


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__				:






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




						def 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							Union[str, Any]      =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

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

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

							return config, pixel_values, labels




						def 					_UpperCamelCase			(   self     ):
							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 					_UpperCamelCase			(   self				,							a_				,							a_				,							a_     ):
							lowerCamelCase_			:							List[Any]      =       DinatModel(config=a_     )
							model.to(a_     )
							model.eval()
							lowerCamelCase_			:							List[str]      =       model(a_     )

							lowerCamelCase_			:							Any      =       (config.image_size // config.patch_size) // (2 ** (len(config.depths     ) - 1))
							lowerCamelCase_			:							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 					_UpperCamelCase			(   self				,							a_				,							a_				,							a_     ):
							lowerCamelCase_			:							Tuple      =       DinatForImageClassification(a_     )
							model.to(a_     )
							model.eval()
							lowerCamelCase_			:							Dict      =       model(a_				,							labels=a_     )
							self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.num_labels)     )

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

							lowerCamelCase_			:							Optional[Any]      =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
							lowerCamelCase_			:							List[Any]      =       model(a_     )
							self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.num_labels)     )




						def 					_UpperCamelCase			(   self				,							a_				,							a_				,							a_     ):
							lowerCamelCase_			:							str      =       DinatBackbone(config=a_     )
							model.to(a_     )
							model.eval()
							lowerCamelCase_			:							Optional[int]      =       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
							lowerCamelCase_			:							str      =       None
							lowerCamelCase_			:							Optional[Any]      =       DinatBackbone(config=a_     )
							model.to(a_     )
							model.eval()
							lowerCamelCase_			:							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 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							Union[str, Any]      =       self.prepare_config_and_inputs()
							lowerCamelCase_			:							Optional[int]      =       config_and_inputs
							lowerCamelCase_			:							List[str]      =       {"pixel_values": pixel_values}
							return config, inputs_dict





@require_natten
@require_torch
class    lowerCAmelCase__				(			__lowerCamelCase,    __lowerCamelCase,    unittest.TestCase       ):






						"""simple docstring"""
						__UpperCAmelCase   :		Optional[Any]  					=	(
						    (
						        DinatModel,
						        DinatForImageClassification,
						        DinatBackbone,
						    )
						    if is_torch_available()
						    else ()
						)
						__UpperCAmelCase   :		Dict  					=	(
						    {'''feature-extraction''': DinatModel, '''image-classification''': DinatForImageClassification}
						    if is_torch_available()
						    else {}
						)
						__UpperCAmelCase   :		Any  					=	False

						__UpperCAmelCase   :		Any  					=	False
						__UpperCAmelCase   :		Any  					=	False
						__UpperCAmelCase   :		Dict  					=	False
						__UpperCAmelCase   :		Union[str, Any]  					=	False




						def 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							Union[str, Any]      =       DinatModelTester(self     )
							lowerCamelCase_			:							Union[str, Any]      =       ConfigTester(self				,							config_class=a_				,							embed_dim=37     )




						def 					_UpperCamelCase			(   self     ):
							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 					_UpperCamelCase			(   self     ):
							return




						def 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							str      =       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*a_     )




						def 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							List[Any]      =       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*a_     )




						def 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							Dict      =       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 					_UpperCamelCase			(   self     ):
							pass




						@unittest.skip(reason="Dinat does not use feedforward chunking"     )
						def 					_UpperCamelCase			(   self     ):
							pass




						def 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							Optional[int]      =       self.model_tester.prepare_config_and_inputs_for_common()

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




						def 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							Union[str, Any]      =       self.model_tester.prepare_config_and_inputs_for_common()

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

								lowerCamelCase_			:							int      =       ["pixel_values"]
								self.assertListEqual(arg_names[:1]				,							a_     )




						def 					_UpperCamelCase			(   self     ):
							self.skipTest("Dinat's attention operation is handled entirely by NATTEN."     )




						def 					_UpperCamelCase			(   self				,							a_				,							a_				,							a_				,							a_     ):
							lowerCamelCase_			:							str      =       model_class(a_     )
							model.to(a_     )
							model.eval()

