'''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     :







				'''simple docstring'''





				def __init__(						self					:						Optional[Any]       ,		lowercase__					:						Optional[int]       ,		lowercase__					:						Dict=13       ,		lowercase__					:						Union[str, Any]=32       ,		lowercase__					:						Optional[int]=3       ,		lowercase__					:						Any=4       ,		lowercase__					:						str=[10, 20, 30, 40]       ,		lowercase__					:						Union[str, Any]=[2, 2, 3, 2]       ,		lowercase__					:						Tuple=True       ,		lowercase__					:						Tuple=True       ,		lowercase__					:						Optional[Any]=37       ,		lowercase__					:						Union[str, Any]="gelu"       ,		lowercase__					:						Tuple=10       ,		lowercase__					:						Any=0.0_2       ,		lowercase__					:						Dict=["stage2", "stage3", "stage4"]       ,		lowercase__					:						Tuple=[2, 3, 4]       ,		lowercase__					:						List[str]=None       ,		)							->Any:


										'''simple docstring'''




										_UpperCamelCase			:			str   			=						parent
										_UpperCamelCase			:			List[str]   			=						batch_size
										_UpperCamelCase			:			Tuple   			=						image_size
										_UpperCamelCase			:			Optional[Any]   			=						num_channels
										_UpperCamelCase			:			Optional[Any]   			=						num_stages
										_UpperCamelCase			:			Dict   			=						hidden_sizes
										_UpperCamelCase			:			int   			=						depths
										_UpperCamelCase			:			List[Any]   			=						is_training
										_UpperCamelCase			:			Optional[int]   			=						use_labels
										_UpperCamelCase			:			str   			=						intermediate_size
										_UpperCamelCase			:			Optional[Any]   			=						hidden_act
										_UpperCamelCase			:			Optional[int]   			=						num_labels
										_UpperCamelCase			:			Tuple   			=						initializer_range
										_UpperCamelCase			:			List[str]   			=						out_features
										_UpperCamelCase			:			Optional[int]   			=						out_indices
										_UpperCamelCase			:			Optional[int]   			=						scope





				def  snake_case__				(						self					:						int					)							->List[Any]:


										'''simple docstring'''




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

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

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

										return config, pixel_values, labels





				def  snake_case__				(						self					:						Union[str, Any]					)							->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=lowercase__       ,		initializer_range=self.initializer_range       ,		out_features=self.out_features       ,		out_indices=self.out_indices       ,		num_labels=self.num_labels       ,		)





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


										'''simple docstring'''




										_UpperCamelCase			:			Union[str, Any]   			=						ConvNextVaModel(config=lowercase__					)
										model.to(lowercase__					)
										model.eval()
										_UpperCamelCase			:			Any   			=						model(lowercase__					)
										# 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  snake_case__				(						self					:						int       ,		lowercase__					:						int       ,		lowercase__					:						List[str]       ,		lowercase__					:						Tuple					)							->Tuple:


										'''simple docstring'''




										_UpperCamelCase			:			List[Any]   			=						ConvNextVaForImageClassification(lowercase__					)
										model.to(lowercase__					)
										model.eval()
										_UpperCamelCase			:			Union[str, Any]   			=						model(lowercase__       ,		labels=lowercase__					)
										self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.num_labels)					)





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


										'''simple docstring'''




										_UpperCamelCase			:			int   			=						ConvNextVaBackbone(config=lowercase__					)
										model.to(lowercase__					)
										model.eval()
										_UpperCamelCase			:			str   			=						model(lowercase__					)

										# 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
										_UpperCamelCase			:			Optional[Any]   			=						None
										_UpperCamelCase			:			Tuple   			=						ConvNextVaBackbone(config=lowercase__					)
										model.to(lowercase__					)
										model.eval()
										_UpperCamelCase			:			List[str]   			=						model(lowercase__					)

										# 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  snake_case__				(						self					:						Tuple					)							->Tuple:


										'''simple docstring'''




										_UpperCamelCase			:			List[Any]   			=						self.prepare_config_and_inputs()
										_UpperCamelCase			,       _UpperCamelCase			,       _UpperCamelCase			:			str   			=						config_and_inputs
										_UpperCamelCase			:			int   			=						{"pixel_values": pixel_values}
										return config, inputs_dict







				def  snake_case__				(						self					:						int					)							->str:


