

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


lowerCAmelCase__	   = logging.get_logger(__name__)

lowerCAmelCase__	   = {
    """facebook/s2t-small-librispeech-asr""": (
        """https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json"""
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text
}

class 					a__						(					UpperCamelCase_			):
			"""simple docstring"""







			__lowerCamelCase        =       'speech_to_text'
			__lowerCamelCase        =       ['past_key_values']
			__lowerCamelCase        =       {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}


			def __init__(    self      ,      lowercase=10000      ,      lowercase=12      ,      lowercase=2048      ,      lowercase=4      ,      lowercase=6      ,      lowercase=2048      ,      lowercase=4      ,      lowercase=0.0      ,      lowercase=0.0      ,      lowercase=True      ,      lowercase=True      ,      lowercase="relu"      ,      lowercase=256      ,      lowercase=0.1      ,      lowercase=0.0      ,      lowercase=0.0      ,      lowercase=0.02      ,      lowercase=2      ,      lowercase=True      ,      lowercase=1      ,      lowercase=0      ,      lowercase=2      ,      lowercase=6000      ,      lowercase=1024      ,      lowercase=2      ,      lowercase=(5, 5)      ,      lowercase=1024      ,      lowercase=80      ,      lowercase=1      ,      **lowercase      ,      )  ->						str:
							'''simple docstring'''






							A__        =			vocab_size
							A__        =			d_model
							A__        =			encoder_ffn_dim
							A__        =			encoder_layers
							A__        =			encoder_attention_heads
							A__        =			decoder_ffn_dim
							A__        =			decoder_layers
							A__        =			decoder_attention_heads
							A__        =			dropout
							A__        =			attention_dropout
							A__        =			activation_dropout
							A__        =			activation_function
							A__        =			init_std
							A__        =			encoder_layerdrop
							A__        =			decoder_layerdrop
							A__        =			use_cache
							A__        =			encoder_layers
							A__        =			scale_embedding  # scale factor will be sqrt(d_model) if True
							A__        =			max_source_positions
							A__        =			max_target_positions
							A__        =			num_conv_layers
							A__        =			list(__a       )
							A__        =			conv_channels
							A__        =			input_feat_per_channel
							A__        =			input_channels

							if len(self.conv_kernel_sizes       ) != self.num_conv_layers:
											raise ValueError(
											    "Configuration for convolutional module is incorrect. "
											    "It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers` "
											    F'but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes       )}`, '
											    F'`config.num_conv_layers = {self.num_conv_layers}`.'       )

							super().__init__(
							    pad_token_id=__a      ,      bos_token_id=__a      ,      eos_token_id=__a      ,      is_encoder_decoder=__a      ,      decoder_start_token_id=__a      ,      **__a      ,      )









from __future__ import annotations



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							list[int]	,       SCREAMING_SNAKE_CASE_:							list[int]	,       SCREAMING_SNAKE_CASE_:							list[int]	,       SCREAMING_SNAKE_CASE_:							list[list[str]]	,       SCREAMING_SNAKE_CASE_:							int	,       )			->      None:




				'''simple docstring'''





				A__        =			len(SCREAMING_SNAKE_CASE_      )

				# 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(SCREAMING_SNAKE_CASE_      ):
								# 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]	,       SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_	,       )



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							int      )			->      None:




				'''simple docstring'''





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

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

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


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	n_queens_solution(4)

