



from __future__ import annotations

SCREAMING_SNAKE_CASE__       =      []


def A						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase					)						->      bool:
			for i in range(len(__UpperCamelCase					)					):
						if board[row][i] == 1:
									return False
			for i in range(len(__UpperCamelCase					)					):
						if board[i][column] == 1:
									return False
			for i, j in zip(range(__UpperCamelCase ,	-1 ,	-1					) ,	range(__UpperCamelCase ,	-1 ,	-1					)					):
						if board[i][j] == 1:
									return False
			for i, j in zip(range(__UpperCamelCase ,	-1 ,	-1					) ,	range(__UpperCamelCase ,	len(__UpperCamelCase					)					)					):
						if board[i][j] == 1:
									return False
			return True


def A						(       __UpperCamelCase ,	__UpperCamelCase					)						->      bool:
			if row >= len(__UpperCamelCase					):
						solution.append(__UpperCamelCase					)
						printboard(__UpperCamelCase					)
						print()
						return True
			for i in range(len(__UpperCamelCase					)					):
						if is_safe(__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase					):
									A__       =    1
									solve(__UpperCamelCase ,	row + 1					)
									A__       =    0
			return False


def A						(       __UpperCamelCase					)						->      None:
			for i in range(len(__UpperCamelCase					)					):
						for j in range(len(__UpperCamelCase					)					):
									if board[i][j] == 1:
												print('Q' ,	end=' '					)
									else:
												print('.' ,	end=' '					)
						print()


# n=int(input("The no. of queens"))
SCREAMING_SNAKE_CASE__       =      8
SCREAMING_SNAKE_CASE__       =      [[0 for i in range(n)] for j in range(n)]
solve(board, 0)






print('''The total no. of solutions are :''', len(solution))









import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
							import optax
							from flax.training.common_utils import onehot

							from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
							from transformers.models.ta.modeling_flax_ta import shift_tokens_right


@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class      __lowerCAmelCase     (		unittest.TestCase      ):
					"""simple docstring"""






					@slow
					def     _a							(     self					:       int					):






								"""simple docstring"""


								A__       =    FlaxMTaForConditionalGeneration.from_pretrained('google/mt5-small'					)
								A__       =    AutoTokenizer.from_pretrained('google/mt5-small'					)

								A__       =    tokenizer('Hello there'				,					return_tensors='np'					).input_ids
								A__       =    tokenizer('Hi I am'				,					return_tensors='np'					).input_ids

								A__       =    shift_tokens_right(_snake_case				,					model.config.pad_token_id				,					model.config.decoder_start_token_id					)

								A__       =    model(_snake_case				,					decoder_input_ids=_snake_case					).logits
								A__       =    optax.softmax_cross_entropy(_snake_case				,					onehot(_snake_case				,					logits.shape[-1]					)					).mean()

								A__       =    -(labels.shape[-1] * loss.item())

								A__       =    -84.9127
								self.assertTrue(abs(mtf_score - EXPECTED_SCORE					) < 1E-4					)




