






import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
     from PIL import Image

     from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast




@require_vision
class       snake_case__ (				unittest.TestCase):

       '''simple docstring'''



       def        __lowercase    (				self     )			->   Union[str, Any]:






            '''simple docstring'''





            __snake_case      :str 							=    tempfile.mkdtemp()

            __snake_case      :Any 							=    BlipImageProcessor()
            __snake_case      :int 							=    GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model"""     )

            __snake_case      :Tuple 							=    BlipaProcessor(_lowerCAmelCase      ,    _lowerCAmelCase     )

            processor.save_pretrained(self.tmpdirname     )



       def        __lowercase    (				self      ,    **a__     )			->   str:






            '''simple docstring'''





            return AutoProcessor.from_pretrained(self.tmpdirname      ,    **_lowerCAmelCase     ).tokenizer



       def        __lowercase    (				self      ,    **a__     )			->   Any:






            '''simple docstring'''





            return AutoProcessor.from_pretrained(self.tmpdirname      ,    **_lowerCAmelCase     ).image_processor



       def        __lowercase    (				self     )			->   Tuple:






            '''simple docstring'''





            shutil.rmtree(self.tmpdirname     )



       def        __lowercase    (				self     )			->   str:






            '''simple docstring'''





            __snake_case      :List[Any] 							=    [np.random.randint(2_55      ,    size=(3, 30, 4_00)      ,    dtype=np.uinta     )]

            __snake_case      :Tuple 							=    [Image.fromarray(np.moveaxis(_lowerCAmelCase      ,    0      ,    -1     )     ) for x in image_inputs]

            return image_inputs



       def        __lowercase    (				self     )			->   Optional[Any]:






            '''simple docstring'''





            __snake_case      :Union[str, Any] 							=    BlipaProcessor(tokenizer=self.get_tokenizer()      ,    image_processor=self.get_image_processor()     )
            processor.save_pretrained(self.tmpdirname     )

            __snake_case      :Dict 							=    self.get_tokenizer(bos_token="""(BOS)"""      ,    eos_token="""(EOS)"""     )
            __snake_case      :str 							=    self.get_image_processor(do_normalize=_lowerCAmelCase      ,    padding_value=1.0     )

            __snake_case      :Optional[Any] 							=    BlipaProcessor.from_pretrained(
                self.tmpdirname      ,    bos_token="""(BOS)"""      ,    eos_token="""(EOS)"""      ,    do_normalize=_lowerCAmelCase      ,    padding_value=1.0     )

            self.assertEqual(processor.tokenizer.get_vocab()      ,    tokenizer_add_kwargs.get_vocab()     )
            self.assertIsInstance(processor.tokenizer      ,    _lowerCAmelCase     )

            self.assertEqual(processor.image_processor.to_json_string()      ,    image_processor_add_kwargs.to_json_string()     )
            self.assertIsInstance(processor.image_processor      ,    _lowerCAmelCase     )



       def        __lowercase    (				self     )			->   int:






            '''simple docstring'''





            __snake_case      :Dict 							=    self.get_image_processor()
            __snake_case      :Optional[int] 							=    self.get_tokenizer()

            __snake_case      :Tuple 							=    BlipaProcessor(tokenizer=_lowerCAmelCase      ,    image_processor=_lowerCAmelCase     )

            __snake_case      :Tuple 							=    self.prepare_image_inputs()

            __snake_case      :str 							=    image_processor(_lowerCAmelCase      ,    return_tensors="""np"""     )
            __snake_case      :int 							=    processor(images=_lowerCAmelCase      ,    return_tensors="""np"""     )

            for key in input_feat_extract.keys():
                 self.assertAlmostEqual(input_feat_extract[key].sum()      ,    input_processor[key].sum()      ,    delta=1e-2     )



       def        __lowercase    (				self     )			->   List[Any]:






            '''simple docstring'''





            __snake_case      :Any 							=    self.get_image_processor()
            __snake_case      :Optional[Any] 							=    self.get_tokenizer()

            __snake_case      :Dict 							=    BlipaProcessor(tokenizer=_lowerCAmelCase      ,    image_processor=_lowerCAmelCase     )

            __snake_case      :Optional[int] 							=    '''lower newer'''

            __snake_case      :Tuple 							=    processor(text=_lowerCAmelCase     )

            __snake_case      :Any 							=    tokenizer(_lowerCAmelCase      ,    return_token_type_ids=_lowerCAmelCase     )

            for key in encoded_tok.keys():
                 self.assertListEqual(encoded_tok[key]      ,    encoded_processor[key]     )



       def        __lowercase    (				self     )			->   List[Any]:






            '''simple docstring'''





            __snake_case      :Optional[int] 							=    self.get_image_processor()
            __snake_case      :Tuple 							=    self.get_tokenizer()

            __snake_case      :List[Any] 							=    BlipaProcessor(tokenizer=_lowerCAmelCase      ,    image_processor=_lowerCAmelCase     )

            __snake_case      :str 							=    '''lower newer'''
            __snake_case      :List[Any] 							=    self.prepare_image_inputs()

            __snake_case      :Union[str, Any] 							=    processor(text=_lowerCAmelCase      ,    images=_lowerCAmelCase     )

            self.assertListEqual(list(inputs.keys()     )      ,    ["""pixel_values""", """input_ids""", """attention_mask"""]     )

            # test if it raises when no input is passed
            with pytest.raises(_lowerCAmelCase     ):
                 processor()



       def        __lowercase    (				self     )			->   List[str]:






            '''simple docstring'''





            __snake_case      :int 							=    self.get_image_processor()
            __snake_case      :Union[str, Any] 							=    self.get_tokenizer()

            __snake_case      :Union[str, Any] 							=    BlipaProcessor(tokenizer=_lowerCAmelCase      ,    image_processor=_lowerCAmelCase     )

            __snake_case      :Any 							=    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

            __snake_case      :Dict 							=    processor.batch_decode(_lowerCAmelCase     )
            __snake_case      :List[str] 							=    tokenizer.batch_decode(_lowerCAmelCase     )

            self.assertListEqual(_lowerCAmelCase      ,    _lowerCAmelCase     )



       def        __lowercase    (				self     )			->   Dict:






            '''simple docstring'''





            __snake_case      :Optional[int] 							=    self.get_image_processor()
            __snake_case      :Optional[Any] 							=    self.get_tokenizer()

            __snake_case      :Optional[Any] 							=    BlipaProcessor(tokenizer=_lowerCAmelCase      ,    image_processor=_lowerCAmelCase     )

            __snake_case      :List[str] 							=    '''lower newer'''
            __snake_case      :List[str] 							=    self.prepare_image_inputs()

            __snake_case      :Any 							=    processor(text=_lowerCAmelCase      ,    images=_lowerCAmelCase     )

            # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
            self.assertListEqual(list(inputs.keys()     )      ,    ["""pixel_values""", """input_ids""", """attention_mask"""]     )









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)



