



import torch.nn as nn

from .imports import is_fpa_available


if is_fpa_available():
		import transformer_engine.pytorch as te





def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase=True       ,			_UpperCAmelCase=True       ,			_UpperCAmelCase=True				):
					"""simple docstring"""






					if not is_fpa_available():
										raise ImportError('Using `convert_model` requires transformer_engine to be installed.'				)
					for name, module in model.named_children():
										if isinstance(__snake_case       ,			nn.Linear				) and to_transformer_engine and _convert_linear:
															# Return early if the linear layer weights are not multiples of 16
															if any(p % 16 != 0 for p in module.weight.shape				):
																				return
															lowercase						   =       module.bias is not None
															lowercase						   =       te.Linear(
															    module.in_features       ,			module.out_features       ,			bias=__snake_case       ,			params_dtype=module.weight.dtype				)
															lowercase						   =       module.weight.data.clone()
															if has_bias:
																				lowercase						   =       module.bias.data.clone()

															setattr(__snake_case       ,			__snake_case       ,			__snake_case				)
										elif isinstance(__snake_case       ,			nn.LayerNorm				) and to_transformer_engine and _convert_ln:
															lowercase						   =       te.LayerNorm(module.normalized_shape[0]       ,			eps=module.eps       ,			params_dtype=module.weight.dtype				)
															lowercase						   =       module.weight.data.clone()
															lowercase						   =       module.bias.data.clone()

															setattr(__snake_case       ,			__snake_case       ,			__snake_case				)
										elif isinstance(__snake_case       ,			te.Linear				) and not to_transformer_engine and _convert_linear:
															lowercase						   =       module.bias is not None
															lowercase						   =       nn.Linear(
															    module.in_features       ,			module.out_features       ,			bias=__snake_case       ,			params_dtype=module.weight.dtype				)
															lowercase						   =       module.weight.data.clone()
															if has_bias:
																				lowercase						   =       module.bias.data.clone()

															setattr(__snake_case       ,			__snake_case       ,			__snake_case				)
										elif isinstance(__snake_case       ,			te.LayerNorm				) and not to_transformer_engine and _convert_ln:
															lowercase						   =       nn.LayerNorm(module.normalized_shape[0]       ,			eps=module.eps       ,			params_dtype=module.weight.dtype				)
															lowercase						   =       module.weight.data.clone()
															lowercase						   =       module.bias.data.clone()

															setattr(__snake_case       ,			__snake_case       ,			__snake_case				)
										else:
															convert_model(
															    __snake_case       ,			to_transformer_engine=__snake_case       ,			_convert_linear=_convert_linear       ,			_convert_ln=_convert_ln       ,			)





def 		__snake_case						(    _UpperCAmelCase				):
					"""simple docstring"""






					if not is_fpa_available():
										raise ImportError('Using `has_transformer_engine_layers` requires transformer_engine to be installed.'				)
					for m in model.modules():
										if isinstance(__snake_case       ,			(te.LayerNorm, te.Linear)				):
															return True
					return False












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[Any]   ,     __UpperCAmelCase   :	Optional[int]   ,     __UpperCAmelCase   :	Union[str, Any]=13   ,     __UpperCAmelCase   :	Optional[int]=64   ,     __UpperCAmelCase   :	List[Any]=4   ,     __UpperCAmelCase   :	Dict=3   ,     __UpperCAmelCase   :	Tuple=16   ,     __UpperCAmelCase   :	Union[str, Any]=[1, 2, 1]   ,     __UpperCAmelCase   :	Tuple=[2, 4, 8]   ,     __UpperCAmelCase   :	int=3   ,     __UpperCAmelCase   :	Optional[Any]=[[3], [1, 2], [1]]   ,     __UpperCAmelCase   :	Union[str, Any]=2.0   ,     __UpperCAmelCase   :	Optional[int]=True   ,     __UpperCAmelCase   :	List[str]=0.0   ,     __UpperCAmelCase   :	str=0.0   ,     __UpperCAmelCase   :	Tuple=0.1   ,     __UpperCAmelCase   :	Optional[int]="gelu"   ,     __UpperCAmelCase   :	int=True   ,     __UpperCAmelCase   :	Tuple=0.02   ,     __UpperCAmelCase   :	List[Any]=1E-5   ,     __UpperCAmelCase   :	Union[str, Any]=True   ,     __UpperCAmelCase   :	Tuple=None   ,     __UpperCAmelCase   :	Optional[Any]=True   ,     __UpperCAmelCase   :	Optional[int]=10   ,     __UpperCAmelCase   :	Optional[Any]=["stage1", "stage2"]   ,     __UpperCAmelCase   :	Tuple=[1, 2]   ,     )		->					int:

