


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:
 _SCREAMING_SNAKE_CASE      :			int      				=   None


_SCREAMING_SNAKE_CASE      :			Union[str, Any]      				=   logging.get_logger(__name__)


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

_SCREAMING_SNAKE_CASE      :			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"
        ),
    },
}

_SCREAMING_SNAKE_CASE      :			int      				=   {
    "facebook/nllb-large-en-ro": 10_24,
    "facebook/nllb-200-distilled-600M": 10_24,
}

# fmt: off
_SCREAMING_SNAKE_CASE      :			str      				=   ["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   A__ (   snake_case__     ):







    """simple docstring"""

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

    __magic_name__        =		[]
    __magic_name__        =		[]
    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      , ):
         # Mask token behave like a normal word, i.e. include the space before it
         snake_case					=    AddedToken(__snake_case      , lstrip=__snake_case      , rstrip=__snake_case   ) if isinstance(__snake_case      , __snake_case   ) else mask_token
         snake_case					=    legacy_behaviour
         super().__init__(
             vocab_file=__snake_case      , tokenizer_file=__snake_case      , bos_token=__snake_case      , eos_token=__snake_case      , sep_token=__snake_case      , cls_token=__snake_case      , unk_token=__snake_case      , pad_token=__snake_case      , mask_token=__snake_case      , src_lang=__snake_case      , tgt_lang=__snake_case      , additional_special_tokens=__snake_case      , legacy_behaviour=__snake_case      , **__snake_case      , )

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

         snake_case					=    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}   )
         snake_case					=    {
             lang_code: self.convert_tokens_to_ids(__snake_case   ) for lang_code in FAIRSEQ_LANGUAGE_CODES
         }

         snake_case					=    src_lang if src_lang is not None else '''eng_Latn'''
         snake_case					=    self.convert_tokens_to_ids(self._src_lang   )
         snake_case					=    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      , __snake_case   ):
         snake_case					=    new_src_lang
         self.set_src_lang_special_tokens(self._src_lang   )
    def 		a_      (	self      , __snake_case      , __snake_case = 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      , __snake_case      , __snake_case = None   ):
         snake_case					=    [self.sep_token_id]
         snake_case					=    [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      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , **__snake_case   ):
         if src_lang is None or tgt_lang is None:
              raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''   )
         snake_case					=    src_lang
         snake_case					=    self(__snake_case      , add_special_tokens=__snake_case      , return_tensors=__snake_case      , **__snake_case   )
         snake_case					=    self.convert_tokens_to_ids(__snake_case   )
         snake_case					=    tgt_lang_id
         return inputs
    def 		a_      (	self      , __snake_case      , __snake_case = "eng_Latn"      , __snake_case = None      , __snake_case = "fra_Latn"      , **__snake_case      , ):
         snake_case					=    src_lang
         snake_case					=    tgt_lang
         return super().prepare_seqaseq_batch(__snake_case      , __snake_case      , **__snake_case   )
    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      , __snake_case   ):
         snake_case					=    self.convert_tokens_to_ids(__snake_case   )

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

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

         snake_case					=    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      , __snake_case   ):
         snake_case					=    self.convert_tokens_to_ids(__snake_case   )
         if self.legacy_behaviour:
              snake_case					=    []
              snake_case					=    [self.eos_token_id, self.cur_lang_code]
         else:
              snake_case					=    [self.cur_lang_code]
              snake_case					=    [self.eos_token_id]

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

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

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

         return (out_vocab_file,)








from __future__ import annotations

import time

_SCREAMING_SNAKE_CASE      :			List[Any]      				=   list[tuple[int, int]]

_SCREAMING_SNAKE_CASE      :			Any      				=   [
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

_SCREAMING_SNAKE_CASE      :			Any      				=   [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

class   A__ :







    """simple docstring"""

    def __init__(	self      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case   ):
         snake_case					=    pos_x
         snake_case					=    pos_y
         snake_case					=    (pos_y, pos_x)
         snake_case					=    goal_x
         snake_case					=    goal_y
         snake_case					=    parent






class   A__ :







