





from collections import OrderedDict
from typing import List, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase							:       List[Any]					 =	logging.get_logger(__name__)

lowerCAmelCase							:       Any					 =	{
    """google/efficientnet-b7""": """https://huggingface.co/google/efficientnet-b7/resolve/main/config.json""",
}





class 				a			(      __lowercase			):
				SCREAMING_SNAKE_CASE__     :   List[Any]		      =     '''efficientnet'''






				def __init__(  self			, _lowerCAmelCase = 3			, _lowerCAmelCase = 600			, _lowerCAmelCase = 2.0			, _lowerCAmelCase = 3.1			, _lowerCAmelCase = 8			, _lowerCAmelCase = [3, 3, 5, 3, 5, 5, 3]			, _lowerCAmelCase = [32, 16, 24, 40, 80, 112, 192]			, _lowerCAmelCase = [16, 24, 40, 80, 112, 192, 320]			, _lowerCAmelCase = []			, _lowerCAmelCase = [1, 2, 2, 2, 1, 2, 1]			, _lowerCAmelCase = [1, 2, 2, 3, 3, 4, 1]			, _lowerCAmelCase = [1, 6, 6, 6, 6, 6, 6]			, _lowerCAmelCase = 0.25			, _lowerCAmelCase = "swish"			, _lowerCAmelCase = 2560			, _lowerCAmelCase = "mean"			, _lowerCAmelCase = 0.02			, _lowerCAmelCase = 0.001			, _lowerCAmelCase = 0.99			, _lowerCAmelCase = 0.5			, _lowerCAmelCase = 0.2			, **_lowerCAmelCase			, ):

										"""simple docstring"""




										super().__init__(**_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				Tuple           =   num_channels
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   image_size
										__SCREAMING_SNAKE_CASE:				Dict           =   width_coefficient
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   depth_coefficient
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   depth_divisor
										__SCREAMING_SNAKE_CASE:				Any           =   kernel_sizes
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   in_channels
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   out_channels
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   depthwise_padding
										__SCREAMING_SNAKE_CASE:				str           =   strides
										__SCREAMING_SNAKE_CASE:				str           =   num_block_repeats
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   expand_ratios
										__SCREAMING_SNAKE_CASE:				int           =   squeeze_expansion_ratio
										__SCREAMING_SNAKE_CASE:				Tuple           =   hidden_act
										__SCREAMING_SNAKE_CASE:				List[Any]           =   hidden_dim
										__SCREAMING_SNAKE_CASE:				Tuple           =   pooling_type
										__SCREAMING_SNAKE_CASE:				Dict           =   initializer_range
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   batch_norm_eps
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   batch_norm_momentum
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   dropout_rate
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   drop_connect_rate
										__SCREAMING_SNAKE_CASE:				Tuple           =   sum(_lowerCAmelCase					) * 4





class 				a			(      __lowercase			):
				SCREAMING_SNAKE_CASE__     :   Any		      =     version.parse('''1.11'''			)






				@property
				def 			snake_case_    (  self					):

										"""simple docstring"""




										return OrderedDict(
										    [
										        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
										    ]					)






				@property
				def 			snake_case_    (  self					):

										"""simple docstring"""




										return 1e-5










from __future__ import annotations






def 		lowerCAmelCase  (	UpperCamelCase__   :   list[int]       ,			UpperCamelCase__   :   list[int]       ,			UpperCamelCase__   :   list[int]       ,			UpperCamelCase__   :   list[list[str]]       ,			UpperCamelCase__   :   int       ,			)      ->    None:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Any           =   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 		lowerCAmelCase  (	UpperCamelCase__   :   int					)      ->    None:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				list[list[str]]           =   []
						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)



