
import unittest

from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
		from PIL import Image
else:







		class        lowerCAmelCase__      :

								@staticmethod
								def  A_				(			*a       ,							**a				)					->      Any:


										'''simple docstring'''

										pass







@is_pipeline_test
@require_vision
@require_torch
class        lowerCAmelCase__      (   unittest.TestCase	):
						UpperCamelCase_							: Optional[Any]	=		MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

						def  A_				(			self       ,							a       ,							a       ,							a				)					->      Optional[int]:


								'''simple docstring'''

								_UpperCamelCase						    =	pipeline(
								    """zero-shot-object-detection"""       ,							model="""hf-internal-testing/tiny-random-owlvit-object-detection"""				)

								_UpperCamelCase						    =	[
								    {
								        """image""": """./tests/fixtures/tests_samples/COCO/000000039769.png""",
								        """candidate_labels""": ["""cat""", """remote""", """couch"""],
								    }
								]
								return object_detector, examples

						def  A_				(			self       ,							a       ,							a				)					->      List[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	object_detector(examples[0]       ,							threshold=0.0				)

								_UpperCamelCase						    =	len(a				)
								self.assertGreater(a       ,							0				)
								self.assertEqual(
								    a       ,							[
								        {
								            """score""": ANY(a				),
								            """label""": ANY(a				),
								            """box""": {"""xmin""": ANY(a				), """ymin""": ANY(a				), """xmax""": ANY(a				), """ymax""": ANY(a				)},
								        }
								        for i in range(a				)
								    ]       ,							)

						@require_tf
						@unittest.skip("""Zero Shot Object Detection not implemented in TF"""				)
						def  A_				(			self				)					->      Union[str, Any]:


								'''simple docstring'''

								pass

						@require_torch
						def  A_				(			self				)					->      Any:


								'''simple docstring'''

								_UpperCamelCase						    =	pipeline(
								    """zero-shot-object-detection"""       ,							model="""hf-internal-testing/tiny-random-owlvit-object-detection"""				)

								_UpperCamelCase						    =	object_detector(
								    """./tests/fixtures/tests_samples/COCO/000000039769.png"""       ,							candidate_labels=["""cat""", """remote""", """couch"""]       ,							threshold=0.64       ,							)

								self.assertEqual(
								    nested_simplify(a       ,							decimals=4				)       ,							[
								        {"""score""": 0.7235, """label""": """cat""", """box""": {"""xmin""": 2_04, """ymin""": 1_67, """xmax""": 2_32, """ymax""": 1_90}},
								        {"""score""": 0.7218, """label""": """remote""", """box""": {"""xmin""": 2_04, """ymin""": 1_67, """xmax""": 2_32, """ymax""": 1_90}},
								        {"""score""": 0.7184, """label""": """couch""", """box""": {"""xmin""": 2_04, """ymin""": 1_67, """xmax""": 2_32, """ymax""": 1_90}},
								        {"""score""": 0.6748, """label""": """remote""", """box""": {"""xmin""": 5_71, """ymin""": 83, """xmax""": 5_98, """ymax""": 1_03}},
								        {"""score""": 0.6656, """label""": """cat""", """box""": {"""xmin""": 5_71, """ymin""": 83, """xmax""": 5_98, """ymax""": 1_03}},
								        {"""score""": 0.6614, """label""": """couch""", """box""": {"""xmin""": 5_71, """ymin""": 83, """xmax""": 5_98, """ymax""": 1_03}},
								        {"""score""": 0.6456, """label""": """remote""", """box""": {"""xmin""": 4_94, """ymin""": 1_05, """xmax""": 5_21, """ymax""": 1_27}},
								        {"""score""": 0.642, """label""": """remote""", """box""": {"""xmin""": 67, """ymin""": 2_74, """xmax""": 93, """ymax""": 2_97}},
								        {"""score""": 0.6419, """label""": """cat""", """box""": {"""xmin""": 4_94, """ymin""": 1_05, """xmax""": 5_21, """ymax""": 1_27}},
								    ]       ,							)

