





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:
					snake_case_     :       Dict						=  None


snake_case_     :       Any						=  logging.get_logger(__name__)


snake_case_     :       List[str]						=  {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

snake_case_     :       Dict						=  {
    "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"
        ),
    },
}

snake_case_     :       List[Any]						=  {
    "facebook/nllb-large-en-ro": 10_24,
    "facebook/nllb-200-distilled-600M": 10_24,
}

# fmt: off
snake_case_     :       Dict						=  ["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      (__a     ):
							__a					:   List[Any]									=       VOCAB_FILES_NAMES
							__a					:   Tuple									=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__a					:   List[str]									=       PRETRAINED_VOCAB_FILES_MAP
							__a					:   List[Any]									=       ["input_ids", "attention_mask"]
							__a					:   Tuple									=       NllbTokenizer

							__a					:   str									=       []
							__a					:   Any									=       []

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







													"""simple docstring"""
													# Mask token behave like a normal word, i.e. include the space before it
													UpperCAmelCase_			:      Optional[int]     						=  AddedToken(A__       ,     lstrip=A__       ,     rstrip=A__   ) if isinstance(A__       ,     A__   ) else mask_token
													UpperCAmelCase_			:      int     						=  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__       ,     )

													UpperCAmelCase_			:      Optional[int]     						=  vocab_file
													UpperCAmelCase_			:      List[Any]     						=  False if not self.vocab_file else True

													UpperCAmelCase_			:      List[Any]     						=  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_			:      Union[str, Any]     						=  {
													    lang_code: self.convert_tokens_to_ids(A__   ) for lang_code in FAIRSEQ_LANGUAGE_CODES
													}

													UpperCAmelCase_			:      Any     						=  src_lang if src_lang is not None else '''eng_Latn'''
													UpperCAmelCase_			:      List[Any]     						=  self.convert_tokens_to_ids(self._src_lang   )
													UpperCAmelCase_			:      Optional[Any]     						=  tgt_lang
													self.set_src_lang_special_tokens(self._src_lang   )

							@property
							def     UpperCAmelCase__	(							self							:     int   )					->       str:







													"""simple docstring"""
													return self._src_lang

							@src_lang.setter
							def     UpperCAmelCase__	(							self							:     int       ,     __magic_name__							:     Dict   )					->       None:







													"""simple docstring"""
													UpperCAmelCase_			:      int     						=  new_src_lang
													self.set_src_lang_special_tokens(self._src_lang   )

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







													"""simple docstring"""
													UpperCAmelCase_			:      Dict     						=  [self.sep_token_id]
													UpperCAmelCase_			:      Tuple     						=  [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							:     str       ,     __magic_name__							:     Optional[int]       ,     __magic_name__							:     Dict       ,     __magic_name__							:     Optional[Any]       ,     __magic_name__							:     List[str]       ,     **__magic_name__							:     List[Any]   )					->       Union[str, Any]:







													"""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_			:      Any     						=  self(A__       ,     add_special_tokens=A__       ,     return_tensors=A__       ,     **A__   )
													UpperCAmelCase_			:      Union[str, Any]     						=  self.convert_tokens_to_ids(A__   )
													UpperCAmelCase_			:      Union[str, Any]     						=  tgt_lang_id
													return inputs

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







													"""simple docstring"""
													UpperCAmelCase_			:      Union[str, Any]     						=  src_lang
													UpperCAmelCase_			:      Optional[Any]     						=  tgt_lang
													return super().prepare_seqaseq_batch(A__       ,     A__       ,     **A__   )

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







													"""simple docstring"""
													return self.set_src_lang_special_tokens(self.src_lang   )

							def     UpperCAmelCase__	(							self							:     Union[str, Any]   )					->       Optional[Any]:







													"""simple docstring"""
													return self.set_tgt_lang_special_tokens(self.tgt_lang   )

							def     UpperCAmelCase__	(							self							:     int       ,     __magic_name__							:     Tuple   )					->       None:







													"""simple docstring"""
													UpperCAmelCase_			:      List[Any]     						=  self.convert_tokens_to_ids(A__   )

													if self.legacy_behaviour:
																			UpperCAmelCase_			:      Optional[int]     						=  []
																			UpperCAmelCase_			:      List[str]     						=  [self.eos_token_id, self.cur_lang_code]
													else:
																			UpperCAmelCase_			:      Dict     						=  [self.cur_lang_code]
																			UpperCAmelCase_			:      str     						=  [self.eos_token_id]

													UpperCAmelCase_			:      Any     						=  self.convert_ids_to_tokens(self.prefix_tokens   )
													UpperCAmelCase_			:      List[str]     						=  self.convert_ids_to_tokens(self.suffix_tokens   )

													UpperCAmelCase_			:      Dict     						=  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							:     Any       ,     __magic_name__							:     str   )					->       None:







													"""simple docstring"""
													UpperCAmelCase_			:      int     						=  self.convert_tokens_to_ids(A__   )
													if self.legacy_behaviour:
																			UpperCAmelCase_			:      Any     						=  []
																			UpperCAmelCase_			:      Any     						=  [self.eos_token_id, self.cur_lang_code]
													else:
																			UpperCAmelCase_			:      Tuple     						=  [self.cur_lang_code]
																			UpperCAmelCase_			:      Union[str, Any]     						=  [self.eos_token_id]

