




"""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, 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''': 10_24,
}

# 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(					__a			):







 '''simple docstring'''





 lowercase__			 = VOCAB_FILES_NAMES
 lowercase__			 = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase__			 = PRETRAINED_VOCAB_FILES_MAP
 lowercase__			 = ["input_ids", "attention_mask"]

 lowercase__			 = []
 lowercase__			 = []


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




   '''simple docstring'''



   _snake_case							:		Optional[int]								=  AddedToken(a_,				lstrip=a_,				rstrip=a_		) if isinstance(a_,				a_		) else mask_token

   _snake_case							:		Union[str, Any]								=  {} if sp_model_kwargs is None else sp_model_kwargs
   _snake_case							:		Any								=  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_,				)

   _snake_case							:		str								=  spm.SentencePieceProcessor(**self.sp_model_kwargs		)
   self.sp_model.Load(str(a_		)		)
   _snake_case							:		str								=  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
   _snake_case							:		List[str]								=  {"""<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
   _snake_case							:		int								=  1

   _snake_case							:		List[str]								=  len(self.sp_model		)
   _snake_case							:		Any								=  {
       code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(a_		)
   }
   _snake_case							:		int								=  {v: k for k, v in self.lang_code_to_id.items()}
   _snake_case							:		int								=  len(self.sp_model		) + len(self.lang_code_to_id		) + self.fairseq_offset

   self.fairseq_tokens_to_ids.update(self.lang_code_to_id		)
   _snake_case							:		List[str]								=  {v: k for k, v in self.fairseq_tokens_to_ids.items()}
   _snake_case							:		Tuple								=  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]		)

   _snake_case							:		Optional[Any]								=  src_lang if src_lang is not None else """eng_Latn"""
   _snake_case							:		List[str]								=  self.lang_code_to_id[self._src_lang]
   _snake_case							:		Union[str, Any]								=  tgt_lang
   self.set_src_lang_special_tokens(self._src_lang		)


 def __getstate__(    self:		Dict		):




   '''simple docstring'''



   _snake_case							:		int								=  self.__dict__.copy()
   _snake_case							:		Tuple								=  None
   _snake_case							:		Dict								=  self.sp_model.serialized_model_proto()
   return state


 def __setstate__(    self:		Optional[int],				a_:		Dict		):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  d

   # for backward compatibility
   if not hasattr(self,				"""sp_model_kwargs"""		):
     _snake_case							:		List[Any]								=  {}

   _snake_case							:		Dict								=  spm.SentencePieceProcessor(**self.sp_model_kwargs		)
   self.sp_model.LoadFromSerializedProto(self.sp_model_proto		)


 @property
 def 			UpperCamelCase_       (    self:		Tuple		):




   '''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 			UpperCamelCase_       (    self:		Tuple		):




   '''simple docstring'''



   return self._src_lang


 @src_lang.setter
 def 			UpperCamelCase_       (    self:		List[str],				a_:		str		):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  new_src_lang
   self.set_src_lang_special_tokens(self._src_lang		)


 def 			UpperCamelCase_       (    self:		Union[str, Any],				a_:		List[int],				a_:		Optional[List[int]] = None,				a_:		bool = 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_		)

   _snake_case							:		str								=  [1] * len(self.prefix_tokens		)
   _snake_case							:		List[str]								=  [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 			UpperCamelCase_       (    self:		List[str],				a_:		List[int],				a_:		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 			UpperCamelCase_       (    self:		Union[str, Any],				a_:		List[int],				a_:		Optional[List[int]] = None		):




   '''simple docstring'''



   _snake_case							:		Dict								=  [self.sep_token_id]
   _snake_case							:		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 			UpperCamelCase_       (    self:		Union[str, Any],				a_:		Dict,				a_:		str,				a_:		Optional[str],				a_:		Optional[str],				**a_:		Optional[int]		):




   '''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"""		)
   _snake_case							:		Optional[int]								=  src_lang
   _snake_case							:		Any								=  self(a_,				add_special_tokens=a_,				return_tensors=a_,				**a_		)
   _snake_case							:		Any								=  self.convert_tokens_to_ids(a_		)
   _snake_case							:		List[Any]								=  tgt_lang_id
   return inputs


 def 			UpperCamelCase_       (    self:		List[str]		):




   '''simple docstring'''



   _snake_case							:		Dict								=  {self.convert_ids_to_tokens(a_		): i for i in range(self.vocab_size		)}
   vocab.update(self.added_tokens_encoder		)
   return vocab


 def 			UpperCamelCase_       (    self:		Tuple,				a_:		str		):




   '''simple docstring'''



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


 def 			UpperCamelCase_       (    self:		Any,				a_:		List[Any]		):




   '''simple docstring'''



   if token in self.fairseq_tokens_to_ids:
     return self.fairseq_tokens_to_ids[token]
   _snake_case							:		List[str]								=  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 			UpperCamelCase_       (    self:		int,				a_:		Union[str, Any]		):




   '''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 			UpperCamelCase_       (    self:		Optional[Any],				a_:		Any		):




   '''simple docstring'''



