code: 
  """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 ,						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] ,						)							->							int:
  								_UpperCAmelCase =						parent
  								_UpperCAmelCase =						batch_size
  								_UpperCAmelCase =						image_size
  								_UpperCAmelCase =						patch_size
  								_UpperCAmelCase =						num_channels
  								_UpperCAmelCase =						embed_dim
  								_UpperCAmelCase =						depths
  								_UpperCAmelCase =						num_heads
  								_UpperCAmelCase =						kernel_size
  								_UpperCAmelCase =						dilations
  								_UpperCAmelCase =						mlp_ratio
  								_UpperCAmelCase =						qkv_bias
  								_UpperCAmelCase =						hidden_dropout_prob
  								_UpperCAmelCase =						attention_probs_dropout_prob
  								_UpperCAmelCase =						drop_path_rate
  								_UpperCAmelCase =						hidden_act
  								_UpperCAmelCase =						patch_norm
  								_UpperCAmelCase =						layer_norm_eps
  								_UpperCAmelCase =						initializer_range
  								_UpperCAmelCase =						is_training
  								_UpperCAmelCase =						scope
  								_UpperCAmelCase =						use_labels
  								_UpperCAmelCase =						num_labels
  								_UpperCAmelCase =						out_features
  								_UpperCAmelCase =						out_indices
  
  
  
  							def       _a   (      self   )							->							Union[str, Any]:
  								_UpperCAmelCase =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )
  
  								_UpperCAmelCase =						None
  								if self.use_labels:
  									_UpperCAmelCase =						ids_tensor([self.batch_size] ,						self.num_labels   )
  
  								_UpperCAmelCase =						self.get_config()
  
  								return config, pixel_values, labels
  
  
  
  							def       _a   (      self   )							->							List[Any]:
  								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       _a   (      self ,						a_ ,						a_ ,						a_   )							->							int:
  								_UpperCAmelCase =						DinatModel(config=a_   )
  								model.to(a_   )
  								model.eval()
  								_UpperCAmelCase =						model(a_   )
  
  								_UpperCAmelCase =						_UpperCAmelCase =						(config.image_size // config.patch_size) // (2 ** (len(config.depths   ) - 1))
  								_UpperCAmelCase =						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       _a   (      self ,						a_ ,						a_ ,						a_   )							->							Any:
  								_UpperCAmelCase =						DinatForImageClassification(a_   )
  								model.to(a_   )
  								model.eval()
  								_UpperCAmelCase =						model(a_ ,						labels=a_   )
  								self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.num_labels)   )
  
  								# test greyscale images
  								_UpperCAmelCase =						1
  								_UpperCAmelCase =						DinatForImageClassification(a_   )
  								model.to(a_   )
  								model.eval()
  
  								_UpperCAmelCase =						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
  								_UpperCAmelCase =						model(a_   )
  								self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.num_labels)   )
  
  
  
  							def       _a   (      self ,						a_ ,						a_ ,						a_   )							->							Dict:
  								_UpperCAmelCase =						DinatBackbone(config=a_   )
  								model.to(a_   )
  								model.eval()
  								_UpperCAmelCase =						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
  								_UpperCAmelCase =						None
  								_UpperCAmelCase =						DinatBackbone(config=a_   )
  								model.to(a_   )
  								model.eval()
  								_UpperCAmelCase =						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       _a   (      self   )							->							Optional[int]:
  								_UpperCAmelCase =						self.prepare_config_and_inputs()
  								_UpperCAmelCase ,      _UpperCAmelCase ,      _UpperCAmelCase =						config_and_inputs
  								_UpperCAmelCase =						{"pixel_values": pixel_values}
  								return config, inputs_dict
  
  
  
  
  
