





import collections
import inspect
import unittest

from transformers import NatConfig
from transformers.testing_utils import require_natten, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
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 NatBackbone, NatForImageClassification, NatModel
      from transformers.models.nat.modeling_nat import NAT_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
      from PIL import Image

      from transformers import AutoImageProcessor

class 						UpperCamelCase__					:



     '''simple docstring'''

     def __init__(				self		:       Optional[Any]		,lowerCamelCase__		:       Dict		,lowerCamelCase__		:       List[Any]=13		,lowerCamelCase__		:       List[Any]=64		,lowerCamelCase__		:       Any=4		,lowerCamelCase__		:       Optional[int]=3		,lowerCamelCase__		:       Optional[int]=16		,lowerCamelCase__		:       Optional[Any]=[1, 2, 1]		,lowerCamelCase__		:       Optional[Any]=[2, 4, 8]		,lowerCamelCase__		:       str=3		,lowerCamelCase__		:       List[str]=2.0		,lowerCamelCase__		:       List[Any]=True		,lowerCamelCase__		:       List[str]=0.0		,lowerCamelCase__		:       Optional[int]=0.0		,lowerCamelCase__		:       str=0.1		,lowerCamelCase__		:       Union[str, Any]="gelu"		,lowerCamelCase__		:       Optional[int]=True		,lowerCamelCase__		:       Dict=0.02		,lowerCamelCase__		:       Optional[int]=1e-5		,lowerCamelCase__		:       Any=True		,lowerCamelCase__		:       Optional[Any]=None		,lowerCamelCase__		:       Optional[Any]=True		,lowerCamelCase__		:       Optional[Any]=10		,lowerCamelCase__		:       Any=["stage1", "stage2"]		,lowerCamelCase__		:       List[Any]=[1, 2]		,)	->						Dict:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		parent
           SCREAMING_SNAKE_CASE						=		batch_size
           SCREAMING_SNAKE_CASE						=		image_size
           SCREAMING_SNAKE_CASE						=		patch_size
           SCREAMING_SNAKE_CASE						=		num_channels
           SCREAMING_SNAKE_CASE						=		embed_dim
           SCREAMING_SNAKE_CASE						=		depths
           SCREAMING_SNAKE_CASE						=		num_heads
           SCREAMING_SNAKE_CASE						=		kernel_size
           SCREAMING_SNAKE_CASE						=		mlp_ratio
           SCREAMING_SNAKE_CASE						=		qkv_bias
           SCREAMING_SNAKE_CASE						=		hidden_dropout_prob
           SCREAMING_SNAKE_CASE						=		attention_probs_dropout_prob
           SCREAMING_SNAKE_CASE						=		drop_path_rate
           SCREAMING_SNAKE_CASE						=		hidden_act
           SCREAMING_SNAKE_CASE						=		patch_norm
           SCREAMING_SNAKE_CASE						=		layer_norm_eps
           SCREAMING_SNAKE_CASE						=		initializer_range
           SCREAMING_SNAKE_CASE						=		is_training
           SCREAMING_SNAKE_CASE						=		scope
           SCREAMING_SNAKE_CASE						=		use_labels
           SCREAMING_SNAKE_CASE						=		num_labels
           SCREAMING_SNAKE_CASE						=		out_features
           SCREAMING_SNAKE_CASE						=		out_indices






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Optional[Any]			)	->						Optional[int]:




           '''simple docstring'''


