
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


lowercase    :   List[str]		       =				logging.get_logger(__name__)

lowercase    :   Tuple		       =				"▁"

lowercase    :   Dict		       =				{"vocab_file": "sentencepiece.bpe.model"}

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

lowercase    :   List[Any]		       =				{
    "facebook/nllb-200-distilled-600M": 1024,
}

# fmt: off
lowercase    :   List[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 __snake_case						( lowerCAmelCase	):
    _a						:		List[Any]=							VOCAB_FILES_NAMES
    _a						:		Optional[Any]=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _a						:		List[str]=							PRETRAINED_VOCAB_FILES_MAP
    _a						:		List[Any]=							["input_ids", "attention_mask"]

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



           '''simple docstring'''







           lowercase				:	List[str]			      = AddedToken(a_						,lstrip=a_						,rstrip=a_ ) if isinstance(a_						,a_ ) else mask_token

           lowercase				:	Tuple			      = {} if sp_model_kwargs is None else sp_model_kwargs
           lowercase				:	str			      = 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_						,)

           lowercase				:	Optional[Any]			      = spm.SentencePieceProcessor(**self.sp_model_kwargs )
           self.sp_model.Load(str(a_ ) )
           lowercase				:	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>' | '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				:	Optional[Any]			      = {"""<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				:	int			      = 1

           lowercase				:	Optional[int]			      = len(self.sp_model )
           lowercase				:	Optional[Any]			      = {
               code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(a_ )
           }
           lowercase				:	int			      = {v: k for k, v in self.lang_code_to_id.items()}
           lowercase				:	str			      = 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				:	int			      = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
           lowercase				:	str			      = 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				:	List[Any]			      = src_lang if src_lang is not None else """eng_Latn"""
           lowercase				:	List[Any]			      = self.lang_code_to_id[self._src_lang]
           lowercase				:	int			      = tgt_lang
           self.set_src_lang_special_tokens(self._src_lang )
    def __getstate__(    self ):



           '''simple docstring'''







           lowercase				:	Optional[int]			      = self.__dict__.copy()
           lowercase				:	List[Any]			      = None
           lowercase				:	List[str]			      = self.sp_model.serialized_model_proto()
           return state
    def __setstate__(    self						,snake_case ):



           '''simple docstring'''







           lowercase				:	str			      = d

           # for backward compatibility
           if not hasattr(self						,"""sp_model_kwargs""" ):
                  lowercase				:	Optional[int]			      = {}

           lowercase				:	List[Any]			      = spm.SentencePieceProcessor(**self.sp_model_kwargs )
           self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
    @property
    def  _SCREAMING_SNAKE_CASE			(    self ):



           '''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  _SCREAMING_SNAKE_CASE			(    self ):



           '''simple docstring'''







           return self._src_lang
    @src_lang.setter
    def  _SCREAMING_SNAKE_CASE			(    self						,snake_case ):



           '''simple docstring'''







           lowercase				:	Tuple			      = new_src_lang
           self.set_src_lang_special_tokens(self._src_lang )
    def  _SCREAMING_SNAKE_CASE			(    self						,snake_case						,snake_case = None						,snake_case = False ):



           '''simple docstring'''







           if already_has_special_tokens:
                  return super().get_special_tokens_mask(
                      token_ids_a=a_						,token_ids_a=a_						,already_has_special_tokens=a_ )

           lowercase				:	str			      = [1] * len(self.prefix_tokens )
           lowercase				:	List[Any]			      = [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  _SCREAMING_SNAKE_CASE			(    self						,snake_case						,snake_case = 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  _SCREAMING_SNAKE_CASE			(    self						,snake_case						,snake_case = None ):



           '''simple docstring'''







           lowercase				:	str			      = [self.sep_token_id]
           lowercase				:	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 + sep + token_ids_a + sep ) * [0]
    def  _SCREAMING_SNAKE_CASE			(    self						,snake_case						,snake_case						,snake_case						,snake_case						,**snake_case ):



           '''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				:	Any			      = src_lang
           lowercase				:	Dict			      = self(a_						,add_special_tokens=a_						,return_tensors=a_						,**a_ )
           lowercase				:	Dict			      = self.convert_tokens_to_ids(a_ )
           lowercase				:	Dict			      = tgt_lang_id
           return inputs
    def  _SCREAMING_SNAKE_CASE			(    self ):



