import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
     from .tokenization_nllb import NllbTokenizer
else:
     __magic_name__    		=None


__magic_name__    		=logging.get_logger(__name__)


__magic_name__    		={'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

__magic_name__    		={
    '''vocab_file''': {
        '''facebook/nllb-200-distilled-600M''': (
            '''https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/nllb-200-distilled-600M''': (
            '''https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json'''
        ),
    },
}

__magic_name__    		={
    '''facebook/nllb-large-en-ro''': 1024,
    '''facebook/nllb-200-distilled-600M''': 1024,
}

# fmt: off
__magic_name__    		=['''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      _A (     SCREAMING_SNAKE_CASE__				):
  SCREAMING_SNAKE_CASE_						:						Union[str, Any]    =VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE_						:						int    =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE_						:						Any    =PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE_						:						str    =["input_ids", "attention_mask"]
  SCREAMING_SNAKE_CASE_						:						Dict    =NllbTokenizer

  SCREAMING_SNAKE_CASE_						:						Any    =[]
  SCREAMING_SNAKE_CASE_						:						Optional[int]    =[]






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





       '''simple docstring'''





       UpperCamelCase__					    =   AddedToken(__lowerCamelCase ,					lstrip=__lowerCamelCase ,					rstrip=__lowerCamelCase						) if isinstance(__lowerCamelCase ,					__lowerCamelCase						) else mask_token
       UpperCamelCase__					    =   legacy_behaviour
       super().__init__(
           vocab_file=__lowerCamelCase ,					tokenizer_file=__lowerCamelCase ,					bos_token=__lowerCamelCase ,					eos_token=__lowerCamelCase ,					sep_token=__lowerCamelCase ,					cls_token=__lowerCamelCase ,					unk_token=__lowerCamelCase ,					pad_token=__lowerCamelCase ,					mask_token=__lowerCamelCase ,					src_lang=__lowerCamelCase ,					tgt_lang=__lowerCamelCase ,					additional_special_tokens=__lowerCamelCase ,					legacy_behaviour=__lowerCamelCase ,					**__lowerCamelCase ,					)

       UpperCamelCase__					    =   vocab_file
       UpperCamelCase__					    =   False if not self.vocab_file else True

       UpperCamelCase__					    =   FAIRSEQ_LANGUAGE_CODES.copy()

       if additional_special_tokens is not None:
            # Only add those special tokens if they are not already there.
            _additional_special_tokens.extend(
                [t for t in additional_special_tokens if t not in _additional_special_tokens]						)

       self.add_special_tokens({'''additional_special_tokens''': _additional_special_tokens}						)
       UpperCamelCase__					    =   {
           lang_code: self.convert_tokens_to_ids(__lowerCamelCase						) for lang_code in FAIRSEQ_LANGUAGE_CODES
       }

       UpperCamelCase__					    =   src_lang if src_lang is not None else '''eng_Latn'''
       UpperCamelCase__					    =   self.convert_tokens_to_ids(self._src_lang						)
       UpperCamelCase__					    =   tgt_lang
       self.set_src_lang_special_tokens(self._src_lang						)






  @property
  def 		_a       (self						)		->   Optional[int]:





       '''simple docstring'''





       return self._src_lang






  @src_lang.setter
  def 		_a       (self ,					SCREAMING_SNAKE_CASE_						)		->   List[Any]:





       '''simple docstring'''





       UpperCamelCase__					    =   new_src_lang
       self.set_src_lang_special_tokens(self._src_lang						)






  def 		_a       (self ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ = None						)		->   List[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 ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ = None						)		->   List[str]:





       '''simple docstring'''





       UpperCamelCase__					    =   [self.sep_token_id]
       UpperCamelCase__					    =   [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 ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					**SCREAMING_SNAKE_CASE_						)		->   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'''						)
       UpperCamelCase__					    =   src_lang
       UpperCamelCase__					    =   self(__lowerCamelCase ,					add_special_tokens=__lowerCamelCase ,					return_tensors=__lowerCamelCase ,					**__lowerCamelCase						)
       UpperCamelCase__					    =   self.convert_tokens_to_ids(__lowerCamelCase						)
       UpperCamelCase__					    =   tgt_lang_id
       return inputs






  def 		_a       (self ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ = "eng_Latn" ,					SCREAMING_SNAKE_CASE_ = None ,					SCREAMING_SNAKE_CASE_ = "fra_Latn" ,					**SCREAMING_SNAKE_CASE_ ,					)		->   Union[str, Any]:





