





"""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						:       Dict,					lowerCAmelCase__						:       int,					lowerCAmelCase__						:       Any=1_3,					lowerCAmelCase__						:       str=6_4,					lowerCAmelCase__						:       Optional[int]=4,					lowerCAmelCase__						:       str=3,					lowerCAmelCase__						:       Optional[Any]=1_6,					lowerCAmelCase__						:       Tuple=[1, 2, 1],					lowerCAmelCase__						:       int=[2, 4, 8],					lowerCAmelCase__						:       Optional[Any]=3,					lowerCAmelCase__						:       str=[[3], [1, 2], [1]],					lowerCAmelCase__						:       Dict=2.0,					lowerCAmelCase__						:       Tuple=True,					lowerCAmelCase__						:       List[Any]=0.0,					lowerCAmelCase__						:       Tuple=0.0,					lowerCAmelCase__						:       Tuple=0.1,					lowerCAmelCase__						:       List[str]="gelu",					lowerCAmelCase__						:       Optional[Any]=True,					lowerCAmelCase__						:       Dict=0.02,					lowerCAmelCase__						:       Optional[Any]=1e-5,					lowerCAmelCase__						:       List[str]=True,					lowerCAmelCase__						:       int=None,					lowerCAmelCase__						:       Any=True,					lowerCAmelCase__						:       Optional[Any]=1_0,					lowerCAmelCase__						:       Any=["stage1", "stage2"],					lowerCAmelCase__						:       Union[str, Any]=[1, 2],					)      -> Any:




										'''simple docstring'''



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






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




										'''simple docstring'''



										_UpperCamelCase     :   Dict      =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

										_UpperCamelCase     :   Dict      =       None
										if self.use_labels:
																_UpperCamelCase     :   int      =       ids_tensor([self.batch_size],					self.num_labels				)

										_UpperCamelCase     :   str      =       self.get_config()

										return config, pixel_values, labels






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




										'''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						:       Optional[Any],					lowerCAmelCase__						:       Union[str, Any],					lowerCAmelCase__						:       Dict,					lowerCAmelCase__						:       List[str]				)      -> List[Any]:




										'''simple docstring'''



										_UpperCamelCase     :   int      =       DinatModel(config=lowerCAmelCase__				)
										model.to(lowerCAmelCase__				)
										model.eval()
										_UpperCamelCase     :   List[str]      =       model(lowerCAmelCase__				)

										_UpperCamelCase     :   Tuple      =       (config.image_size // config.patch_size) // (2 ** (len(config.depths				) - 1))
										_UpperCamelCase     :   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 							snake_case      (					self						:       Any,					lowerCAmelCase__						:       Optional[int],					lowerCAmelCase__						:       List[Any],					lowerCAmelCase__						:       Optional[Any]				)      -> List[str]:




										'''simple docstring'''



										_UpperCamelCase     :   List[str]      =       DinatForImageClassification(lowerCAmelCase__				)
										model.to(lowerCAmelCase__				)
										model.eval()
										_UpperCamelCase     :   Optional[Any]      =       model(lowerCAmelCase__,					labels=lowerCAmelCase__				)
										self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.num_labels)				)

										# test greyscale images
										_UpperCamelCase     :   Any      =       1
										_UpperCamelCase     :   List[str]      =       DinatForImageClassification(lowerCAmelCase__				)
										model.to(lowerCAmelCase__				)
										model.eval()

										_UpperCamelCase     :   str      =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
										_UpperCamelCase     :   Optional[int]      =       model(lowerCAmelCase__				)
										self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.num_labels)				)






				def 							snake_case      (					self						:       str,					lowerCAmelCase__						:       Optional[int],					lowerCAmelCase__						:       Optional[int],					lowerCAmelCase__						:       Dict				)      -> Optional[int]:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[int]      =       DinatBackbone(config=lowerCAmelCase__				)
										model.to(lowerCAmelCase__				)
										model.eval()
										_UpperCamelCase     :   List[Any]      =       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], 1_6, 1_6]				)

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

										# verify backbone works with out_features=None
										_UpperCamelCase     :   List[Any]      =       None
										_UpperCamelCase     :   Union[str, Any]      =       DinatBackbone(config=lowerCAmelCase__				)
										model.to(lowerCAmelCase__				)
										model.eval()
										_UpperCamelCase     :   List[Any]      =       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 							snake_case      (					self						:       str				)      -> Optional[Any]:




										'''simple docstring'''



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




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

				UpperCamelCase								=       False
				UpperCamelCase								=       False
				UpperCamelCase								=       False
				UpperCamelCase								=       False






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




										'''simple docstring'''



										_UpperCamelCase     :   Union[str, Any]      =       DinatModelTester(self				)
										_UpperCamelCase     :   Optional[Any]      =       ConfigTester(self,					config_class=lowerCAmelCase__,					embed_dim=3_7				)






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




										'''simple docstring'''



										return






				def 							snake_case      (					self						:       Dict				)      -> str:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[int]      =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase__				)






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




										'''simple docstring'''



										_UpperCamelCase     :   Any      =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__				)






				def 							snake_case      (					self						:       Any				)      -> int:




										'''simple docstring'''



