






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_      :






 '''simple docstring'''




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

     """simple docstring"""



     _UpperCAmelCase			: List[str]     					=			parent
     _UpperCAmelCase			: List[str]     					=			batch_size
     _UpperCAmelCase			: Tuple     					=			image_size
     _UpperCAmelCase			: List[str]     					=			patch_size
     _UpperCAmelCase			: int     					=			num_channels
     _UpperCAmelCase			: Any     					=			embed_dim
     _UpperCAmelCase			: int     					=			depths
     _UpperCAmelCase			: Union[str, Any]     					=			num_heads
     _UpperCAmelCase			: List[str]     					=			kernel_size
     _UpperCAmelCase			: Tuple     					=			dilations
     _UpperCAmelCase			: List[str]     					=			mlp_ratio
     _UpperCAmelCase			: Optional[Any]     					=			qkv_bias
     _UpperCAmelCase			: Optional[int]     					=			hidden_dropout_prob
     _UpperCAmelCase			: List[str]     					=			attention_probs_dropout_prob
     _UpperCAmelCase			: Optional[int]     					=			drop_path_rate
     _UpperCAmelCase			: Optional[Any]     					=			hidden_act
     _UpperCAmelCase			: int     					=			patch_norm
     _UpperCAmelCase			: int     					=			layer_norm_eps
     _UpperCAmelCase			: Optional[Any]     					=			initializer_range
     _UpperCAmelCase			: Dict     					=			is_training
     _UpperCAmelCase			: Optional[Any]     					=			scope
     _UpperCAmelCase			: Tuple     					=			use_labels
     _UpperCAmelCase			: Any     					=			num_labels
     _UpperCAmelCase			: Optional[int]     					=			out_features
     _UpperCAmelCase			: str     					=			out_indices


 def 	snake_case__			(		self)	->						Optional[Any]:

     """simple docstring"""



     _UpperCAmelCase			: int     					=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

     _UpperCAmelCase			: Union[str, Any]     					=			self.get_config()

     return config, pixel_values, labels


 def 	snake_case__			(		self)	->						Dict:

     """simple docstring"""



     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 	snake_case__			(		self							,					_A							,					_A							,					_A)	->						Any:

     """simple docstring"""



     _UpperCAmelCase			: str     					=			DinatModel(config=_A)
     model.to(_A)
     model.eval()
     _UpperCAmelCase			: Optional[int]     					=			model(_A)

     _UpperCAmelCase			: Dict     					=			(config.image_size // config.patch_size) // (2 ** (len(config.depths) - 1))
     _UpperCAmelCase			: int     					=			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 	snake_case__			(		self							,					_A							,					_A							,					_A)	->						Dict:

     """simple docstring"""



     _UpperCAmelCase			: Any     					=			DinatForImageClassification(_A)
     model.to(_A)
     model.eval()
     _UpperCAmelCase			: Tuple     					=			model(_A							,					labels=_A)
     self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.num_labels))

     # test greyscale images
     _UpperCAmelCase			: Optional[Any]     					=			1
     _UpperCAmelCase			: int     					=			DinatForImageClassification(_A)
     model.to(_A)
     model.eval()

     _UpperCAmelCase			: Dict     					=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
     _UpperCAmelCase			: Union[str, Any]     					=			model(_A)
     self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.num_labels))


 def 	snake_case__			(		self							,					_A							,					_A							,					_A)	->						Optional[int]:

     """simple docstring"""



     _UpperCAmelCase			: List[str]     					=			DinatBackbone(config=_A)
     model.to(_A)
     model.eval()
     _UpperCAmelCase			: Any     					=			model(_A)

     # 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
     _UpperCAmelCase			: int     					=			None
     _UpperCAmelCase			: str     					=			DinatBackbone(config=_A)
     model.to(_A)
     model.eval()
     _UpperCAmelCase			: int     					=			model(_A)

     # 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 	snake_case__			(		self)	->						Any:

     """simple docstring"""



