





import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRetriever
from transformers.models.realm.tokenization_realm import VOCAB_FILES_NAMES, RealmTokenizer





class 				a			(      __lowercase			):






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Tuple           =   tempfile.mkdtemp()
										__SCREAMING_SNAKE_CASE:				Tuple           =   5

										# Realm tok
										__SCREAMING_SNAKE_CASE:				List[str]           =   [
										    '''[UNK]''',
										    '''[CLS]''',
										    '''[SEP]''',
										    '''[PAD]''',
										    '''[MASK]''',
										    '''test''',
										    '''question''',
										    '''this''',
										    '''is''',
										    '''the''',
										    '''first''',
										    '''second''',
										    '''third''',
										    '''fourth''',
										    '''fifth''',
										    '''record''',
										    '''want''',
										    '''##want''',
										    '''##ed''',
										    '''wa''',
										    '''un''',
										    '''runn''',
										    '''##ing''',
										    ''',''',
										    '''low''',
										    '''lowest''',
										]
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   os.path.join(self.tmpdirname			, '''realm_tokenizer'''					)
										os.makedirs(_lowerCAmelCase			, exist_ok=_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   os.path.join(_lowerCAmelCase			, VOCAB_FILES_NAMES['''vocab_file''']					)
										with open(self.vocab_file			, '''w'''			, encoding='''utf-8'''					) as vocab_writer:
																vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]					)					)

										__SCREAMING_SNAKE_CASE:				Dict           =   os.path.join(self.tmpdirname			, '''realm_block_records'''					)
										os.makedirs(_lowerCAmelCase			, exist_ok=_lowerCAmelCase					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname			, '''realm_tokenizer'''					)					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										shutil.rmtree(self.tmpdirname					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   RealmConfig(num_block_records=self.num_block_records					)
										return config






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Any           =   Dataset.from_dict(
										    {
										        '''id''': ['''0''', '''1'''],
										        '''question''': ['''foo''', '''bar'''],
										        '''answers''': [['''Foo''', '''Bar'''], ['''Bar''']],
										    }					)
										return dataset






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Tuple           =   np.array(
										    [
										        b'''This is the first record''',
										        b'''This is the second record''',
										        b'''This is the third record''',
										        b'''This is the fourth record''',
										        b'''This is the fifth record''',
										        b'''This is a longer longer longer record''',
										    ]			, dtype=_lowerCAmelCase			, )
										return block_records






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				str           =   RealmRetriever(
										    block_records=self.get_dummy_block_records()			, tokenizer=self.get_tokenizer()			, )
										return retriever






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Optional[int]           =   self.get_config()
										__SCREAMING_SNAKE_CASE:				str           =   self.get_dummy_retriever()
										__SCREAMING_SNAKE_CASE:				Dict           =   retriever.tokenizer

										__SCREAMING_SNAKE_CASE:				str           =   np.array([0, 3]			, dtype='''long'''					)
										__SCREAMING_SNAKE_CASE:				Dict           =   tokenizer(['''Test question''']					).input_ids
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   tokenizer(
										    ['''the fourth''']			, add_special_tokens=_lowerCAmelCase			, return_token_type_ids=_lowerCAmelCase			, return_attention_mask=_lowerCAmelCase			, ).input_ids
										__SCREAMING_SNAKE_CASE:				List[Any]           =   config.reader_seq_len

										__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				Optional[int]           =   retriever(
										    _lowerCAmelCase			, _lowerCAmelCase			, answer_ids=_lowerCAmelCase			, max_length=_lowerCAmelCase			, return_tensors='''np'''					)

										self.assertEqual(len(_lowerCAmelCase					)			, 2					)
										self.assertEqual(len(_lowerCAmelCase					)			, 2					)
										self.assertEqual(len(_lowerCAmelCase					)			, 2					)
										self.assertEqual(concat_inputs.input_ids.shape			, (2, 10)					)
										self.assertEqual(concat_inputs.attention_mask.shape			, (2, 10)					)
										self.assertEqual(concat_inputs.token_type_ids.shape			, (2, 10)					)
										self.assertEqual(concat_inputs.special_tokens_mask.shape			, (2, 10)					)
										self.assertEqual(
										    tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[0]					)			, ['''[CLS]''', '''test''', '''question''', '''[SEP]''', '''this''', '''is''', '''the''', '''first''', '''record''', '''[SEP]''']			, )
										self.assertEqual(
										    tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[1]					)			, ['''[CLS]''', '''test''', '''question''', '''[SEP]''', '''this''', '''is''', '''the''', '''fourth''', '''record''', '''[SEP]''']			, )






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Any           =   self.get_config()
										__SCREAMING_SNAKE_CASE:				Dict           =   self.get_dummy_retriever()
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   retriever.tokenizer

										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   np.array([0, 3, 5]			, dtype='''long'''					)
										__SCREAMING_SNAKE_CASE:				Tuple           =   tokenizer(['''Test question''']					).input_ids
										__SCREAMING_SNAKE_CASE:				Tuple           =   tokenizer(
										    ['''the fourth''', '''longer longer''']			, add_special_tokens=_lowerCAmelCase			, return_token_type_ids=_lowerCAmelCase			, return_attention_mask=_lowerCAmelCase			, ).input_ids
										__SCREAMING_SNAKE_CASE:				Dict           =   config.reader_seq_len

										__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				List[Any]           =   retriever(
										    _lowerCAmelCase			, _lowerCAmelCase			, answer_ids=_lowerCAmelCase			, max_length=_lowerCAmelCase			, return_tensors='''np'''					)

										self.assertEqual([False, True, True]			, _lowerCAmelCase					)
										self.assertEqual([[-1, -1, -1], [6, -1, -1], [6, 7, 8]]			, _lowerCAmelCase					)
										self.assertEqual([[-1, -1, -1], [7, -1, -1], [7, 8, 9]]			, _lowerCAmelCase					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Tuple           =   self.get_dummy_retriever()
										retriever.save_pretrained(os.path.join(self.tmpdirname			, '''realm_block_records'''					)					)

										# Test local path
										__SCREAMING_SNAKE_CASE:				Any           =   retriever.from_pretrained(os.path.join(self.tmpdirname			, '''realm_block_records'''					)					)
										self.assertEqual(retriever.block_records[0]			, b'''This is the first record'''					)

										# Test mocked remote path
										with patch('''transformers.models.realm.retrieval_realm.hf_hub_download'''					) as mock_hf_hub_download:
																__SCREAMING_SNAKE_CASE:				List[Any]           =   os.path.join(
																    os.path.join(self.tmpdirname			, '''realm_block_records'''					)			, _REALM_BLOCK_RECORDS_FILENAME					)
																__SCREAMING_SNAKE_CASE:				Dict           =   RealmRetriever.from_pretrained('''google/realm-cc-news-pretrained-openqa'''					)

										self.assertEqual(retriever.block_records[0]			, b'''This is the first record'''					)










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)



