
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:
     lowercase  : List[Any]						   =							None


lowercase  : str						   =							logging.get_logger(__name__)


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

lowercase  : 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"
        ),
    },
}

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

# fmt: off
lowercase  : 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 __snake_case		(				lowerCamelCase__					):
    _a		:						Union[str, Any]=    VOCAB_FILES_NAMES
    _a		:						Tuple=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _a		:						Optional[int]=    PRETRAINED_VOCAB_FILES_MAP
    _a		:						int=    ["input_ids", "attention_mask"]
    _a		:						Tuple=    NllbTokenizer

    _a		:						Optional[int]=    []
    _a		:						List[str]=    []
    def __init__(  self		,snake_case=None		,snake_case=None		,snake_case="<s>"		,snake_case="</s>"		,snake_case="</s>"		,snake_case="<s>"		,snake_case="<unk>"		,snake_case="<pad>"		,snake_case="<mask>"		,snake_case=None		,snake_case=None		,snake_case=None		,snake_case=False		,**snake_case		,):



           '''simple docstring'''







           lowercase							:					int	     =				AddedToken(__A		,lstrip=__A		,rstrip=__A				) if isinstance(__A		,__A				) else mask_token
           lowercase							:					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		,)

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

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

           lowercase							:					List[Any]	     =				src_lang if src_lang is not None else '''eng_Latn'''
           lowercase							:					List[Any]	     =				self.convert_tokens_to_ids(self._src_lang				)
           lowercase							:					List[str]	     =				tgt_lang
           self.set_src_lang_special_tokens(self._src_lang				)
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return self._src_lang
    @src_lang.setter
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           lowercase							:					Tuple	     =				new_src_lang
           self.set_src_lang_special_tokens(self._src_lang				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case = 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 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case = None				):



           '''simple docstring'''







           lowercase							:					Union[str, Any]	     =				[self.sep_token_id]
           lowercase							:					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 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case		,snake_case		,snake_case		,**snake_case				):



           '''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"""				)
           lowercase							:					Optional[Any]	     =				src_lang
           lowercase							:					Optional[Any]	     =				self(__A		,add_special_tokens=__A		,return_tensors=__A		,**__A				)
           lowercase							:					Union[str, Any]	     =				self.convert_tokens_to_ids(__A				)
           lowercase							:					int	     =				tgt_lang_id
           return inputs
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case = "eng_Latn"		,snake_case = None		,snake_case = "fra_Latn"		,**snake_case		,):



           '''simple docstring'''







           lowercase							:					Any	     =				src_lang
           lowercase							:					Tuple	     =				tgt_lang
           return super().prepare_seqaseq_batch(__A		,__A		,**__A				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







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



           '''simple docstring'''







           return self.set_tgt_lang_special_tokens(self.tgt_lang				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           lowercase							:					int	     =				self.convert_tokens_to_ids(__A				)

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

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

           lowercase							:					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 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







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

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

           lowercase							:					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 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case = 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(__A				):
                  logger.error(f"Vocabulary path ({save_directory}) should be a directory."				)
                  return
           lowercase							:					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,)


def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Union[str, Any]:
       global f  # a global dp table for knapsack
       if f[i][j] < 0:
              if j < wt[i - 1]:
                     lowercase							:					str	     =				mf_knapsack(i - 1 ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )
              else:
                     lowercase							:					Optional[int]	     =				max(
                         mf_knapsack(i - 1 ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    ) ,     mf_knapsack(i - 1 ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     j - wt[i - 1]    ) + val[i - 1] ,     )
              lowercase							:					List[Any]	     =				val
       return f[i][j]







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Any:
       lowercase							:					Optional[int]	     =				[[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_:
                            lowercase							:					Union[str, Any]	     =				max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]] ,     dp[i - 1][w_]    )
                     else:
                            lowercase							:					Any	     =				dp[i - 1][w_]

       return dp[n][w_], dp







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Optional[Any]:
       if not (isinstance(SCREAMING_SNAKE_CASE__ ,     (list, tuple)    ) and isinstance(SCREAMING_SNAKE_CASE__ ,     (list, tuple)    )):
              raise ValueError(
                  """Both the weights and values vectors must be either lists or tuples"""    )

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

       lowercase     ,      lowercase							:					Any	     =				knapsack(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )
       lowercase							:					set	     =				set()
       _construct_solution(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )

       return optimal_val, example_optional_set







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Optional[int]:
       # 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(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     i - 1 ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )
              else:
                     optimal_set.add(SCREAMING_SNAKE_CASE__    )
                     _construct_solution(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     i - 1 ,     j - wt[i - 1] ,     SCREAMING_SNAKE_CASE__    )


if __name__ == "__main__":
  lowercase  : Dict						   =							[3, 2, 4, 4]
  lowercase  : List[Any]						   =							[4, 3, 2, 3]
  lowercase  : Tuple						   =							4
  lowercase  : Tuple						   =							6
  lowercase  : Tuple						   =							[[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
  lowercase   ,  lowercase  : List[str]						   =							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
  lowercase   ,  lowercase  : Optional[int]						   =							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)