     _UpperCAmelCase			: Optional[Any]     					=			self.prepare_config_and_inputs()
     _UpperCAmelCase			: Union[str, Any]     					=			config_and_inputs
     _UpperCAmelCase			: Dict     					=			{"pixel_values": pixel_values}
     return config, inputs_dict



@require_natten
@require_torch
class 							A_      (  A__	,				A__	,				unittest.TestCase     ):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:     Union[str, Any]	=   (
     (
         DinatModel,
         DinatForImageClassification,
         DinatBackbone,
     )
     if is_torch_available()
     else ()
 )
 _SCREAMING_SNAKE_CASE							:     Union[str, Any]	=   (
     {"feature-extraction": DinatModel, "image-classification": DinatForImageClassification}
     if is_torch_available()
     else {}
 )
 _SCREAMING_SNAKE_CASE							:     List[Any]	=   False

 _SCREAMING_SNAKE_CASE							:     int	=   False
 _SCREAMING_SNAKE_CASE							:     List[Any]	=   False
 _SCREAMING_SNAKE_CASE							:     int	=   False
 _SCREAMING_SNAKE_CASE							:     Dict	=   False


 def 	snake_case__			(		self)	->						Optional[int]:

     """simple docstring"""



     _UpperCAmelCase			: Optional[Any]     					=			DinatModelTester(self)
     _UpperCAmelCase			: Optional[Any]     					=			ConfigTester(self							,					config_class=_A							,					embed_dim=37)


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

     """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 	snake_case__			(		self)	->						List[str]:

     """simple docstring"""



     return


 def 	snake_case__			(		self)	->						Optional[Any]:

     """simple docstring"""



     _UpperCAmelCase			: Any     					=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*_A)


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

     """simple docstring"""



     _UpperCAmelCase			: int     					=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*_A)


 def 	snake_case__			(		self)	->						List[Any]:

     """simple docstring"""



     _UpperCAmelCase			: Optional[Any]     					=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_backbone(*_A)


 @unittest.skip(reason='''Dinat does not use inputs_embeds''')
 def 	snake_case__			(		self)	->						Tuple:

     """simple docstring"""



     pass


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

     """simple docstring"""



     pass


 def 	snake_case__			(		self)	->						int:

     """simple docstring"""



     _UpperCAmelCase			: Tuple     					=			self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         _UpperCAmelCase			: Optional[int]     					=			model_class(_A)
         self.assertIsInstance(model.get_input_embeddings()							,					(nn.Module))
         _UpperCAmelCase			: int     					=			model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(_A							,					nn.Linear))


 def 	snake_case__			(		self)	->						Optional[Any]:

     """simple docstring"""



     _UpperCAmelCase			: List[str]     					=			self.model_tester.prepare_config_and_inputs_for_common()

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

         _UpperCAmelCase			: List[str]     					=			["pixel_values"]
         self.assertListEqual(arg_names[:1]							,					_A)


 def 	snake_case__			(		self)	->						List[str]:

     """simple docstring"""



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


 def 	snake_case__			(		self							,					_A							,					_A							,					_A							,					_A)	->						List[Any]:

     """simple docstring"""



     _UpperCAmelCase			: Dict     					=			model_class(_A)
     model.to(_A)
     model.eval()

     with torch.no_grad():
         _UpperCAmelCase			: Any     					=			model(**self._prepare_for_class(_A							,					_A))

     _UpperCAmelCase			: Any     					=			outputs.hidden_states

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

     # Dinat has a different seq_length
     _UpperCAmelCase			: List[Any]     					=			(
         config.patch_size
         if isinstance(config.patch_size							,					collections.abc.Iterable)
         else (config.patch_size, config.patch_size)
     )

     _UpperCAmelCase			: Union[str, Any]     					=			image_size[0] // patch_size[0]
     _UpperCAmelCase			: Dict     					=			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":
         _UpperCAmelCase			: int     					=			outputs.reshaped_hidden_states
         self.assertEqual(len(_A)							,					_A)

