


from __future__ import annotations
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,):



     """simple docstring"""






     snake_case					=    len(UpperCamelCase_    )

     # 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(UpperCamelCase_    ):
          # 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]							,UpperCamelCase_							,UpperCamelCase_							,)
def       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    []
     depth_first_search([]							,[]							,[]							,UpperCamelCase_							,UpperCamelCase_    )

     # Print all the boards
     for board in boards:
          for column in board:
               print(UpperCamelCase_    )
          print(''''''    )

     print(len(UpperCamelCase_    )							,'''solutions were found.'''    )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 n_queens_solution(4)








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


_SCREAMING_SNAKE_CASE      :			Dict      				=   logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE      :			Optional[Any]      				=   {
    "alibaba-damo/mgp-str-base": "https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json",
}

class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		'mgp-str'
    def __init__(	self      , __snake_case=[3_2, 1_2_8]      , __snake_case=4      , __snake_case=3      , __snake_case=2_7      , __snake_case=3_8      , __snake_case=5_0_2_5_7      , __snake_case=3_0_5_2_2      , __snake_case=7_6_8      , __snake_case=1_2      , __snake_case=1_2      , __snake_case=4.0      , __snake_case=True      , __snake_case=False      , __snake_case=1E-5      , __snake_case=0.0      , __snake_case=0.0      , __snake_case=0.0      , __snake_case=False      , __snake_case=0.02      , **__snake_case      , ):
         super().__init__(**__snake_case   )

         snake_case					=    image_size
         snake_case					=    patch_size
         snake_case					=    num_channels
         snake_case					=    max_token_length
         snake_case					=    num_character_labels
         snake_case					=    num_bpe_labels
         snake_case					=    num_wordpiece_labels
         snake_case					=    hidden_size
         snake_case					=    num_hidden_layers
         snake_case					=    num_attention_heads
         snake_case					=    mlp_ratio
         snake_case					=    distilled
         snake_case					=    layer_norm_eps
         snake_case					=    drop_rate
         snake_case					=    qkv_bias
         snake_case					=    attn_drop_rate
         snake_case					=    drop_path_rate
         snake_case					=    output_aa_attentions
         snake_case					=    initializer_range




