import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


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


__lowerCamelCase      :			Tuple							       =		{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__lowerCamelCase      :			Optional[Any]							       =		{
    '''vocab_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json''',
    },
}

__lowerCamelCase      :			str							       =		{
    '''allenai/led-base-16384''': 1_6384,
}







class __snake_case    (						lowerCamelCase_		):
   lowerCAmelCase_														=							VOCAB_FILES_NAMES
   lowerCAmelCase_														=							PRETRAINED_VOCAB_FILES_MAP
   lowerCAmelCase_														=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCAmelCase_														=							LEDTokenizer
   lowerCAmelCase_														=							["input_ids", "attention_mask"]




   def __init__(   self      :    str	,     _lowercase      :    Union[str, Any]=None	,     _lowercase      :    Tuple=None	,     _lowercase      :    Dict=None	,     _lowercase      :    Union[str, Any]="replace"	,     _lowercase      :    Optional[Any]="<s>"	,     _lowercase      :    Optional[int]="</s>"	,     _lowercase      :    int="</s>"	,     _lowercase      :    List[str]="<s>"	,     _lowercase      :    Tuple="<unk>"	,     _lowercase      :    Optional[Any]="<pad>"	,     _lowercase      :    List[str]="<mask>"	,     _lowercase      :    List[str]=False	,     _lowercase      :    Optional[Any]=True	,     **_lowercase      :    List[str]	,     ):



     """simple docstring"""






     super().__init__(
         _lowercase	,     _lowercase	,     tokenizer_file=_lowercase	,     errors=_lowercase	,     bos_token=_lowercase	,     eos_token=_lowercase	,     sep_token=_lowercase	,     cls_token=_lowercase	,     unk_token=_lowercase	,     pad_token=_lowercase	,     mask_token=_lowercase	,     add_prefix_space=_lowercase	,     trim_offsets=_lowercase	,     **_lowercase	,     )

     SCREAMING_SNAKE_CASE__      							=				json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()				)
     if pre_tok_state.get("""add_prefix_space"""	,     _lowercase				) != add_prefix_space:
       SCREAMING_SNAKE_CASE__      							=				getattr(_lowercase	,     pre_tok_state.pop("""type"""				)				)
       SCREAMING_SNAKE_CASE__      							=				add_prefix_space
       SCREAMING_SNAKE_CASE__      							=				pre_tok_class(**_lowercase				)

     SCREAMING_SNAKE_CASE__      							=				add_prefix_space

     # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
     SCREAMING_SNAKE_CASE__      							=				"""post_processor"""
     SCREAMING_SNAKE_CASE__      							=				getattr(self.backend_tokenizer	,     _lowercase	,     _lowercase				)
     if tokenizer_component_instance:
       SCREAMING_SNAKE_CASE__      							=				json.loads(tokenizer_component_instance.__getstate__()				)

       # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
       if "sep" in state:
         SCREAMING_SNAKE_CASE__      							=				tuple(state["""sep"""]				)
       if "cls" in state:
         SCREAMING_SNAKE_CASE__      							=				tuple(state["""cls"""]				)

       SCREAMING_SNAKE_CASE__      							=				False

       if state.get("""add_prefix_space"""	,     _lowercase				) != add_prefix_space:
         SCREAMING_SNAKE_CASE__      							=				add_prefix_space
         SCREAMING_SNAKE_CASE__      							=				True

       if state.get("""trim_offsets"""	,     _lowercase				) != trim_offsets:
         SCREAMING_SNAKE_CASE__      							=				trim_offsets
         SCREAMING_SNAKE_CASE__      							=				True

       if changes_to_apply:
         SCREAMING_SNAKE_CASE__      							=				getattr(_lowercase	,     state.pop("""type"""				)				)
         SCREAMING_SNAKE_CASE__      							=				component_class(**_lowercase				)
         setattr(self.backend_tokenizer	,     _lowercase	,     _lowercase				)




   @property
   # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
   def      __a						(   self      :    Union[str, Any]				):



     """simple docstring"""






     if self._mask_token is None:
       if self.verbose:
         logger.error("""Using mask_token, but it is not set yet."""				)
       return None
     return str(self._mask_token				)




