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:
	_UpperCAmelCase						=		None


_UpperCAmelCase						=		logging.get_logger(__name__)


_UpperCAmelCase						=		{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

_UpperCAmelCase						=		{
    """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"""
        ),
    },
}

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

# fmt: off
_UpperCAmelCase						=		["""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						(					__a  ):


				'''simple docstring'''






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

				lowerCamelCase_    =					[]
				lowerCamelCase_    =					[]
				def __init__(						self		,	lowercase=None		,	lowercase=None		,	lowercase="<s>"		,	lowercase="</s>"		,	lowercase="</s>"		,	lowercase="<s>"		,	lowercase="<unk>"		,	lowercase="<pad>"		,	lowercase="<mask>"		,	lowercase=None		,	lowercase=None		,	lowercase=None		,	lowercase=False		,	**lowercase		,	):



						"""simple docstring"""





						A_		:				Tuple     =      AddedToken(_UpperCamelCase		,	lstrip=_UpperCamelCase		,	rstrip=_UpperCamelCase					) if isinstance(_UpperCamelCase		,	_UpperCamelCase					) else mask_token
						A_		:				int     =      legacy_behaviour
						super().__init__(
						    vocab_file=_UpperCamelCase		,	tokenizer_file=_UpperCamelCase		,	bos_token=_UpperCamelCase		,	eos_token=_UpperCamelCase		,	sep_token=_UpperCamelCase		,	cls_token=_UpperCamelCase		,	unk_token=_UpperCamelCase		,	pad_token=_UpperCamelCase		,	mask_token=_UpperCamelCase		,	src_lang=_UpperCamelCase		,	tgt_lang=_UpperCamelCase		,	additional_special_tokens=_UpperCamelCase		,	legacy_behaviour=_UpperCamelCase		,	**_UpperCamelCase		,	)

						A_		:				Optional[Any]     =      vocab_file
						A_		:				Dict     =      False if not self.vocab_file else True

						A_		:				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}					)
						A_		:				List[Any]     =      {
						    lang_code: self.convert_tokens_to_ids(_UpperCamelCase					) for lang_code in FAIRSEQ_LANGUAGE_CODES
						}

						A_		:				List[Any]     =      src_lang if src_lang is not None else 'eng_Latn'
						A_		:				Tuple     =      self.convert_tokens_to_ids(self._src_lang					)
						A_		:				int     =      tgt_lang
						self.set_src_lang_special_tokens(self._src_lang					)
				@property
				def   lowerCAmelCase_   (						self					):



						"""simple docstring"""





						return self._src_lang
				@src_lang.setter
				def   lowerCAmelCase_   (						self		,	lowercase					):



						"""simple docstring"""





						A_		:				List[Any]     =      new_src_lang
						self.set_src_lang_special_tokens(self._src_lang					)
				def   lowerCAmelCase_   (						self		,	lowercase		,	lowercase = 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   lowerCAmelCase_   (						self		,	lowercase		,	lowercase = None					):



						"""simple docstring"""





						A_		:				List[str]     =      [self.sep_token_id]
						A_		:				str     =      [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   lowerCAmelCase_   (						self		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	**lowercase					):



						"""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'					)
						A_		:				Optional[int]     =      src_lang
						A_		:				Tuple     =      self(_UpperCamelCase		,	add_special_tokens=_UpperCamelCase		,	return_tensors=_UpperCamelCase		,	**_UpperCamelCase					)
						A_		:				Optional[Any]     =      self.convert_tokens_to_ids(_UpperCamelCase					)
						A_		:				List[str]     =      tgt_lang_id
						return inputs
				def   lowerCAmelCase_   (						self		,	lowercase		,	lowercase = "eng_Latn"		,	lowercase = None		,	lowercase = "fra_Latn"		,	**lowercase		,	):



						"""simple docstring"""





						A_		:				Optional[Any]     =      src_lang
						A_		:				str     =      tgt_lang
						return super().prepare_seqaseq_batch(_UpperCamelCase		,	_UpperCamelCase		,	**_UpperCamelCase					)
				def   lowerCAmelCase_   (						self					):



						"""simple docstring"""





						return self.set_src_lang_special_tokens(self.src_lang					)
				def   lowerCAmelCase_   (						self					):



						"""simple docstring"""





						return self.set_tgt_lang_special_tokens(self.tgt_lang					)
				def   lowerCAmelCase_   (						self		,	lowercase					):



						"""simple docstring"""





						A_		:				List[str]     =      self.convert_tokens_to_ids(_UpperCamelCase					)

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

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

						A_		:				Tuple     =      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   lowerCAmelCase_   (						self		,	lowercase					):



						"""simple docstring"""





						A_		:				str     =      self.convert_tokens_to_ids(_UpperCamelCase					)
						if self.legacy_behaviour:
								A_		:				Optional[int]     =      []
								A_		:				str     =      [self.eos_token_id, self.cur_lang_code]
						else:
								A_		:				int     =      [self.cur_lang_code]
								A_		:				Optional[Any]     =      [self.eos_token_id]

