


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

import sentencepiece as spm

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


_A			=						logging.get_logger(__name__)

_A			=						'▁'

_A			=						{'vocab_file': 'sentencepiece.bpe.model'}

_A			=						{
    'vocab_file': {
        'facebook/nllb-200-distilled-600M': (
            'https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model'
        ),
    }
}

_A			=						{
    'facebook/nllb-200-distilled-600M': 1024,
}

# fmt: off
_A			=						['ace_Arab', 'ace_Latn', 'acm_Arab', 'acq_Arab', 'aeb_Arab', 'afr_Latn', 'ajp_Arab', 'aka_Latn', 'amh_Ethi', 'apc_Arab', 'arb_Arab', 'ars_Arab', 'ary_Arab', 'arz_Arab', 'asm_Beng', 'ast_Latn', 'awa_Deva', 'ayr_Latn', 'azb_Arab', 'azj_Latn', 'bak_Cyrl', 'bam_Latn', 'ban_Latn', 'bel_Cyrl', 'bem_Latn', 'ben_Beng', 'bho_Deva', 'bjn_Arab', 'bjn_Latn', 'bod_Tibt', 'bos_Latn', 'bug_Latn', 'bul_Cyrl', 'cat_Latn', 'ceb_Latn', 'ces_Latn', 'cjk_Latn', 'ckb_Arab', 'crh_Latn', 'cym_Latn', 'dan_Latn', 'deu_Latn', 'dik_Latn', 'dyu_Latn', 'dzo_Tibt', 'ell_Grek', 'eng_Latn', 'epo_Latn', 'est_Latn', 'eus_Latn', 'ewe_Latn', 'fao_Latn', 'pes_Arab', 'fij_Latn', 'fin_Latn', 'fon_Latn', 'fra_Latn', 'fur_Latn', 'fuv_Latn', 'gla_Latn', 'gle_Latn', 'glg_Latn', 'grn_Latn', 'guj_Gujr', 'hat_Latn', 'hau_Latn', 'heb_Hebr', 'hin_Deva', 'hne_Deva', 'hrv_Latn', 'hun_Latn', 'hye_Armn', 'ibo_Latn', 'ilo_Latn', 'ind_Latn', 'isl_Latn', 'ita_Latn', 'jav_Latn', 'jpn_Jpan', 'kab_Latn', 'kac_Latn', 'kam_Latn', 'kan_Knda', 'kas_Arab', 'kas_Deva', 'kat_Geor', 'knc_Arab', 'knc_Latn', 'kaz_Cyrl', 'kbp_Latn', 'kea_Latn', 'khm_Khmr', 'kik_Latn', 'kin_Latn', 'kir_Cyrl', 'kmb_Latn', 'kon_Latn', 'kor_Hang', 'kmr_Latn', 'lao_Laoo', 'lvs_Latn', 'lij_Latn', 'lim_Latn', 'lin_Latn', 'lit_Latn', 'lmo_Latn', 'ltg_Latn', 'ltz_Latn', 'lua_Latn', 'lug_Latn', 'luo_Latn', 'lus_Latn', 'mag_Deva', 'mai_Deva', 'mal_Mlym', 'mar_Deva', 'min_Latn', 'mkd_Cyrl', 'plt_Latn', 'mlt_Latn', 'mni_Beng', 'khk_Cyrl', 'mos_Latn', 'mri_Latn', 'zsm_Latn', 'mya_Mymr', 'nld_Latn', 'nno_Latn', 'nob_Latn', 'npi_Deva', 'nso_Latn', 'nus_Latn', 'nya_Latn', 'oci_Latn', 'gaz_Latn', 'ory_Orya', 'pag_Latn', 'pan_Guru', 'pap_Latn', 'pol_Latn', 'por_Latn', 'prs_Arab', 'pbt_Arab', 'quy_Latn', 'ron_Latn', 'run_Latn', 'rus_Cyrl', 'sag_Latn', 'san_Deva', 'sat_Beng', 'scn_Latn', 'shn_Mymr', 'sin_Sinh', 'slk_Latn', 'slv_Latn', 'smo_Latn', 'sna_Latn', 'snd_Arab', 'som_Latn', 'sot_Latn', 'spa_Latn', 'als_Latn', 'srd_Latn', 'srp_Cyrl', 'ssw_Latn', 'sun_Latn', 'swe_Latn', 'swh_Latn', 'szl_Latn', 'tam_Taml', 'tat_Cyrl', 'tel_Telu', 'tgk_Cyrl', 'tgl_Latn', 'tha_Thai', 'tir_Ethi', 'taq_Latn', 'taq_Tfng', 'tpi_Latn', 'tsn_Latn', 'tso_Latn', 'tuk_Latn', 'tum_Latn', 'tur_Latn', 'twi_Latn', 'tzm_Tfng', 'uig_Arab', 'ukr_Cyrl', 'umb_Latn', 'urd_Arab', 'uzn_Latn', 'vec_Latn', 'vie_Latn', 'war_Latn', 'wol_Latn', 'xho_Latn', 'ydd_Hebr', 'yor_Latn', 'yue_Hant', 'zho_Hans', 'zho_Hant', 'zul_Latn']






