



"""simple docstring"""




import collections
import inspect
import unittest

from transformers import DinatConfig
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 DinatBackbone, DinatForImageClassification, DinatModel
     from transformers.models.dinat.modeling_dinat import DINAT_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
     from PIL import Image

     from transformers import AutoImageProcessor


class 	a__		:


   def __init__(				self   :				List[str],  lowerCAmelCase   :				Union[str, Any],  lowerCAmelCase   :				Optional[int]=13,  lowerCAmelCase   :				Any=64,  lowerCAmelCase   :				Dict=4,  lowerCAmelCase   :				Any=3,  lowerCAmelCase   :				Tuple=16,  lowerCAmelCase   :				str=[1, 2, 1],  lowerCAmelCase   :				Optional[int]=[2, 4, 8],  lowerCAmelCase   :				Optional[int]=3,  lowerCAmelCase   :				Optional[int]=[[3], [1, 2], [1]],  lowerCAmelCase   :				Dict=2.0,  lowerCAmelCase   :				Dict=True,  lowerCAmelCase   :				int=0.0,  lowerCAmelCase   :				Tuple=0.0,  lowerCAmelCase   :				Tuple=0.1,  lowerCAmelCase   :				Any="gelu",  lowerCAmelCase   :				Dict=True,  lowerCAmelCase   :				Optional[Any]=0.02,  lowerCAmelCase   :				Dict=1e-5,  lowerCAmelCase   :				Optional[int]=True,  lowerCAmelCase   :				Tuple=None,  lowerCAmelCase   :				List[str]=True,  lowerCAmelCase   :				Tuple=10,  lowerCAmelCase   :				int=["stage1", "stage2"],  lowerCAmelCase   :				int=[1, 2],  )     ->							Tuple:
         lowercase	:   Any											= parent
         lowercase	:   Optional[Any]											= batch_size
         lowercase	:   Any											= image_size
         lowercase	:   Union[str, Any]											= patch_size
         lowercase	:   Any											= num_channels
         lowercase	:   Optional[int]											= embed_dim
         lowercase	:   Optional[int]											= depths
         lowercase	:   List[str]											= num_heads
         lowercase	:   str											= kernel_size
         lowercase	:   Tuple											= dilations
         lowercase	:   List[str]											= mlp_ratio
         lowercase	:   Optional[Any]											= qkv_bias
         lowercase	:   Union[str, Any]											= hidden_dropout_prob
         lowercase	:   Dict											= attention_probs_dropout_prob
         lowercase	:   Any											= drop_path_rate
         lowercase	:   Tuple											= hidden_act
         lowercase	:   List[str]											= patch_norm
         lowercase	:   Optional[Any]											= layer_norm_eps
         lowercase	:   Dict											= initializer_range
         lowercase	:   int											= is_training
         lowercase	:   Optional[int]											= scope
         lowercase	:   Optional[int]											= use_labels
         lowercase	:   Any											= num_labels
         lowercase	:   int											= out_features
         lowercase	:   str											= out_indices


   def 							lowercase					(				self   :				Union[str, Any]      )     ->							List[Any]:
         lowercase	:   List[Any]											= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

         lowercase	:   int											= None
         if self.use_labels:
               lowercase	:   Union[str, Any]											= ids_tensor([self.batch_size],  self.num_labels      )

         lowercase	:   Optional[int]											= self.get_config()

         return config, pixel_values, labels


   def 							lowercase					(				self   :				Any      )     ->							Any:
         return DinatConfig(
             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,  dilations=self.dilations,  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 							lowercase					(				self   :				List[str],  lowerCAmelCase   :				Optional[Any],  lowerCAmelCase   :				List[Any],  lowerCAmelCase   :				Optional[Any]      )     ->							Optional[int]:
         lowercase	:   List[Any]											= DinatModel(config=lowerCAmelCase      )
         model.to(lowerCAmelCase      )
         model.eval()
         lowercase	:   Any											= model(lowerCAmelCase      )

         lowercase	:   Optional[int]											= (config.image_size // config.patch_size) // (2 ** (len(config.depths      ) - 1))
         lowercase	:   List[str]											= 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 							lowercase					(				self   :				Any,  lowerCAmelCase   :				int,  lowerCAmelCase   :				Dict,  lowerCAmelCase   :				Optional[Any]      )     ->							List[str]:
         lowercase	:   str											= DinatForImageClassification(lowerCAmelCase      )
         model.to(lowerCAmelCase      )
         model.eval()
         lowercase	:   int											= model(lowerCAmelCase,  labels=lowerCAmelCase      )
         self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.num_labels)      )

