





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''': 5_1_2,
}






def      lowerCamelCase_						(				_UpperCamelCase )		->						int:






				"""simple docstring"""


				snake_case_						:    Optional[Any]				=  collections.OrderedDict()
				with open(__a ,				'''r''' ,				encoding='''utf-8''' ) as reader:
								snake_case_						:    int				=  reader.readlines()
				for index, token in enumerate(__a ):
								snake_case_						:    List[str]				=  token.rstrip('''\n''' )
								snake_case_						:    List[Any]				=  index
				return vocab





class 					__lowerCAmelCase       (			__SCREAMING_SNAKE_CASE     ):
			lowerCamelCase_     :     Tuple       			=      VOCAB_FILES_NAMES
			lowerCamelCase_     :     Dict       			=      PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase_     :     int       			=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase_     :     List[Any]       			=      ['''input_ids''', '''attention_mask''']






			def __init__(self					,							__magic_name__					,							__magic_name__="[SEP]"					,							__magic_name__="[SEP]"					,							__magic_name__="[SEP]"					,							__magic_name__="[UNK]"					,							__magic_name__="[PAD]"					,							__magic_name__="[CLS]"					,							__magic_name__="[MASK]"					,							__magic_name__ = None					,							**__magic_name__					,							)     ->   None:


							'''simple docstring'''



							snake_case_						:    int				=  {} if sp_model_kwargs is None else sp_model_kwargs

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

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

							for i in range(10    ):
											snake_case_						:    Optional[int]				=  F'''[unused{i}]'''
											snake_case_						:    int				=  5 + i

							# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
							snake_case_						:    int				=  12
							snake_case_						:    Optional[Any]				=  {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(UpperCAmelCase_    )






			def __getstate__(self    )     ->   Dict:


							'''simple docstring'''



							snake_case_						:    Optional[int]				=  self.__dict__.copy()
							snake_case_						:    Dict				=  None
							return state






			def __setstate__(self					,							__magic_name__    )     ->   Dict:


							'''simple docstring'''



							snake_case_						:    Tuple				=  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_						:    Dict				=  {}

							snake_case_						:    Tuple				=  spm.SentencePieceProcessor(**self.sp_model_kwargs    )
							self.sp_model.Load(self.vocab_file    )






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__ = None					,							__magic_name__ = 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 ([0] * len(UpperCAmelCase_    )) + [1]
							return ([0] * len(UpperCAmelCase_    )) + [1] + ([0] * len(UpperCAmelCase_    )) + [1]






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


							'''simple docstring'''



							snake_case_						:    Any				=  [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 			lowerCamelCase  (self    )     ->   Dict:


							'''simple docstring'''



							return len(self.sp_model    ) + self.fairseq_offset






			def 			lowerCamelCase  (self    )     ->   Tuple:


							'''simple docstring'''



							snake_case_						:    str				=  {self.convert_ids_to_tokens(UpperCAmelCase_    ): i for i in range(self.vocab_size    )}
							vocab.update(self.added_tokens_encoder    )
							return vocab






			def 			lowerCamelCase  (self					,							__magic_name__    )     ->   str:


							'''simple docstring'''



							return self.sp_model.encode(UpperCAmelCase_					,							out_type=UpperCAmelCase_    )






			def 			lowerCamelCase  (self					,							__magic_name__    )     ->   str:


							'''simple docstring'''



							if token in self.fairseq_tokens_to_ids:
											return self.fairseq_tokens_to_ids[token]
							snake_case_						:    str				=  self.sp_model.PieceToId(UpperCAmelCase_    )

							# 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 			lowerCamelCase  (self					,							__magic_name__    )     ->   Optional[int]:


							'''simple docstring'''



							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 			lowerCamelCase  (self					,							__magic_name__    )     ->   Optional[Any]:


							'''simple docstring'''



							snake_case_						:    Union[str, Any]				=  "".join(UpperCAmelCase_    ).replace(UpperCAmelCase_					,							''' '''    ).strip()
							return out_string






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__ = 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_						:    List[str]				=  os.path.join(
							    UpperCAmelCase_					,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']    )

							if os.path.abspath(self.vocab_file    ) != os.path.abspath(UpperCAmelCase_    ) and os.path.isfile(self.vocab_file    ):
											copyfile(self.vocab_file					,							UpperCAmelCase_    )
							elif not os.path.isfile(self.vocab_file    ):
											with open(UpperCAmelCase_					,							'''wb'''    ) as fi:
															snake_case_						:    Dict				=  self.sp_model.serialized_model_proto()
															fi.write(UpperCAmelCase_    )

							return (out_vocab_file,)






