



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""": 1_024,
    """facebook/nllb-200-distilled-600M""": 1_024,
}

# 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  __snake_case      (   SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__				:      List[str]         =					VOCAB_FILES_NAMES
							UpperCamelCase__				:      Optional[Any]         =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCamelCase__				:      Dict         =					PRETRAINED_VOCAB_FILES_MAP
							UpperCamelCase__				:      Tuple         =					["""input_ids""", """attention_mask"""]
							UpperCamelCase__				:      Union[str, Any]         =					NllbTokenizer

							UpperCamelCase__				:      List[int]         =					[]
							UpperCamelCase__				:      List[int]         =					[]


							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_  ,  ):
											# Mask token behave like a normal word, i.e. include the space before it
											a__				      =		AddedToken(a_  ,  lstrip=a_  ,  rstrip=a_		) if isinstance(a_  ,  a_		) else mask_token
											a__				      =		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_  ,  )

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

											a__				      =		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__				      =		{
											    lang_code: self.convert_tokens_to_ids(a_		) for lang_code in FAIRSEQ_LANGUAGE_CODES
											}

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


							@property
							def _a      (							self		):
											return self._src_lang


							@src_lang.setter
							def _a      (							self  ,  a_		):
											a__				      =		new_src_lang
											self.set_src_lang_special_tokens(self._src_lang		)


							def _a      (							self  ,  a_  ,  a_ = None		):
											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  ,  a_  ,  a_ = None		):
											a__				      =		[self.sep_token_id]
											a__				      =		[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  ,  a_  ,  a_  ,  a_  ,  a_  ,  **a_		):
											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__				      =		src_lang
											a__				      =		self(a_  ,  add_special_tokens=a_  ,  return_tensors=a_  ,  **a_		)
											a__				      =		self.convert_tokens_to_ids(a_		)
											a__				      =		tgt_lang_id
											return inputs


							def _a      (							self  ,  a_  ,  a_ = "eng_Latn"  ,  a_ = None  ,  a_ = "fra_Latn"  ,  **a_  ,  ):
											a__				      =		src_lang
											a__				      =		tgt_lang
											return super().prepare_seqaseq_batch(a_  ,  a_  ,  **a_		)


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


							def _a      (							self		):
											return self.set_tgt_lang_special_tokens(self.tgt_lang		)


							def _a      (							self  ,  a_		):
											a__				      =		self.convert_tokens_to_ids(a_		)

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

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

											a__				      =		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  ,  a_		):
											a__				      =		self.convert_tokens_to_ids(a_		)
											if self.legacy_behaviour:
															a__				      =		[]
															a__				      =		[self.eos_token_id, self.cur_lang_code]
											else:
															a__				      =		[self.cur_lang_code]
															a__				      =		[self.eos_token_id]

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

											a__				      =		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  ,  a_  ,  a_ = None		):
											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
											a__				      =		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,)






def 			A_      (	__a     :				Any		):

				"""simple docstring"""
				if not head:
								return True
				# split the list to two parts
				a__      ,			a__				      =		head.next, head
				while fast and fast.next:
								a__				      =		fast.next.next
								a__				      =		slow.next
				a__				      =		slow.next
				a__				      =		None  # Don't forget here! But forget still works!
				# reverse the second part
				a__				      =		None
				while second:
								a__				      =		second.next
								a__				      =		node
								a__				      =		second
								a__				      =		nxt
				# compare two parts
				# second part has the same or one less node
				while node:
								if node.val != head.val:
												return False
								a__				      =		node.next
								a__				      =		head.next
				return True

def 			A_      (	__a     :				int		):

				"""simple docstring"""
				if not head or not head.next:
								return True

				# 1. Get the midpoint (slow)
				a__				      =		a__				      =		a__				      =		head
				while fast and fast.next:
								a__      ,			a__				      =		fast.next.next, slow.next

				# 2. Push the second half into the stack
				a__				      =		[slow.val]
				while slow.next:
								a__				      =		slow.next
								stack.append(slow.val		)

				# 3. Comparison
				while stack:
								if stack.pop() != cur.val:
												return False
								a__				      =		cur.next

				return True




def 			A_      (	__a     :				Any		):

				"""simple docstring"""
				if not head or not head.next:
								return True
				a__				      =		{}
				a__				      =		0
				while head:
								if head.val in d:
												d[head.val].append(__a		)
								else:
												a__				      =		[pos]
								a__				      =		head.next
								pos += 1
				a__				      =		pos - 1
				a__				      =		0
				for v in d.values():
								if len(__a		) % 2 != 0:
												middle += 1
								else:
												a__				      =		0
												for i in range(0 ,		len(__a		)		):
																if v[i] + v[len(__a		) - 1 - step] != checksum:
																				return False
																step += 1
								if middle > 1:
												return False
				return True

