"""simple docstring"""





import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


_UpperCamelCase   :						List[str]      =    logging.get_logger(__name__)

_UpperCamelCase   :						Dict      =    "▁"

_UpperCamelCase   :						Any      =    {"vocab_file": "sentencepiece.bpe.model"}

_UpperCamelCase   :						List[str]      =    {
    "vocab_file": {
        "facebook/mbart-large-50-one-to-many-mmt": (
            "https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model"
        ),
    }
}

_UpperCamelCase   :						str      =    {
    "facebook/mbart-large-50-one-to-many-mmt": 10_24,
}

# fmt: off
_UpperCamelCase   :						Optional[Any]      =    ["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN", "af_ZA", "az_AZ", "bn_IN", "fa_IR", "he_IL", "hr_HR", "id_ID", "ka_GE", "km_KH", "mk_MK", "ml_IN", "mn_MN", "mr_IN", "pl_PL", "ps_AF", "pt_XX", "sv_SE", "sw_KE", "ta_IN", "te_IN", "th_TH", "tl_XX", "uk_UA", "ur_PK", "xh_ZA", "gl_ES", "sl_SI"]


class      UpperCAmelCase_	(    _a):
      lowerCamelCase__		:    Tuple   =  VOCAB_FILES_NAMES
      lowerCamelCase__		:    List[Any]   =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowerCamelCase__		:    Any   =  PRETRAINED_VOCAB_FILES_MAP
      lowerCamelCase__		:    Optional[Any]   =  ["input_ids", "attention_mask"]

      lowerCamelCase__		:    List[int]   =  []
      lowerCamelCase__		:    List[int]   =  []


      def __init__(   self		,			a		,			a=None		,			a=None		,			a="</s>"		,			a="</s>"		,			a="<s>"		,			a="<unk>"		,			a="<pad>"		,			a="<mask>"		,			a = None		,			**a		,			)       ->					None:
             # Mask token behave like a normal word, i.e. include the space before it
             lowercase__			:		Optional[int]       =	AddedToken(a		,			lstrip=a		,			rstrip=a							) if isinstance(a		,			a							) else mask_token

             lowercase__			:		Optional[Any]       =	{} if sp_model_kwargs is None else sp_model_kwargs

             lowercase__			:		Any       =	kwargs.get('additional_special_tokens'		,			[]							)
             kwargs["additional_special_tokens"] += [
                 code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
             ]

             super().__init__(
                 src_lang=a		,			tgt_lang=a		,			eos_token=a		,			unk_token=a		,			sep_token=a		,			cls_token=a		,			pad_token=a		,			mask_token=a		,			sp_model_kwargs=self.sp_model_kwargs		,			**a		,			)

             lowercase__			:		Any       =	spm.SentencePieceProcessor(**self.sp_model_kwargs							)
             self.sp_model.Load(str(a							)							)
             lowercase__			:		Optional[Any]       =	vocab_file

             # Original fairseq vocab and spm vocab must be "aligned":
             # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
             # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
             # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
             # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

             # Mimic fairseq token-to-id alignment for the first 4 token
             lowercase__			:		List[str]       =	{'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

             # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
             lowercase__			:		Optional[int]       =	1

             lowercase__			:		str       =	len(self.sp_model							)
             lowercase__			:		int       =	{
                 code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(a							)
             }
             lowercase__			:		List[str]       =	{v: k for k, v in self.lang_code_to_id.items()}
             lowercase__			:		Dict       =	len(self.sp_model							) + len(self.lang_code_to_id							) + self.fairseq_offset

             self.fairseq_tokens_to_ids.update(self.lang_code_to_id							)
             lowercase__			:		Optional[Any]       =	{v: k for k, v in self.fairseq_tokens_to_ids.items()}

             lowercase__			:		List[Any]       =	src_lang if src_lang is not None else 'en_XX'
             lowercase__			:		Optional[int]       =	self.lang_code_to_id[self._src_lang]
             lowercase__			:		List[str]       =	tgt_lang
             self.set_src_lang_special_tokens(self._src_lang							)