								_UpperCamelCase						    =	object_detector(
								    [
								        {
								            """image""": """./tests/fixtures/tests_samples/COCO/000000039769.png""",
								            """candidate_labels""": ["""cat""", """remote""", """couch"""],
								        }
								    ]       ,							threshold=0.64       ,							)

								self.assertEqual(
								    nested_simplify(a       ,							decimals=4				)       ,							[
								        [
								            {"""score""": 0.7235, """label""": """cat""", """box""": {"""xmin""": 2_04, """ymin""": 1_67, """xmax""": 2_32, """ymax""": 1_90}},
								            {"""score""": 0.7218, """label""": """remote""", """box""": {"""xmin""": 2_04, """ymin""": 1_67, """xmax""": 2_32, """ymax""": 1_90}},
								            {"""score""": 0.7184, """label""": """couch""", """box""": {"""xmin""": 2_04, """ymin""": 1_67, """xmax""": 2_32, """ymax""": 1_90}},
								            {"""score""": 0.6748, """label""": """remote""", """box""": {"""xmin""": 5_71, """ymin""": 83, """xmax""": 5_98, """ymax""": 1_03}},
								            {"""score""": 0.6656, """label""": """cat""", """box""": {"""xmin""": 5_71, """ymin""": 83, """xmax""": 5_98, """ymax""": 1_03}},
								            {"""score""": 0.6614, """label""": """couch""", """box""": {"""xmin""": 5_71, """ymin""": 83, """xmax""": 5_98, """ymax""": 1_03}},
								            {"""score""": 0.6456, """label""": """remote""", """box""": {"""xmin""": 4_94, """ymin""": 1_05, """xmax""": 5_21, """ymax""": 1_27}},
								            {"""score""": 0.642, """label""": """remote""", """box""": {"""xmin""": 67, """ymin""": 2_74, """xmax""": 93, """ymax""": 2_97}},
								            {"""score""": 0.6419, """label""": """cat""", """box""": {"""xmin""": 4_94, """ymin""": 1_05, """xmax""": 5_21, """ymax""": 1_27}},
								        ]
								    ]       ,							)

						@require_torch
						@slow
						def  A_				(			self				)					->      List[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	pipeline("""zero-shot-object-detection"""				)

								_UpperCamelCase						    =	object_detector(
								    """http://images.cocodataset.org/val2017/000000039769.jpg"""       ,							candidate_labels=["""cat""", """remote""", """couch"""]       ,							)
								self.assertEqual(
								    nested_simplify(a       ,							decimals=4				)       ,							[
								        {"""score""": 0.2868, """label""": """cat""", """box""": {"""xmin""": 3_24, """ymin""": 20, """xmax""": 6_40, """ymax""": 3_73}},
								        {"""score""": 0.277, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 72, """xmax""": 1_77, """ymax""": 1_15}},
								        {"""score""": 0.2537, """label""": """cat""", """box""": {"""xmin""": 1, """ymin""": 55, """xmax""": 3_15, """ymax""": 4_72}},
								        {"""score""": 0.1474, """label""": """remote""", """box""": {"""xmin""": 3_35, """ymin""": 74, """xmax""": 3_71, """ymax""": 1_87}},
								        {"""score""": 0.1208, """label""": """couch""", """box""": {"""xmin""": 4, """ymin""": 0, """xmax""": 6_42, """ymax""": 4_76}},
								    ]       ,							)