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

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

           SCREAMING_SNAKE_CASE						=		self.get_config()

           return config, pixel_values, labels






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




           '''simple docstring'''


           return NatConfig(
               num_labels=self.num_labels		,image_size=self.image_size		,patch_size=self.patch_size		,num_channels=self.num_channels		,embed_dim=self.embed_dim		,depths=self.depths		,num_heads=self.num_heads		,kernel_size=self.kernel_size		,mlp_ratio=self.mlp_ratio		,qkv_bias=self.qkv_bias		,hidden_dropout_prob=self.hidden_dropout_prob		,attention_probs_dropout_prob=self.attention_probs_dropout_prob		,drop_path_rate=self.drop_path_rate		,hidden_act=self.hidden_act		,patch_norm=self.patch_norm		,layer_norm_eps=self.layer_norm_eps		,initializer_range=self.initializer_range		,out_features=self.out_features		,out_indices=self.out_indices		,)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       str		,lowerCamelCase__		:       str		,lowerCamelCase__		:       Dict		,lowerCamelCase__		:       Dict			)	->						int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		NatModel(config=lowerCamelCase__			)
           model.to(lowerCamelCase__			)
           model.eval()
           SCREAMING_SNAKE_CASE						=		model(lowerCamelCase__			)

           SCREAMING_SNAKE_CASE						=		(config.image_size // config.patch_size) // (2 ** (len(config.depths			) - 1))
           SCREAMING_SNAKE_CASE						=		int(config.embed_dim * 2 ** (len(config.depths			) - 1)			)

           self.parent.assertEqual(
               result.last_hidden_state.shape		,(self.batch_size, expected_height, expected_width, expected_dim)			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Any		,lowerCamelCase__		:       Optional[Any]		,lowerCamelCase__		:       Dict		,lowerCamelCase__		:       Dict			)	->						Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		NatForImageClassification(lowerCamelCase__			)
           model.to(lowerCamelCase__			)
           model.eval()
           SCREAMING_SNAKE_CASE						=		model(lowerCamelCase__		,labels=lowerCamelCase__			)
           self.parent.assertEqual(result.logits.shape		,(self.batch_size, self.num_labels)			)

           # test greyscale images
           SCREAMING_SNAKE_CASE						=		1
           SCREAMING_SNAKE_CASE						=		NatForImageClassification(lowerCamelCase__			)
           model.to(lowerCamelCase__			)
           model.eval()

           SCREAMING_SNAKE_CASE						=		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
           SCREAMING_SNAKE_CASE						=		model(lowerCamelCase__			)
           self.parent.assertEqual(result.logits.shape		,(self.batch_size, self.num_labels)			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       List[str]		,lowerCamelCase__		:       Tuple		,lowerCamelCase__		:       int		,lowerCamelCase__		:       Optional[Any]			)	->						List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		NatBackbone(config=lowerCamelCase__			)
           model.to(lowerCamelCase__			)
           model.eval()
           SCREAMING_SNAKE_CASE						=		model(lowerCamelCase__			)

           # verify hidden states
           self.parent.assertEqual(len(result.feature_maps			)		,len(config.out_features			)			)
           self.parent.assertListEqual(list(result.feature_maps[0].shape			)		,[self.batch_size, model.channels[0], 16, 16]			)

           # verify channels
           self.parent.assertEqual(len(model.channels			)		,len(config.out_features			)			)

           # verify backbone works with out_features=None
           SCREAMING_SNAKE_CASE						=		None
           SCREAMING_SNAKE_CASE						=		NatBackbone(config=lowerCamelCase__			)
           model.to(lowerCamelCase__			)
           model.eval()
           SCREAMING_SNAKE_CASE						=		model(lowerCamelCase__			)

           # verify feature maps
           self.parent.assertEqual(len(result.feature_maps			)		,1			)
           self.parent.assertListEqual(list(result.feature_maps[0].shape			)		,[self.batch_size, model.channels[-1], 4, 4]			)

           # verify channels
           self.parent.assertEqual(len(model.channels			)		,1			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Dict			)	->						Any:




           '''simple docstring'''


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

@require_natten
@require_torch
class 						UpperCamelCase__					( SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, unittest.TestCase  ):



     '''simple docstring'''

     __snake_case       :      List[Any]						      =     (
         (
             NatModel,
             NatForImageClassification,
             NatBackbone,
         )
         if is_torch_available()
         else ()
     )
     __snake_case       :      List[Any]						      =     (
         {"feature-extraction": NatModel, "image-classification": NatForImageClassification}
         if is_torch_available()
         else {}
     )
     __snake_case       :      Dict						      =     False