   _snake_case							:		Optional[int]								=  """""".join(a_		).replace(a_,				""" """		).strip()
   return out_string


 def 			UpperCamelCase_       (    self:		Optional[Any],				a_:		str,				a_:		Optional[str] = None		):




   '''simple docstring'''



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

   return (out_vocab_file,)


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




   '''simple docstring'''



   _snake_case							:		Any								=  src_lang
   _snake_case							:		Union[str, Any]								=  tgt_lang
   return super().prepare_seqaseq_batch(a_,				a_,				**a_		)


 def 			UpperCamelCase_       (    self:		int		):




   '''simple docstring'''



   return self.set_src_lang_special_tokens(self.src_lang		)


 def 			UpperCamelCase_       (    self:		str		):




   '''simple docstring'''



   return self.set_tgt_lang_special_tokens(self.tgt_lang		)


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




   '''simple docstring'''



   _snake_case							:		List[Any]								=  self.lang_code_to_id[src_lang]
   if self.legacy_behaviour:
     _snake_case							:		List[str]								=  []
     _snake_case							:		List[str]								=  [self.eos_token_id, self.cur_lang_code]
   else:
     _snake_case							:		List[str]								=  [self.cur_lang_code]
     _snake_case							:		Dict								=  [self.eos_token_id]


 def 			UpperCamelCase_       (    self:		Optional[int],				a_:		str		):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  self.lang_code_to_id[lang]
   if self.legacy_behaviour:
     _snake_case							:		List[str]								=  []
     _snake_case							:		List[str]								=  [self.eos_token_id, self.cur_lang_code]
   else:
     _snake_case							:		Tuple								=  [self.cur_lang_code]
     _snake_case							:		int								=  [self.eos_token_id]








"""simple docstring"""


import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase


def  UpperCAmelCase__	(snake_case__							: Optional[int]		,       snake_case__							: Any		,       snake_case__							: List[str]		,       snake_case__							: int   ):
  """simple docstring"""

  if isinstance(snake_case__		,       snake_case__   ):
    _snake_case							:		List[Any]								=  np.full((len(snake_case__   ), sequence_length, 2)		,       snake_case__   )
  else:
    _snake_case							:		Any								=  np.full((len(snake_case__   ), sequence_length)		,       snake_case__   )

  for i, tensor in enumerate(snake_case__   ):
    if padding_side == "right":
      if isinstance(snake_case__		,       snake_case__   ):
        _snake_case							:		Dict								=  tensor[:sequence_length]
      else:
        _snake_case							:		List[Any]								=  tensor[:sequence_length]
    else:
      if isinstance(snake_case__		,       snake_case__   ):
        _snake_case							:		str								=  tensor[:sequence_length]
      else:
        _snake_case							:		Tuple								=  tensor[:sequence_length]

  return out_tensor.tolist()


def  UpperCAmelCase__	(snake_case__							: Optional[int]   ):
  """simple docstring"""

  _snake_case							:		str								=  ord(snake_case__   )
  if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 1_23 and cp <= 1_26):
    return True
  _snake_case							:		Union[str, Any]								=  unicodedata.category(snake_case__   )
  if cat.startswith("""P"""   ):
    return True
  return False






@dataclass
class  lowercase(					__a			):







 '''simple docstring'''





 lowercase__			 = 42
 lowercase__			 = True
 lowercase__			 = None
 lowercase__			 = None
 lowercase__			 = -1_00
 lowercase__			 = "pt"


 def 			UpperCamelCase_       (    self:		Any,				a_:		Union[str, Any]		):




   '''simple docstring'''



   import torch

   _snake_case							:		Optional[Any]								=  """label""" if """label""" in features[0].keys() else """labels"""
   _snake_case							:		str								=  [feature[label_name] for feature in features] if label_name in features[0].keys() else None
   _snake_case							:		Any								=  self.tokenizer.pad(
       a_,				padding=self.padding,				max_length=self.max_length,				pad_to_multiple_of=self.pad_to_multiple_of,				return_tensors="""pt""" if labels is None else None,				)

   if labels is None:
     return batch

   _snake_case							:		Optional[int]								=  torch.tensor(batch["""entity_ids"""]		).shape[1]
   _snake_case							:		Any								=  self.tokenizer.padding_side
   if padding_side == "right":
     _snake_case							:		Optional[int]								=  [
         list(a_		) + [self.label_pad_token_id] * (sequence_length - len(a_		)) for label in labels
     ]
   else:
     _snake_case							:		Union[str, Any]								=  [
         [self.label_pad_token_id] * (sequence_length - len(a_		)) + list(a_		) for label in labels
     ]

   _snake_case							:		List[Any]								=  [feature["""ner_tags"""] for feature in features]
   _snake_case							:		str								=  padding_tensor(a_,				-1,				a_,				a_		)
   _snake_case							:		Any								=  [feature["""original_entity_spans"""] for feature in features]
   _snake_case							:		int								=  padding_tensor(a_,				(-1, -1),				a_,				a_		)
   _snake_case							:		str								=  {k: torch.tensor(a_,				dtype=torch.intaa		) for k, v in batch.items()}

   return batch


