
'''simple docstring'''







import os
from functools import partial
from glob import glob

import faiss
from datasets import Features, Sequence, Value, concatenate_datasets, load_dataset, load_from_disk

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					Tuple , __SCREAMING_SNAKE_CASE   :					str=100 , __SCREAMING_SNAKE_CASE   :					Optional[int]=" "				):



						"""simple docstring"""





						lowercase_      :	List[Any]						=			text.split(__SCREAMING_SNAKE_CASE				)
						return [character.join(text[i : i + n]				).strip() for i in range(0 , len(__SCREAMING_SNAKE_CASE				) , __SCREAMING_SNAKE_CASE				)]
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					str				):



						"""simple docstring"""





						lowercase_		,					lowercase_      :	List[Any]						=			[], []
						for title, text in zip(documents['''title'''] , documents['''text''']				):
												if text is not None:
																		for passage in split_text(__SCREAMING_SNAKE_CASE				):
																								titles.append(title if title is not None else ''''''				)
																								texts.append(__SCREAMING_SNAKE_CASE				)
						return {"title": titles, "text": texts}
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					Optional[int] , __SCREAMING_SNAKE_CASE   :					Tuple , __SCREAMING_SNAKE_CASE   :					List[Any] , __SCREAMING_SNAKE_CASE   :					str , __SCREAMING_SNAKE_CASE   :					Optional[Any] , __SCREAMING_SNAKE_CASE   :					List[Any]				):



						"""simple docstring"""





						lowercase_      :	List[str]						=			load_dataset(
						    '''csv''' , data_files=[csv_path] , split='''train''' , delimiter='''\t''' , column_names=['''title''', '''text''']				)
						lowercase_      :	List[str]						=			kb_dataset.map(
						    __SCREAMING_SNAKE_CASE , batched=__SCREAMING_SNAKE_CASE , num_proc=1				)  # if you want you can load already splitted csv.
						lowercase_      :	Optional[int]						=			[kb_dataset.shard(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , contiguous=__SCREAMING_SNAKE_CASE				) for i in range(__SCREAMING_SNAKE_CASE				)]
						lowercase_      :	Tuple						=			kb_list[process_num]

						lowercase_      :	List[str]						=			'''data_''' + str(__SCREAMING_SNAKE_CASE				)
						lowercase_      :	str						=			os.path.join(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE				)

						lowercase_      :	int						=			DPRContextEncoderTokenizerFast.from_pretrained('''facebook/dpr-ctx_encoder-multiset-base'''				)
						lowercase_      :	Union[str, Any]						=			ctx_encoder.to(device=__SCREAMING_SNAKE_CASE				)

						def embed(
						    __SCREAMING_SNAKE_CASE   :					dict , __SCREAMING_SNAKE_CASE   :					DPRContextEncoder , __SCREAMING_SNAKE_CASE   :					DPRContextEncoderTokenizerFast , __SCREAMING_SNAKE_CASE   :					List[str]				) -> dict:
												lowercase_      :	Optional[int]						=			ctx_tokenizer(
												    documents['''title'''] , documents['''text'''] , truncation=__SCREAMING_SNAKE_CASE , padding='''longest''' , return_tensors='''pt'''				)['''input_ids''']
												lowercase_      :	Any						=			ctx_encoder(input_ids.to(device=__SCREAMING_SNAKE_CASE				) , return_dict=__SCREAMING_SNAKE_CASE				).pooler_output
												return {"embeddings": embeddings.detach().cpu().numpy()}

						lowercase_      :	Union[str, Any]						=			Features(
						    {'''text''': Value('''string'''				), '''title''': Value('''string'''				), '''embeddings''': Sequence(Value('''float32'''				)				)}				)  # optional, save as float32 instead of float64 to save space

						lowercase_      :	List[Any]						=			data_shrad.map(
						    partial(__SCREAMING_SNAKE_CASE , ctx_encoder=__SCREAMING_SNAKE_CASE , ctx_tokenizer=__SCREAMING_SNAKE_CASE , device=__SCREAMING_SNAKE_CASE				) , batched=__SCREAMING_SNAKE_CASE , batch_size=16 , features=__SCREAMING_SNAKE_CASE , )
						dataset.save_to_disk(__SCREAMING_SNAKE_CASE				)




def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					str , __SCREAMING_SNAKE_CASE   :					Optional[Any]				):