     __snake_case       :      Dict						      =     False
     __snake_case       :      int						      =     False
     __snake_case       :      Tuple						      =     False
     __snake_case       :      str						      =     False






     def 							SCREAMING_SNAKE_CASE__    (				self		:       List[str]			)	->						Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		NatModelTester(self			)
           SCREAMING_SNAKE_CASE						=		ConfigTester(self		,config_class=lowerCamelCase__		,embed_dim=37			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       List[Any]			)	->						List[str]:




           '''simple docstring'''


           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 							SCREAMING_SNAKE_CASE__    (				self		:       Optional[Any]			)	->						List[Any]:




           '''simple docstring'''


           return






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Dict			)	->						Dict:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowerCamelCase__			)






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Dict			)	->						str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_backbone(*lowerCamelCase__			)






     @unittest.skip(reason="""Nat does not use inputs_embeds"""			)
     def 							SCREAMING_SNAKE_CASE__    (				self		:       List[Any]			)	->						Optional[Any]:




           '''simple docstring'''


           pass






     @unittest.skip(reason="""Nat does not use feedforward chunking"""			)
     def 							SCREAMING_SNAKE_CASE__    (				self		:       Optional[Any]			)	->						str:




           '''simple docstring'''


           pass






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Optional[Any]			)	->						Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		self.model_tester.prepare_config_and_inputs_for_common()

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






     def 							SCREAMING_SNAKE_CASE__    (				self		:       int			)	->						str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		self.model_tester.prepare_config_and_inputs_for_common()

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

                 SCREAMING_SNAKE_CASE						=		['pixel_values']
                 self.assertListEqual(arg_names[:1]		,lowerCamelCase__			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       List[str]			)	->						Tuple:




           '''simple docstring'''


           self.skipTest("""Nat\'s attention operation is handled entirely by NATTEN."""			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Tuple		,lowerCamelCase__		:       Optional[int]		,lowerCamelCase__		:       Union[str, Any]		,lowerCamelCase__		:       Dict		,lowerCamelCase__		:       List[str]			)	->						int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		model_class(lowerCamelCase__			)
           model.to(lowerCamelCase__			)
           model.eval()

           with torch.no_grad():
                 SCREAMING_SNAKE_CASE						=		model(**self._prepare_for_class(lowerCamelCase__		,lowerCamelCase__			)			)

           SCREAMING_SNAKE_CASE						=		outputs.hidden_states

           SCREAMING_SNAKE_CASE						=		getattr(
               self.model_tester		,"""expected_num_hidden_layers"""		,len(self.model_tester.depths			) + 1			)
           self.assertEqual(len(lowerCamelCase__			)		,lowerCamelCase__			)

           # Nat has a different seq_length
           SCREAMING_SNAKE_CASE						=		(
               config.patch_size
               if isinstance(config.patch_size		,collections.abc.Iterable			)
               else (config.patch_size, config.patch_size)
           )

           SCREAMING_SNAKE_CASE						=		image_size[0] // patch_size[0]
           SCREAMING_SNAKE_CASE						=		image_size[1] // patch_size[1]

           self.assertListEqual(
               list(hidden_states[0].shape[-3:]			)		,[height, width, self.model_tester.embed_dim]		,)

           if model_class.__name__ != "NatBackbone":
                 SCREAMING_SNAKE_CASE						=		outputs.reshaped_hidden_states
                 self.assertEqual(len(lowerCamelCase__			)		,lowerCamelCase__			)

                 SCREAMING_SNAKE_CASE						=		reshaped_hidden_states[0].shape
                 SCREAMING_SNAKE_CASE						=		(
                     reshaped_hidden_states[0].view(lowerCamelCase__		,lowerCamelCase__		,lowerCamelCase__		,lowerCamelCase__			).permute(0		,2		,3		,1			)
                 )
                 self.assertListEqual(
                     list(reshaped_hidden_states.shape[-3:]			)		,[height, width, self.model_tester.embed_dim]		,)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Tuple			)	->						Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		self.model_tester.prepare_config_and_inputs_for_common()