										'''simple docstring'''




										_UpperCamelCase			:			Any   			=						self.prepare_config_and_inputs()
										_UpperCamelCase			,       _UpperCamelCase			,       _UpperCamelCase			:			Union[str, Any]   			=						config_and_inputs
										_UpperCamelCase			:			List[str]   			=						{"pixel_values": pixel_values, "labels": labels}
										return config, inputs_dict





@require_torch
class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , unittest.TestCase	):







				'''simple docstring'''
				UpperCAmelCase__							=							(
				    (
				        ConvNextVaModel,
				        ConvNextVaForImageClassification,
				        ConvNextVaBackbone,
				    )
				    if is_torch_available()
				    else ()
				)
				UpperCAmelCase__							=							(
				    {'''feature-extraction''': ConvNextVaModel, '''image-classification''': ConvNextVaForImageClassification}
				    if is_torch_available()
				    else {}
				)

				UpperCAmelCase__							=							False
				UpperCAmelCase__							=							False
				UpperCAmelCase__							=							False
				UpperCAmelCase__							=							False
				UpperCAmelCase__							=							False





				def  snake_case__				(						self					:						List[str]					)							->Optional[int]:


										'''simple docstring'''




										_UpperCamelCase			:			Dict   			=						ConvNextVaModelTester(self					)
										_UpperCamelCase			:			List[Any]   			=						ConfigTester(self       ,		config_class=lowercase__       ,		has_text_modality=lowercase__       ,		hidden_size=37					)





				def  snake_case__				(						self					:						Dict					)							->List[str]:


										'''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					:						str					)							->List[str]:


										'''simple docstring'''




										return





				@unittest.skip(reason="ConvNextV2 does not use inputs_embeds"					)
				def  snake_case__				(						self					:						int					)							->Dict:


										'''simple docstring'''




										pass





				@unittest.skip(reason="ConvNextV2 does not support input and output embeddings"					)
				def  snake_case__				(						self					:						Union[str, Any]					)							->str:


										'''simple docstring'''




										pass





				@unittest.skip(reason="ConvNextV2 does not use feedforward chunking"					)
				def  snake_case__				(						self					:						Any					)							->Any:


										'''simple docstring'''




										pass





				def  snake_case__				(						self					:						Union[str, Any]					)							->Tuple:


										'''simple docstring'''




										if not self.model_tester.is_training:
																return

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

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

																_UpperCamelCase			:			List[Any]   			=						model_class(lowercase__					)
																model.to(lowercase__					)
																model.train()
																_UpperCamelCase			:			str   			=						self._prepare_for_class(lowercase__       ,		lowercase__       ,		return_labels=lowercase__					)
																_UpperCamelCase			:			List[str]   			=						model(**lowercase__					).loss
																loss.backward()





				def  snake_case__				(						self					:						int					)							->str:


										'''simple docstring'''




										if not self.model_tester.is_training:
																return

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

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

																_UpperCamelCase			:			List[str]   			=						model_class(lowercase__					)
																model.to(lowercase__					)
																model.gradient_checkpointing_enable()
																model.train()
																_UpperCamelCase			:			str   			=						self._prepare_for_class(lowercase__       ,		lowercase__       ,		return_labels=lowercase__					)
																_UpperCamelCase			:			Any   			=						model(**lowercase__					).loss
																loss.backward()





				def  snake_case__				(						self					:						str					)							->Optional[int]:


										'''simple docstring'''




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

										for model_class in self.all_model_classes:
																_UpperCamelCase			:			Dict   			=						model_class(lowercase__					)
																_UpperCamelCase			:			Union[str, Any]   			=						inspect.signature(model.forward					)
																# signature.parameters is an OrderedDict => so arg_names order is deterministic
																_UpperCamelCase			:			Dict   			=						[*signature.parameters.keys()]

																_UpperCamelCase			:			List[str]   			=						["pixel_values"]
																self.assertListEqual(arg_names[:1]       ,		lowercase__					)





				def  snake_case__				(						self					:						int					)							->Optional[Any]:


										'''simple docstring'''




										_UpperCamelCase			:			Optional[Any]   			=						self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowercase__					)