   @mask_token.setter
   def      __a						(   self      :    Union[str, Any]	,     _lowercase      :    Dict				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				AddedToken(_lowercase	,     lstrip=_lowercase	,     rstrip=_lowercase				) if isinstance(_lowercase	,     _lowercase				) else value
     SCREAMING_SNAKE_CASE__      							=				value




   def      __a						(   self      :    Dict	,     *_lowercase      :    str	,     **_lowercase      :    Tuple				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				kwargs.get("""is_split_into_words"""	,     _lowercase				)

     if is_split_into_words and not self.add_prefix_space:
       raise ValueError(
           f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
           """to use it with pretokenized inputs."""				)

     return super()._batch_encode_plus(*_lowercase	,     **_lowercase				)




   def      __a						(   self      :    Optional[int]	,     *_lowercase      :    Any	,     **_lowercase      :    List[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				kwargs.get("""is_split_into_words"""	,     _lowercase				)

     if is_split_into_words and not self.add_prefix_space:
       raise ValueError(
           f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
           """to use it with pretokenized inputs."""				)

     return super()._encode_plus(*_lowercase	,     **_lowercase				)




   def      __a						(   self      :    str	,     _lowercase      :    str	,     _lowercase      :    Optional[str] = None				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self._tokenizer.model.save(_lowercase	,     name=_lowercase				)
     return tuple(_lowercase				)




   def      __a						(   self      :    int	,     _lowercase      :    List[Any]	,     _lowercase      :    Dict=None				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				[self.bos_token_id] + token_ids_a + [self.eos_token_id]
     if token_ids_a is None:
       return output

     return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]




   def      __a						(   self      :    Any	,     _lowercase      :    List[int]	,     _lowercase      :    Optional[List[int]] = None				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				[self.sep_token_id]
     SCREAMING_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      :    str	,     _lowercase      :    Union[Dict[str, EncodedInput], BatchEncoding]	,     _lowercase      :    Optional[int] = None	,     _lowercase      :    PaddingStrategy = PaddingStrategy.DO_NOT_PAD	,     _lowercase      :    Optional[int] = None	,     _lowercase      :    Optional[bool] = None	,     ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				super()._pad(
         encoded_inputs=_lowercase	,     max_length=_lowercase	,     padding_strategy=_lowercase	,     pad_to_multiple_of=_lowercase	,     return_attention_mask=_lowercase	,     )

     # Load from model defaults
     if return_attention_mask is None:
       SCREAMING_SNAKE_CASE__      							=				"""attention_mask""" in self.model_input_names

     if return_attention_mask and "global_attention_mask" in encoded_inputs:
       SCREAMING_SNAKE_CASE__      							=				encoded_inputs[self.model_input_names[0]]
       # `global_attention_mask` need to have the same length as other (sequential) inputs.
       SCREAMING_SNAKE_CASE__      							=				len(encoded_inputs["""global_attention_mask"""]				) != len(_lowercase				)

       if needs_to_be_padded:
         SCREAMING_SNAKE_CASE__      							=				len(_lowercase				) - len(encoded_inputs["""global_attention_mask"""]				)

         if self.padding_side == "right":
           # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
           SCREAMING_SNAKE_CASE__      							=				(
               encoded_inputs["""global_attention_mask"""] + [-1] * difference
           )
         elif self.padding_side == "left":
           SCREAMING_SNAKE_CASE__      							=				[-1] * difference + encoded_inputs[
               """global_attention_mask"""
           ]
         else:
           raise ValueError("""Invalid padding strategy:""" + str(self.padding_side				)				)

     return encoded_inputs

from __future__ import annotations

__lowerCamelCase      :			Tuple							       =		list[list[int]]

# assigning initial values to the grid
__lowerCamelCase      :			Matrix							       =		[
    [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
__lowerCamelCase      :			Matrix							       =		[
    [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      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Matrix			,					__UpperCamelCase   :	int			,					__UpperCamelCase   :	int			,					__UpperCamelCase   :	int     )							-> bool:







  """simple docstring"""





  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      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Matrix     )							-> tuple[int, int] | None:







  """simple docstring"""





  for i in range(9     ):
    for j in range(9     ):
      if grid[i][j] == 0:
        return i, j
  return None



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Matrix     )							-> Matrix | None:







  """simple docstring"""





  if location := find_empty_location(__UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				location
  else:
    # If the location is ``None``, then the grid is solved.
    return grid

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

      if sudoku(__UpperCamelCase     ) is not None:
        return grid

      SCREAMING_SNAKE_CASE__      							=				0

  return None



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Matrix     )							-> None:







  """simple docstring"""





  for row in grid:
    for cell in row:
      print(__UpperCamelCase			,					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:''')
      __lowerCamelCase      :			str							       =		sudoku(example_grid)
      if solution is not None:
         print_solution(solution)
      else:
         print('''Cannot find a solution.''')
