



'''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 				__lowerCAmelCase:






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




					"""simple docstring"""







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






			def 				_lowercase	(   self  :							List[Any]	):




					"""simple docstring"""







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

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

					SCREAMING_SNAKE_CASE_						:List[str]										=      self.get_config()

					return config, pixel_values, labels






			def 				_lowercase	(   self  :							List[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 				_lowercase	(   self  :							Dict      ,       SCREAMING_SNAKE_CASE  :							Union[str, Any]      ,       SCREAMING_SNAKE_CASE  :							List[str]      ,       SCREAMING_SNAKE_CASE  :							List[Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:int										=      DinatModel(config=SCREAMING_SNAKE_CASE	)
					model.to(SCREAMING_SNAKE_CASE	)
					model.eval()
					SCREAMING_SNAKE_CASE_						:Tuple										=      model(SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      (config.image_size // config.patch_size) // (2 ** (len(config.depths	) - 1))
					SCREAMING_SNAKE_CASE_						:Optional[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 				_lowercase	(   self  :							List[Any]      ,       SCREAMING_SNAKE_CASE  :							Union[str, Any]      ,       SCREAMING_SNAKE_CASE  :							List[str]      ,       SCREAMING_SNAKE_CASE  :							Optional[Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Tuple										=      DinatForImageClassification(SCREAMING_SNAKE_CASE	)
					model.to(SCREAMING_SNAKE_CASE	)
					model.eval()
					SCREAMING_SNAKE_CASE_						:str										=      model(SCREAMING_SNAKE_CASE      ,       labels=SCREAMING_SNAKE_CASE	)
					self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.num_labels)	)

					# test greyscale images
					SCREAMING_SNAKE_CASE_						:str										=      1
					SCREAMING_SNAKE_CASE_						:Any										=      DinatForImageClassification(SCREAMING_SNAKE_CASE	)
					model.to(SCREAMING_SNAKE_CASE	)
					model.eval()

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






			def 				_lowercase	(   self  :							str      ,       SCREAMING_SNAKE_CASE  :							Dict      ,       SCREAMING_SNAKE_CASE  :							Tuple      ,       SCREAMING_SNAKE_CASE  :							Optional[int]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Dict										=      DinatBackbone(config=SCREAMING_SNAKE_CASE	)
					model.to(SCREAMING_SNAKE_CASE	)
					model.eval()
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      model(SCREAMING_SNAKE_CASE	)

					# 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_						:Union[str, Any]										=      None
					SCREAMING_SNAKE_CASE_						:Dict										=      DinatBackbone(config=SCREAMING_SNAKE_CASE	)
					model.to(SCREAMING_SNAKE_CASE	)
					model.eval()
					SCREAMING_SNAKE_CASE_						:List[Any]										=      model(SCREAMING_SNAKE_CASE	)

					# 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	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      self.prepare_config_and_inputs()
					SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:List[str]										=      config_and_inputs
					SCREAMING_SNAKE_CASE_						:Tuple										=      {'pixel_values': pixel_values}
					return config, inputs_dict




@require_natten
@require_torch
class 				__lowerCAmelCase(							lowerCAmelCase__		,			lowerCAmelCase__		,			unittest.TestCase      ):
			__snake_case  :		Dict		=      (
			    (
			        DinatModel,
			        DinatForImageClassification,
			        DinatBackbone,
			    )
			    if is_torch_available()
			    else ()
			)
			__snake_case  :		str		=      (
			    {'feature-extraction': DinatModel, 'image-classification': DinatForImageClassification}
			    if is_torch_available()
			    else {}
			)
			__snake_case  :		str		=      False

			__snake_case  :		str		=      False
			__snake_case  :		List[str]		=      False
			__snake_case  :		int		=      False
			__snake_case  :		Dict		=      False






			def 				_lowercase	(   self  :							Any	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:str										=      DinatModelTester(self	)
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      ConfigTester(self      ,       config_class=SCREAMING_SNAKE_CASE      ,       embed_dim=37	)






			def 				_lowercase	(   self  :							List[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  :							Union[str, Any]	):




					"""simple docstring"""







					return






			def 				_lowercase	(   self  :							Tuple	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE	)






			def 				_lowercase	(   self  :							int	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:str										=      self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE	)






			def 				_lowercase	(   self  :							List[str]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:int										=      self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_backbone(*SCREAMING_SNAKE_CASE	)






			@unittest.skip(reason='Dinat does not use inputs_embeds'	)
			def 				_lowercase	(   self  :							str	):




					"""simple docstring"""







					pass






			@unittest.skip(reason='Dinat does not use feedforward chunking'	)
			def 				_lowercase	(   self  :							Dict	):




					"""simple docstring"""







					pass






			def 				_lowercase	(   self  :							int	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:int										=      self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
							SCREAMING_SNAKE_CASE_						:List[str]										=      model_class(SCREAMING_SNAKE_CASE	)
							self.assertIsInstance(model.get_input_embeddings()      ,       (nn.Module)	)
							SCREAMING_SNAKE_CASE_						:Any										=      model.get_output_embeddings()
							self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE      ,       nn.Linear	)	)






			def 				_lowercase	(   self  :							Tuple	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:Optional[int]										=      self.model_tester.prepare_config_and_inputs_for_common()

