
"""simple docstring"""





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														=		None


_a														=		logging.get_logger(__name__)


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

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

# fmt: off
_a														=		["""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(    lowerCamelCase	):
		lowercase__						=      VOCAB_FILES_NAMES
		lowercase__						=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowercase__						=      PRETRAINED_VOCAB_FILES_MAP
		lowercase__						=      ['input_ids', 'attention_mask']
		lowercase__						=      NllbTokenizer

		lowercase__						=      []
		lowercase__						=      []



		def __init__(					self				,		__a=None				,		__a=None				,		__a="<s>"				,		__a="</s>"				,		__a="</s>"				,		__a="<s>"				,		__a="<unk>"				,		__a="<pad>"				,		__a="<mask>"				,		__a=None				,		__a=None				,		__a=None				,		__a=False				,		**__a				,		)       ->       Dict:






							'''simple docstring'''
							# Mask token behave like a normal word, i.e. include the space before it
							_UpperCamelCase							 =  AddedToken(__a				,		lstrip=__a				,		rstrip=__a) if isinstance(__a				,		__a) else mask_token
							_UpperCamelCase							 =  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							 =  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(__a) 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 						UpperCAmelCase							(					self)       ->       str:






							'''simple docstring'''
							return self._src_lang



		@src_lang.setter
		def 						UpperCAmelCase							(					self				,		__a)       ->       None:






							'''simple docstring'''
							_UpperCamelCase							 =  new_src_lang
							self.set_src_lang_special_tokens(self._src_lang)



		def 						UpperCAmelCase							(					self				,		__a				,		__a = 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				,		__a				,		__a = None)       ->       List[int]:






							'''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 						UpperCAmelCase							(					self				,		__a				,		__a				,		__a				,		__a				,		**__a)       ->       Optional[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(__a				,		add_special_tokens=__a				,		return_tensors=__a				,		**__a)
							_UpperCamelCase							 =  self.convert_tokens_to_ids(__a)
							_UpperCamelCase							 =  tgt_lang_id
							return inputs



		def 						UpperCAmelCase							(					self				,		__a				,		__a = "eng_Latn"				,		__a = None				,		__a = "fra_Latn"				,		**__a				,		)       ->       BatchEncoding:






							'''simple docstring'''
							_UpperCamelCase							 =  src_lang
							_UpperCamelCase							 =  tgt_lang
							return super().prepare_seqaseq_batch(__a				,		__a				,		**__a)



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






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



		def 						UpperCAmelCase							(					self)       ->       str:






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



		def 						UpperCAmelCase							(					self				,		__a)       ->       None:






							'''simple docstring'''
							_UpperCamelCase							 =  self.convert_tokens_to_ids(__a)

							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 						UpperCAmelCase							(					self				,		__a)       ->       None:






							'''simple docstring'''
							_UpperCamelCase							 =  self.convert_tokens_to_ids(__a)
							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 						UpperCAmelCase							(					self				,		__a				,		__a = 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							 =  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"""





from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_a														=		{
    """configuration_autoformer""": [
        """AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """AutoformerConfig""",
    ],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a														=		[
				    """AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """AutoformerForPrediction""",
				    """AutoformerModel""",
				    """AutoformerPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_autoformer import (
				    AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    AutoformerConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_autoformer import (
								    AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    AutoformerForPrediction,
								    AutoformerModel,
								    AutoformerPreTrainedModel,
								)

else:
				import sys

				_a														=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