  @require_natten
  @require_torch
  class    _lowerCAmelCase						(						lowerCamelCase    ,       lowerCamelCase    ,       unittest.TestCase						):
  							lowercase_     :     int       =							(
  							    (
  							        DinatModel,
  							        DinatForImageClassification,
  							        DinatBackbone,
  							    )
  							    if is_torch_available()
  							    else ()
  							)
  							lowercase_     :     List[Any]       =							(
  							    {'''feature-extraction''': DinatModel, '''image-classification''': DinatForImageClassification}
  							    if is_torch_available()
  							    else {}
  							)
  							lowercase_     :     List[Any]       =							False
  
  							lowercase_     :     Optional[int]       =							False
  							lowercase_     :     Dict       =							False
  							lowercase_     :     Optional[Any]       =							False
  							lowercase_     :     List[str]       =							False
  
  
  
  							def       _a   (      self   )							->							Tuple:
  								_UpperCAmelCase =						DinatModelTester(self   )
  								_UpperCAmelCase =						ConfigTester(self ,						config_class=a_ ,						embed_dim=37   )
  
  
  
  							def       _a   (      self   )							->							int:
  								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       _a   (      self   )							->							Dict:
  								return
  
  
  
  							def       _a   (      self   )							->							str:
  								_UpperCAmelCase =						self.model_tester.prepare_config_and_inputs()
  								self.model_tester.create_and_check_model(*a_   )
  
  
  
  							def       _a   (      self   )							->							Optional[int]:
  								_UpperCAmelCase =						self.model_tester.prepare_config_and_inputs()
  								self.model_tester.create_and_check_for_image_classification(*a_   )
  
  
  
  							def       _a   (      self   )							->							Dict:
  								_UpperCAmelCase =						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       _a   (      self   )							->							str:
  								pass
  
  
  
  							@unittest.skip(reason="Dinat does not use feedforward chunking"   )
  							def       _a   (      self   )							->							List[Any]:
  								pass
  
  
  
  							def       _a   (      self   )							->							List[Any]:
  								_UpperCAmelCase ,      _UpperCAmelCase =						self.model_tester.prepare_config_and_inputs_for_common()
  
  								for model_class in self.all_model_classes:
  									_UpperCAmelCase =						model_class(a_   )
  									self.assertIsInstance(model.get_input_embeddings() ,						(nn.Module)   )
  									_UpperCAmelCase =						model.get_output_embeddings()
  									self.assertTrue(x is None or isinstance(a_ ,						nn.Linear   )   )
  
  
  
  							def       _a   (      self   )							->							Dict:
  								_UpperCAmelCase ,      _UpperCAmelCase =						self.model_tester.prepare_config_and_inputs_for_common()
  
  								for model_class in self.all_model_classes:
  									_UpperCAmelCase =						model_class(a_   )
  									_UpperCAmelCase =						inspect.signature(model.forward   )
  									# signature.parameters is an OrderedDict => so arg_names order is deterministic
  									_UpperCAmelCase =						[*signature.parameters.keys()]
  
  									_UpperCAmelCase =						["pixel_values"]
  									self.assertListEqual(arg_names[:1] ,						a_   )
  
  
  
  							def       _a   (      self   )							->							Tuple:
  								self.skipTest("Dinat's attention operation is handled entirely by NATTEN."   )
  
  
  
  							def       _a   (      self ,						a_ ,						a_ ,						a_ ,						a_   )							->							int:
  								_UpperCAmelCase =						model_class(a_   )
  								model.to(a_   )
  								model.eval()
  
  								with torch.no_grad():
  									_UpperCAmelCase =						model(**self._prepare_for_class(a_ ,						a_   )   )
  
  								_UpperCAmelCase =						outputs.hidden_states
  
  								_UpperCAmelCase =						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
  								_UpperCAmelCase =						(
  								    config.patch_size
  								    if isinstance(config.patch_size ,						collections.abc.Iterable   )
  								    else (config.patch_size, config.patch_size)
  								)
  
  								_UpperCAmelCase =						image_size[0] // patch_size[0]
  								_UpperCAmelCase =						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":
  									_UpperCAmelCase =						outputs.reshaped_hidden_states
  									self.assertEqual(len(a_   ) ,						a_   )
  