         _UpperCAmelCase			: List[Any]     					=			reshaped_hidden_states[0].shape
         _UpperCAmelCase			: int     					=			(
             reshaped_hidden_states[0].view(_A							,					_A							,					_A							,					_A).permute(0							,					2							,					3							,					1)
         )
         self.assertListEqual(
             list(reshaped_hidden_states.shape[-3:])							,					[height, width, self.model_tester.embed_dim]							,					)


 def 	snake_case__			(		self)	->						List[str]:

     """simple docstring"""



     _UpperCAmelCase			: Union[str, Any]     					=			self.model_tester.prepare_config_and_inputs_for_common()

     _UpperCAmelCase			: Optional[Any]     					=			(
         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:
         _UpperCAmelCase			: Optional[int]     					=			True
         self.check_hidden_states_output(_A							,					_A							,					_A							,					_A)

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         _UpperCAmelCase			: List[Any]     					=			True

         self.check_hidden_states_output(_A							,					_A							,					_A							,					_A)


 @slow
 def 	snake_case__			(		self)	->						int:

     """simple docstring"""



     for model_name in DINAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _UpperCAmelCase			: int     					=			DinatModel.from_pretrained(_A)
         self.assertIsNotNone(_A)


 def 	snake_case__			(		self)	->						Tuple:

     """simple docstring"""



     _UpperCAmelCase			: List[str]     					=			self.model_tester.prepare_config_and_inputs_for_common()

     _UpperCAmelCase			: Any     					=			_config_zero_init(_A)
     for model_class in self.all_model_classes:
         _UpperCAmelCase			: Optional[int]     					=			model_class(config=_A)
         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     ):






 '''simple docstring'''




 @cached_property
 def 	snake_case__			(		self)	->						Optional[Any]:

     """simple docstring"""



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


 @slow
 def 	snake_case__			(		self)	->						str:

     """simple docstring"""



     _UpperCAmelCase			: Dict     					=			DinatForImageClassification.from_pretrained('''shi-labs/dinat-mini-in1k-224''').to(_A)
     _UpperCAmelCase			: Any     					=			self.default_image_processor

     _UpperCAmelCase			: List[str]     					=			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
     _UpperCAmelCase			: Optional[Any]     					=			image_processor(images=_A							,					return_tensors='''pt''').to(_A)

     # forward pass
     with torch.no_grad():
         _UpperCAmelCase			: Any     					=			model(**_A)

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



@require_torch
@require_natten
class 							A_      (  unittest.TestCase	,				A__     ):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:     Tuple	=   (DinatBackbone,) if is_torch_available() else ()
 _SCREAMING_SNAKE_CASE							:     List[Any]	=   DinatConfig


 def 	snake_case__			(		self)	->						Optional[int]:

     """simple docstring"""



     _UpperCAmelCase			: Tuple     					=			DinatModelTester(self)












def        _lowerCamelCase		(  __A      :							dict						,						__A      :							int	) ->   str:
    if not root or root not in binary_tree_mirror_dictionary:
        return
    _UpperCAmelCase					,	_UpperCAmelCase			: int     					=			binary_tree_mirror_dictionary[root][:2]
    _UpperCAmelCase			: int     					=			[right_child, left_child]
    binary_tree_mirror_dict(__A						,						__A	)
    binary_tree_mirror_dict(__A						,						__A	)

def        _lowerCamelCase		(  __A      :							dict						,						__A      :							int = 1	) ->   dict:
    if not binary_tree:
        raise ValueError('''binary tree cannot be empty'''	)
    if root not in binary_tree:
        _UpperCAmelCase			: Dict     					=			f'''root {root} is not present in the binary_tree'''
        raise ValueError(__A	)
    _UpperCAmelCase			: int     					=			dict(__A	)
    binary_tree_mirror_dict(__A						,						__A	)
    return binary_tree_mirror_dictionary


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE								=  {1: [2, 3], 2: [4, 5], 3: [6, 7], 7: [8, 9]}
  print(F'Binary tree: {binary_tree}')
  SCREAMING_SNAKE_CASE								=  binary_tree_mirror(binary_tree, 5)
  print(F'Binary tree mirror: {binary_tree_mirror_dictionary}')




