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      __lowercase					(    lowercase_    ):
      lowercase					 =     VOCAB_FILES_NAMES
      lowercase					 =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowercase					 =     PRETRAINED_VOCAB_FILES_MAP
      lowercase					 =     ['''input_ids''', '''attention_mask''']

      lowercase					 =     []
      lowercase					 =     []

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


         '''simple docstring'''

         lowercase							=		AddedToken(UpperCamelCase__							,							lstrip=UpperCamelCase__							,							rstrip=UpperCamelCase__   ) if isinstance(UpperCamelCase__							,							UpperCamelCase__   ) else mask_token

         lowercase							=		{} if sp_model_kwargs is None else sp_model_kwargs
         lowercase							=		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__							,							)

         lowercase							=		spm.SentencePieceProcessor(**self.sp_model_kwargs   )
         self.sp_model.Load(str(UpperCamelCase__   )   )
         lowercase							=		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
         lowercase							=		{'''<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
         lowercase							=		1

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

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

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

      def __getstate__(	self				:      Any   )							->		int:


         '''simple docstring'''

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

      def __setstate__(	self				:      List[str]							,							__lowerCamelCase				:      List[str]   )							->		List[Any]:


         '''simple docstring'''

         lowercase							=		d

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

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

      @property
      def        __a		(	self				:      Union[str, Any]   )							->		Any:


         '''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        __a		(	self				:      str   )							->		int:


         '''simple docstring'''

         return self._src_lang

      @src_lang.setter
      def        __a		(	self				:      str							,							__lowerCamelCase				:      str   )							->		str:


         '''simple docstring'''

         lowercase							=		new_src_lang
         self.set_src_lang_special_tokens(self._src_lang   )

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


         '''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__   )

         lowercase							=		[1] * len(self.prefix_tokens   )
         lowercase							=		[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        __a		(	self				:      List[str]							,							__lowerCamelCase				:      List[int]							,							__lowerCamelCase				:      Optional[List[int]] = None   )							->		Optional[Any]:


         '''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        __a		(	self				:      int							,							__lowerCamelCase				:      List[int]							,							__lowerCamelCase				:      Optional[List[int]] = None   )							->		Optional[Any]:


         '''simple docstring'''

         lowercase							=		[self.sep_token_id]
         lowercase							=		[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				:      str							,							__lowerCamelCase				:      Tuple							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      Optional[str]							,							__lowerCamelCase				:      Optional[str]							,							**__lowerCamelCase				:      int   )							->		Union[str, Any]:


         '''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'''   )
         lowercase							=		src_lang
         lowercase							=		self(UpperCamelCase__							,							add_special_tokens=UpperCamelCase__							,							return_tensors=UpperCamelCase__							,							**UpperCamelCase__   )
         lowercase							=		self.convert_tokens_to_ids(UpperCamelCase__   )
         lowercase							=		tgt_lang_id
         return inputs

      def        __a		(	self				:      str   )							->		str:


         '''simple docstring'''

         lowercase							=		{self.convert_ids_to_tokens(UpperCamelCase__   ): i for i in range(self.vocab_size   )}
         vocab.update(self.added_tokens_encoder   )
         return vocab

      def        __a		(	self				:      str							,							__lowerCamelCase				:      str   )							->		Tuple:


         '''simple docstring'''

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

      def        __a		(	self				:      int							,							__lowerCamelCase				:      List[Any]   )							->		Optional[Any]:


         '''simple docstring'''

         if token in self.fairseq_tokens_to_ids:
            return self.fairseq_tokens_to_ids[token]
         lowercase							=		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        __a		(	self				:      List[str]							,							__lowerCamelCase				:      Any   )							->		Tuple:


         '''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        __a		(	self				:      Dict							,							__lowerCamelCase				:      Optional[int]   )							->		int:


         '''simple docstring'''

         lowercase							=		''''''.join(UpperCamelCase__   ).replace(UpperCamelCase__							,							''' '''   ).strip()
         return out_string

      def        __a		(	self				:      Optional[Any]							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      Optional[str] = None   )							->		Union[str, Any]:


         '''simple docstring'''

         if not os.path.isdir(UpperCamelCase__   ):
            logger.error(f'Vocabulary path ({save_directory}) should be a directory'   )
            return
         lowercase							=		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:
               lowercase							=		self.sp_model.serialized_model_proto()
               fi.write(UpperCamelCase__   )

         return (out_vocab_file,)

      def        __a		(	self				:      List[str]							,							__lowerCamelCase				:      List[str]							,							__lowerCamelCase				:      str = "eng_Latn"							,							__lowerCamelCase				:      Optional[List[str]] = None							,							__lowerCamelCase				:      str = "fra_Latn"							,							**__lowerCamelCase				:      List[Any]							,							)							->		List[Any]:


         '''simple docstring'''

         lowercase							=		src_lang
         lowercase							=		tgt_lang
         return super().prepare_seqaseq_batch(UpperCamelCase__							,							UpperCamelCase__							,							**UpperCamelCase__   )

      def        __a		(	self				:      str   )							->		Any:


         '''simple docstring'''

         return self.set_src_lang_special_tokens(self.src_lang   )

      def        __a		(	self				:      Any   )							->		str:


         '''simple docstring'''

         return self.set_tgt_lang_special_tokens(self.tgt_lang   )

      def        __a		(	self				:      Tuple							,							__lowerCamelCase				:      Any   )							->		str:


         '''simple docstring'''

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



      def        __a		(	self				:      Optional[int]							,							__lowerCamelCase				:      str   )							->		Tuple:


         '''simple docstring'''

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






import logging

from transformers import PretrainedConfig


A_      =      logging.getLogger(__name__)


A_      =      {
    "bertabs-finetuned-cnndm": "https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json",
}






class      __lowercase					(    _A    ):
						lowercase					 =     'bertabs'

						def __init__(	self				:      Dict							,							__lowerCamelCase				:      Tuple=3_05_22							,							__lowerCamelCase				:      Tuple=5_12							,							__lowerCamelCase				:      List[Any]=6							,							__lowerCamelCase				:      Any=5_12							,							__lowerCamelCase				:      Any=8							,							__lowerCamelCase				:      Union[str, Any]=5_12							,							__lowerCamelCase				:      Tuple=0.2							,							__lowerCamelCase				:      str=6							,							__lowerCamelCase				:      int=7_68							,							__lowerCamelCase				:      int=8							,							__lowerCamelCase				:      List[Any]=20_48							,							__lowerCamelCase				:      Union[str, Any]=0.2							,							**__lowerCamelCase				:      Dict							,							)							->		Dict:


									'''simple docstring'''

									super().__init__(**__lowerCamelCase   )

									lowercase							=		vocab_size
									lowercase							=		max_pos

									lowercase							=		enc_layers
									lowercase							=		enc_hidden_size
									lowercase							=		enc_heads
									lowercase							=		enc_ff_size
									lowercase							=		enc_dropout

									lowercase							=		dec_layers
									lowercase							=		dec_hidden_size
									lowercase							=		dec_heads
									lowercase							=		dec_ff_size
									lowercase							=		dec_dropout