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

							lowerCamelCase_			:							int      =       outputs.hidden_states

							lowerCamelCase_			:							Optional[int]      =       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
							lowerCamelCase_			:							List[str]      =       (
							    config.patch_size
							    if isinstance(config.patch_size				,							collections.abc.Iterable     )
							    else (config.patch_size, config.patch_size)
							)

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

								lowerCamelCase_			:							List[Any]      =       reshaped_hidden_states[0].shape
								lowerCamelCase_			:							Union[str, Any]      =       (
								    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 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							Union[str, Any]      =       self.model_tester.prepare_config_and_inputs_for_common()

							lowerCamelCase_			:							Optional[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:
								lowerCamelCase_			:							List[str]      =       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"]
								lowerCamelCase_			:							Union[str, Any]      =       True

								self.check_hidden_states_output(a_				,							a_				,							a_				,							a_     )




						@slow
						def 					_UpperCamelCase			(   self     ):
							for model_name in DINAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								lowerCamelCase_			:							Dict      =       DinatModel.from_pretrained(a_     )
								self.assertIsNotNone(a_     )




						def 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							str      =       self.model_tester.prepare_config_and_inputs_for_common()

							lowerCamelCase_			:							Optional[int]      =       _config_zero_init(a_     )
							for model_class in self.all_model_classes:
								lowerCamelCase_			:							Any      =       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    lowerCAmelCase__				(			unittest.TestCase       ):






						"""simple docstring"""




						@cached_property
						def 					_UpperCamelCase			(   self     ):
							return AutoImageProcessor.from_pretrained("shi-labs/dinat-mini-in1k-224"     ) if is_vision_available() else None




						@slow
						def 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							Optional[int]      =       DinatForImageClassification.from_pretrained("shi-labs/dinat-mini-in1k-224"     ).to(a_     )
							lowerCamelCase_			:							List[Any]      =       self.default_image_processor

							lowerCamelCase_			:							int      =       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
							lowerCamelCase_			:							List[str]      =       image_processor(images=a_				,							return_tensors="pt"     ).to(a_     )

							# forward pass
							with torch.no_grad():
								lowerCamelCase_			:							Dict      =       model(**a_     )

							# verify the logits
							lowerCamelCase_			:							str      =       torch.Size((1, 1000)     )
							self.assertEqual(outputs.logits.shape				,							a_     )
							lowerCamelCase_			:							Union[str, Any]      =       torch.tensor([-0.15_45, -0.76_67, 0.46_42]     ).to(a_     )
							self.assertTrue(torch.allclose(outputs.logits[0, :3]				,							a_				,							atol=1E-4     )     )





@require_torch
@require_natten
class    lowerCAmelCase__				(			unittest.TestCase,    __lowerCamelCase       ):






						"""simple docstring"""
						__UpperCAmelCase   :		int  					=	(DinatBackbone,) if is_torch_available() else ()
						__UpperCAmelCase   :		Dict  					=	DinatConfig




						def 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							List[str]      =       DinatModelTester(self     )





from __future__ import annotations





class    lowerCAmelCase__				:






						"""simple docstring"""




						def __init__(   self				,							a_     ):
							lowerCamelCase_			:							List[str]      =       value
							lowerCamelCase_			:							Node | None      =       None
							lowerCamelCase_			:							Node | None      =       None





class    lowerCAmelCase__				:






						"""simple docstring"""
						__UpperCAmelCase   :		int




						def __init__(   self     ):
							lowerCamelCase_			:							str      =       0




						def 					_UpperCamelCase			(   self				,							a_				,							a_     ):
							if node is None:
								return

							if path_sum == self.target:
								self.paths += 1

							if node.left:
								self.depth_first_search(node.left				,							path_sum + node.left.value     )
							if node.right:
								self.depth_first_search(node.right				,							path_sum + node.right.value     )




						def 					_UpperCamelCase			(   self				,							a_				,							a_ = None     ):
							if node is None:
								return 0
							if target is not None:
								lowerCamelCase_			:							List[str]      =       target

							self.depth_first_search(a_				,							node.value     )
							self.path_sum(node.left     )
							self.path_sum(node.right     )

							return self.paths


if __name__ == "__main__":
							import doctest

							doctest.testmod()

