


def 					SCREAMING_SNAKE_CASE			(  UpperCAmelCase__				):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE   =			0

				# if input_string is "aba" than new_input_string become "a|b|a"
				_SCREAMING_SNAKE_CASE   =			''''''
				_SCREAMING_SNAKE_CASE   =			''''''

				# append each character + "|" in new_string for range(0, length-1)
				for i in input_string[: len(UpperCAmelCase__				) - 1]:
								new_input_string += i + "|"
				# append last character
				new_input_string += input_string[-1]

				# we will store the starting and ending of previous furthest ending palindromic
				# substring
				_SCREAMING_SNAKE_CASE   =			0, 0

				# length[i] shows the length of palindromic substring with center i
				_SCREAMING_SNAKE_CASE   =			[1 for i in range(len(UpperCAmelCase__				)				)]

				# for each character in new_string find corresponding palindromic string
				_SCREAMING_SNAKE_CASE   =			0
				for j in range(len(UpperCAmelCase__				)				):
								_SCREAMING_SNAKE_CASE   =			1 if j > r else min(length[l + r - j] // 2 ,r - j + 1				)
								while (
								    j - k >= 0
								    and j + k < len(UpperCAmelCase__				)
								    and new_input_string[k + j] == new_input_string[j - k]
								):
												k += 1

								_SCREAMING_SNAKE_CASE   =			2 * k - 1

								# does this string is ending after the previously explored end (that is r) ?
								# if yes the update the new r to the last index of this
								if j + k - 1 > r:
												_SCREAMING_SNAKE_CASE   =			j - k + 1  # noqa: E741
												_SCREAMING_SNAKE_CASE   =			j + k - 1

								# update max_length and start position
								if max_length < length[j]:
												_SCREAMING_SNAKE_CASE   =			length[j]
												_SCREAMING_SNAKE_CASE   =			j

    # create that string
				_SCREAMING_SNAKE_CASE   =			new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
				for i in s:
								if i != "|":
												output_string += i

				return output_string


if __name__ == "__main__":
							import doctest

							doctest.testmod()



'''simple docstring'''





import inspect
import unittest

from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
							from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor





class 		__SCREAMING_SNAKE_CASE							:







				def __init__(       self     :		str		,					UpperCAmelCase__     :		Optional[Any]		,					UpperCAmelCase__     :		List[str]=13		,					UpperCAmelCase__     :		Any=32		,					UpperCAmelCase__     :		List[str]=3		,					UpperCAmelCase__     :		Any=4		,					UpperCAmelCase__     :		List[str]=[10, 20, 30, 40]		,					UpperCAmelCase__     :		Any=[2, 2, 3, 2]		,					UpperCAmelCase__     :		Optional[Any]=True		,					UpperCAmelCase__     :		Tuple=True		,					UpperCAmelCase__     :		Optional[Any]=37		,					UpperCAmelCase__     :		Union[str, Any]="gelu"		,					UpperCAmelCase__     :		Optional[Any]=10		,					UpperCAmelCase__     :		Any=0.02		,					UpperCAmelCase__     :		Optional[int]=["stage2", "stage3", "stage4"]		,					UpperCAmelCase__     :		Dict=[2, 3, 4]		,					UpperCAmelCase__     :		Optional[int]=None		,					):
										'''simple docstring'''
										lowercase :							List[Any]    		=parent
										lowercase :							Tuple    		=batch_size
										lowercase :							List[str]    		=image_size
										lowercase :							List[Any]    		=num_channels
										lowercase :							Union[str, Any]    		=num_stages
										lowercase :							int    		=hidden_sizes
										lowercase :							Any    		=depths
										lowercase :							Tuple    		=is_training
										lowercase :							str    		=use_labels
										lowercase :							List[Any]    		=intermediate_size
										lowercase :							int    		=hidden_act
										lowercase :							Union[str, Any]    		=num_labels
										lowercase :							Optional[int]    		=initializer_range
										lowercase :							int    		=out_features
										lowercase :							List[str]    		=out_indices
										lowercase :							str    		=scope







