
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


_A   =			logging.get_logger(__name__)

_A   =			"▁"

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

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

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

_A   =			{
    "microsoft/xprophetnet-large-wiki100-cased": 5_12,
}



def 						lowercase_      (       A__		)							->							List[Any]:
    """simple docstring"""

    snake_case								= collections.OrderedDict()
    with open(A__			,		"r"			,		encoding="utf-8"		) as reader:
        snake_case								= reader.readlines()
    for index, token in enumerate(A__		):
        snake_case								= token.rstrip("\n"		)
        snake_case								= index
    return vocab







class 					lowerCamelCase		(  A_  ):
     UpperCAmelCase__    :							Dict									=		VOCAB_FILES_NAMES
     UpperCAmelCase__    :							str									=		PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase__    :							Dict									=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase__    :							List[Any]									=		["input_ids", "attention_mask"]





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

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

         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

         snake_case								= spm.SentencePieceProcessor(**self.sp_model_kwargs       )
         self.sp_model.Load(str(_A       )       )
         snake_case								= 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
         snake_case								= {"[PAD]": 0, "[CLS]": 1, "[SEP]": 2, "[UNK]": 3, "[MASK]": 4}

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

         # The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
         snake_case								= 1_2
         snake_case								= {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(_A       )





     def __getstate__(self				:   Tuple       )						->						int:
         snake_case								= self.__dict__.copy()
         snake_case								= None
         return state





     def __setstate__(self				:   Union[str, Any]    ,   _A				:   Any       )						->						Optional[Any]:
         snake_case								= 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"       ):
             snake_case								= {}

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





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

         if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=_A    ,   token_ids_a=_A    ,   already_has_special_tokens=_A       )

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





     def 		UpperCAmelCase(self				:   Tuple    ,   _A				:   List[int]    ,   _A				:   Optional[List[int]] = None       )						->						List[int]:
         snake_case								= [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				:   Any       )						->						Tuple:
         return len(self.sp_model       ) + self.fairseq_offset





     def 		UpperCAmelCase(self				:   Optional[Any]       )						->						List[str]:
         snake_case								= {self.convert_ids_to_tokens(_A       ): i for i in range(self.vocab_size       )}
         vocab.update(self.added_tokens_encoder       )
         return vocab





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





     def 		UpperCAmelCase(self				:   Tuple    ,   _A				:   Union[str, Any]       )						->						Union[str, Any]:
         if token in self.fairseq_tokens_to_ids:
             return self.fairseq_tokens_to_ids[token]
         snake_case								= self.sp_model.PieceToId(_A       )

         # 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				:   Union[str, Any]    ,   _A				:   str       )						->						str:
         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				:   List[str]    ,   _A				:   Dict       )						->						Union[str, Any]:
         snake_case								= "".join(_A       ).replace(_A    ,   " "       ).strip()
         return out_string





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

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

         return (out_vocab_file,)





     def 		UpperCAmelCase(self				:   List[str]    ,   _A				:   List[int]    ,   _A				:   Optional[List[int]] = None       )						->						List[int]:

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



from ..utils import DummyObject, requires_backends







class 					lowerCamelCase		(  metaclass=A_  ):
     UpperCAmelCase__    :							Union[str, Any]									=		["onnx"]





     def __init__(self				:   Tuple    ,   *_A				:   Optional[int]    ,   **_A				:   Any       )						->						Dict:
         requires_backends(self    ,   ["onnx"]       )





     @classmethod
     def 		UpperCAmelCase(cls				:   int    ,   *_A				:   Dict    ,   **_A				:   List[Any]       )						->						Optional[Any]:
         requires_backends(cls    ,   ["onnx"]       )





     @classmethod
     def 		UpperCAmelCase(cls				:   Dict    ,   *_A				:   Tuple    ,   **_A				:   Optional[Any]       )						->						int:
         requires_backends(cls    ,   ["onnx"]       )

