

def       a__					(		UpperCAmelCase							:      Tuple    ,			UpperCAmelCase							:      List[Any]    ,			UpperCAmelCase							:      List[Any]    ,			UpperCAmelCase							:      Tuple      )  ->						Any:
  global f  # a global dp table for knapsack
  if f[i][j] < 0:
    if j < wt[i - 1]:
      UpperCAmelCase	:							Any						=			mf_knapsack(i - 1    ,			UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase      )
    else:
      UpperCAmelCase	:							Optional[Any]						=			max(
          mf_knapsack(i - 1    ,			UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase      )    ,			mf_knapsack(i - 1    ,			UpperCAmelCase    ,			UpperCAmelCase    ,			j - wt[i - 1]      ) + val[i - 1]    ,			)
    UpperCAmelCase	:							Any						=			val
  return f[i][j]

def       a__					(		UpperCAmelCase							:      str    ,			UpperCAmelCase							:      str    ,			UpperCAmelCase							:      int    ,			UpperCAmelCase							:      List[str]      )  ->						str:
  UpperCAmelCase	:							Any						=			[[0] * (w + 1) for _ in range(n + 1      )]

  for i in range(1    ,			n + 1      ):
    for w_ in range(1    ,			w + 1      ):
      if wt[i - 1] <= w_:
        UpperCAmelCase	:							Dict						=			max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]    ,			dp[i - 1][w_]      )
      else:
        UpperCAmelCase	:							List[Any]						=			dp[i - 1][w_]

  return dp[n][w_], dp

def       a__					(		UpperCAmelCase							:      int    ,			UpperCAmelCase							:      list    ,			UpperCAmelCase							:      list      )  ->						List[Any]:
  if not (isinstance(UpperCAmelCase    ,			(list, tuple)      ) and isinstance(UpperCAmelCase    ,			(list, tuple)      )):
    raise ValueError(
        '''Both the weights and values vectors must be either lists or tuples'''      )

  UpperCAmelCase	:							Union[str, Any]						=			len(UpperCAmelCase      )
  if num_items != len(UpperCAmelCase      ):
    UpperCAmelCase	:							Dict						=			(
        '''The number of weights must be the same as the number of values.\n'''
        f'''But got {num_items} weights and {len(UpperCAmelCase      )} values'''
    )
    raise ValueError(UpperCAmelCase      )
  for i in range(UpperCAmelCase      ):
    if not isinstance(wt[i]    ,			UpperCAmelCase      ):
      UpperCAmelCase	:							Tuple						=			(
          '''All weights must be integers but got weight of '''
          f'''type {type(wt[i]      )} at index {i}'''
      )
      raise TypeError(UpperCAmelCase      )

  UpperCAmelCase			,						UpperCAmelCase	:							Any						=			knapsack(UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase      )
  UpperCAmelCase	:							set						=			set()
  _construct_solution(UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase      )

  return optimal_val, example_optional_set

def       a__					(		UpperCAmelCase							:      list    ,			UpperCAmelCase							:      list    ,			UpperCAmelCase							:      int    ,			UpperCAmelCase							:      int    ,			UpperCAmelCase							:      set      )  ->						Union[str, Any]:
  # for the current item i at a maximum weight j to be part of an optimal subset,
  # the optimal value at (i, j) must be greater than the optimal value at (i-1, j).
  # where i - 1 means considering only the previous items at the given maximum weight
  if i > 0 and j > 0:
    if dp[i - 1][j] == dp[i][j]:
      _construct_solution(UpperCAmelCase    ,			UpperCAmelCase    ,			i - 1    ,			UpperCAmelCase    ,			UpperCAmelCase      )
    else:
      optimal_set.add(UpperCAmelCase      )
      _construct_solution(UpperCAmelCase    ,			UpperCAmelCase    ,			i - 1    ,			j - wt[i - 1]    ,			UpperCAmelCase      )


if __name__ == "__main__":
     _lowerCamelCase    :       int        =							[3, 2, 4, 4]
     _lowerCamelCase    :       int        =							[4, 3, 2, 3]
     _lowerCamelCase    :       Tuple        =							4
     _lowerCamelCase    :       Any        =							6
     _lowerCamelCase    :       Any        =							[[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
     _lowerCamelCase       ,   _lowerCamelCase    :       List[Any]        =							knapsack(w, wt, val, n)
     print(optimal_solution)
     print(mf_knapsack(n, wt, val, w))  # switched the n and w

     # testing the dynamic programming problem with example
     # the optimal subset for the above example are items 3 and 4
     _lowerCamelCase       ,   _lowerCamelCase    :       Dict        =							knapsack_with_example_solution(w, wt, val)
     assert optimal_solution == 8
     assert optimal_subset == {3, 4}
     print("optimal_value = ", optimal_solution)
     print("An optimal subset corresponding to the optimal value", optimal_subset)








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:
     _lowerCamelCase    :       List[Any]        =							None


_lowerCamelCase    :       str        =							logging.get_logger(__name__)


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

_lowerCamelCase    :       str        =							{
    "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"
        ),
    },
}

_lowerCamelCase    :       Any        =							{
    "facebook/nllb-large-en-ro": 1_0_2_4,
    "facebook/nllb-200-distilled-600M": 1_0_2_4,
}