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

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






			def 				_lowercase	(   self  :							Tuple	):




					"""simple docstring"""







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






			def 				_lowercase	(   self  :							int      ,       SCREAMING_SNAKE_CASE  :							Optional[int]      ,       SCREAMING_SNAKE_CASE  :							Tuple      ,       SCREAMING_SNAKE_CASE  :							List[Any]      ,       SCREAMING_SNAKE_CASE  :							Tuple	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      model_class(SCREAMING_SNAKE_CASE	)
					model.to(SCREAMING_SNAKE_CASE	)
					model.eval()

					with torch.no_grad():
							SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      model(**self._prepare_for_class(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)	)

					SCREAMING_SNAKE_CASE_						:int										=      outputs.hidden_states

					SCREAMING_SNAKE_CASE_						:List[Any]										=      getattr(
					    self.model_tester      ,       'expected_num_hidden_layers'      ,       len(self.model_tester.depths	) + 1	)
					self.assertEqual(len(SCREAMING_SNAKE_CASE	)      ,       SCREAMING_SNAKE_CASE	)

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

					SCREAMING_SNAKE_CASE_						:List[Any]										=      image_size[0] // patch_size[0]
					SCREAMING_SNAKE_CASE_						:str										=      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":
							SCREAMING_SNAKE_CASE_						:int										=      outputs.reshaped_hidden_states
							self.assertEqual(len(SCREAMING_SNAKE_CASE	)      ,       SCREAMING_SNAKE_CASE	)

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






			def 				_lowercase	(   self  :							Any	):




					"""simple docstring"""







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

					SCREAMING_SNAKE_CASE_						:str										=      (
					    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_						:str										=      True
							self.check_hidden_states_output(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)

							# check that output_hidden_states also work using config
							del inputs_dict["output_hidden_states"]
							SCREAMING_SNAKE_CASE_						:Tuple										=      True

							self.check_hidden_states_output(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)






			@slow
			def 				_lowercase	(   self  :							Optional[int]	):




					"""simple docstring"""







					for model_name in DINAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							SCREAMING_SNAKE_CASE_						:str										=      DinatModel.from_pretrained(SCREAMING_SNAKE_CASE	)
							self.assertIsNotNone(SCREAMING_SNAKE_CASE	)






			def 				_lowercase	(   self  :							Union[str, Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:int										=      self.model_tester.prepare_config_and_inputs_for_common()

					SCREAMING_SNAKE_CASE_						:int										=      _config_zero_init(SCREAMING_SNAKE_CASE	)
					for model_class in self.all_model_classes:
							SCREAMING_SNAKE_CASE_						:List[Any]										=      model_class(config=SCREAMING_SNAKE_CASE	)
							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 				__lowerCAmelCase(							unittest.TestCase      ):






			@cached_property
			def 				_lowercase	(   self  :							int	):




					"""simple docstring"""







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






			@slow
			def 				_lowercase	(   self  :							Any	):




					"""simple docstring"""







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

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

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

					# verify the logits
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      torch.Size((1, 1_000)	)
					self.assertEqual(outputs.logits.shape      ,       SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      torch.tensor([-0.15_45, -0.76_67, 0.46_42]	).to(SCREAMING_SNAKE_CASE	)
					self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,       SCREAMING_SNAKE_CASE      ,       atol=1E-4	)	)




@require_torch
@require_natten
class 				__lowerCAmelCase(							unittest.TestCase		,			lowerCAmelCase__      ):
			__snake_case  :		Optional[int]		=      (DinatBackbone,) if is_torch_available() else ()
			__snake_case  :		Union[str, Any]		=      DinatConfig






			def 				_lowercase	(   self  :							Any	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[Any]										=      DinatModelTester(self	)

"""simple docstring"""




from collections.abc import Callable

UpperCAmelCase       :    Optional[int]									=			Callable[[float], float]  # type alias for a real -> real function



def 				__a       (			_lowercase							, _lowercase							, _lowercase							, ):







					"""simple docstring"""





					lowerCamelCase__		:   Tuple  				=					float(_lowercase						)
					while True:
										try:
															lowerCamelCase__		:   str  				=					prev_guess - function(_lowercase						) / derivative(_lowercase						)
										except ZeroDivisionError:
															raise ZeroDivisionError('''Could not find root'''						) from None
										if abs(prev_guess - next_guess						) < 10**-5:
															return next_guess
										lowerCamelCase__		:   List[str]  				=					next_guess



def 				__a       (			_lowercase						):







					"""simple docstring"""





					return (x**3) - (2 * x) - 5



def 				__a       (			_lowercase						):







					"""simple docstring"""





					return 3 * (x**2) - 2


if __name__ == "__main__":
					print(newton(f, fa, 3))






