"""simple docstring"""







import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

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 torch import nn

       from transformers import SwiftFormerForImageClassification, SwiftFormerModel
       from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import ViTImageProcessor







class        UpperCamelCase		:
   def __init__(			self     ,__UpperCamelCase     ,__UpperCamelCase=13     ,__UpperCamelCase=3     ,__UpperCamelCase=True     ,__UpperCamelCase=True     ,__UpperCamelCase=0.1     ,__UpperCamelCase=0.1     ,__UpperCamelCase=224     ,__UpperCamelCase=1000     ,__UpperCamelCase=[3, 3, 6, 4]     ,__UpperCamelCase=[48, 56, 112, 220]     ,)	->      Optional[int]:


        '''simple docstring'''


        lowercase_	:				Union[str, Any]													=	parent
        lowercase_	:				List[Any]													=	batch_size
        lowercase_	:				Any													=	num_channels
        lowercase_	:				List[str]													=	is_training
        lowercase_	:				Optional[Any]													=	use_labels
        lowercase_	:				Optional[int]													=	hidden_dropout_prob
        lowercase_	:				List[Any]													=	attention_probs_dropout_prob
        lowercase_	:				int													=	num_labels
        lowercase_	:				Any													=	image_size
        lowercase_	:				Any													=	layer_depths
        lowercase_	:				Union[str, Any]													=	embed_dims
   def 			_UpperCAmelCase   (			self			)	->      Tuple:


        '''simple docstring'''


        lowercase_	:				str													=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

        lowercase_	:				Optional[Any]													=	None
        if self.use_labels:
             lowercase_	:				int													=	ids_tensor([self.batch_size]     ,self.num_labels			)

        lowercase_	:				str													=	self.get_config()

        return config, pixel_values, labels
   def 			_UpperCAmelCase   (			self			)	->      Union[str, Any]:


        '''simple docstring'''


        return SwiftFormerConfig(
            depths=self.layer_depths     ,embed_dims=self.embed_dims     ,mlp_ratio=4     ,downsamples=[True, True, True, True]     ,hidden_act='gelu'     ,num_labels=self.num_labels     ,down_patch_size=3     ,down_stride=2     ,down_pad=1     ,drop_rate=0.0     ,drop_path_rate=0.0     ,use_layer_scale=__UpperCamelCase     ,layer_scale_init_value=1e-5     ,)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      int:


        '''simple docstring'''


        lowercase_	:				List[str]													=	SwiftFormerModel(config=__UpperCamelCase			)
        model.to(__UpperCamelCase			)
        model.eval()
        lowercase_	:				int													=	model(__UpperCamelCase			)
        self.parent.assertEqual(result.last_hidden_state.shape     ,(self.batch_size, self.embed_dims[-1], 7, 7)			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      int:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	self.num_labels
        lowercase_	:				str													=	SwiftFormerForImageClassification(__UpperCamelCase			)
        model.to(__UpperCamelCase			)
        model.eval()
        lowercase_	:				Tuple													=	model(__UpperCamelCase     ,labels=__UpperCamelCase			)
        self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.num_labels)			)

        lowercase_	:				Optional[int]													=	SwiftFormerForImageClassification(__UpperCamelCase			)
        model.to(__UpperCamelCase			)
        model.eval()

        lowercase_	:				Any													=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)
        lowercase_	:				str													=	model(__UpperCamelCase			)
        self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.num_labels)			)







   def 			_UpperCAmelCase   (			self			)	->      Dict:


        '''simple docstring'''


        ((lowercase_)							,  (lowercase_)							,  (lowercase_))	:				List[str]													=	self.prepare_config_and_inputs()
        lowercase_	:				List[str]													=	{'pixel_values': pixel_values}
        return config, inputs_dict







@require_torch
class        UpperCamelCase		(			lowercase_						,      lowercase_						,      unittest.TestCase  ):
   lowercase							=  (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
   lowercase							=  (
       {'feature-extraction': SwiftFormerModel, 'image-classification': SwiftFormerForImageClassification}
       if is_torch_available()
       else {}
   )

   lowercase							=  False
   lowercase							=  False
   lowercase							=  False
   lowercase							=  False
   lowercase							=  False
   def 			_UpperCAmelCase   (			self			)	->      Union[str, Any]:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	SwiftFormerModelTester(self			)
        lowercase_	:				Optional[int]													=	ConfigTester(
            self     ,config_class=__UpperCamelCase     ,has_text_modality=__UpperCamelCase     ,hidden_size=37     ,num_attention_heads=12     ,num_hidden_layers=12     ,)
   def 			_UpperCAmelCase   (			self			)	->      str:


        '''simple docstring'''


        self.config_tester.run_common_tests()
   @unittest.skip(reason='SwiftFormer does not use inputs_embeds'			)
   def 			_UpperCAmelCase   (			self			)	->      str:


        '''simple docstring'''


        pass
   def 			_UpperCAmelCase   (			self			)	->      Optional[int]:


        '''simple docstring'''


        lowercase_							,  lowercase_	:				Optional[Any]													=	self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             lowercase_	:				Any													=	model_class(__UpperCamelCase			)
             lowercase_	:				Union[str, Any]													=	model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(__UpperCamelCase     ,nn.Linear			)			)
   def 			_UpperCAmelCase   (			self			)	->      Optional[int]:


        '''simple docstring'''


        lowercase_							,  lowercase_	:				Any													=	self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             lowercase_	:				Any													=	model_class(__UpperCamelCase			)
             lowercase_	:				Optional[int]													=	inspect.signature(model.forward			)
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             lowercase_	:				Tuple													=	[*signature.parameters.keys()]

             lowercase_	:				Any													=	['pixel_values']
             self.assertListEqual(arg_names[:1]     ,__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      Tuple:


