


"""simple docstring"""


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

import sentencepiece as spm

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


A     :   Any			    =			logging.get_logger(__name__)

A     :   int			    =			{"vocab_file": "sentencepiece.bpe.model"}

A     :   str			    =			{
    "vocab_file": {
        "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez-orangesum-title": (
            "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"
        ),
    },
}

A     :   int			    =			{
    "moussaKam/mbarthez": 1_0_2_4,
    "moussaKam/barthez": 1_0_2_4,
    "moussaKam/barthez-orangesum-title": 1_0_2_4,
}

A     :   int			    =			"▁"
class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							__UpperCAmelCase						:    Any				      =VOCAB_FILES_NAMES
							__UpperCAmelCase						:    int				      =PRETRAINED_VOCAB_FILES_MAP
							__UpperCAmelCase						:    int				      =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__UpperCAmelCase						:    Dict				      =["""input_ids""", """attention_mask"""]

							def __init__(     self    ,   __a    ,   __a="<s>"    ,   __a="</s>"    ,   __a="</s>"    ,   __a="<s>"    ,   __a="<unk>"    ,   __a="<pad>"    ,   __a="<mask>"    ,   __a = None    ,   **__a    ,   ):
										# Mask token behave like a normal word, i.e. include the space before it
										__lowerCAmelCase				    =			AddedToken(__a    ,   lstrip=__a    ,   rstrip=__a	) if isinstance(__a    ,   __a	) else mask_token

										__lowerCAmelCase				    =			{} if sp_model_kwargs is None else sp_model_kwargs

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

										__lowerCAmelCase				    =			vocab_file
										__lowerCAmelCase				    =			spm.SentencePieceProcessor(**self.sp_model_kwargs	)
										self.sp_model.Load(str(__a	)	)

										__lowerCAmelCase				    =			{"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

										__lowerCAmelCase				    =			len(self.sp_model	) - 1
										__lowerCAmelCase				    =			{v: k for k, v in self.fairseq_tokens_to_ids.items()}

							def 			snake_case				(     self    ,   __a    ,   __a = None	):

										if token_ids_a is None:
													return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
										__lowerCAmelCase				    =			[self.cls_token_id]
										__lowerCAmelCase				    =			[self.sep_token_id]
										return cls + token_ids_a + sep + sep + token_ids_a + sep

							def 			snake_case				(     self    ,   __a    ,   __a = None    ,   __a = False	):
										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 [1] + ([0] * len(__a	)) + [1]
										return [1] + ([0] * len(__a	)) + [1, 1] + ([0] * len(__a	)) + [1]

							def 			snake_case				(     self    ,   __a    ,   __a = None	):
										__lowerCAmelCase				    =			[self.sep_token_id]
										__lowerCAmelCase				    =			[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]

							@property
							def 			snake_case				(     self	):
										return len(self.sp_model	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			{self.convert_ids_to_tokens(__a	): i for i in range(self.vocab_size	)}
										vocab.update(self.added_tokens_encoder	)
										return vocab

							def 			snake_case				(     self    ,   __a	):
										return self.sp_model.encode(__a    ,   out_type=__a	)

							def 			snake_case				(     self    ,   __a	):
										if token in self.fairseq_tokens_to_ids:
													return self.fairseq_tokens_to_ids[token]
										__lowerCAmelCase				    =			self.sp_model.PieceToId(__a	)

										return spm_id if spm_id else self.unk_token_id

							def 			snake_case				(     self    ,   __a	):
										if index in self.fairseq_ids_to_tokens:
													return self.fairseq_ids_to_tokens[index]
										return self.sp_model.IdToPiece(__a	)

							def 			snake_case				(     self    ,   __a	):
										__lowerCAmelCase				    =			[]
										__lowerCAmelCase				    =			""
										__lowerCAmelCase				    =			False
										for token in tokens:
													# make sure that special tokens are not decoded using sentencepiece model
													if token in self.all_special_tokens:
																if not prev_is_special:
																			out_string += " "
																out_string += self.sp_model.decode(__a	) + token
																__lowerCAmelCase				    =			True
																__lowerCAmelCase				    =			[]
													else:
																current_sub_tokens.append(__a	)
																__lowerCAmelCase				    =			False
										out_string += self.sp_model.decode(__a	)
										return out_string.strip()