    """simple docstring"""

    def __init__(	self      , __snake_case      , __snake_case   ):
         snake_case					=    Node(start[1]      , start[0]      , goal[1]      , goal[0]      , __snake_case   )
         snake_case					=    Node(goal[1]      , goal[0]      , goal[1]      , goal[0]      , __snake_case   )

         snake_case					=    [self.start]
         snake_case					=    False
    def 		a_      (	self   ):
         while self.node_queue:
              snake_case					=    self.node_queue.pop(0   )

              if current_node.pos == self.target.pos:
                   snake_case					=    True
                   return self.retrace_path(__snake_case   )

              snake_case					=    self.get_successors(__snake_case   )

              for node in successors:
                   self.node_queue.append(__snake_case   )

         if not self.reached:
              return [self.start.pos]
         return None
    def 		a_      (	self      , __snake_case   ):
         snake_case					=    []
         for action in delta:
              snake_case					=    parent.pos_x + action[1]
              snake_case					=    parent.pos_y + action[0]
              if not (0 <= pos_x <= len(grid[0]   ) - 1 and 0 <= pos_y <= len(__snake_case   ) - 1):
                   continue

              if grid[pos_y][pos_x] != 0:
                   continue

              successors.append(
                  Node(__snake_case      , __snake_case      , self.target.pos_y      , self.target.pos_x      , __snake_case   )   )
         return successors




    def 		a_      (	self      , __snake_case   ):
         snake_case					=    node
         snake_case					=    []
         while current_node is not None:
              path.append((current_node.pos_y, current_node.pos_x)   )
              snake_case					=    current_node.parent
         path.reverse()
         return path






class   A__ :







    """simple docstring"""

    def __init__(	self      , __snake_case      , __snake_case   ):
         snake_case					=    BreadthFirstSearch(__snake_case      , __snake_case   )
         snake_case					=    BreadthFirstSearch(__snake_case      , __snake_case   )
         snake_case					=    False
    def 		a_      (	self   ):
         while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
              snake_case					=    self.fwd_bfs.node_queue.pop(0   )
              snake_case					=    self.bwd_bfs.node_queue.pop(0   )

              if current_bwd_node.pos == current_fwd_node.pos:
                   snake_case					=    True
                   return self.retrace_bidirectional_path(
                       __snake_case      , __snake_case   )

              snake_case					=    current_bwd_node
              snake_case					=    current_fwd_node

              snake_case					=    {
                  self.fwd_bfs: self.fwd_bfs.get_successors(__snake_case   ),
                  self.bwd_bfs: self.bwd_bfs.get_successors(__snake_case   ),
              }

              for bfs in [self.fwd_bfs, self.bwd_bfs]:
                   for node in successors[bfs]:
                        bfs.node_queue.append(__snake_case   )

         if not self.reached:
              return [self.fwd_bfs.start.pos]
         return None




    def 		a_      (	self      , __snake_case      , __snake_case   ):
         snake_case					=    self.fwd_bfs.retrace_path(__snake_case   )
         snake_case					=    self.bwd_bfs.retrace_path(__snake_case   )
         bwd_path.pop()
         bwd_path.reverse()
         snake_case					=    fwd_path + bwd_path
         return path


if __name__ == "__main__":
 # all coordinates are given in format [y,x]
 import doctest

 doctest.testmod()
 _SCREAMING_SNAKE_CASE      :			Optional[Any]      				=   (0, 0)
 _SCREAMING_SNAKE_CASE      :			List[Any]      				=   (len(grid) - 1, len(grid[0]) - 1)
 for elem in grid:
  print(elem)

 _SCREAMING_SNAKE_CASE      :			List[Any]      				=   time.time()
 _SCREAMING_SNAKE_CASE      :			List[Any]      				=   BreadthFirstSearch(init, goal)
 _SCREAMING_SNAKE_CASE      :			List[str]      				=   bfs.search()
 _SCREAMING_SNAKE_CASE      :			int      				=   time.time() - start_bfs_time

 print("Unidirectional BFS computation time : ", bfs_time)

 _SCREAMING_SNAKE_CASE      :			Any      				=   time.time()
 _SCREAMING_SNAKE_CASE      :			Union[str, Any]      				=   BidirectionalBreadthFirstSearch(init, goal)
 _SCREAMING_SNAKE_CASE      :			Union[str, Any]      				=   bd_bfs.search()
 _SCREAMING_SNAKE_CASE      :			Tuple      				=   time.time() - start_bd_bfs_time

 print("Bidirectional BFS computation time : ", bd_bfs_time)




