






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   :							Tuple						= logging.get_logger(__name__)

__a   :							Tuple						= "▁"

__a   :							List[str]						= {"vocab_file": "sentencepiece.bpe.model"}

__a   :							List[str]						= {
    "vocab_file": {
        "facebook/nllb-200-distilled-600M": (
            "https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model"
        ),
    }
}

__a   :							Optional[int]						= {
    "facebook/nllb-200-distilled-600M": 1024,
}

# fmt: off
__a   :							Any						= ["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    __lowercase   (						lowercase_	):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE     =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE     =					PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE     =					["input_ids", "attention_mask"]

     SCREAMING_SNAKE_CASE     =					[]
     SCREAMING_SNAKE_CASE     =					[]




     def __init__(					self							:      int			,						UpperCamelCase_							:      Dict			,						UpperCamelCase_							:      List[Any]="<s>"			,						UpperCamelCase_							:      List[str]="</s>"			,						UpperCamelCase_							:      Optional[Any]="</s>"			,						UpperCamelCase_							:      int="<s>"			,						UpperCamelCase_							:      List[str]="<unk>"			,						UpperCamelCase_							:      List[Any]="<pad>"			,						UpperCamelCase_							:      Optional[Any]="<mask>"			,						UpperCamelCase_							:      List[str]=None			,						UpperCamelCase_							:      Union[str, Any]=None			,						UpperCamelCase_							:      List[Any]=None			,						UpperCamelCase_							:      Optional[Dict[str, Any]] = None			,						UpperCamelCase_							:      str=None			,						UpperCamelCase_							:      int=False			,						**UpperCamelCase_							:      str			,						):


         """simple docstring"""



         __A       =						AddedToken(UpperCamelCase_			,						lstrip=UpperCamelCase_			,						rstrip=UpperCamelCase_					) if isinstance(UpperCamelCase_			,						UpperCamelCase_					) else mask_token

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

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

         __A       =						spm.SentencePieceProcessor(**self.sp_model_kwargs					)
         self.sp_model.Load(str(UpperCamelCase_					)					)
         __A       =						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
         __A       =						{"""<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
         __A       =						1

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

         self.fairseq_tokens_to_ids.update(self.lang_code_to_id					)
         __A       =						{v: k for k, v in self.fairseq_tokens_to_ids.items()}
         __A       =						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]					)

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




     def __getstate__(					self							:      Dict					):


         """simple docstring"""



         __A       =						self.__dict__.copy()
         __A       =						None
         __A       =						self.sp_model.serialized_model_proto()
         return state




     def __setstate__(					self							:      str			,						UpperCamelCase_							:      str					):


         """simple docstring"""



         __A       =						d

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

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




     @property
     def 		lowerCAmelCase_  (					self							:      str					):


         """simple docstring"""



         return len(self.sp_model					) + len(self.lang_code_to_id					) + self.fairseq_offset + 1  # Plus 1 for the mask token




     @property
     def 		lowerCAmelCase_  (					self							:      str					):


         """simple docstring"""



         return self._src_lang




     @src_lang.setter
     def 		lowerCAmelCase_  (					self							:      Tuple			,						UpperCamelCase_							:      str					):


         """simple docstring"""



         __A       =						new_src_lang
         self.set_src_lang_special_tokens(self._src_lang					)




     def 		lowerCAmelCase_  (					self							:      List[Any]			,						UpperCamelCase_							:      List[int]			,						UpperCamelCase_							:      Optional[List[int]] = None			,						UpperCamelCase_							:      bool = False					):


         """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_					)

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




     def 		lowerCAmelCase_  (					self							:      str			,						UpperCamelCase_							:      List[int]			,						UpperCamelCase_							:      Optional[List[int]] = None					):


         """simple docstring"""



         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 		lowerCAmelCase_  (					self							:      Optional[int]			,						UpperCamelCase_							:      List[int]			,						UpperCamelCase_							:      Optional[List[int]] = None					):


         """simple docstring"""



         __A       =						[self.sep_token_id]
         __A       =						[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 		lowerCAmelCase_  (					self							:      List[Any]			,						UpperCamelCase_							:      int			,						UpperCamelCase_							:      str			,						UpperCamelCase_							:      Optional[str]			,						UpperCamelCase_							:      Optional[str]			,						**UpperCamelCase_							:      Tuple					):


         """simple docstring"""



         if src_lang is None or tgt_lang is None:
             raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""					)
         __A       =						src_lang
         __A       =						self(UpperCamelCase_			,						add_special_tokens=UpperCamelCase_			,						return_tensors=UpperCamelCase_			,						**UpperCamelCase_					)
         __A       =						self.convert_tokens_to_ids(UpperCamelCase_					)
         __A       =						tgt_lang_id
         return inputs




     def 		lowerCAmelCase_  (					self							:      Optional[int]					):