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Union[str, Any]              =  VOCAB_FILES_NAMES
    UpperCAmelCase__						:		Optional[Any]              =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase__						:		Union[str, Any]              =  PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase__						:		str              =  ["input_ids", "attention_mask"]

    UpperCAmelCase__						:		List[int]              =  []
    UpperCAmelCase__						:		List[int]              =  []






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

         __UpperCamelCase   							={} if sp_model_kwargs is None else sp_model_kwargs
         __UpperCamelCase   							=legacy_behaviour

         super().__init__(
             bos_token=A_ ,       eos_token=A_ ,       unk_token=A_ ,       sep_token=A_ ,       cls_token=A_ ,       pad_token=A_ ,       mask_token=A_ ,       tokenizer_file=A_ ,       src_lang=A_ ,       tgt_lang=A_ ,       additional_special_tokens=A_ ,       sp_model_kwargs=self.sp_model_kwargs ,       legacy_behaviour=A_ ,       **A_ ,       )

         __UpperCamelCase   							=spm.SentencePieceProcessor(**self.sp_model_kwargs   )
         self.sp_model.Load(str(A_   )   )
         __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>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
         # spm      | '<unk>' | '<s>'   | '</s>' | 'an'    | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'

         # Mimic fairseq token-to-id alignment for the first 4 token
         __UpperCamelCase   							={'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

         # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
         __UpperCamelCase   							=1

         __UpperCamelCase   							=len(self.sp_model   )
         __UpperCamelCase   							={
             code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(A_   )
         }
         __UpperCamelCase   							={v: k for k, v in self.lang_code_to_id.items()}
         __UpperCamelCase   							=len(self.sp_model   ) + len(self.lang_code_to_id   ) + self.fairseq_offset

         self.fairseq_tokens_to_ids.update(self.lang_code_to_id   )
         __UpperCamelCase   							={v: k for k, v in self.fairseq_tokens_to_ids.items()}
         __UpperCamelCase   							=list(self.lang_code_to_id.keys()   )

         if additional_special_tokens is not None:
              # Only add those special tokens if they are not already there.
              self._additional_special_tokens.extend(
                  [t for t in additional_special_tokens if t not in self._additional_special_tokens]   )

         __UpperCamelCase   							=src_lang if src_lang is not None else 'eng_Latn'
         __UpperCamelCase   							=self.lang_code_to_id[self._src_lang]
         __UpperCamelCase   							=tgt_lang
         self.set_src_lang_special_tokens(self._src_lang   )






    def __getstate__(      self   )  ->	int:
         __UpperCamelCase   							=self.__dict__.copy()
         __UpperCamelCase   							=None
         __UpperCamelCase   							=self.sp_model.serialized_model_proto()
         return state






    def __setstate__(      self ,       A_   )  ->	List[str]:
         __UpperCamelCase   							=d

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

         __UpperCamelCase   							=spm.SentencePieceProcessor(**self.sp_model_kwargs   )
         self.sp_model.LoadFromSerializedProto(self.sp_model_proto   )






    @property
    def _a (      self   )  ->	Union[str, Any]:
         return len(self.sp_model   ) + len(self.lang_code_to_id   ) + self.fairseq_offset + 1  # Plus 1 for the mask token






    @property
    def _a (      self   )  ->	str:
         return self._src_lang






    @src_lang.setter
    def _a (      self ,       A_   )  ->	None:
         __UpperCamelCase   							=new_src_lang
         self.set_src_lang_special_tokens(self._src_lang   )






    def _a (      self ,       A_ ,       A_ = None ,       A_ = 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_   )

         __UpperCamelCase   							=[1] * len(self.prefix_tokens   )
         __UpperCamelCase   							=[1] * len(self.suffix_tokens   )
         if token_ids_a is None:
              return prefix_ones + ([0] * len(A_   )) + suffix_ones
         return prefix_ones + ([0] * len(A_   )) + ([0] * len(A_   )) + suffix_ones






    def _a (      self ,       A_ ,       A_ = None   )  ->	List[int]:
         if token_ids_a is None:
              return self.prefix_tokens + token_ids_a + self.suffix_tokens
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens






    def _a (      self ,       A_ ,       A_ = None   )  ->	List[int]:
         __UpperCamelCase   							=[self.sep_token_id]
         __UpperCamelCase   							=[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 _a (      self ,       A_ ,       A_ ,       A_ ,       A_ ,       **A_   )  ->	int:
         if src_lang is None or tgt_lang is None:
              raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'   )
         __UpperCamelCase   							=src_lang
         __UpperCamelCase   							=self(A_ ,       add_special_tokens=A_ ,       return_tensors=A_ ,       **A_   )
         __UpperCamelCase   							=self.convert_tokens_to_ids(A_   )
         __UpperCamelCase   							=tgt_lang_id
         return inputs






    def _a (      self   )  ->	Union[str, Any]:
         __UpperCamelCase   							={self.convert_ids_to_tokens(A_   ): i for i in range(self.vocab_size   )}
         vocab.update(self.added_tokens_encoder   )
         return vocab