  									_UpperCAmelCase ,      _UpperCAmelCase ,      _UpperCAmelCase ,      _UpperCAmelCase =						reshaped_hidden_states[0].shape
  									_UpperCAmelCase =						(
  									    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       _a   (      self   )							->							List[str]:
  								_UpperCAmelCase ,      _UpperCAmelCase =						self.model_tester.prepare_config_and_inputs_for_common()
  
  								_UpperCAmelCase =						(
  								    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:
  									_UpperCAmelCase =						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"]
  									_UpperCAmelCase =						True
  
  									self.check_hidden_states_output(a_ ,						a_ ,						a_ ,						a_   )
  
  
  
  							@slow
  							def       _a   (      self   )							->							int:
  								for model_name in DINAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
  									_UpperCAmelCase =						DinatModel.from_pretrained(a_   )
  									self.assertIsNotNone(a_   )
  
  
  
  							def       _a   (      self   )							->							List[Any]:
  								_UpperCAmelCase ,      _UpperCAmelCase =						self.model_tester.prepare_config_and_inputs_for_common()
  
  								_UpperCAmelCase =						_config_zero_init(a_   )
  								for model_class in self.all_model_classes:
  									_UpperCAmelCase =						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						):
  
  
  
  							@cached_property
  							def       _a   (      self   )							->							Optional[Any]:
  								return AutoImageProcessor.from_pretrained("shi-labs/dinat-mini-in1k-224"   ) if is_vision_available() else None
  
  
  
  							@slow
  							def       _a   (      self   )							->							Optional[Any]:
  								_UpperCAmelCase =						DinatForImageClassification.from_pretrained("shi-labs/dinat-mini-in1k-224"   ).to(a_   )
  								_UpperCAmelCase =						self.default_image_processor
  
  								_UpperCAmelCase =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
  								_UpperCAmelCase =						image_processor(images=a_ ,						return_tensors="pt"   ).to(a_   )
  
  								# forward pass
  								with torch.no_grad():
  									_UpperCAmelCase =						model(**a_   )
  
  								# verify the logits
  								_UpperCAmelCase =						torch.Size((1, 1000)   )
  								self.assertEqual(outputs.logits.shape ,						a_   )
  								_UpperCAmelCase =						torch.tensor([-0.1545, -0.7667, 0.4642]   ).to(a_   )
  								self.assertTrue(torch.allclose(outputs.logits[0, :3] ,						a_ ,						atol=1e-4   )   )
  
  
  
  
  
  @require_torch
  @require_natten
  class    _lowerCAmelCase						(						unittest.TestCase    ,       lowerCamelCase						):
  							lowercase_     :     Dict       =							(DinatBackbone,) if is_torch_available() else ()
  							lowercase_     :     List[str]       =							DinatConfig
  
  
  
  							def       _a   (      self   )							->							List[str]:
  								_UpperCAmelCase =						DinatModelTester(self   )
  
  
  
  
  
style_context: 
  """simple docstring"""
  from .stack import Stack
  
  
  
  
  def     __lowerCamelCase  (		UpperCamelCase__      ):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	_UpperCAmelCase =						Stack()
  	_UpperCAmelCase =						{"(": ")", "[": "]", "{": "}"}
  	for bracket in parentheses:
  		if bracket in bracket_pairs:
  			stack.push(UpperCamelCase__      )
  		elif bracket in (")", "]", "}"):
  			if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
  				return False
  	return stack.is_empty()
  
  
  if __name__ == "__main__":
  			from doctest import testmod
  
  			testmod()
  
  			__magic_name__		       =				['''((()))''', '''((())''', '''(()))''']
  			print('''Balanced parentheses demonstration:\n''')
  			for example in examples:
  						__magic_name__		       =				'''''' if balanced_parentheses(example) else '''not '''
  						print(f'''{example} is {not_str}balanced''')
  
  
  
  
  