      @property
      def 		_UpperCAmelCase       (   self							)       ->					int:
             return len(self.sp_model							) + len(self.lang_code_to_id							) + self.fairseq_offset + 1  # Plus 1 for the mask token


      @property
      def 		_UpperCAmelCase       (   self							)       ->					str:
             return self._src_lang


      @src_lang.setter
      def 		_UpperCAmelCase       (   self		,			a							)       ->					None:
             lowercase__			:		List[str]       =	new_src_lang
             self.set_src_lang_special_tokens(self._src_lang							)


      def __getstate__(   self							)       ->					Dict:
             lowercase__			:		int       =	self.__dict__.copy()
             lowercase__			:		Any       =	None
             return state


      def __setstate__(   self		,			a							)       ->					None:
             lowercase__			:		Any       =	d

             # for backward compatibility
             if not hasattr(self		,			'sp_model_kwargs'							):
                    lowercase__			:		Tuple       =	{}

             lowercase__			:		Tuple       =	spm.SentencePieceProcessor(**self.sp_model_kwargs							)
             self.sp_model.Load(self.vocab_file							)


      def 		_UpperCAmelCase       (   self							)       ->					Dict:
             lowercase__			:		Dict       =	{self.convert_ids_to_tokens(a							): i for i in range(self.vocab_size							)}
             vocab.update(self.added_tokens_encoder							)
             return vocab


      def 		_UpperCAmelCase       (   self		,			a							)       ->					List[str]:
             return self.sp_model.encode(a		,			out_type=a							)


      def 		_UpperCAmelCase       (   self		,			a							)       ->					int:
             if token in self.fairseq_tokens_to_ids:
                    return self.fairseq_tokens_to_ids[token]
             lowercase__			:		Optional[int]       =	self.sp_model.PieceToId(a							)

             # Need to return unknown token if the SP model returned 0
             return spm_id + self.fairseq_offset if spm_id else self.unk_token_id


      def 		_UpperCAmelCase       (   self		,			a							)       ->					str:
             if index in self.fairseq_ids_to_tokens:
                    return self.fairseq_ids_to_tokens[index]
             return self.sp_model.IdToPiece(index - self.fairseq_offset							)


      def 		_UpperCAmelCase       (   self		,			a							)       ->					Dict:
             lowercase__			:		str       =	[]
             lowercase__			:		Optional[int]       =	''
             lowercase__			:		str       =	False
             for token in tokens:
                    # make sure that special tokens are not decoded using sentencepiece model
                    if token in self.all_special_tokens:
                           if not prev_is_special:
                                  out_string += " "
                           out_string += self.sp_model.decode(a							) + token
                           lowercase__			:		List[Any]       =	True
                           lowercase__			:		Any       =	[]
                    else:
                           current_sub_tokens.append(a							)
                           lowercase__			:		Any       =	False
             out_string += self.sp_model.decode(a							)
             return out_string.strip()


      def 		_UpperCAmelCase       (   self		,			a		,			a = None							)       ->					Tuple[str]:
             if not os.path.isdir(a							):
                    logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
                    return
             lowercase__			:		Dict       =	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							) and os.path.isfile(self.vocab_file							):
                    copyfile(self.vocab_file		,			a							)
             elif not os.path.isfile(self.vocab_file							):
                    with open(a		,			'wb'							) as fi:
                           lowercase__			:		int       =	self.sp_model.serialized_model_proto()
                           fi.write(a							)

             return (out_vocab_file,)


      def 		_UpperCAmelCase       (   self		,			a		,			a = None		,			a = False							)       ->					List[int]:

             if already_has_special_tokens:
                    return super().get_special_tokens_mask(
                        token_ids_a=a		,			token_ids_a=a		,			already_has_special_tokens=a							)