										"""simple docstring"""



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







							def        lowercase__							(    self   :	Dict				)		->					Tuple:

										"""simple docstring"""



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

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

										UpperCamelCase_     				= self.get_config()

										return config, pixel_values, labels







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

										"""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        lowercase__							(    self   :	str   ,     __UpperCAmelCase   :	Tuple   ,     __UpperCAmelCase   :	List[Any]   ,     __UpperCAmelCase   :	Optional[int]				)		->					Union[str, Any]:

										"""simple docstring"""



										UpperCamelCase_     				= DinatModel(config=__UpperCAmelCase				)
										model.to(__UpperCAmelCase				)
										model.eval()
										UpperCamelCase_     				= model(__UpperCAmelCase				)

										UpperCamelCase_     				= UpperCamelCase_     				= (config.image_size // config.patch_size) // (2 ** (len(config.depths				) - 1))
										UpperCamelCase_     				= 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   :	List[str]   ,     __UpperCAmelCase   :	Any   ,     __UpperCAmelCase   :	Optional[Any]   ,     __UpperCAmelCase   :	Optional[Any]				)		->					str:

										"""simple docstring"""



										UpperCamelCase_     				= DinatForImageClassification(__UpperCAmelCase				)
										model.to(__UpperCAmelCase				)
										model.eval()
										UpperCamelCase_     				= model(__UpperCAmelCase   ,     labels=__UpperCAmelCase				)
										self.parent.assertEqual(result.logits.shape   ,     (self.batch_size, self.num_labels)				)

										# test greyscale images
										UpperCamelCase_     				= 1
										UpperCamelCase_     				= DinatForImageClassification(__UpperCAmelCase				)
										model.to(__UpperCAmelCase				)
										model.eval()

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







							def        lowercase__							(    self   :	List[Any]   ,     __UpperCAmelCase   :	Dict   ,     __UpperCAmelCase   :	Union[str, Any]   ,     __UpperCAmelCase   :	Dict				)		->					Any:

										"""simple docstring"""



										UpperCamelCase_     				= DinatBackbone(config=__UpperCAmelCase				)
										model.to(__UpperCAmelCase				)
										model.eval()
										UpperCamelCase_     				= model(__UpperCAmelCase				)

										# 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_     				= None
										UpperCamelCase_     				= DinatBackbone(config=__UpperCAmelCase				)
										model.to(__UpperCAmelCase				)
										model.eval()
										UpperCamelCase_     				= model(__UpperCAmelCase				)

										# 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   :	int				)		->					Any:

										"""simple docstring"""



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







@require_natten
@require_torch
class     A					(				lowerCamelCase_  ,						lowerCamelCase_  ,						unittest.TestCase       ):
							_SCREAMING_SNAKE_CASE  :		int			  =       (
							    (
							        DinatModel,
							        DinatForImageClassification,
							        DinatBackbone,
							    )
							    if is_torch_available()
							    else ()
							)
							_SCREAMING_SNAKE_CASE  :		int			  =       (
							    {'''feature-extraction''': DinatModel, '''image-classification''': DinatForImageClassification}
							    if is_torch_available()
							    else {}
							)
							_SCREAMING_SNAKE_CASE  :		List[str]			  =       False

							_SCREAMING_SNAKE_CASE  :		Any			  =       False
							_SCREAMING_SNAKE_CASE  :		Dict			  =       False
							_SCREAMING_SNAKE_CASE  :		Union[str, Any]			  =       False
							_SCREAMING_SNAKE_CASE  :		Optional[Any]			  =       False







							def        lowercase__							(    self   :	Any				)		->					Any:

										"""simple docstring"""



										UpperCamelCase_     				= DinatModelTester(self				)
										UpperCamelCase_     				= ConfigTester(self   ,     config_class=__UpperCAmelCase   ,     embed_dim=37				)







							def        lowercase__							(    self   :	Optional[Any]				)		->					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        lowercase__							(    self   :	Dict				)		->					List[str]:

										"""simple docstring"""



										return







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

										"""simple docstring"""



										UpperCamelCase_     				= self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__UpperCAmelCase				)







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

										"""simple docstring"""



										UpperCamelCase_     				= self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase				)







							def        lowercase__							(    self   :	Dict				)		->					Optional[int]:

										"""simple docstring"""



										UpperCamelCase_     				= self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_backbone(*__UpperCAmelCase				)







