
import inspect
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, 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, _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 ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
					from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image







class 			__A:




				def __init__(     self		,     _snake_case		,     _snake_case=13		,     _snake_case=64		,     _snake_case=2		,     _snake_case=3		,     _snake_case=True		,     _snake_case=True		,     _snake_case=32		,     _snake_case=5		,     _snake_case=4		,     _snake_case=37		,     _snake_case="gelu"		,     _snake_case=0.1		,     _snake_case=0.1		,     _snake_case=10		,     _snake_case=0.02		,     _snake_case=[1, 16, 4, 4]		,     _snake_case=None		,     )    ->      List[str]:


								'''simple docstring'''




								__a										=    parent
								__a										=    batch_size
								__a										=    image_size
								__a										=    patch_size
								__a										=    num_channels
								__a										=    is_training
								__a										=    use_labels
								__a										=    hidden_size
								__a										=    num_hidden_layers
								__a										=    num_attention_heads
								__a										=    intermediate_size
								__a										=    hidden_act
								__a										=    hidden_dropout_prob
								__a										=    attention_probs_dropout_prob
								__a										=    type_sequence_label_size
								__a										=    initializer_range
								__a										=    scope
								__a										=    backbone_featmap_shape

								# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
								# the number of patches is based on the feature map of the backbone, which by default uses an output stride
								# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
								__a										=    (self.image_size // 32) ** 2
								__a										=    num_patches + 1




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      List[str]:


								'''simple docstring'''




								__a										=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

								__a										=    None
								if self.use_labels:
												__a										=    ids_tensor([self.batch_size]		,     self.type_sequence_label_size				)

								__a										=    self.get_config()

								return config, pixel_values, labels




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Dict:


								'''simple docstring'''




								__a										=    {
								    '''global_padding''': '''same''',
								    '''layer_type''': '''bottleneck''',
								    '''depths''': [3, 4, 9],
								    '''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
								    '''embedding_dynamic_padding''': True,
								    '''hidden_sizes''': [4, 8, 16, 32],
								    '''num_groups''': 2,
								}

								return ViTHybridConfig(
								    image_size=self.image_size		,     patch_size=self.patch_size		,     num_channels=self.num_channels		,     hidden_size=self.hidden_size		,     num_hidden_layers=self.num_hidden_layers		,     num_attention_heads=self.num_attention_heads		,     intermediate_size=self.intermediate_size		,     hidden_act=self.hidden_act		,     hidden_dropout_prob=self.hidden_dropout_prob		,     attention_probs_dropout_prob=self.attention_probs_dropout_prob		,     is_decoder=_snake_case		,     initializer_range=self.initializer_range		,     backbone_featmap_shape=self.backbone_featmap_shape		,     backbone_config=_snake_case		,     )




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case				)    ->      List[str]:


								'''simple docstring'''




								__a										=    ViTHybridModel(config=_snake_case				)
								model.to(_snake_case				)
								model.eval()
								__a										=    model(_snake_case				)
								self.parent.assertEqual(result.last_hidden_state.shape		,     (self.batch_size, self.seq_length, self.hidden_size)				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case				)    ->      Optional[int]:


								'''simple docstring'''




								__a										=    self.type_sequence_label_size
								__a										=    ViTHybridForImageClassification(_snake_case				)
								model.to(_snake_case				)
								model.eval()
								__a										=    model(_snake_case		,     labels=_snake_case				)
								self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.type_sequence_label_size)				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[Any]:


								'''simple docstring'''




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







@require_torch
class 			__A( a  ,   a  ,   unittest.TestCase			):
				snake_case_						   =    (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
				snake_case_						   =    (
				    {'''feature-extraction''': ViTHybridModel, '''image-classification''': ViTHybridForImageClassification}
				    if is_torch_available()
				    else {}
				)
				snake_case_						   =    False
				snake_case_						   =    False
				snake_case_						   =    False




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      int:


								'''simple docstring'''




