






"""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, PreTrainedTokenizer
from ...utils import logging


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

__A		:       Tuple  				=		{'''vocab_file''': '''sentencepiece.bpe.model'''}

__A		:       Optional[Any]  				=		{
    '''vocab_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
    }
}

__A		:       str  				=		{
    '''camembert-base''': 512,
}

__A		:       Any  				=		'''▁'''





class 					_UpperCAmelCase       ( _A							):
					SCREAMING_SNAKE_CASE_		:				Any							    =   VOCAB_FILES_NAMES
					SCREAMING_SNAKE_CASE_		:				str							    =   PRETRAINED_VOCAB_FILES_MAP
					SCREAMING_SNAKE_CASE_		:				Optional[Any]							    =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					SCREAMING_SNAKE_CASE_		:				Tuple							    =   ["input_ids", "attention_mask"]




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

							lowercase_ : str							       =   {} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    bos_token=A    ,						eos_token=A    ,						unk_token=A    ,						sep_token=A    ,						cls_token=A    ,						pad_token=A    ,						mask_token=A    ,						additional_special_tokens=A    ,						sp_model_kwargs=self.sp_model_kwargs    ,						**A    ,						)
							lowercase_ : Tuple							       =   spm.SentencePieceProcessor(**self.sp_model_kwargs   )
							self.sp_model.Load(str(A   )   )
							lowercase_ : Union[str, Any]							       =   vocab_file
							# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
							# sentencepiece vocabulary (this is the case for <s> and </s>
							lowercase_ : List[Any]							       =   {'''<s>NOTUSED''': 0, '''<pad>''': 1, '''</s>NOTUSED''': 2, '''<unk>''': 3}
							lowercase_ : Dict							       =   len(self.fairseq_tokens_to_ids   )
							lowercase_ : List[Any]							       =   len(self.sp_model   ) + len(self.fairseq_tokens_to_ids   )
							lowercase_ : int							       =   {v: k for k, v in self.fairseq_tokens_to_ids.items()}




					def   A  (				self     :						Any    ,						A     :						List[int]    ,						A     :						Optional[List[int]] = None   )  ->  List[int]:

							if token_ids_a is None:
									return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							lowercase_ : List[str]							       =   [self.cls_token_id]
							lowercase_ : Union[str, Any]							       =   [self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep




					def   A  (				self     :						List[Any]    ,						A     :						List[int]    ,						A     :						Optional[List[int]] = None    ,						A     :						bool = 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   )

							if token_ids_a is None:
									return [1] + ([0] * len(A   )) + [1]
							return [1] + ([0] * len(A   )) + [1, 1] + ([0] * len(A   )) + [1]




					def   A  (				self     :						Union[str, Any]    ,						A     :						List[int]    ,						A     :						Optional[List[int]] = None   )  ->  List[int]:
							lowercase_ : List[Any]							       =   [self.sep_token_id]
							lowercase_ : int							       =   [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]




					@property
					def   A  (				self     :						List[str]   )  ->  Union[str, Any]:
							return len(self.fairseq_tokens_to_ids   ) + len(self.sp_model   )




					def   A  (				self     :						Any   )  ->  Optional[Any]:
							lowercase_ : List[Any]							       =   {self.convert_ids_to_tokens(A   ): i for i in range(self.vocab_size   )}
							vocab.update(self.added_tokens_encoder   )
							return vocab




					def   A  (				self     :						Optional[int]    ,						A     :						str   )  ->  List[str]:
							return self.sp_model.encode(A    ,						out_type=A   )




					def   A  (				self     :						Union[str, Any]    ,						A     :						str   )  ->  Optional[Any]:
							if token in self.fairseq_tokens_to_ids:
									return self.fairseq_tokens_to_ids[token]
							elif self.sp_model.PieceToId(A   ) == 0:
									# Convert sentence piece unk token to fairseq unk token index
									return self.unk_token_id
							return self.fairseq_offset + self.sp_model.PieceToId(A   )




					def   A  (				self     :						int    ,						A     :						List[str]   )  ->  Dict:
							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   A  (				self     :						Tuple    ,						A     :						Tuple   )  ->  Optional[Any]:
							lowercase_ : Tuple							       =   []
							lowercase_ : int							       =   ''''''
							lowercase_ : int							       =   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_ : Dict							       =   True
											lowercase_ : Any							       =   []
									else:
											current_sub_tokens.append(A   )
											lowercase_ : int							       =   False
							out_string += self.sp_model.decode(A   )
							return out_string.strip()




					def __getstate__(				self     :						Dict   )  ->  Union[str, Any]:
							lowercase_ : Optional[int]							       =   self.__dict__.copy()
							lowercase_ : Tuple							       =   None
							return state




					def __setstate__(				self     :						Optional[int]    ,						A     :						Optional[Any]   )  ->  Union[str, Any]:
							lowercase_ : Any							       =   d

							# for backward compatibility
							if not hasattr(self    ,						'''sp_model_kwargs'''   ):
									lowercase_ : List[str]							       =   {}

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






					def   A  (				self     :						List[Any]    ,						A     :						str    ,						A     :						Optional[str] = None   )  ->  Tuple[str]:
							if not os.path.isdir(A   ):
									logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''   )
									return
							lowercase_ : Union[str, Any]							       =   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_ : Union[str, Any]							       =   self.sp_model.serialized_model_proto()
											fi.write(A   )

							return (out_vocab_file,)











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.")