								_UpperCamelCase						    =	object_detector(
								    [
								        {
								            """image""": """http://images.cocodataset.org/val2017/000000039769.jpg""",
								            """candidate_labels""": ["""cat""", """remote""", """couch"""],
								        },
								        {
								            """image""": """http://images.cocodataset.org/val2017/000000039769.jpg""",
								            """candidate_labels""": ["""cat""", """remote""", """couch"""],
								        },
								    ]       ,							)
								self.assertEqual(
								    nested_simplify(a       ,							decimals=4				)       ,							[
								        [
								            {"""score""": 0.2868, """label""": """cat""", """box""": {"""xmin""": 3_24, """ymin""": 20, """xmax""": 6_40, """ymax""": 3_73}},
								            {"""score""": 0.277, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 72, """xmax""": 1_77, """ymax""": 1_15}},
								            {"""score""": 0.2537, """label""": """cat""", """box""": {"""xmin""": 1, """ymin""": 55, """xmax""": 3_15, """ymax""": 4_72}},
								            {"""score""": 0.1474, """label""": """remote""", """box""": {"""xmin""": 3_35, """ymin""": 74, """xmax""": 3_71, """ymax""": 1_87}},
								            {"""score""": 0.1208, """label""": """couch""", """box""": {"""xmin""": 4, """ymin""": 0, """xmax""": 6_42, """ymax""": 4_76}},
								        ],
								        [
								            {"""score""": 0.2868, """label""": """cat""", """box""": {"""xmin""": 3_24, """ymin""": 20, """xmax""": 6_40, """ymax""": 3_73}},
								            {"""score""": 0.277, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 72, """xmax""": 1_77, """ymax""": 1_15}},
								            {"""score""": 0.2537, """label""": """cat""", """box""": {"""xmin""": 1, """ymin""": 55, """xmax""": 3_15, """ymax""": 4_72}},
								            {"""score""": 0.1474, """label""": """remote""", """box""": {"""xmin""": 3_35, """ymin""": 74, """xmax""": 3_71, """ymax""": 1_87}},
								            {"""score""": 0.1208, """label""": """couch""", """box""": {"""xmin""": 4, """ymin""": 0, """xmax""": 6_42, """ymax""": 4_76}},
								        ],
								    ]       ,							)

						@require_tf
						@unittest.skip("""Zero Shot Object Detection not implemented in TF"""				)
						def  A_				(			self				)					->      Optional[Any]:


								'''simple docstring'''

								pass

						@require_torch
						@slow
						def  A_				(			self				)					->      Union[str, Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	0.2
								_UpperCamelCase						    =	pipeline("""zero-shot-object-detection"""				)

								_UpperCamelCase						    =	object_detector(
								    """http://images.cocodataset.org/val2017/000000039769.jpg"""       ,							candidate_labels=["""cat""", """remote""", """couch"""]       ,							threshold=a       ,							)
								self.assertEqual(
								    nested_simplify(a       ,							decimals=4				)       ,							[
								        {"""score""": 0.2868, """label""": """cat""", """box""": {"""xmin""": 3_24, """ymin""": 20, """xmax""": 6_40, """ymax""": 3_73}},
								        {"""score""": 0.277, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 72, """xmax""": 1_77, """ymax""": 1_15}},
								        {"""score""": 0.2537, """label""": """cat""", """box""": {"""xmin""": 1, """ymin""": 55, """xmax""": 3_15, """ymax""": 4_72}},
								    ]       ,							)







						@require_torch
						@slow
						def  A_				(			self				)					->      Optional[int]:


								'''simple docstring'''

								_UpperCamelCase						    =	2
								_UpperCamelCase						    =	pipeline("""zero-shot-object-detection"""				)

								_UpperCamelCase						    =	object_detector(
								    """http://images.cocodataset.org/val2017/000000039769.jpg"""       ,							candidate_labels=["""cat""", """remote""", """couch"""]       ,							top_k=a       ,							)
								self.assertEqual(
								    nested_simplify(a       ,							decimals=4				)       ,							[
								        {"""score""": 0.2868, """label""": """cat""", """box""": {"""xmin""": 3_24, """ymin""": 20, """xmax""": 6_40, """ymax""": 3_73}},
								        {"""score""": 0.277, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 72, """xmax""": 1_77, """ymax""": 1_15}},
								    ]       ,							)





from __future__ import annotations


def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  )		->				None:

		"""simple docstring"""






		_UpperCamelCase						    =	len(lowerCAmelCase )

		# 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(lowerCAmelCase ):
				# 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]						,  lowerCAmelCase						,  lowerCAmelCase						,  )


def 							__A(lowerCAmelCase )		->				None:

		"""simple docstring"""






		_UpperCamelCase						    =	[]
		depth_first_search([]						,  []						,  []						,  lowerCAmelCase						,  lowerCAmelCase )

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

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


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		n_queens_solution(4)