								__a										=    ViTHybridModelTester(self				)
								__a										=    ConfigTester(self		,     config_class=_snake_case		,     has_text_modality=_snake_case		,     hidden_size=37				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Any:


								'''simple docstring'''




								self.config_tester.run_common_tests()




				@unittest.skip(reason='''ViT does not use inputs_embeds'''				)
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[Any]:


								'''simple docstring'''




								pass




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      str:


								'''simple docstring'''




								__a							,     __a										=    self.model_tester.prepare_config_and_inputs_for_common()

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




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[int]:


								'''simple docstring'''




								__a							,     __a										=    self.model_tester.prepare_config_and_inputs_for_common()

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

												__a										=    ['''pixel_values''']
												self.assertListEqual(arg_names[:1]		,     _snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      str:


								'''simple docstring'''




								__a										=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Any:


								'''simple docstring'''




								__a										=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_snake_case				)




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


								'''simple docstring'''




								__a							,     __a										=    self.model_tester.prepare_config_and_inputs_for_common()

								__a										=    _config_zero_init(_snake_case				)
								for model_class in self.all_model_classes:
												__a										=    model_class(config=_snake_case				)
												# Skip the check for the backbone
												for name, module in model.named_modules():
																if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
																				__a										=    [F"""{name}.{key}""" for key in module.state_dict().keys()]
																				break

												for name, param in model.named_parameters():
																if param.requires_grad:
																				if name in backbone_params:
																								continue
																				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"""		,     )




				@slow
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[int]:


								'''simple docstring'''




								for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												__a										=    ViTHybridModel.from_pretrained(_snake_case				)
												self.assertIsNotNone(_snake_case				)



def     __lowerCAmelCase			(			)       ->  Optional[int]:
				__a										=    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
				return image







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




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


								'''simple docstring'''




								return (
								    ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)
								    if is_vision_available()
								    else None
								)




				@slow
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Union[str, Any]:


								'''simple docstring'''




								__a										=    ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]				).to(
								    _snake_case				)

								__a										=    self.default_image_processor
								__a										=    prepare_img()
								__a										=    image_processor(images=_snake_case		,     return_tensors='''pt'''				).to(_snake_case				)

								# forward pass
								with torch.no_grad():
												__a										=    model(**_snake_case				)

								# verify the logits
								__a										=    torch.Size((1, 1_000)				)
								self.assertEqual(outputs.logits.shape		,     _snake_case				)

								__a										=    torch.tensor([-1.9090, -0.4993, -0.2389]				).to(_snake_case				)

								self.assertTrue(torch.allclose(outputs.logits[0, :3]		,     _snake_case		,     atol=1E-4				)				)




				@slow
				@require_accelerate
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Tuple:


								'''simple docstring'''




								__a										=    ViTHybridImageProcessor.from_pretrained('''google/vit-hybrid-base-bit-384'''				)
								__a										=    ViTHybridForImageClassification.from_pretrained('''google/vit-hybrid-base-bit-384'''		,     device_map='''auto'''				)

								__a										=    prepare_img()

								__a										=    image_processor(images=_snake_case		,     return_tensors='''pt'''				)
								__a										=    model(**_snake_case				)
								__a										=    outputs.logits
								# model predicts one of the 1000 ImageNet classes
								__a										=    logits.argmax(-1				).item()

								self.assertTrue(model.config.idalabel[predicted_class_idx]		,     '''tabby, tabby cat'''				)

def     __lowerCAmelCase			(			a__  )       ->  str:
				__a										=    []
				__a										=    set({'''(''', '''[''', '''{'''}  )
				__a										=    set({''')''', ''']''', '''}'''}  )
				__a										=    {'''{''': '''}''', '''[''': ''']''', '''(''': ''')'''}

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

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

				return len(a__  ) == 0


def     __lowerCAmelCase			(			)       ->  Dict:
				__a										=    input('''Enter sequence of brackets: '''  )
				if is_balanced(a__  ):
								print(a__						,    '''is balanced'''  )
				else:
								print(a__						,    '''is not balanced'''  )


if __name__ == "__main__":
					main()