




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:
		__A						:							Optional[Any]							=  None


__A						:							Tuple							=  logging.get_logger(__name__)


__A						:							Union[str, Any]							=  {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

__A						:							Any							=  {
    '''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'''
        ),
    },
}

__A						:							int							=  {
    '''facebook/nllb-large-en-ro''': 1_0_2_4,
    '''facebook/nllb-200-distilled-600M''': 1_0_2_4,
}

# fmt: off
__A						:							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 			UpperCAmelCase_      (   snake_case__       ):




							'''simple docstring'''



							a__						  =     VOCAB_FILES_NAMES
							a__						  =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							a__						  =     PRETRAINED_VOCAB_FILES_MAP
							a__						  =     ['''input_ids''', '''attention_mask''']
							a__						  =     NllbTokenizer

							a__						  =     []
							a__						  =     []





							def __init__(					self       :	Optional[int]       ,     a       :	Union[str, Any]=None       ,     a       :	Optional[int]=None       ,     a       :	str="<s>"       ,     a       :	Tuple="</s>"       ,     a       :	Dict="</s>"       ,     a       :	Optional[Any]="<s>"       ,     a       :	List[str]="<unk>"       ,     a       :	Dict="<pad>"       ,     a       :	List[Any]="<mask>"       ,     a       :	int=None       ,     a       :	List[Any]=None       ,     a       :	Tuple=None       ,     a       :	Optional[Any]=False       ,     **a       :	int       ,     )			->					Optional[Any]:
										# Mask token behave like a normal word, i.e. include the space before it
										SCREAMING_SNAKE_CASE           =					AddedToken(_A       ,     lstrip=_A       ,     rstrip=_A			) if isinstance(_A       ,     _A			) else mask_token
										SCREAMING_SNAKE_CASE           =					legacy_behaviour
										super().__init__(
										    vocab_file=_A       ,     tokenizer_file=_A       ,     bos_token=_A       ,     eos_token=_A       ,     sep_token=_A       ,     cls_token=_A       ,     unk_token=_A       ,     pad_token=_A       ,     mask_token=_A       ,     src_lang=_A       ,     tgt_lang=_A       ,     additional_special_tokens=_A       ,     legacy_behaviour=_A       ,     **_A       ,     )

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

										SCREAMING_SNAKE_CASE           =					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}			)
										SCREAMING_SNAKE_CASE           =					{
										    lang_code: self.convert_tokens_to_ids(_A			) for lang_code in FAIRSEQ_LANGUAGE_CODES
										}

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





							@property
							def    _UpperCAmelCase     (					self       :	Union[str, Any]			)			->					Tuple:
										return self._src_lang





							@src_lang.setter
							def    _UpperCAmelCase     (					self       :	Tuple       ,     a       :	int			)			->					int:
										SCREAMING_SNAKE_CASE           =					new_src_lang
										self.set_src_lang_special_tokens(self._src_lang			)





							def    _UpperCAmelCase     (					self       :	Tuple       ,     a       :	int       ,     a       :	str = None			)			->					int:
										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       :	List[str]       ,     a       :	Any       ,     a       :	int = None			)			->					Any:
										SCREAMING_SNAKE_CASE           =					[self.sep_token_id]
										SCREAMING_SNAKE_CASE           =					[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       :	int       ,     a       :	Optional[Any]       ,     a       :	List[str]       ,     a       :	int       ,     a       :	Dict       ,     **a       :	int			)			->					str:
										if src_lang is None or tgt_lang is None:
													raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""			)
										SCREAMING_SNAKE_CASE           =					src_lang
										SCREAMING_SNAKE_CASE           =					self(_A       ,     add_special_tokens=_A       ,     return_tensors=_A       ,     **_A			)
										SCREAMING_SNAKE_CASE           =					self.convert_tokens_to_ids(_A			)
										SCREAMING_SNAKE_CASE           =					tgt_lang_id
										return inputs





							def    _UpperCAmelCase     (					self       :	Any       ,     a       :	List[str]       ,     a       :	List[Any] = "eng_Latn"       ,     a       :	List[str] = None       ,     a       :	str = "fra_Latn"       ,     **a       :	str       ,     )			->					Optional[Any]:
										SCREAMING_SNAKE_CASE           =					src_lang
										SCREAMING_SNAKE_CASE           =					tgt_lang
										return super().prepare_seqaseq_batch(_A       ,     _A       ,     **_A			)





							def    _UpperCAmelCase     (					self       :	Dict			)			->					Optional[Any]:
										return self.set_src_lang_special_tokens(self.src_lang			)





							def    _UpperCAmelCase     (					self       :	Dict			)			->					List[Any]:
										return self.set_tgt_lang_special_tokens(self.tgt_lang			)





							def    _UpperCAmelCase     (					self       :	Any       ,     a       :	Optional[Any]			)			->					Optional[Any]:
										SCREAMING_SNAKE_CASE           =					self.convert_tokens_to_ids(_A			)

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

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

										SCREAMING_SNAKE_CASE           =					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    _UpperCAmelCase     (					self       :	List[str]       ,     a       :	Optional[Any]			)			->					str:
										SCREAMING_SNAKE_CASE           =					self.convert_tokens_to_ids(_A			)
										if self.legacy_behaviour:
													SCREAMING_SNAKE_CASE           =					[]
													SCREAMING_SNAKE_CASE           =					[self.eos_token_id, self.cur_lang_code]
										else:
													SCREAMING_SNAKE_CASE           =					[self.cur_lang_code]
													SCREAMING_SNAKE_CASE           =					[self.eos_token_id]

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

										SCREAMING_SNAKE_CASE           =					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    _UpperCAmelCase     (					self       :	Optional[int]       ,     a       :	Optional[int]       ,     a       :	List[Any] = None			)			->					str:
										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(_A			):
													logger.error(f"""Vocabulary path ({save_directory}) should be a directory."""			)
													return
										SCREAMING_SNAKE_CASE           =					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			):
													copyfile(self.vocab_file       ,     _A			)

										return (out_vocab_file,)












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 			UpperCAmelCase_      (   unittest.TestCase       ):




							'''simple docstring'''



							@slow
							def    _UpperCAmelCase     (					self       :	Optional[int]			)			->					Tuple:
										SCREAMING_SNAKE_CASE           =					AutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""       ,     return_dict=a			).to(a			)
										SCREAMING_SNAKE_CASE           =					AutoTokenizer.from_pretrained("""google/mt5-small"""			)

										SCREAMING_SNAKE_CASE           =					tokenizer("""Hello there"""       ,     return_tensors="""pt"""			).input_ids
										SCREAMING_SNAKE_CASE           =					tokenizer("""Hi I am"""       ,     return_tensors="""pt"""			).input_ids

										SCREAMING_SNAKE_CASE           =					model(input_ids.to(a			)       ,     labels=labels.to(a			)			).loss
										SCREAMING_SNAKE_CASE           =					-(labels.shape[-1] * loss.item())

										SCREAMING_SNAKE_CASE           =					-84.9127
										self.assertTrue(abs(mtf_score - EXPECTED_SCORE			) < 1E-4			)