       '''simple docstring'''





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






  def 		_a       (self						)		->   Optional[Any]:





       '''simple docstring'''





       return self.set_src_lang_special_tokens(self.src_lang						)






  def 		_a       (self						)		->   Dict:





       '''simple docstring'''





       return self.set_tgt_lang_special_tokens(self.tgt_lang						)






  def 		_a       (self ,					SCREAMING_SNAKE_CASE_						)		->   List[str]:





       '''simple docstring'''





       UpperCamelCase__					    =   self.convert_tokens_to_ids(__lowerCamelCase						)

       if self.legacy_behaviour:
            UpperCamelCase__					    =   []
            UpperCamelCase__					    =   [self.eos_token_id, self.cur_lang_code]
       else:
            UpperCamelCase__					    =   [self.cur_lang_code]
            UpperCamelCase__					    =   [self.eos_token_id]

       UpperCamelCase__					    =   self.convert_ids_to_tokens(self.prefix_tokens						)
       UpperCamelCase__					    =   self.convert_ids_to_tokens(self.suffix_tokens						)

       UpperCamelCase__					    =   processors.TemplateProcessing(
           single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str ,					pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str ,					special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str ,					self.prefix_tokens + self.suffix_tokens						)						) ,					)






  def 		_a       (self ,					SCREAMING_SNAKE_CASE_						)		->   Any:





       '''simple docstring'''





       UpperCamelCase__					    =   self.convert_tokens_to_ids(__lowerCamelCase						)
       if self.legacy_behaviour:
            UpperCamelCase__					    =   []
            UpperCamelCase__					    =   [self.eos_token_id, self.cur_lang_code]
       else:
            UpperCamelCase__					    =   [self.cur_lang_code]
            UpperCamelCase__					    =   [self.eos_token_id]

       UpperCamelCase__					    =   self.convert_ids_to_tokens(self.prefix_tokens						)
       UpperCamelCase__					    =   self.convert_ids_to_tokens(self.suffix_tokens						)

       UpperCamelCase__					    =   processors.TemplateProcessing(
           single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str ,					pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str ,					special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str ,					self.prefix_tokens + self.suffix_tokens						)						) ,					)






  def 		_a       (self ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ = None						)		->   Optional[int]:





       '''simple docstring'''





       if not self.can_save_slow_tokenizer:
            raise ValueError(
                '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
                '''tokenizer.'''						)

       if not os.path.isdir(__lowerCamelCase						):
            logger.error(F"Vocabulary path ({save_directory}) should be a directory."						)
            return
       UpperCamelCase__					    =   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						):
            copyfile(self.vocab_file ,					__lowerCamelCase						)

       return (out_vocab_file,)










"""simple docstring"""



import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
       from transformers import AutoModelForSeqaSeqLM, AutoTokenizer






@require_torch
@require_sentencepiece
@require_tokenizers
class 						__snake_case   (						unittest.TestCase	):





 """simple docstring"""


 @slow
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Any							=	AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small'''   ,		return_dict=__lowerCamelCase    ).to(__lowerCamelCase    )
     __A       :		Optional[Any]							=	AutoTokenizer.from_pretrained('''google/mt5-small'''    )

     __A       :		Optional[int]							=	tokenizer('''Hello there'''   ,		return_tensors='''pt'''    ).input_ids
     __A       :		str							=	tokenizer('''Hi I am'''   ,		return_tensors='''pt'''    ).input_ids

     __A       :		Any							=	model(input_ids.to(__lowerCamelCase    )   ,		labels=labels.to(__lowerCamelCase    )    ).loss
     __A       :		Tuple							=	-(labels.shape[-1] * loss.item())

     __A       :		int							=	-8_4.9_1_2_7
     self.assertTrue(abs(mtf_score - EXPECTED_SCORE    ) < 1e-4    )