										_UpperCamelCase     :   int      =       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 							snake_case      (					self						:       str				)      -> int:




										'''simple docstring'''



										pass






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




										'''simple docstring'''



										pass






				def 							snake_case      (					self						:       Any				)      -> str:




										'''simple docstring'''



										_UpperCamelCase  ,				_UpperCamelCase     :   List[Any]      =       self.model_tester.prepare_config_and_inputs_for_common()

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






				def 							snake_case      (					self						:       Optional[Any]				)      -> Tuple:




										'''simple docstring'''



										_UpperCamelCase  ,				_UpperCamelCase     :   str      =       self.model_tester.prepare_config_and_inputs_for_common()

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

																_UpperCamelCase     :   str      =       ['''pixel_values''']
																self.assertListEqual(arg_names[:1],					lowerCAmelCase__				)






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




										'''simple docstring'''



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






				def 							snake_case      (					self						:       str,					lowerCAmelCase__						:       List[Any],					lowerCAmelCase__						:       List[Any],					lowerCAmelCase__						:       int,					lowerCAmelCase__						:       List[Any]				)      -> Optional[int]:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[Any]      =       model_class(lowerCAmelCase__				)
										model.to(lowerCAmelCase__				)
										model.eval()

										with torch.no_grad():
																_UpperCamelCase     :   Optional[int]      =       model(**self._prepare_for_class(lowerCAmelCase__,					lowerCAmelCase__				)				)

										_UpperCamelCase     :   Dict      =       outputs.hidden_states

										_UpperCamelCase     :   List[str]      =       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
										_UpperCamelCase     :   Dict      =       (
										    config.patch_size
										    if isinstance(config.patch_size,					collections.abc.Iterable				)
										    else (config.patch_size, config.patch_size)
										)

										_UpperCamelCase     :   Any      =       image_size[0] // patch_size[0]
										_UpperCamelCase     :   Optional[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":
																_UpperCamelCase     :   Optional[int]      =       outputs.reshaped_hidden_states
																self.assertEqual(len(lowerCAmelCase__				),					lowerCAmelCase__				)

																_UpperCamelCase  ,				_UpperCamelCase  ,				_UpperCamelCase  ,				_UpperCamelCase     :   Optional[Any]      =       reshaped_hidden_states[0].shape
																_UpperCamelCase     :   int      =       (
																    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 							snake_case      (					self						:       Tuple				)      -> Tuple:




										'''simple docstring'''



										_UpperCamelCase  ,				_UpperCamelCase     :   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     :   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"]
																_UpperCamelCase     :   int      =       True

																self.check_hidden_states_output(lowerCAmelCase__,					lowerCAmelCase__,					lowerCAmelCase__,					lowerCAmelCase__				)






				@slow
				def 							snake_case      (					self						:       List[Any]				)      -> Tuple:




										'''simple docstring'''



										for model_name in DINAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																_UpperCamelCase     :   Tuple      =       DinatModel.from_pretrained(lowerCAmelCase__				)
																self.assertIsNotNone(lowerCAmelCase__				)






				def 							snake_case      (					self						:       Any				)      -> Any:




										'''simple docstring'''



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

										_UpperCamelCase     :   Any      =       _config_zero_init(lowerCAmelCase__				)
										for model_class in self.all_model_classes:
																_UpperCamelCase     :   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 							snake_case      (					self						:       List[Any]				)      -> List[str]:




										'''simple docstring'''



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






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




										'''simple docstring'''



										_UpperCamelCase     :   Optional[int]      =       DinatForImageClassification.from_pretrained('''shi-labs/dinat-mini-in1k-224'''				).to(lowerCAmelCase__				)
										_UpperCamelCase     :   List[Any]      =       self.default_image_processor

										_UpperCamelCase     :   Union[str, Any]      =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
										_UpperCamelCase     :   Any      =       image_processor(images=lowerCAmelCase__,					return_tensors='''pt'''				).to(lowerCAmelCase__				)

										# forward pass
										with torch.no_grad():
																_UpperCamelCase     :   str      =       model(**lowerCAmelCase__				)

										# verify the logits
										_UpperCamelCase     :   Any      =       torch.Size((1, 1_0_0_0)				)
										self.assertEqual(outputs.logits.shape,					lowerCAmelCase__				)
										_UpperCamelCase     :   str      =       torch.tensor([-0.1_545, -0.7_667, 0.4_642]				).to(lowerCAmelCase__				)
										self.assertTrue(torch.allclose(outputs.logits[0, :3],					lowerCAmelCase__,					atol=1e-4				)				)




@require_torch
@require_natten
class 					_a		( unittest.TestCase      ,							_lowerCAmelCase							):
				UpperCamelCase								=       (DinatBackbone,) if is_torch_available() else ()
				UpperCamelCase								=       DinatConfig






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




										'''simple docstring'''



										_UpperCamelCase     :   str      =       DinatModelTester(self				)










def 				_lowerCAmelCase			(	_a :       str      )						->      int:
     lowerCAmelCase_       :							List[str]			 =		53_81
     for x in s:
          lowerCAmelCase_       :							Dict			 =		((hash_value << 5) + hash_value) + ord(_a      )
     return hash_value & 0XFFFFFFFF