         """simple docstring"""



         __A       =						{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							:      str			,						UpperCamelCase_							:      str					):


         """simple docstring"""



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




     def 		lowerCAmelCase_  (					self							:      Dict			,						UpperCamelCase_							:      List[Any]					):


         """simple docstring"""



         if token in self.fairseq_tokens_to_ids:
             return self.fairseq_tokens_to_ids[token]
         __A       =						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							:      List[str]			,						UpperCamelCase_							:      List[str]					):


         """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							:      str			,						UpperCamelCase_							:      Tuple					):


         """simple docstring"""



         __A       =						"""""".join(UpperCamelCase_					).replace(UpperCamelCase_			,						""" """					).strip()
         return out_string




     def 		lowerCAmelCase_  (					self							:      Optional[int]			,						UpperCamelCase_							:      str			,						UpperCamelCase_							:      Optional[str] = None					):


         """simple docstring"""



         if not os.path.isdir(UpperCamelCase_					):
             logger.error(F"Vocabulary path ({save_directory}) should be a directory"					)
             return
         __A       =						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:
                 __A       =						self.sp_model.serialized_model_proto()
                 fi.write(UpperCamelCase_					)

         return (out_vocab_file,)




     def 		lowerCAmelCase_  (					self							:      Tuple			,						UpperCamelCase_							:      List[str]			,						UpperCamelCase_							:      str = "eng_Latn"			,						UpperCamelCase_							:      Optional[List[str]] = None			,						UpperCamelCase_							:      str = "fra_Latn"			,						**UpperCamelCase_							:      str			,						):


         """simple docstring"""



         __A       =						src_lang
         __A       =						tgt_lang
         return super().prepare_seqaseq_batch(UpperCamelCase_			,						UpperCamelCase_			,						**UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      str					):


         """simple docstring"""



         return self.set_src_lang_special_tokens(self.src_lang					)




     def 		lowerCAmelCase_  (					self							:      int					):


         """simple docstring"""



         return self.set_tgt_lang_special_tokens(self.tgt_lang					)




     def 		lowerCAmelCase_  (					self							:      Optional[int]			,						UpperCamelCase_							:      int					):


         """simple docstring"""



         __A       =						self.lang_code_to_id[src_lang]
         if self.legacy_behaviour:
             __A       =						[]
             __A       =						[self.eos_token_id, self.cur_lang_code]
         else:
             __A       =						[self.cur_lang_code]
             __A       =						[self.eos_token_id]





     def 		lowerCAmelCase_  (					self							:      Optional[Any]			,						UpperCamelCase_							:      str					):


         """simple docstring"""



         __A       =						self.lang_code_to_id[lang]
         if self.legacy_behaviour:
             __A       =						[]
             __A       =						[self.eos_token_id, self.cur_lang_code]
         else:
             __A       =						[self.cur_lang_code]
             __A       =						[self.eos_token_id]












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


__a   :							Optional[Any]						= logging.get_logger(__name__)

__a   :							Dict						= {
    "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    __lowercase   (						lowercase_	):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					"realm"




     def __init__(					self							:      str			,						UpperCamelCase_							:      List[Any]=30_522			,						UpperCamelCase_							:      Dict=768			,						UpperCamelCase_							:      Union[str, Any]=128			,						UpperCamelCase_							:      Tuple=12			,						UpperCamelCase_							:      Optional[Any]=12			,						UpperCamelCase_							:      Optional[int]=8			,						UpperCamelCase_							:      str=3_072			,						UpperCamelCase_							:      List[str]="gelu_new"			,						UpperCamelCase_							:      Optional[int]=0.1			,						UpperCamelCase_							:      Optional[int]=0.1			,						UpperCamelCase_							:      str=512			,						UpperCamelCase_							:      int=2			,						UpperCamelCase_							:      str=0.02			,						UpperCamelCase_							:      List[Any]=1e-12			,						UpperCamelCase_							:      str=256			,						UpperCamelCase_							:      List[str]=10			,						UpperCamelCase_							:      int=1e-3			,						UpperCamelCase_							:      List[str]=5			,						UpperCamelCase_							:      Tuple=320			,						UpperCamelCase_							:      List[str]=13_353_718			,						UpperCamelCase_							:      Tuple=5_000			,						UpperCamelCase_							:      Optional[int]=1			,						UpperCamelCase_							:      List[str]=0			,						UpperCamelCase_							:      Union[str, Any]=2			,						**UpperCamelCase_							:      List[Any]			,						):


         """simple docstring"""



         super().__init__(pad_token_id=UpperCamelCase_			,						bos_token_id=UpperCamelCase_			,						eos_token_id=UpperCamelCase_			,						**UpperCamelCase_					)

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

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

         # Retrieval config
         __A       =						num_block_records
         __A       =						searcher_beam_size




