






import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


lowerCamelCase__							=     get_tests_dir("""fixtures/test_sentencepiece.model""")
lowerCamelCase__							=     get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")

lowerCamelCase__							=     """pt""" if is_torch_available() else """tf"""




@require_sentencepiece
@require_tokenizers
class 			snake_case__   (							lowercase_	,    unittest.TestCase):

       '''simple docstring'''
       lowerCamelCase							:				Union[str, Any] 				= CamembertTokenizer
       lowerCamelCase							:				int 				= CamembertTokenizerFast
       lowerCamelCase							:				int 				= True
       lowerCamelCase							:				Any 				= True



       def 					__lowercase						(							self    ) ->      Optional[Any]:






            '''simple docstring'''





            super().setUp()

            # We have a SentencePiece fixture for testing
            __snake_case			:Optional[int]          =						CamembertTokenizer(a__    )
            tokenizer.save_pretrained(self.tmpdirname    )



       def 					__lowercase						(							self    ) ->      Union[str, Any]:






            '''simple docstring'''





            __snake_case			:Optional[Any]          =						"""<pad>"""
            __snake_case			:List[str]          =						1

            self.assertEqual(self.get_tokenizer()._convert_token_to_id(a__    )			,						a__    )
            self.assertEqual(self.get_tokenizer()._convert_id_to_token(a__    )			,						a__    )



       def 					__lowercase						(							self    ) ->      List[Any]:






            '''simple docstring'''





            __snake_case			:Dict          =						list(self.get_tokenizer().get_vocab().keys()    )

            self.assertEqual(vocab_keys[0]			,						"""<s>NOTUSED"""    )
            self.assertEqual(vocab_keys[1]			,						"""<pad>"""    )
            self.assertEqual(vocab_keys[-1]			,						"""<mask>"""    )
            self.assertEqual(len(a__    )			,						10_04    )



       def 					__lowercase						(							self    ) ->      Union[str, Any]:






            '''simple docstring'''





            self.assertEqual(self.get_tokenizer().vocab_size			,						10_05    )



       def 					__lowercase						(							self    ) ->      Dict:






            '''simple docstring'''





            __snake_case			:Any          =						CamembertTokenizer(a__    )
            tokenizer.save_pretrained(self.tmpdirname    )
            __snake_case			:str          =						CamembertTokenizerFast.from_pretrained(self.tmpdirname    )

            __snake_case			:Optional[Any]          =						"""I was born in 92000, and this is falsé."""

            __snake_case			:Dict          =						tokenizer.encode(a__    )
            __snake_case			:Optional[Any]          =						rust_tokenizer.encode(a__    )
            self.assertListEqual(a__			,						a__    )

            __snake_case			:Union[str, Any]          =						tokenizer.encode(a__			,						add_special_tokens=a__    )
            __snake_case			:List[str]          =						rust_tokenizer.encode(a__			,						add_special_tokens=a__    )
            self.assertListEqual(a__			,						a__    )

            # <unk> tokens are not the same for `rust` than for `slow`.
            # Because spm gives back raw token instead of `unk` in EncodeAsPieces
            # tokens = tokenizer.tokenize(sequence)
            __snake_case			:List[Any]          =						tokenizer.convert_ids_to_tokens(a__    )
            __snake_case			:int          =						rust_tokenizer.tokenize(a__    )
            self.assertListEqual(a__			,						a__    )



       def 					__lowercase						(							self    ) ->      Any:






            '''simple docstring'''





            if not self.test_rust_tokenizer:
                 return

            __snake_case			:Tuple          =						self.get_tokenizer()
            __snake_case			:str          =						self.get_rust_tokenizer()

            __snake_case			:Union[str, Any]          =						"""I was born in 92000, and this is falsé."""

            __snake_case			:Tuple          =						tokenizer.tokenize(a__    )
            __snake_case			:Dict          =						rust_tokenizer.tokenize(a__    )
            self.assertListEqual(a__			,						a__    )

            __snake_case			:int          =						tokenizer.encode(a__			,						add_special_tokens=a__    )
            __snake_case			:Optional[int]          =						rust_tokenizer.encode(a__			,						add_special_tokens=a__    )
            self.assertListEqual(a__			,						a__    )

            __snake_case			:List[Any]          =						self.get_rust_tokenizer()
            __snake_case			:Optional[int]          =						tokenizer.encode(a__    )
            __snake_case			:Union[str, Any]          =						rust_tokenizer.encode(a__    )
            self.assertListEqual(a__			,						a__    )



       @slow
       def 					__lowercase						(							self    ) ->      Optional[int]:






            '''simple docstring'''





            __snake_case			:str          =						{"""input_ids""": [[5, 54, 71_96, 2_97, 30, 23, 7_76, 18, 11, 32_15, 37_05, 82_52, 22, 31_64, 11_81, 21_16, 29, 16, 8_13, 25, 7_91, 33_14, 20, 34_46, 38, 2_75_75, 1_20, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_68, 17, 11, 90_88, 20, 15_17, 8, 2_28_04, 1_88_18, 10, 38, 6_29, 6_07, 6_07, 1_42, 19, 71_96, 8_67, 56, 1_03_26, 24, 22_67, 20, 4_16, 50_72, 1_56_12, 2_33, 7_34, 7, 23_99, 27, 16, 30_15, 16_49, 7, 24, 20, 43_38, 23_99, 27, 13, 34_00, 14, 13, 61_89, 8, 9_30, 9, 6]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
            # fmt: on

            # camembert is a french model. So we also use french texts.
            __snake_case			:Optional[int]          =						[
                """Le transformeur est un modèle d'apprentissage profond introduit en 2017, """
                """utilisé principalement dans le domaine du traitement automatique des langues (TAL).""",
                """À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus """
                """pour gérer des données séquentielles, telles que le langage naturel, pour des tâches """
                """telles que la traduction et la synthèse de texte.""",
            ]

            self.tokenizer_integration_test_util(
                expected_encoding=a__			,						model_name="""camembert-base"""			,						revision="""3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"""			,						sequences=a__			,						)










from __future__ import annotations

lowerCamelCase__							=     list[list[int]]

# assigning initial values to the grid
lowerCamelCase__							=     [
    [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__							=     [
    [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   UpperCamelCase  (       snake_case__     :   Matrix	,snake_case__     :   int	,snake_case__     :   int	,snake_case__     :   int  ):




     '''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   UpperCamelCase  (       snake_case__     :   Matrix  ):




     '''simple docstring'''






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






def   UpperCamelCase  (       snake_case__     :   Matrix  ):




     '''simple docstring'''






     if location := find_empty_location(snake_case__  ):
          __snake_case       , __snake_case			:Optional[int]          =						location
     else:
          # If the location is ``None``, then the grid is solved.
          return grid

     for digit in range(1	,10  ):
          if is_safe(snake_case__	,snake_case__	,snake_case__	,snake_case__  ):
               __snake_case			:Union[str, Any]          =						digit

               if sudoku(snake_case__  ) is not None:
                    return grid

               __snake_case			:Tuple          =						0

     return None






def   UpperCamelCase  (       snake_case__     :   Matrix  ):




     '''simple docstring'''






     for row in grid:
          for cell in row:
               print(snake_case__	,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__							=     sudoku(example_grid)
          if solution is not None:
               print_solution(solution)
          else:
               print("""Cannot find a solution.""")