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


										'''simple docstring'''




										def check_hidden_states_output(lowercase__					:						List[Any]       ,		lowercase__					:						Tuple       ,		lowercase__					:						List[str]					):
																_UpperCamelCase			:			Optional[int]   			=						model_class(lowercase__					)
																model.to(lowercase__					)
																model.eval()

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

																_UpperCamelCase			:			str   			=						outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																_UpperCamelCase			:			Dict   			=						self.model_tester.num_stages
																self.assertEqual(len(lowercase__					)       ,		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]       ,		)

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

										for model_class in self.all_model_classes:
																_UpperCamelCase			:			Union[str, Any]   			=						True
																check_hidden_states_output(lowercase__       ,		lowercase__       ,		lowercase__					)

																# check that output_hidden_states also work using config
																del inputs_dict["output_hidden_states"]
																_UpperCamelCase			:			Optional[int]   			=						True

																check_hidden_states_output(lowercase__       ,		lowercase__       ,		lowercase__					)





				def  snake_case__				(						self					:						str					)							->str:


										'''simple docstring'''




										_UpperCamelCase			:			Any   			=						self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*lowercase__					)







				@slow
				def  snake_case__				(						self					:						str					)							->str:


										'''simple docstring'''




										for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																_UpperCamelCase			:			Any   			=						ConvNextVaModel.from_pretrained(lowercase__					)
																self.assertIsNotNone(lowercase__					)





def 						__A							(     )     ->			Dict:




						'''simple docstring'''
						_UpperCamelCase			:			Any   			=						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					)
						return image






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







				'''simple docstring'''





				@cached_property
				def  snake_case__				(						self					:						Any					)							->List[str]:


										'''simple docstring'''




										return AutoImageProcessor.from_pretrained("facebook/convnextv2-tiny-1k-224"					) if is_vision_available() else None







				@slow
				def  snake_case__				(						self					:						List[str]					)							->str:


										'''simple docstring'''




										_UpperCamelCase			:			int   			=						ConvNextVaForImageClassification.from_pretrained("facebook/convnextv2-tiny-1k-224"					).to(lowercase__					)

										_UpperCamelCase			:			Dict   			=						self.default_image_processor
										_UpperCamelCase			:			Any   			=						prepare_img()
										_UpperCamelCase			:			List[Any]   			=						preprocessor(images=lowercase__       ,		return_tensors="pt"					).to(lowercase__					)

										# forward pass
										with torch.no_grad():
																_UpperCamelCase			:			str   			=						model(**lowercase__					)

										# verify the logits
										_UpperCamelCase			:			Any   			=						torch.Size((1, 1_000)					)
										self.assertEqual(outputs.logits.shape       ,		lowercase__					)

										_UpperCamelCase			:			List[str]   			=						torch.tensor([0.9_9_9_6, 0.1_9_6_6, -0.4_3_8_6]					).to(lowercase__					)
										self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,		lowercase__       ,		atol=1e-4					)					)



'''simple docstring'''





def 						__A							(     UpperCAmelCase					)     ->			List[Any]:




						'''simple docstring'''
						_UpperCamelCase			:			str   			=						[]
						_UpperCamelCase			:			Optional[Any]   			=						set({"(", "[", "{"}					)
						_UpperCamelCase			:			int   			=						set({")", "]", "}"}					)
						_UpperCamelCase			:			Dict   			=						{"{": "}", "[": "]", "(": ")"}

						for i in range(len(UpperCAmelCase					)					):
												if s[i] in open_brackets:
																		stack.append(s[i]					)

												elif s[i] in closed_brackets and (
												    len(UpperCAmelCase					) == 0 or (len(UpperCAmelCase					) > 0 and open_to_closed[stack.pop()] != s[i])
												):
																		return False

						return len(UpperCAmelCase					) == 0
def 						__A							(     )     ->			str:




						'''simple docstring'''
						_UpperCamelCase			:			Any   			=						input("Enter sequence of brackets: "					)
						if is_balanced(UpperCAmelCase					):
												print(UpperCAmelCase     ,"is balanced"					)
						else:
												print(UpperCAmelCase     ,"is not balanced"					)


if __name__ == "__main__":
			main()