           '''simple docstring'''







           lowercase				:	Any			      = {self.convert_ids_to_tokens(a_ ): i for i in range(self.vocab_size )}
           vocab.update(self.added_tokens_encoder )
           return vocab
    def  _SCREAMING_SNAKE_CASE			(    self						,snake_case ):



           '''simple docstring'''







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



           '''simple docstring'''







           if token in self.fairseq_tokens_to_ids:
                  return self.fairseq_tokens_to_ids[token]
           lowercase				:	Any			      = 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  _SCREAMING_SNAKE_CASE			(    self						,snake_case ):



           '''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  _SCREAMING_SNAKE_CASE			(    self						,snake_case ):



           '''simple docstring'''







           lowercase				:	Tuple			      = """""".join(a_ ).replace(a_						,""" """ ).strip()
           return out_string
    def  _SCREAMING_SNAKE_CASE			(    self						,snake_case						,snake_case = None ):



           '''simple docstring'''







           if not os.path.isdir(a_ ):
                  logger.error(f"Vocabulary path ({save_directory}) should be a directory" )
                  return
           lowercase				:	List[Any]			      = 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:
                         lowercase				:	Optional[Any]			      = self.sp_model.serialized_model_proto()
                         fi.write(a_ )

           return (out_vocab_file,)
    def  _SCREAMING_SNAKE_CASE			(    self						,snake_case						,snake_case = "eng_Latn"						,snake_case = None						,snake_case = "fra_Latn"						,**snake_case						,):



           '''simple docstring'''







           lowercase				:	Tuple			      = src_lang
           lowercase				:	List[Any]			      = tgt_lang
           return super().prepare_seqaseq_batch(a_						,a_						,**a_ )
    def  _SCREAMING_SNAKE_CASE			(    self ):



           '''simple docstring'''







           return self.set_src_lang_special_tokens(self.src_lang )
    def  _SCREAMING_SNAKE_CASE			(    self ):



           '''simple docstring'''







           return self.set_tgt_lang_special_tokens(self.tgt_lang )
    def  _SCREAMING_SNAKE_CASE			(    self						,snake_case ):



           '''simple docstring'''







           lowercase				:	Union[str, Any]			      = self.lang_code_to_id[src_lang]
           if self.legacy_behaviour:
                  lowercase				:	Optional[Any]			      = []
                  lowercase				:	Any			      = [self.eos_token_id, self.cur_lang_code]
           else:
                  lowercase				:	int			      = [self.cur_lang_code]
                  lowercase				:	List[str]			      = [self.eos_token_id]
    def  _SCREAMING_SNAKE_CASE			(    self						,snake_case ):



           '''simple docstring'''







           lowercase				:	Optional[Any]			      = self.lang_code_to_id[lang]
           if self.legacy_behaviour:
                  lowercase				:	Optional[int]			      = []
                  lowercase				:	Union[str, Any]			      = [self.eos_token_id, self.cur_lang_code]
           else:
                  lowercase				:	Dict			      = [self.cur_lang_code]
                  lowercase				:	Dict			      = [self.eos_token_id]





from typing import Dict

from .base import GenericTensor, Pipeline


class 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_=None    ,a_=None    ,a_=None    ,**a_						):







						"""simple docstring"""
						if tokenize_kwargs is None:
										lowerCAmelCase__		  =  {}

						if truncation is not None:
										if "truncation" in tokenize_kwargs:
														raise ValueError(
														    'truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'						)
										lowerCAmelCase__		  =  truncation

						lowerCAmelCase__		  =  tokenize_kwargs

						lowerCAmelCase__		  =  {}
						if return_tensors is not None:
										lowerCAmelCase__		  =  return_tensors

						return preprocess_params, {}, postprocess_params
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,**a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.framework
						lowerCAmelCase__		  =  self.tokenizer(a_    ,return_tensors=a_    ,**a_						)
						return model_inputs
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.model(**a_						)
						return model_outputs
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_=False						):







						"""simple docstring"""
						# [0] is the first available tensor, logits or last_hidden_state.
						if return_tensors:
										return model_outputs[0]
						if self.framework == "pt":
										return model_outputs[0].tolist()
						elif self.framework == "tf":
										return model_outputs[0].numpy().tolist()



		def __call__(							self    ,*a_    ,**a_						):







						"""simple docstring"""
						return super().__call__(*a_    ,**a_						)