				def  lowerCamelCase_	(       self     :		int   ):
										'''simple docstring'''
										lowercase :							str    		=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

										lowercase :							Dict    		=None
										if self.use_labels:
																lowercase :							List[Any]    		=ids_tensor([self.batch_size]		,					self.num_labels   )

										lowercase :							Dict    		=self.get_config()

										return config, pixel_values, labels







				def  lowerCamelCase_	(       self     :		Any   ):
										'''simple docstring'''
										return ConvNextVaConfig(
										    num_channels=self.num_channels		,					hidden_sizes=self.hidden_sizes		,					depths=self.depths		,					num_stages=self.num_stages		,					hidden_act=self.hidden_act		,					is_decoder=UpperCAmelCase__		,					initializer_range=self.initializer_range		,					out_features=self.out_features		,					out_indices=self.out_indices		,					num_labels=self.num_labels		,					)







				def  lowerCamelCase_	(       self     :		List[Any]		,					UpperCAmelCase__     :		Union[str, Any]		,					UpperCAmelCase__     :		Optional[Any]		,					UpperCAmelCase__     :		Optional[Any]   ):
										'''simple docstring'''
										lowercase :							Dict    		=ConvNextVaModel(config=UpperCAmelCase__   )
										model.to(UpperCAmelCase__   )
										model.eval()
										lowercase :							Optional[Any]    		=model(UpperCAmelCase__   )
										# expected last hidden states: B, C, H // 32, W // 32
										self.parent.assertEqual(
										    result.last_hidden_state.shape		,					(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)		,					)







				def  lowerCamelCase_	(       self     :		Any		,					UpperCAmelCase__     :		Dict		,					UpperCAmelCase__     :		Dict		,					UpperCAmelCase__     :		int   ):
										'''simple docstring'''
										lowercase :							Dict    		=ConvNextVaForImageClassification(UpperCAmelCase__   )
										model.to(UpperCAmelCase__   )
										model.eval()
										lowercase :							str    		=model(UpperCAmelCase__		,					labels=UpperCAmelCase__   )
										self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.num_labels)   )







				def  lowerCamelCase_	(       self     :		Dict		,					UpperCAmelCase__     :		Union[str, Any]		,					UpperCAmelCase__     :		List[Any]		,					UpperCAmelCase__     :		int   ):
										'''simple docstring'''
										lowercase :							Union[str, Any]    		=ConvNextVaBackbone(config=UpperCAmelCase__   )
										model.to(UpperCAmelCase__   )
										model.eval()
										lowercase :							Optional[int]    		=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, self.hidden_sizes[1], 4, 4]   )

										# verify channels
										self.parent.assertEqual(len(model.channels   )		,					len(config.out_features   )   )
										self.parent.assertListEqual(model.channels		,					config.hidden_sizes[1:]   )

										# verify backbone works with out_features=None
										lowercase :							Optional[Any]    		=None
										lowercase :							str    		=ConvNextVaBackbone(config=UpperCAmelCase__   )
										model.to(UpperCAmelCase__   )
										model.eval()
										lowercase :							Optional[Any]    		=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, self.hidden_sizes[-1], 1, 1]   )

										# verify channels
										self.parent.assertEqual(len(model.channels   )		,					1   )
										self.parent.assertListEqual(model.channels		,					[config.hidden_sizes[-1]]   )







				def  lowerCamelCase_	(       self     :		List[Any]   ):
										'''simple docstring'''
										lowercase :							Any    		=self.prepare_config_and_inputs()
										lowercase		,	lowercase		,	lowercase :							str    		=config_and_inputs
										lowercase :							Any    		={'''pixel_values''': pixel_values}
										return config, inputs_dict