        '''simple docstring'''


        lowercase_	:				Tuple													=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      str:


        '''simple docstring'''


        lowercase_	:				int													=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase			)
   @slow
   def 			_UpperCAmelCase   (			self			)	->      Union[str, Any]:


        '''simple docstring'''


        for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowercase_	:				str													=	SwiftFormerModel.from_pretrained(__UpperCamelCase			)
             self.assertIsNotNone(__UpperCamelCase			)
   @unittest.skip(reason='SwiftFormer does not output attentions'			)
   def 			_UpperCAmelCase   (			self			)	->      List[Any]:


        '''simple docstring'''


        pass
   def 			_UpperCAmelCase   (			self			)	->      Tuple:


        '''simple docstring'''


        def check_hidden_states_output(__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			):
             lowercase_	:				List[str]													=	model_class(__UpperCamelCase			)
             model.to(__UpperCamelCase			)
             model.eval()

             with torch.no_grad():
                  lowercase_	:				str													=	model(**self._prepare_for_class(__UpperCamelCase     ,__UpperCamelCase			)			)

             lowercase_	:				Dict													=	outputs.hidden_states

             lowercase_	:				Union[str, Any]													=	8
             self.assertEqual(len(__UpperCamelCase			)     ,__UpperCamelCase			)  # TODO

             # SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
             # with the width and height being successively divided by 2, after every 2 blocks
             for i in range(len(__UpperCamelCase			)			):
                  self.assertEqual(
                      hidden_states[i].shape     ,torch.Size(
                          [
                              self.model_tester.batch_size,
                              self.model_tester.embed_dims[i // 2],
                              (self.model_tester.image_size // 4) // 2 ** (i // 2),
                              (self.model_tester.image_size // 4) // 2 ** (i // 2),
                          ]			)     ,)

        lowercase_							,  lowercase_	:				Any													=	self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             lowercase_	:				Any													=	True
             check_hidden_states_output(__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             lowercase_	:				Dict													=	True

             check_hidden_states_output(__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      Optional[Any]:


        '''simple docstring'''


        def _config_zero_init(__UpperCamelCase			):
             lowercase_	:				Optional[Any]													=	copy.deepcopy(__UpperCamelCase			)
             for key in configs_no_init.__dict__.keys():
                  if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
                       setattr(__UpperCamelCase     ,__UpperCamelCase     ,1e-10			)
                  if isinstance(getattr(__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)     ,__UpperCamelCase			):
                       lowercase_	:				Optional[Any]													=	_config_zero_init(getattr(__UpperCamelCase     ,__UpperCamelCase			)			)
                       setattr(__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)
             return configs_no_init

        lowercase_							,  lowercase_	:				Dict													=	self.model_tester.prepare_config_and_inputs_for_common()

        lowercase_	:				Union[str, Any]													=	_config_zero_init(__UpperCamelCase			)
        for model_class in self.all_model_classes:
             lowercase_	:				Tuple													=	model_class(config=__UpperCamelCase			)
             for name, param in model.named_parameters():
                  if param.requires_grad:
                       self.assertIn(
                           ((param.data.mean() * 1e9) / 1e9).round().item()     ,[0.0, 1.0]     ,msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''     ,)







   @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'			)
   def 			_UpperCAmelCase   (			self			)	->      Union[str, Any]:


        '''simple docstring'''


        pass






def 			lowercase__(       ):
     lowercase_	:				List[Any]													=	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
     return image







@require_torch
@require_vision
class        UpperCamelCase		(			unittest.TestCase  ):
   @cached_property
   def 			_UpperCAmelCase   (			self			)	->      List[Any]:


        '''simple docstring'''


        return ViTImageProcessor.from_pretrained('MBZUAI/swiftformer-xs'			) if is_vision_available() else None







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


        '''simple docstring'''


        lowercase_	:				Dict													=	SwiftFormerForImageClassification.from_pretrained('MBZUAI/swiftformer-xs'			).to(__UpperCamelCase			)

        lowercase_	:				List[Any]													=	self.default_image_processor
        lowercase_	:				Dict													=	prepare_img()
        lowercase_	:				int													=	image_processor(images=__UpperCamelCase     ,return_tensors='pt'			).to(__UpperCamelCase			)

        # forward pass
        with torch.no_grad():
             lowercase_	:				Union[str, Any]													=	model(**__UpperCamelCase			)

        # verify the logits
        lowercase_	:				Tuple													=	torch.Size((1, 1000)			)
        self.assertEqual(outputs.logits.shape     ,__UpperCamelCase			)

        lowercase_	:				Optional[Any]													=	torch.tensor([[-2.1703e00, 2.1107e00, -2.0811e00]]			).to(__UpperCamelCase			)
        self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,__UpperCamelCase     ,atol=1e-4			)			)


"""simple docstring"""







from __future__ import annotations

__SCREAMING_SNAKE_CASE    	=[]



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     list[list[int]]     ,			__SCREAMING_SNAKE_CASE							:     int     ,			__SCREAMING_SNAKE_CASE							:     int      ):
     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 			lowercase__(       __SCREAMING_SNAKE_CASE							:     list[list[int]]     ,			__SCREAMING_SNAKE_CASE							:     int      ):
     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      ):
               lowercase_	:				int													=	1
               solve(__SCREAMING_SNAKE_CASE     ,			row + 1      )
               lowercase_	:				Dict													=	0
     return False



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     list[list[int]]      ):
     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"))
__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))