							def __getstate__(     self	):
										__lowerCAmelCase				    =			self.__dict__.copy()
										__lowerCAmelCase				    =			None
										return state

							def __setstate__(     self    ,   __a	):
										__lowerCAmelCase				    =			d

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

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



							def 			snake_case				(     self    ,   __a    ,   __a = None	):
										if not os.path.isdir(__a	):
													logger.error(f"Vocabulary path ({save_directory}) should be a directory"	)
													return
										__lowerCAmelCase				    =			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:
																__lowerCAmelCase				    =			self.sp_model.serialized_model_proto()
																fi.write(__a	)

										return (out_vocab_file,)











import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer


_UpperCAmelCase				:       Optional[int]	   =				logging.get_logger(__name__)

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

_UpperCAmelCase				:       int	   =				{
    """vocab_file""": {
        """google/realm-cc-news-pretrained-embedder""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt"""
        ),
        """google/realm-cc-news-pretrained-encoder""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt"""
        ),
        """google/realm-cc-news-pretrained-scorer""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt"""
        ),
        """google/realm-cc-news-pretrained-openqa""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt"""
        ),
        """google/realm-orqa-nq-openqa""": """https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt""",
        """google/realm-orqa-nq-reader""": """https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt""",
        """google/realm-orqa-wq-openqa""": """https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt""",
        """google/realm-orqa-wq-reader""": """https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt""",
    },
    """tokenizer_file""": {
        """google/realm-cc-news-pretrained-embedder""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont"""
        ),
        """google/realm-cc-news-pretrained-encoder""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json"""
        ),
        """google/realm-cc-news-pretrained-scorer""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json"""
        ),
        """google/realm-cc-news-pretrained-openqa""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json"""
        ),
        """google/realm-orqa-nq-openqa""": (
            """https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json"""
        ),
        """google/realm-orqa-nq-reader""": (
            """https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json"""
        ),
        """google/realm-orqa-wq-openqa""": (
            """https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json"""
        ),
        """google/realm-orqa-wq-reader""": (
            """https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json"""
        ),
    },
}

_UpperCAmelCase				:       int	   =				{
    """google/realm-cc-news-pretrained-embedder""": 5_12,
    """google/realm-cc-news-pretrained-encoder""": 5_12,
    """google/realm-cc-news-pretrained-scorer""": 5_12,
    """google/realm-cc-news-pretrained-openqa""": 5_12,
    """google/realm-orqa-nq-openqa""": 5_12,
    """google/realm-orqa-nq-reader""": 5_12,
    """google/realm-orqa-wq-openqa""": 5_12,
    """google/realm-orqa-wq-reader""": 5_12,
}

_UpperCAmelCase				:       Any	   =				{
    """google/realm-cc-news-pretrained-embedder""": {"""do_lower_case""": True},
    """google/realm-cc-news-pretrained-encoder""": {"""do_lower_case""": True},
    """google/realm-cc-news-pretrained-scorer""": {"""do_lower_case""": True},
    """google/realm-cc-news-pretrained-openqa""": {"""do_lower_case""": True},
    """google/realm-orqa-nq-openqa""": {"""do_lower_case""": True},
    """google/realm-orqa-nq-reader""": {"""do_lower_case""": True},
    """google/realm-orqa-wq-openqa""": {"""do_lower_case""": True},
    """google/realm-orqa-wq-reader""": {"""do_lower_case""": True},
}

