


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


__UpperCamelCase				:				List[Any]  				= logging.get_logger(__name__)

__UpperCamelCase				:				List[str]  				= "▁"

__UpperCamelCase				:				Tuple  				= {"vocab_file": "sentencepiece.bpe.model"}

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

__UpperCamelCase				:				Any  				= {
    "facebook/nllb-200-distilled-600M": 1024,
}

# fmt: off
__UpperCamelCase				:				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 					__magic_name__		(   __lowerCAmelCase):
     A:						List[str]								=					VOCAB_FILES_NAMES
     A:						Optional[Any]								=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     A:						str								=					PRETRAINED_VOCAB_FILES_MAP
     A:						str								=					['input_ids', 'attention_mask']

     A:						List[int]								=					[]
     A:						List[int]								=					[]


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





      '''simple docstring'''






      UpperCamelCase__    :			List[Any]      				=      AddedToken(lowerCamelCase__							,		lstrip=lowerCamelCase__							,		rstrip=lowerCamelCase__	) if isinstance(lowerCamelCase__							,		lowerCamelCase__	) else mask_token

      UpperCamelCase__    :			int      				=      {} if sp_model_kwargs is None else sp_model_kwargs
      UpperCamelCase__    :			Optional[Any]      				=      legacy_behaviour

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

      UpperCamelCase__    :			Optional[Any]      				=      spm.SentencePieceProcessor(**self.sp_model_kwargs	)
      self.sp_model.Load(str(lowerCamelCase__	)	)
      UpperCamelCase__    :			Tuple      				=      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__    :			Union[str, 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
      UpperCamelCase__    :			Tuple      				=      1

      UpperCamelCase__    :			List[Any]      				=      len(self.sp_model	)
      UpperCamelCase__    :			Dict      				=      {
          code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(lowerCamelCase__	)
      }
      UpperCamelCase__    :			Dict      				=      {v: k for k, v in self.lang_code_to_id.items()}
      UpperCamelCase__    :			Tuple      				=      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__    :			int      				=      {v: k for k, v in self.fairseq_tokens_to_ids.items()}
      UpperCamelCase__    :			Dict      				=      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__    :			str      				=      src_lang if src_lang is not None else '''eng_Latn'''
      UpperCamelCase__    :			Tuple      				=      self.lang_code_to_id[self._src_lang]
      UpperCamelCase__    :			List[Any]      				=      tgt_lang
      self.set_src_lang_special_tokens(self._src_lang	)


     def __getstate__(					self							:   Any	)     ->					Tuple:





      '''simple docstring'''






      UpperCamelCase__    :			str      				=      self.__dict__.copy()
      UpperCamelCase__    :			Tuple      				=      None
      UpperCamelCase__    :			int      				=      self.sp_model.serialized_model_proto()
      return state


     def __setstate__(					self							:   Any							,		lowerCamelCase__							:   Optional[Any]	)     ->					Dict:





      '''simple docstring'''






      UpperCamelCase__    :			Union[str, Any]      				=      d

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

      UpperCamelCase__    :			Any      				=      spm.SentencePieceProcessor(**self.sp_model_kwargs	)
      self.sp_model.LoadFromSerializedProto(self.sp_model_proto	)


     @property
     def    UpperCAmelCase__		(					self							:   Optional[Any]	)     ->					List[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    UpperCAmelCase__		(					self							:   Any	)     ->					str:





      '''simple docstring'''






      return self._src_lang


     @src_lang.setter
     def    UpperCAmelCase__		(					self							:   Any							,		lowerCamelCase__							:   Union[str, Any]	)     ->					None:





      '''simple docstring'''






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


     def    UpperCAmelCase__		(					self							:   Optional[Any]							,		lowerCamelCase__							:   Union[str, Any]							,		lowerCamelCase__							:   int = None							,		lowerCamelCase__							:   Union[str, Any] = False	)     ->					List[int]:





      '''simple docstring'''






