



import inspect
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
 from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

class     _A       :



       def __init__(		self							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=13     ,		__SCREAMING_SNAKE_CASE							:     int=64     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=2     ,		__SCREAMING_SNAKE_CASE							:     str=3     ,		__SCREAMING_SNAKE_CASE							:     Any=True     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=True     ,		__SCREAMING_SNAKE_CASE							:     Dict=32     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=5     ,		__SCREAMING_SNAKE_CASE							:     str=4     ,		__SCREAMING_SNAKE_CASE							:     str=37     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]="gelu"     ,		__SCREAMING_SNAKE_CASE							:     List[str]=0.1     ,		__SCREAMING_SNAKE_CASE							:     Any=0.1     ,		__SCREAMING_SNAKE_CASE							:     Tuple=10     ,		__SCREAMING_SNAKE_CASE							:     Dict=0.02     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=[1, 16, 4, 4]     ,		__SCREAMING_SNAKE_CASE							:     List[str]=None     ,		):


           '''simple docstring'''

           __a						       =				parent
           __a						       =				batch_size
           __a						       =				image_size
           __a						       =				patch_size
           __a						       =				num_channels
           __a						       =				is_training
           __a						       =				use_labels
           __a						       =				hidden_size
           __a						       =				num_hidden_layers
           __a						       =				num_attention_heads
           __a						       =				intermediate_size
           __a						       =				hidden_act
           __a						       =				hidden_dropout_prob
           __a						       =				attention_probs_dropout_prob
           __a						       =				type_sequence_label_size
           __a						       =				initializer_range
           __a						       =				scope
           __a						       =				backbone_featmap_shape

           # in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
           # the number of patches is based on the feature map of the backbone, which by default uses an output stride
           # of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
           __a						       =				(self.image_size // 32) ** 2
           __a						       =				num_patches + 1



       def 		_lowerCamelCase			(		self							:     Optional[int]):


           '''simple docstring'''

           __a						       =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

           __a						       =				None
           if self.use_labels:
               __a						       =				ids_tensor([self.batch_size]     ,		self.type_sequence_label_size)

           __a						       =				self.get_config()

           return config, pixel_values, labels



       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           __a						       =				{
               '''global_padding''': '''same''',
               '''layer_type''': '''bottleneck''',
               '''depths''': [3, 4, 9],
               '''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
               '''embedding_dynamic_padding''': True,
               '''hidden_sizes''': [4, 8, 16, 32],
               '''num_groups''': 2,
           }

           return ViTHybridConfig(
               image_size=self.image_size     ,		patch_size=self.patch_size     ,		num_channels=self.num_channels     ,		hidden_size=self.hidden_size     ,		num_hidden_layers=self.num_hidden_layers     ,		num_attention_heads=self.num_attention_heads     ,		intermediate_size=self.intermediate_size     ,		hidden_act=self.hidden_act     ,		hidden_dropout_prob=self.hidden_dropout_prob     ,		attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,		is_decoder=__UpperCamelCase     ,		initializer_range=self.initializer_range     ,		backbone_featmap_shape=self.backbone_featmap_shape     ,		backbone_config=__UpperCamelCase     ,		)



       def 		_lowerCamelCase			(		self							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     int     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]):


           '''simple docstring'''

           __a						       =				ViTHybridModel(config=__UpperCamelCase)
           model.to(__UpperCamelCase)
           model.eval()
           __a						       =				model(__UpperCamelCase)
           self.parent.assertEqual(result.last_hidden_state.shape     ,		(self.batch_size, self.seq_length, self.hidden_size))



       def 		_lowerCamelCase			(		self							:     Union[str, Any]     ,		__SCREAMING_SNAKE_CASE							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     List[Any]):


           '''simple docstring'''

           __a						       =				self.type_sequence_label_size
           __a						       =				ViTHybridForImageClassification(__UpperCamelCase)
           model.to(__UpperCamelCase)
           model.eval()
           __a						       =				model(__UpperCamelCase     ,		labels=__UpperCamelCase)
           self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.type_sequence_label_size))





       def 		_lowerCamelCase			(		self							:     str):


           '''simple docstring'''

           __a						       =				self.prepare_config_and_inputs()
           __a       ,   __a       ,   __a						       =				config_and_inputs
           __a						       =				{'''pixel_values''': pixel_values}
           return config, inputs_dict


@require_torch
class     _A       (	lowercase_      ,lowercase_      ,unittest.TestCase    ):
       UpperCamelCase__  :    str											=						(ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
       UpperCamelCase__  :    str											=						(
           {'''feature-extraction''': ViTHybridModel, '''image-classification''': ViTHybridForImageClassification}
           if is_torch_available()
           else {}
       )
       UpperCamelCase__  :    Dict											=						False
       UpperCamelCase__  :    Any											=						False
       UpperCamelCase__  :    int											=						False



       def 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

           __a						       =				ViTHybridModelTester(self)
           __a						       =				ConfigTester(self     ,		config_class=__UpperCamelCase     ,		has_text_modality=__UpperCamelCase     ,		hidden_size=37)



       def 		_lowerCamelCase			(		self							:     Any):


           '''simple docstring'''

           self.config_tester.run_common_tests()