# fmt: off
_lowerCamelCase    :       int        =							["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__						):
   UpperCamelCase       					=					VOCAB_FILES_NAMES
   UpperCamelCase       					=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   UpperCamelCase       					=					PRETRAINED_VOCAB_FILES_MAP
   UpperCamelCase       					=					["""input_ids""", """attention_mask"""]
   UpperCamelCase       					=					NllbTokenizer

   UpperCamelCase       					=					[]
   UpperCamelCase       					=					[]



   def __init__(							self							:					Optional[Any],		__A							:					Tuple=None,		__A							:					int=None,		__A							:					List[Any]="<s>",		__A							:					Tuple="</s>",		__A							:					Any="</s>",		__A							:					Optional[Any]="<s>",		__A							:					Tuple="<unk>",		__A							:					str="<pad>",		__A							:					Dict="<mask>",		__A							:					Optional[Any]=None,		__A							:					List[Any]=None,		__A							:					List[Any]=None,		__A							:					str=False,		**__A							:					Tuple,		):
     # Mask token behave like a normal word, i.e. include the space before it
     UpperCAmelCase	:							int						=			AddedToken(__A,		lstrip=__A,		rstrip=__A						) if isinstance(__A,		__A						) else mask_token
     UpperCAmelCase	:							str						=			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	:							Optional[Any]						=			False if not self.vocab_file else True

     UpperCAmelCase	:							Optional[int]						=			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	:							List[Any]						=			{
         lang_code: self.convert_tokens_to_ids(__A						) for lang_code in FAIRSEQ_LANGUAGE_CODES
     }

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



   @property
   def 	__magic_name__   (							self							:					Optional[int]						):
     return self._src_lang



   @src_lang.setter
   def 	__magic_name__   (							self							:					Union[str, Any],		__A							:					str						):
     UpperCAmelCase	:							Tuple						=			new_src_lang
     self.set_src_lang_special_tokens(self._src_lang						)



   def 	__magic_name__   (							self							:					Any,		__A							:					List[int],		__A							:					Optional[List[int]] = 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 	__magic_name__   (							self							:					Tuple,		__A							:					List[int],		__A							:					Optional[List[int]] = None						):
     UpperCAmelCase	:							Union[str, Any]						=			[self.sep_token_id]
     UpperCAmelCase	:							Any						=			[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 	__magic_name__   (							self							:					str,		__A							:					Optional[int],		__A							:					str,		__A							:					Optional[str],		__A							:					Optional[str],		**__A							:					Union[str, Any]						):
     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[Any]						=			src_lang
     UpperCAmelCase	:							Optional[Any]						=			self(__A,		add_special_tokens=__A,		return_tensors=__A,		**__A						)
     UpperCAmelCase	:							Union[str, Any]						=			self.convert_tokens_to_ids(__A						)
     UpperCAmelCase	:							int						=			tgt_lang_id
     return inputs



   def 	__magic_name__   (							self							:					List[str],		__A							:					List[str],		__A							:					str = "eng_Latn",		__A							:					Optional[List[str]] = None,		__A							:					str = "fra_Latn",		**__A							:					Tuple,		):
     UpperCAmelCase	:							Any						=			src_lang
     UpperCAmelCase	:							Tuple						=			tgt_lang
     return super().prepare_seqaseq_batch(__A,		__A,		**__A						)



   def 	__magic_name__   (							self							:					List[str]						):
     return self.set_src_lang_special_tokens(self.src_lang						)



   def 	__magic_name__   (							self							:					Optional[Any]						):
     return self.set_tgt_lang_special_tokens(self.tgt_lang						)



   def 	__magic_name__   (							self							:					Union[str, Any],		__A							:					List[str]						):
     UpperCAmelCase	:							int						=			self.convert_tokens_to_ids(__A						)

     if self.legacy_behaviour:
       UpperCAmelCase	:							Tuple						=			[]
       UpperCAmelCase	:							Optional[int]						=			[self.eos_token_id, self.cur_lang_code]
     else:
       UpperCAmelCase	:							Any						=			[self.cur_lang_code]
       UpperCAmelCase	:							Optional[Any]						=			[self.eos_token_id]

     UpperCAmelCase	:							Dict						=			self.convert_ids_to_tokens(self.prefix_tokens						)
     UpperCAmelCase	:							Union[str, Any]						=			self.convert_ids_to_tokens(self.suffix_tokens						)

     UpperCAmelCase	:							int						=			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 	__magic_name__   (							self							:					Dict,		__A							:					str						):
     UpperCAmelCase	:							Optional[int]						=			self.convert_tokens_to_ids(__A						)
     if self.legacy_behaviour:
       UpperCAmelCase	:							List[Any]						=			[]
       UpperCAmelCase	:							Any						=			[self.eos_token_id, self.cur_lang_code]
     else:
       UpperCAmelCase	:							int						=			[self.cur_lang_code]
       UpperCAmelCase	:							List[Any]						=			[self.eos_token_id]

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

     UpperCAmelCase	:							List[str]						=			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 	__magic_name__   (							self							:					Dict,		__A							:					str,		__A							:					Optional[str] = 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
     UpperCAmelCase	:							Tuple						=			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,)