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

      UpperCamelCase__    :			str      				=      [1] * len(self.prefix_tokens	)
      UpperCamelCase__    :			List[str]      				=      [1] * len(self.suffix_tokens	)
      if token_ids_a is None:
       return prefix_ones + ([0] * len(lowerCamelCase__	)) + suffix_ones
      return prefix_ones + ([0] * len(lowerCamelCase__	)) + ([0] * len(lowerCamelCase__	)) + suffix_ones


     def    UpperCAmelCase__		(					self							:   Union[str, Any]							,		lowerCamelCase__							:   Any							,		lowerCamelCase__							:   Tuple = None	)     ->					List[int]:





      '''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]							,		lowerCamelCase__							:   List[str]							,		lowerCamelCase__							:   Dict = None	)     ->					List[int]:





      '''simple docstring'''






      UpperCamelCase__    :			Tuple      				=      [self.sep_token_id]
      UpperCamelCase__    :			Dict      				=      [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]							,		lowerCamelCase__							:   Optional[int]							,		lowerCamelCase__							:   Optional[int]							,		lowerCamelCase__							:   Tuple							,		lowerCamelCase__							:   Dict							,		**lowerCamelCase__							:   Optional[int]	)     ->					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'''	)
      UpperCamelCase__    :			Optional[int]      				=      src_lang
      UpperCamelCase__    :			Dict      				=      self(lowerCamelCase__							,		add_special_tokens=lowerCamelCase__							,		return_tensors=lowerCamelCase__							,		**lowerCamelCase__	)
      UpperCamelCase__    :			List[str]      				=      self.convert_tokens_to_ids(lowerCamelCase__	)
      UpperCamelCase__    :			Any      				=      tgt_lang_id
      return inputs


     def    UpperCAmelCase__		(					self							:   str	)     ->					str:





      '''simple docstring'''






      UpperCamelCase__    :			Optional[Any]      				=      {self.convert_ids_to_tokens(lowerCamelCase__	): i for i in range(self.vocab_size	)}
      vocab.update(self.added_tokens_encoder	)
      return vocab


     def    UpperCAmelCase__		(					self							:   Any							,		lowerCamelCase__							:   Dict	)     ->					List[str]:





      '''simple docstring'''






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


     def    UpperCAmelCase__		(					self							:   str							,		lowerCamelCase__							:   str	)     ->					str:





      '''simple docstring'''






      if token in self.fairseq_tokens_to_ids:
       return self.fairseq_tokens_to_ids[token]
      UpperCamelCase__    :			Tuple      				=      self.sp_model.PieceToId(lowerCamelCase__	)

      # 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							,		lowerCamelCase__							:   Tuple	)     ->					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[int]							,		lowerCamelCase__							:   Optional[int]	)     ->					int:





      '''simple docstring'''






      UpperCamelCase__    :			Tuple      				=      ''''''.join(lowerCamelCase__	).replace(lowerCamelCase__							,		''' '''	).strip()
      return out_string


     def    UpperCAmelCase__		(					self							:   str							,		lowerCamelCase__							:   List[str]							,		lowerCamelCase__							:   str = None	)     ->					Tuple[str]:





      '''simple docstring'''






      if not os.path.isdir(lowerCamelCase__	):
       logger.error(F"Vocabulary path ({save_directory}) should be a directory"	)
       return
      UpperCamelCase__    :			Any      				=      os.path.join(
          lowerCamelCase__							,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']	)

      if os.path.abspath(self.vocab_file	) != os.path.abspath(lowerCamelCase__	) and os.path.isfile(self.vocab_file	):
       copyfile(self.vocab_file							,		lowerCamelCase__	)
      elif not os.path.isfile(self.vocab_file	):
       with open(lowerCamelCase__							,		'''wb'''	) as fi:
        UpperCamelCase__    :			int      				=      self.sp_model.serialized_model_proto()
        fi.write(lowerCamelCase__	)

      return (out_vocab_file,)


     def    UpperCAmelCase__		(					self							:   Any							,		lowerCamelCase__							:   Tuple							,		lowerCamelCase__							:   Any = "eng_Latn"							,		lowerCamelCase__							:   Tuple = None							,		lowerCamelCase__							:   Union[str, Any] = "fra_Latn"							,		**lowerCamelCase__							:   Optional[Any]							,		)     ->					BatchEncoding:





