



'''simple docstring'''

import inspect
import unittest
from math import floor

from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import CvtForImageClassification, CvtModel
				from transformers.models.cvt.modeling_cvt import CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import AutoImageProcessor

class __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						def     __snake_case   ( self				)     ->     int:
							lowerCAmelCase   =     self.config_class(**self.inputs_dict				)
							self.parent.assertTrue(hasattr(A_     ,	"""embed_dim"""				)				)
							self.parent.assertTrue(hasattr(A_     ,	"""num_heads"""				)				)







class __snake_case:


						'''simple docstring'''



						def __init__( self     ,	A_     ,	A_=13     ,	A_=64     ,	A_=3     ,	A_=[16, 48, 96]     ,	A_=[1, 3, 6]     ,	A_=[1, 2, 10]     ,	A_=[7, 3, 3]     ,	A_=[4, 2, 2]     ,	A_=[2, 1, 1]     ,	A_=[2, 2, 2]     ,	A_=[False, False, True]     ,	A_=[0.0, 0.0, 0.0]     ,	A_=0.0_2     ,	A_=1e-12     ,	A_=True     ,	A_=True     ,	A_=2     ,	)     ->     List[str]:
							lowerCAmelCase   =     parent
							lowerCAmelCase   =     batch_size
							lowerCAmelCase   =     image_size
							lowerCAmelCase   =     patch_sizes
							lowerCAmelCase   =     patch_stride
							lowerCAmelCase   =     patch_padding
							lowerCAmelCase   =     is_training
							lowerCAmelCase   =     use_labels
							lowerCAmelCase   =     num_labels
							lowerCAmelCase   =     num_channels
							lowerCAmelCase   =     embed_dim
							lowerCAmelCase   =     num_heads
							lowerCAmelCase   =     stride_kv
							lowerCAmelCase   =     depth
							lowerCAmelCase   =     cls_token
							lowerCAmelCase   =     attention_drop_rate
							lowerCAmelCase   =     initializer_range
							lowerCAmelCase   =     layer_norm_eps


						def     __snake_case   ( self				)     ->     Optional[Any]:
							lowerCAmelCase   =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

							lowerCAmelCase   =     None
							if self.use_labels:
								lowerCAmelCase   =     ids_tensor([self.batch_size]     ,	self.num_labels				)

							lowerCAmelCase   =     self.get_config()
							return config, pixel_values, labels


						def     __snake_case   ( self				)     ->     Tuple:
							return CvtConfig(
							    image_size=self.image_size     ,	num_labels=self.num_labels     ,	num_channels=self.num_channels     ,	embed_dim=self.embed_dim     ,	num_heads=self.num_heads     ,	patch_sizes=self.patch_sizes     ,	patch_padding=self.patch_padding     ,	patch_stride=self.patch_stride     ,	stride_kv=self.stride_kv     ,	depth=self.depth     ,	cls_token=self.cls_token     ,	attention_drop_rate=self.attention_drop_rate     ,	initializer_range=self.initializer_range     ,	)


						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_				)     ->     str:
							lowerCAmelCase   =     CvtModel(config=A_				)
							model.to(A_				)
							model.eval()
							lowerCAmelCase   =     model(A_				)
							lowerCAmelCase   =     (self.image_size, self.image_size)
							lowerCAmelCase,       lowerCAmelCase   =     image_size[0], image_size[1]
							for i in range(len(self.depth				)				):
								lowerCAmelCase   =     floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1				)
								lowerCAmelCase   =     floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1				)
							self.parent.assertEqual(result.last_hidden_state.shape     ,	(self.batch_size, self.embed_dim[-1], height, width)				)


						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_				)     ->     str:
							lowerCAmelCase   =     self.num_labels
							lowerCAmelCase   =     CvtForImageClassification(A_				)
							model.to(A_				)
							model.eval()
							lowerCAmelCase   =     model(A_     ,	labels=A_				)
							self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.num_labels)				)




						def     __snake_case   ( self				)     ->     List[str]:
							lowerCAmelCase   =     self.prepare_config_and_inputs()
							lowerCAmelCase,       lowerCAmelCase,       lowerCAmelCase   =     config_and_inputs
							lowerCAmelCase   =     {"""pixel_values""": pixel_values}
							return config, inputs_dict







@require_torch
class __snake_case(				_lowerCAmelCase		,							_lowerCAmelCase		,							unittest.TestCase ):


						'''simple docstring'''



						UpperCAmelCase							:   Union[str, Any]									=		(CvtModel, CvtForImageClassification) if is_torch_available() else ()
						UpperCAmelCase							:   int									=		(
						    {"feature-extraction": CvtModel, "image-classification": CvtForImageClassification}
						    if is_torch_available()
						    else {}
						)

						UpperCAmelCase							:   Optional[Any]									=		False
						UpperCAmelCase							:   int									=		False
						UpperCAmelCase							:   Optional[int]									=		False
						UpperCAmelCase							:   Any									=		False
						UpperCAmelCase							:   Optional[int]									=		False


						def     __snake_case   ( self				)     ->     List[Any]:
							lowerCAmelCase   =     CvtModelTester(self				)
							lowerCAmelCase   =     ConfigTester(self     ,	config_class=A_     ,	has_text_modality=A_     ,	hidden_size=37				)


						def     __snake_case   ( self				)     ->     Optional[Any]:
							self.create_and_test_config_common_properties()
							self.config_tester.create_and_test_config_to_json_string()
							self.config_tester.create_and_test_config_to_json_file()
							self.config_tester.create_and_test_config_from_and_save_pretrained()
							self.config_tester.create_and_test_config_with_num_labels()
							self.config_tester.check_config_can_be_init_without_params()
							self.config_tester.check_config_arguments_init()


						def     __snake_case   ( self				)     ->     int:
							return


						@unittest.skip(reason="""Cvt does not output attentions"""				)
						def     __snake_case   ( self				)     ->     str:
							pass