    def _a (      self ,       A_   )  ->	List[str]:
         return self.sp_model.encode(A_ ,       out_type=A_   )






    def _a (      self ,       A_   )  ->	List[str]:
         if token in self.fairseq_tokens_to_ids:
              return self.fairseq_tokens_to_ids[token]
         __UpperCamelCase   							=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 _a (      self ,       A_   )  ->	List[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 _a (      self ,       A_   )  ->	List[Any]:
         __UpperCamelCase   							=''.join(A_   ).replace(A_ ,       ' '   ).strip()
         return out_string






    def _a (      self ,       A_ ,       A_ = None   )  ->	Tuple[str]:
         if not os.path.isdir(A_   ):
              logger.error(f'Vocabulary path ({save_directory}) should be a directory'   )
              return
         __UpperCamelCase   							=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:
                   __UpperCamelCase   							=self.sp_model.serialized_model_proto()
                   fi.write(A_   )

         return (out_vocab_file,)






    def _a (      self ,       A_ ,       A_ = "eng_Latn" ,       A_ = None ,       A_ = "fra_Latn" ,       **A_ ,       )  ->	BatchEncoding:
         __UpperCamelCase   							=src_lang
         __UpperCamelCase   							=tgt_lang
         return super().prepare_seqaseq_batch(A_ ,       A_ ,       **A_   )






    def _a (      self   )  ->	Optional[Any]:
         return self.set_src_lang_special_tokens(self.src_lang   )






    def _a (      self   )  ->	List[Any]:
         return self.set_tgt_lang_special_tokens(self.tgt_lang   )






    def _a (      self ,       A_   )  ->	None:
         __UpperCamelCase   							=self.lang_code_to_id[src_lang]
         if self.legacy_behaviour:
              __UpperCamelCase   							=[]
              __UpperCamelCase   							=[self.eos_token_id, self.cur_lang_code]
         else:
              __UpperCamelCase   							=[self.cur_lang_code]
              __UpperCamelCase   							=[self.eos_token_id]






    def _a (      self ,       A_   )  ->	None:
         __UpperCamelCase   							=self.lang_code_to_id[lang]
         if self.legacy_behaviour:
              __UpperCamelCase   							=[]
              __UpperCamelCase   							=[self.eos_token_id, self.cur_lang_code]
         else:
              __UpperCamelCase   							=[self.cur_lang_code]
              __UpperCamelCase   							=[self.eos_token_id]









from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A			=						logging.get_logger(__name__)

_A			=						{
    'google/realm-cc-news-pretrained-embedder': (
        'https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json'
    ),
    'google/realm-cc-news-pretrained-encoder': (
        'https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/config.json'
    ),
    'google/realm-cc-news-pretrained-scorer': (
        'https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/config.json'
    ),
    'google/realm-cc-news-pretrained-openqa': (
        'https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/config.json'
    ),
    'google/realm-orqa-nq-openqa': 'https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/config.json',
    'google/realm-orqa-nq-reader': 'https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/config.json',
    'google/realm-orqa-wq-openqa': 'https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/config.json',
    'google/realm-orqa-wq-reader': 'https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/config.json',
    # See all REALM models at https://huggingface.co/models?filter=realm
}






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    UpperCAmelCase__						:		str              =  "realm"






    def __init__(      self ,       A_=30522 ,       A_=768 ,       A_=128 ,       A_=12 ,       A_=12 ,       A_=8 ,       A_=3072 ,       A_="gelu_new" ,       A_=0.1 ,       A_=0.1 ,       A_=512 ,       A_=2 ,       A_=0.02 ,       A_=1E-12 ,       A_=256 ,       A_=10 ,       A_=1E-3 ,       A_=5 ,       A_=320 ,       A_=13353718 ,       A_=5000 ,       A_=1 ,       A_=0 ,       A_=2 ,       **A_ ,       )  ->	Dict:
         super().__init__(pad_token_id=A_ ,       bos_token_id=A_ ,       eos_token_id=A_ ,       **A_   )

         # Common config
         __UpperCamelCase   							=vocab_size
         __UpperCamelCase   							=max_position_embeddings
         __UpperCamelCase   							=hidden_size
         __UpperCamelCase   							=retriever_proj_size
         __UpperCamelCase   							=num_hidden_layers
         __UpperCamelCase   							=num_attention_heads
         __UpperCamelCase   							=num_candidates
         __UpperCamelCase   							=intermediate_size
         __UpperCamelCase   							=hidden_act
         __UpperCamelCase   							=hidden_dropout_prob
         __UpperCamelCase   							=attention_probs_dropout_prob
         __UpperCamelCase   							=initializer_range
         __UpperCamelCase   							=type_vocab_size
         __UpperCamelCase   							=layer_norm_eps

         # Reader config
         __UpperCamelCase   							=span_hidden_size
         __UpperCamelCase   							=max_span_width
         __UpperCamelCase   							=reader_layer_norm_eps
         __UpperCamelCase   							=reader_beam_size
         __UpperCamelCase   							=reader_seq_len

         # Retrieval config
         __UpperCamelCase   							=num_block_records
         __UpperCamelCase   							=searcher_beam_size





