



'''simple docstring'''





from __future__ import annotations




def lowerCAmelCase						(__A						,			__A						,			__A						,			__A						,			__A						,			):



     """simple docstring"""



     _a											=						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):



     """simple docstring"""



     _a											=						[]
     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)










'''simple docstring'''





from ....configuration_utils import PretrainedConfig
from ....utils import logging


lowercase_	 =						logging.get_logger(__name__)

# TODO: upload to AWS
lowercase_	 =						{
    "yjernite/retribert-base-uncased": (
        "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json"
    ),
}






class __A    (						A		):



 '''simple docstring'''







 __lowerCamelCase					: Optional[Any]    = 'retribert'



 def __init__(self				,     A=30_522				,     A=768				,     A=8				,     A=12				,     A=3_072				,     A="gelu"				,     A=0.1				,     A=0.1				,     A=512				,     A=2				,     A=0.02				,     A=1E-12				,     A=True				,     A=128				,     A=0				,     **A				,     )					->  Any:


      """simple docstring"""



      super().__init__(pad_token_id=A				,     **A )

      _a											=						vocab_size
      _a											=						hidden_size
      _a											=						num_hidden_layers
      _a											=						num_attention_heads
      _a											=						hidden_act
      _a											=						intermediate_size
      _a											=						hidden_dropout_prob
      _a											=						attention_probs_dropout_prob
      _a											=						max_position_embeddings
      _a											=						type_vocab_size
      _a											=						initializer_range
      _a											=						layer_norm_eps
      _a											=						share_encoders
      _a											=						projection_dim