         # test greyscale images
         lowercase	:   Optional[Any]											= 1
         lowercase	:   str											= DinatForImageClassification(lowerCAmelCase      )
         model.to(lowerCAmelCase      )
         model.eval()

         lowercase	:   Any											= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
         lowercase	:   Optional[int]											= model(lowerCAmelCase      )
         self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.num_labels)      )


   def 							lowercase					(				self   :				Union[str, Any],  lowerCAmelCase   :				str,  lowerCAmelCase   :				Tuple,  lowerCAmelCase   :				Dict      )     ->							Union[str, Any]:
         lowercase	:   Union[str, Any]											= DinatBackbone(config=lowerCAmelCase      )
         model.to(lowerCAmelCase      )
         model.eval()
         lowercase	:   Tuple											= 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
         lowercase	:   Any											= None
         lowercase	:   Optional[int]											= DinatBackbone(config=lowerCAmelCase      )
         model.to(lowerCAmelCase      )
         model.eval()
         lowercase	:   int											= 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 							lowercase					(				self   :				str      )     ->							List[str]:
         lowercase	:   Tuple											= self.prepare_config_and_inputs()
         lowercase						,     lowercase						,     lowercase	:   Any											= config_and_inputs
         lowercase	:   str											= {'pixel_values': pixel_values}
         return config, inputs_dict




@require_natten
@require_torch
class 	a__		(    SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__,	unittest.TestCase  ):
   _lowerCamelCase       				=					(
       (
           DinatModel,
           DinatForImageClassification,
           DinatBackbone,
       )
       if is_torch_available()
       else ()
   )
   _lowerCamelCase       				=					(
       {'feature-extraction': DinatModel, 'image-classification': DinatForImageClassification}
       if is_torch_available()
       else {}
   )
   _lowerCamelCase       				=					False

   _lowerCamelCase       				=					False
   _lowerCamelCase       				=					False
   _lowerCamelCase       				=					False
   _lowerCamelCase       				=					False


   def 							lowercase					(				self   :				Any      )     ->							List[str]:
         lowercase	:   List[Any]											= DinatModelTester(self      )
         lowercase	:   List[str]											= ConfigTester(self,  config_class=lowerCAmelCase,  embed_dim=37      )


   def 							lowercase					(				self   :				str      )     ->							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 							lowercase					(				self   :				Optional[int]      )     ->							Union[str, Any]:
         return


   def 							lowercase					(				self   :				Optional[Any]      )     ->							str:
         lowercase	:   List[Any]											= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowerCAmelCase      )


   def 							lowercase					(				self   :				Any      )     ->							Optional[int]:
         lowercase	:   List[Any]											= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase      )


   def 							lowercase					(				self   :				int      )     ->							int:
         lowercase	:   List[Any]											= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_backbone(*lowerCAmelCase      )


   @unittest.skip(reason='Dinat does not use inputs_embeds'      )
   def 							lowercase					(				self   :				Any      )     ->							Optional[Any]:
         pass


   @unittest.skip(reason='Dinat does not use feedforward chunking'      )
   def 							lowercase					(				self   :				Optional[int]      )     ->							str:
         pass


   def 							lowercase					(				self   :				Optional[Any]      )     ->							Union[str, Any]:
         lowercase						,     lowercase	:   Tuple											= self.model_tester.prepare_config_and_inputs_for_common()

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


   def 							lowercase					(				self   :				Dict      )     ->							List[Any]:
         lowercase						,     lowercase	:   int											= self.model_tester.prepare_config_and_inputs_for_common()