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


							'''simple docstring'''



							if token_ids_a is None:
											return token_ids_a + [self.sep_token_id]
							snake_case_						:    Union[str, Any]				=  [self.sep_token_id]
							return token_ids_a + sep + token_ids_a + sep







import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer


__A									=				logging.get_logger(__name__)

__A									=				{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

__A									=				{
    "vocab_file": {
        "yjernite/retribert-base-uncased": (
            "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "yjernite/retribert-base-uncased": (
            "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json"
        ),
    },
}

__A									=				{
    "yjernite/retribert-base-uncased": 512,
}


__A									=				{
    "yjernite/retribert-base-uncased": {"do_lower_case": True},
}



class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''
 lowercase_		=			VOCAB_FILES_NAMES
 lowercase_		=			PRETRAINED_VOCAB_FILES_MAP
 lowercase_		=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase_		=			PRETRAINED_INIT_CONFIGURATION
 lowercase_		=			RetriBertTokenizer
 lowercase_		=			["input_ids", "attention_mask"]

 def __init__(self				:   int       , UpperCAmelCase_				:   List[str]=None       , UpperCAmelCase_				:   str=None       , UpperCAmelCase_				:   Optional[Any]=True       , UpperCAmelCase_				:   Union[str, Any]="[UNK]"       , UpperCAmelCase_				:   Any="[SEP]"       , UpperCAmelCase_				:   List[str]="[PAD]"       , UpperCAmelCase_				:   Optional[Any]="[CLS]"       , UpperCAmelCase_				:   Optional[Any]="[MASK]"       , UpperCAmelCase_				:   Dict=True       , UpperCAmelCase_				:   str=None       , **UpperCAmelCase_				:   str       , )       ->List[Any]:



   '''simple docstring'''



   super().__init__(
       UpperCAmelCase_       , tokenizer_file=UpperCAmelCase_       , do_lower_case=UpperCAmelCase_       , unk_token=UpperCAmelCase_       , sep_token=UpperCAmelCase_       , pad_token=UpperCAmelCase_       , cls_token=UpperCAmelCase_       , mask_token=UpperCAmelCase_       , tokenize_chinese_chars=UpperCAmelCase_       , strip_accents=UpperCAmelCase_       , **UpperCAmelCase_       , )

   lowerCamelCase__:      List[Any]			  =json.loads(self.backend_tokenizer.normalizer.__getstate__())
   if (
       normalizer_state.get("lowercase"       , UpperCAmelCase_) != do_lower_case
       or normalizer_state.get("strip_accents"       , UpperCAmelCase_) != strip_accents
       or normalizer_state.get("handle_chinese_chars"       , UpperCAmelCase_) != tokenize_chinese_chars
   ):
     lowerCamelCase__:      Dict			  =getattr(UpperCAmelCase_       , normalizer_state.pop("type"))
     lowerCamelCase__:      int			  =do_lower_case
     lowerCamelCase__:      int			  =strip_accents
     lowerCamelCase__:      List[str]			  =tokenize_chinese_chars
     lowerCamelCase__:      Tuple			  =normalizer_class(**UpperCAmelCase_)

   lowerCamelCase__:      Any			  =do_lower_case

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[str]       , UpperCAmelCase_				:   Dict       , UpperCAmelCase_				:   Optional[Any]=None)       ->List[str]:



   '''simple docstring'''



   lowerCamelCase__:      Optional[Any]			  =[self.cls_token_id] + token_ids_a + [self.sep_token_id]

   if token_ids_a:
     output += token_ids_a + [self.sep_token_id]

   return output

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



   '''simple docstring'''



   lowerCamelCase__:      Tuple			  =[self.sep_token_id]
   lowerCamelCase__:      Optional[int]			  =[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 							SCREAMING_SNAKE_CASE_					(self				:   Tuple       , UpperCAmelCase_				:   str       , UpperCAmelCase_				:   Optional[str] = None)       ->Tuple[str]:



   '''simple docstring'''



   lowerCamelCase__:      Tuple			  =self._tokenizer.model.save(UpperCAmelCase_       , name=UpperCAmelCase_)
   return tuple(UpperCAmelCase_)

