from __future__ import annotations



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: list[int]				,  __UpperCamelCase					: list[int]				,  __UpperCamelCase					: list[int]				,  __UpperCamelCase					: list[list[str]]				,  __UpperCamelCase					: int				,  )							->      None:







  """simple docstring"""





  SCREAMING_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 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: int			)							->      None:







  """simple docstring"""





  SCREAMING_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 typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy







class __snake_case       (		lowerCamelCase_	):
   lowerCAmelCase_														=							"EncodecFeatureExtractor"
   lowerCAmelCase_														=							("T5Tokenizer", "T5TokenizerFast")




   def __init__(					self    :       List[Any] ,			_lowercase    :       int ,			_lowercase    :       Any   ):



     """simple docstring"""






     super().__init__(_lowercase ,			_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   self.feature_extractor
     SCREAMING_SNAKE_CASE__    							=   False




   def 			__a		(					self    :       Tuple ,			_lowercase    :       str=None ,			_lowercase    :       int=None ,			_lowercase    :       Any=True   ):



     """simple docstring"""






     return self.tokenizer.get_decoder_prompt_ids(task=_lowercase ,			language=_lowercase ,			no_timestamps=_lowercase   )




   def __call__(					self    :       Tuple ,			*_lowercase    :       Tuple ,			**_lowercase    :       Union[str, Any]   ):



     """simple docstring"""






     if self._in_target_context_manager:
       return self.current_processor(*_lowercase ,			**_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""audio""" ,			_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""sampling_rate""" ,			_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""text""" ,			_lowercase   )
     if len(_lowercase   ) > 0:
       SCREAMING_SNAKE_CASE__    							=   args[0]
       SCREAMING_SNAKE_CASE__    							=   args[1:]

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

     if text is not None:
       SCREAMING_SNAKE_CASE__    							=   self.tokenizer(_lowercase ,			**_lowercase   )

     if audio is not None:
       SCREAMING_SNAKE_CASE__    							=   self.feature_extractor(_lowercase ,			*_lowercase ,			sampling_rate=_lowercase ,			**_lowercase   )

     if audio is None:
       return inputs

     elif text is None:
       return audio_inputs

     else:
       SCREAMING_SNAKE_CASE__    							=   audio_inputs["""input_values"""]
       if "padding_mask" in audio_inputs:
         SCREAMING_SNAKE_CASE__    							=   audio_inputs["""padding_mask"""]
       return inputs




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



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""audio""" ,			_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""padding_mask""" ,			_lowercase   )

     if len(_lowercase   ) > 0:
       SCREAMING_SNAKE_CASE__    							=   args[0]
       SCREAMING_SNAKE_CASE__    							=   args[1:]

     if audio_values is not None:
       return self._decode_audio(_lowercase ,			padding_mask=_lowercase   )
     else:
       return self.tokenizer.batch_decode(*_lowercase ,			**_lowercase   )




   def 			__a		(					self    :       List[str] ,			*_lowercase    :       Tuple ,			**_lowercase    :       Optional[Any]   ):



     """simple docstring"""






     return self.tokenizer.decode(*_lowercase ,			**_lowercase   )




   def 			__a		(					self    :       List[str] ,			_lowercase    :       Tuple ,			_lowercase    :       Optional = None   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   to_numpy(_lowercase   )
     SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   audio_values.shape

     if padding_mask is None:
       return list(_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   to_numpy(_lowercase   )

     # match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
     # token (so that the generated audio values are **not** treated as padded tokens)
     SCREAMING_SNAKE_CASE__    							=   seq_len - padding_mask.shape[-1]
     SCREAMING_SNAKE_CASE__    							=   1 - self.feature_extractor.padding_value
     SCREAMING_SNAKE_CASE__    							=   np.pad(_lowercase ,			((0, 0), (0, difference)) ,			"""constant""" ,			constant_values=_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   audio_values.tolist()
     for i in range(_lowercase   ):
       SCREAMING_SNAKE_CASE__    							=   np.asarray(audio_values[i]   )[
           padding_mask[i][None, :] != self.feature_extractor.padding_value
       ]
       SCREAMING_SNAKE_CASE__    							=   sliced_audio.reshape(_lowercase ,			-1   )

     return audio_values