						@unittest.skip(reason="""Cvt does not use inputs_embeds"""				)
						def     __snake_case   ( self				)     ->     Dict:
							pass


						@unittest.skip(reason="""Cvt does not support input and output embeddings"""				)
						def     __snake_case   ( self				)     ->     Dict:
							pass


						def     __snake_case   ( self				)     ->     Tuple:
							lowerCAmelCase,       lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
								lowerCAmelCase   =     model_class(A_				)
								lowerCAmelCase   =     inspect.signature(model.forward				)
								# signature.parameters is an OrderedDict => so arg_names order is deterministic
								lowerCAmelCase   =     [*signature.parameters.keys()]

								lowerCAmelCase   =     ["""pixel_values"""]
								self.assertListEqual(arg_names[:1]     ,	A_				)


						def     __snake_case   ( self				)     ->     int:
							lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*A_				)


						def     __snake_case   ( self				)     ->     Dict:
							def check_hidden_states_output(A_     ,	A_     ,	A_				):
								lowerCAmelCase   =     model_class(A_				)
								model.to(A_				)
								model.eval()

								with torch.no_grad():
									lowerCAmelCase   =     model(**self._prepare_for_class(A_     ,	A_				)				)

								lowerCAmelCase   =     outputs.hidden_states

								lowerCAmelCase   =     len(self.model_tester.depth				)
								self.assertEqual(len(A_				)     ,	A_				)

								# verify the first hidden states (first block)
								self.assertListEqual(
								    list(hidden_states[0].shape[-3:]				)     ,	[
								        self.model_tester.embed_dim[0],
								        self.model_tester.image_size // 4,
								        self.model_tester.image_size // 4,
								    ]     ,	)

							lowerCAmelCase,       lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
								lowerCAmelCase   =     True
								check_hidden_states_output(A_     ,	A_     ,	A_				)

								# check that output_hidden_states also work using config
								del inputs_dict["output_hidden_states"]
								lowerCAmelCase   =     True

								check_hidden_states_output(A_     ,	A_     ,	A_				)


						def     __snake_case   ( self				)     ->     Union[str, Any]:
							lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*A_				)


						@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""				)
						def     __snake_case   ( self				)     ->     Tuple:
							pass




						@slow
						def     __snake_case   ( self				)     ->     Any:
							for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								lowerCAmelCase   =     CvtModel.from_pretrained(A_				)
								self.assertIsNotNone(A_				)







def 		_snake_case		( )      ->						int:







	"""simple docstring"""
	lowerCAmelCase   =     Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
	return image





@require_torch
@require_vision
class __snake_case(				unittest.TestCase ):


						'''simple docstring'''



						@cached_property
						def     __snake_case   ( self				)     ->     str:
							return AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)




						@slow
						def     __snake_case   ( self				)     ->     Dict:
							lowerCAmelCase   =     CvtForImageClassification.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]				).to(A_				)

							lowerCAmelCase   =     self.default_image_processor
							lowerCAmelCase   =     prepare_img()
							lowerCAmelCase   =     image_processor(images=A_     ,	return_tensors="""pt"""				).to(A_				)

							# forward pass
							with torch.no_grad():
								lowerCAmelCase   =     model(**A_				)

							# verify the logits
							lowerCAmelCase   =     torch.Size((1, 1000)				)
							self.assertEqual(outputs.logits.shape     ,	A_				)

							lowerCAmelCase   =     torch.tensor([0.9_2_8_5, 0.9_0_1_5, -0.3_1_5_0]				).to(A_				)

							self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,	A_     ,	atol=1e-4				)				)




'''simple docstring'''

from __future__ import annotations

UpperCAmelCase     		=   []






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						list[list[int]]							,		_SCREAMING_SNAKE_CASE					:						int							,		_SCREAMING_SNAKE_CASE					:						int							)      ->						bool:







	"""simple docstring"""
	for i in range(len(_SCREAMING_SNAKE_CASE							)							):
		if board[row][i] == 1:
			return False
	for i in range(len(_SCREAMING_SNAKE_CASE							)							):
		if board[i][column] == 1:
			return False
	for i, j in zip(range(_SCREAMING_SNAKE_CASE							,		-1							,		-1							)							,		range(_SCREAMING_SNAKE_CASE							,		-1							,		-1							)							):
		if board[i][j] == 1:
			return False
	for i, j in zip(range(_SCREAMING_SNAKE_CASE							,		-1							,		-1							)							,		range(_SCREAMING_SNAKE_CASE							,		len(_SCREAMING_SNAKE_CASE							)							)							):
		if board[i][j] == 1:
			return False
	return True






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						list[list[int]]							,		_SCREAMING_SNAKE_CASE					:						int							)      ->						bool:







	"""simple docstring"""
	if row >= len(_SCREAMING_SNAKE_CASE							):
		solution.append(_SCREAMING_SNAKE_CASE							)
		printboard(_SCREAMING_SNAKE_CASE							)
		print()
		return True
	for i in range(len(_SCREAMING_SNAKE_CASE							)							):
		if is_safe(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							):
			lowerCAmelCase   =     1
			solve(_SCREAMING_SNAKE_CASE							,		row + 1							)
			lowerCAmelCase   =     0
	return False






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						list[list[int]]							)      ->						None:







	"""simple docstring"""
	for i in range(len(_SCREAMING_SNAKE_CASE							)							):
		for j in range(len(_SCREAMING_SNAKE_CASE							)							):
			if board[i][j] == 1:
				print("""Q"""							,		end=""" """							)
			else:
				print("""."""							,		end=""" """							)
		print()


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





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