						"""simple docstring"""





						lowercase_      :	List[Any]						=			[]

						for shard_address in glob(str(__SCREAMING_SNAKE_CASE				) + '''/*/'''				):
												data_shard_list.append(load_from_disk(__SCREAMING_SNAKE_CASE				)				)

						lowercase_      :	str						=			concatenate_datasets(__SCREAMING_SNAKE_CASE				)
						faiss.omp_set_num_threads(96				)

						lowercase_      :	Optional[int]						=			faiss.IndexHNSWFlat(768 , 128 , faiss.METRIC_INNER_PRODUCT				)
						concat.add_faiss_index('''embeddings''' , custom_index=__SCREAMING_SNAKE_CASE				)
						concat.get_index('''embeddings'''				).save(
						    __SCREAMING_SNAKE_CASE				)  # since we load the index in to memory,we can directly update the index in the disk





'''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							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE=13							,				__SCREAMING_SNAKE_CASE=64							,				__SCREAMING_SNAKE_CASE=4							,				__SCREAMING_SNAKE_CASE=3							,				__SCREAMING_SNAKE_CASE=16							,				__SCREAMING_SNAKE_CASE=[1, 2, 1]							,				__SCREAMING_SNAKE_CASE=[2, 4, 8]							,				__SCREAMING_SNAKE_CASE=3							,				__SCREAMING_SNAKE_CASE=[[3], [1, 2], [1]]							,				__SCREAMING_SNAKE_CASE=2.0							,				__SCREAMING_SNAKE_CASE=True							,				__SCREAMING_SNAKE_CASE=0.0							,				__SCREAMING_SNAKE_CASE=0.0							,				__SCREAMING_SNAKE_CASE=0.1							,				__SCREAMING_SNAKE_CASE="gelu"							,				__SCREAMING_SNAKE_CASE=True							,				__SCREAMING_SNAKE_CASE=0.02							,				__SCREAMING_SNAKE_CASE=1E-5							,				__SCREAMING_SNAKE_CASE=True							,				__SCREAMING_SNAKE_CASE=None							,				__SCREAMING_SNAKE_CASE=True							,				__SCREAMING_SNAKE_CASE=10							,				__SCREAMING_SNAKE_CASE=["stage1", "stage2"]							,				__SCREAMING_SNAKE_CASE=[1, 2]							,				):


											"""simple docstring"""





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






					def       _snake_case       (  self						):


											"""simple docstring"""





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

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

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

											return config, pixel_values, labels






					def       _snake_case       (  self						):


											"""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							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											lowercase_      :	Optional[int]						=			DinatModel(config=__SCREAMING_SNAKE_CASE						)
											model.to(__SCREAMING_SNAKE_CASE						)
											model.eval()
											lowercase_      :	Union[str, Any]						=			model(__SCREAMING_SNAKE_CASE						)

											lowercase_      :	int						=			(config.image_size // config.patch_size) // (2 ** (len(config.depths						) - 1))
											lowercase_      :	Dict						=			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							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											lowercase_      :	int						=			DinatForImageClassification(__SCREAMING_SNAKE_CASE						)
											model.to(__SCREAMING_SNAKE_CASE						)
											model.eval()
											lowercase_      :	Union[str, Any]						=			model(__SCREAMING_SNAKE_CASE							,				labels=__SCREAMING_SNAKE_CASE						)
											self.parent.assertEqual(result.logits.shape							,				(self.batch_size, self.num_labels)						)

											# test greyscale images
											lowercase_      :	Union[str, Any]						=			1
											lowercase_      :	List[str]						=			DinatForImageClassification(__SCREAMING_SNAKE_CASE						)
											model.to(__SCREAMING_SNAKE_CASE						)
											model.eval()

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






					def       _snake_case       (  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											lowercase_      :	Dict						=			DinatBackbone(config=__SCREAMING_SNAKE_CASE						)
											model.to(__SCREAMING_SNAKE_CASE						)
											model.eval()
											lowercase_      :	Union[str, Any]						=			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
											lowercase_      :	str						=			None
											lowercase_      :	Optional[int]						=			DinatBackbone(config=__SCREAMING_SNAKE_CASE						)
											model.to(__SCREAMING_SNAKE_CASE						)
											model.eval()
											lowercase_      :	str						=			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       _snake_case       (  self						):