           SCREAMING_SNAKE_CASE						=		(
               self.model_tester.image_size
               if isinstance(self.model_tester.image_size		,collections.abc.Iterable			)
               else (self.model_tester.image_size, self.model_tester.image_size)
           )

           for model_class in self.all_model_classes:
                 SCREAMING_SNAKE_CASE						=		True
                 self.check_hidden_states_output(lowerCamelCase__		,lowerCamelCase__		,lowerCamelCase__		,lowerCamelCase__			)

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

                 self.check_hidden_states_output(lowerCamelCase__		,lowerCamelCase__		,lowerCamelCase__		,lowerCamelCase__			)






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




           '''simple docstring'''


           for model_name in NAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 SCREAMING_SNAKE_CASE						=		NatModel.from_pretrained(lowerCamelCase__			)
                 self.assertIsNotNone(lowerCamelCase__			)






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		self.model_tester.prepare_config_and_inputs_for_common()

           SCREAMING_SNAKE_CASE						=		_config_zero_init(lowerCamelCase__			)
           for model_class in self.all_model_classes:
                 SCREAMING_SNAKE_CASE						=		model_class(config=lowerCamelCase__			)
                 for name, param in model.named_parameters():
                       if "embeddings" not in name and param.requires_grad:
                             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"""		,)

@require_natten
@require_vision
@require_torch
class 						UpperCamelCase__					( unittest.TestCase  ):



     '''simple docstring'''

     @cached_property
     def 							SCREAMING_SNAKE_CASE__    (				self		:       Any			)	->						List[str]:




           '''simple docstring'''


           return AutoImageProcessor.from_pretrained("""shi-labs/nat-mini-in1k-224"""			) if is_vision_available() else None






     @slow
     def 							SCREAMING_SNAKE_CASE__    (				self		:       str			)	->						Dict:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		NatForImageClassification.from_pretrained("""shi-labs/nat-mini-in1k-224"""			).to(lowerCamelCase__			)
           SCREAMING_SNAKE_CASE						=		self.default_image_processor

           SCREAMING_SNAKE_CASE						=		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
           SCREAMING_SNAKE_CASE						=		image_processor(images=lowerCamelCase__		,return_tensors="""pt"""			).to(lowerCamelCase__			)

           # forward pass
           with torch.no_grad():
                 SCREAMING_SNAKE_CASE						=		model(**lowerCamelCase__			)

           # verify the logits
           SCREAMING_SNAKE_CASE						=		torch.Size((1, 1000)			)
           self.assertEqual(outputs.logits.shape		,lowerCamelCase__			)
           SCREAMING_SNAKE_CASE						=		torch.tensor([0.3805, -0.8676, -0.3912]			).to(lowerCamelCase__			)
           self.assertTrue(torch.allclose(outputs.logits[0, :3]		,lowerCamelCase__		,atol=1e-4			)			)

@require_torch
@require_natten
class 						UpperCamelCase__					( unittest.TestCase				, SCREAMING_SNAKE_CASE__  ):



     '''simple docstring'''

     __snake_case       :      str						      =     (NatBackbone,) if is_torch_available() else ()
     __snake_case       :      Optional[int]						      =     NatConfig






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		NatModelTester(self			)







"""simple docstring"""




def 	lowercase__  (      )  ->      Any:






      '''simple docstring'''


      lowercase	:   Dict											= 1

      while True:
            if (
                sorted(str(_UpperCAmelCase					)					)
                == sorted(str(2 * i					)					)
                == sorted(str(3 * i					)					)
                == sorted(str(4 * i					)					)
                == sorted(str(5 * i					)					)
                == sorted(str(6 * i					)					)
            ):
                  return i

            i += 1


if __name__ == "__main__":
     print(solution())
