_file

   # Original fairseq vocab and spm vocab must be "aligned":
   # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
   # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
   # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
   # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

   # put special tokens and [unused] tokens into the vocab
   UpperCAmelCase					      =  {"[PAD]": 0, "[CLS]": 1, "[SEP]": 2, "[UNK]": 3, "[MASK]": 4}

   for i in range(1_0    ):
     UpperCAmelCase					      =  f"[unused{i}]"
     UpperCAmelCase					      =  5 + i

   # The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
   UpperCAmelCase					      =  1_2
   UpperCAmelCase					      =  {v: k for k, v in self.fairseq_tokens_to_ids.items()}
   for k in self.fairseq_tokens_to_ids.keys():
     self.unique_no_split_tokens.append(lowerCAmelCase__    )


 def __getstate__(						self		: Any    )    ->      List[Any]:
   UpperCAmelCase					      =  self.__dict__.copy()
   UpperCAmelCase					      =  None
   return state


 def __setstate__(						self		: List[Any]			,						lowerCAmelCase__		: str    )    ->      str:
   UpperCAmelCase					      =  d
   try:
     import sentencepiece as spm
   except ImportError:
     logger.warning(
         "You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"
         " pip install sentencepiece"    )
     raise

   # for backward compatibility
   if not hasattr(self			,						"sp_model_kwargs"    ):
     UpperCAmelCase					      =  {}

   UpperCAmelCase					      =  spm.SentencePieceProcessor(**self.sp_model_kwargs    )
   self.sp_model.Load(self.vocab_file    )


 def _UpperCamelCase	(						self		: Optional[Any]			,						lowerCAmelCase__		: List[int]			,						lowerCAmelCase__		: Optional[List[int]] = None			,						lowerCAmelCase__		: bool = False    )    ->      List[int]:

   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 ([0] * len(lowerCAmelCase__    )) + [1]
   return ([0] * len(lowerCAmelCase__    )) + [1] + ([0] * len(lowerCAmelCase__    )) + [1]


 def _UpperCamelCase	(						self		: int			,						lowerCAmelCase__		: List[int]			,						lowerCAmelCase__		: Optional[List[int]] = None    )    ->      List[int]:
   UpperCAmelCase					      =  [self.sep_token_id]

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


 @property
 def _UpperCamelCase	(						self		: int    )    ->      int:
   return len(self.sp_model    ) + self.fairseq_offset


 def _UpperCamelCase	(						self		: Tuple    )    ->      Optional[int]:
   UpperCAmelCase					      =  {self.convert_ids_to_tokens(lowerCAmelCase__    ): i for i in range(self.vocab_size    )}
   vocab.update(self.added_tokens_encoder    )
   return vocab


 def _UpperCamelCase	(						self		: Optional[Any]			,						lowerCAmelCase__		: str    )    ->      str:
   return self.sp_model.encode(lowerCAmelCase__			,						out_type=lowerCAmelCase__    )


 def _UpperCamelCase	(						self		: Any			,						lowerCAmelCase__		: int    )    ->      Tuple:
   if token in self.fairseq_tokens_to_ids:
     return self.fairseq_tokens_to_ids[token]
   UpperCAmelCase					      =  self.sp_model.PieceToId(lowerCAmelCase__    )

   # Need to return unknown token if the SP model returned 0
   return spm_id + self.fairseq_offset if spm_id else self.unk_token_id


 def _UpperCamelCase	(						self		: str			,						lowerCAmelCase__		: int    )    ->      Tuple:
   if index in self.fairseq_ids_to_tokens:
     return self.fairseq_ids_to_tokens[index]
   return self.sp_model.IdToPiece(index - self.fairseq_offset    )


 def _UpperCamelCase	(						self		: Union[str, Any]			,						lowerCAmelCase__		: Any    )    ->      str:
   UpperCAmelCase					      =  "".join(lowerCAmelCase__    ).replace(lowerCAmelCase__			,						" "    ).strip()
   return out_string


 def _UpperCamelCase	(						self		: Any			,						lowerCAmelCase__		: str			,						lowerCAmelCase__		: Optional[str] = None    )    ->      Tuple[str]:
   if not os.path.isdir(lowerCAmelCase__    ):
     logger.error(f"Vocabulary path ({save_directory}) should be a directory"    )
     return
   UpperCAmelCase					      =  os.path.join(
       lowerCAmelCase__			,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]    )