											"""simple docstring"""





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







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

					lowerCAmelCase_	  =	False
					lowerCAmelCase_	  =	False
					lowerCAmelCase_	  =	False
					lowerCAmelCase_	  =	False






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	Optional[int]						=			DinatModelTester(self						)
											lowercase_      :	Optional[Any]						=			ConfigTester(self							,				config_class=__SCREAMING_SNAKE_CASE							,				embed_dim=37						)






					def       _snake_case       (  self						):


											"""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						):


											"""simple docstring"""





											return






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	Tuple						=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE						)






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	List[str]						=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE						)






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	Any						=			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       _snake_case       (  self						):


											"""simple docstring"""





											pass






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


											"""simple docstring"""





											pass






					def       _snake_case       (  self						):


											"""simple docstring"""





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

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






					def       _snake_case       (  self						):


											"""simple docstring"""





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

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

																	lowercase_      :	Dict						=			['''pixel_values''']
																	self.assertListEqual(arg_names[:1]							,				__SCREAMING_SNAKE_CASE						)






					def       _snake_case       (  self						):


											"""simple docstring"""





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






					def       _snake_case       (  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											lowercase_      :	Union[str, Any]						=			model_class(__SCREAMING_SNAKE_CASE						)
											model.to(__SCREAMING_SNAKE_CASE						)
											model.eval()

											with torch.no_grad():
																	lowercase_      :	Optional[Any]						=			model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						)						)

											lowercase_      :	int						=			outputs.hidden_states

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

											lowercase_      :	Union[str, Any]						=			image_size[0] // patch_size[0]
											lowercase_      :	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":
																	lowercase_      :	Any						=			outputs.reshaped_hidden_states
																	self.assertEqual(len(__SCREAMING_SNAKE_CASE						)							,				__SCREAMING_SNAKE_CASE						)

																	lowercase_		,					lowercase_		,					lowercase_		,					lowercase_      :	int						=			reshaped_hidden_states[0].shape
																	lowercase_      :	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       _snake_case       (  self						):


											"""simple docstring"""





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

											lowercase_      :	List[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:
																	lowercase_      :	Dict						=			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"]
																	lowercase_      :	str						=			True

																	self.check_hidden_states_output(__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						)






					@slow
					def       _snake_case       (  self						):


											"""simple docstring"""





											for model_name in DINAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	lowercase_      :	Optional[Any]						=			DinatModel.from_pretrained(__SCREAMING_SNAKE_CASE						)
																	self.assertIsNotNone(__SCREAMING_SNAKE_CASE						)






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_		,					lowercase_      :	Tuple						=			self.model_tester.prepare_config_and_inputs_for_common()

											lowercase_      :	Tuple						=			_config_zero_init(__SCREAMING_SNAKE_CASE						)
											for model_class in self.all_model_classes:
																	lowercase_      :	List[str]						=			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       _snake_case       (  self						):


											"""simple docstring"""





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






					@slow
					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	Optional[int]						=			DinatForImageClassification.from_pretrained('''shi-labs/dinat-mini-in1k-224'''						).to(__SCREAMING_SNAKE_CASE						)
											lowercase_      :	str						=			self.default_image_processor

											lowercase_      :	Tuple						=			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
											lowercase_      :	Tuple						=			image_processor(images=__SCREAMING_SNAKE_CASE							,				return_tensors='''pt'''						).to(__SCREAMING_SNAKE_CASE						)

											# forward pass
											with torch.no_grad():
																	lowercase_      :	str						=			model(**__SCREAMING_SNAKE_CASE						)

											# verify the logits
											lowercase_      :	List[Any]						=			torch.Size((1, 10_00)						)
											self.assertEqual(outputs.logits.shape							,				__SCREAMING_SNAKE_CASE						)
											lowercase_      :	Union[str, Any]						=			torch.tensor([-0.1_545, -0.7_667, 0.4_642]						).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_	):
					lowerCAmelCase_	  =	(DinatBackbone,) if is_torch_available() else ()
					lowerCAmelCase_	  =	DinatConfig






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	Tuple						=			DinatModelTester(self						)