				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										lowercase :							str    		=self.prepare_config_and_inputs()
										lowercase		,	lowercase		,	lowercase :							List[str]    		=config_and_inputs
										lowercase :							Optional[Any]    		={'''pixel_values''': pixel_values, '''labels''': labels}
										return config, inputs_dict





@require_torch
class 		__SCREAMING_SNAKE_CASE							(			lowercase__						,							lowercase__						,							unittest.TestCase      ):
				lowerCamelCase_		       =    (
				    (
				        ConvNextVaModel,
				        ConvNextVaForImageClassification,
				        ConvNextVaBackbone,
				    )
				    if is_torch_available()
				    else ()
				)
				lowerCamelCase_		       =    (
				    {'feature-extraction': ConvNextVaModel, 'image-classification': ConvNextVaForImageClassification}
				    if is_torch_available()
				    else {}
				)

				lowerCamelCase_		       =    False
				lowerCamelCase_		       =    False
				lowerCamelCase_		       =    False
				lowerCamelCase_		       =    False
				lowerCamelCase_		       =    False







				def  lowerCamelCase_	(       self     :		Optional[int]   ):
										'''simple docstring'''
										lowercase :							Dict    		=ConvNextVaModelTester(self   )
										lowercase :							str    		=ConfigTester(self		,					config_class=UpperCAmelCase__		,					has_text_modality=UpperCAmelCase__		,					hidden_size=37   )







				def  lowerCamelCase_	(       self     :		Optional[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  lowerCamelCase_	(       self     :		Any   ):
										'''simple docstring'''
										return







				@unittest.skip(reason='''ConvNextV2 does not use inputs_embeds'''   )
				def  lowerCamelCase_	(       self     :		Optional[int]   ):
										'''simple docstring'''
										pass







				@unittest.skip(reason='''ConvNextV2 does not support input and output embeddings'''   )
				def  lowerCamelCase_	(       self     :		List[str]   ):
										'''simple docstring'''
										pass







				@unittest.skip(reason='''ConvNextV2 does not use feedforward chunking'''   )
				def  lowerCamelCase_	(       self     :		Tuple   ):
										'''simple docstring'''
										pass







				def  lowerCamelCase_	(       self     :		List[Any]   ):
										'''simple docstring'''
										if not self.model_tester.is_training:
																return

										for model_class in self.all_model_classes:
																lowercase		,	lowercase :							Union[str, Any]    		=self.model_tester.prepare_config_and_inputs_with_labels()
																lowercase :							Optional[int]    		=True

																if model_class.__name__ in [
																    *get_values(UpperCAmelCase__   ),
																    *get_values(UpperCAmelCase__   ),
																]:
																						continue

																lowercase :							Dict    		=model_class(UpperCAmelCase__   )
																model.to(UpperCAmelCase__   )
																model.train()
																lowercase :							Optional[Any]    		=self._prepare_for_class(UpperCAmelCase__		,					UpperCAmelCase__		,					return_labels=UpperCAmelCase__   )
																lowercase :							List[Any]    		=model(**UpperCAmelCase__   ).loss
																loss.backward()







				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										if not self.model_tester.is_training:
																return

										for model_class in self.all_model_classes:
																lowercase		,	lowercase :							Any    		=self.model_tester.prepare_config_and_inputs_with_labels()
																lowercase :							List[Any]    		=False
																lowercase :							Any    		=True

																if (
																    model_class.__name__
																    in [*get_values(UpperCAmelCase__   ), *get_values(UpperCAmelCase__   )]
																    or not model_class.supports_gradient_checkpointing
																):
																						continue

																lowercase :							Any    		=model_class(UpperCAmelCase__   )
																model.to(UpperCAmelCase__   )
																model.gradient_checkpointing_enable()
																model.train()
																lowercase :							Optional[Any]    		=self._prepare_for_class(UpperCAmelCase__		,					UpperCAmelCase__		,					return_labels=UpperCAmelCase__   )
																lowercase :							int    		=model(**UpperCAmelCase__   ).loss
																loss.backward()