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

										"""simple docstring"""



										pass







							@unittest.skip(reason='Dinat does not use feedforward chunking'				)
							def        lowercase__							(    self   :	List[Any]				)		->					List[Any]:

										"""simple docstring"""



										pass







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

										"""simple docstring"""



										UpperCamelCase_				,   UpperCamelCase_     				= self.model_tester.prepare_config_and_inputs_for_common()

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







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

										"""simple docstring"""



										UpperCamelCase_				,   UpperCamelCase_     				= self.model_tester.prepare_config_and_inputs_for_common()

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

													UpperCamelCase_     				= ['pixel_values']
													self.assertListEqual(arg_names[:1]   ,     __UpperCAmelCase				)







							def        lowercase__							(    self   :	Dict				)		->					Optional[int]:

										"""simple docstring"""



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







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

										"""simple docstring"""



										UpperCamelCase_     				= model_class(__UpperCAmelCase				)
										model.to(__UpperCAmelCase				)
										model.eval()

										with torch.no_grad():
													UpperCamelCase_     				= model(**self._prepare_for_class(__UpperCAmelCase   ,     __UpperCAmelCase				)				)

										UpperCamelCase_     				= outputs.hidden_states

										UpperCamelCase_     				= getattr(
										    self.model_tester   ,     'expected_num_hidden_layers'   ,     len(self.model_tester.depths				) + 1				)
										self.assertEqual(len(__UpperCAmelCase				)   ,     __UpperCAmelCase				)

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

										UpperCamelCase_     				= image_size[0] // patch_size[0]
										UpperCamelCase_     				= 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_     				= outputs.reshaped_hidden_states
													self.assertEqual(len(__UpperCAmelCase				)   ,     __UpperCAmelCase				)

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







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

										"""simple docstring"""



										UpperCamelCase_				,   UpperCamelCase_     				= self.model_tester.prepare_config_and_inputs_for_common()

										UpperCamelCase_     				= (
										    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_     				= True
													self.check_hidden_states_output(__UpperCAmelCase   ,     __UpperCAmelCase   ,     __UpperCAmelCase   ,     __UpperCAmelCase				)

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

													self.check_hidden_states_output(__UpperCAmelCase   ,     __UpperCAmelCase   ,     __UpperCAmelCase   ,     __UpperCAmelCase				)







							@slow
							def        lowercase__							(    self   :	List[Any]				)		->					Dict:

										"""simple docstring"""



										for model_name in DINAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													UpperCamelCase_     				= DinatModel.from_pretrained(__UpperCAmelCase				)
													self.assertIsNotNone(__UpperCAmelCase				)







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

										"""simple docstring"""



										UpperCamelCase_				,   UpperCamelCase_     				= self.model_tester.prepare_config_and_inputs_for_common()

										UpperCamelCase_     				= _config_zero_init(__UpperCAmelCase				)
										for model_class in self.all_model_classes:
													UpperCamelCase_     				= model_class(config=__UpperCAmelCase				)
													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   :	Dict				)		->					Dict:

										"""simple docstring"""



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







							@slow
							def        lowercase__							(    self   :	Tuple				)		->					int:

										"""simple docstring"""



										UpperCamelCase_     				= DinatForImageClassification.from_pretrained('shi-labs/dinat-mini-in1k-224'				).to(__UpperCAmelCase				)
										UpperCamelCase_     				= self.default_image_processor

										UpperCamelCase_     				= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
										UpperCamelCase_     				= image_processor(images=__UpperCAmelCase   ,     return_tensors='pt'				).to(__UpperCAmelCase				)

										# forward pass
										with torch.no_grad():
													UpperCamelCase_     				= model(**__UpperCAmelCase				)

										# verify the logits
										UpperCamelCase_     				= torch.Size((1, 1000)				)
										self.assertEqual(outputs.logits.shape   ,     __UpperCAmelCase				)
										UpperCamelCase_     				= torch.tensor([-0.1_545, -0.7_667, 0.4_642]				).to(__UpperCAmelCase				)
										self.assertTrue(torch.allclose(outputs.logits[0, :3]   ,     __UpperCAmelCase   ,     atol=1E-4				)				)







@require_torch
@require_natten
class     A					(				unittest.TestCase  ,						lowerCamelCase_       ):
							_SCREAMING_SNAKE_CASE  :		str			  =       (DinatBackbone,) if is_torch_available() else ()
							_SCREAMING_SNAKE_CASE  :		int			  =       DinatConfig







							def        lowercase__							(    self   :	Optional[Any]				)		->					List[Any]:

										"""simple docstring"""



										UpperCamelCase_     				= DinatModelTester(self				)