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

               lowercase	:   str											= ['pixel_values']
               self.assertListEqual(arg_names[:1],  lowerCAmelCase      )


   def 							lowercase					(				self   :				Dict      )     ->							Optional[int]:
         self.skipTest('Dinat\'s attention operation is handled entirely by NATTEN.'      )


   def 							lowercase					(				self   :				Dict,  lowerCAmelCase   :				Tuple,  lowerCAmelCase   :				List[Any],  lowerCAmelCase   :				Tuple,  lowerCAmelCase   :				List[str]      )     ->							List[str]:
         lowercase	:   Dict											= model_class(lowerCAmelCase      )
         model.to(lowerCAmelCase      )
         model.eval()

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

         lowercase	:   List[Any]											= outputs.hidden_states

         lowercase	:   Dict											= getattr(
             self.model_tester,  'expected_num_hidden_layers',  len(self.model_tester.depths      ) + 1      )
         self.assertEqual(len(lowerCAmelCase      ),  lowerCAmelCase      )

         # Dinat has a different seq_length
         lowercase	:   str											= (
             config.patch_size
             if isinstance(config.patch_size,  collections.abc.Iterable      )
             else (config.patch_size, config.patch_size)
         )

         lowercase	:   List[Any]											= image_size[0] // patch_size[0]
         lowercase	:   List[Any]											= 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__ != "DinatBackbone":
               lowercase	:   Union[str, Any]											= outputs.reshaped_hidden_states
               self.assertEqual(len(lowerCAmelCase      ),  lowerCAmelCase      )

               lowercase						,     lowercase						,     lowercase						,     lowercase	:   Dict											= reshaped_hidden_states[0].shape
               lowercase	:   List[Any]											= (
                   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 							lowercase					(				self   :				Tuple      )     ->							int:
         lowercase						,     lowercase	:   Optional[Any]											= self.model_tester.prepare_config_and_inputs_for_common()

         lowercase	:   int											= (
             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:
               lowercase	:   List[Any]											= 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"]
               lowercase	:   Any											= True

               self.check_hidden_states_output(lowerCAmelCase,  lowerCAmelCase,  lowerCAmelCase,  lowerCAmelCase      )


   @slow
   def 							lowercase					(				self   :				Optional[int]      )     ->							Union[str, Any]:
         for model_name in DINAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               lowercase	:   List[Any]											= DinatModel.from_pretrained(lowerCAmelCase      )
               self.assertIsNotNone(lowerCAmelCase      )






   def 							lowercase					(				self   :				Optional[Any]      )     ->							Tuple:
         lowercase						,     lowercase	:   Any											= self.model_tester.prepare_config_and_inputs_for_common()

         lowercase	:   Optional[Any]											= _config_zero_init(lowerCAmelCase      )
         for model_class in self.all_model_classes:
               lowercase	:   List[str]											= 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 	a__		(    unittest.TestCase  ):


   @cached_property
   def 							lowercase					(				self   :				Optional[Any]      )     ->							str:
         return AutoImageProcessor.from_pretrained('shi-labs/dinat-mini-in1k-224'      ) if is_vision_available() else None






   @slow
   def 							lowercase					(				self   :				str      )     ->							Any:
         lowercase	:   Union[str, Any]											= DinatForImageClassification.from_pretrained('shi-labs/dinat-mini-in1k-224'      ).to(lowerCAmelCase      )
         lowercase	:   Union[str, Any]											= self.default_image_processor

         lowercase	:   str											= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
         lowercase	:   Any											= image_processor(images=lowerCAmelCase,  return_tensors='pt'      ).to(lowerCAmelCase      )

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

         # verify the logits
         lowercase	:   Optional[Any]											= torch.Size((1, 1000)      )
         self.assertEqual(outputs.logits.shape,  lowerCAmelCase      )
         lowercase	:   List[Any]											= torch.tensor([-0.1545, -0.7667, 0.4642]      ).to(lowerCAmelCase      )
         self.assertTrue(torch.allclose(outputs.logits[0, :3],  lowerCAmelCase,  atol=1e-4      )      )




@require_torch
@require_natten
class 	a__		(    unittest.TestCase,	SCREAMING_SNAKE_CASE__  ):
   _lowerCamelCase       				=					(DinatBackbone,) if is_torch_available() else ()
   _lowerCamelCase       				=					DinatConfig


   def 							lowercase					(				self   :				Optional[Any]      )     ->							int:
         lowercase	:   Tuple											= DinatModelTester(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())