class    lowerCAmelCase							(					__UpperCamelCase			):
 UpperCAmelCase__													=		VOCAB_FILES_NAMES
 UpperCAmelCase__													=		PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase__													=		PRETRAINED_INIT_CONFIGURATION
 UpperCAmelCase__													=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase__													=		RealmTokenizer
 def __init__(      self  :      Optional[int]			,     UpperCAmelCase  :      Tuple=None			,     UpperCAmelCase  :      Any=None			,     UpperCAmelCase  :      List[Any]=True			,     UpperCAmelCase  :      Optional[Any]="[UNK]"			,     UpperCAmelCase  :      Any="[SEP]"			,     UpperCAmelCase  :      Tuple="[PAD]"			,     UpperCAmelCase  :      List[Any]="[CLS]"			,     UpperCAmelCase  :      Union[str, Any]="[MASK]"			,     UpperCAmelCase  :      Optional[Any]=True			,     UpperCAmelCase  :      Any=None			,     **UpperCAmelCase  :      Optional[int]			,     )			->	str:
     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__    :      Optional[int]					 =		getattr(UpperCAmelCase			,     normalizer_state.pop('type'	)	)
         lowerCamelCase__    :      Optional[Any]					 =		do_lower_case
         lowerCamelCase__    :      str					 =		strip_accents
         lowerCamelCase__    :      Optional[Any]					 =		tokenize_chinese_chars
         lowerCamelCase__    :      int					 =		normalizer_class(**UpperCAmelCase	)

     lowerCamelCase__    :      str					 =		do_lower_case
 def      A_	(      self  :      Optional[int]			,     UpperCAmelCase  :      int			,     **UpperCAmelCase  :      int	)			->	List[Any]:
     lowerCamelCase__    :      List[Any]					 =		PaddingStrategy.MAX_LENGTH

     lowerCamelCase__    :      Optional[int]					 =		text
     lowerCamelCase__    :      Dict					 =		kwargs.pop('text_pair'			,     UpperCAmelCase	)
     lowerCamelCase__    :      List[Any]					 =		kwargs.pop('return_tensors'			,     UpperCAmelCase	)

     lowerCamelCase__    :      List[Any]					 =		{
         'input_ids': [],
         'attention_mask': [],
         'token_type_ids': [],
     }

     for idx, candidate_text in enumerate(UpperCAmelCase	):
         if batch_text_pair is not None:
             lowerCamelCase__    :      Tuple					 =		batch_text_pair[idx]
         else:
             lowerCamelCase__    :      Dict					 =		None

         lowerCamelCase__    :      Optional[int]					 =		super().__call__(UpperCAmelCase			,     UpperCAmelCase			,     return_tensors=UpperCAmelCase			,     **UpperCAmelCase	)

         lowerCamelCase__    :      Any					 =		encoded_candidates.get('input_ids'	)
         lowerCamelCase__    :      Union[str, Any]					 =		encoded_candidates.get('attention_mask'	)
         lowerCamelCase__    :      Tuple					 =		encoded_candidates.get('token_type_ids'	)

         if encoded_input_ids is not None:
             output_data["input_ids"].append(UpperCAmelCase	)
         if encoded_attention_mask is not None:
             output_data["attention_mask"].append(UpperCAmelCase	)
         if encoded_token_type_ids is not None:
             output_data["token_type_ids"].append(UpperCAmelCase	)

     lowerCamelCase__    :      int					 =		{key: item for key, item in output_data.items() if len(UpperCAmelCase	) != 0}

     return BatchEncoding(UpperCAmelCase			,     tensor_type=UpperCAmelCase	)
 def      A_	(      self  :      int			,     UpperCAmelCase  :      Optional[Any]			,     UpperCAmelCase  :      Optional[int]=None	)			->	List[str]:
     lowerCamelCase__    :      Tuple					 =		[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      A_	(      self  :      Tuple			,     UpperCAmelCase  :      List[int]			,     UpperCAmelCase  :      Optional[List[int]] = None	)			->	List[int]:
     lowerCamelCase__    :      List[Any]					 =		[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      A_	(      self  :      Optional[Any]			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      Optional[str] = None	)			->	Tuple[str]:
     lowerCamelCase__    :      int					 =		self._tokenizer.model.save(UpperCAmelCase			,     name=UpperCAmelCase	)
     return tuple(UpperCAmelCase	)