   if os.path.abspath(self.vocab_file    ) != os.path.abspath(lowerCAmelCase__    ) and os.path.isfile(self.vocab_file    ):
     copyfile(self.vocab_file			,						lowerCAmelCase__    )
   elif not os.path.isfile(self.vocab_file    ):
     with open(lowerCAmelCase__			,						"wb"    ) as fi:
       UpperCAmelCase					      =  self.sp_model.serialized_model_proto()
       fi.write(lowerCAmelCase__    )

   return (out_vocab_file,)


 def _UpperCamelCase	(						self		: Dict			,						lowerCAmelCase__		: List[int]			,						lowerCAmelCase__		: Optional[List[int]] = None    )    ->      List[int]:

   if token_ids_a is None:
     return token_ids_a + [self.sep_token_id]
   UpperCAmelCase					      =  [self.sep_token_id]
   return token_ids_a + sep + token_ids_a + sep






from . import (
    albert,
    align,
    altclip,
    audio_spectrogram_transformer,
    auto,
    autoformer,
    bark,
    bart,
    barthez,
    bartpho,
    beit,
    bert,
    bert_generation,
    bert_japanese,
    bertweet,
    big_bird,
    bigbird_pegasus,
    biogpt,
    bit,
    blenderbot,
    blenderbot_small,
    blip,
    blip_a,
    bloom,
    bridgetower,
    byta,
    camembert,
    canine,
    chinese_clip,
    clap,
    clip,
    clipseg,
    codegen,
    conditional_detr,
    convbert,
    convnext,
    convnextva,
    cpm,
    cpmant,
    ctrl,
    cvt,
    dataavec,
    deberta,
    deberta_va,
    decision_transformer,
    deformable_detr,
    deit,
    deprecated,
    deta,
    detr,
    dialogpt,
    dinat,
    distilbert,
    dit,
    donut,
    dpr,
    dpt,
    efficientformer,
    efficientnet,
    electra,
    encodec,
    encoder_decoder,
    ernie,
    ernie_m,
    esm,
    falcon,
    flaubert,
    flava,
    fnet,
    focalnet,
    fsmt,
    funnel,
    git,
    glpn,
    gpta,
    gpt_bigcode,
    gpt_neo,
    gpt_neox,
    gpt_neox_japanese,
    gpt_swa,
    gptj,
    gptsan_japanese,
    graphormer,
    groupvit,
    herbert,
    hubert,
    ibert,
    imagegpt,
    informer,
    instructblip,
    jukebox,
    layoutlm,
    layoutlmva,
    layoutlmva,
    layoutxlm,
    led,
    levit,
    lilt,
    llama,
    longformer,
    longta,
    luke,
    lxmert,
    mam_aaa,
    marian,
    markuplm,
    maskaformer,
    maskformer,
    mbart,
    mbartaa,
    mega,
    megatron_bert,
    megatron_gpta,
    mgp_str,
    mluke,
    mobilebert,
    mobilenet_va,
    mobilenet_va,
    mobilevit,
    mobilevitva,
    mpnet,
    mra,
    mta,
    musicgen,
    mvp,
    nat,
    nezha,
    nllb,
    nllb_moe,
    nystromformer,
    oneformer,
    open_llama,
    openai,
    opt,
    owlvit,
    pegasus,
    pegasus_x,
    perceiver,
    phobert,
    pixastruct,
    plbart,
    poolformer,
    prophetnet,
    qdqbert,
    rag,
    realm,
    reformer,
    regnet,
    rembert,
    resnet,
    roberta,
    roberta_prelayernorm,
    roc_bert,
    roformer,
    rwkv,
    sam,
    segformer,
    sew,
    sew_d,
    speech_encoder_decoder,
    speech_to_text,
    speech_to_text_a,
    speechta,
    splinter,
    squeezebert,
    swiftformer,
    swin,
    swinasr,
    swinva,
    switch_transformers,
    ta,
    table_transformer,
    tapas,
    time_series_transformer,
    timesformer,
    timm_backbone,
    transfo_xl,
    trocr,
    tvlt,
    umta,
    unispeech,
    unispeech_sat,
    upernet,
    videomae,
    vilt,
    vision_encoder_decoder,
    vision_text_dual_encoder,
    visual_bert,
    vit,
    vit_hybrid,
    vit_mae,
    vit_msn,
    vivit,
    wavaveca,
    wavaveca_conformer,
    wavaveca_phoneme,
    wavaveca_with_lm,
    wavlm,
    whisper,
    x_clip,
    xglm,
    xlm,
    xlm_prophetnet,
    xlm_roberta,
    xlm_roberta_xl,
    xlnet,
    xmod,
    yolos,
    yoso,
)



