import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
   import torch







class __snake_case    (						lowerCamelCase_		):
   lowerCAmelCase_														=							"char"
   lowerCAmelCase_														=							"bpe"
   lowerCAmelCase_														=							"wp"





__lowerCamelCase      :			List[Any]							       =		(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)







class __snake_case    (						lowerCamelCase_		):
   lowerCAmelCase_														=							["image_processor", "char_tokenizer"]
   lowerCAmelCase_														=							"ViTImageProcessor"
   lowerCAmelCase_														=							"MgpstrTokenizer"




   def __init__(   self      :    List[Any]	,     _lowercase      :    List[Any]=None	,     _lowercase      :    Union[str, Any]=None	,     **_lowercase      :    Optional[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				None
     if "feature_extractor" in kwargs:
       warnings.warn(
           """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
           """ instead."""	,     _lowercase	,     )
       SCREAMING_SNAKE_CASE__      							=				kwargs.pop("""feature_extractor"""				)

     SCREAMING_SNAKE_CASE__      							=				image_processor if image_processor is not None else feature_extractor
     if image_processor is None:
       raise ValueError("""You need to specify an `image_processor`."""				)
     if tokenizer is None:
       raise ValueError("""You need to specify a `tokenizer`."""				)

     SCREAMING_SNAKE_CASE__      							=				tokenizer
     SCREAMING_SNAKE_CASE__      							=				AutoTokenizer.from_pretrained("""gpt2"""				)
     SCREAMING_SNAKE_CASE__      							=				AutoTokenizer.from_pretrained("""bert-base-uncased"""				)

     super().__init__(_lowercase	,     _lowercase				)




   def __call__(   self      :    Tuple	,     _lowercase      :    Tuple=None	,     _lowercase      :    List[Any]=None	,     _lowercase      :    Union[str, Any]=None	,     **_lowercase      :    List[str]				):



     """simple docstring"""






     if images is None and text is None:
       raise ValueError("""You need to specify either an `images` or `text` input to process."""				)

     if images is not None:
       SCREAMING_SNAKE_CASE__      							=				self.image_processor(_lowercase	,     return_tensors=_lowercase	,     **_lowercase				)
     if text is not None:
       SCREAMING_SNAKE_CASE__      							=				self.char_tokenizer(_lowercase	,     return_tensors=_lowercase	,     **_lowercase				)

     if text is None:
       return inputs
     elif images is None:
       return encodings
     else:
       SCREAMING_SNAKE_CASE__      							=				encodings["""input_ids"""]
       return inputs




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



     """simple docstring"""






     SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				sequences
     SCREAMING_SNAKE_CASE__      							=				char_preds.size(0				)

     SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				self._decode_helper(_lowercase	,     """char"""				)
     SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				self._decode_helper(_lowercase	,     """bpe"""				)
     SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				self._decode_helper(_lowercase	,     """wp"""				)

     SCREAMING_SNAKE_CASE__      							=				[]
     SCREAMING_SNAKE_CASE__      							=				[]
     for i in range(_lowercase				):
       SCREAMING_SNAKE_CASE__      							=				[char_scores[i], bpe_scores[i], wp_scores[i]]
       SCREAMING_SNAKE_CASE__      							=				[char_strs[i], bpe_strs[i], wp_strs[i]]
       SCREAMING_SNAKE_CASE__      							=				scores.index(max(_lowercase				)				)
       final_strs.append(strs[max_score_index]				)
       final_scores.append(scores[max_score_index]				)

     SCREAMING_SNAKE_CASE__      							=				{}
     SCREAMING_SNAKE_CASE__      							=				final_strs
     SCREAMING_SNAKE_CASE__      							=				final_scores
     SCREAMING_SNAKE_CASE__      							=				char_strs
     SCREAMING_SNAKE_CASE__      							=				bpe_strs
     SCREAMING_SNAKE_CASE__      							=				wp_strs
     return out




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



     """simple docstring"""






     if format == DecodeType.CHARACTER:
       SCREAMING_SNAKE_CASE__      							=				self.char_decode
       SCREAMING_SNAKE_CASE__      							=				1
       SCREAMING_SNAKE_CASE__      							=				"""[s]"""
     elif format == DecodeType.BPE:
       SCREAMING_SNAKE_CASE__      							=				self.bpe_decode
       SCREAMING_SNAKE_CASE__      							=				2
       SCREAMING_SNAKE_CASE__      							=				"""#"""
     elif format == DecodeType.WORDPIECE:
       SCREAMING_SNAKE_CASE__      							=				self.wp_decode
       SCREAMING_SNAKE_CASE__      							=				1_02
       SCREAMING_SNAKE_CASE__      							=				"""[SEP]"""
     else:
       raise ValueError(f"""Format {format} is not supported."""				)

     SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				[], []
     SCREAMING_SNAKE_CASE__      							=				pred_logits.size(0				)
     SCREAMING_SNAKE_CASE__      							=				pred_logits.size(1				)
     SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				pred_logits.topk(1	,     dim=-1	,     largest=_lowercase	,     sorted=_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				preds_index.view(-1	,     _lowercase				)[:, 1:]
     SCREAMING_SNAKE_CASE__      							=				decoder(_lowercase				)
     SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				torch.nn.functional.softmax(_lowercase	,     dim=2				).max(dim=2				)
     SCREAMING_SNAKE_CASE__      							=				preds_max_prob[:, 1:]

     for index in range(_lowercase				):
       SCREAMING_SNAKE_CASE__      							=				preds_str[index].find(_lowercase				)
       SCREAMING_SNAKE_CASE__      							=				preds_str[index][:pred_eos]
       SCREAMING_SNAKE_CASE__      							=				preds_index[index].cpu().tolist()
       SCREAMING_SNAKE_CASE__      							=				pred_index.index(_lowercase				) if eos_token in pred_index else -1
       SCREAMING_SNAKE_CASE__      							=				preds_max_prob[index][: pred_eos_index + 1]
       SCREAMING_SNAKE_CASE__      							=				pred_max_prob.cumprod(dim=0				)[-1] if pred_max_prob.nelement() != 0 else 0.0
       dec_strs.append(_lowercase				)
       conf_scores.append(_lowercase				)

     return dec_strs, conf_scores




   def      __a						(   self      :    Any	,     _lowercase      :    Any				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				[seq.replace(""" """	,     """"""				) for seq in self.char_tokenizer.batch_decode(_lowercase				)]
     return decode_strs




   def      __a						(   self      :    Dict	,     _lowercase      :    int				):



     """simple docstring"""






     return self.bpe_tokenizer.batch_decode(_lowercase				)




   def      __a						(   self      :    Union[str, Any]	,     _lowercase      :    Optional[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				[seq.replace(""" """	,     """"""				) for seq in self.wp_tokenizer.batch_decode(_lowercase				)]
     return decode_strs

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.''')