													UpperCAmelCase_			:      Dict     						=  self.convert_ids_to_tokens(self.prefix_tokens   )
													UpperCAmelCase_			:      Tuple     						=  self.convert_ids_to_tokens(self.suffix_tokens   )

													UpperCAmelCase_			:      Optional[Any]     						=  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[Any]       ,     __magic_name__							:     Optional[int]       ,     __magic_name__							:     Optional[Any] = None   )					->       Tuple[str]:







													"""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(A__   ):
																			logger.error(F"""Vocabulary path ({save_directory}) should be a directory."""   )
																			return
													UpperCAmelCase_			:      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__   ):
																			copyfile(self.vocab_file       ,     A__   )

													return (out_vocab_file,)












'''simple docstring'''







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
					import torch

					from transformers.generation import DisjunctiveConstraint


@require_torch
class 					__a      (unittest.TestCase     ):

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







													"""simple docstring"""
													# For consistency across different places the DisjunctiveConstraint is called,
													# dc.token_ids is a list of integers. It is also initialized only by integers.

													UpperCAmelCase_			:      List[str]     						=  [[1, 2, 4], [1, 2, 3, 4]]
													UpperCAmelCase_			:      List[str]     						=  DisjunctiveConstraint(__magic_name__   )
													self.assertTrue(isinstance(dc.token_ids       ,     __magic_name__   )   )

													with self.assertRaises(__magic_name__   ):
																			DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]   )   )

													with self.assertRaises(__magic_name__   ):
																			DisjunctiveConstraint([torch.LongTensor([1, 2, 4]   ), torch.LongTensor([1, 2, 3, 4, 5]   )]   )

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







													"""simple docstring"""
													# We can't have constraints that are complete subsets of another. This leads to a preverse
													# interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
													# It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
													# fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
													# will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
													UpperCAmelCase_			:      Tuple     						=  [[1, 2], [1, 2, 3, 4]]

													with self.assertRaises(__magic_name__   ):
																			DisjunctiveConstraint(__magic_name__   )  # fails here

							def     UpperCAmelCase__	(							self							:     Optional[int]   )					->       Union[str, Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[int]     						=  [[1, 2, 3], [1, 2, 4]]

													UpperCAmelCase_			:      List[str]     						=  DisjunctiveConstraint(__magic_name__   )

													UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      List[Any]     						=  dc.update(1   )
													UpperCAmelCase_			:      Dict     						=  stepped is True and completed is False and reset is False
													self.assertTrue(__magic_name__   )
													self.assertTrue(not dc.completed   )
													self.assertTrue(dc.current_seq == [1]   )

													UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      str     						=  dc.update(2   )
													UpperCAmelCase_			:      Optional[Any]     						=  stepped is True and completed is False and reset is False
													self.assertTrue(__magic_name__   )
													self.assertTrue(not dc.completed   )
													self.assertTrue(dc.current_seq == [1, 2]   )

													UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      List[Any]     						=  dc.update(3   )
													UpperCAmelCase_			:      Dict     						=  stepped is True and completed is True and reset is False
													self.assertTrue(__magic_name__   )
													self.assertTrue(dc.completed   )  # Completed!
													self.assertTrue(dc.current_seq == [1, 2, 3]   )



							def     UpperCAmelCase__	(							self							:     int   )					->       Dict:







													"""simple docstring"""
													UpperCAmelCase_			:      Any     						=  [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

													UpperCAmelCase_			:      Tuple     						=  DisjunctiveConstraint(__magic_name__   )

													UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      List[Any]     						=  dc.update(1   )
													self.assertTrue(not dc.completed   )
													self.assertTrue(dc.current_seq == [1]   )

													UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      Optional[int]     						=  dc.update(2   )
													self.assertTrue(not dc.completed   )
													self.assertTrue(dc.current_seq == [1, 2]   )

													UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      int     						=  dc.update(4   )
													self.assertTrue(not dc.completed   )
													self.assertTrue(dc.current_seq == [1, 2, 4]   )

													UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      int     						=  dc.update(5   )
													self.assertTrue(dc.completed   )  # Completed!
													self.assertTrue(dc.current_seq == [1, 2, 4, 5]   )

													dc.reset()

													UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      List[Any]     						=  dc.update(1   )
													self.assertTrue(not dc.completed   )
													self.assertTrue(dc.remaining() == 3   )
													self.assertTrue(dc.current_seq == [1]   )

													UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      Optional[Any]     						=  dc.update(2   )
													self.assertTrue(not dc.completed   )
													self.assertTrue(dc.remaining() == 2   )
													self.assertTrue(dc.current_seq == [1, 2]   )

													UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      Optional[int]     						=  dc.update(5   )
													self.assertTrue(dc.completed   )  # Completed!
													self.assertTrue(dc.remaining() == 0   )
													self.assertTrue(dc.current_seq == [1, 2, 5]   )