				def  lowerCamelCase_	(       self     :		Tuple   ):
										'''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(UpperCAmelCase__   )
																lowercase :							Union[str, Any]    		=inspect.signature(model.forward   )
																# signature.parameters is an OrderedDict => so arg_names order is deterministic
																lowercase :							int    		=[*signature.parameters.keys()]

																lowercase :							Optional[Any]    		=['''pixel_values''']
																self.assertListEqual(arg_names[:1]		,					UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		Optional[Any]   ):
										'''simple docstring'''
										lowercase :							Tuple    		=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		int   ):
										'''simple docstring'''
										def check_hidden_states_output(UpperCAmelCase__     :		List[str]		,					UpperCAmelCase__     :		int		,					UpperCAmelCase__     :		Tuple   ):
																lowercase :							int    		=model_class(UpperCAmelCase__   )
																model.to(UpperCAmelCase__   )
																model.eval()

																with torch.no_grad():
																						lowercase :							Any    		=model(**self._prepare_for_class(UpperCAmelCase__		,					UpperCAmelCase__   )   )

																lowercase :							Dict    		=outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																lowercase :							List[Any]    		=self.model_tester.num_stages
																self.assertEqual(len(UpperCAmelCase__   )		,					expected_num_stages + 1   )

																# ConvNextV2's feature maps are of shape (batch_size, num_channels, height, width)
																self.assertListEqual(
																    list(hidden_states[0].shape[-2:]   )		,					[self.model_tester.image_size // 4, self.model_tester.image_size // 4]		,					)

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

										for model_class in self.all_model_classes:
																lowercase :							List[str]    		=True
																check_hidden_states_output(UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__   )

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

																check_hidden_states_output(UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		Dict   ):
										'''simple docstring'''
										lowercase :							str    		=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase__   )







				@slow
				def  lowerCamelCase_	(       self     :		Optional[int]   ):
										'''simple docstring'''
										for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																lowercase :							List[Any]    		=ConvNextVaModel.from_pretrained(UpperCAmelCase__   )
																self.assertIsNotNone(UpperCAmelCase__   )






def 				_lowerCAmelCase			(   )				->  List[Any]:
						lowercase :							Union[str, Any]    		=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
						return image







@require_torch
@require_vision
class 		__SCREAMING_SNAKE_CASE							(			unittest.TestCase      ):







				@cached_property
				def  lowerCamelCase_	(       self     :		List[str]   ):
										'''simple docstring'''
										return AutoImageProcessor.from_pretrained('''facebook/convnextv2-tiny-1k-224'''   ) if is_vision_available() else None







				@slow
				def  lowerCamelCase_	(       self     :		Optional[Any]   ):
										'''simple docstring'''
										lowercase :							Tuple    		=ConvNextVaForImageClassification.from_pretrained('''facebook/convnextv2-tiny-1k-224'''   ).to(UpperCAmelCase__   )

										lowercase :							int    		=self.default_image_processor
										lowercase :							List[str]    		=prepare_img()
										lowercase :							List[Any]    		=preprocessor(images=UpperCAmelCase__		,					return_tensors='''pt'''   ).to(UpperCAmelCase__   )

										# forward pass
										with torch.no_grad():
																lowercase :							Dict    		=model(**UpperCAmelCase__   )

										# verify the logits
										lowercase :							Optional[Any]    		=torch.Size((1, 1000)   )
										self.assertEqual(outputs.logits.shape		,					UpperCAmelCase__   )

										lowercase :							Tuple    		=torch.tensor([0.99_96, 0.19_66, -0.43_86]   ).to(UpperCAmelCase__   )
										self.assertTrue(torch.allclose(outputs.logits[0, :3]		,					UpperCAmelCase__		,					atol=1E-4   )   )





