




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


UpperCAmelCase_									=	get_tests_dir('fixtures/test_sentencepiece.model')
UpperCAmelCase_									=	get_tests_dir('fixtures/test_sentencepiece_bpe.model')

UpperCAmelCase_									=	'pt' if is_torch_available() else 'tf'





@require_sentencepiece
@require_tokenizers
class     lowerCamelCase__(					__lowerCamelCase ,     unittest.TestCase):
      UpperCAmelCase__      :	Tuple    		=   CamembertTokenizer
      UpperCAmelCase__      :	Tuple    		=   CamembertTokenizerFast
      UpperCAmelCase__      :	List[Any]    		=   True
      UpperCAmelCase__      :	str    		=   True
      def 			lowerCAmelCase__						(				self: int   ):
             super().setUp()

             # We have a SentencePiece fixture for testing
             __lowerCamelCase   =						CamembertTokenizer(UpperCamelCase_   )
             tokenizer.save_pretrained(self.tmpdirname   )
      def 			lowerCAmelCase__						(				self: Union[str, Any]   ):
             __lowerCamelCase   =						"""<pad>"""
             __lowerCamelCase   =						1

             self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCamelCase_   )    ,       UpperCamelCase_   )
             self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCamelCase_   )    ,       UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Dict   ):
             __lowerCamelCase   =						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(UpperCamelCase_   )    ,       10_04   )
      def 			lowerCAmelCase__						(				self: Any   ):
             self.assertEqual(self.get_tokenizer().vocab_size    ,       10_05   )
      def 			lowerCAmelCase__						(				self: str   ):
             __lowerCamelCase   =						CamembertTokenizer(UpperCamelCase_   )
             tokenizer.save_pretrained(self.tmpdirname   )
             __lowerCamelCase   =						CamembertTokenizerFast.from_pretrained(self.tmpdirname   )

             __lowerCamelCase   =						"""I was born in 92000, and this is falsé."""

             __lowerCamelCase   =						tokenizer.encode(UpperCamelCase_   )
             __lowerCamelCase   =						rust_tokenizer.encode(UpperCamelCase_   )
             self.assertListEqual(UpperCamelCase_    ,       UpperCamelCase_   )

             __lowerCamelCase   =						tokenizer.encode(UpperCamelCase_    ,       add_special_tokens=UpperCamelCase_   )
             __lowerCamelCase   =						rust_tokenizer.encode(UpperCamelCase_    ,       add_special_tokens=UpperCamelCase_   )
             self.assertListEqual(UpperCamelCase_    ,       UpperCamelCase_   )

             # <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)
             __lowerCamelCase   =						tokenizer.convert_ids_to_tokens(UpperCamelCase_   )
             __lowerCamelCase   =						rust_tokenizer.tokenize(UpperCamelCase_   )
             self.assertListEqual(UpperCamelCase_    ,       UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Tuple   ):
             if not self.test_rust_tokenizer:
                    return

             __lowerCamelCase   =						self.get_tokenizer()
             __lowerCamelCase   =						self.get_rust_tokenizer()

             __lowerCamelCase   =						"""I was born in 92000, and this is falsé."""

             __lowerCamelCase   =						tokenizer.tokenize(UpperCamelCase_   )
             __lowerCamelCase   =						rust_tokenizer.tokenize(UpperCamelCase_   )
             self.assertListEqual(UpperCamelCase_    ,       UpperCamelCase_   )

             __lowerCamelCase   =						tokenizer.encode(UpperCamelCase_    ,       add_special_tokens=UpperCamelCase_   )
             __lowerCamelCase   =						rust_tokenizer.encode(UpperCamelCase_    ,       add_special_tokens=UpperCamelCase_   )
             self.assertListEqual(UpperCamelCase_    ,       UpperCamelCase_   )

             __lowerCamelCase   =						self.get_rust_tokenizer()
             __lowerCamelCase   =						tokenizer.encode(UpperCamelCase_   )
             __lowerCamelCase   =						rust_tokenizer.encode(UpperCamelCase_   )
             self.assertListEqual(UpperCamelCase_    ,       UpperCamelCase_   )




      @slow
      def 			lowerCAmelCase__						(				self: Dict   ):
             # fmt: off
             __lowerCamelCase   =						{"""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.
             __lowerCamelCase   =						[
                 """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=UpperCamelCase_    ,       model_name="""camembert-base"""    ,       revision="""3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"""    ,       sequences=UpperCamelCase_    ,       )







from __future__ import annotations




def 					lowerCamelCase__       (						A__       :					list[int]  ,			A__       :					list[int]  ,			A__       :					list[int]  ,			A__       :					list[list[str]]  ,			A__       :					int  ,			):





       '''simple docstring'''



       __lowerCamelCase   =						len(A__	)

       # If row is equal to the size of the board it means there are a queen in each row in
       # the current board (possible_board)
       if row == n:
              # We convert the variable possible_board that looks like this: [1, 3, 0, 2] to
              # this: ['. Q . . ', '. . . Q ', 'Q . . . ', '. . Q . ']
              boards.append([""". """ * i + """Q """ + """. """ * (n - 1 - i) for i in possible_board]	)
              return

       # We iterate each column in the row to find all possible results in each row
       for col in range(A__	):
              # We apply that we learned previously. First we check that in the current board
              # (possible_board) there are not other same value because if there is it means
              # that there are a collision in vertical. Then we apply the two formulas we
              # learned before:
              #
              # 45º: y - x = b or 45: row - col = b
              # 135º: y + x = b or row + col = b.
              #
              # And we verify if the results of this two formulas not exist in their variables
              # respectively.  (diagonal_right_collisions, diagonal_left_collisions)
              #
              # If any or these are True it means there is a collision so we continue to the
              # next value in the for loop.
              if (
                  col in possible_board
                  or row - col in diagonal_right_collisions
                  or row + col in diagonal_left_collisions
              ):
                     continue

              # If it is False we call dfs function again and we update the inputs
              depth_first_search(
                  [*possible_board, col]  ,			[*diagonal_right_collisions, row - col]  ,			[*diagonal_left_collisions, row + col]  ,			A__  ,			A__  ,			)




def 					lowerCamelCase__       (						A__       :					int	):





       '''simple docstring'''



       __lowerCamelCase   =						[]
       depth_first_search([]  ,			[]  ,			[]  ,			A__  ,			A__	)

       # Print all the boards
       for board in boards:
              for column in board:
                     print(A__	)
              print(""""""	)

       print(len(A__	)  ,			"""solutions were found."""	)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       n_queens_solution(4)