						A_		:				str     =      self.convert_ids_to_tokens(self.prefix_tokens					)
						A_		:				Tuple     =      self.convert_ids_to_tokens(self.suffix_tokens					)

						A_		:				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   lowerCAmelCase_   (						self		,	lowercase		,	lowercase = None					):



						"""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(_UpperCamelCase					):
								logger.error(F'''Vocabulary path ({save_directory}) should be a directory.'''					)
								return
						A_		:				int     =      os.path.join(
						    _UpperCamelCase		,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']					)

						if os.path.abspath(self.vocab_file					) != os.path.abspath(_UpperCamelCase					):
								copyfile(self.vocab_file		,	_UpperCamelCase					)

						return (out_vocab_file,)








from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


_A   =     logging.get_logger(__name__)


_A   =     TypeVar("DatasetType", Dataset, IterableDataset)






def    lowerCamelCase__				(				__lowerCAmelCase  :  List[DatasetType]    ,							__lowerCAmelCase  :  Optional[List[float]] = None    ,							__lowerCAmelCase  :  Optional[int] = None    ,							__lowerCAmelCase  :  Optional[DatasetInfo] = None    ,							__lowerCAmelCase  :  Optional[NamedSplit] = None    ,							__lowerCAmelCase  :  Literal["first_exhausted", "all_exhausted"] = "first_exhausted"    ,							):




			"""simple docstring"""


			from .arrow_dataset import Dataset
			from .iterable_dataset import IterableDataset

			if not datasets:
						raise ValueError("Unable to interleave an empty list of datasets."				)
			for i, dataset in enumerate(__lowerCAmelCase				):
						if not isinstance(__lowerCAmelCase    ,							(Dataset, IterableDataset)				):
									if isinstance(__lowerCAmelCase    ,							(DatasetDict, IterableDatasetDict)				):
												if not dataset:
															raise ValueError(
															    F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
															    "is an empty dataset dictionary."				)
												raise ValueError(
												    F"""Dataset at position {i} has at least one split: {list(__lowerCAmelCase				)}\n"""
												    F"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(__lowerCAmelCase				)				)}']"""				)
									raise ValueError(
									    F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__lowerCAmelCase				).__name__}."""				)
						if i == 0:
									lowerCAmelCase_	,  lowerCAmelCase_						=	(
									    (Dataset, IterableDataset) if isinstance(__lowerCAmelCase    ,							__lowerCAmelCase				) else (IterableDataset, Dataset)
									)
						elif not isinstance(__lowerCAmelCase    ,							__lowerCAmelCase				):
									raise ValueError(
									    F"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."""				)
			if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
						raise ValueError(F"""{stopping_strategy} is not supported. Please enter a valid stopping_strategy."""				)
			if dataset_type is Dataset:
						return _interleave_map_style_datasets(
						    __lowerCAmelCase    ,							__lowerCAmelCase    ,							__lowerCAmelCase    ,							info=__lowerCAmelCase    ,							split=__lowerCAmelCase    ,							stopping_strategy=__lowerCAmelCase				)
			else:
						return _interleave_iterable_datasets(
						    __lowerCAmelCase    ,							__lowerCAmelCase    ,							__lowerCAmelCase    ,							info=__lowerCAmelCase    ,							split=__lowerCAmelCase    ,							stopping_strategy=__lowerCAmelCase				)






def    lowerCamelCase__				(				__lowerCAmelCase  :  List[DatasetType]    ,							__lowerCAmelCase  :  Optional[DatasetInfo] = None    ,							__lowerCAmelCase  :  Optional[NamedSplit] = None    ,							__lowerCAmelCase  :  int = 0    ,							):




			"""simple docstring"""


			if not dsets:
						raise ValueError("Unable to concatenate an empty list of datasets."				)
			for i, dataset in enumerate(__lowerCAmelCase				):
						if not isinstance(__lowerCAmelCase    ,							(Dataset, IterableDataset)				):
									if isinstance(__lowerCAmelCase    ,							(DatasetDict, IterableDatasetDict)				):
												if not dataset:
															raise ValueError(
															    F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
															    "is an empty dataset dictionary."				)
												raise ValueError(
												    F"""Dataset at position {i} has at least one split: {list(__lowerCAmelCase				)}\n"""
												    F"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(__lowerCAmelCase				)				)}']"""				)
									raise ValueError(
									    F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__lowerCAmelCase				).__name__}."""				)
						if i == 0:
									lowerCAmelCase_	,  lowerCAmelCase_						=	(
									    (Dataset, IterableDataset) if isinstance(__lowerCAmelCase    ,							__lowerCAmelCase				) else (IterableDataset, Dataset)
									)
						elif not isinstance(__lowerCAmelCase    ,							__lowerCAmelCase				):
									raise ValueError(
									    F"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."""				)
			if dataset_type is Dataset:
						return _concatenate_map_style_datasets(__lowerCAmelCase    ,							info=__lowerCAmelCase    ,							split=__lowerCAmelCase    ,							axis=__lowerCAmelCase				)
			else:
						return _concatenate_iterable_datasets(__lowerCAmelCase    ,							info=__lowerCAmelCase    ,							split=__lowerCAmelCase    ,							axis=__lowerCAmelCase				)