             lowercase__			:		Dict       =	[1] * len(self.prefix_tokens							)
             lowercase__			:		Tuple       =	[1] * len(self.suffix_tokens							)
             if token_ids_a is None:
                    return prefix_ones + ([0] * len(a							)) + suffix_ones
             return prefix_ones + ([0] * len(a							)) + ([0] * len(a							)) + suffix_ones


      def 		_UpperCAmelCase       (   self		,			a		,			a = None							)       ->					List[int]:
             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		,			a		,			a		,			**a							)       ->					Optional[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'							)
             lowercase__			:		Dict       =	src_lang
             lowercase__			:		Optional[Any]       =	self(a		,			add_special_tokens=a		,			return_tensors=a		,			**a							)
             lowercase__			:		Dict       =	self.convert_tokens_to_ids(a							)
             lowercase__			:		Dict       =	tgt_lang_id
             return inputs


      def 		_UpperCAmelCase       (   self		,			a		,			a = "en_XX"		,			a = None		,			a = "ro_RO"		,			**a		,			)       ->					BatchEncoding:
             lowercase__			:		Optional[Any]       =	src_lang
             lowercase__			:		Any       =	tgt_lang
             return super().prepare_seqaseq_batch(a		,			a		,			**a							)


      def 		_UpperCAmelCase       (   self							)       ->					int:
             return self.set_src_lang_special_tokens(self.src_lang							)


      def 		_UpperCAmelCase       (   self							)       ->					Tuple:
             return self.set_tgt_lang_special_tokens(self.tgt_lang							)


      def 		_UpperCAmelCase       (   self		,			a							)       ->					None:
             lowercase__			:		List[str]       =	self.lang_code_to_id[src_lang]
             lowercase__			:		Optional[Any]       =	[self.cur_lang_code_id]
             lowercase__			:		List[str]       =	[self.eos_token_id]


      def 		_UpperCAmelCase       (   self		,			a							)       ->					None:
             lowercase__			:		str       =	self.lang_code_to_id[tgt_lang]
             lowercase__			:		List[str]       =	[self.cur_lang_code_id]
             lowercase__			:		List[Any]       =	[self.eos_token_id]









from __future__ import annotations

UpperCAmelCase__				   =		list[list[int]]

# assigning initial values to the grid
UpperCAmelCase__				   =		[
    [3, 0, 6, 5, 0, 8, 4, 0, 0],
    [5, 2, 0, 0, 0, 0, 0, 0, 0],
    [0, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]

# a grid with no solution
UpperCAmelCase__				   =		[
    [5, 0, 6, 5, 0, 8, 4, 0, 3],
    [5, 2, 0, 0, 0, 0, 0, 0, 2],
    [1, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]
def 						_a    (      a :Matrix	,							a :int	,							a :int	,							a :int )							->			bool:
 for i in range(9 ):
  if grid[row][i] == n or grid[i][column] == n:
   return False

 for i in range(3 ):
  for j in range(3 ):
   if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
    return False

 return True
def 						_a    (      a :Matrix )							->			tuple[int, int] | None:
 for i in range(9 ):
  for j in range(9 ):
   if grid[i][j] == 0:
    return i, j
 return None
def 						_a    (      a :Matrix )							->			Matrix | None:
 if location := find_empty_location(a ):
  a  ,     a		  =      location
 else:
  # If the location is ``None``, then the grid is solved.
  return grid

 for digit in range(1	,							10 ):
  if is_safe(a	,							a	,							a	,							a ):
   a		  =      digit

   if sudoku(a ) is not None:
    return grid

   a		  =      0

 return None
def 						_a    (      a :Matrix )							->			None:
 for row in grid:
  for cell in row:
   print(a	,							end=''' ''' )
  print()


if __name__ == "__main__":
     # make a copy of grid so that you can compare with the unmodified grid
     for example_grid in (initial_grid, no_solution):
          print("\nExample grid:\n" + "=" * 20)
          print_solution(example_grid)
          print("\nExample grid solution:")
          UpperCAmelCase__				   =		sudoku(example_grid)
          if solution is not None:
               print_solution(solution)
          else:
               print("Cannot find a solution.")