      '''simple docstring'''






      UpperCamelCase__    :			Dict      				=      src_lang
      UpperCamelCase__    :			str      				=      tgt_lang
      return super().prepare_seqaseq_batch(lowerCamelCase__							,		lowerCamelCase__							,		**lowerCamelCase__	)


     def    UpperCAmelCase__		(					self							:   List[Any]	)     ->					List[str]:





      '''simple docstring'''






      return self.set_src_lang_special_tokens(self.src_lang	)


     def    UpperCAmelCase__		(					self							:   List[str]	)     ->					Any:





      '''simple docstring'''






      return self.set_tgt_lang_special_tokens(self.tgt_lang	)


     def    UpperCAmelCase__		(					self							:   List[str]							,		lowerCamelCase__							:   Dict	)     ->					None:





      '''simple docstring'''






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


     def    UpperCAmelCase__		(					self							:   Dict							,		lowerCamelCase__							:   Dict	)     ->					None:





      '''simple docstring'''






      UpperCamelCase__    :			Any      				=      self.lang_code_to_id[lang]
      if self.legacy_behaviour:
       UpperCamelCase__    :			List[Any]      				=      []
       UpperCamelCase__    :			Optional[Any]      				=      [self.eos_token_id, self.cur_lang_code]
      else:
       UpperCamelCase__    :			List[str]      				=      [self.cur_lang_code]
       UpperCamelCase__    :			Dict      				=      [self.eos_token_id]







'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase_		       =  logging.get_logger(__name__)

lowercase_		       =  {
    "facebook/xmod-base": "https://huggingface.co/facebook/xmod-base/resolve/main/config.json",
    "facebook/xmod-large-prenorm": "https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json",
    "facebook/xmod-base-13-125k": "https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json",
    "facebook/xmod-base-30-125k": "https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json",
    "facebook/xmod-base-30-195k": "https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json",
    "facebook/xmod-base-60-125k": "https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json",
    "facebook/xmod-base-60-265k": "https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json",
    "facebook/xmod-base-75-125k": "https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json",
    "facebook/xmod-base-75-269k": "https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json",
}






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       Any          =       'xmod'



 def __init__(self ,   A=30_522 ,   A=768 ,   A=12 ,   A=12 ,   A=3_072 ,   A="gelu" ,   A=0.1 ,   A=0.1 ,   A=512 ,   A=2 ,   A=0.02 ,   A=1E-12 ,   A=1 ,   A=0 ,   A=2 ,   A="absolute" ,   A=True ,   A=None ,   A=False ,   A=2 ,   A=False ,   A=True ,   A=True ,   A=("en_XX",) ,   A=None ,   **A ,   )    ->     List[str]:


      """simple docstring"""



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

      _a      =  vocab_size
      _a      =  hidden_size
      _a      =  num_hidden_layers
      _a      =  num_attention_heads
      _a      =  hidden_act
      _a      =  intermediate_size
      _a      =  hidden_dropout_prob
      _a      =  attention_probs_dropout_prob
      _a      =  max_position_embeddings
      _a      =  type_vocab_size
      _a      =  initializer_range
      _a      =  layer_norm_eps
      _a      =  position_embedding_type
      _a      =  use_cache
      _a      =  classifier_dropout
      _a      =  pre_norm
      _a      =  adapter_reduction_factor
      _a      =  adapter_layer_norm
      _a      =  adapter_reuse_layer_norm
      _a      =  ln_before_adapter
      _a      =  list(A       )
      _a      =  default_language






class __A			(  A						):



 '''simple docstring'''







 @property
 def 					a__  (self       )    ->     Mapping[str, Mapping[int, str]]:


      """simple docstring"""



      if self.task == "multiple-choice":
           _a      =  {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
      else:
           _a      =  {0: '''batch''', 1: '''sequence'''}
      return OrderedDict(
          [
              ('''input_ids''', dynamic_axis),
              ('''attention_mask''', dynamic_axis),
          ]       )