       @unittest.skip(reason='''ViT does not use inputs_embeds''')
       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           pass



       def 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

           __a       ,   __a						       =				self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               __a						       =				model_class(__UpperCamelCase)
               self.assertIsInstance(model.get_input_embeddings()     ,		(nn.Module))
               __a						       =				model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(__UpperCamelCase     ,		nn.Linear))



       def 		_lowerCamelCase			(		self							:     Any):


           '''simple docstring'''

           __a       ,   __a						       =				self.model_tester.prepare_config_and_inputs_for_common()

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

               __a						       =				['''pixel_values''']
               self.assertListEqual(arg_names[:1]     ,		__UpperCamelCase)



       def 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

           __a						       =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__UpperCamelCase)



       def 		_lowerCamelCase			(		self							:     Union[str, Any]):


           '''simple docstring'''

           __a						       =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase)



       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           __a       ,   __a						       =				self.model_tester.prepare_config_and_inputs_for_common()

           __a						       =				_config_zero_init(__UpperCamelCase)
           for model_class in self.all_model_classes:
               __a						       =				model_class(config=__UpperCamelCase)
               # Skip the check for the backbone
               for name, module in model.named_modules():
                   if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
                       __a						       =				[F'{name}.{key}' for key in module.state_dict().keys()]
                       break

               for name, param in model.named_parameters():
                   if param.requires_grad:
                       if name in backbone_params:
                           continue
                       self.assertIn(
                           ((param.data.mean() * 1E9).round() / 1E9).item()     ,		[0.0, 1.0]     ,		msg=F'Parameter {name} of model {model_class} seems not properly initialized'     ,		)





       @slow
       def 		_lowerCamelCase			(		self							:     Tuple):


           '''simple docstring'''

           for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __a						       =				ViTHybridModel.from_pretrained(__UpperCamelCase)
               self.assertIsNotNone(__UpperCamelCase)





def      __snake_case	(			):
    __a						       =				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
    return image

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



       @cached_property
       def 		_lowerCamelCase			(		self							:     Optional[int]):


           '''simple docstring'''

           return (
               ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0])
               if is_vision_available()
               else None
           )



       @slow
       def 		_lowerCamelCase			(		self							:     Optional[Any]):


           '''simple docstring'''

           __a						       =				ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(
               __UpperCamelCase)

           __a						       =				self.default_image_processor
           __a						       =				prepare_img()
           __a						       =				image_processor(images=__UpperCamelCase     ,		return_tensors='''pt''').to(__UpperCamelCase)

           # forward pass
           with torch.no_grad():
               __a						       =				model(**__UpperCamelCase)

           # verify the logits
           __a						       =				torch.Size((1, 1_000))
           self.assertEqual(outputs.logits.shape     ,		__UpperCamelCase)

           __a						       =				torch.tensor([-1.90_90, -0.49_93, -0.23_89]).to(__UpperCamelCase)

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





       @slow
       @require_accelerate
       def 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

           __a						       =				ViTHybridImageProcessor.from_pretrained('''google/vit-hybrid-base-bit-384''')
           __a						       =				ViTHybridForImageClassification.from_pretrained('''google/vit-hybrid-base-bit-384'''     ,		device_map='''auto''')

           __a						       =				prepare_img()

           __a						       =				image_processor(images=__UpperCamelCase     ,		return_tensors='''pt''')
           __a						       =				model(**__UpperCamelCase)
           __a						       =				outputs.logits
           # model predicts one of the 1000 ImageNet classes
           __a						       =				logits.argmax(-1).item()

           self.assertTrue(model.config.idalabel[predicted_class_idx]     ,		'''tabby, tabby cat''')












"""simple docstring"""
from __future__ import annotations

_snake_case     :		str       						= []






def 					A__				(			UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase       ):
							for i in range(len(UpperCamelCase       )       ):
														if board[row][i] == 1:
																					return False
							for i in range(len(UpperCamelCase       )       ):
														if board[i][column] == 1:
																					return False
							for i, j in zip(range(UpperCamelCase					,  -1					,  -1       )					,  range(UpperCamelCase					,  -1					,  -1       )       ):
														if board[i][j] == 1:
																					return False
							for i, j in zip(range(UpperCamelCase					,  -1					,  -1       )					,  range(UpperCamelCase					,  len(UpperCamelCase       )       )       ):
														if board[i][j] == 1:
																					return False
							return True






def 					A__				(			UpperCamelCase					,  UpperCamelCase       ):
							if row >= len(UpperCamelCase       ):
														solution.append(UpperCamelCase       )
														printboard(UpperCamelCase       )
														print()
														return True
							for i in range(len(UpperCamelCase       )       ):
														if is_safe(UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase       ):
																					A		=  1
																					solve(UpperCamelCase					,  row + 1       )
																					A		=  0
							return False






def 					A__				(			UpperCamelCase       ):
							for i in range(len(UpperCamelCase       )       ):
														for j in range(len(UpperCamelCase       )       ):
																					if board[i][j] == 1:
																												print("Q"					,  end=" "       )
																					else:
																												print("."					,  end=" "       )
														print()


# n=int(input("The no. of queens"))
_snake_case     :		List[str]       						= 8
_snake_case     :		List[str]       						= [[0 for i in range(n)] for j in range(n)]
solve(board, 0)
print('The total no. of solutions are :', len(solution))


